Underground space flood emergency rescue and dispatching command model construction method and device under rainstorm situation

Abstract

The invention discloses an underground space flood emergency rescue and dispatching command model construction method and device under a rainstorm situation, belongs to the technical field of construction safety management, and solves the problem that effective landing of an emergency rescue scheme cannot be ensured in the prior art. According to the method, flood simulation under multiple scenes of different rainfall capacities is carried out based on rainstorm scene data and a cellular automaton model, flood simulation results are obtained, key risk influence factors are identified, severity evaluation is carried out, and risk grade evaluation is carried out based on an underground space disaster risk evaluation index system under the rainstorm scene. The method comprises the following steps: constructing a knowledge base, matching a target underground space emergency rescue resource list with the constructed knowledge base to obtain a first emergency task list, applying the first emergency task list to a corresponding rainfall scene, performing pre-deduction by adopting a cellular automaton model, and generating a second emergency resource list according to a pre-deduction result to obtain an emergency rescue and scheduling command decision. The method is used for underground space flood emergency rescue and dispatching command under the rainstorm situation.

Description

Technical Field

[0001] A method and device for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation, which is used for underground space flood emergency rescue and dispatch command under a rainstorm situation and belongs to the technical field of construction safety management. Background Art

[0002] In recent years, against the backdrop of global climate change, extreme weather events have occurred frequently, among which urban waterlogging caused by heavy rains is particularly prominent, posing a severe challenge to urban infrastructure. Especially in densely populated metropolises with a high degree of underground space development and utilization, underground space construction, as an important part of public facilities construction, has a direct relationship with the public's safe travel and the safety of life and property. Therefore, studying the emergency rescue and dispatch command methods of underground space floods under heavy rain scenarios has become a key issue that needs to be urgently addressed in the field of urban management and public safety.

[0003] With the development of emergency management concepts and technologies, the application of modern information technology has become increasingly widespread, providing strong support for improving the speed and efficiency of emergency response. The government and relevant departments have actively built an emergency management information system, using big data, cloud computing, the Internet of Things, artificial intelligence and other technical means to achieve accurate early warning, efficient response and scientific decision-making for emergencies. For example, by establishing an urban drainage system model and combining it with meteorological forecast data, it is possible to predict in advance the waterlogging points that may be caused by heavy rains; with the help of GIS (geographic information system), it is possible to quickly locate the disaster-stricken areas and optimize the rescue path; using intelligent monitoring and sensor networks, the water level changes inside the underground space can be monitored in real time to provide a decision-making basis for rescue dispatch. On this basis, formulating and improving emergency plans has become the core of emergency management work. The emergency plan details the entire process from early warning information release, emergency resource allocation, on-site disposal measures to post-disaster reconstruction, and clarifies the responsibilities and cooperation mechanisms of responsible entities at all levels. The implementation of the plan depends on an efficient dispatching and command system to ensure that when heavy rains strike, emergency response can be quickly initiated, human and material resources can be reasonably dispatched, the disaster situation can be effectively controlled, and losses can be minimized.

[0004] However, the prior art has the following technical problems:

[0005] 1. The existing disaster emergency rescue and dispatch command decision-making system performs simple knowledge base statistics to match the brief situation on site with the emergency rescue plan. However, there is a lack of adaptability correction of the plan to the on-site situation, and the effective implementation of the emergency rescue plan cannot be guaranteed;

[0006] 2. Domestic and foreign research on underground space flood emergency rescue and dispatch command simulation is concentrated on the operation period, and usually takes floods, personnel and materials as the main research objects. Insufficient consideration is given to factors such as flood risk factors and construction equipment. Due to the lack of construction and simulation analysis of disaster scenarios of different levels, the emergency rescue plan is not intelligent enough, and the rescue effect cannot be quantitatively evaluated, making it difficult to guide the practice of flood emergency rescue during the construction period of underground space. Summary of the invention

[0007] The purpose of the present invention is to provide a method and device for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm scenario, so as to solve the problem that the existing relevant disaster emergency rescue and dispatch command decision-making system performs simple knowledge base statistics, matches the brief situation on the scene with the emergency rescue plan, lacks the adaptability correction of the plan to the on-site situation, and cannot ensure the effective implementation of the emergency rescue plan; and the research related to underground space flood emergency rescue and dispatch command simulation at home and abroad is concentrated on the operation period, and usually takes floods, personnel, and materials as the main research objects, and insufficient consideration is given to flood risk factors and construction equipment and other factors. Due to the lack of construction and simulation analysis of disaster scenarios of different levels, the emergency rescue plan is not intelligent enough, and the rescue effect cannot be quantitatively evaluated, making it difficult to guide the practice of underground space flood emergency rescue during the construction period.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] A method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm scenario comprises the following steps:

[0010] Step S1, based on the rainstorm scenario data and the cellular automaton model, a flood simulation is performed under multiple scenarios with different rainfall amounts, the spatiotemporal evolution process of the waterlogging disaster is simulated and analyzed, and the flood simulation results are obtained, wherein the flood simulation results include the catchment area, water depth, arrival time and flooding range;

[0011] Step S2: Based on the flood simulation results, identify key risk factors to conduct severity evaluation, and conduct risk level evaluation based on the underground space disaster risk evaluation index system under the rainstorm scenario, wherein the key risk factors include construction personnel, important facilities, protection plans and underground space flooding scope;

[0012] Step S3, matching the target underground space emergency rescue resource list obtained based on the severity evaluation and risk level assessment results and statistics with the constructed knowledge base to obtain a multi-angle first emergency task list for the underground space, wherein the multi-angle first emergency task list for the underground space includes the types and quantities of emergency resources required for emergency response to the flood accident according to the state of the flood accident;

[0013] Step S4: Apply the content in the first emergency resource list to the corresponding rainfall scenario, and use the cellular automaton model for pre-simulation. According to the pre-simulation results, generate the second emergency resource list, that is, obtain the emergency rescue and dispatch command decision.

[0014] Further, the severity evaluation in step 2 includes the sum of the depth analysis and the disaster loss assessment;

[0015] The specific steps of the in-depth analysis include:

[0016] Step 2.1: Verify the key risk influencing factors IN(t)=(x c 0 (t)), t=1,2,3……M, whether S is satisfied c ={x c (1),x c (1),……,x c (M)},c=1,2,3 data format requirements, if the requirements are met, use the formula Normalization is performed. If the requirements are not met, data verification is performed and input data is obtained again for judgment. M represents the frame sequence of input data with time tags, IN and S are both set letters, and x c 0 (t) represents the input data before normalization;

[0017] Step 2.2: Based on the normalized data obtained in step 2.1, a loss assessment model is constructed. That is, based on three types of disaster factors, namely, disaster variables, carrier variables and control variables, a description value is assigned to each disaster factor to construct a loss assessment model. Among them, disaster variables refer to disaster factors, including the scope of underground space flooding; carrier variables refer to the construction site, including construction personnel; and control variables refer to protective facilities, including important facilities and protection plans.

