CN106878375B - A kind of cockpit pollutant monitoring method based on distributed combination sensor network - Google Patents
A kind of cockpit pollutant monitoring method based on distributed combination sensor network Download PDFInfo
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- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 207
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 188
- 238000012544 monitoring process Methods 0.000 title claims abstract description 58
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- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 12
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 claims description 9
- YMWUJEATGCHHMB-UHFFFAOYSA-N Dichloromethane Chemical compound ClCCl YMWUJEATGCHHMB-UHFFFAOYSA-N 0.000 claims description 9
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 9
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 claims description 9
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 6
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 claims description 6
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 6
- 239000001569 carbon dioxide Substances 0.000 claims description 6
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 3
- 239000002158 endotoxin Substances 0.000 claims description 3
- WSFSSNUMVMOOMR-NJFSPNSNSA-N methanone Chemical compound O=[14CH2] WSFSSNUMVMOOMR-NJFSPNSNSA-N 0.000 claims description 3
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Abstract
The invention discloses a kind of cockpit pollutant monitoring methods based on distributed combination sensor network, to improve the accuracy and reliability of cockpit pollutant measurement.The described method includes: establish distributed combination sensor network in cockpit to be measured, each node in the distributed combination sensor network is provided with for monitoring same specified pollutant but the different main and auxiliary sensor of measuring principle；To each node initializing；The main and auxiliary sensor of each node measures respectively and calculates measurement value sensor error；Each node determines the nodal information of itself and propagates to neighbor node；Measurement value sensor error and its information of neighbor nodes to each node carry out consistency Kalman filtering and obtain the optimal error estimates value of each junction sensor；Using the corresponding master reference measured value of the optimal error estimates value correction respective nodes of sensor, the specified pollutant concentration value of respective nodes is obtained.The method increase the accuracy and reliabilities of cockpit pollutant monitoring.
Description
Technical Field
The invention belongs to the field of aircraft cabin environment monitoring, and particularly relates to a cabin pollutant monitoring method based on a distributed combined sensor network.
Background
The airtight pressurized cabin ensures the safe flight of passengers and crew members in severe high-altitude environment. With the continuous development of the aviation industry, all the aspects of civil aviation also put higher requirements on the environmental quality in the cockpit, and all the countries and regions clearly stipulate the limit values of relevant environmental parameters in corresponding civil aviation standards. However, these standards only address basic environmental parameters such as temperature, pressure and humidity, and the only specified pollutant indicator, smoke, is also only an auxiliary means for fire monitoring. The practical situation is: the aircraft cabin is narrow and closed in space and dense in personnel, and pollutants which are not found in time can directly damage passengers and crew members within the flight time of hours. The safe and comfortable cabin environment is an important factor for passengers to select airline flights, and is also a necessary guarantee for the physical health of members working in the cabin environment for a long time, particularly whether a newly-shipped airplane can meet the comfort of the passengers on the airplane or not, and a research method can be provided for airworthiness approval of the cabin environment.
Relevant actual measurement work is carried out for aircraft cockpit pollutants at home and abroad in recent years, and in the process of monitoring the pollutants in the cockpit, due to the fact that a local single sensor is excessively sensitive, a measurement result has large errors under noise interference and false alarms often occur. In actual flight, once serious warning occurs, a pilot must immediately operate the airplane for standby landing, so that the normal flight plan is seriously influenced by false warning, and a great deal of manpower and financial resources are lost. The method of increasing the alarm threshold can reduce the false alarm rate, but generates serious potential safety hazard.
Disclosure of Invention
The invention provides a cabin pollutant monitoring method based on a distributed combined sensor network, which is used for improving the accuracy and reliability of cabin pollutant measurement.
The invention provides a cabin pollutant monitoring method based on a distributed combined sensor network, which comprises the following steps:
establishing a distributed combined sensor network in a cabin to be tested; each node in the distributed combined sensor network is provided with a sensor for monitoring at least one specified pollutant, and for each specified pollutant, a main sensor and an auxiliary sensor with different measurement principles are arranged;
defining neighbor nodes of each node, and initializing and setting an estimation gain array P of each node i corresponding to the sensor of the l & ltth & gt specified pollutantilAnd state estimation；
For each node, a main sensor and an auxiliary sensor for monitoring the same pollutant are respectively measured to obtain a main sensor measured value Z of the current node corresponding to the appointed pollutant of the first kindPilAnd the measured value Z of the auxiliary sensorSil；
According to the formula Zil＝ZPil-ZSilCalculating the error Z of the measured value of the sensor corresponding to various specified pollutants by each nodeil；
Each node determines the node information of the node and transmits the node information to the neighbor nodes; the node information comprises state estimation values of the sensors of which the current nodes correspond to various specified pollutants, information vectors and information matrixes of which the current nodes correspond to the various specified pollutants;
for each node, the sensor measurement error Z for each specified contaminant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filter
For each node, the optimal error estimation value of the sensor corresponding to the I & ltth & gt specified pollutant by the current node is adoptedCorrecting the main sensor measurement value of the current node corresponding to the l type of specified pollutant to obtain the l type of specified pollutant concentration value of the current node:
wherein, i is 1, 2.. times.n; n is the number of nodes in the distributed combined sensor network; l, L is the specified number of pollutant species monitored by each node.
