Industrial park risk emergency collaborative management and control method and system

Abstract

The invention relates to the technical field of data processing, in particular to an industrial park risk emergency collaborative management and control method and system. The invention discloses an industrial park risk emergency collaborative management and control system. Comprising a danger source information data packet acquisition module, an evaluation index set establishment module, an evaluation grade set establishment module, a fuzzy comprehensive scoring matrix generation module, a fuzzy comprehensive evaluation matrix generation module, a weight vector generation module, a theoretical risk grade output module and a collaborative management and control scheme acquisition module. According to the method, the weight vector is subjected to simulation calculation through the genetic algorithm, the weight vector is optimized in combination with experience knowledge of an expert end, and the accuracy and rationality of theoretical risk level calculation through a fuzzy comprehensive evaluation model are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, it relates to a method and system for collaborative risk emergency management and control in industrial parks. Background Art

[0002] With the continuous popularization of industrial parks, the risk management and control of industrial parks has become increasingly important. At present, for the risk management and control of industrial parks, generally, the risks of industrial parks are first classified by level, and then corresponding collaborative management and control methods are implemented according to the risk level, such as the allocation of emergency resources. When classifying the risks of industrial parks, one method is to use a fuzzy comprehensive evaluation model for evaluation. When setting the weights of evaluation indicators, generally, it is set by experts based on experience and professional knowledge, but the subjectivity is relatively strong. If the average value or weighted average value is used, some information will be lost, resulting in inaccurate classification of the final risk level. Summary of the Invention

[0003] The present invention provides a method and system for collaborative risk emergency management and control in industrial parks. By simulating and calculating the weight vector through a genetic algorithm, and combining the experience and knowledge of the expert side, the weight vector is optimized to improve the accuracy and rationality of calculating the theoretical risk level through the fuzzy comprehensive evaluation model.

[0004] A method for collaborative risk emergency management and control in industrial parks includes: Obtaining a hazard information data packet A corresponding to the internal hazards of the industrial park b , where b = 1, 2, 3 ······ B, and B is the total number of internal hazards in the industrial park; Establishing an evaluation index set U corresponding to the internal risks of the industrial park. The evaluation set U exists in the form of {u 1 , u 2 , u 3 …u n …u N}, where n = 1, 2, 3 ······ N, and u n is the evaluation index corresponding to the inside of the industrial park; Establishing an evaluation grade set V corresponding to the internal risks of the industrial park. The evaluation grade V exists in the form of {v 1 , v 2 , v 3 …v m …v M}, where m = 1, 2, 3 ······ M, and v m is the evaluation grade corresponding to the inside of the industrial park, and M is the total number of evaluation grades corresponding to the inside of the industrial park; Establish a fuzzy comprehensive evaluation matrix template according to the evaluation index set U and the evaluation grade set V, and send the fuzzy comprehensive evaluation matrix template and the hazard source information data packet A b to the expert side to obtain all the fuzzy comprehensive scoring matrices Y p fed back by the expert side, where p = 1, 2, 3 ······ P, and P is the total number of experts on the expert side. According to all the fuzzy comprehensive scoring matrices Y p fed back by the expert side, generate the fuzzy comprehensive evaluation matrix R b corresponding to this hazard source information data packet A b，n×m ; Obtain the weight vector W corresponding to the evaluation index set U. The existence form of the weight vector W is [w 1 , w 2 , w 3 … w n … w N , where w n is the vector corresponding to the evaluation index u n . The weight vector W is established based on the genetic algorithm; Multiply the fuzzy comprehensive evaluation matrix R b corresponding to this hazard source information data packet A b，n×m by the weight vector W through the matrix multiplication operator, then transpose it to generate the evaluation grade vector, and project the evaluation grade vector into the evaluation grade set V. Select the evaluation grade with the largest value in the evaluation grade set V as the theoretical risk level b corresponding to this hazard source information data packet A Select the corresponding collaborative control plan from the collaborative control plan library according to the theoretical risk level corresponding to this hazard source information data packet A b , and send the corresponding prompt information to each department according to this collaborative control plan.

