Multi-source remote sensing cooperative monitoring method and system for breach dangerous case emergency scene

Abstract

The invention discloses a multi-source remote sensing cooperative monitoring method and system for a breach dangerous case emergency scene. The method comprises the following steps: constructing a performance matrix of a multi-source cooperative monitoring sensor; expressing each performance index in the performance matrix by adopting a triangular fuzzy number to obtain a fuzzy matrix of the performance matrix; adopting an entropy weight method to configure a weight vector of the performance index, and adopting a triangular fuzzy number to express the weight vector; calculating the membership degree of each sensor belonging to the fuzzy positive ideal according to the weighted fuzzy decision matrix, and sorting the sensors according to the descending order of the membership degree to obtain a sensor performance ranking sequence; and performing optimal priority search on the sensor performance ranking sequence to obtain an optimal sensor combination scheme. According to the invention, the actual requirements of emergency monitoring tasks of breach dangerous cases and flood evolution on response timeliness, high frequency and dynamics can be met.

Description

Technical Field

[0001] The present application belongs to the technical field of water conservancy remote sensing and water resources three-dimensional monitoring, and specifically relates to a multi-source remote sensing collaborative monitoring method and system for emergency scenarios of breach hazards. Background Art

[0002] River and lake embankments are important barriers for flood control and disaster relief. In recent years, due to heavy rainfall and high flood levels, breaches have occurred frequently and are accompanied by flood disasters. Emergency response to breaches has high requirements for response timeliness, information acquisition accuracy, and observation coordination. How to scientifically and effectively monitor breaches in real time and provide technical support for post-disaster rapid assessment and flood emergency rescue decisions is an urgent practical need.

[0003] Traditional ground monitoring methods are real-time, direct, and highly accurate, but they are limited by the sparseness of monitoring sites and cannot reflect the global information of flood disasters. Satellite remote sensing plays an important role in flood prevention and disaster reduction with its advantages of all-weather, short cycle, dynamic continuity, and wide coverage. With the rapid development of remote sensing technology, the types of monitoring data include multi-spectral, hyperspectral, radar, etc. The satellite resolution is as high as centimeter level, and the revisit cycle can reach daily scale, which greatly enhances the performance of earth observation. The introduction of concepts such as spatial information grid framework and integrated air-space-ground earth observation sensor network has opened up new ideas for multi-source remote sensing collaborative monitoring. Compared with single satellite / sensor, multi-satellite / sensor collaborative networking has the advantages of broadening the temporal and spatial coverage, improving target recognition accuracy, strong anti-interference ability, and the ability to detect multi-dimensional stereoscopic data, which shows great potential in flood and drought disaster prevention.

[0004] Multi-source remote sensing collaboration includes two aspects: collaborative monitoring process and collaborative data inversion. The former focuses on the performance of the sensor itself, selecting the satellite / sensor combination that best matches the target requirements from the inherent physical parameters; the latter focuses on data application and belongs to the post-evaluation mode of sensor performance. At present, domestic and foreign technical experts use massive satellite data for flood monitoring, including: using aerial images and ERS-1 synthetic aperture radar to extract basin flood boundaries and draw inundation area maps; using Landsat-7ETM+ and Sentinel-1SAR to obtain land use and coverage in floodplains; conducting major flood monitoring and disaster damage assessment based on FY3 and MODIS; extracting flood inundation range information based on GF-3 and Sentinel-1 radar data, and dynamically estimating the duration of flood disaster inundation; using Landsat8 and Sentinel-1A change detection to obtain flood inundation range, etc. Most existing technologies focus on collaborative data inversion, but lack dynamic real-time monitoring of flood disaster processes, making it difficult to meet the needs of breach dangers and flood evolution for response timeliness and process dynamic tracking.

[0005] It should be noted that since a satellite carries multiple different types of sensors at the same time, single satellite / sensor, multiple satellites / sensors, and satellite / sensor are used to represent satellites and the sensors they carry. Summary of the invention

[0006] The purpose of this application is to provide a multi-source remote sensing collaborative monitoring method and system for breach emergency scenarios, aiming to meet the actual needs of emergency monitoring tasks for breach and flood evolution in terms of response timeliness, high frequency and dynamism.

[0007] To achieve the above objectives, on the one hand, the present application provides a multi-source remote sensing collaborative monitoring method for emergency scenarios of breach hazards, including:

[0008] Construct a performance matrix of multi-source collaborative monitoring sensors based on the performance indicator data of sensors;

[0009] The triangular fuzzy numbers are used to express each performance index in the performance matrix, and the triangular fuzzy numbers of the same type of performance index are normalized to obtain the fuzzy matrix of the performance matrix.

[0010] The entropy weight method is used to configure the weight vector of performance indicators, and the weight vector is expressed by triangular fuzzy numbers;

[0011] The fuzzy matrix is ​​weighted by the fuzzy number weight vector to obtain the weighted fuzzy decision matrix:

[0012] The membership degree of each sensor belonging to the fuzzy positive ideal is calculated according to the weighted fuzzy decision matrix, and the sensors are sorted from large to small according to the membership degree to obtain the sensor performance ranking sequence;

[0013] The best priority search is performed on the sensor performance ranking sequence to obtain the best sensor combination solution.

[0014] In some specific implementations, triangular fuzzy numbers are used to express each performance index in the performance matrix, including:

[0015] For a quantitative performance indicator p, when p is an exact number, it is expressed as p = (p, p, p);

[0016] When p is an interval number, it is expressed as p=(p min ,p med ,p max ), where p min and p max represent the lower and upper bounds of the interval respectively.

[0017] For the qualitative performance index p, the triangular fuzzy number ratio method is used to quantify it into the corresponding triangular fuzzy number.

[0018] In some specific implementations, the triangle fuzzy numbers of the same type of performance indicators are normalized respectively, including:

[0019] For cost-based performance indicators, the normalization process is:

[0020]

[0021] For the revenue-based performance indicators, the normalization process is:

[0022]

[0023] The triangular fuzzy number of the g-th performance index of sensor f is denoted as s fg =(a fg ,b fg ,c fg ), where min(a fg ) and max(a fg ) represent the triangular fuzzy number a of the g-th performance index of all sensors. fg The minimum and maximum values ​​of min(b fg ) and max(b fg ) represent the triangular fuzzy number b of the g-th performance index of all sensors. fg The minimum and maximum values ​​of min(c fg ) and max(c fg ) represent the triangular fuzzy number c of the g-th performance index of all sensors. fg The minimum and maximum values ​​of x; Λ1 represents the normalization operation; fg is the normalized s fg .

