WO2022012295A1 - Fire detection method and apparatus - Google Patents
Fire detection method and apparatus Download PDFInfo
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- WO2022012295A1 WO2022012295A1 PCT/CN2021/102072 CN2021102072W WO2022012295A1 WO 2022012295 A1 WO2022012295 A1 WO 2022012295A1 CN 2021102072 W CN2021102072 W CN 2021102072W WO 2022012295 A1 WO2022012295 A1 WO 2022012295A1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
- G08B29/188—Data fusion; cooperative systems, e.g. voting among different detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
Definitions
- the invention relates to the technical field of fire detection, in particular to a fire detection method and device.
- the gray fuzzy neural network information fusion algorithm is usually used to fuse the measurement data obtained by the smoke detection module, the CO gas detection module and the temperature detection module, or the fuzzy logic control system with feedback (that is, the fuzzy logic control system with feedback is used).
- Logic fusion algorithm fuses the measurement data obtained by different sensors or modules to determine whether a fire has occurred, but these methods have low detection accuracy, are prone to misjudgment of fire, and have poor detection timeliness, requiring too long to detect time.
- the implementation of the present invention provides a fire detection method and device.
- the technical solution is as follows:
- a fire detection method including:
- the evaluation value of each node to the fire occurrence probability in the cabin is determined
- the determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node includes:
- Consistency test is carried out on the judgment matrix of each fire influencing factor
- the fire influence weight value of each fire influence factor is calculated.
- the fire influence weight of each fire influencing factor is calculated according to the judgment matrix of each fire influencing factor that has passed the consistency check values, including:
- a preset number of fire-influencing weight values for the fire-influencing factors are calculated, wherein the preset number is equal to all the fire-influencing factors that pass the consistency check.
- the number of judgment matrices for each fire influencing factor is a positive integer greater than or equal to 2;
- the fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
- determining the evaluation value of the fire occurrence probability in the cabin by each node according to the fire influence weight value of each fire influence factor including:
- each fire influence factor determines the fire influence weight interval of each fire influence factor
- the evaluation value of each node to the fire occurrence probability in the cabin is determined.
- determining the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node includes:
- each node On the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
- the probability that a fire actually occurs in the cabin is determined.
- the determining the probability of a fire actually occurring in the cabin according to the augmented support matrix includes:
- the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
- the judging whether to issue a fire alarm according to the probability that a fire actually occurs in the cabin includes:
- a fire detection device comprising:
- a first determination module used for determining multiple fire-influencing factors of each node in the engine room
- a second determination module configured to determine the fire influence weight value of each fire influence factor among the plurality of fire influence factors of each node
- a third determination module configured to determine the evaluation value of each node on the probability of fire occurrence in the cabin according to the fire influence weight value of each fire influence factor
- a fourth determination module configured to determine the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of fire occurrence in the cabin by each node;
- the judging module is used for judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
- the second determining module includes:
- a check sub-module used to check the consistency of the judgment matrix of each fire influencing factor
- the calculation sub-module is configured to calculate the fire influence weight value of each fire influence factor according to the judgment matrix of each fire influence factor that has passed the consistency check.
- the calculation submodule is specifically used for:
- a preset number of fire influence weight values for each fire influencing factor are calculated. , wherein the preset number is equal to the number of judgment matrices of the respective fire influencing factors that have passed the consistency check; the preset number is a positive integer greater than or equal to 2;
- the fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
- the third determining module includes:
- a first determination sub-module configured to determine the fire influence weight interval of each fire influence factor according to the fire influence weight value of each fire influence factor
- the second determination submodule is configured to determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;
- a calculation submodule configured to calculate the optimal fire influence weight value of each fire influence factor of each node according to the target influence function of each fire influence factor and the constraint condition of the target influence function;
- the third determination sub-module is configured to determine, according to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the effect of each node on the interior of the cabin. An estimate of the probability of fire occurrence.
- the fourth determining module is specifically configured to:
- each node On the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
- the probability that a fire actually occurs in the cabin is determined.
- the fourth determining module is further configured to:
- the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
- the judging module is specifically used for:
- Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
- the estimated value of each node to the probability of fire occurrence in the cabin can be preliminarily calculated.
- the estimated value may not be accurate and needs to be adjusted. Therefore, the estimated value can be used to calculate The probability of a fire actually occurring in the cabin, so as to accurately detect the fire, and accurately judge whether to issue a fire alarm, so as to ensure the timeliness and accuracy of the fire alarm.
- FIG. 1 is a schematic diagram of the deployment of a fire detection sensor node provided by the present invention.
- FIG. 2 is a fire probability error curve provided by the present invention.
- FIG. 3 is a comparison diagram of detection time between a fire detection method provided by the present invention and two fire detection algorithms in the related art.
- FIG. 4 is a relationship curve between the number k of judgment matrices and the false alarm rate of fire detection provided by the present invention.
- FIG. 5 is a comparison diagram of detection accuracy of a fire detection method provided by the present invention compared with two fire detection algorithms in the related art.
- FIG. 6 is a flowchart of a fire detection method provided by the present invention.
- an embodiment of the present invention provides a fire detection method, which can be used in a fire detection program, system or device. As shown in FIG. 6 , the method includes steps S101 to S105:
- a plurality of fire-influencing factors of each node in the engine room are determined; the fire-influencing factors include but are not limited to: temperature, smoke concentration, CO concentration, and infrared light intensity.
- the cabin can be an aircraft cabin, an aviation cabin, etc., and each node is each sensor node.
- each sensor node can have one or more sensors of different types, which are used to detect the actual factor value of the fire influencing factor.
- step S102 the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node is determined; the fire influence weight value is used to represent the weight of each fire influence factor in the occurrence of the fire.
- each node adopts the same fire influence factor.
- step S103 according to the fire influence weight value of each fire influence factor, the evaluation value of each node on the probability of fire occurrence in the cabin is determined; the evaluation value is used to characterize the probability of fire occurrence at each node, The greater the evaluation value of a node, the greater the probability of fire at the node, and vice versa.
- step S104 the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node;
- the present invention can be based on the support matrix. Multi-sensor node data is fused (that is, the evaluation value of fire occurrence probability of each node is fused).
- step S105 it is judged whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
- the estimated value of each node to the probability of fire occurrence in the cabin can be preliminarily calculated.
- the estimated value may not be accurate and needs to be adjusted. Therefore, the estimated value can be used to calculate The probability of a fire actually occurring in the cabin, so as to accurately detect the fire, and accurately judge whether to issue a fire alarm, so as to ensure the timeliness and accuracy of the fire alarm.
- the determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node includes:
- Consistency test is carried out on the judgment matrix of each fire influencing factor
- the fire influence weight value of each fire influence factor is calculated.
- the judgment matrix can be checked for consistency. If the consistency check is passed, the judgment matrix is determined to be a reasonable matrix; otherwise, it should be discarded. Through the judgment matrix of each fire-influencing factor through the consistency check, the fire-influencing weight value of each fire-influencing factor is calculated, so as to determine the weight of each fire-influencing factor in the occurrence of fire.
- the relationship of the degree of influence between variables is quantitatively represented by a judgment matrix.
- the judgment matrix be C ⁇ R n ⁇ n , where n represents the number of variables.
- the judgment matrix C has the form:
- c ij can take 2, 4, 6, and 8; c ji can take the values of 1/2, 1/4, 1/6, and 1/8. Therefore, the element c ij can take any integer from 1 to 9 according to the above rules.
- the fire influence weight of each fire influencing factor is calculated according to the judgment matrix of each fire influencing factor that has passed the consistency check values, including:
- a preset number of fire-influencing weight values for the fire-influencing factors are calculated, wherein the preset number is equal to all the fire-influencing factors that pass the consistency check.
- the number of judgment matrices for each fire influencing factor is a positive integer greater than or equal to 2;
- the fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
- determining the evaluation value of the fire occurrence probability in the cabin by each node according to the fire influence weight value of each fire influence factor including:
- each fire influence factor determines the fire influence weight interval of each fire influence factor
- each fire influence weight interval of each fire influence factor determines the target influence function of each fire influence factor (for finding the smallest weight deviation in the interval) and the constraint condition of the target influence function (the constraint condition may be each The sum of the optimal fire influence weights of each fire influence factor of the node is 1);
- the evaluation value of each node to the fire occurrence probability in the cabin is determined.
- the actual factor value of each fire-influencing factor is the actual value of each fire-influencing factor.
- the actual value range of the fire-influencing factor temperature (unit °C) is [0, 100]
- the actual value of the smoke concentration (unit ppm) is The interval is [100, 1000]
- the actual value interval where the CO concentration (unit ppm) is located is [10, 100]
- the actual value interval where the infrared light intensity (unit Lux) is located is [100, 1000].
- Different classes may affect the evaluation value.
- the actual factor value of each fire-influencing factor can be normalized.
- the target influence function of each fire influence factor and the constraint conditions of the target influence function can be determined, so that according to the target influence function and the constraint conditions, the fire influence of each fire influence factor can be obtained from the fire influence factor.
- the optimal fire influence weight value is automatically selected from the weight interval, and the optimal fire influence weight value should be able to take into account all the values in the weight interval to the greatest extent.
- the optimal fire influence weight value of each fire influence factor and all The actual factor value of each fire influencing factor of each node is described, and the determined evaluation value of each node for the probability of fire occurrence in the cabin can ensure that the final evaluation value can better reflect the actual situation; at the same time, the optimal weight is used for weighting Fusion can effectively avoid the tedious calculation process during fusion, and improve the response speed to fire.
- determining the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node includes:
- each node On the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
- the parameter ⁇ is adjustable and is used to adjust the fusion accuracy, which is generally set to 0.8 [16].
- the probability that a fire actually occurs in the cabin is determined.
- the support degree function can be converted into an initial support degree matrix about the evaluation value, and in order to facilitate the adaptive allocation of weight coefficients for each node (ie, The evaluation credibility coefficient of the evaluation value of each node) to fuse and adjust the evaluation value of the probability of fire occurrence in the cabin to achieve the purpose of improving the accuracy of the probability of actual fire occurrence in the cabin, which can be constructed based on the initial support matrix
- the support matrix is extended to improve the accuracy of the calculated probability of a fire actually occurring in the cabin, thereby enabling accurate prediction of the fire.
- the determining the probability of a fire actually occurring in the cabin according to the augmented support matrix includes:
- the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
- a higher evaluation credibility coefficient can be assigned to the evaluation value of each node with high reliability, and a lower evaluation credibility coefficient can be assigned to the evaluation value of each node with low credibility, so that Avoid failure or inaccurate detection of sensor nodes deployed in the cabin, which will adversely affect the accuracy of the fusion results of the evaluation values of each node, making the fusion of evaluation values fault-tolerant, thereby greatly improving the probability of actual fire in the cabin.
- the technical scheme of the present invention conducts experiments with the gray-fuzzy neural network fusion algorithm and the fuzzy logic fusion algorithm in the related art from the perspectives of the time required to detect a fire and issue an alarm signal and the system false alarm rate. After comparison, it is found that the fire detection scheme of the present invention can detect fire within 10s, while reducing the false alarm rate to less than 0.5%, greatly reducing the fire false alarm rate and improving the accuracy and timeliness of fire detection.
- the judging whether to issue a fire alarm according to the probability that a fire actually occurs in the cabin includes:
- Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
- the probability threshold of fire occurrence in the cabin can be accurately calculated, and then the currently calculated probability of actual fire occurrence in the cabin and the fire occurrence in the cabin can be calculated.
- the probability threshold of the cabin it can be accurately judged whether a fire alarm is issued. Specifically, if the currently calculated probability of actual fire in the cabin is greater than or equal to the probability threshold of fire in the cabin, it means that a fire is about to occur, then Fire alarm is carried out, otherwise, it means that fire has not yet occurred, and there is no need to carry out fire alarm.
- a multi-sensor data fusion method is proposed to detect fire.
- the weights of temperature, smoke concentration, CO concentration and infrared light intensity in the occurrence of fire are calculated by the improved Analytic Hierarchy Process (AHP) on each sensor node of the wireless sensor network (WSN), and the variable weighted fusion method is used.
- AHP Analytic Hierarchy Process
- the data fusion method under the action of various factors is considered in the study of the fire detection and alarm problem based on WSN.
- the present invention uses the improved AHP at each sensor node in the WSN to calculate the weight of each variable affecting the probability of fire occurrence, and calculates the weight of each variable affecting the probability of fire occurrence according to the multiple
- the variable weighted fusion evaluates the probability of fire occurrence in the cabin; at the same time, in order to avoid the influence of the faulty sensor on the detection accuracy, the present invention uses the adaptive weighted fusion method to fuse the evaluation data of each node.
- the credibility of the fire assessment results of each node is calculated by constructing a support matrix, and a higher weight is assigned to a node with high credibility, and a lower weight is assigned otherwise. This minimizes the impact of faulty sensors on fusion results.
- the main contribution of the present invention is to avoid the problems of reduced timeliness when the detection system fuses multi-variable data, and large deviations in fusion results when some sensors are faulty.
- the present invention considers the weight of each variable that affects the occurrence of fire, and designs a new "multi-variable weighted fusion" fire evaluation algorithm based on the improved AHP variable weight calculation method, which can still detect quickly when there are many fusion variables.
- the present invention fuses more related variables, which greatly reduces the false alarm rate of the system;
- the present invention uses adaptive weighted fusion to fuse the evaluation values of the fire occurrence probability of each sensor node. , by constructing a support matrix to assign weight coefficients to each node adaptively, to a certain extent, it avoids the problem of decreased fusion accuracy caused by the measurement deviation of some faulty sensors.
- the structure of the invention is as follows: the first section introduces a method for evaluating the probability of fire occurrence by using the improved AHP and multivariate weighted fusion algorithm at each sensor node; the second section introduces a method by constructing an augmented support matrix , a method of adaptively weighted fusion of the estimated value of fire occurrence probability by each node.