In one embodiment, the determining, by each node, node information of the node itself includes:
each node corresponds to the observation noise covariance matrix R of various specified pollutants according to the preset current nodeilAccording to the formulaAndcalculating an information matrix U of the current node corresponding to various specified pollutantsiAnd an information vector ui(ii) a Wherein HilA combined error observation matrix corresponding to the ith specified pollutant for the known ith node;
each node corresponds the current node to the state estimation value of the sensor of various specified pollutantsInformation vector u of current node corresponding to various specified pollutantsiAnd information matrix UiAnd determining the node information as the current node.
In one embodiment, the sensor measurement error Z for each specified contaminant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filterThe method comprises the following steps:
the current node receives node information of all neighbor nodes of the current node;
for the l type of specified pollutant monitored by the current node, performing the following information fusion on information vectors and information matrixes of all neighbor nodes of the current node corresponding to the same type of specified pollutant:
updating the estimation error covariance matrix M of the sensor corresponding to the I & ltth & gt specified pollutant at the current node by the current nodeilComprises the following steps:
according to the formulaPerforming state estimation on the sensor to obtain a state estimation value of the sensor corresponding to the first specified pollutant at the current node
According to the formulaCalculating the optimal error estimation value of the sensor corresponding to the I type of specified pollutant at the current node
Wherein J ∈ Ji＝Ni∪{i}，NiIs a neighbor node set of the ith node, and the variable rilComprises the following steps:
theta is a system sampling period, and a Frobenius norm is taken as a matrix.
In one embodiment, after obtaining the/th designated pollutant concentration value of the current node, the method further includes:
judging whether the concentration value of each appointed pollutant of each node is greater than a preset appointed pollutant concentration threshold value of a corresponding type;
when the concentration value of any one appointed pollutant of the node is larger than the preset appointed pollutant concentration threshold value of the corresponding type, the pollutant concentration overproof alarm is sent out.
In one embodiment, after the determining whether the concentration value of each specified pollutant at each node is greater than the preset threshold value of the concentration of each specified pollutant in the corresponding class, the method further includes:
when the concentration value of each appointed pollutant of each node is not more than the preset appointed pollutant concentration threshold value of the corresponding type, calculating the air quality index AQI of each node according to the following formulai：
Wherein Z isblFor a predetermined first specified pollutant concentration threshold, αlAnd the preset indicator weight value of the first designated pollutant.
In one embodiment, after the sending out the pollutant concentration over-limit alarm or calculating the air quality index of each node, the method further comprises the following steps:
each node corresponds the current node to the estimated gain array P of the sensor of each specified pollutantilAnd state estimationThe updating is as follows: pil＝AMilAT，
And returning to the step of executing the measurement of the main sensor and the auxiliary sensor for monitoring the same pollutant respectively for each node.
In one embodiment, the specified contaminant is at least one of carbon dioxide, carbon monoxide, ozone, inhalable particles, acetone, ethanol, formaldehyde, toluene, dichloromethane, endotoxins, microorganisms.
Some of the benefits of the present invention may include:
the cabin pollutant monitoring method based on the distributed combined sensor network provided by the invention introduces the distributed combined sensor network into cabin pollutant monitoring for use, the sensors for measuring the same pollutant but different working principles are arranged on a single measurement node, the measurement data are fused by utilizing the consistency Kalman filtering algorithm, and finally, an accurate pollutant concentration value is obtained, so that the accurate measurement of the cabin pollutant is realized, and the reliability of the monitoring result is high. Each node sensor does not need to exchange information with all nodes, communication only occurs between neighbor sensor nodes, complexity of data fusion calculation can be effectively reduced, monitoring real-time performance is improved, energy consumption of the sensor is reduced, the problem of short reliability in global information fusion is solved, and global consistent estimation is achieved by combining measured values of each node sensor and information of neighbor nodes.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram illustrating output correction data fusion of a node i according to an embodiment of the present invention;
FIG. 2 is a flow chart of a cabin pollutant monitoring method based on a distributed combination sensor network according to an embodiment of the present invention;
FIG. 3 is a flow chart of another cabin pollutant monitoring method based on a distributed combination sensor network according to the embodiment of the invention;
FIG. 4 is a flowchart of steps S106/S306 according to an embodiment of the present invention;
FIG. 5 is a flow chart of another cabin pollutant monitoring method based on a distributed combination sensor network according to the embodiment of the invention;
FIG. 6 is a schematic diagram of a digital simulation of node i according to an embodiment of the present invention;
FIG. 7 is a diagram of a cabin contaminant monitoring network topology with 10 nodes simulated in an embodiment of the present invention;
FIG. 8 is a diagram of the variation of the state averaged estimation error in a simulation example;
FIG. 9 is a diagram of the variation of the state average consistency error in a simulation example.
Detailed Description
In order to solve the problems in the prior art, the distributed sensor network is adopted, the sensors for monitoring different types of pollutants are arranged in each node area, the sensors with different measurement principles are selected according to a certain pollutant category, and the measurement results of the sensors are subjected to data fusion to make up for the defects of the sensors of the single type. It is known that each sensor in a sensor network can obtain similar but not completely consistent measurement results under the action of a plurality of random factors. The traditional solution is to set a data fusion center to perform weighted fusion on the data monitored by all the sensors. However, the scheme has large data traffic, and the data fusion center can cause the whole system to fail once failing, so that the reliability is poor. The cabin pollutant monitoring needs high real-time performance and accuracy, the occurrence of pollutants can be timely monitored through a monitoring network, and whether the release amount of the pollutants meets the standard or not can be judged. Therefore, the invention adopts the distributed consistency Kalman filter to perform data fusion so as to achieve accurate measurement and improve the reliability of the measurement result.