[0024] In some specific implementations, the entropy weight method is used to configure the weight vector of the performance indicator, including: standardizing the performance indicator data in the performance matrix; solving the information entropy of each type of performance indicator respectively; solving the weight coefficient of each type of performance indicator by using the information entropy;

[0025] The standardization of the performance indicator data in the performance matrix includes: when the performance indicators include both cost-based performance indicators and benefit-based performance indicators, firstly performing the same trend processing on the performance indicators, and then standardizing the performance indicator data after the same trend processing;

[0026] The weight coefficient

[0027] Among them, w g represents the weight coefficient of the g-th performance indicator; h t 、h gThey represent the information entropy of the t-th and g-th performance indicators respectively; t represents the performance indicator number, and there are m performance indicators in total; exp() represents exponential function operation.

[0028] In some specific implementations, the performance indicator data after the same trend processing is standardized according to the following formula:

[0029]

[0030] Among them, s' fg represents the standardized s fg ; Indicates s after the same trend processing fg ;s fg represents the g-th performance indicator of sensor f; n represents the number of sensors; and m represents the dimension of the performance indicator.

[0031] In some specific implementations, calculating the membership degree of each sensor to a fuzzy positive ideal according to a weighted fuzzy decision matrix includes:

[0032] Determine the fuzzy positive ideal and the fuzzy negative ideal according to the weighted fuzzy decision matrix;

[0033] Calculate the distance between each sensor and the fuzzy positive ideal and the fuzzy negative ideal respectively and

[0034]

[0035] in, and They represent the mutual information positive adjustment parameter and the mutual information negative adjustment parameter respectively; Y fg represents the weighted fuzzy value of the g-th performance indicator of sensor f in the weighted fuzzy decision matrix; and They represent the fuzzy positive ideal and fuzzy negative ideal corresponding to the g-th dimension performance index respectively;

[0036] According to the distance of each sensor and Calculate the membership degree of the sensor to the fuzzy positive ideal.

[0037] In some specific implementations, performing a best-first search on a sensor performance ranking sequence includes:

[0038] Step 1: Add the sensor node with the largest fuzzy membership to the collaborative solution set {S o , o∈(1,2,...e)}, where o represents the serial number of the sensor currently added to the collaborative solution set, and e is the number of sensors in the collaborative solution set;

[0039] Step 2: Traverse the remaining sensor nodes in the sensor performance ranking sequence in order, and judge whether the comprehensive fitness of the coordination solution set is optimized after each remaining sensor node is added to the current coordination solution set. If it is optimized, add the sensor node; otherwise, do not add it; until the traversal is completed, the sensor nodes in the obtained coordination solution set are the best sensor combination;

[0040] The calculation of the comprehensive fitness is as follows:

[0041] First, logical operations are used to remove duplicates and sum the performance indicators of the sensors in the current collaborative solution:

[0042] s g *U=s g1 *u1+s g2 *u2+...s ge *u e ;

[0043] Among them, g represents the number of each performance indicator; s go represents the g-th performance indicator value of the o-th sensor in the current solution set, obtained from the performance matrix S, o = 1, 2, ... e; u o represents the fuzzy membership of the oth sensor o, o = 1, 2, ... e; s go *u o represents the fuzzy adaptability of the oth sensor to the gth performance indicator; s g *U represents the logical operation of the current collaborative solution set on the g-th performance indicator; * represents the deduplication summation operator;

[0044] Then, calculate the individual score F of the current collaborative solution set for each performance indicator g (U):

[0045] When the monitoring task has clear and quantifiable performance indicator requirements, the individual scores of the current collaborative solution set are expressed as follows:

[0046]

[0047] When the monitoring task has vague requirements for performance indicators, the individual scores of the current collaborative solution set are expressed as follows:

[0048] F g (U)=rank(s g *U) / 2 e-1 ;

[0049] Among them, q g Indicates the g-th performance indicator; F g (U) represents the single score of the current collaborative solution set for the g-th performance indicator; rank() represents the ranking function. gis a cost-based performance index, sort in descending order and take the minimum value; when the performance index q i It is a profit-based performance indicator, sorted in positive order and takes the maximum value;

[0050] Finally, calculate the comprehensive fitness of the current cooperative solution set Among them, w g Represents the weight coefficient of the g-th performance indicator.

[0051] On the other hand, the present application also provides a multi-source remote sensing collaborative monitoring system for emergency scenarios of breach hazards, including:

[0052] The first module is used to construct a performance matrix of multi-source collaborative monitoring sensors based on the performance indicator data of the sensors;

[0053] The second module is used to express each performance index in the performance matrix using triangular fuzzy numbers, and normalize the triangular fuzzy numbers of the same type of performance indexes to obtain the fuzzy matrix of the performance matrix;

[0054] The third module is used to configure the weight vector of the performance index using the entropy weight method and express the weight vector using triangular fuzzy numbers;

[0055] The fourth module is used to weight the fuzzy matrix with the fuzzy number weight vector to obtain the weighted fuzzy decision matrix:

[0056] The fifth module is used to calculate the membership degree of each sensor belonging to the fuzzy positive ideal according to the weighted fuzzy decision matrix, sort the sensors from large to small according to the membership degree, and obtain the sensor performance ranking sequence;

[0057] The sixth module is used to perform the best priority search on the sensor performance ranking sequence to obtain the best sensor combination solution.

[0058] In some specific embodiments, the fifth module further includes:

[0059] Submodule 1, used to determine the fuzzy positive ideal and the fuzzy negative ideal according to the weighted fuzzy decision matrix;

[0060] Submodule 2 is used to calculate the distance between each sensor and the fuzzy positive ideal and the fuzzy negative ideal respectively and

[0061]

[0062] in, and They represent the mutual information positive adjustment parameter and the mutual information negative adjustment parameter respectively; Y fg represents the weighted fuzzy value of the g-th performance indicator of sensor f in the weighted fuzzy decision matrix; and They represent the fuzzy positive ideal and fuzzy negative ideal corresponding to the g-th dimension performance index respectively;

[0063] Submodule 3 is used to determine the distance between each sensor. and Calculate the membership degree of the sensor to the fuzzy positive ideal.