- the third section is the experimental part, which verifies the timeliness, accuracy and fault tolerance of the fire detection algorithm proposed by the present invention.
- the fourth section is the conclusion of the present invention.
- the present invention mainly adopts the improved AHP method to determine the variable weight [11].
- the traditional AHP calculates the relative weight of the lowest layer to the highest layer by selecting a judgment matrix, and sorts the various schemes and measures in the lowest layer according to this weight.
- the accuracy of the required weights is not high.
- the present invention uses the improved AHP to calculate the weights occupied by each influencing factor variable in the process of fire occurrence, and by selecting multiple judgment matrices and constructing the objective function in the weight interval, the method is Optimal weights for variables.
- the basic process of the improved AHP method for evaluating fire problems is: 1) Determine the factors and variables that affect the occurrence of fire; 2) Establish a judgment matrix for each variable; 3) Consistency test on the judgment matrix; 4) Eigenvector method to calculate the variable weight 5) Set the objective function to obtain the optimal weight.
- the weight calculation steps are as follows:
- Step 1 Set the factor variables that affect the occurrence of fire as temperature, smoke concentration, CO concentration and infrared light intensity.
- x 1 represent temperature
- x 2 represent smoke concentration
- x 3 represent CO concentration
- x 4 represent infrared light intensity
- Step 2 Establish a judgment matrix for each variable:
- the influence degree relationship between variables is quantitatively represented by a judgment matrix.
- the judgment matrix be C ⁇ R n ⁇ n , where n represents the number of variables.
- the judgment matrix C has the form:
- c ij can take 2, 4, 6, and 8; c ji can take the values of 1/2, 1/4, 1/6, and 1/8. Therefore, the element c ij can take any integer from 1 to 9 according to the above rules.
- Step 3 Check the consistency of the judgment matrix:
- Step 4 Eigenvector method to find the variable weight interval:
- the weight of each variable is calculated by the eigenvector method, and the calculated weight is written into the form of an interval, which is recorded as the weight interval of the variable (this interval is composed of independent weight points).
- a weight of the variable can be calculated according to the eigenvector method.
- the weight calculation formula is as follows:
- the k weights of each variable obtained above are written in the form of an interval, and the interval is the weight interval of the variable.
- a weight interval of the variable i can be obtained based on formulas (2) to (4), and the interval includes k weights.
- Step 5 Set the objective function to obtain the optimal weight:
- the optimal weight should be able to take into account all the value information in the weight interval to the greatest extent, so as to ensure that the final evaluation result can better reflect the actual situation; at the same time, using the optimal weight for weighted fusion can effectively avoid the tedious calculation process during fusion, improve Speed of reaction to fire.
- the temperature (unit °C) is set in the interval [0, 100], the smoke concentration (in ppm) in the interval [100, 1000], and the CO concentration (in ppm) in the interval [ 10, 100], the infrared light intensity (unit Lux) is in the interval [100, 1000].
- a conversion function is constructed to map the real value of the variable to [0-1] to avoid affecting the calculation result due to different dimensions.
- the conversion function is:
- max( xi ) is the maximum value in the interval where the variable i is located
- min( xi ) is the minimum value in the interval where the variable i is located.
- this section proposes a multi-sensor node data fusion method based on support matrix. This method objectively reflects the support degree between the evaluation data of nodes without knowing the evaluation ability of each node to the probability of fire occurrence.
- the weight coefficient of each node's fire probability evaluation value in the fusion process is adaptively adjusted to achieve the best fusion effect.
- the feature of this method is that it can fuse a large amount of data online, and in the process of adaptive weight coefficient allocation, by assigning a higher weight to the fire evaluation value with high reliability, and otherwise assigning a lower weight, the algorithm has a certain fault tolerance. ability.
- each node is composed of temperature, smoke concentration, CO concentration and infrared light intensity sensors to measure environmental variables, and the probability of fire occurrence in the cabin at this moment can be obtained through variable weighted fusion calculation at each node.
- the parameter ⁇ is adjustable and is used to adjust the fusion accuracy, which is generally set to 0.8 [16].
- Support function Represents the mutual support of node i * and node j * at the kth moment, which can generally be expressed in matrix form:
- the mutual support relationship between the nodes can be determined.
- the i * th column of The larger the value, the higher the reliability of the fire probability evaluation value obtained at the node i *, and the lower the reliability otherwise.
- an augmented support matrix which increases the dimension of the support matrix by one row and one column.
- the purpose of constructing this new dimension support matrix is to measure the mutual support between all current evaluation values and previous evaluation values, so as to adaptively assign weight coefficients to each node.
- the augmented support matrix at time k can be defined as:
- i * ,j * 1,2,...,N+1.
- formula (14) becomes:
- W [w 1 (k),w 2 (k),...w N+1 (k)] T
- A [a 1 (k),a 2 (k),...a N+1 (k )] T .
- i * ,j * 1,2,...,N+1.
- the critical value of fire occurrence is set as: the temperature is 55°C, the smoke concentration is 700ppm, the CO concentration is 20ppm, and the infrared light intensity is 760Lux.
- the environmental data detected by the sensor is first normalized by formula (7) on each node, and then the fire probability is evaluated by formula (8); then based on the node data, mutual support
- the probability value of each node is estimated by self-adaptive weight coefficient distribution, and the actual fire probability value in the cabin is calculated after weighted fusion.
- each node is composed of temperature, smoke concentration, CO concentration and infrared light intensity sensors.
- the schematic diagram of node deployment is shown in Figure 1.
- the environmental parameters collected by each node are: Variable units x1 is °C, x2 is ppm, x3 is ppm and x4 is Lux.
- the variables that affect the probability of fire occurrence temperature, smoke concentration, CO concentration and infrared light intensity are firstly calculated.
- the fusion algorithm is compared with the fuzzy logic fusion algorithm to verify the superiority of the online detection method (ie, the fire detection method of the present invention) in terms of timeliness, accuracy and fault tolerance of fire detection.
- this experiment calculates the weight interval of each variable when k is 5 (where B1 to B5 are the judgment matrix), and uses formulas (5) and (6) to calculate each variable in the weight interval.
- Optimal weights for variables Table 2 describes the relevant process of weight calculation in Section 1 (where Table 2 records the weight interval obtained under assumption 13), and Table 3 lists the corresponding output results:
- the online detection algorithm of the present invention can complete the fire detection and alarm within 10s, while the gray-fuzzy neural network fusion algorithm in the related art completes the fire within 23s Detection and alarm, the fuzzy logic fusion algorithm in the related art completes fire detection and alarm within 20s.
- the online detection algorithm proposed by the present invention is superior to the other two algorithms in terms of fire detection timeliness .
- the core of the online detection algorithm is to calculate the weight occupied by each variable.
- the set of weights [0.6492, 0.1474, 0.1439, 0.0595] in the above experiment is obtained when the number of judgment matrices is 5.
- k the fire false alarm rate is 3% under the background of this experiment, and taking this as the initial condition, the relationship between the number k selected for judgment matrix and the fire detection false alarm rate is analyzed.
- the simulation curve in Fig. 4 shows that the more reasonable judgment matrices are selected, the more accurate the obtained variable weights will be, and the more accurate the evaluation results of fire occurrence probability of each node will be.
- the online detection algorithm reduces the false alarm rate to less than 0.5%, which effectively improves the accuracy of fire detection.
- SE squared error
- p j represents the true value of the probability of fire occurrence under the j algorithm; It represents the estimated value of the probability of fire occurrence after fusing 50 nodes under the j algorithm.
- the online detection algorithm proposed by the present invention performs self-adaptive distribution of weight coefficients based on the mutual support between data due to node data fusion. Therefore, when the number of faulty nodes increases, its SE value increases gently, and after the number of faulty nodes reaches 30, a large deviation occurs. Therefore, in an actual fire detection system, the fault tolerance capability of the online detection algorithm of the present invention is better.
- the fire online detection algorithm of the present invention first calculates the weight of each variable by using the improved AHP at each node of the WSN, and proposes a new "variable weighted fusion algorithm" to evaluate the probability of fire occurrence, so that the system can still be used when there are many input variables. It can detect fire in time and accurately. Experiments show that the algorithm can complete the detection and alarm of fire within 10s. Compared with other fire detection algorithms, it greatly reduces the time required for fire detection and alarm, effectively avoids the spread of fire, and to a certain extent makes the follow-up fire. Firefighting has become smoother. When using WSN to collect environmental data, there is a problem that the detection accuracy is damaged or even the system is paralyzed due to sensor failure. The fusion is given a higher weight, and vice versa. By adaptively assigning weight coefficients to each node, the influence of deviation data collected by faulty sensors on fusion results is reduced, so the online detection algorithm has strong fault tolerance.
- the embodiment of the present invention further provides a fire detection device, the device comprising:
- a first determination module used for determining multiple fire-influencing factors of each node in the engine room
- a second determination module configured to determine the fire influence weight value of each fire influence factor among the plurality of fire influence factors of each node
- a third determination module configured to determine the evaluation value of each node on the probability of fire occurrence in the cabin according to the fire influence weight value of each fire influence factor
- a fourth determination module configured to determine the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of fire occurrence in the cabin by each node;
- the judging module is used for judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
- the second determining module includes:
- a check sub-module used to check the consistency of the judgment matrix of each fire influencing factor
- the calculation sub-module is configured to calculate the fire influence weight value of each fire influence factor according to the judgment matrix of each fire influence factor that has passed the consistency check.
- the calculation submodule is specifically used for:
- a preset number of fire influence weight values for each fire influencing factor are calculated. , wherein the preset number is equal to the number of judgment matrices of the respective fire influencing factors that have passed the consistency check; the preset number is a positive integer greater than or equal to 2;
- the fire influence weight interval of each fire influence factor is determined.
- the third determining module includes:
- a first determination sub-module configured to determine the fire influence weight interval of each fire influence factor according to the fire influence weight value of each fire influence factor
- the second determination submodule is configured to determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;
- a calculation submodule configured to calculate the optimal fire influence weight value of each fire influence factor of each node according to the target influence function of each fire influence factor and the constraint condition of the target influence function;
- the third determination sub-module is configured to determine, according to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the effect of each node on the interior of the cabin. An estimate of the probability of fire occurrence.
- the fourth determining module is specifically configured to:
- each node On the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
- the probability that a fire actually occurs in the cabin is determined.
- the fourth determining module is further configured to:
- the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
- the judging module is specifically used for:
- Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
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Abstract
A fire detection method and apparatus. The method comprises: determining a plurality of fire influence factors of nodes in a cabin (S101); determining a fire influence weight value of each fire influence factor in the plurality of fire influence factors of the nodes (S102); according to the fire influence weight value of each fire influence factor, determining an evaluation value of each node for the fire occurrence probability in the cabin (S103); determining the probability of actual fire in the cabin according to the evaluation value of each node for the fire occurrence probability in the cabin (S104); and determining, according to the probability of actual fire in the cabin, whether a fire alarm is given (S105). The fire detection method and apparatus can accurately detect a fire, and accurately determine whether a fire alarm is given, so that the timeliness and the accuracy of a fire alarm are ensured.
Description
本发明涉及火灾检测技术领域,尤其涉及一种火灾检测方法及装置。The invention relates to the technical field of fire detection, in particular to a fire detection method and device.
目前,在对火灾进行检测时,通常采用灰模糊神经网络信息融合算法将烟雾探测模块,CO气体检测模块和温度检测模块所获取的测量数据进行融合或者采用带反馈的模糊逻辑控制***(即模糊逻辑融合算法)将不同传感器或者模块所获取的测量数据进行融合然后判断是否发生火灾,但这些方法检测准确率均较低,容易发生火灾误判,且检测及时性较差,需要消耗过长检测时间。At present, in the detection of fire, the gray fuzzy neural network information fusion algorithm is usually used to fuse the measurement data obtained by the smoke detection module, the CO gas detection module and the temperature detection module, or the fuzzy logic control system with feedback (that is, the fuzzy logic control system with feedback is used). Logic fusion algorithm) fuses the measurement data obtained by different sensors or modules to determine whether a fire has occurred, but these methods have low detection accuracy, are prone to misjudgment of fire, and have poor detection timeliness, requiring too long to detect time.
发明内容SUMMARY OF THE INVENTION
本发明实施提供了一种火灾检测方法及装置。所述技术方案如下:The implementation of the present invention provides a fire detection method and device. The technical solution is as follows:
根据本发明实施例的第一方面,提供一种火灾检测方法,包括:According to a first aspect of the embodiments of the present invention, a fire detection method is provided, including:
确定机舱内各节点的多个火灾影响因素;Identify multiple fire-influencing factors at each node in the engine room;
确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node;
根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;According to the fire influence weight value of each fire influence factor, the evaluation value of each node to the fire occurrence probability in the cabin is determined;
根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;According to the evaluation value of the probability of fire occurrence in the cabin by each node, determine the probability of the actual occurrence of fire in the cabin;
根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。According to the probability of a fire actually occurring in the cabin, it is judged whether to issue a fire alarm.
在一个实施例中,所述确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值,包括:In one embodiment, the determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node includes:
确定所述各火灾影响因素的判断矩阵;determining the judgment matrix for each of the fire influencing factors;
对所述各火灾影响因素的判断矩阵进行一致性检验;Consistency test is carried out on the judgment matrix of each fire influencing factor;
根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。According to the judgment matrix of each fire influence factor that has passed the consistency check, the fire influence weight value of each fire influence factor is calculated.
