CN104616416A - Multi-sensor information fusion-based wireless fire alarm system - Google Patents
Multi-sensor information fusion-based wireless fire alarm system Download PDFInfo
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- CN104616416A CN104616416A CN201510018777.5A CN201510018777A CN104616416A CN 104616416 A CN104616416 A CN 104616416A CN 201510018777 A CN201510018777 A CN 201510018777A CN 104616416 A CN104616416 A CN 104616416A
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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
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/009—Signalling of the alarm condition to a substation whose identity is signalled to a central station, e.g. relaying alarm signals in order to extend communication range
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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Abstract
The invention relates to a multi-sensor information fusion-based wireless fire alarm system. The system comprises an industrial personal computer, a wireless transmission unit and a control unit, wherein the wireless transmission unit is used for connecting the industrial personal computer with the control unit; the control unit comprises a monitoring server, an analog-digital converter, an active low-pass filter and a plurality of data sensors; the multiple data sensors are connected with the monitoring server through the respective active low-pass filter and analog-digital converter respectively; the monitoring server, on one hand, is used for receiving data of the analog-digital converter, and on the other hand, is used for analyzing and processing acquired information by using a fuzzy reasoning algorithm-based multi-sensor information fusion technology and performing judgment processing on the acquired information according to a fusion database and an expert knowledge base; previously recorded data sets acquired by the multiple data sensors are stored in the fusion data base; different fuzzy reasoning rules corresponding to different application scenes are stored in the expert knowledge base. The occurrence of a fire disaster can be accurately and quickly judged.
Description
Technical field
The present invention relates to automatic control technology field, particularly relate to a kind of wireless fire disaster alarm system based on multi-sensor information fusion.
Background technology
The accuracy and reliability required along with fire alarm system is more and more higher, and the fire detection alarm system of general type can not satisfy the demands, and the warning system of single Monitoring Indexes often produces false alarm, brings inconvenience for producing, living.Adopt multiple sensor to gather the various abnormal informations of fire generation comprehensively, and by the fire information that multi-sensor information fusion technology processes sensor gathers, the reliability of whole alarm monitoring system can be improved widely.Multinomial monitor control index needs multiple sensor Information Monitoring, but multisensor brings the sharp increase of information, and the process of information in enormous quantities is difficult to formulate rational control strategy, is difficult to the testing requirement meeting fire alarm system indices high precision, high speed.In current fire alarm system, great majority all adopt cable network, and wired network system has poor mobility, dumb, expansibility is poor, networking and the shortcoming such as maintenance is not convenient.For numerous places needing fire alarm such as school, factory, hospitals, need the control point of setting a lot, adopt not only constructional difficulties, the maintenance inconvenience of wired wire laying mode, and high cost.Along with the development of wireless communication technology, the stability of wireless network and real-time have had very large improvement, are enough to ensure that monitor control system safely and steadily runs.
Along with the development of theoretical research and development technique, in environmental monitoring and commercial production, various types of sensor emerges in multitude, performance also improves constantly, the sensor can selected for intelligent system gets more and more, in same system, how effectively overall treatment, the information that utilizes various sensor to provide seem more and more important.Novel fire alarm system adopts the sensor detecting many index to monitor monitoring area, in this multisensor syste, each sensor provide the space of information, time, expression way different, confidence level, uncertainty degree are different, emphasis and purposes are also different, and this proposes new requirement to the process of information and management.In traditional mode, the information of each sensor collection separately, isolatedly carries out processing process, work for the treatment of amount not only can be caused to increase, and cut off the contact of information between each sensor, the information characteristics that the organic assembling that lost information contains, causes the waste of information resources.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of wireless fire disaster alarm system based on multi-sensor information fusion, can judge the generation of fire accurately and rapidly.
The technical solution adopted for the present invention to solve the technical problems is: provide a kind of wireless fire disaster alarm system based on multi-sensor information fusion, comprise industrial computer, wireless transmission unit and control module, described wireless transmission unit is for connecting industrial computer and control module; Described control module comprises monitoring server, analog to digital converter, active low-pass filter and multiple data transducer; Described multiple data transducer is connected with monitoring server with analog to digital converter respectively by respective active low-pass filter; Described monitoring server is on the one hand for receiving the data of analog to digital converter, multi-sensor information fusion technology on the other hand based on Fuzzy Logic Reasoning Algorithm is analyzed the information gathered, is processed, and carries out judgement process according to amalgamation database and expert knowledge library to Information Monitoring; The data group that the multiple data transducers recorded before depositing in described amalgamation database gather; The different fuzzy reasoning criterions that corresponding different application scenarios stores are deposited in described expert knowledge library.