The method for monitoring the pollutants in the cabin selects a typical position in the cabin to be tested as a network node, the typical position comprises the top, the bottom, a window, a seat and the like of the cabin, sensors are placed at the positions, so that a distributed combined sensor network is formed, each node is provided with a sensor for monitoring at least one specified pollutant, and each specified pollutant is provided with a main sensor and an auxiliary sensor which have different measurement principles. Defining G ═ (V, E, a) as a topological structure diagram of an optional distributed combinational sensor network, where V ═ V { (V }1,v2,...vnThe nodes are a sensor node set;and exchanging the connection edge set for the sensor node information. N for neighbor node set of ith nodei＝{vj∈V|(vi,vj) E, and the number of its neighbor nodes is called the degree of the node and is marked as di＝|NiL. The degree matrix D of the topology G is defined as a diagonal matrix with the degrees of the sensor nodes as diagonal elements, i.e., D ═ diag { D {1,...dN}。A＝[aij]A is an adjacency matrix representing communication relations of the sensors, when the ith sensor and the jth sensor communicate with each otherijThe value is 1, otherwise 0. The laplacian matrix of graph G is defined as L ═ D-a, and if matrix L contains a non-zero eigenvalue, it is said thatThe undirected topology G is connected.
When data fusion is carried out on the sensor measurement value of each node of the distributed combined sensor network, the output correction data fusion structure shown in FIG. 1 is adopted, and the consistency Kalman filter measures the measurement result Z of the main sensor and the auxiliary sensor of the same pollutant on the node iPiAnd ZSiThe difference between the two sensors is subjected to data fusion, and the consistency Kalman filter outputs the error Z of the two sensorsiTo estimate the optimal state ofAnd then the error estimation value is used for carrying out output correction on the measured value of the main sensor to obtain the optimal estimation value of the actual pollutant concentration.
In the data fusion structure shown in fig. 1, the consensus kalman filter performs data fusion on the measurement results of the primary and secondary sensors, so that an error dynamic equation of the combined sensor combining the two sensors needs to be established first. And according to the statistical analysis of the actual performance of the system and the error analysis of the sensors, an error model of each sensor can be obtained, and for simple marking, the superscript + is adopted to represent the next moment update of the variable state.
The main sensor error dynamic equation is shown in formula (1):
the auxiliary sensor error dynamic equation is shown as formula (2):
wherein,as state vector of primary and secondary sensors；ZPi∈Rp,ZSi∈RsThe vector is the output vector of the main sensor and the auxiliary sensor; (A)P,BP,HPi) And (A)S,BS,HSi) Is a parameter matrix of appropriate dimensions. w is aP,vPi,wS,vSiThe white Gaussian noise signal is a one-dimensional mutually independent white Gaussian noise signal, and satisfies the following conditions:
E[w(k)w(l)T]＝Q(k)δkl
E[vi(k)vj(l)T]＝Rij(k)δkl
wherein, deltaklThe pulse function is a unit pulse function, namely, when k is equal to l, the value is 1, otherwise, the value is 0; q and RijFor the corresponding noise covariance matrix, R is assigned for ease of labelingijAbbreviated as Ri。
According to the actual performance and the error analysis of the system, the overall error equation of the combined sensor can be obtained by the formulas (1) and (2) and is used as the system equation of the consistency Kalman filter. Because the sampling time of the main sensor and the sampling time of the auxiliary sensor can not be ensured to be completely consistent, in order to simplify a system observation equation of the alignment time, the measurement noise of the auxiliary sensor can be ignored, and various errors of the main sensor can be classified as the measurement noise of the combined sensor. The error equation for the combi sensor is derived as follows:
for convenience of the following analysis, formula (3) is represented as a uniform form as follows:
comparing the formula (3) with the formula (4), the combined error state vector is x ═ col (x)P,xS) Combining error systematic noiseIs w ═ col (w)P,wS) (ii) a Assuming combined error observation noise to take the dominant sensor observation noise, i.e. vi＝vPi. The parameter of the combined error system equation is A ═ diag (A)P,AS)；B＝diag(BP,BS)；HiThe combined error observed value matrix of the ith node for the pollutants monitored by the current combined sensor can be obtained according to the sensor measurement error principle and engineering practice.
For the distributed system represented by equation (4), the discrete form of the kalman filter structure is as follows.
Wherein KiAnd CiRespectively a filter gain matrix and a consistency matrix.Andthe statistical meanings of the estimated value and the predicted value of the measured state x are as follows:
wherein Z (k) col { Z1(k),...,ZN(k) And the row vector is formed by the observed values of the sensors. Defining the state estimation error e separatelyiAnd state prediction errorThe following were used:
then there is MijAnd PijRespectively representing an estimation error covariance matrix and a prediction error covariance matrix, and having the following statistical meanings:
Mij(k)＝E[ηi(k)ηj(k)T] (10)
for the sake of simplicity, when the node i is equal to j, M is respectively addedijAnd PijAbbreviated as MiAnd Pi. Defining weighted measurementsAnd information matrixThe above filter can be represented as the following information form:
the above filter is called a consistency Kalman information filter, and uses a state prediction error covariance matrix PiCovariance matrix M of sum state estimation errorsiTo correct the predicted value of the stateTo obtain an estimate of the error stateThe data fusion effect has good expandability and can be suitable for large-scale distributed monitoring networks. The invention uses the consistency kalman information filter in the proposed distributed combinational sensor network.