在一个实施例中,当所述各火灾影响因素的判断矩阵存在多个时,所述根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值,包括:In one embodiment, when there are multiple judgment matrices of each fire influencing factor, the fire influence weight of each fire influencing factor is calculated according to the judgment matrix of each fire influencing factor that has passed the consistency check values, including:
根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或 等于2的正整数;According to the judgment matrix of each of the fire-influencing factors that pass the consistency check, a preset number of fire-influencing weight values for the fire-influencing factors are calculated, wherein the preset number is equal to all the fire-influencing factors that pass the consistency check. the number of judgment matrices for each fire influencing factor; the preset number is a positive integer greater than or equal to 2;
根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间。The fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
在一个实施例中,根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值,包括:In one embodiment, determining the evaluation value of the fire occurrence probability in the cabin by each node according to the fire influence weight value of each fire influence factor, including:
根据所述各火灾影响因素的火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间;According to the fire influence weight value of each fire influence factor, determine the fire influence weight interval of each fire influence factor;
根据所述各火灾影响因素的火灾影响权重区间,确定所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件;Determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;
根据所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件,计算所述各节点的各火灾影响因素的最优火灾影响权重值;According to the target influence function of each fire influence factor and the constraint condition of the target influence function, calculate the optimal fire influence weight value of each fire influence factor of each node;
根据所述各节点的各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定所述各节点对所述舱内火灾发生概率的评估值。According to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the evaluation value of each node to the fire occurrence probability in the cabin is determined.
在一个实施例中,所述根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率,包括:In one embodiment, determining the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node includes:
根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数;According to the evaluation value of each node on the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
根据所述任意两个节点之间关于评估值之间的支持度函数,建立所述各节点之间关于评估值的初始的支持度矩阵;According to the support function between the any two nodes with respect to the evaluation value, establish an initial support degree matrix between the nodes with respect to the evaluation value;
为所述各节点关于评估值之间的初始的支持度矩阵构建增广支持度矩阵;其中,所述增广支持度矩阵相比于所述初始的支持度矩阵多了一行与一列;Constructing an augmented support matrix for the initial support matrix between the evaluation values of each node; wherein, the augmented support matrix has one more row and one column compared to the initial support matrix;
根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率。According to the augmented support matrix, the probability that a fire actually occurs in the cabin is determined.
在一个实施例中,所述根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率,包括:In one embodiment, the determining the probability of a fire actually occurring in the cabin according to the augmented support matrix includes:
根据所述增广支持度矩阵,确定所述各节点对所述舱内火灾发生概率的评估值的评估可信系数;determining, according to the augmented support matrix, an evaluation credibility coefficient of each node for the evaluation value of the probability of occurrence of fire in the cabin;
根据所述各节点对所述舱内火灾发生概率的评估值和所述各节点对所述舱内火灾发生概率的评估值的评估可信系数,确定所述舱内实际发生火灾的概率。The probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
在一个实施例中,所述根据所述舱内实际发生火灾的概率,判断是否发出火灾报警,包括:In one embodiment, the judging whether to issue a fire alarm according to the probability that a fire actually occurs in the cabin includes:
根据所述各节点的各火灾影响因素的火灾发生临界因素值,计算所述舱内发生火灾的概率阈值;Calculate the probability threshold of fire occurrence in the cabin according to the fire occurrence critical factor value of each fire influencing factor of each node;
根据所述舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值,判断 是否发出火灾报警。According to the actual probability of fire in the cabin and the probability threshold of fire in the cabin, it is judged whether to issue a fire alarm.
根据本发明实施例的第二方面,提供一种火灾检测装置,包括:According to a second aspect of the embodiments of the present invention, a fire detection device is provided, comprising:
第一确定模块,用于确定机舱内各节点的多个火灾影响因素;a first determination module, used for determining multiple fire-influencing factors of each node in the engine room;
第二确定模块,用于确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;a second determination module, configured to determine the fire influence weight value of each fire influence factor among the plurality of fire influence factors of each node;
第三确定模块,用于根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;a third determination module, configured to determine the evaluation value of each node on the probability of fire occurrence in the cabin according to the fire influence weight value of each fire influence factor;
第四确定模块,用于根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;a fourth determination module, configured to determine the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of fire occurrence in the cabin by each node;
判断模块,用于根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。The judging module is used for judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
在一个实施例中,所述第二确定模块包括:In one embodiment, the second determining module includes:
确定子模块,用于确定所述各火灾影响因素的判断矩阵;a determination sub-module for determining the judgment matrix of each of the fire-influencing factors;
检验子模块,用于对所述各火灾影响因素的判断矩阵进行一致性检验;a check sub-module, used to check the consistency of the judgment matrix of each fire influencing factor;
计算子模块,用于根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。The calculation sub-module is configured to calculate the fire influence weight value of each fire influence factor according to the judgment matrix of each fire influence factor that has passed the consistency check.
在一个实施例中,所述计算子模块具体用于:In one embodiment, the calculation submodule is specifically used for:
当所述各火灾影响因素的判断矩阵存在多个时,根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或等于2的正整数;When there are multiple judgment matrices for each fire influencing factor, according to each judgment matrix of each fire influencing factor that passes the consistency check, a preset number of fire influence weight values for each fire influencing factor are calculated. , wherein the preset number is equal to the number of judgment matrices of the respective fire influencing factors that have passed the consistency check; the preset number is a positive integer greater than or equal to 2;
根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间。The fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
在一个实施例中,所述第三确定模块包括:In one embodiment, the third determining module includes:
第一确定子模块,用于根据所述各火灾影响因素的火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间;a first determination sub-module, configured to determine the fire influence weight interval of each fire influence factor according to the fire influence weight value of each fire influence factor;
第二确定子模块,用于根据所述各火灾影响因素的火灾影响权重区间,确定所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件;The second determination submodule is configured to determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;
计算子模块,用于根据所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件,计算所述各节点的各火灾影响因素的最优火灾影响权重值;a calculation submodule, configured to calculate the optimal fire influence weight value of each fire influence factor of each node according to the target influence function of each fire influence factor and the constraint condition of the target influence function;
第三确定子模块,用于根据所述各节点的各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定所述各节点对所述舱内火灾发生概率的评估值。The third determination sub-module is configured to determine, according to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the effect of each node on the interior of the cabin. An estimate of the probability of fire occurrence.
在一个实施例中,所述第四确定模块具体用于:In one embodiment, the fourth determining module is specifically configured to:
根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数;According to the evaluation value of each node on the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
根据所述任意两个节点之间关于评估值之间的支持度函数,建立所述各节点之间关于评估值的初始的支持度矩阵;According to the support function between the any two nodes with respect to the evaluation value, establish an initial support degree matrix between the nodes with respect to the evaluation value;
为所述各节点关于评估值之间的初始的支持度矩阵构建增广支持度矩阵;其中,所述增广支持度矩阵相比于所述初始的支持度矩阵多了一行与一列;Constructing an augmented support matrix for the initial support matrix between the evaluation values of each node; wherein, the augmented support matrix has one more row and one column compared to the initial support matrix;
根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率。According to the augmented support matrix, the probability that a fire actually occurs in the cabin is determined.
在一个实施例中,所述第四确定模块具体还用于:In one embodiment, the fourth determining module is further configured to:
根据所述增广支持度矩阵,确定所述各节点对所述舱内火灾发生概率的评估值的评估可信系数;determining, according to the augmented support matrix, an evaluation credibility coefficient of each node for the evaluation value of the probability of occurrence of fire in the cabin;
根据所述各节点对所述舱内火灾发生概率的评估值和所述各节点对所述舱内火灾发生概率的评估值的评估可信系数,确定所述舱内实际发生火灾的概率。The probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
在一个实施例中,所述判断模块具体用于:In one embodiment, the judging module is specifically used for:
根据所述各节点的各火灾影响因素的火灾发生临界因素值,计算所述舱内发生火灾的概率阈值;Calculate the probability threshold of fire occurrence in the cabin according to the fire occurrence critical factor value of each fire influencing factor of each node;
根据所述舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值,判断是否发出火灾报警。Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
通过本发明的技术方案可实现以下技术效果:The following technical effects can be achieved through the technical solutions of the present invention:
通过计算各节点的各火灾影响因素的火灾影响权重值,可初步算出各节点对舱内火灾发生概率的评估值,而该评估值可能不太准确,需要进行调整,因而,利用评估值可计算出舱内实际发生火灾的概率,从而对火灾进行准确检测,并准确判断是否发出火灾报警,以确保火灾报警的及时性和准确性。By calculating the fire influence weight value of each fire influencing factor at each node, the estimated value of each node to the probability of fire occurrence in the cabin can be preliminarily calculated. However, the estimated value may not be accurate and needs to be adjusted. Therefore, the estimated value can be used to calculate The probability of a fire actually occurring in the cabin, so as to accurately detect the fire, and accurately judge whether to issue a fire alarm, so as to ensure the timeliness and accuracy of the fire alarm.
本发明的其它特征和优点将在随后的说明书中阐述,并且,部分地从说明书中变得显而易见,或者通过实施本发明而了解。本发明的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description, claims, and drawings.
附图用来提供对本发明的进一步理解,并且构成说明书的一部分,与本发明的实施例一起用于解释本发明,并不构成对本发明的限制。The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.
图1为本发明所提供的一种火灾检测传感器节点部署示意图。FIG. 1 is a schematic diagram of the deployment of a fire detection sensor node provided by the present invention.
图2为本发明所提供的一种火灾发生概率误差曲线。FIG. 2 is a fire probability error curve provided by the present invention.
图3为本发明所提供的一种火灾检测方法相比于相关技术中的两种火灾检测算法的检测时间对比图。FIG. 3 is a comparison diagram of detection time between a fire detection method provided by the present invention and two fire detection algorithms in the related art.
图4为本发明所提供的一种判断矩阵个数k与火灾检测误报率关系曲线。FIG. 4 is a relationship curve between the number k of judgment matrices and the false alarm rate of fire detection provided by the present invention.
图5为本发明所提供的一种火灾检测方法相比于相关技术中的两种火灾检测算法的检测精度对比图。FIG. 5 is a comparison diagram of detection accuracy of a fire detection method provided by the present invention compared with two fire detection algorithms in the related art.
图6为本发明所提供的一种火灾检测方法的流程图。FIG. 6 is a flowchart of a fire detection method provided by the present invention.
这里将详细地对示例性实施例进行说明,其示例表示在附图中。下面的描述涉及附图时,除非另有表示,不同附图中的相同数字表示相同或相似的要素。以下示例性实施例中所描述的实施方式并不代表与本公开相一致的所有实施方式。相反,它们仅是与如所附权利要求书中所详述的、本公开的一些方面相一致的装置和方法的例子。Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with this disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as recited in the appended claims.
为了解决上述技术问题,本发明实施例提供了一种火灾检测方法,该方法可用于火灾检测程序、***或装置中,如图6所示,该方法包括步骤S101至步骤S105:In order to solve the above technical problems, an embodiment of the present invention provides a fire detection method, which can be used in a fire detection program, system or device. As shown in FIG. 6 , the method includes steps S101 to S105:
在步骤S101中,确定机舱内各节点的多个火灾影响因素;火灾影响因素包括但不限于:温度、烟雾浓度、CO浓度及红外光线强度。机舱可以是飞机舱、航空舱等,而每个节点即每个传感器节点,当然,每个传感器节点可以有一个或多个类型不同的传感器,用于检测火灾影响因素的实际因素值。In step S101, a plurality of fire-influencing factors of each node in the engine room are determined; the fire-influencing factors include but are not limited to: temperature, smoke concentration, CO concentration, and infrared light intensity. The cabin can be an aircraft cabin, an aviation cabin, etc., and each node is each sensor node. Of course, each sensor node can have one or more sensors of different types, which are used to detect the actual factor value of the fire influencing factor.
在步骤S102中,确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;火灾影响权重值用于表征各火灾影响因素在火灾发生中所占权重。另外,为了确保火灾检测的准确性,各节点采用相同的火灾影响因素。In step S102, the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node is determined; the fire influence weight value is used to represent the weight of each fire influence factor in the occurrence of the fire. In addition, in order to ensure the accuracy of fire detection, each node adopts the same fire influence factor.
在步骤S103中,根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;该评估值用于表征各节点发生火灾的概率的大小,某个节点的评估值越大,该节点发生火灾的概率越大,反之,则越小。In step S103, according to the fire influence weight value of each fire influence factor, the evaluation value of each node on the probability of fire occurrence in the cabin is determined; the evaluation value is used to characterize the probability of fire occurrence at each node, The greater the evaluation value of a node, the greater the probability of fire at the node, and vice versa.
在步骤S104中,根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;In step S104, the probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node;
为确保火灾报警准确性,需要将各节点的火灾发生概率的评估值进行融合,同时为避免部署在舱内的传感器节点出现故障时对融合结果准确性的影响,本发明可基于支持度矩阵对多传感器节点数据进行融合(即对各节点的火灾发生概率的评估值进行融合)。In order to ensure the accuracy of the fire alarm, it is necessary to fuse the evaluation values of the fire probability of each node. At the same time, in order to avoid the influence on the accuracy of the fusion result when the sensor nodes deployed in the cabin fail, the present invention can be based on the support matrix. Multi-sensor node data is fused (that is, the evaluation value of fire occurrence probability of each node is fused).
在步骤S105中,根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。In step S105, it is judged whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
通过计算各节点的各火灾影响因素的火灾影响权重值,可初步算出各节点对舱内火灾发生概率的评估值,而该评估值可能不太准确,需要进行调整,因而,利用评估值可计算出舱内实际发生火灾的概率,从而对火灾进行准确检测,并准确判断是否发出火灾报警,以确保火灾报警的及时性和准确性。By calculating the fire influence weight value of each fire influencing factor at each node, the estimated value of each node to the probability of fire occurrence in the cabin can be preliminarily calculated. However, the estimated value may not be accurate and needs to be adjusted. Therefore, the estimated value can be used to calculate The probability of a fire actually occurring in the cabin, so as to accurately detect the fire, and accurately judge whether to issue a fire alarm, so as to ensure the timeliness and accuracy of the fire alarm.
在一个实施例中,所述确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值,包括:In one embodiment, the determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node includes:
确定所述各火灾影响因素的判断矩阵;determining the judgment matrix for each of the fire influencing factors;
对所述各火灾影响因素的判断矩阵进行一致性检验;Consistency test is carried out on the judgment matrix of each fire influencing factor;
根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。According to the judgment matrix of each fire influence factor that has passed the consistency check, the fire influence weight value of each fire influence factor is calculated.