[0005] Furthermore, it includes: generating the fuzzy comprehensive evaluation matrix R p corresponding to this hazard source information data packet A b according to all the fuzzy comprehensive scoring matrices Y b，n×m fed back by the expert side, including the following steps: T1: Store all the fuzzy comprehensive scoring matrices Y p in the fuzzy comprehensive scoring matrix set ε, and let j = 1. j is used as the number to select the fuzzy comprehensive scoring matrix; T2: Select the fuzzy comprehensive scoring matrix Y j , and calculate the similarity G j between the fuzzy comprehensive scoring matrix Y p and the remaining fuzzy comprehensive scoring matrices Y f in the fuzzy comprehensive scoring matrix set ε respectively, where f = 1, 2, 3 ······ P - 1, and traverse all the similarities Gf and compare them with the similarity threshold one by one, and count the number Q of similarities G less than the similarity threshold f and store them in the central matrix confirmation set ζ; j ; T3: Determine whether "j < P" holds. If "j < P" does not hold, assign j + 1 to j and return to T2; if "j < P" holds, enter T4; T4: Obtain the central matrix confirmation set ζ, and select the number J corresponding to the element with the largest value from the central matrix confirmation set ζ, and use the fuzzy comprehensive evaluation matrix Y in the fuzzy comprehensive evaluation matrix set ε J as the central matrix; T5: Based on the central matrix, select all fuzzy comprehensive evaluation matrices Y in the fuzzy comprehensive evaluation matrix set ε whose similarity to the central matrix is less than the similarity threshold p , calculate the average value matrix of all selected fuzzy comprehensive evaluation matrices Y p and the central matrix, and then generate the fuzzy comprehensive evaluation matrix R after normalizing the average value matrix b，n×m .

[0006] Furthermore, the establishment of the weight vector W includes the following steps: S1: Obtain the hazard source information data packet A b , the corresponding fuzzy comprehensive evaluation matrix R of this hazard source information data packet A b and the corresponding actual risk level of this hazard source information data packet A b，n×m , initialize a series of simulated weight vectors W b , where x = 1, 2, 3 ······ X, X is the total number of simulated weight vectors, and store these simulated weight vectors W x in the simulated weight vector set δ; x ; S2: Let i = 1, and i is used as the number to select the simulated weight vector; S3: Select the simulated weight vector W from the simulated weight vector parent set δ i , and calculate the theoretical risk level according to this simulated weight vector W i and the corresponding fuzzy comprehensive evaluation matrix R of this hazard source information data packet A b , match this theoretical risk level with the actual risk level. If the match is consistent, retain this simulated weight vector W in the simulated weight vector set δ b，n×m ; if the match is inconsistent, delete this simulated weight vector W in the simulated weight vector set δ i ; i ; S4: Determine whether "i < X" holds. If "i < X" holds, it indicates that not all elements in the simulated weight vector set δ have been traversed. Assign i + 1 to i and return to S3. If "i < X" does not hold, it indicates that all elements in the simulated weight vector set δ have been traversed, and proceed to S5; S5: Obtain the simulated weight vector set δ, and denote the simulated weight vector W in the current simulated weight vector set δ x as the simulated weight vector parent. Perform mutation and recombination operations on the simulated weight vector parent of the simulated weight vector set δ to generate a simulated weight vector offspring, store the simulated weight vector offspring in the simulated weight vector set δ, delete the simulated weight vector parent in the simulated weight vector set δ, assign the total number of elements in the current simulated weight vector set δ to X, return to S2 for iteration, record the iteration count t, and when "t > t_max", proceed to S6; S6: Obtain the current simulated weight vector set δ, and perform unsupervised clustering analysis on all the simulated weight vectors in the simulated weight vector set δ to obtain the simulated weight vector at the center point as the weight vector W.

[0007] Further, the actual risk level is obtained through the following steps: Obtain the marked risk levels of this hazard source information data packet A by experts at the expert side b and traverse all the marked risk levels, and select the marked risk level with the highest occurrence frequency as the actual risk level.