[0064] In some specific embodiments, the sixth module further includes:

[0065] Submodule 4 is used to add the sensor node with the largest fuzzy membership into the collaborative solution set {S o , o∈(1,2,...e)}, where o represents the serial number of the sensor currently added to the collaborative solution set, and e is the number of sensors in the collaborative solution set;

[0066] Submodule 5 is used to sequentially traverse the remaining sensor nodes in the sensor performance ranking sequence, and judge one by one whether the comprehensive fitness of the coordination solution set is optimized after each remaining sensor node is added to the current coordination solution set. If it is optimized, the sensor node is added; otherwise, it is not added; until the traversal is completed, the sensor nodes in the obtained coordination solution set are the best sensor combination;

[0067] The calculation of the comprehensive fitness is as follows:

[0068] First, logical operations are used to remove duplicates and sum the performance indicators of the sensors in the current collaborative solution:

[0069] s g *U=s g1 *u1+s g2 *u2+...s ge *u e ;

[0070] Among them, g represents the number of each performance indicator; s go represents the g-th performance indicator value of the o-th sensor in the current solution set, obtained from the performance matrix S, o = 1, 2, ... e; u o represents the fuzzy membership of the oth sensor o, o = 1, 2, ... e; s go *u o represents the fuzzy adaptability of the oth sensor to the gth performance indicator; s g *U represents the logical operation of the current collaborative solution set on the g-th performance indicator; * represents the deduplication summation operator;

[0071] Then, calculate the individual score F of the current collaborative solution set for each performance indicator g (U):

[0072] When the monitoring task has clear and quantifiable performance indicator requirements, the individual scores of the current collaborative solution set are expressed as follows:

[0073]

[0074] When the monitoring task has vague requirements for performance indicators, the individual scores of the current collaborative solution set are expressed as follows:

[0075] F g (U)=rank(s g *U) / 2 e-1 ;

[0076] Among them, q g Indicates the g-th performance indicator; F g (U) represents the single score of the current collaborative solution set for the g-th performance indicator; rank() represents the ranking function. g is a cost-based performance index, sort in descending order and take the minimum value; when the performance index q i It is a profit-based performance indicator, sorted in positive order and takes the maximum value;

[0077] Finally, calculate the comprehensive fitness of the current cooperative solution set Among them, w g Represents the weight coefficient of the g-th performance indicator.

[0078] Compared with the prior art, this application has the following advantages and beneficial effects:

[0079] This application can meet the practical needs of monitoring breach danger and flood evolution for response timeliness, high frequency and dynamism. In the specific application cases of this application, the monitoring time can run through the whole process before, during and after the breach, and can truly reflect the pipe seepage process and expansion trend before the breach danger occurs, which can effectively make up for the problem of lack of pre-breach data in in-situ hydrological observation and flood reenactment model. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic diagram of the specific process of this application method;

[0081] Figure 2 This is a monitoring diagram of the change in the breach width obtained by using the method of the present application and hydrological in-situ monitoring in the embodiment;

[0082] Figure 3 This is a monitoring map of flooded area changes calculated using the method of the present application and the flood reenactment model in the embodiment. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solutions and advantages of this application clearer, the specific implementation methods of this application will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the specific implementation methods described are only part of the specific implementation methods of this application, not all of the specific implementation methods. Based on the specific implementation methods in this application, all other specific implementation methods obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0084] See also Figure 1 , shown is a specific flow chart of the present application method, which will be combined with Figure 1 Provide the technical principles, ideas and specific implementation process on which this application method is based.

[0085] 1. Construction of multi-source remote sensing collaborative monitoring model.

[0086] Before executing the multi-source remote sensing collaborative monitoring for the breach emergency scenario in this application, a multi-source remote sensing collaborative monitoring model is pre-defined and constructed. This application constructs a multi-source remote sensing collaborative monitoring model Q=S according to the monitoring task of the breach emergency scenario and the monitoring requirements of the monitoring task. T U, where S represents the performance matrix of multi-source collaborative monitoring sensors, S T represents the transpose operation of the matrix S, Q represents the monitoring demand vector, and U represents the decision vector.

[0087] The monitoring task of this application is the breach hazard. The monitoring demand is represented by a vector Q = {q1, q2, ...q m}, q i Represents a single monitoring factor, i = 1, 2, ... m. In this specific implementation, based on the basic demand library of sensor performance parameters, combined with the practical requirements of the emergency task of the breach and the secondary flood disaster caused by the breach on the timeliness of response, information accuracy, coverage completeness, etc., the monitoring factors are quantified into sensor performance indicators such as re-entry period, frequency, resolution, coverage, spectral band, and number of strips. If the performance indicator q i If max or min is required, the performance indicator is "fuzzy", where max means the higher the better and min means the lower the better; if the performance indicator q i If a specific value is required, the performance indicator is "clear".

[0088] The basic requirement library of sensor performance parameters was summarized by the National Disaster Reduction Committee during the construction of the small satellite constellation project for environmental and disaster monitoring and forecasting. The basic requirement library describes the basic requirements of different disaster types in applications at different development stages. Disaster types include floods, droughts, rainstorms, forest fires, earthquakes, etc.; development stages include monitoring and early warning, emergency response, emergency rescue, disaster assessment, etc.; basic requirements include re-entry time, spatial resolution, observation spectrum, all-weather observation capabilities, etc. This application selects the basic requirements corresponding to the monitoring tasks based on the basic requirement library and quantifies them into corresponding performance indicators.

[0089] The performance matrix S of the multi-source collaborative monitoring sensor is composed of the performance indicators of each sensor and is expressed as follows:

[0090]

[0091] In formula (1), f represents the sensor number, n represents the number of sensors, m represents the dimension of sensor performance index, s fg represents the g-th performance indicator of sensor f.

[0092] The performance matrix S of multi-source collaborative monitoring sensors can be simplified as: S = {S f ,f=1,2,...n},S f represents the performance indicator vector of sensor f.

[0093] Considering the fuzzy situation of “same transmission but different needs” or “different transmission but same needs” of sensors, a fuzzy multi-attribute decision vector U={u1,u2,...u n},u f ∈[0,1] represents the fuzzy membership of sensor f to the monitoring task, u f The larger the value, the better the match of sensor f to the monitoring task, and vice versa.

[0094] In this application, the multi-source remote sensing collaborative monitoring model Q = S T U inputs the performance matrix S of the multi-source collaborative monitoring sensor and the demand vector Q of the monitoring task, and then outputs the decision vector U and the satellite / sensor combination scheme determined by the fuzzy multi-attribute decision-making method.

[0095] 2. Use fuzzy multi-attribute decision-making method to solve the optimal sensor combination solution.