通过确定各火灾影响因素的判断矩阵,可对该判断矩阵进行一致性检验,如果一致性检验通过则确定该判断矩阵为合理矩阵,否则,应该舍去,而在确定为合理矩阵后,可根据通过一致性检验的各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值,以便确定各火灾影响因素在火灾发生中所占权重。By determining the judgment matrix of each fire influencing factor, the judgment matrix can be checked for consistency. If the consistency check is passed, the judgment matrix is determined to be a reasonable matrix; otherwise, it should be discarded. Through the judgment matrix of each fire-influencing factor through the consistency check, the fire-influencing weight value of each fire-influencing factor is calculated, so as to determine the weight of each fire-influencing factor in the occurrence of fire.
例如:变量间的影响程度关系用判断矩阵定量表示,设判断矩阵为C∈R
n×n,其中n代表变量个数。
For example, the relationship of the degree of influence between variables is quantitatively represented by a judgment matrix. Let the judgment matrix be C∈R n×n , where n represents the number of variables.
判断矩阵C形式为:The judgment matrix C has the form:
矩阵元素
表示第i与第j个变量对火灾状况的影响程度重要性比值。
matrix element Indicates the importance ratio of the influence degree of the i-th and j-th variables on the fire situation.
元素c
ij的填写规则为:
The rules for filling in element c ij are:
若x
i与x
j同样重要,则取c
ij=1,c
ji=1;
If x i and x j are equally important, then take c ij =1, c ji =1;
若x
i比x
j稍微重要,则取c
ij=3,c
ji=1/3;
If x i is slightly more important than x j , then take c ij =3, c ji =1/3;
若x
i比x
j明显重要,则取c
ij=5,c
ji=1/5;
If x i is obviously more important than x j , then take c ij =5, c ji =1/5;
若x
i比x
j重要得多,则取c
ij=7,c
ji=1/7;
If x i is much more important than x j , then take c ij =7, c ji =1/7;
若x
i比x
j绝对重要,则取c
ij=9,c
ji=1/9;
If x i is absolutely more important than x j , then take c ij =9, c ji =1/9;
若x
i与x
j的重要程度介于上述关系之间,则c
ij可取2,4,6,8;c
ji可取1/2,1/4,1/6,1/8各值。因此,元素c
ij按如上规则可取1~9内任意整数。
If the importance of x i and x j is between the above relationships, then c ij can take 2, 4, 6, and 8; c ji can take the values of 1/2, 1/4, 1/6, and 1/8. Therefore, the element c ij can take any integer from 1 to 9 according to the above rules.
在一个实施例中,当所述各火灾影响因素的判断矩阵存在多个时,所述根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值,包括:In one embodiment, when there are multiple judgment matrices of each fire influencing factor, the fire influence weight of each fire influencing factor is calculated according to the judgment matrix of each fire influencing factor that has passed the consistency check values, including:
根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或 等于2的正整数;According to the judgment matrix of each of the fire-influencing factors that pass the consistency check, a preset number of fire-influencing weight values for the fire-influencing factors are calculated, wherein the preset number is equal to all the fire-influencing factors that pass the consistency check. the number of judgment matrices for each fire influencing factor; the preset number is a positive integer greater than or equal to 2;
根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间。The fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
判断矩阵也可以是多个,当存在多个判断矩阵时,基于每个通过一致性检验的判断矩阵均可计算出一个火灾影响权重值,因而,通过一致性检验的判断矩阵有多个时,计算出的各火灾影响因素的火灾影响权重值就会有多个,因而,可形成各火灾影响因素的火灾影响权重区间。There can also be multiple judgment matrices. When there are multiple judgment matrices, a fire impact weight value can be calculated based on each judgment matrix that passes the consistency check. Therefore, when there are multiple judgment matrices that pass the consistency check, There will be multiple calculated fire influence weight values for each fire influence factor, and thus, the fire influence weight interval of each fire influence factor can be formed.
在一个实施例中,根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值,包括:In one embodiment, determining the evaluation value of the fire occurrence probability in the cabin by each node according to the fire influence weight value of each fire influence factor, including:
根据所述各火灾影响因素的火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间;According to the fire influence weight value of each fire influence factor, determine the fire influence weight interval of each fire influence factor;
根据所述各火灾影响因素的火灾影响权重区间,确定所述各火灾影响因素的(用于求区间内权重偏差最小的)目标影响函数和所述目标影响函数的约束条件(约束条件可以是各节点的各火灾影响因素的最优火灾影响权重值之和为1);According to the fire influence weight interval of each fire influence factor, determine the target influence function of each fire influence factor (for finding the smallest weight deviation in the interval) and the constraint condition of the target influence function (the constraint condition may be each The sum of the optimal fire influence weights of each fire influence factor of the node is 1);
根据所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件,计算所述各节点的各火灾影响因素的最优火灾影响权重值;According to the target influence function of each fire influence factor and the constraint condition of the target influence function, calculate the optimal fire influence weight value of each fire influence factor of each node;
根据所述各节点的各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定所述各节点对所述舱内火灾发生概率的评估值。各火灾影响因素的实际因素值即各火灾影响因素的实际取值,例如:火灾影响因素温度(单位℃)所在的实际取值区间为[0,100],烟雾浓度(单位ppm)所在的实际取值区间为[100,1000],CO浓度(单位ppm)所在的实际取值区间为[10,100],红外光线强度(单位Lux)所在的实际取值区间为[100,1000],而为了避免因量纲不同而对评估值造成影响,在计算评估值之前,可对各火灾影响因素的实际因素值进行归一化。According to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the evaluation value of each node to the fire occurrence probability in the cabin is determined. The actual factor value of each fire-influencing factor is the actual value of each fire-influencing factor. For example, the actual value range of the fire-influencing factor temperature (unit ℃) is [0, 100], and the actual value of the smoke concentration (unit ppm) is The interval is [100, 1000], the actual value interval where the CO concentration (unit ppm) is located is [10, 100], and the actual value interval where the infrared light intensity (unit Lux) is located is [100, 1000]. Different classes may affect the evaluation value. Before calculating the evaluation value, the actual factor value of each fire-influencing factor can be normalized.
在确定各火灾影响因素的火灾影响权重区间之后,可确定各火灾影响因素的目标影响函数和该目标影响函数的约束条件,以便根据该目标影响函数和约束条件,从各火灾影响因素的火灾影响权重区间中自动筛选出最优火灾影响权重值,而最优火灾影响权重值应该能够最大限度的兼顾权重区间内的所有权值值,因而,利用各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定出的各节点对所述舱内火灾发生概率的评估值可以保证最终的评估值能够更好的反应实际情况;同时利用最优权重进行加权融合可以有效避免融合时繁琐的计算过程,提高对火情的反应速度。After the fire influence weight interval of each fire influence factor is determined, the target influence function of each fire influence factor and the constraint conditions of the target influence function can be determined, so that according to the target influence function and the constraint conditions, the fire influence of each fire influence factor can be obtained from the fire influence factor. The optimal fire influence weight value is automatically selected from the weight interval, and the optimal fire influence weight value should be able to take into account all the values in the weight interval to the greatest extent. Therefore, the optimal fire influence weight value of each fire influence factor and all The actual factor value of each fire influencing factor of each node is described, and the determined evaluation value of each node for the probability of fire occurrence in the cabin can ensure that the final evaluation value can better reflect the actual situation; at the same time, the optimal weight is used for weighting Fusion can effectively avoid the tedious calculation process during fusion, and improve the response speed to fire.
在一个实施例中,所述根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率,包括:In one embodiment, determining the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node includes:
根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数;According to the evaluation value of each node on the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
令
和
分别表示传感器节点i
*和j
*在k时刻对舱内火灾发生概率的评估值(i
*,j
*∈1,2,…,N)。如果
和
的差值较大,则表示这两个传感器节点在k时刻彼此不支持,如果差值小,则表示这两个节点彼此支持。如果一个节点被大多节点同时支持,它被认为是一个有效的火灾概率评估值。否则,该节点所评估的概率值在融合过程中将被赋予较低权重;
make and Represents the evaluation value (i* ,j * ∈1,2,…,N) of sensor nodes i * and j * on the probability of fire occurrence in the cabin at time k, respectively. if and If the difference is large, it means that the two sensor nodes do not support each other at time k, and if the difference is small, it means that the two nodes support each other. If a node is simultaneously supported by a majority of nodes, it is considered a valid fire probability estimate. Otherwise, the probability value evaluated by this node will be given a lower weight in the fusion process;
为了表示
和
之间的相互支持度,将支持度函数定义为模糊集合中的衰减指数函数:
in order to express and The mutual support between , and the support function is defined as a decaying exponential function in the fuzzy set:
参数α可调,用于调整融合精度,一般设为0.8[16]。The parameter α is adjustable and is used to adjust the fusion accuracy, which is generally set to 0.8 [16].
而支持度函数
表示节点i
*和节点j
*在第k时刻的相互支持度。
while the support function Represents the mutual support of node i * and node j * at the kth moment.
根据所述任意两个节点之间关于评估值之间的支持度函数,建立所述各节点之间关于评估值的初始的支持度矩阵;According to the support function between the any two nodes with respect to the evaluation value, establish an initial support degree matrix between the nodes with respect to the evaluation value;
为所述各节点关于评估值之间的初始的支持度矩阵构建增广支持度矩阵;其中,所述增广支持度矩阵相比于所述初始的支持度矩阵多了一行与一列;Constructing an augmented support matrix for the initial support matrix between the evaluation values of each node; wherein, the augmented support matrix has one more row and one column compared to the initial support matrix;
根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率。According to the augmented support matrix, the probability that a fire actually occurs in the cabin is determined.
在根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数之后,由于每两个节点之间都存在一个支持度函数,也即所有节点之间存在很多个支持度函数,为了方便处理,可将支持度函数转换为关于评估值的初始的支持度矩阵,而为了便于为各节点自适应分配权重系数(即各节点的评估值的评估可信系数),以对舱内火灾发生概率的评估值进行融合和调整,达到提高舱内实际发生火灾的概率的准确性的目的,可基于初始的支持度矩阵构建增广支持度矩阵,以提高所计算的所述舱内实际发生火灾的概率的准确性,进而能够对火灾进行准确预测。After establishing a support function between any two nodes of the nodes with respect to the evaluation value according to the evaluation value of the fire probability in the cabin by the nodes, since there is a support function between every two nodes Degree function, that is, there are many support degree functions between all nodes. In order to facilitate processing, the support degree function can be converted into an initial support degree matrix about the evaluation value, and in order to facilitate the adaptive allocation of weight coefficients for each node (ie, The evaluation credibility coefficient of the evaluation value of each node) to fuse and adjust the evaluation value of the probability of fire occurrence in the cabin to achieve the purpose of improving the accuracy of the probability of actual fire occurrence in the cabin, which can be constructed based on the initial support matrix The support matrix is extended to improve the accuracy of the calculated probability of a fire actually occurring in the cabin, thereby enabling accurate prediction of the fire.
在一个实施例中,所述根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率,包括:In one embodiment, the determining the probability of a fire actually occurring in the cabin according to the augmented support matrix includes:
根据所述增广支持度矩阵,确定所述各节点对所述舱内火灾发生概率的评估值的评估可信系数;determining, according to the augmented support matrix, an evaluation credibility coefficient of each node for the evaluation value of the probability of occurrence of fire in the cabin;
根据所述各节点对所述舱内火灾发生概率的评估值和所述各节点对所述舱内火灾发生概率的评估值的评估可信系数,确定所述舱内实际发生火灾的概率。The probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
基于该增广支持度矩阵可为可信性高的各节点的评估值分配较高的评估可信系数,而为可信性低的各节点的评估值分配较低的评估可信系数,以避免部署在舱内的传感器节点出现故障或者检测不准而对各节点的评估值的融合结果准确性造成不良影响,使得评估值的融合具有容错性,从而大幅度提高舱内实际发生火灾的概率的预测准确性,具体地,本发明的技术方案从检测到火 灾并发出报警信号所需时间和***误报率的角度分别与相关技术中的灰模糊神经网络融合算法和模糊逻辑融合算法进行实验比较之后,发现本发明的火灾检测方案能够在10s内检测到火情,同时将误报率降低到了0.5%以下,极大地降低了火灾误报率,提高了火灾检测的准确性和及时性。Based on the augmented support matrix, a higher evaluation credibility coefficient can be assigned to the evaluation value of each node with high reliability, and a lower evaluation credibility coefficient can be assigned to the evaluation value of each node with low credibility, so that Avoid failure or inaccurate detection of sensor nodes deployed in the cabin, which will adversely affect the accuracy of the fusion results of the evaluation values of each node, making the fusion of evaluation values fault-tolerant, thereby greatly improving the probability of actual fire in the cabin. Specifically, the technical scheme of the present invention conducts experiments with the gray-fuzzy neural network fusion algorithm and the fuzzy logic fusion algorithm in the related art from the perspectives of the time required to detect a fire and issue an alarm signal and the system false alarm rate. After comparison, it is found that the fire detection scheme of the present invention can detect fire within 10s, while reducing the false alarm rate to less than 0.5%, greatly reducing the fire false alarm rate and improving the accuracy and timeliness of fire detection.