[0008] Further, the establishment of the collaborative control plan library includes the following steps: For each evaluation level, obtain the collaborative control plans feedback by the expert side, and store these collaborative control plans in the collaborative control plan library in one-to-one correspondence with the evaluation levels.

[0009] An industrial park risk emergency collaborative control system includes: A hazard source information data packet acquisition module for acquiring the hazard source information data packet corresponding to the hazard sources inside the industrial park; An evaluation index set establishment module for establishing the evaluation index set corresponding to the risks inside the industrial park; An evaluation level set establishment module for establishing the evaluation level set corresponding to the risks inside the industrial park; A fuzzy comprehensive scoring matrix generation module for generating a fuzzy comprehensive scoring matrix based on the evaluation index set, the evaluation level set, and the information feedback by the expert side; A fuzzy comprehensive evaluation matrix generation module for generating a fuzzy comprehensive evaluation matrix based on the fuzzy comprehensive scoring matrix; A weight vector generation module for generating a weight vector; The theoretical risk level output module is used to generate the theoretical risk level according to the fuzzy comprehensive evaluation matrix and the weight vector; The collaborative control plan acquisition module is used to select the corresponding collaborative control plan from the collaborative control plan library according to the theoretical risk level.

[0010] Furthermore, the weight vector generation module includes: The actual risk level acquisition unit is used to acquire the actual risk level; The weight vector generation unit is used to generate the weight vector according to the fuzzy comprehensive evaluation matrix corresponding to the hazard source information data packet and the actual risk level corresponding to this hazard source information data packet.

[0011] The present invention has the following advantages: The present invention performs simulation calculation on the weight vector through the genetic algorithm, and optimizes the weight vector in combination with the empirical knowledge of the expert side, so as to improve the accuracy and rationality of calculating the theoretical risk level through the fuzzy comprehensive evaluation model. Brief Description of the Drawings

[0012] Figure 1 It is a schematic structural diagram of the industrial park risk emergency collaborative control system adopted in the embodiment of the present invention.

[0013] Figure 2 It is a schematic structural diagram of the weight vector generation module in the industrial park risk emergency collaborative control system adopted in the embodiment of the present invention. Detailed Embodiment

[0014] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention.

