A nuclear accident emergency action planning system based on multi-source heterogeneous data processing

Abstract

The application relates to the technical field of nuclear accident emergency treatment, and discloses a nuclear accident emergency action planning system based on multi-source heterogeneous data processing, which comprises the following steps: acquiring a geographical map of a place where a nuclear accident occurs; generating a directed graph for representing spatial relations based on a graph theory method according to the geographical map; calculating the sparsity of each rescue node in the directed graph, screening a plurality of sparse rescue nodes in the directed graph according to the sparsity, and simplifying the directed graph according to the sparse rescue nodes to form a new directed graph; connecting the directed edges between an accident source node and each rescue node in the new directed graph to form a plurality of rescue paths; acquiring the best rescue path from the plurality of rescue paths by using a pre-configured genetic algorithm, and inputting the best rescue path for feedback; and the application can avoid the redundancy of the planned rescue path, and is beneficial to guaranteeing the safety of rescue task personnel while ensuring the rescue efficiency in a nuclear accident rescue scene.

Description

Technical Field

[0001] This invention relates to the field of nuclear accident emergency response technology, and more specifically, to a nuclear accident emergency action planning system based on multi-source heterogeneous data processing. Background Technology

[0002] Nuclear accidents are usually accompanied by the widespread dissemination of radioactive materials, posing a serious threat to the surrounding environment and personnel safety. In the emergency response process, the rapid planning and optimization of rescue routes is a core issue. However, due to the uneven distribution of rescue nodes and the fact that factors such as the sparseness of nodes and spatial topology are not fully considered in route planning, existing methods often fail to provide reliable solutions that are adapted to the actual environment.

[0003] Existing technologies in emergency route planning mainly rely on fixed geographic maps and simple route selection rules, neglecting the complex spatial relationships between nodes and the impact of the importance of different nodes on the overall planning results. For example, sparse nodes may become bottlenecks in route planning due to limited connections with other nodes, leading to redundant routes or a lack of flexibility. At the same time, existing methods focus more on route length or a single target, failing to comprehensively consider the radiation dose, distance, and structural properties of the route. This can easily lead to planning results that are not applicable to dynamic rescue needs. Furthermore, it is difficult to ensure the safety of personnel performing rescue missions while ensuring rescue efficiency. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a nuclear accident emergency response planning system based on multi-source heterogeneous data processing.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A nuclear accident emergency response planning system based on multi-source heterogeneous data processing, the system comprising:

[0007] The data processing module is used to obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into one accident source node, multiple rescue nodes and multiple intermediate nodes.

[0008] The data update module is used to calculate the sparsity of each rescue node in the directed graph, filter out multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph.

[0009] The path generation module is used to connect directed edges from the accident source node to each rescue node in the new directed graph, forming multiple rescue paths;

[0010] The action planning module is used to obtain the optimal rescue path from multiple rescue paths using a pre-configured genetic algorithm, and to input the optimal rescue path for feedback.

[0011] Furthermore, the calculation of the sparsity of each rescue node in the directed graph includes:

[0012] Traverse the directed graph and record each rescue node and the set of neighbors of each rescue node. The set of neighbors contains neighbor rescue nodes that are directly adjacent to the rescue node, and the number of neighbor rescue nodes is N(v).

[0013] Obtain the number of directed edges E(v) that directly connect a rescue node to each of its neighboring rescue nodes. Substitute the number of neighboring rescue nodes N(v) and the number of directed edges E(v) into the pre-constructed calculation model of the local clustering coefficient C(v) to calculate the local clustering coefficient C(v) for each rescue node.

[0014] The expression for the calculation model of the local clustering coefficient C(v) is as follows:

[0015]

[0016] In the formula: μ represents the neighboring rescue node directly connected to the rescue node, ∑ μ∈ N(v) means that for each neighboring rescue node μ, the rescue node is counted once;

[0017] Substitute the local clustering coefficient C(v) of the rescue nodes into the sparsity calculation formula to obtain the sparsity S(v) of each rescue node;

[0018] The sparsity S(v) of each rescue node is calculated using the following formula:

[0019]

[0020] In the formula: e represents the natural constant.