在一个实施例中,所述根据所述舱内实际发生火灾的概率,判断是否发出火灾报警,包括:In one embodiment, the judging whether to issue a fire alarm according to the probability that a fire actually occurs in the cabin includes:
根据所述各节点的各火灾影响因素的火灾发生临界因素值(即达到发生火灾的临界状态时,各火灾影响因素的取值),计算所述舱内发生火灾的概率阈值;Calculate the probability threshold of fire occurrence in the cabin according to the fire occurrence critical factor value of each fire influencing factor of each node (that is, the value of each fire influencing factor when the critical state of fire occurrence is reached);
根据所述舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值,判断是否发出火灾报警。Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
根据所述各节点的各火灾影响因素的火灾发生临界因素值,可准确计算所述舱内发生火灾的概率阈值,然后将当前计算出的舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值进行比较,即可准确判断是否发出火灾报警,具体地,如果当前计算出的舱内实际发生火灾的概率大于或等于所述舱内发生火灾的概率阈值,说明要发生了火灾,则进行火灾报警,否则,说明尚未发生火灾,则不需要进行火灾报警。According to the fire occurrence critical factor value of each fire influencing factor of each node, the probability threshold of fire occurrence in the cabin can be accurately calculated, and then the currently calculated probability of actual fire occurrence in the cabin and the fire occurrence in the cabin can be calculated. By comparing the probability threshold of the cabin, it can be accurately judged whether a fire alarm is issued. Specifically, if the currently calculated probability of actual fire in the cabin is greater than or equal to the probability threshold of fire in the cabin, it means that a fire is about to occur, then Fire alarm is carried out, otherwise, it means that fire has not yet occurred, and there is no need to carry out fire alarm.
最后,需要明确的是:本领域技术人员可根据实际需求,将上述多个实施例进行自由组合。Finally, it should be clarified that those skilled in the art can freely combine the above-mentioned multiple embodiments according to actual needs.
下面将进一步详细说明本发明的技术方案:The technical scheme of the present invention will be described in further detail below:
针对飞机舱内使用单一传感器检测火灾时存在较高误报率的情况,提出了多传感器数据融合方法对火灾进行检测。首先在无线传感网络(WSN)的各传感器节点上利用改进的层次分析法(AHP)计算温度、烟雾浓度、CO浓度和红外光线强度在火灾发生时所占的权重,并利用变量加权融合方法评估舱内火灾发生的可能性概率;然后基于各节点对火灾发生概率评估数据间的相互支持度,为各评估值自适应分配权重系数,并将所有节点评估值进行加权融合,最终获得火灾发生概率;最后通过与阈值概率相比,判断是否有火灾发生。从***检测到火灾并发出报警信号所需时间和***误报率的角度分别与灰模糊神经网络融合算法和模糊逻辑融合算法进行比较,实验表明该火灾检测算法能够在10s内检测到火情,同时将误报率降低到了0.5%以下,验证了算法在及时性与准确性方面的优越性。并通过设置故障传感器节点,证明该算法具有一定的容错能力。Aiming at the high false alarm rate when using a single sensor to detect fire in the aircraft cabin, a multi-sensor data fusion method is proposed to detect fire. First, the weights of temperature, smoke concentration, CO concentration and infrared light intensity in the occurrence of fire are calculated by the improved Analytic Hierarchy Process (AHP) on each sensor node of the wireless sensor network (WSN), and the variable weighted fusion method is used. Evaluate the probability of fire occurrence in the cabin; then based on the mutual support between the evaluation data of fire occurrence probability of each node, adaptively assign weight coefficients to each evaluation value, and weight and fuse the evaluation values of all nodes, and finally obtain the fire Occurrence probability; finally, it is judged whether there is a fire by comparing with the threshold probability. From the perspective of the time required for the system to detect a fire and issue an alarm signal and the system's false alarm rate, it is compared with the gray-fuzzy neural network fusion algorithm and the fuzzy logic fusion algorithm respectively. The experiments show that the fire detection algorithm can detect fire within 10s. At the same time, the false alarm rate is reduced to less than 0.5%, which verifies the superiority of the algorithm in terms of timeliness and accuracy. And by setting faulty sensor nodes, it is proved that the algorithm has a certain fault tolerance.
基于对上述方法的研究,针对飞机环境狭小、封闭等特点,在研究基于WSN中火灾检测报警问题中,考虑多种因素作用下的数据融合方法。为了在融合变量较多的情况下,检测算法仍然能够准确、及时的检测到火灾,本发明在WSN中的各传感器节点处,利用改进的AHP计算影响火灾发生概率的各变量权重,并根据多变量加权融合对舱内火灾发生概率进行评估;同时,为了避 免故障传感器对检测精度造成的影响,本发明利用自适应加权融合的方法,将各节点的评估数据进行融合。其中,在不知道各节点检测能力的情况下,通过构造支持度矩阵,计算各节点对火灾评估结果的可信度,对可信度高的节点分配较高权重,反之则分配较低权重,从而将故障传感器对融合结果造成的影响降到最低。Based on the research of the above methods, in view of the characteristics of small and closed aircraft environment, the data fusion method under the action of various factors is considered in the study of the fire detection and alarm problem based on WSN. In order that the detection algorithm can still detect the fire accurately and timely in the case of many fusion variables, the present invention uses the improved AHP at each sensor node in the WSN to calculate the weight of each variable affecting the probability of fire occurrence, and calculates the weight of each variable affecting the probability of fire occurrence according to the multiple The variable weighted fusion evaluates the probability of fire occurrence in the cabin; at the same time, in order to avoid the influence of the faulty sensor on the detection accuracy, the present invention uses the adaptive weighted fusion method to fuse the evaluation data of each node. Among them, in the case of not knowing the detection ability of each node, the credibility of the fire assessment results of each node is calculated by constructing a support matrix, and a higher weight is assigned to a node with high credibility, and a lower weight is assigned otherwise. This minimizes the impact of faulty sensors on fusion results.
本发明的主要贡献在于:为避免检测***对多变量数据进行融合时出现及时性降低以及在部分传感器存在故障的情况下造成融合结果有较大偏差的问题。首先,本发明考虑影响火灾发生的各变量所占权重,基于改进的AHP变量权重计算方法,设计了一种新的“多变量加权融合”火灾评估算法,在融合变量较多时仍能快速的检测出火灾状况,同时本发明将较多的相关变量进行融合,极大的降低了***误报率;其次,本发明利用自适应加权融合的方式将各传感器节点对火灾发生概率的评估值进行融合,通过构建支持度矩阵为各节点自适应分配权系数,在一定程度上避免了由于部分故障传感器的测量偏差而造成融合精度下降的问题。The main contribution of the present invention is to avoid the problems of reduced timeliness when the detection system fuses multi-variable data, and large deviations in fusion results when some sensors are faulty. First, the present invention considers the weight of each variable that affects the occurrence of fire, and designs a new "multi-variable weighted fusion" fire evaluation algorithm based on the improved AHP variable weight calculation method, which can still detect quickly when there are many fusion variables. In addition, the present invention fuses more related variables, which greatly reduces the false alarm rate of the system; secondly, the present invention uses adaptive weighted fusion to fuse the evaluation values of the fire occurrence probability of each sensor node. , by constructing a support matrix to assign weight coefficients to each node adaptively, to a certain extent, it avoids the problem of decreased fusion accuracy caused by the measurement deviation of some faulty sensors.
本发明结构为:第一节介绍了一种在各传感器节点处利用改进的AHP和多变量加权融合算法对火灾发生概率进行评估的方法;第二节介绍了一种通过构建增广支持度矩阵,将各节点对火灾发生概率的评估值进行自适应加权融合的方法。第三节为实验部分,验证了本发明提出的火灾检测算法具有及时性、准确性和容错性。第四节为本发明结论。The structure of the invention is as follows: the first section introduces a method for evaluating the probability of fire occurrence by using the improved AHP and multivariate weighted fusion algorithm at each sensor node; the second section introduces a method by constructing an augmented support matrix , a method of adaptively weighted fusion of the estimated value of fire occurrence probability by each node. The third section is the experimental part, which verifies the timeliness, accuracy and fault tolerance of the fire detection algorithm proposed by the present invention. The fourth section is the conclusion of the present invention.
1、基于多变量加权融合的火灾评估算法1. Fire assessment algorithm based on multivariate weighted fusion
本节研究了一种改进的AHP法,提出了多变量权重计算方法,进而确定温度、烟雾浓度、CO浓度及红外光线强度在火灾发生中所占权重。在WSN的每个传感器节点处提出了一种新的“多变量加权融合”算法,获得各节点对舱内火灾发生概率的评估值。In this section, an improved AHP method is studied, and a multivariate weight calculation method is proposed to determine the weights of temperature, smoke concentration, CO concentration and infrared light intensity in fire occurrence. A new "multi-variable weighted fusion" algorithm is proposed at each sensor node of WSN to obtain the evaluation value of each node's probability of fire occurrence in the cabin.
1.1改进的AHP多变量权重算法1.1 Improved AHP multivariate weighting algorithm
要对舱内火灾发生情况进行评估与判断,则要确定相关的影响因素变量,而变量的权重就表示它们在评估过程中所处的地位。一个变量权重的大小将对火灾评估结果产生重要的影响。因此,既科学又合理的确定变量权重是是一个值得注意的问题,本发明主要采用改进的AHP法确定变量权重[11]。传统的AHP通过选取一个判断矩阵,计算最低层对最高层的相对权重,并按此权重对最低层中的各种方案、措施进行排序。但由于选取的判断矩阵个数单一,所求权重的准确性不高。为保证所求权重的准确性,本发明利用改进的AHP计算各影响因素变量在火灾发生过程中所占的权重,通过选取多个判断矩阵并在权重区间内构建目标函数的方式,求取各变量的最优权重。To evaluate and judge the occurrence of fire in the cabin, it is necessary to determine the relevant influencing factor variables, and the weight of the variables indicates their position in the evaluation process. The size of a variable weight will have an important impact on the fire assessment results. Therefore, it is a problem worth noting to determine the variable weight scientifically and reasonably. The present invention mainly adopts the improved AHP method to determine the variable weight [11]. The traditional AHP calculates the relative weight of the lowest layer to the highest layer by selecting a judgment matrix, and sorts the various schemes and measures in the lowest layer according to this weight. However, due to the single number of judgment matrices selected, the accuracy of the required weights is not high. In order to ensure the accuracy of the required weights, the present invention uses the improved AHP to calculate the weights occupied by each influencing factor variable in the process of fire occurrence, and by selecting multiple judgment matrices and constructing the objective function in the weight interval, the method is Optimal weights for variables.
改进的AHP法评估火灾问题的基本过程是:1)确定影响火灾发生的各因素变量;2)建立各变量的判断矩阵;3)对判断矩阵进行一致性检验;4)特征向量法求变量权重区间;5)设置目标函数求取最优权重。权重计算步骤如下:The basic process of the improved AHP method for evaluating fire problems is: 1) Determine the factors and variables that affect the occurrence of fire; 2) Establish a judgment matrix for each variable; 3) Consistency test on the judgment matrix; 4) Eigenvector method to calculate the variable weight 5) Set the objective function to obtain the optimal weight. The weight calculation steps are as follows:
步骤一:设影响火灾发生的因素变量为温度、烟雾浓度、CO浓度及红外光线强度,变量用x
i(i=1,2,3,,n)表示,i为变量下标。设x
1代表温度、x
2代表烟雾浓度、x
3代表CO浓度,x
4代表红外光线强度;
Step 1: Set the factor variables that affect the occurrence of fire as temperature, smoke concentration, CO concentration and infrared light intensity. The variables are represented by x i (i=1,2,3,,n), and i is the variable subscript. Let x 1 represent temperature, x 2 represent smoke concentration, x 3 represent CO concentration, and x 4 represent infrared light intensity;
步骤二:建立各变量的判断矩阵:Step 2: Establish a judgment matrix for each variable:
变量间的影响程度关系用判断矩阵定量表示,设判断矩阵为C∈R
n×n,其中n代表变量个数。
The influence degree relationship between variables is quantitatively represented by a judgment matrix. Let the judgment matrix be C∈R n×n , where n represents the number of variables.
判断矩阵C形式为:The judgment matrix C has the form:
矩阵元素
表示第i与第j个变量对火灾状况的影响程度重要性比值。
matrix element Indicates the importance ratio of the influence degree of the i-th and j-th variables on the fire situation.
元素c
ij的填写规则为:
The rules for filling in element c ij are:
若x
i与x
j同样重要,则取c
ij=1,c
ji=1;
If x i and x j are equally important, then take c ij =1, c ji =1;
若x
i比x
j稍微重要,则取c
ij=3,c
ji=1/3;
If x i is slightly more important than x j , then take c ij =3, c ji =1/3;
若x
i比x
j明显重要,则取c
ij=5,c
ji=1/5;
If x i is obviously more important than x j , then take c ij =5, c ji =1/5;
若x
i比x
j重要得多,则取c
ij=7,c
ji=1/7;
If x i is much more important than x j , then take c ij =7, c ji =1/7;
若x
i比x
j绝对重要,则取c
ij=9,c
ji=1/9;
If x i is absolutely more important than x j , then take c ij =9, c ji =1/9;
若x
i与x
j的重要程度介于上述关系之间,则c
ij可取2,4,6,8;c
ji可取1/2,1/4,1/6,1/8各值。因此,元素c
ij按如上规则可取1~9内任意整数。
If the importance of x i and x j is between the above relationships, then c ij can take 2, 4, 6, and 8; c ji can take the values of 1/2, 1/4, 1/6, and 1/8. Therefore, the element c ij can take any integer from 1 to 9 according to the above rules.
步骤三:对判断矩阵进行一致性检验:Step 3: Check the consistency of the judgment matrix:
设一致性比率
其中,
(λ为C的最大特征值,n为判断矩阵阶数)。RI为平均随机一致性指标,其取值由判断矩阵的不同阶数决定(如表1所示)。当CR<0.1时相应的判断矩阵C符合一致性检验,为合理矩阵,否则应舍去。
Set Consistency Ratio in, (λ is the largest eigenvalue of C, and n is the order of the judgment matrix). RI is the average random consistency index, and its value is determined by the different orders of the judgment matrix (as shown in Table 1). When CR<0.1, the corresponding judgment matrix C conforms to the consistency test and is a reasonable matrix, otherwise it should be discarded.