Described wireless transmission unit comprises admin site, receiving node, control website, relay reception node and relay; Each control module connects one and controls website, described relay reception node and relay form one group jointly for extending the repeater of Internet Transmission distance, radio communication is set up between all control websites and repeater, realize radio communication by receiving node between relay and admin site, described admin site is connected with industrial computer.
Shown data transducer comprises temperature sensor, combustable gas concentration sensor and smokescope sensor.
The multi-sensor information fusion based on Fuzzy Logic Reasoning Algorithm that described monitoring server adopts comprises to be chosen suitable sensor and detects, determine fuzzy set membership function and fuzzy reasoning criterion, by various Fuzzy Logic Operators, calculate the conclusion of each fuzzy reasoning criterion, by fuzzy composition, the fuzzy conclusion obtained is carried out overall treatment, draw a conclusion, and anti fuzzy method is carried out to conclusion.
Described monitoring server adopts subjective experience method, analysis ratiocination method or investigation statistics method determination fuzzy set membership function.
Beneficial effect
Owing to have employed above-mentioned technical scheme, the present invention compared with prior art, has following advantage and good effect:
The present invention adopts Wi-Fi network as transmission medium, and layout simple, the duration of arranging net is short, easily recovers after network is destroyed.
The present invention adopts multilayer distributed network, can expand neatly or reduce monitoring range, and convenient increase and decrease needs the quantity of guarded region, improves monitoring efficiency simultaneously.
The present invention adopt software filtering, the compound digital filter algorithm that limit filtration is combined with recurrence average filtering effectively in collected signal random noise and periodic noise carry out filtering.
The present invention adopts the multi-sensor information fusion technology based on Fuzzy Logic Reasoning Algorithm to process Information Monitoring.Fire detector did many technological improvements in sensitivity, reliability and usability, but still not having a kind of single parameter fire monitoring device effectively to monitor so far surveys all kinds of condition of a fire.Multi-sensor information fusion technology can effectively utilize image data in enormous quantities, converts different information sources to unified representation, effectively realizes comparison between each information source and communicates, and is convenient to the fusion of information.Adopt suitable blending algorithm to carry out various treatment and analysis to multi-sensor information, and carry out reasoning, judge the generation of fire accurately and rapidly.
Accompanying drawing explanation
Fig. 1 is supervisory system structural representation;
Fig. 2 is information fusion control principle drawing;
Fig. 3 is fuzzy reasoning information fusion schematic diagram;
Fig. 4 is data fusion method schematic diagram in embodiment;
Fig. 5 is the fuzzy Fusion result schematic diagram of multisensor syste.
Embodiment
Below in conjunction with specific embodiment, set forth the present invention further.Should be understood that these embodiments are only not used in for illustration of the present invention to limit the scope of the invention.In addition should be understood that those skilled in the art can make various changes or modifications the present invention, and these equivalent form of values fall within the application's appended claims limited range equally after the content of having read the present invention's instruction.
Embodiments of the present invention relate to a kind of wireless fire disaster alarm system based on multi-sensor information fusion, and its structure as shown in Figure 1, comprises industrial computer, wireless transmission unit and control module; Wireless transmission unit comprises admin site MST, receiving node AP, controls website CST, relay reception node R AP and relay RST; Each control module connects one and controls website CST, relay reception node R AP and relay RST forms one group jointly for extending the repeater of Internet Transmission distance, radio communication is set up between all control website CST and repeater, realize radio communication by receiving node AP between relay RST and admin site MST, admin site MST is connected with industrial computer.