[0018] The loss assessment model is expressed as:

[0019] x(t)=(x 1 (t),x 2 (t),x 3 (t)) T

[0020] IN:(x i 0 (t))(i=1,2,3)

[0021] OUT:(x i 0 (t+1))(i=1,2,3)

[0022] Among them, x 1(t) represents the description value of a certain disaster variable of underground space flood at time t, x 2 (t) represents the description value of a certain carrier variable of the underground station flood at time t, x 3 (t) represents the description value of a certain control variable of underground station flood at time t, IN: (x i 0 (t))(i=1,2,3) represents the format of input data of loss assessment model, OUT:(x i 0 (t+1))(i=1,2,3) represents the format of the output data of the loss assessment model, and x(t) represents the normalized input data;

[0023] Step 2.3: Based on the data set including the disaster-causing factors, the loss assessment model is trained on the corresponding relationship between the disaster-causing factors and the loss assessment. After the training, the disaster-causing factors to be predicted are deeply analyzed and evaluated to obtain the deep analysis evaluation values ​​of the corresponding disaster-causing factors;

[0024] The disaster loss assessment includes the sum of the risk population assessment and the economic loss assessment;

[0025] The risk population assessment refers to statistically analyzing the total value of the risk population, and obtaining the risk population assessment score corresponding to the total value according to the risk population assessment standard, wherein the risk population includes the casualties on site due to the disaster, the missing persons due to the disaster, the emergency relocated and resettled population, and the trapped population. The risk population assessment standard includes that if the total value of the risk population is greater than or equal to 100 people, the corresponding risk population assessment value is 5 points; if the total value of the risk population is greater than or equal to 50 people and less than 100 people, the corresponding risk population assessment value is 4 points; if the total value of the risk population is greater than or equal to 10 people and less than 50 people, the corresponding risk population assessment value is 3 points; if the total value of the risk population is greater than or equal to 3 people and less than 10 people, the corresponding risk population assessment value is 2 points; if the total value of the risk population is less than 3 people, the corresponding risk population assessment value is 1 point;

[0026] The economic loss evaluation refers to the statistical total economic loss, and obtaining the economic loss evaluation score corresponding to the total economic loss according to the economic loss evaluation standard, wherein the total economic loss includes the economic losses of construction site equipment loss, construction period loss and material loss at the construction site after the flood occurs. The economic loss evaluation standard includes the total economic loss is greater than or equal to 1 million yuan, and the corresponding economic loss evaluation score is 5 points, indicating that particularly serious economic losses are caused; the total economic loss is greater than or equal to 500,000 yuan and less than 1 million yuan, and the corresponding economic loss evaluation score is 4 points, indicating that serious economic losses are caused; the total economic loss is greater than or equal to 100,000 yuan and less than 500,000 yuan, and the corresponding economic loss evaluation score is 3 points, indicating that relatively heavy economic losses are caused; the total economic loss is greater than or equal to 10,000 yuan and less than 100,000 yuan, and the corresponding economic loss evaluation score is 2 points, indicating that certain economic losses are caused; the total economic loss is less than 10,000 yuan, and the corresponding economic loss evaluation score is 1 point, indicating that basically no economic losses are caused.

[0027] Furthermore, the specific steps of risk level assessment in step 2 are:

[0028] Construct a multi-index comprehensive evaluation system, that is, construct an underground space disaster risk evaluation index system under heavy rain scenarios, which includes general indicators, primary indicators and secondary indicators. Among them, the general indicator is the underground space flood risk under heavy rain scenarios. The primary indicators include the hazard of disaster factors, the sensitivity of the disaster-prone environment, the vulnerability of the disaster-bearing body and the disaster prevention and mitigation capabilities. The secondary indicators include the 24-hour maximum rainfall, rainfall duration and construction management and responsibility implementation of the hazard factor hazard, the catchment area, drainage network density, complex surrounding environment and rivers of the disaster-prone environment sensitivity, the drainage ditch drainage capacity and foundation pit depth of the disaster-bearing body vulnerability, and the height of the retaining wall, disaster monitoring and early warning technology and emergency material reserves of the disaster prevention and mitigation capabilities.

[0029] The grey clustering evaluation method is constructed to score the secondary indicators in the multi-indicator comprehensive evaluation system to determine the primary indicator score. Finally, the risk level assessment of the total indicator is obtained according to the weight of the given primary indicator. The specific steps are as follows:

[0030] Step 2-1, establish the whitening weight function for the quantitative risk assessment of rainstorm floods around underground space, that is, the gray clustering evaluation method. Use the whitening weight function combined with the quantitative risk characteristics of rainstorm floods around underground space to divide the risk level into 5 gray classes, and based on the threshold center point vector U = (9, 7, 5, 3, 1), obtain the reference threshold and corresponding risk level of each gray class. Based on the reference threshold and risk level of each gray class, assign weight w to all secondary indicators under the i-th primary indicator. i =(w i1 ,w i2 ,w i3 ,...wij ), where the quantitative risk characteristics of rainstorm floods around underground space are the secondary indicators in the multi-indicator comprehensive evaluation system. When the gray category is 1, the reference threshold is [0, 9, ∞], and the risk level is very safe. When the gray category is 2, the reference threshold is [0, 7, 14], and the risk level is safe. When the gray category is 3, the reference threshold is [0, 5, 10], and the risk level is general. When the gray category is 4, the reference threshold is [0, 3, 6], and the risk level is dangerous. When the gray category is 5, the reference threshold is [0, 1, 2], and the risk level is very dangerous.

[0031] The formula of the whitening weight function is:

[0032]

[0033] Where, d ijk represents the score of expert k on the jth secondary indicator under the i-th primary indicator, k = 1, 2, 3, ..., f 1 1 (d ijk ), f 1 2 (d ijk ), f 1 3 (d ijk ), f 1 4 (d ijk ), f 1 5 (d ijk ) respectively represent the risk level of the jth secondary indicator under the ith primary indicator in each gray category;

[0034] Step 2-2: Construct a secondary grey evaluation matrix. The specific steps are as follows:

[0035] By ijk Obtain the jth secondary index c under the i-th primary index ij The evaluation coefficient in each gray class is as follows:

[0036]

[0037] Calculate the secondary index c separately ij The evaluation coefficient in the five gray categories is as follows:

[0038]

[0039] Among them, e represents the e-th gray class, Y ij represents the total clustering coefficient,

[0040] Based on the total clustering coefficient Y ijand evaluation coefficient Y ije Get the gray evaluation weight value:

[0041]

[0042] Arrange the grey evaluation weight values ​​of all secondary indicators under the i-th primary indicator under each grey category, and establish the secondary grey evaluation matrix R i :

[0043]

[0044] Step 2-3: Calculate the comprehensive evaluation result, that is, the weights w of all secondary indicators under the i-th primary indicator i =(w i1 ,w i2 ,w i3 ,...w ij ) and the secondary grey evaluation matrix R i Multiply them to get the weighted evaluation result Z i , that is, Z i =W i R i A is the first level indicator i The risk level assessment results are similarly based on the first-level indicator A i The first-level index weight W is constructed based on the risk level assessment results, and the first-level grey evaluation matrix Z is constructed to obtain the risk level assessment result M = WZ of the total quantitative index of rainstorm floods around the underground space. * =M·UT converts the primary matrix M into a comprehensive evaluation value W * , and through the comprehensive evaluation value W * The reference quantitative value range that falls into determines the risk level of the target, where ·UT represents the execution of the transposition combination transformation.