[0021] Furthermore, the filtering of multiple sparse rescue nodes in the directed graph includes:

[0022] The sparsity S(v) is compared with a preset sparsity threshold S. threshould Compare;

[0023] If S(v)≥S threshould If so, the corresponding rescue node is determined to be a sparse rescue node;

[0024] If S(v) < S threshould If so, the corresponding rescue node is determined to be a non-sparse rescue node;

[0025] Statistical analysis of all S(V)≥S threshould The rescue nodes were obtained, resulting in multiple sparse rescue nodes.

[0026] Furthermore, the simplification of the directed graph based on sparse rescue nodes includes:

[0027] Mark all sparse rescue nodes in the directed graph;

[0028] Remove all sparse rescue nodes from the directed graph and update the spatial relationships between the remaining nodes to obtain a new directed graph.

[0029] Furthermore, the process of obtaining the optimal rescue route from multiple rescue routes includes:

[0030] a1: Initialize the population: Generate an original population based on multiple rescue paths. The original population contains P individuals, each representing a rescue path, where P is a positive integer.

[0031] a2: Fitness assessment: For each individual, obtain the cumulative dose, path length, and cumulative sparsity of each rescue path; input the cumulative dose, path length, and cumulative sparsity into a pre-constructed fitness function to calculate the fitness of each individual;

[0032] a3: Selection: Two individuals with high fitness from the original population were selected as the father and mother using the roulette wheel method;

[0033] a4: Crossover: Perform a crossover operation on the parent and parent to produce new individuals;

[0034] a5: Mutation: Perform a mutation operation on the new individual to obtain W new individuals, combine the W new individuals into a new population, replace the original population with the new population, and return to step a2, where W is an integer greater than zero;

[0035] a6: Repeat steps a2 to a5 above until the fitness of an individual in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold. Then, output the rescue path represented by the corresponding individual as the best rescue path.

[0036] Furthermore, the logic for obtaining the cumulative dose is as follows:

[0037] When a nuclear accident occurs, acquire on-site environmental data of the nuclear accident site, as well as on-site accident data of the accident source nodes within the nuclear accident site;

[0038] The on-site environmental data and on-site accident data are input into a preset Gaussian plume model to obtain the concentration distribution of radionuclides emitted by the nuclear accident. The concentration distribution refers to the concentration of radionuclides at each node in the directed graph.

[0039] The basic data of the radionuclides are obtained, and the dose rate at each node in the rescue path is calculated based on the basic data and concentration distribution of the radionuclides, and the cumulative dose of the rescue path is calculated based on the dose rate.

[0040] Furthermore, the formula for calculating the dose rate is as follows:

[0041]

[0042] In the formula: S r The calculation formula is as follows:

[0043] S r =C r ×λ r ×Y r ;

[0044] In the formula: S r k represents the source intensity of the r-th radionuclide within the node; r λ is the dose conversion factor for the r-th radionuclide; r Let be the decay constant of the r-th radionuclide within the node, and its calculation formula is: In the formula: T 1 / 2 Y represents the half-life of a radioactive nuclide. r Let r be the radiation particle release yield of the r-th radionuclide within the node;

[0045] The formula for calculating the cumulative dose is as follows:

[0046]

[0047] In the formula: Δd is the path distance of a single node in the rescue route; D i Δt is the dose rate of the i-th node; Δt is the dwell time; Δd ​​is the spatial distance of the i-th node on the path; v is the average speed of the personnel.

[0048] Furthermore, the formula for calculating the pre-constructed fitness function is as follows: In the formula: Fitness is the fitness level, D total For cumulative dose, L total Let S(v) be the path length. total For cumulative sparsity, ω1, ω2, and ω3 are adjustment factors greater than zero, where ω1>ω2>ω3. D0, L0, and S(v)0 are reference values ​​for cumulative dose, path length, and cumulative sparsity, respectively.

[0049] A nuclear accident emergency response planning method based on multi-source heterogeneous data processing, implemented based on any one of the aforementioned nuclear accident emergency response planning systems based on multi-source heterogeneous data processing, the method comprising:

[0050] Obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into one accident source node, multiple rescue nodes, and multiple intermediate nodes.

[0051] Calculate the sparsity of each rescue node in the directed graph, select multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph.