[0096] This application uses the compromise fuzzy multi-attribute decision-making method for solution. The specific steps are as follows:

[0097] (1) Using triangular fuzzy numbers to express each performance index in the performance matrix S, the triangular fuzzy numbers of each performance index are normalized respectively to obtain the fuzzy matrix R of the performance matrix S;

[0098] The specific implementation process of this step is as follows:

[0099] 1.1 Use triangular fuzzy numbers to express the performance indicators in the performance matrix S:

[0100] J(S) is used to represent the fitness fuzzy set of the sensor set S, the triangular fuzzy number Z∈J(S), μ Z is the transformation formula of the triangular fuzzy number Z, which is expressed as Z = (l, z, r) = (z L ,z,z R ), where z represents the basic data value of the performance indicator; L Indicates the lower bound of the performance index value; z R Indicates the upper bound of the performance indicator value.

[0101] For the quantitative performance index p, when p is an exact number, it is converted into the corresponding triangular fuzzy number using formula (2);

[0102] p=(p,p,p)(2)

[0103] When p is an interval number, it is converted into the corresponding triangular fuzzy number using formula (3):

[0104]

[0105] In formula (3), p min and p max Represent the lower and upper bounds of the interval respectively.

[0106] The qualitative performance index p is quantized into a corresponding triangular fuzzy number using the triangular fuzzy number ratio method. The quantization conversion form in this specific implementation is shown in Table 1.

[0107] Table 1 Conversion of qualitative indicators

[0108] Quantization value (001) (112) (234) (456) (678) (789) (91010) Cost-based indicators Highest Very high high generally Low Very low lowest Profitability Index lowest Very low Low generally high Very high Highest

[0109] 1.2 Normalize the triangle fuzzy numbers of various performance indicators respectively:

[0110] The object to be evaluated is S = {S f ,f=1,2,...n}, where S f represents the performance index vector of sensor f. For the g-th performance index, corresponding to n fuzzy index values, the triangular fuzzy number of the g-th performance index of sensor f is recorded as s fg =(a fg ,b fg ,c fg ), then the normalized fuzzy matrix is ​​recorded as R = (x fg ) m×n .

[0111] If fg If is a cost-type indicator, the normalization process is:

[0112]

[0113] In formula (4):

[0114] min(a fg ) represents the triangular fuzzy number a of the g-th performance index of all sensors fg The minimum value of

[0115] min(b fg ) represents the triangular fuzzy number b of the g-th performance index of all sensors fg The minimum value of

[0116] min(c fg ) represents the triangular fuzzy number c of the g-th performance index of all sensors fg The minimum value of

[0117] Λ1 represents a normalization operation.

[0118] If fg If is a profit indicator, the normalization process is:

[0119]

[0120] In formula (5):

[0121] max(a fg ) represents the triangular fuzzy number a of the g-th performance index of all sensors fg The maximum value of

[0122] max(b fg ) represents the triangular fuzzy number b of the g-th performance index of all sensors fg The maximum value of

[0123] max(c fg ) represents the triangular fuzzy number c of the g-th performance index of all sensors fg The maximum value of .

[0124] Using formulas (4) to (5), S = {S f ,f=1,2,...n}, the fuzzy index values ​​of each performance index are normalized respectively to obtain the normalized fuzzy matrix R=(x fg ) m×n , where x fg A triangular fuzzy number representing the g-th performance indicator of sensor f.

[0125] (2) Use the entropy weight method to configure the weight vector of performance indicators;

[0126] The weight coefficient of the performance indicator can objectively reflect the importance of each performance indicator, and the configuration difference of the weight coefficient greatly affects the performance evaluation quality of the multi-source collaborative monitoring sensor. For different monitoring tasks, the importance of the same performance indicator varies greatly. Therefore, when using performance indicators as the basis for evaluating monitoring capabilities, their weight coefficients need to be reasonably configured. In this application, the information entropy of the performance indicator is used to solve the weight coefficient. The information entropy value can be used to judge the degree of discreteness of the performance indicator. The smaller the information entropy value, the greater the degree of discreteness, and the greater the impact of the performance indicator on the comprehensive evaluation, and a larger weight coefficient should be assigned. If the values ​​of a certain performance indicator are all equal, then the performance indicator has no effect in the comprehensive evaluation.

[0127] The entropy weight method is used to configure the weight vector of the performance indicator, including: standardizing the performance indicator data; solving the information entropy of each performance indicator respectively; solving the weight vector of the performance indicator by using the information entropy.

[0128] In the traditional entropy weight method, the following formula (6) is used to standardize the performance index:

[0129]

[0130] In formula (6):

[0131] s fg represents the g-th performance indicator of sensor f;

[0132] s' fg represents the standardized s fg ;

[0133] min(s 1g ,...s fg ,...s ng ) represents the minimum value of the g-th performance indicator of n sensors;

[0134] max(s 1g ,...s fg ,...s ng ) represents the maximum value of the g-th performance indicator of n sensors.

[0135] In the traditional entropy weight method, the following formula (7) is used to solve the information entropy of the performance index:

[0136]

[0137] In formula (7):

[0138] h g represents the information entropy of the g-th performance indicator;

[0139] k is a constant, and its size is determined by the number of sensors n;

[0140] r fg Represents the standardized index s' fg The weight matrix.

[0141] In the traditional entropy weight method, the following formula (8) is used to solve the weight vector of the performance index:

[0142]

[0143] In formula (8):

[0144] w g represents the weight coefficient of the g-th performance indicator;

[0145] m represents the dimension of sensor performance index.

[0146] According to the weight coefficient w g Obtain the weight vector W of the performance matrix S of the multi-source collaborative monitoring sensor = (w1, w2, ... w m ).

[0147] The objective weights determined by the traditional entropy weight method can avoid subjective experience interference to a certain extent. However, the indicators involved in performance evaluation are diversified, and the trends and dimensions are not unified, which is not convenient for indicator analysis and weighting; in addition, when the difference between similar indicators is small, the traditional entropy weight calculation result is close to 1, which will over-magnify the weight gap and cause large errors.

[0148] To solve the above defects, this application proposes the following improvements:

[0149] ① Considering the inconsistency of the trends and dimensions of various performance indicators, this application performs dimensionless and extremely large normalization on the performance indicators:

[0150] In this specific implementation manner, the performance indicators involved in the sensor monitoring performance evaluation include re-entry period, frequency, resolution, coverage, spectral band, number of stripes, etc. In the emergency monitoring task of breach danger, the smaller the re-entry period, the better, with the dimension ranging from "seconds" of geostationary satellites to "days" of normal satellites. This indicator belongs to the extremely small scale, which is also called cost-type indicator; the more frequency, the better, with the dimension ranging from "0" times to "infinite" times, which belongs to the extremely large scale, which is also called benefit-type indicator; the finer the resolution, the better, with the dimension ranging from "sub-meter" to "kilometer", which belongs to the extremely small scale; the greater the coverage, the better, with the dimension ranging from "0%" to "100%", which belongs to the extremely large scale; the spectral band needs to meet the inversion requirements of breach width and flooded area, covering multi-spectral or microwave corresponding spectral bands, with the dimension description of "1 if the spectral band requirements are met" and "0 if they are not met", which belongs to the extremely large discrete interval; the number of scanning strips is directly related to the workload of satellite image preprocessing, and generally requires "some and as few as possible", with the dimension ranging from "1" scene to "infinite" scene, which is closely related to the sensor scanning bandwidth, which belongs to the extremely small scale.