表1不同阶数下的RI取值Table 1 RI values under different orders
矩阵阶数 |
11 | 22 | 33 | 44 | 55 | 66 | 77 | 88 |
RI |
00 | 00 | 0.520.52 | 0.890.89 | 1.121.12 | 1.261.26 | 1.361.36 | 1.411.41 |
步骤四:特征向量法求变量权重区间:Step 4: Eigenvector method to find the variable weight interval:
基于上述构建的判断矩阵,利用特征向量法计算各变量权重,并将计算出的权重写成区间的形式,记此区间为变量的权重区间(该区间由独立的权重点组成)。Based on the judgment matrix constructed above, the weight of each variable is calculated by the eigenvector method, and the calculated weight is written into the form of an interval, which is recorded as the weight interval of the variable (this interval is composed of independent weight points).
对应任意选取的一个合理判断矩阵,根据特征向量法可以计算出变量的一个权重。权重计算公式如下:Corresponding to an arbitrarily selected reasonable judgment matrix, a weight of the variable can be calculated according to the eigenvector method. The weight calculation formula is as follows:
式中i为变量下标(i=1,2,3,,n),k代表选取的合理判断矩阵个数(k=1,2,3,,m;为正整数);
是在第k个判断矩阵下求得的第i个变量的一个权重,因此对于每个变量i可求得k个权重。
In the formula, i is the variable subscript (i=1,2,3,,n), and k represents the number of reasonable judgment matrices selected (k=1,2,3,,m; is a positive integer); is a weight of the i-th variable obtained under the k-th judgment matrix, so k weights can be obtained for each variable i.
将上述求得的各变量的k个权重写成区间的形式,该区间即为变量的权重区间。对于任一变量i,基于公式(2)~(4)可得变量i的一个权重区间,且区间内包含k个权值。The k weights of each variable obtained above are written in the form of an interval, and the interval is the weight interval of the variable. For any variable i, a weight interval of the variable i can be obtained based on formulas (2) to (4), and the interval includes k weights.
步骤五:设置目标函数求取最优权重:Step 5: Set the objective function to obtain the optimal weight:
最优权重应该能够最大限度的兼顾权重区间内的所有权值信息,从而保证最终的评估结果能够更好的反应实际情况;同时利用最优权重进行加权融合可以有效避免融合时繁琐的计算过程,提高对火情的反应速度。设w
i表示第i个变量的一个权重,构建求区间内权重偏差最小的目标函数(即上文的目标影响函数)为:
The optimal weight should be able to take into account all the value information in the weight interval to the greatest extent, so as to ensure that the final evaluation result can better reflect the actual situation; at the same time, using the optimal weight for weighted fusion can effectively avoid the tedious calculation process during fusion, improve Speed of reaction to fire. Let w i represent a weight of the i-th variable, and construct the objective function that minimizes the weight deviation in the interval (that is, the objective influence function above) as:
目标函数的约束条件为:The constraints of the objective function are:
基于公式(5)-(6)求得第i个变量的最优权重记为
Based on formulas (5)-(6), the optimal weight of the i-th variable is obtained as
1.2变量加权融合的火灾发生概率评估算法1.2 Fire probability evaluation algorithm based on variable weighted fusion
针对舱内不同环境下各变量的实际取值情况,设温度(单位℃)所在区间为[0,100],烟雾浓度(单位ppm)区间为[100,1000],CO浓度(单位ppm)区间为[10,100],红外光线强度(单位Lux)区间为[100,1000]。构建转换函数将变量真实值映射到[0-1]之间,以避免因量纲不同而对计算结果造成影响,转换函数为:According to the actual value of each variable in different environments in the cabin, the temperature (unit °C) is set in the interval [0, 100], the smoke concentration (in ppm) in the interval [100, 1000], and the CO concentration (in ppm) in the interval [ 10, 100], the infrared light intensity (unit Lux) is in the interval [100, 1000]. A conversion function is constructed to map the real value of the variable to [0-1] to avoid affecting the calculation result due to different dimensions. The conversion function is:
其中,max(x
i)为变量i所在区间内的最大值,min(x
i)为变量i所在区间的最小值。
Among them, max( xi ) is the maximum value in the interval where the variable i is located, and min( xi ) is the minimum value in the interval where the variable i is located.
设舱内部署N个传感器节点,各节点对舱内火灾发生概率的评估值为p,归一化后的变量值记为
变量最优权重为
变量加权结果为火灾发生概率,计算公式为:
Suppose N sensor nodes are deployed in the cabin, the evaluation value of each node for the probability of fire occurrence in the cabin is p, and the normalized variable value is recorded as The optimal weight of the variable is The variable weighted result is the probability of fire occurrence, and the calculation formula is:
其中
即为节点i
*对火灾发生概率的评估结果。
in It is the evaluation result of the probability of fire occurrence by node i *.
2、基于节点数据支持度的融合算法2. Fusion algorithm based on node data support
火灾检测时,如果舱内火灾发生概率大于阈值概率,需要发出火警信号。为确保火灾报警准确性,需要将各节点评估的火灾发生概率值进行融合。同时为避免部署在舱内的传感器节点出现故障时对融合结果准确性的影响,本节提出了一种基于支持度矩阵的多传感器节点数据融合方法。该方法在不了解各节点对火灾发生概率的评估能力的情况下,客观地反映了节点评估数据之间的支持程度。通过构建增广矩阵,自适应调整各节点火灾概率评估值在融合过程中的权重系数,使融合效果达到最佳。该方法的特点在于可以在线融合大量数据同时在自适应权系数分配过程中,通过对可信度高的火灾评估值分配较高权重,反之则分配较低权重的方式,使算法具有一定的容错能力。During fire detection, if the probability of fire in the cabin is greater than the threshold probability, a fire alarm signal needs to be issued. In order to ensure the accuracy of fire alarm, it is necessary to fuse the fire probability values evaluated by each node. At the same time, in order to avoid the influence on the accuracy of fusion results when the sensor nodes deployed in the cabin fail, this section proposes a multi-sensor node data fusion method based on support matrix. This method objectively reflects the support degree between the evaluation data of nodes without knowing the evaluation ability of each node to the probability of fire occurrence. By constructing an augmented matrix, the weight coefficient of each node's fire probability evaluation value in the fusion process is adaptively adjusted to achieve the best fusion effect. The feature of this method is that it can fuse a large amount of data online, and in the process of adaptive weight coefficient allocation, by assigning a higher weight to the fire evaluation value with high reliability, and otherwise assigning a lower weight, the algorithm has a certain fault tolerance. ability.
2.1构造支持度矩阵2.1 Constructing the support matrix
设有N个传感器节点(每个节点由温度、烟雾浓度、CO浓度和红外光线强度传感器组成)来测量环境变量,在每个节点处经变量加权融合计算均能获得此刻舱内火灾发生概率,令
和
分别表示传感器节点i
*和j
*在k时刻对舱内火灾发生概率的评估值(i
*,j
*∈1,2,…,N)。如果
和
的差值较大,则表示这两个传感器节点在k时刻彼此不支持,如果差值小,则表示这两个节点彼此支持。如果一个节点被大多节点同时支持,它被认为是一个有效的火灾概率评估值。否则,该节点所评估的概率值在融合过程中将被赋予较低权重。
There are N sensor nodes (each node is composed of temperature, smoke concentration, CO concentration and infrared light intensity sensors) to measure environmental variables, and the probability of fire occurrence in the cabin at this moment can be obtained through variable weighted fusion calculation at each node. make and Represents the evaluation value (i* ,j * ∈1,2,…,N) of sensor nodes i * and j * on the probability of fire occurrence in the cabin at time k, respectively. if and If the difference is large, it means that the two sensor nodes do not support each other at time k, and if the difference is small, it means that the two nodes support each other. If a node is simultaneously supported by a majority of nodes, it is considered a valid fire probability estimate. Otherwise, the probability value evaluated by this node will be given a lower weight in the fusion process.
为了表示
和
之间的相互支持度,将支持度函数定义为模糊集合中的衰减指数函数:
in order to express and The mutual support between , and the support function is defined as a decaying exponential function in the fuzzy set:
参数α可调,用于调整融合精度,一般设为0.8[16]。The parameter α is adjustable and is used to adjust the fusion accuracy, which is generally set to 0.8 [16].
支持度函数
表示节点i
*和节点j
*在第k时刻的相互支持度,一般可用矩阵形式表示:
Support function Represents the mutual support of node i * and node j * at the kth moment, which can generally be expressed in matrix form:
在得到支持度矩阵
后,可以确定各节点之间的相互支持度关系。对于
的第i
*列,
越大,在节点i
*处得到的火灾概率评估值的可信度越高,反之则可信度较低。
After getting the support matrix After that, the mutual support relationship between the nodes can be determined. for the i * th column of , The larger the value, the higher the reliability of the fire probability evaluation value obtained at the node i *, and the lower the reliability otherwise.
2.2构造增广支持度矩阵2.2 Constructing the Augmented Support Matrix
在实际检测中会得到大量的火灾概率评估值,为了在每个采样时间内集成所有的评估值,定义一个增广支持度矩阵,它将支持度矩阵的维数增加一行和一列。构建这个新维度支持度矩阵的目的是测量当前所有评估值与以前评估值之间的相互支持度,从而为各节点自适应分配权重系数。In actual detection, a large number of fire probability evaluation values will be obtained. In order to integrate all evaluation values in each sampling time, an augmented support matrix is defined, which increases the dimension of the support matrix by one row and one column. The purpose of constructing this new dimension support matrix is to measure the mutual support between all current evaluation values and previous evaluation values, so as to adaptively assign weight coefficients to each node.
构建增广矩阵的具体步骤如下:The specific steps of constructing the augmented matrix are as follows:
当k=1时,用前N个评估值的平均值
作为初始火灾概率评估值
When k=1, use the average of the first N evaluation values As an initial fire probability assessment value
计算k时刻增广支持度矩阵新增的行和列:Calculate the new rows and columns of the augmented support matrix at time k:
表示(k-1)时刻所有节点火灾概率评估值的融合结果。
Represents the fusion result of the fire probability evaluation values of all nodes at time (k-1).
第k时刻的增广支持度矩阵可定义为:The augmented support matrix at time k can be defined as:
2.3加权融合节点评估值2.3 Weighted fusion node evaluation value
设
表示
的融合权重系数(w
N+1(k)为(k-1)时刻融合结果
的权重系数),
满足:
Assume Express The fusion weight coefficient (w N+1 (k) is the fusion result at (k-1) time weight coefficient), satisfy:
在增广支持度矩阵
中,通过对
的第i
*列进行积分得到
的可信 度,因此,
是对
的积分。设一组矢量
每个元素是对sj*i*(k)的第i*进行积分的结果,,
为:
The augmented support matrix , through the Integrate the i * th column to get reliability, therefore, is true 's points. set a vector Each element is the result of integrating the i*th of sj*i*(k), for:
其中,i
*,j
*=1,2,…,N+1。
Among them, i * ,j * =1,2,...,N+1.
根据公式(12),式(14)变为:According to formula (12), formula (14) becomes:
其中,W=[w
1(k),w
2(k),…w
N+1(k)]
T,A=[a
1(k),a
2(k),…a
N+1(k)]
T。
Wherein, W=[w 1 (k),w 2 (k),…w N+1 (k)] T , A=[a 1 (k),a 2 (k),…a N+1 (k )] T .
由于
是一个非负对称矩阵,由弗罗贝尼乌斯-佩龙定理,
具有最大特征值λ
*(λ
*>0)。
because is a nonnegative symmetric matrix, by the Frobenius-Perron theorem, Has the largest eigenvalue λ * (λ * > 0).
因此,therefore,
计算λ
*对应的正特征向量A,有:
Calculate the positive eigenvector A corresponding to λ*, there are:
W=λ
*A (17)
W = λ * A (17)
现在可得权系数与特征向量的比例关系为:Now the proportional relationship between the available weight coefficient and the eigenvector is:
根据公式(13),计算每个节点的权系数:According to formula (13), calculate the weight coefficient of each node:
其中,i
*,j
*=1,2,…,N+1。
Among them, i * ,j * =1,2,...,N+1.
然后,得到最终的融合表达式:Then, to get the final fusion expression:
其中
代表将所有传感器节点评估值融合后的舱内火灾发生概率,将其与火灾阈值概率进行比较,若大于阈值概率则***发出火警信号。
in It represents the probability of fire occurrence in the cabin after fusing the evaluation values of all sensor nodes, and compares it with the fire threshold probability. If it is greater than the threshold probability, the system sends out a fire alarm signal.
火灾发生临界值设成:温度为55℃、烟雾浓度为700ppm、CO浓度为20ppm,红外光线强度为760Lux。将以上变量归一化后代入公式(8),所得结果即为火灾阈值概率。The critical value of fire occurrence is set as: the temperature is 55℃, the smoke concentration is 700ppm, the CO concentration is 20ppm, and the infrared light intensity is 760Lux. After the above variables are normalized and entered into formula (8), the result is the fire threshold probability.
利用变量加权融合与节点火灾概率评估数据加权融合方法计算舱内火灾 发生概率时,第一节中步骤一到五为离线计算过程,旨在获取变量x
i(i=1,2,3,4)权重。对舱内火灾状况进行在线检测时,首先在各节点上将传感器检测到的环境数据利用公式(7)进行归一化再由式(8)对火灾发生概率进行评估;然后基于节点数据相互支持度,为各节点评估出的概率值进行自适应权重系数分配,经加权融合后计算出舱内实际火灾发生概率值
最终,将
与阈值概率比较,判断是否发出火警信号。
When using the variable weighted fusion and the node fire probability evaluation data weighted fusion method to calculate the fire probability in the cabin, steps 1 to 5 in the first section are offline calculation processes, which aim to obtain the variables x i (i=1,2,3,4 )Weights. When online detection of the fire condition in the cabin, the environmental data detected by the sensor is first normalized by formula (7) on each node, and then the fire probability is evaluated by formula (8); then based on the node data, mutual support The probability value of each node is estimated by self-adaptive weight coefficient distribution, and the actual fire probability value in the cabin is calculated after weighted fusion. Ultimately, will Compare with the threshold probability to judge whether to send out a fire alarm signal.