[0052] In the new directed graph, directed edges connecting the accident source node to each rescue node form multiple rescue paths;

[0053] The optimal rescue path is selected from multiple rescue routes using a pre-configured genetic algorithm, and the optimal rescue path is then used as feedback.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] This application discloses a nuclear accident emergency response planning system based on multi-source heterogeneous data processing, comprising: acquiring a geographic map of the nuclear accident location; generating a directed graph representing spatial relationships based on the geographic map and graph theory methods; calculating the sparsity of each rescue node in the directed graph; selecting multiple sparse rescue nodes in the directed graph based on the sparsity; simplifying the directed graph based on the sparse rescue nodes to form a new directed graph; connecting directed edges between the accident source node and each rescue node in the new directed graph to form multiple rescue paths; using a pre-configured genetic algorithm to obtain the optimal rescue path from the multiple rescue paths, and inputting the optimal rescue path for feedback; based on the above technical features, this invention effectively reduces computational redundancy by calculating the sparsity of rescue nodes and simplifying the directed graph, thus optimizing... The invention optimizes the overall graph structure, significantly reducing the complexity of path planning while preserving key network characteristics, ensuring the connectivity and reachability of the graph structure. Secondly, it utilizes a pre-configured genetic algorithm to dynamically optimize rescue paths. By comprehensively considering multiple factors such as path length, cumulative radiation dose, and sparsity, it can quickly generate optimal rescue paths, effectively avoiding issues like path unavailability and significantly improving the adaptability and fault tolerance of the planning. Furthermore, by combining radionuclide concentration distribution calculations and rescue path radiation dose assessments in path planning, and through the effective fusion of multi-source data, it achieves comprehensive quantification and optimization of path safety. While ensuring rescue efficiency, it minimizes the radiation exposure risk to rescue personnel, further enhancing the practical feasibility of the planning scheme. Attached Figure Description

[0056] Figure 1 A module structure diagram of a nuclear accident emergency response planning system based on multi-source heterogeneous data processing provided by the present invention;

[0057] Figure 2 The flowchart provided by this invention is a nuclear accident emergency response planning method based on multi-source heterogeneous data processing. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] Please see Figure 1 As shown in the figure, this embodiment discloses a nuclear accident emergency response planning system based on multi-source heterogeneous data processing. The system includes:

[0061] The data processing module 101 is used to obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into an accident source node, multiple rescue nodes and multiple intermediate nodes.

[0062] It should be understood that: there are several geographic maps, all pre-stored in the system database. Each geographic map is used to reveal relevant geographic data about nuclear sites (such as nuclear power plants) and their surroundings, including but not limited to road networks, terrain features, building distribution, rescue node locations, and the specific location of the nuclear site (where a nuclear accident occurred). Among them, intermediate nodes refer to nodes located between the accident source node and the rescue node in the directed graph. They are neither the accident source node (the location of the accident) nor the direct rescue node (the endpoint of the rescue mission), but they play a bridging or transitional role in path connection and optimization. For example, the intermediate nodes are intersections in the road network.

[0063] It should be noted that the method of generating a directed graph to represent spatial relationships based on a geographic map and graph theory is an existing technology. The generation process is as follows: The specific location of the nuclear accident is marked as the accident source node; emergency rescue sites related to the nuclear accident are marked on the geographic map, and the locations of these emergency rescue sites are defined as rescue nodes; key intersections and turning points in the road network are extracted, and these points are used as intermediate nodes; edges are established between nodes to represent spatial connections; each edge is assigned a direction based on the actual situation, giving it directionality and representing the accessibility of a path from one node to another. Simultaneously, weights and attributes are assigned to each edge based on information such as the actual path length; based on the connections between all nodes and edges, the topological structure of the directed graph is generated to ensure the graph's connectivity and reachability.

[0064] The data update module 102 is used to calculate the sparsity of each rescue node in the directed graph, filter out multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph.

[0065] In implementation, the calculation of the sparsity of each rescue node in the directed graph includes:

[0066] Traverse the directed graph and record each rescue node and the set of neighbors of each rescue node. The set of neighbors contains neighbor rescue nodes that are directly adjacent to the rescue node, and the number of neighbor rescue nodes is N(v).

[0067] It should be understood that the neighbor set refers to the set of all (original) rescue nodes that are directly connected to a specific (original) rescue node;

[0068] Obtain the number of directed edges E(v) that directly connect a rescue node to each of its neighboring rescue nodes. Substitute the number of neighboring rescue nodes N(v) and the number of directed edges E(v) into the pre-constructed calculation model of the local clustering coefficient C(v) to calculate the local clustering coefficient C(v) for each rescue node.

[0069] The expression for the calculation model of the local clustering coefficient C(v) is as follows:

[0070]

[0071] In the formula: μ represents the neighboring rescue node directly connected to the rescue node, ∑ μ∈ N(v) represents counting each neighboring rescue node μ once for each rescue node; in other words, summing the counts of these neighboring rescue nodes.