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Fig. 2 is a flowchart of a cabin pollutant monitoring method based on a distributed combination sensor network according to an embodiment of the present invention. As shown in fig. 2, the method comprises the following steps S101-S107:
s101: establishing a distributed combined sensor network in a cabin to be tested; each node in the distributed combined sensor network is provided with a sensor for monitoring at least one specified pollutant, and for each specified pollutant, a main sensor and an auxiliary sensor with different measurement principles are arranged.
For convenience of description, the first designated pollutant monitored by any two nodes is set as the same type of pollutant. For example, if the distributed combined sensor network has 2 nodes, each node is provided with a sensor for monitoring 3 specified pollutants including carbon dioxide, carbon monoxide and ozone, the 1 st, 2 nd and 3 rd specified pollutants monitored by the node 1 are sequentially designated as carbon dioxide, carbon monoxide and ozone, and the 1 st, 2 nd and 3 rd specified pollutants monitored by the node 2 are also sequentially designated as carbon dioxide, carbon monoxide and ozone.
S102: defining neighbor nodes of each node, and initializing and setting an estimation gain array P of each node i corresponding to the sensor of the l & ltth & gt specified pollutantilAnd state estimationComprises the following steps:
Pil＝Pil(0)，
wherein, i is 1, 2.. times.n; n is the number of nodes in the distributed combined sensor network; l, L is the specified number of pollutant species monitored by each node, i.e.: the index i indicates the node coefficient and l indicates the parameter corresponding to the l-th specified contaminant.
S103: for each node i, a main sensor and an auxiliary sensor which are used for monitoring the same pollutant and have different measurement principles are respectively measured to obtain a main sensor measurement value Z of the current node corresponding to the l-th specified pollutantPilAnd the measured value Z of the auxiliary sensorSil。
S104: calculating the error Z of the measured value of the sensor corresponding to various specified pollutants by each nodeilThe calculation formula is as follows: zil＝ZPil-ZSil。
S105: each node determines its own node information (uil，Uil) And propagates to neighboring nodes. Wherein,state estimates, u, of the sensors corresponding to various specified pollutants for the current nodeilInformation vectors, U, corresponding to various specified pollutants for the current nodeilAn information matrix corresponding to various specified contaminants for the current node.
Wherein, each node firstly corresponds to the observation noise covariance matrix R of various specified pollutants according to the preset current nodeilAccording to the formulaAndcalculating an information matrix U of the current node corresponding to various specified pollutantsilAnd an information vector uil(ii) a Each node then assigns the current node to state estimates for sensors of various specified pollutantsInformation vector u of current node corresponding to various specified pollutantsilAnd information matrix UilNode information determined as a current node (uil，Uil) (ii) a Wherein HilA combined error observation matrix corresponding to the l-th designated contaminant for the known i-th node.
S106: for each node, the sensor measurement error Z for each specified contaminant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filter
S107: for each node, the optimal error estimation value of the sensor corresponding to the I & ltth & gt specified pollutant by the current node is adoptedCorrecting the main sensor measurement value of the current node corresponding to the l-th appointed pollutant to obtain the l-th appointed pollutant concentration value Z of the current nodeOil。
Wherein, the correction formula is:ZOilspecifying a contaminant concentration value, Z, for the first kind of node iPilThe primary sensor measurement corresponding to the l-th designated pollutant for node i,and (4) the optimal error estimation value of the sensor corresponding to the l & ltth & gt specified pollutant for the node i.
According to the technical scheme provided by the embodiment of the invention, the distributed combined sensor network is introduced into the monitoring of the pollutants in the cockpit for use, the sensors for measuring the same pollutant but with different working principles are arranged on a single measuring node, the measured data are fused by utilizing the consistency Kalman filtering algorithm, and finally, the accurate pollutant concentration value is obtained, so that the accurate measurement of the pollutants in the cockpit is realized, and the reliability of the monitoring result is high.
Fig. 3 is a flowchart of another cabin pollutant monitoring method based on a distributed combination sensor network according to an embodiment of the present invention. As shown in fig. 3, the method comprises the following steps S201-S210:
s201: and establishing a distributed combined sensor network in the cockpit to be tested.
S202: defining neighbor nodes of each node, and initializing and setting an estimation gain array P of each node i corresponding to the sensor of the l & ltth & gt specified pollutantilAnd state estimation
S203: for each node i, a main sensor and an auxiliary sensor which are used for monitoring the same pollutant and have different measurement principles are respectively measured to obtain a main sensor measurement value Z of the current node corresponding to the l-th specified pollutantPilAnd the measured value Z of the auxiliary sensorSil。
S204: calculating the error Z of the measured value of the sensor corresponding to various specified pollutants by each nodeil。
S205: each node determines its own node information and propagates to neighboring nodes.