[0045] Further, the specific steps of step S4 are:

[0046] Step S4.1: Starting from the three levels of leadership decision-making, professional handling and public response, a hierarchical emergency decision-making mechanism is formed;

[0047] Step S4.2: Based on the professional handling in the emergency decision-making mechanism and the first emergency rescue resource list, the content of the first emergency resource list is applied to the corresponding rainfall scenario, and the cellular automaton model is used for pre-simulation. According to the pre-simulation results, the second emergency resource list is generated, that is, the emergency rescue and dispatch command decision is obtained, wherein the emergency rescue and dispatch decision includes generating disaster evacuation paths for personnel and generating material utilization directions.

[0048] Further, in step S4.2,

[0049] The specific steps of generating the disaster evacuation path for personnel are:

[0050] Step 4.21, obtain the basic information of the underground space, and use 3D modeling software to build a digital underground space model, wherein the basic information of the underground space includes CAD drawings of the underground space, 3D scanning data, camera distribution and access control system information;

[0051] Step 4.22: Conduct disaster simulation based on the pre-simulation results of the underground space to determine all possible disaster avoidance paths in the underground space under the current situation;

[0052] Step 4.23: Determine the optimal disaster avoidance path based on the determined underground space disaster avoidance paths under the current situation and the Dijkstra algorithm; the specific steps are:

[0053] Step 4.231, establish a directed weighted graph G (V, E, W), where the set V is used to store all nodes of the underground space disaster avoidance path in the current situation, the set E is used to store the edges connecting two nodes in the underground space disaster avoidance path in the current situation, and W represents all navigable paths in the underground space disaster avoidance path in the current situation, that is, the disaster avoidance path;

[0054] Step 4.232, define a set D for storing emergency evacuation nodes in the disaster avoidance path of the underground space under the current situation;

[0055] Step 4.233, create a set P to store the sequence of all nodes in the directed weighted graph G(V,E,W), and create a set Q to record all node paths connected to each node in the directed weighted graph G(V,E,W);

[0056] Step 4.234, based on each emergency evacuation node in set D, use the Dijkstar algorithm to perform path resolution on set P and set Q, obtain a shortest path, synchronously construct sets M and N, traverse and store the points and edges on the shortest path in sets M and N respectively;

[0057] Step 4.235: for each emergency evacuation node, remove the edge of the shortest path stored in the set N in step 4.234 from the corresponding directed weighted graph G, so that the directed weighted graph G becomes a directed weighted graph G1;

[0058] Step 4.236: If there is no traversable path from each emergency evacuation node to the target node in the directed weighted graph G1, go to step 42.37; otherwise, go to step 4.233 and execute intermittently based on the directed weighted graph G1 until all the shortest paths from each emergency evacuation node to the target node or the first z shortest paths are obtained to end the loop; when all the shortest paths are less than z, all the shortest paths from each emergency evacuation node to the target node are obtained; otherwise, the first z shortest paths are obtained;

[0059] Step 42.37: Given a threshold target, if all shortest paths or the shortest paths of the first z paths are less than or equal to the threshold target, all shortest paths are taken as the optimal disaster avoidance paths; otherwise, a screening function is used to determine the paths with the same number of paths as the threshold target among all shortest paths or the shortest paths of the first z paths as the optimal disaster avoidance paths;

[0060] Step 4.24: During the disaster avoidance process, according to the real-time monitored rainfall conditions and changes in the underground space, re-execute step 4.21;

[0061] The material utilization direction is generated by using a CNN neural network model and pre-deduction results, and the CNN neural network model includes two objective functions and five constraints that need to be satisfied;

[0062] Objective function 1 is used to indicate the lowest cost of emergency material dispatch:

[0063]

[0064] Among them, m represents the number of emergency material rescue points, n represents the number of disaster-affected points, and x(Z v ) qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q Dispatch the vth emergency supplies Z v The number of G qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q The number of times the vth type of material is dispatched;

[0065] Objective function 2 is used to indicate the shortest adjustment time for emergency supplies;

[0066]

[0067] in, Indicates that the emergency supplies at point w are sent to rescue point R w To the qth disaster point D q Triangular fuzzy numbers for dispatching emergency supplies. t wq1 is the pessimistic value of the fuzzy number, t wq2 is the normal value of the fuzzy number, t wq3 is the optimistic value of the fuzzy number, Indicates the emergency supplies from point w to rescue point R w Whether to deploy materials to the qth disaster site D q ,

[0068] There are five constraints:

[0069] Constraint 1 means that the actual total supply of emergency supplies at each emergency rescue point is equal to the total demand for emergency supplies at each disaster site. The formula is:

[0070]

[0071] Among them, r(Z v ) w Indicates the w-th emergency supplies rescue point R w The vth material Z v The actual supply of The emergency supplies Z at the qth disaster site v The actual demand;

[0072] Constraint 2 indicates that the vth type of emergency material Z is actually dispatched from each emergency material rescue point to the qth disaster point. v The quantity is equal to the emergency supplies Z of the disaster site v The demand is:

[0073]

[0074] Among them, x(Z v ) wq The emergency supplies Z actually dispatched from the w-th emergency supply rescue point to the q-th disaster site v The number of represents the demand for emergency supplies at the qth disaster site;

[0075] Constraint 3 indicates that the emergency supplies Z allocated from the w-th emergency supply rescue point to each disaster site v The sum of the quantities of emergency supplies is equal to the emergency supplies transported from the rescue point Z v The actual supply is:

[0076]

[0077] Constraint 4 indicates that the emergency supplies from the wth point are rescued from point R. w Deployed to the qth disaster site D q The quantity of emergency supplies is a non-negative number, and the formula is:

[0078] x(Z v ) wq ≥0

[0079] Constraint 5 means that the emergency supplies from the wth point are sent to the rescue point R. w Whether to deploy emergency supplies to the qth disaster site D q If there are emergency supplies from the wth emergency supplies rescue point R w Deployed to the qth disaster site Dq , then x~ wq The value of is 1, otherwise it is 0;

[0080]

[0081] Here, k represents the number of species.

[0082] Furthermore, the cellular automaton model simulation in steps S1 and S4 is based on the rainstorm scenario data, the underground space construction model and the catchment area, using the cellular automaton model to perform physical simulation and numerical simulation of the disaster evolution to obtain disaster data, including the underground space flooded area, flooded water depth and flood arrival time;

[0083] Among them, the underground space construction model is obtained by acquiring underground space data and importing it based on the underground space data after remodeling according to the drawings. The underground space data includes DEM data, terrain model, remote sensing image, BIM model, rainstorm data and meteorological data. The terrain model refers to the underground space model. The rainstorm scenario data and the catchment area are obtained based on the rainfall parameter information in the underground space data and the obtained rainstorm intensity.

[0084] A device for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm scenario includes a memory, a processor and a computer program stored in the memory. The processor executes the computer program to implement the steps of a method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm scenario.

