Urban emergency management simulation method and device, electronic equipment and storage medium

Abstract

The invention discloses a city emergency management simulation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring emergency knowledge and emergency data corresponding to an emergency task; determining an emergency role according to the emergency knowledge and the emergency data; constructing a nested execution pair of the emergency role; extracting a system demand of the emergency role according to the nested execution pair, and generating an emergency system analysis template according to the system demand; performing task decomposition on the emergency task by adopting the emergency system analysis template to obtain a task decomposition result; performing interactive logic modeling by adopting the task decomposition result to form a logic process; adopting the logic process to generate an Agent execution graph; and generating an executable simulation model of the Agent execution graph, and combining the executable simulation model with a cloud simulation model.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management, and in particular to a city emergency management simulation method, device, electronic equipment and storage medium. Background Art

[0002] In modern society, urban emergency response faces many challenges. Real-life emergency drills and actual combat simulations are often costly, expensive, and risky, and difficult to organize and implement; traditional map operations and sand table simulations are difficult to accurately grasp the details of emergency actions; relying solely on mathematical analytical calculations, it is difficult to fully describe the intelligent behavior of emergency systems and their components. The use of human-in-the-loop and real-device-in-the-loop simulation evaluation can realize emergency simulation of single equipment and small-scale teams, but the model system is complex and it is difficult to further expand to higher-level urban emergency response simulation functions. Computer simulation uses computers and special equipment to simulate the real emergency response process through simulation models. In the actual emergency response process, various emergency activities can be described by certain models. Through the study of these models, emergency response simulation using computer simulation as a means can reveal the basic laws of the emergency response process. In the information age, computer emergency simulation, as a basic method for studying emergency problems, has become a core way to evaluate emergency response capabilities and has received increasing attention.

[0003] However, in the urban emergency response, there is no application in computer simulation at present. This is because the reality factors are numerous and complex, and it is difficult for general simulation models to fully describe them. Since each city has its own unique features in terms of geography, population density, infrastructure, climate conditions, etc., the existing simulation models lack sufficient adaptability and are difficult to effectively adjust and apply according to the specific characteristics of the city. Each city has its own particularity, which makes it difficult to directly apply the simulation model. In addition, urban emergency response requires the support of real-time data, such as traffic flow, weather conditions, public resource status, etc. However, the current simulation model is difficult to integrate and process these dynamic data in real time, affecting the accuracy and practicality of the simulation. Urban emergency response involves the collaboration of multiple departments, such as fire protection, medical care, and traffic management. The existing simulation system is difficult to effectively simulate and coordinate the interaction between these departments, resulting in the limited credibility and application effect of the simulation results. At present, there is a lack of stable and reliable urban emergency response simulation calculation methods that can accurately describe and comprehensively reflect urban emergency response. Summary of the invention

[0004] The present invention provides a city emergency management simulation method, device, electronic device and storage medium, which are used to solve the technical problem that the existing simulation system is difficult to effectively simulate and coordinate the interaction between departments, resulting in the limited credibility and application effect of the simulation results.

[0005] The present invention provides a city emergency management simulation method, comprising:

[0006] Acquire emergency knowledge and emergency data corresponding to emergency tasks;

[0007] Determining an emergency role according to the emergency knowledge and the emergency data;

[0008] constructing a nested execution pair of the emergency roles;

[0009] Extracting the system requirements of the emergency role according to the nested execution, and generating an emergency system analysis template according to the system requirements;

[0010] Decomposing the emergency task by using the emergency system analysis template to obtain a task decomposition result;

[0011] Using the task decomposition results to perform interactive logic modeling to form a logical process;

[0012] Generate an Agent execution graph using the logic flow;

[0013] An executable simulation model of the Agent execution graph is generated, and the executable simulation model is combined with a cloud simulation model.

[0014] Optionally, the step of determining the emergency role according to the emergency knowledge and the emergency data includes:

[0015] Using a natural language processing algorithm to parse the emergency knowledge and the emergency data to obtain parsed data;

[0016] Converting the parsed data into structured data;

[0017] Cluster analysis is performed on the structured data, and the emergency role corresponding to each clustering result is determined.