S206: for each node, the sensor measurement error Z for each specified contaminant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filter
S207: for each node, the optimal error estimation value of the sensor corresponding to the I & ltth & gt specified pollutant by the current node is adoptedCorrecting the main sensor measurement value of the current node corresponding to the l-th appointed pollutant to obtain the l-th appointed pollutant concentration value Z of the current nodeOil。
In this embodiment, the specific implementation method of steps S201 to S207 is similar to that of steps S101 to S107 in the above embodiment, and is not described here again.
S208: judging whether the concentration value of each appointed pollutant of each node is greater than a preset appointed pollutant concentration threshold value of a corresponding type; when the concentration value of any one of the designated pollutants of the node is greater than the preset threshold value of the designated pollutant concentration of the corresponding type, executing step S209; otherwise, that is, when the concentration value of each designated pollutant at each node is not greater than the preset threshold value of the concentration of the designated pollutant of the corresponding type, step S210 is executed.
As the current civil aviation airworthiness regulations and design standards do not bring the pollutant concentration monitoring into the scope of investigation. The invention refers to the existing measured data and the indoor environment standard of the building, combines the characteristic of the limited space of the aircraft cabin, considers the following pollutant categories, and the related pollutant concentration threshold value and the measuring method are shown in the table 1:
TABLE 1 cabin contaminant monitoring categories
Preferably, the specified contaminant monitored by each node is at least one of carbon dioxide, carbon monoxide, ozone, inhalable particles, acetone, ethanol, formaldehyde, toluene, dichloromethane, endotoxins, microorganisms.
S209: and (5) sending out an alarm for exceeding the pollutant concentration.
In the step, the node identification with the pollutant concentration exceeding the standard and the pollutant type exceeding the standard can be given during alarming.
S210: calculating Air Quality Index (AQI) of each nodei：
Wherein Z isblFor a predetermined first specified pollutant concentration threshold, αlAnd the preset indicator weight value of the first designated pollutant.
In the embodiment, after data fusion is carried out through distributed combined sensor network measurement and a consistency Kalman filter, and the pollutant concentration of each node is obtained, whether the pollutant concentration of each monitoring node exceeds the standard or not can be judged through a preset pollutant concentration threshold value, an alarm body reminds a user when the pollutant concentration exceeds the standard, in addition, a cabin air quality evaluation index calculation method is provided, the air quality index of each node is calculated when the air quality index does not exceed the standard, and the user can conveniently monitor the air quality of each key position of a cabin in real time.
In one embodiment, as shown in FIG. 4, the filtering method of the Kalman Filter of step S106/S206 may be implemented as the following steps S301-S305:
s301: each node i receives node information of all neighbor nodes j of the node i: (ujl，Ujl)。
S302: for the l-th specified pollutant monitored by the current node i, all the neighbor nodes j of the current node i correspond to the information vector u of the same specified pollutantjlAnd information matrix UjlInformation fusion is performed according to the following formula (14):
wherein J ∈ Ji＝Ni∪{i}，NiIs the neighbor node set of the ith node.
S303: updating the estimation error covariance matrix M of the sensor corresponding to the l-th specified pollutant at the current node i according to the formula (15) by the current node iilComprises the following steps:
s304: performing state estimation on the sensor according to a formula (16) to obtain a state estimation value of the sensor of the current node i corresponding to the l-th specified pollutant
S305: calculating the optimal error estimation value of the sensor corresponding to the l type of specified pollutant at the current node i according to the formula (17)
Wherein, the variable rilComprises the following steps:
theta is a system sampling period, and a Frobenius norm is taken as a matrix.
Fig. 5 is a flowchart of another cabin pollutant monitoring method based on a distributed combination sensor network according to an embodiment of the present invention. As shown in fig. 5, the method includes the following steps S401 to S411:
s401: and establishing a distributed combined sensor network in the cockpit to be tested.
S402: defining neighbor nodes of each node, and initializing and setting an estimation gain array P of each node i corresponding to the sensor of the l & ltth & gt specified pollutantilAnd state estimation
S403: for each node i, a main sensor and an auxiliary sensor for monitoring the same pollutant are respectively measured to obtain a main sensor measured value Z of the current node corresponding to the appointed pollutant of the first kindPilAnd the measured value Z of the auxiliary sensorSil。
S404: calculating the error Z of the measured value of the sensor corresponding to various specified pollutants by each nodeil。
S405: each node determines its own node information and propagates to neighboring nodes.
S406: for each node, according toSensor measurement error Z for each specified pollutant at the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filterIn this embodiment, this step is implemented by using the method shown in fig. 4, and details are not described again.
S407: for each node, the optimal error estimation value of the sensor corresponding to the I & ltth & gt specified pollutant by the current node is adoptedCorrecting the main sensor measurement value of the current node corresponding to the l-th appointed pollutant to obtain the l-th appointed pollutant concentration value Z of the current nodeOil。
S408: judging whether the concentration value of each appointed pollutant of each node is greater than a preset appointed pollutant concentration threshold value of a corresponding type; when the concentration value of any one of the designated pollutants of the node is greater than the preset threshold value of the designated pollutant concentration of the corresponding type, executing step S409; otherwise, that is, when the concentration value of each designated pollutant at each node is not greater than the preset threshold value of the concentration of the designated pollutant of the corresponding type, step S410 is executed.
S409: an alarm indicating that the concentration of the pollutant exceeds the standard is issued, and then step S411 is performed.
S410: calculating Air Quality Index (AQI) of each nodeiSubsequently, step S411 is performed.