[0151] The performance indicators selected in this specific implementation include extremely large, extremely small and extremely large discrete intervals. This application performs extremely large same-trend processing on the sensor performance indicator data, and then performs maximum value normalization processing on all performance indicators after the same-trend processing.

[0152] Extremely large same trend processing refers to processing extremely small performance indicators into extremely large ones. The formula is as follows:

[0153]

[0154] In formula (9), Indicates s after the same trend processing fg .

[0155] Normalize the performance indicators after extremely large homogeneous trend processing:

[0156]

[0157] ② Considering that the difference between similar performance indicators is small, which will lead to the amplification of the weight gap, it is proposed to modify the entropy weight to configure the performance indicator weight, as follows:

[0158]

[0159] In formula (11):

[0160] t represents the performance index number, with a total of m performance indexes;

[0161] h t 、h g denote the information entropy of the t-th and g-th performance indicators respectively;

[0162] exp() represents exponential function operation.

[0163] The weight vector W of the performance matrix S of the multi-source cooperative monitoring sensor solved by equation (11) is W = (w1, w2, ... w m ), which can effectively widen the differences between performance indicators and reduce calculation errors.

[0164] The improved entropy weight method is used in the present application to configure the weight vector of the performance indicator, including: using formulas (9) to (10) to standardize the performance indicator; using formula (7) to solve the information entropy of the performance indicator; and using formula (11) to solve the performance indicator weight vector.

[0165] (3) The weight vector is represented by a triangular fuzzy number, and then the fuzzy matrix R is weighted by the fuzzy number weight vector to obtain the weighted fuzzy decision matrix Y;

[0166] The weight vector W triangular fuzzy number is expressed as:

[0167] W=[(w1,w1,w1),(w2,w2,w2),...(w m ,w m ,w m )](12)

[0168] The weighted processing formula is as follows:

[0169] Y=(w g × fg ) m×n (13)

[0170] (4) According to the weighted fuzzy value Y of each sensor performance index in the weighted fuzzy decision matrix fg , calculate the membership degree of each sensor belonging to the fuzzy positive ideal, and sort the sensors from large to small according to the membership degree, and obtain the sensor performance ranking sequence;

[0171] In this specific implementation, this step further includes the following sub-steps:

[0172] 4.1 Determining the fuzzy positive ideal Z + and fuzzy negative ideal Z - :

[0173]

[0174] In formula (14):

[0175] represents the fuzzy maximum value corresponding to the g-th dimension performance index in the weighted fuzzy decision matrix Y;

[0176] It represents the fuzzy minimum value corresponding to the g-th dimension performance index in the fuzzy decision matrix Y.

[0177] 4.2 According to the weighted fuzzy value of the sensor performance index, the relationship between each sensor and the fuzzy positive ideal Z is calculated respectively. + , fuzzy negative ideal Z - The distance is recorded as and

[0178] distance and The Euclidean distance can be selected and the calculation formula is as follows:

[0179]

[0180] The Euclidean distance is simple and intuitive, but it is more sensitive in dealing with high-dimensional and highly correlated indicator sets. Therefore, the preferred solution of this specific implementation method proposes: using the adaptive mutual information correlation coefficient as the fuzzy distance measure to solve the fuzzy positive ideal Z + , fuzzy negative ideal Z - The fuzzy distance measure has translation invariance and scale (dimension) invariance, and is more suitable for solving the fuzzy distance of the sensor performance index set of this application.

[0181] The calculation formula of fuzzy distance measurement is as follows:

[0182]

[0183] In formula (16), and They represent the mutual information positive adjustment parameter and the mutual information negative adjustment parameter, respectively, which takes into account the correlation between the sensor performance index and the fuzzy ideal vector.

[0184] 4.3 Calculation of the sensor's fuzzy positive ideal Z + The fuzzy membership u f :

[0185]

[0186] u f The larger it is, the better the corresponding sensor is suited to the monitoring task.

[0187] 4.4 According to the fuzzy membership u f Sort the sensors from large to small to obtain a ranking sequence of sensor performance;

[0188] The sensor performance ranking sequence is recorded as {S d ,d=rank(U,descend)}, where d represents the sorting number, Sd represents the order of merit, U represents the fuzzy multi-attribute decision vector, descend is the descending instruction, and rank(U,descend) represents the ranking function of drainage in descending order according to the fuzzy membership.

[0189] 4.5 Ranking sequence S of sensor performance d Perform best-first search to obtain the best sensor combination solution.

[0190] In this application, the optimal number of combinations is not determined, so all possible combinations need to be checked. If the traditional random permutation and combination method is used to find the optimal sensor combination solution, it will take 2 iterations. n -1 times, which is time-consuming and computationally difficult, where n represents the number of sensors. To reduce the computational difficulty, this application uses the best-first search method to solve the collaborative monitoring solution of sensors. The best-first search method is a heuristic search algorithm that uses a heuristic function to sort the sensor performance sequence S d For scoring, this method only needs n iterations, which greatly reduces the computational cost.

[0191] The specific implementation process of this step is provided below:

[0192] Step 1: Initialization of the cooperative solution set:

[0193] Take the sensor node with the highest priority in the sensor performance ranking sequence and add it to the collaborative solution set {S o , o∈(1,2,...e)}, where o represents the serial number of the sensor currently added to the collaborative solution set, and e is the number of sensors in the collaborative solution set. The priority is determined according to the fuzzy membership. The larger the fuzzy membership, the higher the priority. The sensor node with the highest priority is the sensor with the largest fuzzy membership.

[0194] Step 2: Traverse the remaining sensor nodes in the superior and inferior sorting sequence in order, and judge one by one whether the comprehensive fitness of the coordinated solution set is optimized after each sensor node joins the current coordinated solution set. If it is optimized, the sensor node is added to the current coordinated solution set; otherwise, it is not added; until the superior and inferior sorting sequence is traversed, the sensor nodes in the obtained coordinated solution set are the best sensor combination.