Each control module comprises monitoring server MMF, analog to digital converter ADI, active low-pass filter LPF, temperature sensor, combustable gas concentration sensor, smokescope sensor.Monitoring server is on the one hand for receiving the data of analog to digital converter, according to the multi-sensor information fusion technology based on Fuzzy Logic Reasoning Algorithm, the information gathered is analyzed on the other hand, process, information fusion control principle as shown in Figure 2, according to amalgamation database and expert knowledge library, judgement process is carried out to Information Monitoring, the sensor image data group recorded before depositing in amalgamation database, the different fuzzy reasoning criterions that corresponding different application scenarios stores are deposited in expert knowledge library, the warning of fire is carried out according to the Fuzzy Logic Reasoning Algorithm of multi-sensor information fusion technology, fast, the guarded region that fire occurs is shown in real time on monitoring server, monitoring server is also connected with control website.Analog to digital converter then connects each monitoring variable sensing device of guarded region by active low-pass filter.
Industrial computer is stable performance, the technical grade PC that processing speed is fast.The connected mode of itself and described MST is wired connection.The mode of the described MMF described in industrial computer access is for inputting the access of corresponding IP address by webpage.All information transmission modes between MST, AP, CST, RAP, RST in wireless transmission unit are wireless transmission.ADI and MMF in control module is integrated in a device, and is directly connected by wired mode with LPF.
Fuzzy reasoning adopts fuzzy logic by the given mapping process being input to output.Fuzzy reasoning comprises five aspects:
1. input variable obfuscation;
2. in the former piece of fuzzy rule, fuzzy operator is applied;
3. conclusion is inferred according to Fuzzy implication computing by prerequisite;
4. synthesize the conclusion part of each rule, draw total conclusion;
5. the anti fuzzy method of output variable.
(1) obfuscation of input variable
The obfuscation of input variable, is namely converted into the input variable determined the fuzzy set described by degree of membership.The input variable of fuzzy inference system is the some numbers determined in input variable domain, and input variable, after obfuscation, is transformed to certain number in [0,1] interval of being represented by degree of membership.Obfuscation is often tried to achieve by subordinate function or table look-up.For this reason, in input variable domain, determine that subordinate function is the basis that application fuzzy reasoning carries out information fusion exactly.
At present, determine that the method for subordinate function roughly has following several:
1. subjective experience method.When domain is discrete, according to subjective understanding or personal experience, directly or indirectly provide the occurrence of degree of membership, determine subordinate function thus.Concrete implementation method comprises:
A) expert point rating method: namely the method for subordinate function is determined in the scoring of comprehensive most expert, this method is widely used in every field that is economic and management;
B) factor weighted comprehensive method: if fuzzy concept is interacted by some questions to form, and each factor itself is fuzzy, then can consider the significance level of each factor to select subordinate function;
C) dyadic ordering method: by determining the order under certain feature to contrast between two between multiple things, decide the general shape of these things to the subordinate function of this feature thus.
2. analysis ratiocination method.When domain consecutive hours, according to the character of problem, apply certain analysis and reasoning, determine that some representative function selected is as subordinate function, such as triangular function, trapezoidal function etc.
3. investigation statistics method.The empirical curve drawn using investigation statistics result, as subordinate function curve, finds out corresponding function expression according to curve.
(2) in the former piece of fuzzy rule, fuzzy operator is applied
In general, fuzzy rule is determined by each application expertise, is the crystallization of the wisdom of humanity, is the foundation that fuzzy inference system carries out fuzzy reasoning.Fuzzy rule mainly provides with the form of " If ... then ".In fuzzy rule, the former piece of the so-called fuzzy rule of " If " part; The consequent of the so-called fuzzy rule of " then " part.
The form of the simplest " If ... then " rule is that " if x is A, so y is B." compound If ... the form of then rule is a lot, such as:
" If m be A and x is B, then y be C, else z be D ";
" If m be A and x is B and y is C, then z is D ";
" If m is A or x be B, then y be C or z be D ";
" If m be A and x is B, then y be C and z is D ".
Here A, B, C, D are domain M respectively, the semantic values of the fuzzy set in X, Y, Z.
After input variable obfuscation, we just know the degree that each proposition in every rule former piece is satisfied.If a more than proposition in the former piece of given rule, then need to obtain by fuzzy operator the degree that this regular former piece is satisfied.The input of fuzzy operator be two or more input variable obtain after obfuscation be subordinate to angle value, it exports is the degree of membership of whole former piece.The desirable T operator of fuzzy operator and any one in association T operator, conventional "AND" operator has min (fuzzy friendship) and prod (algebra product), and the "or" operator commonly used has max (also fuzzy) and probor (probability or).Probability or be defined as
probor(μ
A(x),μ
B(x))＝μ
A(x)+μ
B(x)-μ
A(x)μ
B(x)
Wherein, A, B are the fuzzy set on domain X, μ
a(x), μ
bx () is corresponding subordinate function.