[0085] Compared with the prior art, the advantages of the present invention are:

[0086] The present invention uses a flood simulation model to conduct a severity assessment and risk level assessment after conducting a basic disaster situation assessment, and generates a first emergency resource list. The first emergency resource list is pre-simulated to generate a second emergency resource list as the final basis for on-site emergency resource configuration and scheduling simulation, and the relevant data is simultaneously stored in the knowledge base to provide a theoretical basis for subsequent flood emergency management. Through the analysis of the rationality of the on-site human-machine-object settings, the impact of flood risks is controlled to a minimum, and through the personnel evacuation path and the generation of material utilization directions, the real-time response and plan adjustment of the flood emergency rescue command are realized, further reducing the losses caused by the disaster on site, which is specifically reflected in:

[0087] 1. The present invention uses a flood simulation model to conduct a severity assessment and risk level assessment after conducting a basic disaster situation assessment, and generates a first emergency resource list. The first emergency resource list is pre-simulated to generate a second emergency resource list as the final basis for on-site emergency resource configuration and scheduling simulation, and can perform adaptability correction on the site to ensure the effective implementation of the emergency rescue plan;

[0088] 2. The present invention fully considers factors such as flood risk factors and construction equipment, and can construct and simulate analysis for disaster scenarios of different levels. The emergency rescue plan has a high degree of intelligence, so that the rescue effect can be quantitatively evaluated. That is, through severity evaluation and risk level evaluation, a method for quantitative evaluation of underground space disasters under heavy rain scenarios is given, and a flood risk level evaluation mechanism is established, which provides a quantitative basis for emergency rescue and dispatch command of heavy rain and floods under different scenarios, so as to effectively guide the practice of emergency rescue of floods during the construction period of underground space. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0090] Figure 1 It is a basic logic schematic diagram of the present invention;

[0091] Figure 2 It is a flowchart of the application of severity evaluation and risk level assessment results;

[0092] Figure 3 It is a flow chart generated by the emergency rescue and dispatch command plan. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] A method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation, characterized in that it includes the following steps:

[0095] Step S1, based on the rainstorm scenario data and the cellular automaton model, a flood simulation is performed under multiple scenarios with different rainfall amounts, the spatiotemporal evolution process of the waterlogging disaster is simulated and analyzed, and the flood simulation results are obtained, wherein the flood simulation results include the catchment area, water depth, arrival time and flooding range; the cellular automaton model simulation is based on the rainstorm scenario data, the underground space construction model and the catchment area, and the cellular automaton model is used to perform physical simulation and numerical simulation of the disaster evolution to obtain the disaster data, including the underground space flooding area, the flooding depth and the flood arrival time; wherein, the underground space construction model is obtained by obtaining the underground space data, and importing it based on the underground space data after reproducing the model according to the drawing, and the underground space data includes DEM data, terrain model, remote sensing image, BIM model, rainstorm data and meteorological data, and the terrain model refers to the underground space model, and the rainstorm scenario data and the catchment area are obtained based on the rainfall parameter information in the underground space data and the obtained rainstorm intensity.

[0096] Step S2: Based on the flood simulation results, identify key risk factors to conduct severity evaluation, and conduct risk level evaluation based on the underground space disaster risk evaluation index system under the rainstorm scenario, wherein the key risk factors include construction personnel, important facilities, protection plans and underground space flooding scope;

[0097] Severity assessment includes the sum of in-depth analysis and disaster loss assessment;

[0098] The specific steps of the in-depth analysis include:

[0099] Step 2.1: Verify the key risk influencing factors IN(t)=(x c 0 (t)), t=1,2,3……M, whether S is satisfied c ={x c (1),x c (1),……,x c (M)},c=1,2,3 data format requirements, if the requirements are met, use the formula Normalization is performed. If the requirements are not met, data verification is performed and input data is obtained again for judgment. M represents the frame sequence of input data with time tags, IN and S are both set letters, and x c 0 (t) represents the input data before normalization;

[0100] Step 2.2: Based on the normalized data obtained in step 2.1, a loss assessment model is constructed. That is, based on three types of disaster factors, namely, disaster variables, carrier variables and control variables, a description value is assigned to each disaster factor to construct a loss assessment model. Among them, disaster variables refer to disaster factors, including the scope of underground space flooding; carrier variables refer to the construction site, including construction personnel; and control variables refer to protective facilities, including important facilities and protection plans.

[0101] The loss assessment model is expressed as:

[0102] x(t)=(x 1 (t),x 2 (t),x 3 (t)) T

[0103] IN:(x i 0 (t))(i=1,2,3)

[0104] OUT:(x i 0 (t+1))(i=1,2,3)

[0105] Among them, x 1 (t) represents the description value of a certain disaster variable of underground space flood at time t, x 2 (t) represents the description value of a certain carrier variable of the underground station flood at time t, x 3 (t) represents the description value of a certain control variable of underground station flood at time t, IN: (x i 0 (t))(i=1,2,3) represents the format of input data of loss assessment model, OUT:(x i 0 (t+1))(i=1,2,3) represents the format of the output data of the loss assessment model, and x(t) represents the normalized input data;

[0106] Step 2.3: Based on the data set including disaster-causing factors, the loss assessment model is trained on the corresponding relationship between disaster-causing factors and loss assessment. After the training, the predicted disaster-causing factors are deeply analyzed and evaluated to obtain the deep analysis evaluation values ​​of the corresponding disaster-causing factors.

[0107] The disaster loss assessment includes the sum of the risk population assessment and the economic loss assessment;

[0108] The risk population assessment refers to statistically analyzing the total value of the risk population, and obtaining the risk population assessment score corresponding to the total value according to the risk population assessment standard, wherein the risk population includes the casualties on site due to the disaster, the missing persons due to the disaster, the emergency relocated and resettled population, and the trapped population. The risk population assessment standard includes that if the total value of the risk population is greater than or equal to 100 people, the corresponding risk population assessment value is 5 points, if it is greater than or equal to 50 people and less than 100 people, the corresponding risk population assessment value is 4 points, if it is greater than or equal to 10 people and less than 50 people, the corresponding risk population assessment value is 3 points, if it is greater than or equal to 3 people and less than 10 people, the corresponding risk population assessment value is 2 points, and if it is less than 3 people, the corresponding risk population assessment value is 1 point, as shown in Table 1:

[0109] Table 1 Risk population assessment table

[0110] Population at risk Corresponding score More than 100 people 5 More than or equal to 50 people and less than 100 people 4 More than or equal to 10 people and less than 50 people 3 More than or equal to 3 people and less than 10 people 2 Less than 3 people 1

[0111] The economic loss evaluation refers to the statistical total economic loss, and the economic loss evaluation score corresponding to the total economic loss is obtained according to the economic loss evaluation standard, wherein the total economic loss includes the economic losses of the construction site equipment loss, construction period loss and material loss at the construction site after the flood occurs. The economic loss evaluation standard includes that the total economic loss is greater than or equal to 1 million yuan, and the corresponding economic loss evaluation score is 5 points, indicating that particularly serious economic losses are caused. The total economic loss is greater than or equal to 500,000 yuan and less than 1 million yuan, and the corresponding economic loss evaluation score is 4 points, indicating that serious economic losses are caused. The total economic loss is greater than or equal to 100,000 yuan and less than 500,000 yuan, and the corresponding economic loss evaluation score is 3 points, indicating that relatively heavy economic losses are caused. The total economic loss is greater than or equal to 10,000 yuan and less than 100,000 yuan, and the corresponding economic loss evaluation score is 2 points, indicating that certain economic losses are caused. The total economic loss is less than 10,000 yuan, and the corresponding economic loss evaluation score is 1 point, indicating that basically no economic losses are caused, as shown in Table 2:

[0112] Table 2 Economic loss evaluation table

[0113] Economic loss impact Evaluation Criteria Corresponding score Causing serious economic losses Greater than or equal to 1 million yuan 5 Cause serious economic losses Greater than or equal to 500,000 yuan, less than 1 million yuan 4 Caused heavy economic losses Greater than or equal to 100,000 yuan, less than 500,000 yuan 3 Cause certain economic losses Greater than or equal to 10,000 yuan, less than 100,000 yuan 2 Basically no economic losses were caused Less than 10,000 yuan 1

[0114] The specific steps of risk level assessment are:

[0115] A multi-index comprehensive evaluation system is constructed, that is, an underground space disaster risk evaluation index system under heavy rain scenarios is constructed, which includes total indicators, primary indicators and secondary indicators. Among them, the total indicator is the underground space flood risk under heavy rain scenarios, the primary indicators include the hazard of disaster factors, the sensitivity of the disaster-prone environment, the vulnerability of the disaster-bearing body and the disaster prevention and mitigation capabilities, the secondary indicators include the 24-hour maximum rainfall, rainfall duration and construction management and responsibility implementation of the hazard factor hazard, the catchment area, drainage network density, complex surrounding environment and rivers of the disaster-prone environment sensitivity, the drainage capacity of the drainage ditch and the depth of the foundation pit of the disaster-bearing body vulnerability, the height of the retaining wall, the disaster monitoring and early warning technology and the emergency material reserves of the disaster prevention and mitigation capabilities, as shown in Table 3, the total indicator is represented by A, the primary indicator is represented by B, and the secondary indicator is represented by C:

[0116] Table 3. Deep foundation pit engineering disaster risk assessment index system under heavy rain scenario

[0117]

[0118]

[0119] The grey clustering evaluation method is constructed to score the secondary indicators in the multi-indicator comprehensive evaluation system to determine the primary indicator score. Finally, the risk level assessment of the total indicator is obtained according to the weight of the given primary indicator. The specific steps are as follows:

[0120] Step 2-1, establish the whitening weight function for the quantitative risk assessment of rainstorm floods around underground space, that is, the gray clustering evaluation method. Use the whitening weight function combined with the quantitative risk characteristics of rainstorm floods around underground space to divide the risk level into 5 gray classes, and based on the threshold center point vector U = (9, 7, 5, 3, 1), obtain the reference threshold and corresponding risk level of each gray class. Based on the reference threshold and risk level of each gray class, assign weight w to all secondary indicators under the i-th primary indicator. i =(w i1 ,w i2 ,w i3 ,...w ij ), among which, the quantitative risk characteristics of rainstorm floods around underground space are the secondary indicators in the multi-index comprehensive evaluation system. When the gray category is 1, the reference threshold is [0, 9, ∞], and the risk level is very safe. When the gray category is 2, the reference threshold is [0, 7, 14], and the risk level is safe. When the gray category is 3, the reference threshold is [0, 5, 10], and the risk level is general. When the gray category is 4, the reference threshold is [0, 3, 6], and the risk level is dangerous. When the gray category is 5, the reference threshold is [0, 1, 2], and the risk level is very dangerous, as shown in Table 4:

[0121] Table 4 Reference thresholds and risk levels for each gray category

[0122]

[0123]

[0124] The formula of the whitening weight function is:

[0125]

[0126] Where, d ijk represents the score of expert k on the jth secondary indicator under the i-th primary indicator, k = 1, 2, 3, ..., f 1 1 (d ijk ), f 1 2 (d ijk ), f 1 3 (d ijk ), f 1 4 (d ijk ), f 1 5 (d ijk ) respectively represent the risk level of the jth secondary indicator under the ith primary indicator in each gray category;

[0127] Step 2-2: Construct a secondary grey evaluation matrix. The specific steps are as follows:

[0128] By ijk Obtain the jth secondary index c under the i-th primary index ij The evaluation coefficient in each gray class is as follows:

[0129]

[0130] Calculate the secondary index c separately ij The evaluation coefficient in the five gray categories is as follows:

[0131]

[0132] Among them, e represents the e-th gray class, Y ij represents the total clustering coefficient,

[0133] Based on the total clustering coefficient Y ij and evaluation coefficient Y ije Get the gray evaluation weight value:

[0134]

[0135] Arrange the grey evaluation weight values ​​of all secondary indicators under the i-th primary indicator under each grey category, and establish the secondary grey evaluation matrix Ri :

[0136]

[0137] Step 2-3: Calculate the comprehensive evaluation result, that is, the weights w of all secondary indicators under the i-th primary indicator i =(w i1 ,w i2 ,w i3 ,...w ij ) and the secondary grey evaluation matrix R i Multiply them to get the weighted evaluation result Z i , that is, Z i =W i R i A is the first level indicator i The risk level assessment results are similarly based on the first-level indicator A i The first-level index weight W is constructed based on the risk level assessment results, and the first-level grey evaluation matrix Z is constructed to obtain the risk level assessment result M = WZ of the total quantitative index of rainstorm floods around the underground space. * =M·UT converts the primary matrix M into a comprehensive evaluation value W * , and through the comprehensive evaluation value W * The reference quantitative value range that falls into determines the risk level of the target, where ·UT represents the execution of the transposition combination transformation.

[0138] Step S3, based on the severity evaluation and risk level assessment results and the statistically obtained target underground space emergency rescue resource list and the constructed knowledge base, a multi-angle first emergency task list of the underground space is obtained, wherein the multi-angle first emergency task list of the underground space includes the types and quantities of emergency resources required for the emergency response to the flood accident according to the state of the flood accident; Figure 2 As shown, Figure 2 This is a flowchart of the application of severity evaluation and risk level assessment results provided by the embodiment of the present application. By sorting out the flood emergency rescue and dispatch command plans under the heavy rain scenarios in the historical course, an emergency rescue knowledge base can be formed, and simultaneously combined with the on-site materials, personnel, equipment and other related conditions of the target underground space, an emergency rescue resource list can be formed. Based on the target underground space emergency rescue resource list and the constructed knowledge base, combined with the simulation of the on-site disaster situation, the first emergency task list that matches the level of the heavy rain disaster can be obtained.

[0139] Step S4: Apply the content in the first emergency resource list to the corresponding rainfall scenario, and use the cellular automaton model for pre-simulation (same as the simulation step in step S1). According to the pre-simulation results, generate the second emergency resource list, that is, obtain the emergency rescue and dispatch command decision. The specific steps are:

[0140] Step S4.1: Starting from the three levels of leadership decision-making, professional handling and public response, a hierarchical emergency decision-making mechanism is formed;

[0141] Step S4.2: Based on the professional handling in the emergency decision-making mechanism and the first emergency rescue resource list, the content of the first emergency resource list is applied to the corresponding rainfall scenario, and the cellular automaton model is used for pre-simulation. According to the pre-simulation results, the second emergency resource list is generated, that is, the emergency rescue and dispatch command decision is obtained, wherein the emergency rescue and dispatch decision includes generating disaster evacuation paths for personnel and generating material utilization directions.