In this embodiment, the specific implementation method of steps S401 to S410 is similar to that of steps S301 to S310 in the above embodiment, and is not described herein again.
S411: each node corresponds the current node to the estimated gain array P of the sensor of each specified pollutantilAnd state estimationUpdated according to the formula (19), and then returns to execute S403.
Wherein,the state estimate of the sensor corresponding to the i-th designated pollutant for the current node i is obtained in step S406.
In the embodiment, after data fusion is carried out through the distributed combined sensor network measurement and the consistency Kalman filter to obtain the pollutant concentration of the nodes, each node updates the estimation gain array P of the sensor in timeilAnd state estimationTherefore, the method can be returned to be recycled, and the real-time monitoring of the cabin pollutant concentration is realized.
In the distributed combined sensor network provided by the invention, the measurement errors of the main sensor and the auxiliary sensor of each node are estimated by using a consistency Kalman filtering algorithm and are used for correcting the measured value of the main sensor to obtain the final measured value of pollutants. Therefore, the stability of the consensus kalman filter algorithm determines the stability of the entire monitoring method. Under the action of system noise, the state estimation value of the local Kalman filter deviates from the true value, and the state estimation values of all nodes cannot completely approach to be consistent due to observation noise. The following provides an analysis of the consistency Kalman filtering algorithm stability under the interference of system noise and observation noise by the method provided by the embodiment of the invention. For convenience of explanation hereinafter, assuming that the distributed combinational sensor network monitors only one type of contaminant, the subscript l used to indicate the type of contaminant for all parameters in the above method may not be considered.
At publicIn the classic consistency Kalman filtering algorithm represented by the formula (5), an intermediate variable M is usediSubstitution into Pi +And will beSubstitution intoA more compact form of the filtering algorithm can be obtained as follows.
Before stability analysis is carried out on the consistency Kalman filtering algorithm represented by the formula (20), two premise assumptions for the algorithm parameters are given, and three lemmas related to the algorithm are given at the same time.
Assume that 1: the system matrix a in the distributed system represented by equation (4) is always non-singular.
Assume 2: presence of positive real numbers q,r,p,So that the algorithm parameter matrix has the following boundaries:
wherein the matrix I is a unit matrix. It should be noted that these two preconditions are not critical. For a discrete distributed system represented by equation (4) sampled from a continuous system, assume 1 always holds. According to the document "Andersen B and Moore J.Detectability and stability of time-varying characterization-time systems [ J].Siam Journal on Control&Optimization.1981,19, (1):20-32.doi:10.1137/0319002. ", if the system represented by equation (4) is consistent and observable, then P isiWith upper and lower bounds.
Introduction 1: for a random process Vk(ξk) If there is a real numberAnd 0 < α ≦ 1 so that
E{Vk(ξk)|ξk-1}≤(1-α)Vk-1(ξk-1)+μ (23)
When true, then the random process is bounded in mean square, i.e.And the random process is bounded by probability 1.
In the following analysis, lemma 1 is used to determine the stability of the state estimates of the consensus kalman filter algorithm. For each measurement node, the parameter boundary conditions are as described in theorem 2 and theorem 3:
2, leading: under the premises of assumptions 1 and 2, for each node, there is a real number 0 < κi1(i ═ 1.., N), so that
(A-AKiHi)T(Pi +)-1(A-AKiHi)≤(1-κi)Pi -1 (24)
Wherein
And 3, introduction: under the premises of assumptions 1 and 2, for each node there is a real number εi> 0(i ═ 1.., N), such that
The main conclusions of the algorithm stability analysis when considering noise are as follows:
theorem 1: considering a wireless sensor network composed of N nodes, the consistency kalman filter algorithm represented by formula (5) is applied to the distributed discrete time-varying system represented by formula (4). With the assumptions 1 and 2, if the initial prediction error is bounded, the prediction error mean square index of the algorithm is bounded and bounded by the probability 1.
Theorem 1 proves that:
according to definitions (8) and (11), a prediction error vector is first definedAnd the corresponding block diagonal array P ═ diag { P ═ diag }1,...PN}. The following lyapunov function was constructed as a random process in lemma 1:
according to assumption 2Can obtain
Wherein,andp＝min{p 1,...,p N}. The inequality (28) satisfies the first condition in theorem 1, and in order to prove that the random process E is bounded by the mean-square exponent, the mathematical expectation E { V } of the Lyapunov function at the next moment in time must be considered+(e+)}. According to the prediction error eiCan be obtained as follows with respect to eiThe dynamic equation of (c):
when substituted into the definition formula (21) of the Lyapunov function, there are
Wherein
Taking the mathematical expectation from both sides of the above formula, and applying theorem 2 and theorem 3, the formula (30) can be simplified to
To facilitate the analysis of terms 3 and 4 on the right side of the above equation, we will apply a consistency gain of CiIs defined as the following form
Λi＝(A-AKiHi)T(Pi +)-1(A-AKiHi) (32)
Ci＝σ[(A-AKiHi)T(Pi +)-1A]-1Λi(33) In formula (3)3) Simultaneous right-hand multiplication on both sides (A-AK)iHi)T(Pi +)-1A, can be conveniently obtained
Then, the definition (32) is substituted into the above formula, and the terms are shifted to obtain
Ci＝σA-1(A-AKiHi) (35)
(A-AKiHi)-1ACi＝σ (36)
Continuing to substitute formula (36) for formula (34), there is
The terms 3 and 4 on the right side of the formula (31) can be expressed by the formulas (34) and (37)
Define Λ ═ diag { Λ1,...ΛNQ ═ col { q }1,...qN}. The laplacian matrix of the wireless sensor network is represented by L as described above, and q can be represented byWhereinTherefore, the formula (38) can be further simplified to
Wherein λ isminAnd λmaxRepresenting the maximum and minimum eigenvalues of the matrix, respectively. Combining formula (31) and formula (39), we have
Whereinκ＝min{κ1,...κN}. As described in the introduction 2, the following inequality is apparently true
The above formula is substituted into formula (40) as
To satisfy the 2 nd condition in theorem 1, we must ensure that the following inequality holds
Equation (43) can be regarded as a unary-quadratic inequality with respect to σ, and when σ < σ*When the inequality is true, wherein
Meanwhile, according to the theorem 3, the inequality (44) is obviously established. Thus, the prediction error eiBounded by a probability 1 mean square index.