3、仿真实验验证3. Simulation experiment verification
在空间内随机均匀部署50个传感器节点,每个节点由温度、烟雾浓度、CO浓度和红外光线强度传感器组成,节点部署示意图如图1所示。每个节点采集的环境参数为
变量单位x1为℃,x2为ppm,x3为ppm和x4为Lux。在相同实验背景下,首先计算影响火灾发生概率的变量:温度、烟雾浓度、CO浓度和红外光线强度所占权重,然后将本发明所提出的火灾在线检测方法与相关技术中的灰模糊神经网络融合算法和模糊逻辑融合算法进行比较,验证在线检测方法(即本发明的火灾检测方法)在火灾检测及时性、准确性与容错性方面的优越性。
50 sensor nodes are randomly and uniformly deployed in the space. Each node is composed of temperature, smoke concentration, CO concentration and infrared light intensity sensors. The schematic diagram of node deployment is shown in Figure 1. The environmental parameters collected by each node are: Variable units x1 is °C, x2 is ppm, x3 is ppm and x4 is Lux. Under the same experimental background, the variables that affect the probability of fire occurrence: temperature, smoke concentration, CO concentration and infrared light intensity are firstly calculated. The fusion algorithm is compared with the fuzzy logic fusion algorithm to verify the superiority of the online detection method (ie, the fire detection method of the present invention) in terms of timeliness, accuracy and fault tolerance of fire detection.
3.1各变量权重计算3.1 Calculation of the weight of each variable
根据第一节关于变量权重计算的相关介绍,下面求取各变量权重。在计算各变量权重之前,首先做出如下假设,变量x
i(i=1,2,3,4)对火灾产生的影响程度重要性关系排序为:
According to the relevant introduction on the calculation of variable weights in Section 1, the weights of each variable are calculated as follows. Before calculating the weight of each variable, first make the following assumptions, the importance relationship of the degree of influence of variables x i (i=1, 2, 3, 4) on the fire is as follows:
假设1:x
1>x
2>x
3>x
4;
Assumption 1: x 1 >x 2 >x 3 >x 4 ;
假设2:x
1>x
2>x
4>x
3;
Assumption 2: x 1 >x 2 >x 4 >x 3 ;
假设3:x
1>x
3>x
2>x
4;
Assumption 3: x 1 >x 3 >x 2 >x 4 ;
假设4:x
1>x
3>x
4>x
2;
Assumption 4: x 1 >x 3 >x 4 >x 2 ;
假设5:x
1>x
4>x
2>x
3;
Assumption 5: x 1 >x 4 >x 2 >x 3 ;
假设6:x
1>x
4>x
3>x
2;
Assumption 6: x 1 >x 4 >x 3 >x 2 ;
假设24:x
4>x
3>x
2>x
1
Assumption 24: x 4 >x 3 >x 2 >x 1
共24种可能性情况,记上述24种假设为A1,A2,…,A24。There are a total of 24 possible situations, and the above 24 hypotheses are recorded as A1, A2, …, A24.
表2假设13下变量权重区间计算结果Table 2 Calculation results of variable weight interval under assumption 13
依据上述假设中变量间的影响程度关系,本实验在k取5时计算各变量的权重区间(其中B1~B5为判断矩阵),并利用公式(5)和(6)在权重区间内计算各变量的最优权重。表2描述了第1节中关于权重计算的相关过程(其中表2记录的是在假设13下得出的权重区间),表3列举了相应的输出结果:According to the relationship between the influence degrees of the variables in the above assumptions, this experiment calculates the weight interval of each variable when k is 5 (where B1 to B5 are the judgment matrix), and uses formulas (5) and (6) to calculate each variable in the weight interval. Optimal weights for variables. Table 2 describes the relevant process of weight calculation in Section 1 (where Table 2 records the weight interval obtained under assumption 13), and Table 3 lists the corresponding output results:
表3所有假设下各变量最优权重的输出结果Table 3 Output results of optimal weights for each variable under all assumptions
本实验选取五组环境参数X1=[38,500,18,188];X2=[89,580,30,200];X3=[39,450,70,229];X4=[51,750,38,199];X5=[39,480,19,705]。在五组参数下利用本发明的在线检测方法,分别得到在A1,A2,…,A24假设下火灾发生的可能性概率。在输入参数相同时,将假设A1,A2,…,A24下计算得到的火灾发生概率与基于“IF THEN”语句的模糊融合算法求得的火灾发生概率进行比较(通过在汽车上进行实验,证明该方法在火灾检测方面具有可行性),图2所示曲线代表A1,A2,…,A24相对本发明所提方法得到的火灾发生概率的误差曲线,从仿真结果可以看出,在假设条件A13下误差最小。由此可见,变量x
i(i=1,2,3,4)对火灾发生概率的影响程度关系为x
3>x
1>x
2>x
4,各变量权重为[0.1474,0.1439,0.6492,0.0595]。
In this experiment, five sets of environmental parameters were selected: X1=[38,500,18,188]; X2=[89,580,30,200]; X3=[39,450,70,229]; X4=[51,750,38,199]; X5=[39,480,19,705]. Using the online detection method of the present invention under five sets of parameters, the probability of fire occurrence under the assumptions of A1, A2, . . . , A24 is obtained respectively. When the input parameters are the same, compare the fire occurrence probability calculated under the assumptions A1, A2,..., A24 with the fire occurrence probability obtained by the fuzzy fusion algorithm based on the "IF THEN" statement (through experiments on the car, it is proved that This method is feasible in fire detection), the curves shown in Figure 2 represent the error curves of A1, A2, ..., A24 relative to the fire probability obtained by the method proposed in the present invention. It can be seen from the simulation results that under the assumption condition A13 The lower error is the smallest. It can be seen that the relationship between the influence degree of variable x i (i=1, 2, 3, 4) on the probability of fire occurrence is x 3 > x 1 > x 2 > x 4 , and the weight of each variable is [0.1474, 0.1439, 0.6492, 0.0595].
3.2在线检测算法的及时性、准确性分析3.2 Analysis of timeliness and accuracy of online detection algorithm
从对火灾的检测与发出报警信号所需时间的角度入,。将空间内50个节点检测的环境变量参数
分别代入以上三种火灾检测算法,进行100次独立重复实验,记录各自的检测时间。其中x
1在区间(55,100]内取值,x
2在区间(700,1000]内取值,x
3在区间(20,100]内取值,x
4在区间(760,1000]内取值。从图3中的仿真曲线可以看出,在输入变量相同的条件下,本发明的在线检测算法能够在10s内完成火灾检测与报警,而相关技术中的灰模糊神经网络融合算法在23s内完成火灾检测与报警,相关技术中的模糊逻辑融合算法在20s内完成火灾检测与报警。实验表明,当输入变量相同时,本发明所提的在线检测算法在火灾检测及时性方面优于其他两种算法。
From the perspective of fire detection and the time required to issue an alarm signal, Environment variable parameters to detect 50 nodes in the space Substitute the above three fire detection algorithms respectively, carry out 100 independent repeated experiments, and record the respective detection time. where x 1 takes a value in the interval (55, 100], x 2 takes a value in the interval (700, 1000], x 3 takes a value in the interval (20, 100], and x 4 takes a value in the interval (760, 1000]. From It can be seen from the simulation curve in Fig. 3 that under the condition of the same input variables, the online detection algorithm of the present invention can complete the fire detection and alarm within 10s, while the gray-fuzzy neural network fusion algorithm in the related art completes the fire within 23s Detection and alarm, the fuzzy logic fusion algorithm in the related art completes fire detection and alarm within 20s. Experiments show that when the input variables are the same, the online detection algorithm proposed by the present invention is superior to the other two algorithms in terms of fire detection timeliness .
在线检测算法的核心在于计算各变量所占的权重,上述实验中的一组权重[0.6492,0.1474,0.1439,0.0595]是当判断矩阵个数为5的情况下求得的。当k取5时,本实验背景下火灾误报率为3%,并以此作为初始条件,分析判断矩阵选取个数k与火灾检测误报率之间的关系。图4仿真曲线表明,选取的合理判断矩阵越多,求得的变量权重就越精准,同时各节点对火灾发生概率的评估结果就越准确。当k大于20时,在线检测算法将误报率降到0.5%以下,有效提高了火灾检测的准确度。The core of the online detection algorithm is to calculate the weight occupied by each variable. The set of weights [0.6492, 0.1474, 0.1439, 0.0595] in the above experiment is obtained when the number of judgment matrices is 5. When k is 5, the fire false alarm rate is 3% under the background of this experiment, and taking this as the initial condition, the relationship between the number k selected for judgment matrix and the fire detection false alarm rate is analyzed. The simulation curve in Fig. 4 shows that the more reasonable judgment matrices are selected, the more accurate the obtained variable weights will be, and the more accurate the evaluation results of fire occurrence probability of each node will be. When k is greater than 20, the online detection algorithm reduces the false alarm rate to less than 0.5%, which effectively improves the accuracy of fire detection.
3.3在线检测算法的容错性分析3.3 Fault Tolerance Analysis of Online Detection Algorithms
在50个传感器节点的基础上,当故障节点逐渐递增时,观察火灾发生概率的检测值与真实值之间的偏差。设环境温度为38℃、烟雾浓度为650ppm、CO浓度为14ppm,红外光线强度为700lux且假定不变,假设节点全部正常工作时检测到的火灾发生概率为真实值,如表4所示。On the basis of 50 sensor nodes, when the number of faulty nodes is gradually increased, the deviation between the detected value and the true value of the probability of fire occurrence is observed. Assuming that the ambient temperature is 38°C, the smoke concentration is 650ppm, the CO concentration is 14ppm, and the infrared light intensity is 700lux and the assumption remains unchanged, it is assumed that the fire probability detected when all nodes are working normally is the true value, as shown in Table 4.
表4三种检测算法下对应的火灾概率真实值Table 4 The corresponding real values of fire probability under three detection algorithms
火灾检测算法fire detection algorithm | 火灾概率fire probability |
灰模糊神经网络融合法Grey Fuzzy Neural Network Fusion Method | 30.56%30.56% |
模糊逻辑融合法Fuzzy logic fusion method | 29.48%29.48% |
本发明的在线检测法On-line detection method of the present invention | 28.62%28.62% |
采用误差平方(SE)作为评价上述三种算法检测精度的标准,定义SE为:The squared error (SE) is used as the standard to evaluate the detection accuracy of the above three algorithms, and SE is defined as:
其中,j为检测算法下标(j=1,2,3);p
j代表j算法下火灾发生概率真实值;
代表j算法下融合50个节点后的火灾发生概率估计值。
Among them, j is the subscript of the detection algorithm (j=1, 2, 3); p j represents the true value of the probability of fire occurrence under the j algorithm; It represents the estimated value of the probability of fire occurrence after fusing 50 nodes under the j algorithm.
从图5仿真曲线可见,当故障节点达到10个后,相关技术中采用灰模糊神经网络信息融合算法和采用带反馈的模糊逻辑控制***的SE将急剧增加,算法检测精度下降,无法满足实际应用***要求。而本发明提出的在线检测算法由于节点数据融合时,是基于数据间的相互支持度进行自适应分配权系数的。 因此,当故障节点增加时,其SE值增加平缓,且在故障节点达到30后,才出现较大偏差。因此,在实际火灾检测***中,本发明的在线检测算法的容错能力更佳。It can be seen from the simulation curve in Fig. 5 that when the number of faulty nodes reaches 10, the SE of the gray-fuzzy neural network information fusion algorithm and the fuzzy logic control system with feedback in the related technology will increase sharply, and the detection accuracy of the algorithm will decrease, which cannot meet the practical application. System Requirements. However, the online detection algorithm proposed by the present invention performs self-adaptive distribution of weight coefficients based on the mutual support between data due to node data fusion. Therefore, when the number of faulty nodes increases, its SE value increases gently, and after the number of faulty nodes reaches 30, a large deviation occurs. Therefore, in an actual fire detection system, the fault tolerance capability of the online detection algorithm of the present invention is better.
4结论4 Conclusion
本发明的火灾在线检测算法首先在WSN的各节点处利用改进的AHP算得各变量权重,并提出了一种新的“变量加权融合算法”以评估火灾发生概率,使***在输入变量较多时仍能及时、准确的对火灾进行检测。实验表明,算法可以在10s内完成对火灾的检测与报警,相比其他火灾检测算法,大大降低了对火灾进行检测与报警所需的时间,有效避免了火势蔓延,在一定程度上使后续的灭火工作变得更加顺利。在利用WSN采集环境数据时,存在由于传感器故障导致检测精度受损甚至***瘫痪的问题,本发明提出了基于构建增广支持度矩阵的多传感器节点数据融合方法,使可信度高的节点在融合中被赋予较高权重,反之较低。通过对各节点自适应分配权系数,降低了故障传感器采集到的偏差数据对融合结果造成的影响,因此该在线检测算法具有较强的容错能力。The fire online detection algorithm of the present invention first calculates the weight of each variable by using the improved AHP at each node of the WSN, and proposes a new "variable weighted fusion algorithm" to evaluate the probability of fire occurrence, so that the system can still be used when there are many input variables. It can detect fire in time and accurately. Experiments show that the algorithm can complete the detection and alarm of fire within 10s. Compared with other fire detection algorithms, it greatly reduces the time required for fire detection and alarm, effectively avoids the spread of fire, and to a certain extent makes the follow-up fire. Firefighting has become smoother. When using WSN to collect environmental data, there is a problem that the detection accuracy is damaged or even the system is paralyzed due to sensor failure. The fusion is given a higher weight, and vice versa. By adaptively assigning weight coefficients to each node, the influence of deviation data collected by faulty sensors on fusion results is reduced, so the online detection algorithm has strong fault tolerance.