[0015] Embodiment 1, an industrial park risk emergency collaborative control method, includes: Obtain the hazard source information data packet A corresponding to the hazard sources inside the industrial park b , where b = 1, 2, 3 ······ B, and B is the total number of hazard sources inside the industrial park. The hazard source can be dangerous chemicals or production facilities, etc. inside the industrial park. The hazard source information data packet A b includes on-site photo files and sensor detection data, etc.; Establish an evaluation index set U corresponding to the risks inside the industrial park. The evaluation set U exists in the form of {u 1 , u 2 , u 3 …u n …u N}], where n = 1, 2, 3 ······ N, and for u nare the corresponding evaluation indicators within the industrial park, such as the status of hazardous sources, confidence in safety measures, emergency rescue capabilities, surrounding environmental conditions, and impact on the industrial chain, etc. N is the total number of corresponding evaluation indicators within the industrial park; Establish an evaluation level set V corresponding to the internal risks of the industrial park. The existence form of the evaluation level V is {v 1 , v 2 , v 3 …v m …v M}, where m=1,2,3······M, v m is the corresponding evaluation level within the industrial park, M is the total number of corresponding evaluation levels within the industrial park, and in this embodiment, the evaluation level V is set to {v 1 , v 2 , v 3 , v 4}, where v 1 Refers to major risks, 2 Refers to greater risk, v 3 Refers to general risk, v 4 Refers to low risk; According to the evaluation index set U and the evaluation level set V, a fuzzy comprehensive evaluation matrix template is established. The fuzzy comprehensive evaluation matrix template and the hazard source information data package A are combined. b Send it to the expert side. Each expert at the expert side obtains the fuzzy comprehensive evaluation matrix template and the hazard source information data package A b After that, according to the hazard source information data package A b Score in the fuzzy comprehensive evaluation matrix template, generate a fuzzy comprehensive scoring matrix, and obtain all fuzzy comprehensive scoring matrices Y fed back by the experts p , where p=1,2,3······P, P is the total number of experts on the expert side, and the fuzzy comprehensive scoring matrix Y based on all the feedback from the experts p Generate this hazard source information data package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m ; According to all the fuzzy comprehensive scoring matrix Y of the expert feedback p Generate this hazard source information data package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m The steps include: T1: All the fuzzy comprehensive score matrix Y p Store in the fuzzy comprehensive scoring matrix set ε, let j = 1, j is used as a number to select the fuzzy comprehensive scoring matrix; T2: Select the fuzzy comprehensive scoring matrix Y j , respectively calculate the fuzzy comprehensive score matrix Y jwith the remaining fuzzy comprehensive evaluation matrix Y in the fuzzy comprehensive evaluation matrix set ε p similarity G f , where f = 1, 2, 3 ······ P - 1. The specific calculation method of this similarity is to expand the fuzzy comprehensive evaluation matrix into a one-dimensional vector and then calculate it through the cosine similarity calculation method; traverse all similarities G f , and compare them with the similarity threshold one by one. The similarity threshold is used to represent the upper limit of the similarity between two fuzzy comprehensive evaluation matrices. If the similarity exceeds this upper limit, it can be considered that the two fuzzy comprehensive evaluation matrices are not similar, so as to eliminate the isolated fuzzy comprehensive evaluation matrices in the fuzzy comprehensive evaluation matrix set ε; count the number Q f of similarities G j less than the similarity threshold, and store them in the central matrix confirmation set ζ; T3: Judge whether "j < P" holds. If "j < P" does not hold, it means that not all elements in the fuzzy comprehensive evaluation matrix set ε have been traversed. Assign j + 1 to j and return to T2; if "j < P" holds, it means that all elements in the fuzzy comprehensive evaluation matrix set ε have been traversed, and enter T4; T4: Obtain the central matrix confirmation set ζ, and select the number J corresponding to the element with the largest value from the central matrix confirmation set ζ. Take the fuzzy comprehensive evaluation matrix Y J in the fuzzy comprehensive evaluation matrix set ε as the central matrix; T5: Based on the central matrix, select all fuzzy comprehensive evaluation matrices Y p from the fuzzy comprehensive evaluation matrix set ε whose similarity with the central matrix is less than the similarity threshold, calculate the average matrix of all selected fuzzy comprehensive evaluation matrices Y p and the central matrix, and then normalize the average matrix to generate the fuzzy comprehensive evaluation matrix R b，n×m .

[0016] Obtain the weight vector W corresponding to the evaluation index set U. The existence form of the weight vector W is [w 1 , w 2 , w 3 … w n … w N , where w n is the vector corresponding to the evaluation index u n , and the weight vector W is established based on the genetic algorithm; Take the fuzzy comprehensive evaluation matrix R b corresponding to this hazard source information data packet A b，n×mIt is calculated with the weight vector W through a matrix multiplication operator, and then transposed to generate an evaluation grade vector. The evaluation grade vector is projected into the evaluation grade set V, and the evaluation grade with the largest value in the evaluation grade set V is selected as the hazard information data packet A of this b corresponding theoretical risk level; According to this hazard information data packet A b Select the corresponding collaborative control plan from the collaborative control plan library according to the corresponding theoretical risk level, and send the corresponding prompt information to each department according to this collaborative control plan to realize the risk collaborative control of the industrial park.

[0017] The establishment of the collaborative control plan library includes the following steps: for each evaluation grade, obtain the collaborative control plan feedback from the expert side, and store this collaborative control plan in the collaborative control plan library in one-to-one correspondence with the evaluation grade. For example, for the high-risk level, it is necessary to send warning information to the person in charge of the industrial park and the enterprise corresponding to the hazard source to remind relevant personnel to take corresponding measures, such as the regulation of emergency materials and the risk investigation of the hazard source.