[0072] Substitute the local clustering coefficient C(v) of the rescue nodes into the sparsity calculation formula to obtain the sparsity S(v) of each rescue node;

[0073] The sparsity S(v) of each rescue node is calculated using the following formula:

[0074]

[0075] In the formula: e represents the natural constant;

[0076] In implementation, the selection of multiple sparse rescue nodes in the directed graph includes:

[0077] The sparsity S(V) is compared with a preset sparsity threshold S. threshould Compare;

[0078] If S(V)≥S threshould If so, the corresponding rescue node is determined to be a sparse rescue node;

[0079] If S(v) < S threshould If so, the corresponding rescue node is determined to be a non-sparse rescue node;

[0080] Statistical analysis of all S(v)≥S threshould The rescue nodes were obtained, resulting in multiple sparse rescue nodes;

[0081] It is understandable that a larger sparsity S(v) means a lower local clustering coefficient C(v) for the corresponding rescue node, indicating fewer connections between the corresponding rescue node and its directly connected neighboring rescue nodes. Therefore, this rescue node is relatively isolated in the directed graph. Conversely, a smaller sparsity S(v) means a larger local clustering coefficient C(v) for the corresponding rescue node, indicating more connections between the corresponding rescue node and its directly connected neighboring rescue nodes. Therefore, this rescue node is relatively closely connected with other rescue nodes in the directed graph.

[0082] Specifically, the simplification of the directed graph based on sparse rescue nodes includes:

[0083] Mark all sparse rescue nodes in the directed graph;

[0084] Remove all sparse rescue nodes from the directed graph and update the spatial relationships between the remaining nodes to obtain a new directed graph;

[0085] It should be understood that after removing all sparse rescue nodes from the directed graph, there will be some directed edges that are not connected to any node. Therefore, when updating the spatial relationships between the remaining nodes, these directed edges that are not connected to any node will also be removed.

[0086] Furthermore, it can be understood that obtaining the sparsity of rescue nodes provides important data support for subsequent simplification of the directed graph. In addition, simplifying the directed graph can reduce computational complexity while retaining key structural information. Furthermore, path planning and selection based on the simplified directed graph can improve the safety and efficiency of rescue missions in actual situations. This prevents rescuers from being unable to find alternative paths when planned rescue routes become unavailable in reality (such as sudden road damage or blockage), thereby effectively improving the fault tolerance of the optimal rescue route in actual situations and ensuring the safety of personnel involved in nuclear accident rescue as much as possible.

[0087] The path generation module 103 is used to connect the directed edges between the accident source node and each rescue node in the new directed graph to form multiple rescue paths;

[0088] Each rescue path includes an accident source node, a rescue node, multiple intermediate nodes, and multiple directed edges.

[0089] It should be understood that in the process of connecting the accident source node to each rescue node with directed edges, the system will take the rescue node as the starting point, the accident source node as the ending point, and the intermediate node as the transfer point. Therefore, when the rescue node is taken as the starting point, the directed edges between the starting point and the intermediate node are connected, and the directed edges between the intermediate nodes are connected, until the directed edges between the intermediate nodes and the accident source node are completed, thus forming a complete rescue path.

[0090] Action planning module 104 is used to obtain the best rescue path from multiple rescue paths using a pre-configured genetic algorithm, and input the best rescue path for feedback;

[0091] In practice, obtaining the optimal rescue route from multiple rescue routes includes:

[0092] a1: Initialize the population: Generate an original population based on multiple rescue paths. The original population contains P individuals, each representing a rescue path, where P is a positive integer.

[0093] a2: Fitness assessment: For each individual (i.e. each rescue path in the original population), obtain the cumulative dose, path length, and cumulative sparsity of each rescue path; input the cumulative dose, path length, and cumulative sparsity into a pre-constructed fitness function to calculate the fitness of each individual;

[0094] Cumulative dose refers to the sum of dose rates at all nodes along the rescue path multiplied by the dwell time (path segment time), reflecting the total radiation dose received by personnel throughout the entire rescue path;

[0095] In implementation, the logic for obtaining the cumulative dose is as follows:

[0096] When a nuclear accident occurs, acquire on-site environmental data of the nuclear accident site, as well as on-site accident data of the accident source nodes within the nuclear accident site;

[0097] The on-site environmental data includes, but is not limited to, temperature, humidity, wind speed, and air pressure; the on-site accident data includes, but is not limited to, release rate and emission height.