[0195] The calculation process of the comprehensive fitness of the collaborative solution set is as follows:

[0196] First, logical operations are used to remove duplicates and sum the performance indicators of the current collaborative solution set:

[0197] s g *U=s g1 *u1+s g2 *u2+...s ge *u e (18)

[0198] In formula (18):

[0199] g represents the number of each performance indicator, g = 1, 2, ... m, m represents the dimension of the performance indicator;

[0200] s go represents the g-th performance indicator value of the o-th sensor in the current solution set, obtained from the performance matrix S, o = 1, 2, ... e;

[0201] u o represents the fuzzy membership of the oth sensor o, o = 1, 2, ... e;

[0202] s go *u o represents the fuzzy fitness of the oth sensor to the gth performance indicator;

[0203] s g *U represents the logical operation of the current collaborative solution set on the g-th performance indicator;

[0204] * indicates the deduplication summation operator.

[0205] In this specific implementation, considering the requirements of emergency scenarios of breach hazards, the performance indicators include re-entry period, frequency, resolution, spectrum, coverage, and number of strips.

[0206] Then, calculate the individual score F of the current collaborative solution set for each performance indicator g (U):

[0207] When the monitoring task is to monitor the performance indicator q g If the demand is clear and quantifiable, the individual scores of the collaborative solution set are expressed as follows:

[0208]

[0209] When the monitoring task is to monitor the performance indicator q g If the demand is fuzzy, the individual scores of the collaborative solution set are expressed as follows:

[0210] F g (U)=rank(s g *U) / 2 e-1 (20)

[0211] q g The smaller (lower) the better, sort in descending order; when q g When the bigger (higher) the better, arrange them in positive order.

[0212] In formulas (19) to (20):

[0213] q grepresents the g-th performance indicator;

[0214] F g (U) represents the single score of the collaborative solution set for the g-th performance indicator;

[0215] rank() represents the ranking function. When the performance index q g For a very small index, sort in descending order and take the minimum value; when the performance index q g If it is an extremely large index, sort it in positive order and take the maximum value.

[0216] Finally, the individual scores of each performance indicator F g (U) Calculate the comprehensive fitness F of the current collaborative solution set e :

[0217]

[0218] w g The weight coefficient of the g-th performance indicator is expressed as the weight vector W = (w1, w2, ... w m )

[0219] The obtained optimal sensor combination is used to perform collaborative monitoring of the monitoring task.

[0220] Example

[0221] The following will take the "Dongting Lake Tuanzhouwan Lake Dike Breach Danger" on July 5, 2024 as an example to verify the application effect of this application in emergency response to breach dangers and flood disasters.

[0222] In this embodiment, based on the basic demand library of sensor performance parameters, combined with the practical requirements of the emergency task of flood disasters caused by the breach of Tuanzhou Dike in Dongting Lake and the response timeliness, information accuracy, and coverage completeness, the demand indicators are quantified into parameters such as re-entry cycle, frequency, resolution, coverage, spectral band, and number of strips. The breach occurred in a local position of the lake embankment on the north side of Tuanzhou Dike. With the influence of the high flood pressure of Dongting Lake, the width of the breach continued to expand. Later, with the intervention of blocking measures, the width of the breach gradually decreased until it was completely closed. Therefore, in the emergency monitoring task of the breach, the dynamic change value of the breach width is used as the monitoring indicator. The specific task requirements are: the re-entry cycle is as fast as possible, the frequency is as high as possible, the spatial resolution is better than 20 meters, the lake embankment is covered locally, and the visual interpretation requirements of the breach width are met.

[0223] The flood disaster caused by the breach resulted in large-scale inundation of Tuanzhou Dike. In the flood emergency monitoring task, the submerged area of ​​the dike is used as the monitoring indicator. The specific task requirements are: the return cycle is as fast as possible, the frequency is as high as possible, the spatial resolution is better than 50 meters, the entire dike area is covered, the number of scanning strips is as small as possible, and the monitoring spectrum meets the water body index inversion requirements.

[0224] It should be noted that breach monitoring has high requirements for reentry period, frequency, and resolution, and relatively low requirements for coverage, which is in line with the local, dynamic, and rapid nature of breaches. However, breach monitoring has no requirements for the number of spectral bands and strips, because the visual interpretation of breach width depends more on the professional cognition of the reader of optical or radar satellite image features, and is less affected by the number of spectral bands or strips. Therefore, the number of spectral bands and strips are not included in the evaluation indicators of breach monitoring tasks.

[0225] As a secondary disaster of breach, floods are relatively delayed, dynamic, and diffuse, endangering the entire embankment area from point to surface, so the resolution requirements are relatively lower than those of breaches, and the requirements for frequency, recurrence cycle, and coverage are relatively higher. In addition, the optical or radar spectrum is directly related to the inversion of water index, and the number of strips is related to the workload of data processing. Therefore, the spectrum and the number of strips are also essential evaluation indicators for flood monitoring tasks.

[0226] After connecting with the Earth Observation and Data Center of the National Space Administration and the European Space Agency's official website, we applied to dispatch civilian and commercial satellites to photograph the areas in danger of breach.

[0227] Time window: [2024 / 7 / 4,2024 / 7 / 8]

[0228] Space window: [112°44′44″, 29°18′26″, 112°51′18″, 29°18′42″]

[0229] After query and screening, the satellites that meet the time window and space window constraints include high-resolution satellites (GF-1 / 1C / 1D, GF-3 / 3B / 3C, GF-4), resource satellites (ZY3-02), environmental satellites (HJ-2B / 2E / 2F), Jilin-1 (JL1-KF01A), Tianyi Chaohu-1 (BC2), Dongfang Huiyan (DFHY-1), micro-nano starry sky (TJ04-1), European Sentinel-1 / 2, etc., covering optical satellites (multispectral, hyperspectral) and radar satellites (microwave). The performance parameters of sensors carried by multi-source satellites can be found in the Natural Resources Satellite Remote Sensing Cloud Service Platform and the official website of Civilian Commercial Satellite.

[0230] Imaging satellites are relatively independent, and their inherent capabilities (such as resolution, re-entry period, spectrum, etc.) and application capabilities (elapsed time, cloud cover, number of strips, etc.) are also different. Constrained by their own performance, imaging conditions, application scenarios, etc., a single satellite / sensor cannot independently complete emergency monitoring of breaches and floods. It is necessary to coordinate multi-source satellites / sensors to perform logical operations on various monitoring indicators to achieve the most efficient dynamic monitoring of breach hazards.