[0018] When classifying the risk equivalence of hazard sources, the contributions of each evaluation index are different. Therefore, in order to better achieve the risk level division, it is necessary to assign weights to each evaluation index. When generating the weight vector W corresponding to the evaluation index set U, this application adopts a genetic algorithm, which specifically includes the following steps: S1: Obtain the hazard information data packet A b and the fuzzy comprehensive evaluation matrix R b corresponding to this hazard information data packet A b，n×m as well as the actual risk level corresponding to this hazard information data packet A b Initialize a series of simulated weight vectors W x where x = 1, 2, 3 ······ X, X is the total number of simulated weight vectors, and store these simulated weight vectors W x in the simulated weight vector set δ; The actual risk level is obtained by the following steps: Obtain the marked risk level marked by the expert on the expert side for this hazard information data packet A b Traverse all the marked risk levels and select the marked risk level with the highest frequency as the actual risk level; S2: Let i = 1, and i is used as a number to select a simulated weight vector; S3: Select the simulated weight vector W i from the simulated weight vector parent set δ, and according to this simulated weight vector W i and the fuzzy comprehensive evaluation matrix R b corresponding to this hazard information data packet A b，n×mCalculate the theoretical risk level, match this theoretical risk level with the actual risk level. If they match, retain this simulated weight vector W in the simulated weight vector set δ; if they do not match, delete this simulated weight vector W in the simulated weight vector set δ. i If they match, retain this simulated weight vector W in the simulated weight vector set δ; if they do not match, delete this simulated weight vector W in the simulated weight vector set δ. i Delete it; S4: Determine whether "i < X" holds. If "i < X" holds, it means that not all elements in the simulated weight vector set δ have been traversed. Assign i + 1 to i and return to S3; if "i < X" does not hold, it means that all elements in the simulated weight vector set δ have been traversed, and enter S5. S5: Obtain the simulated weight vector set δ, and denote the simulated weight vector W in the current simulated weight vector set δ as the simulated weight vector parent. Perform mutation and recombination operations on the simulated weight vector parent of the simulated weight vector set δ to generate simulated weight vector children, and store the simulated weight vector children in the simulated weight vector set δ. Delete the simulated weight vector parent in the simulated weight vector set δ, assign the total number of elements in the current simulated weight vector set δ to X, and return to S2 for iteration. Record the number of iterations t. When "t > t_max", enter S6, where t_max is the artificially set iteration upper limit, defaulting to 100. x S6: Obtain the current simulated weight vector set δ, and perform clustering analysis on all simulated weight vectors in the simulated weight vector set δ using the k-means algorithm to obtain the simulated weight vector at the center point as the weight vector W. S6: Obtain the current simulated weight vector set δ, and perform clustering analysis on all simulated weight vectors in the simulated weight vector set δ using the k-means algorithm to obtain the simulated weight vector at the center point as the weight vector W.

[0019] The present invention performs simulation calculations on the weight vector through a genetic algorithm, and optimizes the weight vector in combination with the empirical knowledge of the expert side, improving the accuracy and rationality of calculating the theoretical risk level through the fuzzy comprehensive evaluation model.

[0020] Embodiment 2, an industrial park risk emergency collaborative control system, as Figure 1 shown, includes: A hazard source information data packet acquisition module, used to acquire the hazard source information data packet corresponding to the hazard sources inside the industrial park; An evaluation index set establishment module, used to establish an evaluation index set corresponding to the risks inside the industrial park; An evaluation grade set establishment module, used to establish an evaluation grade set corresponding to the risks inside the industrial park; A fuzzy comprehensive scoring matrix generation module, used to generate a fuzzy comprehensive scoring matrix according to the evaluation index set, the evaluation grade set, and the information feedback from the expert side; A fuzzy comprehensive evaluation matrix generation module, used to generate a fuzzy comprehensive evaluation matrix according to the fuzzy comprehensive scoring matrix; A weight vector generation module for generating a weight vector; A theoretical risk level output module for generating a theoretical risk level according to a fuzzy comprehensive evaluation matrix and a weight vector; A collaborative control solution acquisition module for selecting a corresponding collaborative control solution from a collaborative control solution library according to the theoretical risk level.