[0098] It should be noted that the on-site environmental data and on-site accident data are collected based on various sensors that have been pre-deployed, including but not limited to wind speed sensors, temperature and humidity sensors, distance sensors and gas sensors.

[0099] The on-site environmental data and on-site accident data are input into a preset Gaussian plume model to obtain the concentration distribution of radionuclides emitted by the nuclear accident. The concentration distribution refers to the concentration of radionuclides at each node in the directed graph.

[0100] The expression for the Gaussian plume model is as follows:

[0101]

[0102] In the formula: C r σ represents the concentration of the radionuclide at location (x, y, z); (x, y, z) represents the node coordinates; Q is the emission rate of the radionuclide; η is the wind speed; σ y and σ z is the diffusion coefficient (determined by atmospheric stability); H is the height of the emission source;

[0103] The basic data of radionuclides are obtained, and the dose rate at each node in the rescue path is calculated based on the basic data and concentration distribution of radionuclides, as well as the cumulative dose of the rescue path is calculated based on the dose rate; the basic data includes, but is not limited to, the types of radionuclides and the yield of radiation particles released.

[0104] The formula for calculating the dose rate is as follows:

[0105]

[0106] In the formula: S r The calculation formula is as follows:

[0107] S r =C r ×λ r ×Y r ;

[0108] In the formula: S r is the source intensity of the r-th radionuclide within the node, measured in becquerels per second (Bq / s), representing the number of radiant particles emitted per second; k r λ is the dose conversion factor for the r-th radionuclide, measured in millisieverts per becquerel (msv / Bq), which is pre-set by technicians based on experimental data; r Let be the decay constant of the r-th radionuclide within the node, and its calculation formula is: In the formula: T 1 / 2 The half-life of a radioactive nuclide, measured in seconds, refers to the time required for the number of nuclides to decrease to half of their initial quantity during radioactive decay. It is pre-set by technicians based on experimental data. r The radiation particle release yield of the r-th radionuclide within the node is expressed in particles / decay, representing the number of neutrons, gamma photons, or other particles released in each decay of the radionuclide.

[0109] The formula for calculating the cumulative dose is as follows:

[0110]

[0111] In the formula: Δd is the path distance of a single node in the rescue route; D i Δt is the dose rate at the i-th node, expressed in millisieverts per hour (msv / h), representing the amount of radiation received per unit time within that node; Δt is the dwell time, representing the time spent by personnel passing through the i-th node on the path, which is preset by technicians based on experiments; Δd is the spatial distance between the i-th nodes on the path, i.e., the spatial span of each node, which is preset by technicians based on experiments; v is the average speed of personnel, expressed in meters per second (m / s), which is preset by technicians based on experiments.

[0112] It should be noted that the path length of each rescue path is obtained by summing the lengths of all directed edges in the rescue path; correspondingly, the cumulative sparsity of each rescue path is obtained by summing the sparsity of all nodes in the rescue path. The calculation process of the sparsity of each node is the same as the above-mentioned content, and will not be repeated here.

[0113] The formula for calculating the pre-constructed fitness function is as follows: In the formula: Fitness is the fitness level, D total For cumulative dose, L total Let S(v) be the path length. total For cumulative sparsity, ω1, ω2 and ω3 are adjustment factors greater than zero, ω1>ω2>ω3, and D0, L0 and S(v)0 are reference values ​​for cumulative dose, path length and cumulative sparsity, respectively.

[0114] a3: Selection: Two individuals with high fitness from the original population were selected as the father and mother using the roulette wheel method;

[0115] Roulette wheel selection is a commonly used selection method in genetic algorithms to select individuals with higher fitness to enter the next generation. It simulates the process of roulette, where each individual receives a corresponding "roulette" area based on its fitness. The higher the fitness of an individual, the larger its corresponding area and the higher its probability of being selected.

[0116] a4: Crossover: Perform a crossover operation on the parent and parent to produce new individuals;

[0117] It should be noted that the crossover operation between the parent and mother lines is based on the crossover operation, which includes, but is not limited to, one of the following: single-point crossover, uniform crossover, or sequential crossover.