The embodiment of the invention also simulates the cabin pollutant monitoring method based on the distributed combined sensor network to verify the implementation effect.
Fig. 6 is a schematic diagram of digital simulation of a node i, where for convenience of description, each node in the distributed combined sensor network is provided with only a primary sensor simulation model and a secondary sensor simulation model for monitoring a pollutant concentration. As shown in FIG. 6, the simulation models of the primary and secondary sensors at node i are at respective system noise WiAnd measure the noise ViRespectively output the measured values Z of the pollutant concentrationPi,ZSiThe difference is the error Z of the measured valuei. The consistency Kalman filter also receives the measurement error Z of the neighbor node j of the node ijAfter being filtered by a consistency Kalman filter, the estimated value is outputTo correct the main sensor measurement Z of node iPiTo obtain the final measured value ZOi. The simulation experiment adopts a Monte Carlo method to carry out a large number of independent repeated tests, and the performance of the monitoring network is analyzed by using the parameter statistical average value at each moment. The following performance indicators are defined:
mean Estimation Error (MEE):
ei,k＝ZO,k-ZOi,k (47)
state Mean consistency Error (Mean Consensus Error, MCE):
wherein, subscript k represents the simulation running time, and N is the total number of nodes.
The mathematical models of the main sensor and the auxiliary sensor in fig. 6 are respective basic control equations, which can be obtained by theoretical analysis of measurement errors and system identification of actual measurement data, and the measurement data obtained by considering system noise and measurement noise is closer to the actual measurement. It should be noted that the system equations and measurement equations adopted by the distributed kalman filter are only approximations to the real sensor combination, limited by the system identification accuracy and the performance of the embedded device.
A simulation example of the present invention is given next. A sensor detection network having 10 nodes is deployed, and the network topology is shown in fig. 7, in which each node is provided with only a primary sensor and a secondary sensor for monitoring the concentration of one pollutant. The laplace matrix is:
the measurement error model considering the combi-sensor is:
error initial state xi(0)＝(8,12)TThe system noise and the measurement noise respectively adopt mutually independent white Gaussian noise interferences with covariance of 10 and 100i, wherein i is a sectionAnd point serial numbers are used for reflecting different measurement errors of each node. The sampling period of the system is 10ms, and P is taken by the initial prediction error matrix of the Kalman filteri(0)＝10I2。
The system was subjected to a stability analysis, first verifying the two hypotheses mentioned in the stability analysis above. For hypothesis 1, each time instant AkApparently non-exotic. With regard to hypothesis 2, the following inequality holds for this system:
from equation (25), it can be calculatedκ0.4515, satisfies the precondition 0 < (r) > of theorem 2κIs less than 1. According toCan be calculated asThen, the formula (45) obtains σ*0.7417 to determine the value range of the consistency parameter.
In the existing cockpit pollutant monitoring, each sensor independently processes data by using a local Kalman filtering algorithm. In order to verify the effectiveness of the method provided by the invention, after 1000 independent monte carlo simulations are carried out on the local kalman filtering algorithm and the consistency kalman filtering algorithm, the state average estimation error change of the statistical formula (46) is shown in fig. 8, and the state average consistency error of the statistical formula (48) is shown in fig. 9.