[0021] See Figure 2 , an industrial park risk emergency collaborative control system, the weight vector generation module includes: An actual risk level acquisition unit for acquiring an actual risk level; A weight vector generation unit for generating a weight vector according to the fuzzy comprehensive evaluation matrix corresponding to the hazard information data packet and the actual risk level corresponding to this hazard information data packet.

[0022] It should be understood that for those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention. The parts not detailedly described in this specification belong to the well-known prior art of those skilled in the art.

Claims

1. A method for collaborative risk emergency management in an industrial park, characterized in that: include: Obtain the hazard source information data package A corresponding to the internal hazard source of the industrial park b , where b=1,2,3······B, B is the total number of hazardous sources inside the industrial park; Establish an evaluation index set U corresponding to the internal risk of the industrial park. The evaluation set U exists in the form of {u1, u2, u3…u n …u N }, where n=1,2,3······N, is u n It is the corresponding evaluation index within the industrial park; Establish an evaluation level set V corresponding to the internal risks of the industrial park. The evaluation level V exists in the form of {v1, v2, v3…v m …v M }, where m=1,2,3······M, v m is the corresponding evaluation level within the industrial park, and M is the total number of corresponding evaluation levels within the industrial park; According to the evaluation index set U and the evaluation level set V, a fuzzy comprehensive evaluation matrix template is established. The fuzzy comprehensive evaluation matrix template and the hazard source information data package A are combined. b Send to the expert side to obtain all fuzzy comprehensive score matrices Y fed back by the expert side p , where p=1,2,3······P, P is the total number of experts on the expert side, and the fuzzy comprehensive scoring matrix Y based on all the feedback from the experts p Generate this hazard source information data package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m ; Get the weight vector W corresponding to the evaluation index set U. The weight vector W exists in the form of [w1, w2, w3…w n …w N ], where w n is the evaluation index u n The corresponding vector, the weight vector W, is established based on the genetic algorithm; This hazard source information package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m The weight vector W is calculated by matrix multiplication operator, and then transposed to generate the evaluation level vector. The evaluation level vector is projected into the evaluation level set V, and the evaluation level with the largest value in the evaluation level set V is selected as the hazard source information data package A. b The corresponding theoretical risk level; According to this hazard source information package A b The corresponding collaborative control plan is selected from the collaborative control plan library according to the corresponding theoretical risk level, and corresponding prompt information is sent to each department according to the collaborative control plan.

2. The method for collaborative risk emergency management and control of industrial parks according to claim 1 is characterized in that: include: According to all the fuzzy comprehensive scoring matrix Y of the expert feedback p Generate this hazard source information data package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m The steps include: T1: All the fuzzy comprehensive score matrix Y p Store in the fuzzy comprehensive scoring matrix set ε, let j = 1, j is used as a number to select the fuzzy comprehensive scoring matrix; T2: Select the fuzzy comprehensive scoring matrix Y j , respectively calculate the fuzzy comprehensive score matrix Y j and the rest of the fuzzy comprehensive scoring matrices Y in the fuzzy comprehensive scoring matrix set ε p The similarity G f , where f=1,2,3······P-1, traverse all similarities G f , and compare them with the similarity threshold one by one, and count the similarities G that are less than the similarity threshold f Number Q j , and stored in the central matrix confirmation set ζ; T3: Determine whether "j<P" holds. If "j<P" does not hold, assign j+1 to j and return to T2; if "j<P" holds, enter T4; T4: Get the central matrix confirmation set ζ, and select the number J corresponding to the element with the largest value from the central matrix confirmation set ζ, and replace the fuzzy comprehensive score matrix Y in the fuzzy comprehensive score matrix set ε with the number J corresponding to the element with the largest value. J As the central matrix; T5: Taking the central matrix as the benchmark, select all fuzzy comprehensive score matrices Y whose similarity with the central matrix is ​​less than the similarity threshold from the fuzzy comprehensive score matrix set ε p , calculate all the fuzzy comprehensive score matrices Y selected p The average value matrix of the center matrix is ​​then normalized to generate the fuzzy comprehensive evaluation matrix R b，n×m .