[0018] Optionally, the emergency knowledge includes emergency command logic, emergency command rules and emergency confrontation experience; the emergency data includes emergency history data, constraint rule data and emergency simulation data.

[0019] Optionally, the step of extracting the system requirements of the emergency role according to the nested execution and generating an emergency system analysis template according to the system requirements includes:

[0020] Extracting system requirements of each emergency role according to the interaction relationship between the emergency roles in the nested execution pair;

[0021] Obtaining task decomposition information corresponding to the emergency task;

[0022] An emergency system analysis template is generated using the emergency role, the system requirements and the task decomposition information.

[0023] Optionally, the step of using the emergency system analysis template to decompose the emergency task to obtain a task decomposition result includes:

[0024] Using the task decomposition information, the emergency task is decomposed into a spatial domain to obtain a regional scheduling decomposition result;

[0025] The task decomposition information is used to perform time domain task decomposition on the emergency task to obtain a time scheduling task decomposition result.

[0026] Optionally, the step of using the task decomposition result to perform interactive logic modeling to form a logic flow includes:

[0027] Determine the emergency entity according to the task decomposition result;

[0028] Determine the interaction between emergency entities;

[0029] Interaction logic modeling is performed according to the interaction relationship to form a logical process.

[0030] Optionally, the step of generating an Agent execution graph using the logic flow includes:

[0031] The logic flow is used to obtain the actions and interaction information of each emergency role;

[0032] The actions and interaction information are used to generate a single agent execution graph of each emergency role;

[0033] The actions and interaction information of multiple emergency roles are used to generate a multi-agent execution graph.

[0034] The present invention also provides a city emergency management simulation device, comprising:

[0035] An emergency knowledge and emergency data acquisition module is used to acquire emergency knowledge and emergency data corresponding to emergency tasks;

[0036] An emergency role determination module, used to determine the emergency role according to the emergency knowledge and the emergency data;

[0037] A nested execution pair construction module, used to construct a nested execution pair of the emergency role;

[0038] An emergency system analysis template generation module, used for extracting the system requirements of the emergency role according to the nested execution, and generating an emergency system analysis template according to the system requirements;

[0039] A task decomposition module, used to decompose the emergency task using the emergency system analysis template to obtain a task decomposition result;

[0040] A logic flow generation module, used to use the task decomposition results to perform interactive logic modeling to form a logic flow;

[0041] An Agent execution graph generation module, used to generate an Agent execution graph using the logic flow;

[0042] The combining module is used to generate an executable simulation model of the Agent execution graph and combine the executable simulation model with the cloud simulation model.

[0043] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0044] The memory is used to store program code and transmit the program code to the processor;

[0045] The processor is used to execute the urban emergency management simulation method as described in any one of the above items according to the instructions in the program code.

[0046] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the urban emergency management simulation method as described in any one of the above items.

[0047] It can be seen from the above technical scheme that the present invention has the following advantages: firstly, the present invention constructs the emergency system by proposing emergency roles and nested execution pairs, which can accurately identify and define the key roles and behaviors in the emergency scenario, improve the accuracy and adaptability of the emergency system model, and enable it to better reflect the complex situations and role interactions in the actual emergency handling; then, based on the system requirement extraction and the generation of the emergency system analysis template of the nested execution pairs, it is helpful to systematically and structuredly analyze the emergency requirements, ensure the comprehensiveness and accuracy of the emergency system requirements, and increase the planning and design capabilities of the emergency simulation system; then, the emergency tasks are decomposed, which can refine and clarify each The specific content and execution sequence of the emergency task are analyzed; the operability and execution efficiency of the emergency task are improved; then, interactive logic modeling is carried out based on the task decomposition results to form a logical process, and an Agent execution diagram is generated according to the logical process, which can intuitively display the execution process and interactive relationship of each Agent, improve the visualization and usability of the model, and facilitate the verification and optimization of the model; finally, the Agent is compiled into an executable simulation model, which is dynamically combined with the cloud simulation model to realize the automatic generation and deployment of the simulation model, enhance the flexibility and scalability of the system, and improve the development efficiency and operation performance of the simulation model, so that it can respond to actual emergency needs more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 A flowchart of a method for simulating urban emergency management provided by an embodiment of the present invention;

[0050] Figure 2 A structural block diagram of a city emergency management simulation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention provide a city emergency management simulation method, device, electronic device and storage medium, which are used to solve the technical problem that the existing simulation system is difficult to effectively simulate and coordinate the interaction between departments, resulting in the limited credibility and application effect of the simulation results.