对应本发明实施例提供的上述火灾检测方法,本发明实施例还提供一种火灾检测装置,该装置包括:Corresponding to the above fire detection method provided by the embodiment of the present invention, the embodiment of the present invention further provides a fire detection device, the device comprising:
第一确定模块,用于确定机舱内各节点的多个火灾影响因素;a first determination module, used for determining multiple fire-influencing factors of each node in the engine room;
第二确定模块,用于确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;a second determination module, configured to determine the fire influence weight value of each fire influence factor among the plurality of fire influence factors of each node;
第三确定模块,用于根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;a third determination module, configured to determine the evaluation value of each node on the probability of fire occurrence in the cabin according to the fire influence weight value of each fire influence factor;
第四确定模块,用于根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;a fourth determination module, configured to determine the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of fire occurrence in the cabin by each node;
判断模块,用于根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。The judging module is used for judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
在一个实施例中,所述第二确定模块包括:In one embodiment, the second determining module includes:
确定子模块,用于确定所述各火灾影响因素的判断矩阵;a determination sub-module for determining the judgment matrix of each of the fire-influencing factors;
检验子模块,用于对所述各火灾影响因素的判断矩阵进行一致性检验;a check sub-module, used to check the consistency of the judgment matrix of each fire influencing factor;
计算子模块,用于根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。The calculation sub-module is configured to calculate the fire influence weight value of each fire influence factor according to the judgment matrix of each fire influence factor that has passed the consistency check.
在一个实施例中,所述计算子模块具体用于:In one embodiment, the calculation submodule is specifically used for:
当所述各火灾影响因素的判断矩阵存在多个时,根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或等于2的正整数;When there are multiple judgment matrices for each fire influencing factor, according to each judgment matrix of each fire influencing factor that passes the consistency check, a preset number of fire influence weight values for each fire influencing factor are calculated. , wherein the preset number is equal to the number of judgment matrices of the respective fire influencing factors that have passed the consistency check; the preset number is a positive integer greater than or equal to 2;
根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾 影响因素的火灾影响权重区间。According to the preset number of fire influence weight values of each fire influence factor, the fire influence weight interval of each fire influence factor is determined.
在一个实施例中,所述第三确定模块包括:In one embodiment, the third determining module includes:
第一确定子模块,用于根据所述各火灾影响因素的火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间;a first determination sub-module, configured to determine the fire influence weight interval of each fire influence factor according to the fire influence weight value of each fire influence factor;
第二确定子模块,用于根据所述各火灾影响因素的火灾影响权重区间,确定所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件;The second determination submodule is configured to determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;
计算子模块,用于根据所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件,计算所述各节点的各火灾影响因素的最优火灾影响权重值;a calculation submodule, configured to calculate the optimal fire influence weight value of each fire influence factor of each node according to the target influence function of each fire influence factor and the constraint condition of the target influence function;
第三确定子模块,用于根据所述各节点的各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定所述各节点对所述舱内火灾发生概率的评估值。The third determination sub-module is configured to determine, according to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the effect of each node on the interior of the cabin. An estimate of the probability of fire occurrence.
在一个实施例中,所述第四确定模块具体用于:In one embodiment, the fourth determining module is specifically configured to:
根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数;According to the evaluation value of each node on the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;
根据所述任意两个节点之间关于评估值之间的支持度函数,建立所述各节点之间关于评估值的初始的支持度矩阵;According to the support function between the any two nodes with respect to the evaluation value, establish an initial support degree matrix between the nodes with respect to the evaluation value;
为所述各节点关于评估值之间的初始的支持度矩阵构建增广支持度矩阵;其中,所述增广支持度矩阵相比于所述初始的支持度矩阵多了一行与一列;Constructing an augmented support matrix for the initial support matrix between the evaluation values of each node; wherein, the augmented support matrix has one more row and one column compared to the initial support matrix;
根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率。According to the augmented support matrix, the probability that a fire actually occurs in the cabin is determined.
在一个实施例中,所述第四确定模块具体还用于:In one embodiment, the fourth determining module is further configured to:
根据所述增广支持度矩阵,确定所述各节点对所述舱内火灾发生概率的评估值的评估可信系数;determining, according to the augmented support matrix, an evaluation credibility coefficient of each node for the evaluation value of the probability of occurrence of fire in the cabin;
根据所述各节点对所述舱内火灾发生概率的评估值和所述各节点对所述舱内火灾发生概率的评估值的评估可信系数,确定所述舱内实际发生火灾的概率。The probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility coefficient of the evaluation value of the probability of occurrence of fire in the cabin by each node.
在一个实施例中,所述判断模块具体用于:In one embodiment, the judging module is specifically used for:
根据所述各节点的各火灾影响因素的火灾发生临界因素值,计算所述舱内发生火灾的概率阈值;Calculate the probability threshold of fire occurrence in the cabin according to the fire occurrence critical factor value of each fire influencing factor of each node;
根据所述舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值,判断是否发出火灾报警。Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
本领域技术用户员在考虑说明书及实践这里公开的公开后,将容易想到本公开的其它实施方案。本申请旨在涵盖本公开的任何变型、用途或者适应性变化,这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的,本公开的真正范围和精神由下面的权利要求指出。Other embodiments of the present disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not disclosed by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.
应当理解的是,本公开并不局限于上面已经描述并在附图中示出的精确结构,并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。It is to be understood that the present disclosure is not limited to the precise structures described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
- 一种火灾检测方法,其特征在于,包括以下步骤:A fire detection method is characterized in that, comprises the following steps:确定机舱内各节点的多个火灾影响因素;Identify multiple fire-influencing factors at each node in the engine room;确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;determining the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node;根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;According to the fire influence weight value of each fire influence factor, the evaluation value of each node to the probability of fire occurrence in the cabin is determined;根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;According to the evaluation value of the probability of fire occurrence in the cabin by each node, determine the probability of the actual occurrence of fire in the cabin;根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。According to the probability of a fire actually occurring in the cabin, it is judged whether to issue a fire alarm.
- 根据权利要求1所述方法,其特征在于,The method of claim 1, wherein:所述确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值,包括:The determining of the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node includes:确定所述各火灾影响因素的判断矩阵;determining the judgment matrix for each of the fire influencing factors;对所述各火灾影响因素的判断矩阵进行一致性检验;Consistency test is carried out on the judgment matrix of each fire influencing factor;根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。According to the judgment matrix of each fire influence factor that has passed the consistency check, the fire influence weight value of each fire influence factor is calculated.
- 根据权利要求2所述方法,其特征在于,The method according to claim 2, characterized in that:当所述各火灾影响因素的判断矩阵存在多个时,所述根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值,包括:When there are multiple judgment matrices of each fire influencing factor, calculating the fire influence weight value of each fire influencing factor according to the judgment matrix of each fire influencing factor that has passed the consistency check, including:根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或等于2的正整数;According to the judgment matrix of each fire influence factor that passes the consistency check, a preset number of fire influence weight values for each fire influence factor are calculated, wherein the preset number is equal to all the fire influence factors that pass the consistency check. the number of judgment matrices for each fire influencing factor; the preset number is a positive integer greater than or equal to 2;根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间。The fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
- 根据权利要求1所述方法,其特征在于,The method of claim 1, wherein:根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值,包括:According to the fire influence weight value of each fire influence factor, the evaluation value of each node to the fire occurrence probability in the cabin is determined, including:根据所述各火灾影响因素的火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间;According to the fire influence weight value of each fire influence factor, determine the fire influence weight interval of each fire influence factor;根据所述各火灾影响因素的火灾影响权重区间,确定所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件;Determine the target influence function of each fire influence factor and the constraint condition of the target influence function according to the fire influence weight interval of each fire influence factor;根据所述各火灾影响因素的目标影响函数和所述目标影响函数的约束条件,计算所述各节点的各火灾影响因素的最优火灾影响权重值;According to the target influence function of each fire influence factor and the constraint condition of the target influence function, calculate the optimal fire influence weight value of each fire influence factor of each node;根据所述各节点的各火灾影响因素的最优火灾影响权重值以及所述各节点的各火灾影响因素的实际因素值,确定所述各节点对所述舱内火灾发生概率的评估值。According to the optimal fire influence weight value of each fire influence factor of each node and the actual factor value of each fire influence factor of each node, the evaluation value of each node to the fire occurrence probability in the cabin is determined.
- 根据权利要求1所述方法,其特征在于,The method of claim 1, wherein:所述根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率,包括:The determining the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node includes:根据所述各节点对所述舱内火灾发生概率的评估值,建立所述各节点中任意两个节点之间关于评估值的支持度函数;According to the evaluation value of each node on the probability of fire occurrence in the cabin, establish a support function between any two nodes in each node with respect to the evaluation value;根据所述任意两个节点之间关于评估值之间的支持度函数,建立所述各节点之间关于评估值的初始的支持度矩阵;According to the support function between the any two nodes with respect to the evaluation value, establish an initial support degree matrix between the nodes with respect to the evaluation value;为所述各节点关于评估值之间的初始的支持度矩阵构建增广支持度矩阵;其中,所述增广支持度矩阵相比于所述初始的支持度矩阵多了一行与一列;Constructing an augmented support matrix for the initial support matrix between the evaluation values of each node; wherein, the augmented support matrix has one more row and one column compared to the initial support matrix;根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率。According to the augmented support matrix, the probability that a fire actually occurs in the cabin is determined.
- 根据权利要求5所述方法,其特征在于,The method of claim 5, wherein:所述根据所述增广支持度矩阵,确定所述舱内实际发生火灾的概率,包括:The determining, according to the augmented support matrix, the probability that a fire actually occurs in the cabin includes:根据所述增广支持度矩阵,确定所述各节点对所述舱内火灾发生概率的评估值的评估可信系数;determining, according to the augmented support matrix, the evaluation credibility coefficient of each node for the evaluation value of the probability of occurrence of fire in the cabin;根据所述各节点对所述舱内火灾发生概率的评估值和所述各节点对所述舱内火灾发生概率的评估值的评估可信系数,确定所述舱内实际发生火灾的概率。The probability of a fire actually occurring in the cabin is determined according to the evaluation value of the probability of occurrence of fire in the cabin by each node and the evaluation credibility factor of the evaluation value of the probability of occurrence of fire in the cabin by each node.
- 根据权利要求1至6中任一项所述方法,其特征在于,The method according to any one of claims 1 to 6, wherein,所述根据所述舱内实际发生火灾的概率,判断是否发出火灾报警,包括:The judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin includes:根据所述各节点的各火灾影响因素的火灾发生临界因素值,计算所述舱内发生火灾的概率阈值;Calculate the probability threshold of fire occurrence in the cabin according to the fire occurrence critical factor value of each fire influencing factor of each node;根据所述舱内实际发生火灾的概率和所述舱内发生火灾的概率阈值,判断是否发出火灾报警。Whether to issue a fire alarm is determined according to the actual probability of fire in the cabin and the probability threshold of fire in the cabin.
- 一种火灾检测装置,其特征在于,该装置包括:A fire detection device, characterized in that the device comprises:第一确定模块,用于确定机舱内各节点的多个火灾影响因素;a first determination module, used for determining multiple fire-influencing factors of each node in the engine room;第二确定模块,用于确定所述各节点的多个火灾影响因素中各火灾影响因素的火灾影响权重值;a second determination module, configured to determine the fire influence weight value of each fire influence factor among the multiple fire influence factors of each node;第三确定模块,用于根据所述各火灾影响因素的火灾影响权重值,确定所述各节点对所述舱内火灾发生概率的评估值;a third determination module, configured to determine the evaluation value of each node on the probability of fire occurrence in the cabin according to the fire influence weight value of each fire influence factor;第四确定模块,用于根据所述各节点对所述舱内火灾发生概率的评估值,确定所述舱内实际发生火灾的概率;a fourth determination module, configured to determine the probability of a fire actually occurring in the cabin according to the evaluation value of the probability of occurrence of fire in the cabin by each node;判断模块,用于根据所述舱内实际发生火灾的概率,判断是否发出火灾报警。The judging module is used for judging whether to issue a fire alarm according to the probability of a fire actually occurring in the cabin.
- 根据权利要求8所述装置,其特征在于,The device of claim 8, wherein:所述第二确定模块包括:The second determining module includes:确定子模块,用于确定所述各火灾影响因素的判断矩阵;a determination sub-module for determining the judgment matrix of each of the fire-influencing factors;检验子模块,用于对所述各火灾影响因素的判断矩阵进行一致性检验;a check sub-module, used to check the consistency of the judgment matrix of each fire influencing factor;计算子模块,用于根据通过一致性检验的所述各火灾影响因素的判断矩阵,计算所述各火灾影响因素的火灾影响权重值。The calculation sub-module is configured to calculate the fire influence weight value of each fire influence factor according to the judgment matrix of each fire influence factor that has passed the consistency check.
- 根据权利要求9所述装置,其特征在于,The device according to claim 9, characterized in that:所述计算子模块具体用于:The calculation sub-module is specifically used for:当所述各火灾影响因素的判断矩阵存在多个时,根据每个通过一致性检验的所述各火灾影响因素的判断矩阵,计算出所述各火灾影响因素的预设数目个火灾影响权重值,其中,所述预设数目等于通过一致性检验的所述各火灾影响因素的判断矩阵的数目;所述预设数目为大于或等于2的正整数;When there are multiple judgment matrices for each fire influencing factor, according to each judgment matrix for each fire influencing factor that passes the consistency check, calculate a preset number of fire influence weight values for each fire influencing factor , wherein the preset number is equal to the number of judgment matrices of the respective fire influencing factors that have passed the consistency check; the preset number is a positive integer greater than or equal to 2;根据所述各火灾影响因素的预设数目个火灾影响权重值,确定所述各火灾影响因素的火灾影响权重区间。The fire influence weight interval of each fire influence factor is determined according to the preset number of fire influence weight values of each fire influence factor.
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