3. The method for collaborative risk emergency management and control of industrial parks according to claim 2 is characterized in that: The establishment of the weight vector W includes the following steps: S1: Get hazard source information data package A b 、This hazard source information data package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m And this hazard source information data package A b Corresponding to the actual risk level, initialize a series of simulation weight vectors W x , where x=1,2,3······X, X is the total amount of simulation weight vectors, and these simulation weight vectors W x Stored in the simulation weight vector set δ; S2: Let i = 1, i is used as the number to select the simulation weight vector; S3: Select a simulated weight vector W from the simulated weight vector parent set δ i , and simulates the weight vector W based on this i and this hazard source information package A b The corresponding fuzzy comprehensive evaluation matrix R b，n×m Calculate the theoretical risk level and match the theoretical risk level with the actual risk level. If the match is consistent, add the simulated weight vector W in the simulated weight vector set δ i If the match is inconsistent, the simulated weight vector W is retained in the simulated weight vector set δ. i Delete it; S4: Determine whether "i<X" holds. If "i<X" holds, it means that all elements in the simulation weight vector set δ have not been traversed yet, assign i+1 to i, and return to S3; if "i<X" does not hold, it means that all elements in the simulation weight vector set δ have been traversed, and enter S5; S5: Get the simulation weight vector set δ, and replace the simulation weight vector W in the current simulation weight vector set δ x Denoted as the simulated weight vector parent, the simulated weight vector parent of the simulated weight vector set δ is mutated and recombined to generate a simulated weight vector child, and the simulated weight vector child is stored in the simulated weight vector set δ, the simulated weight vector parent in the simulated weight vector set δ is deleted, the total number of elements in the current simulated weight vector set δ is assigned to X, and then return to S2, iterate, record the number of iterations t, and when "t>t_max", enter S6; S6: Obtain the current simulation weight vector set δ, and perform unsupervised cluster analysis on all simulation weight vectors in the simulation weight vector set δ to obtain the simulation weight vector at the center point as the weight vector W.

4. The method for collaborative risk emergency management and control of industrial parks according to claim 3 is characterized in that: The actual risk level is obtained by the following steps: Obtain the expert's opinion on the hazard source information data package A b The annotated risk level is marked. All the annotated risk levels are traversed and the annotated risk level with the highest frequency is selected as the actual risk level.

5. The method for collaborative risk emergency management and control of industrial parks according to claim 4 is characterized in that: The establishment of the collaborative control solution library includes the following steps: for each evaluation level, obtaining the collaborative control solution fed back by the expert side, and storing the collaborative control solution and the evaluation level in the collaborative control solution library in a one-to-one correspondence.

6. An industrial park risk emergency collaborative management and control system, characterized in that: The system applies an industrial park risk emergency collaborative management method as described in any one of claims 1 to 5 above, including: A hazard source information data packet acquisition module is used to acquire hazard source information data packets corresponding to the hazard sources within the industrial park; An evaluation index set establishment module is used to establish an evaluation index set corresponding to the internal risks of the industrial park; An evaluation level set establishment module is used to establish an evaluation level set corresponding to the internal risks of the industrial park; A fuzzy comprehensive scoring matrix generation module is used to generate a fuzzy comprehensive scoring matrix based on the evaluation index set, the evaluation level set and the information fed back by the expert end; A fuzzy comprehensive evaluation matrix generation module is used to generate a fuzzy comprehensive evaluation matrix according to the fuzzy comprehensive scoring matrix; A weight vector generation module, used for generating a weight vector; Theoretical risk level output module, used to generate theoretical risk level according to fuzzy comprehensive evaluation matrix and weight vector; The collaborative management and control scheme acquisition module is used to select the corresponding collaborative management and control scheme from the collaborative management and control scheme library according to the theoretical risk level.

7. The industrial park risk emergency collaborative management and control system according to claim 6 is characterized in that: The weight vector generation module includes: An actual risk level acquisition unit, used to acquire the actual risk level; The weight vector generating unit is used to generate a weight vector according to the fuzzy comprehensive evaluation matrix corresponding to the hazard source information data packet and the actual risk level corresponding to the hazard source information data packet.

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