(3) conclusion is inferred according to Fuzzy implication computing by former piece
Application fuzzy operator, achieves the mapping being subordinate to angle value and the whole former piece of each fuzzy rule after input vector obfuscation, next just can derive conclusion according to fuzzy implication operator by the angle value that is subordinate to of former piece.Fuzzy implication is the degree that the former piece of fuzzy rule is satisfied, and output is a fuzzy set.Rule " if x is A, so y is B " illustrates the Fuzzy implication relation between A and B, is designated as A → B.
(4) fuzzy composition
After above-mentioned a few step work, each rules and regulations is obtained for a conclusion, and each conclusion is that the form of a fuzzy set provides.We must be synthesized these fuzzy sets by fuzzy operator, obtain one and comprehensively export fuzzy set.Conventional Fuzzy Arithmetic Operators has max (also fuzzy), probor (probability or) and sum (algebraic sum).
(5) anti fuzzy method of output variable
What obtained by fuzzy reasoning is one and comprehensively exports fuzzy set, reflect the reasoning results fuzzy behaviour this be a kind of combination of different value.But, in practical application, use be all precise volume, so the precise volume that can represent this fuzzy set possibility distrabtion must be found out from fuzzy output subordinate function, Here it is anti fuzzy method (Defuzzification).Ambiguity solution can adopt diverse ways, and the result obtained with diverse ways is also different.The method of conventional anti fuzzy method has following several:
1. centroid method (Centroid).
So-called centroid method, gets fuzzy membership functions curve and abscissa axis exactly and surrounds domain element value corresponding to the mass centre in region as output valve.
2. dichotomy (Bisector).
Getting the element value that the subordinate function curve and the abscissa axis that export fuzzy set surround the equal branch of the area in region corresponding is output valve.
3. the mean value of fuzzy set maximum value is exported.
4. the maximal value of fuzzy set maximum value is exported.
5. the minimum value of fuzzy set maximum value is exported.
It should be noted that, relatively more reasonable with centroid method in theory, but calculate more complicated, therefore sometimes do not adopt in this way in the system that requirement of real-time is high.The simplest method is maximum membership degree method, this method gets that maximum value of degree of membership in all fuzzy sets or subordinate function as output, but this method does not take the impact of those less values of other degrees of membership into account, representative bad, so it is through being usually used in simple system.The various method of average in addition between both: as method of weighted mean, degree of membership amplitude limit (α-cut) the element method of average etc.
Fig. 3 is fuzzy reasoning information fusion schematic diagram, and fusion treatment process is as follows:
(1) selection of sensor.According to concrete practical problems, choose suitable sensor and detect, in acquisition practical problems, research object (comprises data and signal etc.) for information about.
(2) design of fuzzy inference system, comprises the determination of fuzzy set membership function and fuzzy inference rule.Describe sensor information variable with fuzzy set and subordinate function, for different problems, utilize the knowledge and experience determination fuzzy inference rule of different field expert.
(3) fuzzy reasoning is carried out.By various Fuzzy Logic Operators, calculate the conclusion of each rule, each conclusion represents the former piece of rule to the satisfaction degree of rule.
(4) fuzzy composition.After fuzzy reasoning, each fuzzy rule, is just derived a fuzzy conclusion, then is realized the overall treatment of these fuzzy conclusion by fuzzy composition, draws a total conclusion.
(5) anti fuzzy method.For the ease of practical application, by various anti fuzzy method process, by the result that fuzzy reasoning merges, namely a fuzzy set membership function is converted into an exact value.
Fire early-warning system of the present invention employs temperature sensor, combustable gas concentration sensor and smokescope sensor, and data fusion method as shown in Figure 4.Applying in fire alarm system based on the data fusion method of fuzzy reasoning is first determine the weight of each sensor, and our weight of design temperature sensor, combustable gas concentration sensor and smokescope sensor device is respectively W in this design
1=0.5, W
2=0.3, W
3=0.2, last court verdict is divided into two kinds: have fire Y1 and without fire Y2.According to current duty, determine the membership function of each sensor x for each judgement y; Carry out linear transformation computing again, last result can be determined.Such as, in certain moment, be respectively μ according to the degree of membership that the data of temperature sensor define without fire
11=0.45, μ
12=0.55 degree of membership defined without fire according to the data of combustible gas sensor is respectively μ
21=0.7, μ
22=0.3, be respectively μ according to the degree of membership that the data of smoke transducer define without fire
31=0.9, μ
32=0.1, adopt linear transformation computing to obtain Y,
Be judged as disaster hidden-trouble according to result, should have prepared to implement fire suppression measures.Fig. 5 is the test figure under fuzzy Fusion three groups of differences in fire hazard monitoring system are subordinate to angle value.