[0231] The implementation process of this application is implemented by MATLAB programming. The satellite / sensor performance set is read in according to the steps, and the data is pre-processed by dimensionless and normalized. The modified entropy weight method is used to determine the weights of satellite / sensor indicators under the breach and flood monitoring tasks. The indicator set and weight set are converted into triangular fuzzy number expressions, and the fuzzy decision matrix of the breach and flood monitoring tasks is obtained by weighted processing, and the fuzzy positive ideal distance, fuzzy negative ideal distance, and fuzzy membership are solved respectively. Satellites are enumerated in order of fuzzy membership, and the single score of the current queue is calculated by deduplication and summation, and the comprehensive fitness value of each combination scheme is solved by weight. Among them, the re-entry period and resolution are logical intersection operations, the coverage rate is logical union operation, and the frequency is cumulative sum operation.

[0232] For breach monitoring tasks, in order to verify the reliability of the breach monitoring coordination scheme, the breach width value is inverted based on the corresponding satellite image as the breach hazard monitoring indicator, and interpolation fitting is performed to form a change monitoring curve. The breach width change trend is then compared and demonstrated in combination with the hydrological in-situ monitoring data. The resulting breach width change monitoring diagram can be found in Figure 2 .

[0233] from Figure 2 It can be seen that the overall trend of the multi-source remote sensing collaborative monitoring in this application is consistent with the hydrological in-situ monitoring data, and the collaborative monitoring time runs through the entire process before, during and after the breach, effectively making up for the defect of the hydrological in-situ monitoring without pre-breach data. During the calculation overlap period, the relative error between the collaborative inversion breach width value and the hydrological in-situ monitoring value is 0.98%, which is within the allowable error range.

[0234] For flood monitoring tasks, in order to verify the reliability of the flood monitoring collaborative scheme, the inundated area of ​​Tuanzhou embankment area was inverted as a flood monitoring indicator based on the corresponding satellite images, and interpolation fitting was performed to form a change monitoring curve. Because the inundated area cannot be measured hydrologically in situ, the inundated area calculated by the flood reenactment model and the inverted area of ​​the combined scheme of this application were used for mutual demonstration. The obtained inundated area change monitoring map can be found in Figure 3 shown.

[0235] from Figure 3 It can be seen that the multi-source remote sensing collaborative monitoring curve shows that before the breach occurred (July 4 to July 5 15:30), a small area was flooded and the area gradually expanded, which further verified that there was seepage near the pipe outlet in the embankment before the breach. After the breach occurred, the multi-source remote sensing collaborative monitoring scheme and the flood reenactment model calculation results were generally consistent. During the calculation overlap period, the relative error between the collaborative inversion flooded area and the flood reenactment model value was -2.39%. The error value was affected by the discrete value fitting accuracy of the multi-source remote sensing inversion and the model calculation system error, and the result was within an acceptable range.

[0236] Note that the above are only specific implementation methods of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific implementation methods described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above specific implementation methods, the present application is not limited to the above specific implementation methods, and may also include more other equivalent embodiments without departing from the concept of the present application, all of which belong to the scope of protection of the present application.

Claims

1. A multi-source remote sensing collaborative monitoring method for emergency scenarios of breach hazards, characterized by: include: Construct a performance matrix of multi-source collaborative monitoring sensors based on the performance indicator data of sensors; The triangular fuzzy numbers are used to express each performance index in the performance matrix, and the triangular fuzzy numbers of the same type of performance index are normalized to obtain the fuzzy matrix of the performance matrix. The entropy weight method is used to configure the weight vector of performance indicators, and the weight vector is expressed by triangular fuzzy numbers; The fuzzy matrix is ​​weighted by the fuzzy number weight vector to obtain the weighted fuzzy decision matrix: The membership degree of each sensor belonging to the fuzzy positive ideal is calculated according to the weighted fuzzy decision matrix, and the sensors are sorted from large to small according to the membership degree to obtain the sensor performance ranking sequence; The best priority search is performed on the sensor performance ranking sequence to obtain the best sensor combination solution.

2. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 1 is characterized by: The performance indicators in the performance matrix expressed by triangular fuzzy numbers include: For a quantitative performance indicator p, when p is an exact number, it is expressed as p = (p, p, p); When p is an interval number, it is expressed as p=(p min ,p med ,p max ), where p min and p max represent the lower and upper bounds of the interval respectively. For the qualitative performance index p, the triangular fuzzy number ratio method is used to quantify it into the corresponding triangular fuzzy number.

3. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 1 is characterized by: The normalizing of the triangle fuzzy numbers of the same type of performance indicators respectively includes: For cost-based performance indicators, the normalization process is: For the revenue-based performance indicators, the normalization process is: The triangular fuzzy number of the g-th performance index of sensor f is denoted as s fg =(a fg ,b fg ,c fg ), where min(a fg ) and max(a fg ) represent the triangular fuzzy number a of the g-th performance index of all sensors. fg The minimum and maximum values ​​of min(b fg ) and max(b fg ) represent the triangular fuzzy number b of the g-th performance index of all sensors. fg The minimum and maximum values ​​of min(c fg ) and max(c fg ) represent the triangular fuzzy number c of the g-th performance index of all sensors. fg The minimum and maximum values ​​of x; Λ1 represents the normalization operation; fg is the normalized s fg .

4. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 1 is characterized by: The method of using the entropy weight method to configure the weight vector of the performance indicator includes: standardizing the performance indicator data in the performance matrix; solving the information entropy of each type of performance indicator respectively; solving the weight coefficient of each type of performance indicator by using the information entropy; The standardization of the performance indicator data in the performance matrix includes: when the performance indicators include both cost-based performance indicators and benefit-based performance indicators, firstly performing the same trend processing on the performance indicators, and then standardizing the performance indicator data after the same trend processing; The weight coefficient Among them, w g represents the weight coefficient of the g-th performance indicator; h t 、h g They represent the information entropy of the t-th and g-th performance indicators respectively; t represents the performance indicator number, and there are m performance indicators in total; exp() represents exponential function operation.

5. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 4 is characterized by: The performance index data after the same trend processing is standardized according to the following formula: Among them, s' fg represents the standardized s fg ; Indicates s after the same trend processing fg ;s fg represents the g-th performance indicator of sensor f; n represents the number of sensors; and m represents the dimension of the performance indicator.

6. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 1 is characterized by: The calculation of the membership degree of each sensor belonging to the fuzzy positive ideal according to the weighted fuzzy decision matrix includes: Determine the fuzzy positive ideal and the fuzzy negative ideal according to the weighted fuzzy decision matrix; Calculate the distance between each sensor and the fuzzy positive ideal and the fuzzy negative ideal respectively and in, and They represent the mutual information positive adjustment parameter and the mutual information negative adjustment parameter respectively; Y fg represents the weighted fuzzy value of the g-th performance indicator of sensor f in the weighted fuzzy decision matrix; and They represent the fuzzy positive ideal and fuzzy negative ideal corresponding to the g-th dimension performance index respectively; According to the distance of each sensor and Calculate the membership degree of the sensor to the fuzzy positive ideal.