[0118] a5: Mutation: Perform a mutation operation on the new individual to obtain W new individuals, combine the W new individuals into a new population, replace the original population with the new population, and return to step a2, where W is an integer greater than zero;

[0119] In genetic algorithms, mutation is used to introduce genetic diversity and prevent the algorithm from getting stuck in local optima; the mutation operation on new individuals is achieved through methods such as uniform mutation or Gaussian mutation.

[0120] a6: Repeat steps a2 to a5 above until the fitness of an individual in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold. Then, output the rescue path represented by the corresponding individual as the best rescue path.

[0121] For example: Assuming the maximum number of iterations is 100, after each iteration, record the individual with the highest fitness in the current population and its fitness value; if the fitness value does not change significantly in a certain generation, it is considered that the convergence condition has been met, the iteration stops, and the rescue path represented by the corresponding individual is output as the best rescue path;

[0122] The scientific design improved the accuracy and reliability of route planning. The solution is based on a directed graph constructed from a geographic map to accurately depict the spatial relationships between accident sources, rescue nodes and intermediate nodes. Combined with the analysis of node sparsity, the graph structure was optimized, reducing the computational complexity caused by redundant information, while retaining key network characteristics, laying the foundation for planning more efficient rescue routes.

[0123] Furthermore, this scheme incorporates a genetic algorithm to achieve dynamic optimization by comprehensively considering multiple factors such as path length, cumulative dose, and sparsity, effectively balancing the safety and time cost of the rescue path. In a dynamic environment, the system can adjust the graph structure and path planning results based on real-time data to ensure the adaptability and executability of the planning scheme, thereby providing more efficient support in sudden nuclear accident scenarios.

[0124] Overall, this approach not only improves the scientific rigor of emergency planning but also enhances the fault tolerance and adaptability in practical operations, providing robust and effective technical support for nuclear accident emergency response missions. This method demonstrates the systematic nature of the planning process and its high degree of alignment with actual needs, significantly optimizing the overall efficiency and safety of nuclear accident emergency operations.

[0125] Example 2

[0126] Please see Figure 2 As shown in the figure, this embodiment discloses a nuclear accident emergency response planning method based on multi-source heterogeneous data processing, the method comprising:

[0127] S201: Obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into one accident source node, multiple rescue nodes, and multiple intermediate nodes.

[0128] S202: Calculate the sparsity of each rescue node in the directed graph, select multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph.

[0129] S203: In the new directed graph, directed edges connecting the accident source node to each rescue node form multiple rescue paths;

[0130] S204: Use a pre-configured genetic algorithm to obtain the best rescue path from multiple rescue paths, and input the best rescue path as feedback.

[0131] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0132] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0139] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A nuclear accident emergency response planning system based on multi-source heterogeneous data processing, characterized in that, The system includes: The data processing module is used to obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into one accident source node, multiple rescue nodes and multiple intermediate nodes. The data update module is used to calculate the sparsity of each rescue node in the directed graph, filter out multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph. The simplification of the directed graph based on sparse rescue nodes includes: Mark all sparse rescue nodes in the directed graph; Remove all sparse rescue nodes from the directed graph and update the spatial relationships between the remaining nodes to obtain a new directed graph; The path generation module is used to connect directed edges from the accident source node to each rescue node in the new directed graph, forming multiple rescue paths; The action planning module is used to obtain the optimal rescue path from multiple rescue paths using a pre-configured genetic algorithm, and input the optimal rescue path for feedback; wherein, the genetic algorithm determines the fitness of each rescue path based on the cumulative dose, path length and cumulative sparsity.

2. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 1, characterized in that, The calculation of the sparsity of each rescue node in the directed graph includes: Traverse the directed graph and record each rescue node and the set of neighbors of each rescue node. The set of neighbors contains neighbor rescue nodes that are directly adjacent to the rescue node, and the number of neighbor rescue nodes is N(v). Obtain the number of directed edges E(v) that directly connect a rescue node to each of its neighboring rescue nodes. Substitute the number of neighboring rescue nodes N(v) and the number of directed edges E(v) into the pre-constructed calculation model of the local clustering coefficient C(v) to calculate the local clustering coefficient C(v) for each rescue node. The expression for the calculation model of the local clustering coefficient C(v) is as follows: ； In the formula: This represents a neighboring rescue node that is directly connected to the rescue node. This indicates that for each neighboring rescue node of a rescue node... Count them all once; Substitute the local clustering coefficient C(v) of the rescue nodes into the sparsity calculation formula to obtain the sparsity of each rescue node. ; Among them, the sparsity of each rescue node The calculation formula is as follows: ； In the formula: Represents the natural constant.

3. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 2, characterized in that, The selection of multiple sparse rescue nodes in the directed graph includes: sparsity Compared with the preset sparsity threshold Compare; like ≥ If so, the corresponding rescue node is determined to be a sparse rescue node; like < If so, the corresponding rescue node is determined to be a non-sparse rescue node; Statistics of all ≥ The rescue nodes were obtained, resulting in multiple sparse rescue nodes.

4. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 3, characterized in that, The process of selecting the optimal rescue route from multiple rescue routes includes: a1: Initialize the population: Generate an original population based on multiple rescue paths. The original population contains P individuals, each representing a rescue path, where P is a positive integer. a2: Fitness assessment: For each individual, obtain the cumulative dose, path length, and cumulative sparsity of each rescue path; input the cumulative dose, path length, and cumulative sparsity into a pre-constructed fitness function to calculate the fitness of each individual; a3: Selection: Two individuals with high fitness from the original population were selected as the father and mother using the roulette wheel method; a4: Crossover: Perform a crossover operation on the parent and parent to produce new individuals; a5: Mutation: Perform a mutation operation on the new individual to obtain W new individuals, combine the W new individuals into a new population, replace the original population with the new population, and return to step a2, where W is an integer greater than zero; a6: Repeat steps a2 to a5 above until the fitness of an individual in the original population or the new population is greater than or equal to the preset fitness threshold, or the number of iterations is greater than or equal to the preset maximum number of iterations threshold. Then, output the rescue path represented by the corresponding individual as the best rescue path.

5. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 4, characterized in that, The logic for obtaining the cumulative dose is as follows: When a nuclear accident occurs, acquire on-site environmental data of the nuclear accident site, as well as on-site accident data of the accident source nodes within the nuclear accident site; The on-site environmental data and on-site accident data are input into a preset Gaussian plume model to obtain the concentration distribution of radionuclides emitted by the nuclear accident. The concentration distribution refers to the concentration of radionuclides at each node in the directed graph. The basic data of the radionuclides are obtained, and the dose rate at each node in the rescue path is calculated based on the basic data and concentration distribution of the radionuclides, and the cumulative dose of the rescue path is calculated based on the dose rate.

6. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 5, characterized in that, The formula for calculating the dose rate is as follows: ； In the formula: The calculation formula is as follows: ； In the formula: Let be the source strength of the r-th radionuclide within the node; is the dose conversion factor for the r-th radionuclide; Let be the decay constant of the r-th radionuclide within the node, and its calculation formula is: In the formula: The half-life of a radioactive nuclide; Let r be the radiation particle release yield of the r-th radionuclide within the node; The formula for calculating the cumulative dose is as follows: ； In the formula: The path distance to a single node in the rescue route; Let be the dose rate of the i-th node; For length of stay; This represents the spatial distance to the i-th node on the path. The average speed of the personnel.

7. The nuclear accident emergency response planning system based on multi-source heterogeneous data processing according to claim 6, characterized in that, The formula for calculating the pre-constructed fitness function is as follows: In the formula: For fitness, For cumulative dose, For path length, To accumulate sparsity, , and An adjustment factor greater than zero. , , and These are reference values ​​for cumulative dose, path length, and cumulative sparsity, respectively.

8. A nuclear accident emergency response planning method based on multi-source heterogeneous data processing, characterized in that, It is implemented based on the nuclear accident emergency response planning system based on multi-source heterogeneous data processing as described in any one of claims 1 to 7, and the method includes: Obtain a geographic map of the nuclear accident site, and generate a directed graph to represent spatial relationships based on the geographic map and graph theory methods. The directed graph contains multiple directed edges and multiple nodes, and the nodes are divided into one accident source node, multiple rescue nodes, and multiple intermediate nodes. Calculate the sparsity of each rescue node in the directed graph, select multiple sparse rescue nodes in the directed graph based on the sparsity, and simplify the directed graph based on the sparse rescue nodes to form a new directed graph. In the new directed graph, directed edges connecting the accident source node to each rescue node form multiple rescue paths; The optimal rescue path is obtained from multiple rescue paths using a pre-configured genetic algorithm, and the optimal rescue path is input for feedback; wherein, the genetic algorithm determines the fitness of each rescue path based on the cumulative dose, path length and cumulative sparsity.

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