Claims (5)
1. based on a wireless fire disaster alarm system for multi-sensor information fusion, comprise industrial computer, wireless transmission unit and control module, it is characterized in that, described wireless transmission unit is for connecting industrial computer and control module; Described control module comprises monitoring server, analog to digital converter, active low-pass filter and multiple data transducer; Described multiple data transducer is connected with monitoring server with analog to digital converter respectively by respective active low-pass filter; Described monitoring server is on the one hand for receiving the data of analog to digital converter, multi-sensor information fusion technology on the other hand based on Fuzzy Logic Reasoning Algorithm is analyzed the information gathered, is processed, and carries out judgement process according to amalgamation database and expert knowledge library to Information Monitoring; The data group that the multiple data transducers recorded before depositing in described amalgamation database gather; The different fuzzy reasoning criterions that corresponding different application scenarios stores are deposited in described expert knowledge library.
2. the wireless fire disaster alarm system based on multi-sensor information fusion according to claim 1, is characterized in that, described wireless transmission unit comprises admin site, receiving node, control website, relay reception node and relay; Each control module connects one and controls website, described relay reception node and relay form one group jointly for extending the repeater of Internet Transmission distance, radio communication is set up between all control websites and repeater, realize radio communication by receiving node between relay and admin site, described admin site is connected with industrial computer.
3. the wireless fire disaster alarm system based on multi-sensor information fusion according to claim 1, is characterized in that, shown data transducer comprises temperature sensor, combustable gas concentration sensor and smokescope sensor.
4. the wireless fire disaster alarm system based on multi-sensor information fusion according to claim 1, it is characterized in that, the multi-sensor information fusion based on Fuzzy Logic Reasoning Algorithm that described monitoring server adopts comprises to be chosen suitable sensor and detects, determine fuzzy set membership function and fuzzy reasoning criterion, by various Fuzzy Logic Operators, calculate the conclusion of each fuzzy reasoning criterion, by fuzzy composition, the fuzzy conclusion obtained is carried out overall treatment, draw a conclusion, and anti fuzzy method is carried out to conclusion.
5. the wireless fire disaster alarm system based on multi-sensor information fusion according to claim 4, is characterized in that, described monitoring server adopts subjective experience method, analysis ratiocination method or investigation statistics method determination fuzzy set membership function.
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| CN105894706B (en) * | 2016-05-03 | 2019-01-04 | 南京林业大学 | A kind of forest fire prediction technique and its system |
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| CN106066640B (en) * | 2016-06-22 | 2018-12-11 | 吉林建筑大学 | A kind of CAN bus based fire-fighting equipment control system and fuzzy control method |
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| CN106875613A (en) * | 2016-12-23 | 2017-06-20 | 北京富邦智慧物联科技有限公司 | A kind of fire alarm Situation analysis method |
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| CN107025747A (en) * | 2017-05-31 | 2017-08-08 | 桂林电子科技大学 | The smart home intruding detection system and detection method of space-time two-dimensional information fusion |
| CN107767613A (en) * | 2017-09-30 | 2018-03-06 | 吉林建筑大学 | A kind of universal fire-fighting control device and method |
| CN108170060A (en) * | 2018-01-03 | 2018-06-15 | 邦正科技股份有限公司 | Danger source and environment monitoring warning system and method |
| CN109542099A (en) * | 2018-11-26 | 2019-03-29 | 江苏农牧科技职业学院 | A kind of agricultural machinery control method |
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| CN110801593A (en) * | 2019-10-30 | 2020-02-18 | 忻州师范学院 | Extremely early fire early warning system and method fusing multi-mode data |
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| CN114611294A (en) * | 2022-03-10 | 2022-06-10 | 苏州中材建设有限公司 | PLC cement mill multi-sensor load detection processing method |
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