7. The multi-source remote sensing collaborative monitoring method for emergency scenarios of breach danger as claimed in claim 1 is characterized by: The best priority search for the sensor performance ranking sequence includes: Step 1: Add the sensor node with the largest fuzzy membership to the collaborative solution set {S o , o∈(1,2,...e)}, where o represents the serial number of the sensor currently added to the collaborative solution set, and e is the number of sensors in the collaborative solution set; Step 2: Traverse the remaining sensor nodes in the sensor performance ranking sequence in order, and judge whether the comprehensive fitness of the coordination solution set is optimized after each remaining sensor node is added to the current coordination solution set. If it is optimized, add the sensor node; otherwise, do not add it; until the traversal is completed, the sensor nodes in the obtained coordination solution set are the best sensor combination; The calculation of the comprehensive fitness is as follows: First, logical operations are used to remove duplicates and sum the performance indicators of the sensors in the current collaborative solution: s g *U=s g1 *u1+s g2 *u2+...s ge *u e ; Among them, g represents the number of each performance indicator; s go represents the g-th performance indicator value of the o-th sensor in the current solution set, obtained from the performance matrix S, o = 1, 2, ... e; u o represents the fuzzy membership of the oth sensor o, o = 1, 2, ... e; s go *u o represents the fuzzy adaptability of the oth sensor to the gth performance indicator; s g *U represents the logical operation of the current collaborative solution set on the g-th performance indicator; * represents the deduplication summation operator; Then, calculate the individual score F of the current collaborative solution set for each performance indicator g (U): When the monitoring task has clear and quantifiable performance indicator requirements, the individual scores of the current collaborative solution set are expressed as follows: When the monitoring task has vague requirements for performance indicators, the individual scores of the current collaborative solution set are expressed as follows: F g (U)=rank(s g *U) / 2 e-1 ; Among them, q g Indicates the g-th performance indicator; F g (U) represents the single score of the current collaborative solution set for the g-th performance indicator; rank() represents the ranking function. g is a cost-based performance index, sort in descending order and take the minimum value; when the performance index q i It is a profit-based performance indicator, sorted in positive order and takes the maximum value; Finally, calculate the comprehensive fitness of the current cooperative solution set Among them, w g Represents the weight coefficient of the g-th performance indicator.

8. A multi-source remote sensing collaborative monitoring system for emergency scenarios of breach hazards, characterized by: include: The first module is used to construct a performance matrix of multi-source collaborative monitoring sensors based on the performance indicator data of the sensors; The second module is used to express each performance index in the performance matrix using triangular fuzzy numbers, and normalize the triangular fuzzy numbers of the same type of performance indexes to obtain the fuzzy matrix of the performance matrix; The third module is used to configure the weight vector of the performance index using the entropy weight method and express the weight vector using triangular fuzzy numbers; The fourth module is used to weight the fuzzy matrix with the fuzzy number weight vector to obtain the weighted fuzzy decision matrix: The fifth module is used to calculate the membership degree of each sensor belonging to the fuzzy positive ideal according to the weighted fuzzy decision matrix, sort the sensors from large to small according to the membership degree, and obtain the sensor performance ranking sequence; The sixth module is used to perform the best priority search on the sensor performance ranking sequence to obtain the best sensor combination solution.

9. The multi-source remote sensing collaborative monitoring system for emergency scenarios of breach danger as claimed in claim 8, characterized in that: The fifth module further includes: Submodule 1, used to determine the fuzzy positive ideal and the fuzzy negative ideal according to the weighted fuzzy decision matrix; Submodule 2 is used to calculate the distance between each sensor and the fuzzy positive ideal and the fuzzy negative ideal respectively and in, and They represent the mutual information positive adjustment parameter and the mutual information negative adjustment parameter respectively; Y fg represents the weighted fuzzy value of the g-th performance indicator of sensor f in the weighted fuzzy decision matrix; and They represent the fuzzy positive ideal and fuzzy negative ideal corresponding to the g-th dimension performance index respectively; Submodule 3 is used to determine the distance between each sensor. and Calculate the membership degree of the sensor to the fuzzy positive ideal.

10. The multi-source remote sensing collaborative monitoring system for emergency scenarios of breach danger as claimed in claim 8, characterized in that: Module 6 further includes: Submodule 4 is used to add the sensor node with the largest fuzzy membership into the collaborative solution set {S o , o∈(1,2,...e)}, where o represents the serial number of the sensor currently added to the collaborative solution set, and e is the number of sensors in the collaborative solution set; Submodule 5 is used to sequentially traverse the remaining sensor nodes in the sensor performance ranking sequence, and judge one by one whether the comprehensive fitness of the coordination solution set is optimized after each remaining sensor node is added to the current coordination solution set. If it is optimized, the sensor node is added; otherwise, it is not added; until the traversal is completed, the sensor nodes in the obtained coordination solution set are the best sensor combination; The calculation of the comprehensive fitness is as follows: First, logical operations are used to remove duplicates and sum the performance indicators of the sensors in the current collaborative solution: s g *U=s g1 *u1+s g2 *u2+...s ge *u e ; Among them, g represents the number of each performance indicator; s go represents the g-th performance indicator value of the o-th sensor in the current solution set, obtained from the performance matrix S, o = 1, 2, ... e; u o represents the fuzzy membership of the oth sensor o, o = 1, 2, ... e; s go *u o represents the fuzzy adaptability of the oth sensor to the gth performance indicator; s g *U represents the logical operation of the current collaborative solution set on the g-th performance indicator; * represents the deduplication summation operator; Then, calculate the individual score F of the current collaborative solution set for each performance indicator g (U): When the monitoring task has clear and quantifiable performance indicator requirements, the individual scores of the current collaborative solution set are expressed as follows: When the monitoring task has vague requirements for performance indicators, the individual scores of the current collaborative solution set are expressed as follows: F g (U)=rank(s g *U) / 2 e-1 ; Among them, q g Indicates the g-th performance indicator; F g (U) represents the single score of the current collaborative solution set for the g-th performance indicator; rank() represents the ranking function. g is a cost-based performance index, sort in descending order and take the minimum value; when the performance index q i It is a profit-based performance indicator, sorted in positive order and takes the maximum value; Finally, calculate the comprehensive fitness of the current cooperative solution set Among them, w g Represents the weight coefficient of the g-th performance indicator.

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