The local kalman filter algorithm does not consider information from surrounding nodes, so the overall effect of state estimation is poor: FIG. 8 shows that the state estimation error is higher than the consistency Kalman filtering algorithm provided by the present invention, and the convergence rate is slower; more importantly, the consistency error of the local kalman filtering algorithm in fig. 9 is much higher than that of the consistent kalman filtering algorithm provided by the present invention, which indicates that the difference between the estimated values of the sensors in the same state is large, and this explains why false alarm easily occurs in a single sensor. Because the method provided by the invention considers system noise and measurement noise in the pollutant monitoring process, the estimated value of each node cannot be completely and accurately converged to the true value, and meanwhile, the estimated value between each node has certain errors, but the errors are quickly converged to the limited value. Compared with the traditional local Kalman filtering algorithm, the method provided by the invention has higher state estimation precision, the measured values of all sensors tend to be consistent, and the problem of false alarm caused by larger error of a local single sensor is effectively solved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. A cabin pollutant monitoring method based on a distributed combined sensor network is characterized by comprising the following steps:
establishing a distributed combined sensor network in a cabin to be tested; each node in the distributed combined sensor network is provided with a sensor for monitoring at least one specified pollutant, and for each specified pollutant, a main sensor and an auxiliary sensor with different measurement principles are arranged;
defining neighbor nodes of each node, and initializing and setting sensors corresponding to the l & ltth & gt specified pollutants of each node iEstimating gain array PilAnd state estimation
For each node, a main sensor and an auxiliary sensor for monitoring the same pollutant are respectively measured to obtain a main sensor measured value Z of the current node corresponding to the appointed pollutant of the first kindPilAnd the measured value Z of the auxiliary sensorSil；
According to the formula Zil＝ZPil-ZSilCalculating the error Z of the measured value of the sensor corresponding to various specified pollutants by each nodeil；
Each node determines the node information of the node and transmits the node information to the neighbor nodes; the node information comprises state estimation values of the sensors of which the current nodes correspond to various specified pollutants, information vectors and information matrixes of which the current nodes correspond to the various specified pollutants;
for each node, the sensor measurement error Z for each specified contaminant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filter
For each node, the optimal error estimation value of the sensor corresponding to the I & ltth & gt specified pollutant by the current node is adoptedCorrecting the main sensor measurement value of the current node corresponding to the l type of specified pollutant to obtain the l type of specified pollutant concentration value of the current node:
wherein, i is 1, 2.. times.n; n is the number of nodes in the distributed combined sensor network; l, L is the specified number of pollutant species monitored by each node;
the method for determining the node information of each node comprises the following steps:
each node corresponds to the observation noise covariance matrix R of various specified pollutants according to the preset current nodeilAccording to the formulaAndcalculating an information matrix U of the current node corresponding to various specified pollutantsilAnd an information vector uil(ii) a Wherein HilA combined error observation matrix corresponding to the ith specified pollutant for the known ith node;
each node corresponds the current node to the state estimation value of the sensor of various specified pollutantsInformation vector u of current node corresponding to various specified pollutantsilAnd information matrix UilAnd determining the node information as the current node.
2. The cabin pollutant monitoring method based on distributed combined sensor network, according to claim 1, characterized in that the sensor measurement value error Z of each specified pollutant according to the current nodeilAnd node information of all neighbor nodes of the current node, and obtaining the optimal error estimation value of the sensor of each specified pollutant corresponding to the current node through the filtering of a consistency Kalman filterThe method comprises the following steps:
the current node receives node information of all neighbor nodes of the current node;
for the l type of specified pollutant monitored by the current node, performing the following information fusion on information vectors and information matrixes of all neighbor nodes of the current node corresponding to the same type of specified pollutant:
updating the estimation error covariance matrix M of the sensor corresponding to the I & ltth & gt specified pollutant at the current node by the current nodeilComprises the following steps:
according to the formulaPerforming state estimation on the sensor to obtain a state estimation value of the sensor corresponding to the first specified pollutant at the current node
According to the formulaCalculating the optimal error estimation value of the sensor corresponding to the I type of specified pollutant at the current node
Wherein J ∈ Ji＝Ni∪{i}，NiIs a neighbor node set of the ith node, and the variable rilComprises the following steps:
theta is a system sampling period, and a Frobenius norm is taken as a matrix.
3. The cockpit contaminant monitoring method based on the distributed combinational sensor network of any of claims 1-2, further comprising, after said obtaining the l-th designated contaminant concentration value of the current node:
judging whether the concentration value of each appointed pollutant of each node is greater than a preset appointed pollutant concentration threshold value of a corresponding type;
when the concentration value of any one appointed pollutant of the node is larger than the preset appointed pollutant concentration threshold value of the corresponding type, the pollutant concentration overproof alarm is sent out.
4. The cabin pollutant monitoring method based on the distributed combination sensor network is characterized in that after the step of judging whether the concentration value of each specified pollutant of each node is larger than the preset threshold value of the concentration of each specified pollutant, the method further comprises the following steps:
when the concentration value of each appointed pollutant of each node is not more than the preset appointed pollutant concentration threshold value of the corresponding type, calculating the air quality index AQI of each node according to the following formulai：
Wherein Z isblFor a predetermined first specified pollutant concentration threshold, αlAnd the preset indicator weight value of the first designated pollutant.
5. The cabin pollutant monitoring method based on the distributed combination sensor network is characterized in that after the pollutant concentration exceeding alarm is sent out or the air quality index of each node is calculated, the method further comprises the following steps:
each node corresponds the current node to the estimated gain array P of the sensor of each specified pollutantilAnd state estimationThe updating is as follows: pil＝AMilAT，A is an adjacency matrix representing the communication relationship of each sensor;
and returning to the step of executing the measurement of the main sensor and the auxiliary sensor for monitoring the same pollutant respectively for each node.
6. The cabin pollutant monitoring method based on the distributed combination sensor network according to claim 1, wherein the specified pollutant is at least one of carbon dioxide, carbon monoxide, ozone, inhalable particles, acetone, ethanol, formaldehyde, toluene, dichloromethane, endotoxin and microorganism.
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| CN109061068B (en) * | 2018-08-15 | 2019-05-21 | 中国民航大学 | The fault-tolerant measurement estimation method of cabin pollutant concentration |
| CN109459527A (en) * | 2018-09-25 | 2019-03-12 | 中国商用飞机有限责任公司 | Method and system for monitoring carbon monoxide concentration of aircraft cabin |
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