[0052] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below are only 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.

[0053] See also Figure 1 , Figure 1 A flowchart of the steps of a city emergency management simulation method provided by an embodiment of the present invention.

[0054] The present invention provides a method for simulating urban emergency management, which may specifically include the following steps:

[0055] Step 101, acquiring emergency knowledge and emergency data corresponding to the emergency task;

[0056] In the embodiment of the present invention, emergency knowledge and emergency data can be extracted from the emergency management database, wherein the emergency knowledge may include emergency command logic, emergency command rules and emergency confrontation experience, and the emergency data may include emergency history data, constraint rule data and emergency simulation data.

[0057] Step 102, determining the emergency role based on emergency knowledge and emergency data;

[0058] After acquiring emergency knowledge and emergency data, the emergency role can be determined based on the emergency knowledge and emergency data.

[0059] In one example, step 102 may include the following sub-steps:

[0060] S21, using a natural language processing algorithm to parse emergency knowledge and emergency data to obtain parsed data;

[0061] S22, converting the parsed data into structured data;

[0062] S23, performing cluster analysis on the structured data, and determining the emergency role corresponding to each clustering result.

[0063] In specific implementations, natural language processing (NLP) algorithms can be used to automatically parse emergency knowledge (such as fire extinguishing strategies, emergency command rules, and emergency confrontation experience) and convert them into structured data. Cluster analysis of historical emergency data based on machine learning models can automatically identify and define emergency roles. Taking fire extinguishing events as an example, the roles of the fire brigade, medical team, and traffic management department can be automatically defined based on historical data by using cluster analysis algorithms.

[0064] Step 103, constructing a nested execution pair of emergency roles;

[0065] In the embodiment of the present invention, the nested execution pair of emergency roles includes emergency roles and their corresponding responsibilities. For example:

[0066] Emergency roles: Fire brigade, responsible for extinguishing fires. Medical team, responsible for treating the wounded. Traffic management department, responsible for traffic diversion.

[0067] Step 104, extracting system requirements of emergency roles according to the nested execution, and generating an emergency system analysis template according to the system requirements;

[0068] After obtaining the nested execution pairs, the system requirements of the emergency role can be extracted according to the nested execution pairs, and an emergency system analysis template can be generated according to the system requirements.

[0069] In one example, step 104 may include the following sub-steps:

[0070] S41, extracting system requirements of each emergency role according to the interaction relationship between the emergency roles in the nested execution pair;

[0071] S42, obtaining task decomposition information corresponding to the emergency task;

[0072] S43, generates an emergency system analysis template using emergency roles, system requirements, and task decomposition information.

[0073] In the embodiment of the present invention, the role relationship and interaction logic in the nested execution pair can be analyzed to extract the specific requirements of each role, such as the fire truck dispatching requirements, medical resource allocation requirements and traffic diversion requirements. Then, an emergency system analysis template containing task decomposition information and information required for interaction logic modeling is generated.

[0074] In the specific implementation, data mining technology can be used to extract system requirements from emergency roles and nested execution pairs, and then deep learning algorithms can be applied to predict and optimize the demand data to generate an emergency system analysis template.

[0075] Step 105, using the emergency system analysis template to decompose the emergency task and obtain a task decomposition result;

[0076] In the embodiment of the present invention, the emergency tasks are decomposed, so that the specific content and execution order of each emergency task can be refined and clarified, thereby improving the operability and execution efficiency of the emergency tasks.

[0077] In one example, step 105 may include the following sub-steps:

[0078] S51, using the task decomposition information to perform spatial domain task decomposition on the emergency task to obtain a regional scheduling decomposition result;

[0079] S52, using the task decomposition information to perform time domain task decomposition on the emergency task to obtain a time scheduling task decomposition result.