[0142] In step S4.2, the specific steps of generating a disaster evacuation path for personnel are:

[0143] Step 4.21, obtain the basic information of the underground space, and use 3D modeling software to build a digital underground space model, wherein the basic information of the underground space includes CAD drawings of the underground space, 3D scanning data, camera distribution and access control system information;

[0144] Step 4.22: Conduct disaster simulation based on the pre-simulation results of the underground space to determine all possible disaster avoidance paths in the underground space under the current situation;

[0145] Step 4.23: Determine the optimal disaster avoidance path based on the determined underground space disaster avoidance paths under the current situation and the Dijkstra algorithm; the specific steps are:

[0146] Step 4.231, establish a directed weighted graph G (V, E, W), where the set V is used to store all nodes of the underground space disaster avoidance path in the current situation, the set E is used to store the edges connecting two nodes in the underground space disaster avoidance path in the current situation, and W represents all navigable paths in the underground space disaster avoidance path in the current situation, that is, the disaster avoidance path;

[0147] Step 4.232, define a set D for storing emergency evacuation nodes in the disaster avoidance path of the underground space under the current situation;

[0148] Step 4.233, create a set P to store the sequence of all nodes in the directed weighted graph G(V,E,W), and create a set Q to record all node paths connected to each node in the directed weighted graph G(V,E,W);

[0149] Step 4.234, based on each emergency evacuation node in set D, use the Dijkstar algorithm to perform path resolution on set P and set Q, obtain a shortest path, synchronously construct sets M and N, traverse and store the points and edges on the shortest path in sets M and N respectively;

[0150] Step 4.235: for each emergency evacuation node, remove the edge of the shortest path stored in the set N in step 4.234 from the corresponding directed weighted graph G, so that the directed weighted graph G becomes a directed weighted graph G1;

[0151] Step 4.236: If there is no traversable path from each emergency evacuation node to the target node in the directed weighted graph G1, go to step 42.37; otherwise, go to step 4.233 and execute intermittently based on the directed weighted graph G1 until all the shortest paths from each emergency evacuation node to the target node or the first z shortest paths are obtained to end the loop; when all the shortest paths are less than z, all the shortest paths from each emergency evacuation node to the target node are obtained; otherwise, the first z shortest paths are obtained;

[0152] Step 42.37: Given a threshold target, if all shortest paths or the shortest paths of the first z paths are less than or equal to the threshold target, all shortest paths are taken as the optimal disaster avoidance paths; otherwise, a screening function is used to determine the paths with the same number of paths as the threshold target among all shortest paths or the shortest paths of the first z paths as the optimal disaster avoidance paths;

[0153] Step 4.24: During the disaster avoidance process, according to the real-time monitored rainfall conditions and changes in the underground space, re-execute step 4.21;

[0154] The material utilization direction is generated by using a CNN neural network model and pre-deduction results, and the CNN neural network model includes two objective functions and five constraints that need to be satisfied;

[0155] Objective function 1 is used to indicate the lowest cost of emergency material dispatch:

[0156]

[0157] Among them, m represents the number of emergency material rescue points, n represents the number of disaster-affected points, and x(Z v ) qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q Dispatch the vth emergency supplies Z v The number of G qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q The number of times the vth type of material is dispatched;

[0158] Objective function 2 is used to indicate the shortest adjustment time for emergency supplies;

[0159]

[0160] in, Indicates that the emergency supplies at point w are sent to rescue point R w To the qth disaster point D q Triangular fuzzy numbers for dispatching emergency supplies. t wq1 is the pessimistic value of the fuzzy number, t wq2 is the normal value of the fuzzy number, t wq3 is the optimistic value of the fuzzy number, Indicates the emergency supplies from point w to rescue point R w Whether to deploy materials to the qth disaster site D q ,

[0161] There are five constraints:

[0162] Constraint 1 means that the actual total supply of emergency supplies at each emergency rescue point is equal to the total demand for emergency supplies at each disaster site. The formula is:

[0163]

[0164] Among them, r(Z v ) w Indicates the w-th emergency supplies rescue point R w The vth material Z v The actual supply of The emergency supplies Z at the qth disaster site v The actual demand;

[0165] Constraint 2 indicates that the vth type of emergency material Z is actually dispatched from each emergency material rescue point to the qth disaster point. v The quantity is equal to the emergency supplies Z of the disaster site v The demand is:

[0166]

[0167] Among them, x(Z v ) wq The emergency supplies Z actually dispatched from the w-th emergency supply rescue point to the q-th disaster site v The number of represents the demand for emergency supplies at the qth disaster site;

[0168] Constraint 3 indicates that the emergency supplies Z allocated from the w-th emergency supply rescue point to each disaster site vThe sum of the quantities of emergency supplies is equal to the emergency supplies transported from the rescue point Z v The actual supply is:

[0169]

[0170] Constraint 4 indicates that the emergency supplies from the wth point are rescued from point R. w Deployed to the qth disaster site D q The quantity of emergency supplies is a non-negative number, and the formula is:

[0171] x(Z v ) wq ≥0

[0172] Constraint 5 means that the emergency supplies from the wth point are sent to the rescue point R. w Whether to deploy emergency supplies to the qth disaster site D q If there are emergency supplies from the wth emergency supplies rescue point R w Deployed to the qth disaster site D q ,but The value of is 1, otherwise it is 0;

[0173]

[0174] Here, k represents the number of species.

[0175] like Figure 3 As shown, Figure 3 It is a schematic diagram of a dispatch command decision output provided by an embodiment of the present application. The content in the first emergency resource list is applied to the corresponding rainfall scenario, and the cellular automaton model is used for pre-deduction to realize the verification of the first emergency resource. Here, if the verification directly meets the requirements, the first emergency resource list is converted into a second emergency resource list for outputting emergency rescue and dispatch command decisions. If it does not meet the requirements, it is necessary to re-acquire the flood-related information, and combine the deduction results to adjust the type and quantity information of emergency resources for emergency response to flood accidents, and use the cellular automaton model for secondary deduction so that the emergency resource list meets the requirements, thereby generating a second emergency resource list.

Claims

1. A method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation, characterized in that: The steps include: Step S1, based on the rainstorm scenario data and the cellular automaton model, a flood simulation is performed under multiple scenarios with different rainfall amounts, the spatiotemporal evolution process of the waterlogging disaster is simulated and analyzed, and the flood simulation results are obtained, wherein the flood simulation results include the catchment area, water depth, arrival time and flooding range; Step S2: Based on the flood simulation results, identify key risk factors to conduct severity evaluation, and conduct risk level evaluation based on the underground space disaster risk evaluation index system under the rainstorm scenario, wherein the key risk factors include construction personnel, important facilities, protection plans and underground space flooding scope; Step S3, matching the target underground space emergency rescue resource list obtained based on the severity evaluation and risk level assessment results and statistics with the constructed knowledge base to obtain a multi-angle first emergency task list for the underground space, wherein the multi-angle first emergency task list for the underground space includes the types and quantities of emergency resources required for emergency response to the flood accident according to the state of the flood accident; Step S4: Apply the content in the first emergency resource list to the corresponding rainfall scenario, and use the cellular automaton model for pre-simulation. According to the pre-simulation results, generate the second emergency resource list, that is, obtain the emergency rescue and dispatch command decision.

2. The method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation according to claim 1 is characterized in that: The severity evaluation in step 2 includes the sum of the in-depth analysis and the disaster loss assessment; The specific steps of the in-depth analysis include: Step 2.1: Verify the key risk influencing factors IN(t)=(x c 0 (t)), t=1,2,3……M, whether S is satisfied c ={x c (1),x c (1),……,x c (M)},c=1,2,3 data format requirements, if the requirements are met, use the formula Normalization is performed. If the requirements are not met, data verification is performed and input data is obtained again for judgment. M represents the frame sequence of input data with time tags, IN and S are both set letters, and x c 0 (t) represents the input data before normalization; Step 2.2: Based on the normalized data obtained in step 2.1, a loss assessment model is constructed. That is, based on three types of disaster factors, namely, disaster variables, carrier variables and control variables, a description value is assigned to each disaster factor to construct a loss assessment model. Among them, disaster variables refer to disaster factors, including the scope of underground space flooding; carrier variables refer to the construction site, including construction personnel; and control variables refer to protective facilities, including important facilities and protection plans. The loss assessment model is expressed as: x(t)=(x1(t),x2(t),x3(t)) T IN:(x i 0 (t))(i=1,2,3) OUT:(x i 0 (t+1))(i=1,2,3) Among them, x1(t) represents the description value of a certain disaster variable of underground space flood at time t, x2(t) represents the description value of a certain carrier variable of underground station flood at time t, and x3(t) represents the description value of a certain control variable of underground station flood at time t. i 0 (t))(i=1,2,3) represents the format of input data of loss assessment model, OUT:(x i 0 (t+1))(i=1,2,3) represents the format of the output data of the loss assessment model, and x(t) represents the normalized input data; Step 2.3: Based on the data set including the disaster-causing factors, the loss assessment model is trained on the corresponding relationship between the disaster-causing factors and the loss assessment. After the training, the disaster-causing factors to be predicted are deeply analyzed and evaluated to obtain the deep analysis evaluation values ​​of the corresponding disaster-causing factors; The disaster loss assessment includes the sum of the risk population assessment and the economic loss assessment; The risk population assessment refers to statistically analyzing the total value of the risk population, and obtaining the risk population assessment score corresponding to the total value according to the risk population assessment standard, wherein the risk population includes the casualties on site due to the disaster, the missing persons due to the disaster, the emergency relocated and resettled population, and the trapped population. The risk population assessment standard includes that if the total value of the risk population is greater than or equal to 100 people, the corresponding risk population assessment value is 5 points; if the total value of the risk population is greater than or equal to 50 people and less than 100 people, the corresponding risk population assessment value is 4 points; if the total value of the risk population is greater than or equal to 10 people and less than 50 people, the corresponding risk population assessment value is 3 points; if the total value of the risk population is greater than or equal to 3 people and less than 10 people, the corresponding risk population assessment value is 2 points; if the total value of the risk population is less than 3 people, the corresponding risk population assessment value is 1 point; The economic loss evaluation refers to the statistical total economic loss, and obtaining the economic loss evaluation score corresponding to the total economic loss according to the economic loss evaluation standard, wherein the total economic loss includes the economic losses of construction site equipment loss, construction period loss and material loss at the construction site after the flood occurs. The economic loss evaluation standard includes the total economic loss is greater than or equal to 1 million yuan, and the corresponding economic loss evaluation score is 5 points, indicating that particularly serious economic losses are caused; the total economic loss is greater than or equal to 500,000 yuan and less than 1 million yuan, and the corresponding economic loss evaluation score is 4 points, indicating that serious economic losses are caused; the total economic loss is greater than or equal to 100,000 yuan and less than 500,000 yuan, and the corresponding economic loss evaluation score is 3 points, indicating that relatively heavy economic losses are caused; the total economic loss is greater than or equal to 10,000 yuan and less than 100,000 yuan, and the corresponding economic loss evaluation score is 2 points, indicating that certain economic losses are caused; the total economic loss is less than 10,000 yuan, and the corresponding economic loss evaluation score is 1 point, indicating that basically no economic losses are caused.

3. The method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation according to claim 1 is characterized in that: The specific steps of risk level assessment in step 2 are: Construct a multi-index comprehensive evaluation system, that is, construct an underground space disaster risk evaluation index system under heavy rain scenarios, which includes general indicators, primary indicators and secondary indicators. Among them, the general indicator is the underground space flood risk under heavy rain scenarios. The primary indicators include the hazard of disaster factors, the sensitivity of the disaster-prone environment, the vulnerability of the disaster-bearing body and the disaster prevention and mitigation capabilities. The secondary indicators include the 24-hour maximum rainfall, rainfall duration and construction management and responsibility implementation of the hazard factor hazard, the catchment area, drainage network density, complex surrounding environment and rivers of the disaster-prone environment sensitivity, the drainage ditch drainage capacity and foundation pit depth of the disaster-bearing body vulnerability, and the height of the retaining wall, disaster monitoring and early warning technology and emergency material reserves of the disaster prevention and mitigation capabilities. The grey clustering evaluation method is constructed to score the secondary indicators in the multi-indicator comprehensive evaluation system to determine the primary indicator score. Finally, the risk level assessment of the total indicator is obtained according to the weight of the given primary indicator. The specific steps are as follows: Step 2-1, establish the whitening weight function for the quantitative risk assessment of rainstorm floods around underground space, that is, the gray clustering evaluation method. Use the whitening weight function combined with the quantitative risk characteristics of rainstorm floods around underground space to divide the risk level into 5 gray classes, and based on the threshold center point vector U = (9, 7, 5, 3, 1), obtain the reference threshold and corresponding risk level of each gray class. Based on the reference threshold and risk level of each gray class, assign weight w to all secondary indicators under the i-th primary indicator. i =(w i1 ,w i2 ,w i3 ,...w ij ), where the quantitative risk characteristics of rainstorm floods around underground space are the secondary indicators in the multi-indicator comprehensive evaluation system. When the gray category is 1, the reference threshold is [0, 9, ∞], and the risk level is very safe. When the gray category is 2, the reference threshold is [0, 7, 14], and the risk level is safe. When the gray category is 3, the reference threshold is [0, 5, 10], and the risk level is general. When the gray category is 4, the reference threshold is [0, 3, 6], and the risk level is dangerous. When the gray category is 5, the reference threshold is [0, 1, 2], and the risk level is very dangerous. The formula of the whitening weight function is: Where, d ijk represents the score of expert k on the jth secondary indicator under the i-th primary indicator, k = 1, 2, 3, ..., f1 1 (d ijk )、f1 2 (d ijk )、f1 3 (d ijk )、f1 4 (d ijk )、f1 5 (d ijk ) respectively represent the risk level of the jth secondary indicator under the ith primary indicator in each gray category; Step 2-2: Construct a secondary grey evaluation matrix. The specific steps are as follows: By ijk Obtain the jth secondary index c under the i-th primary index ij The evaluation coefficient in each gray class is as follows: Calculate the secondary index c separately ij The evaluation coefficient in the five gray categories is as follows: Among them, e represents the e-th gray class, Y ij represents the total clustering coefficient, Based on the total clustering coefficient Y ij and evaluation coefficient Y ije Get the gray evaluation weight value: Arrange the grey evaluation weight values ​​of all secondary indicators under the i-th primary indicator under each grey category, and establish the secondary grey evaluation matrix R i : Step 2-3: Calculate the comprehensive evaluation result, that is, the weights w of all secondary indicators under the i-th primary indicator i =(w i1 ,w i2 ,w i3 ,...w ij ) and the secondary grey evaluation matrix R i Multiply them to get the weighted evaluation result Z i , that is, Z i =W i R i A is the first level indicator i The risk level assessment results are similarly based on the first-level indicator A i The first-level index weight W is constructed based on the risk level assessment results, and the first-level grey evaluation matrix Z is constructed to obtain the risk level assessment result M = WZ of the total quantitative index of rainstorm floods around the underground space. * =M·UT converts the primary matrix M into a comprehensive evaluation value W * , and through the comprehensive evaluation value W * The reference quantitative value range that falls into determines the risk level of the target, where ·UT represents the execution of the transposition combination transformation.

4. The method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation according to claim 1 is characterized in that: The specific steps of step S4 are: Step S4.1: Starting from the three levels of leadership decision-making, professional handling and public response, a hierarchical emergency decision-making mechanism is formed; Step S4.2: Based on the professional handling in the emergency decision-making mechanism and the first emergency rescue resource list, the content of the first emergency resource list is applied to the corresponding rainfall scenario, and the cellular automaton model is used for pre-simulation. According to the pre-simulation results, the second emergency resource list is generated, that is, the emergency rescue and dispatch command decision is obtained, wherein the emergency rescue and dispatch decision includes generating disaster evacuation paths for personnel and generating material utilization directions.

5. The method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation according to claim 4 is characterized in that: In the step S4.2, The specific steps of generating the disaster evacuation path for personnel are: Step 4.21, obtain the basic information of the underground space, and use 3D modeling software to build a digital underground space model, wherein the basic information of the underground space includes CAD drawings of the underground space, 3D scanning data, camera distribution and access control system information; Step 4.22: Conduct disaster simulation based on the pre-simulation results of the underground space to determine all possible disaster avoidance paths in the underground space under the current situation; Step 4.23: Determine the optimal disaster avoidance path based on the determined underground space disaster avoidance paths under the current situation and the Dijkstra algorithm; the specific steps are: Step 4.231, establish a directed weighted graph G (V, E, W), where the set V is used to store all nodes of the underground space disaster avoidance path in the current situation, the set E is used to store the edges connecting two nodes in the underground space disaster avoidance path in the current situation, and W represents all navigable paths in the underground space disaster avoidance path in the current situation, that is, the disaster avoidance path; Step 4.232, define a set D for storing emergency evacuation nodes in the disaster avoidance path of the underground space under the current situation; Step 4.233, create a set P to store the sequence of all nodes in the directed weighted graph G(V,E,W), and create a set Q to record all node paths connected to each node in the directed weighted graph G(V,E,W); Step 4.234, based on each emergency evacuation node in set D, use the Dijkstar algorithm to perform path resolution on set P and set Q, obtain a shortest path, synchronously construct sets M and N, traverse and store the points and edges on the shortest path in sets M and N respectively; Step 4.235: for each emergency evacuation node, remove the edge of the shortest path stored in the set N in step 4.234 from the corresponding directed weighted graph G, so that the directed weighted graph G becomes a directed weighted graph G1; Step 4.236: If there is no traversable path from each emergency evacuation node to the target node in the directed weighted graph G1, go to step 42.37; otherwise, go to step 4.233 and execute intermittently based on the directed weighted graph G1 until all the shortest paths from each emergency evacuation node to the target node or the first z shortest paths are obtained to end the loop, wherein when all the shortest paths are less than z, all the shortest paths from each emergency evacuation node to the target node are obtained; otherwise, the first z shortest paths are obtained; Step 42.37: Given a threshold target, if all shortest paths or the shortest paths of the first z paths are less than or equal to the threshold target, all shortest paths are taken as the optimal disaster avoidance paths; otherwise, a screening function is used to determine the paths with the same number of paths as the threshold target among all shortest paths or the shortest paths of the first z paths as the optimal disaster avoidance paths; Step 4.24: During the disaster avoidance process, according to the real-time monitored rainfall conditions and changes in the underground space, re-execute step 4.21; The material utilization direction is generated by using a CNN neural network model and pre-deduction results, and the CNN neural network model includes two objective functions and five constraints that need to be satisfied; Objective function 1 is used to indicate the lowest cost of emergency material dispatch: Among them, m represents the number of emergency material rescue points, n represents the number of disaster-affected points, and x(Z v ) qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q Dispatch the vth emergency supplies Z v The number of G qw Indicates the emergency supplies from point w to rescue point R w To the qth disaster point D q The number of times the vth type of material is dispatched; Objective function 2 is used to indicate the shortest adjustment time for emergency supplies; in, Indicates that the emergency supplies at point w are sent to rescue point R w To the qth disaster point D q Triangular fuzzy numbers for dispatching emergency supplies. t wq1 is the pessimistic value of the fuzzy number, t wq2 is the normal value of the fuzzy number, t wq3 is the optimistic value of the fuzzy number, Indicates the emergency supplies from point w to rescue point R w Whether to deploy materials to the qth disaster site D q , There are five constraints: Constraint 1 means that the actual total supply of emergency supplies at each emergency rescue point is equal to the total demand for emergency supplies at each disaster site. The formula is: Among them, r(Z v ) w Indicates the w-th emergency supplies rescue point R w The vth material Z v The actual supply of The emergency supplies Z at the qth disaster site v The actual demand; Constraint 2 indicates that the vth type of emergency material Z is actually dispatched from each emergency material rescue point to the qth disaster point. v The quantity is equal to the emergency supplies Z of the disaster site v The demand is: Among them, x(Z v ) wq The emergency supplies Z actually dispatched from the w-th emergency supply rescue point to the q-th disaster site v The number of represents the demand for emergency supplies at the qth disaster site; Constraint 3 indicates that the emergency supplies Z allocated from the w-th emergency supply rescue point to each disaster site v The sum of the quantities of emergency supplies is equal to the emergency supplies transported from the rescue point Z v The actual supply is: Constraint 4 indicates that the emergency supplies from the wth point are rescued from point R. w Deployed to the qth disaster site D q The quantity of emergency supplies is a non-negative number, and the formula is: x(Z v ) wq ≥0 Constraint 5 means that the emergency supplies from the wth point are sent to the rescue point R. w Whether to deploy emergency supplies to the qth disaster site D q If there are emergency supplies from the wth emergency supplies rescue point R w Deployed to the qth disaster site D q , then x~ wq The value of is 1, otherwise it is 0; Here, k represents the number of species.

6. The method for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation according to claim 1, characterized in that: The cellular automaton model simulation in steps S1 and S4 is based on the rainstorm scenario data, the underground space construction model and the catchment area, using the cellular automaton model to perform physical simulation and numerical simulation of the disaster evolution to obtain disaster data, including the underground space flooded area, flooded water depth and flood arrival time; Among them, the underground space construction model is obtained by acquiring underground space data and importing it based on the underground space data after remodeling according to the drawings. The underground space data includes DEM data, terrain model, remote sensing image, BIM model, rainstorm data and meteorological data. The terrain model refers to the underground space model. The rainstorm scenario data and the catchment area are obtained based on the rainfall parameter information in the underground space data and the obtained rainstorm intensity.

7. A device for constructing an underground space flood emergency rescue and dispatch command model under a rainstorm situation, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

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