[0080] In the specific implementation, task decomposition can include spatial domain task decomposition and temporal domain task decomposition. Among them, spatial domain task decomposition can be performed based on the strategic game method to ensure the integrity of tasks in each area. Temporal domain task decomposition can be performed based on the task hierarchy network to ensure the timeliness and priority of tasks. Finally, the task decomposition results of spatial domain and temporal domain are integrated to form a complete task decomposition result.

[0081] In another embodiment, optimization algorithms such as genetic algorithms and ant colony algorithms may be used to decompose tasks in the space domain and time domain to ensure the integrity and timeliness of the tasks.

[0082] Step 106, using the task decomposition results to perform interactive logic modeling to form a logical process;

[0083] In the embodiment of the present invention, the task decomposition result can be used to perform interactive logic modeling to establish the interactive relationship between the emergency roles and form a logical process for subsequent generation of the Agent execution graph conceptual model to ensure the effectiveness of the interaction between the emergency roles.

[0084] In one example, step 106 may include the following sub-steps:

[0085] S61, determine the emergency entity based on the task decomposition results;

[0086] S62, determining the interaction relationship between various emergency entities;

[0087] S63, performing interactive logic modeling according to the interactive relationship to form a logical process.

[0088] In the specific implementation, the key variables and relationships in task execution can be determined based on statistical analysis, and then the Bayesian network and Markov Decision Process (MDP) can be applied. Then, based on the key variables and relationships, a distributed computing platform can be used to perform large-scale emergency entity interaction logic modeling. Then, based on a custom symbolic rule system, a semantic representation is constructed to form a logical process for emergency event management.

[0089] Step 107, using the logic flow to generate an Agent execution graph;

[0090] After obtaining the logical process, the logical process can be used to generate an Agent execution graph. The Agent execution graph includes a single-agent execution graph and a multi-agent execution graph. The specific steps may include:

[0091] S71, using a logical process to obtain the actions and interaction information of each emergency role;

[0092] S72, using the action and interaction information to generate a single agent execution graph of each emergency role;

[0093] S73, generating a multi-agent execution graph using the actions and interaction information of multiple emergency roles.

[0094] In the specific implementation, the single-agent execution graph is used to describe the actions and interactions of a single emergency entity, while the multi-agent execution graph is used to describe the coordinated actions and interactions of multiple emergency entities.

[0095] The Agent execution graph includes attribute graph and process graph. Attribute graph is used to describe the static attributes of emergency entities, such as roles and resources. Process graph is used to describe the dynamic behaviors of emergency entities, such as actions and decisions.

[0096] In one example, the agent attribute graph can be set based on psychological attributes, thinking attributes, and emergency command and dispatch algorithms. A diagram of the action optimization strategy design process based on behavior modeling, reasoning and planning mechanisms, dynamic strategy optimization, and deep reinforcement learning based on generative adversarial networks.

[0097] Furthermore, for the design of multi-agent execution graph algorithm, it is necessary to complete two aspects of work: the design of multi-agent execution behavior rule intelligent generation algorithm and the design of multi-agent execution behavior interaction protocol algorithm. The former is the basis for the implementation of the latter. Through these two aspects of work, the multi-agent execution process graph interaction mechanism is realized to express the interactive actions of multiple emergency entities to perform emergency tasks.

[0098] The design of the intelligent generation algorithm for multi-agent execution behavior rules includes: multi-agent execution behavior prediction and classification based on convolutional neural networks; multi-agent execution behavior rule selection based on deep reinforcement learning; generation of optimal behavior rules and reward and punishment feedback. Behavior prediction and classification: Use convolutional neural networks to predict and classify multi-agent execution behaviors. Behavior rule selection: Select multi-agent execution behavior rules based on DRL. Reward and punishment feedback: Generate the optimal behavior rules and optimize them through reward and punishment mechanisms.

[0099] Furthermore, the design of the multi-agent execution behavior interaction collaborative algorithm includes: modeling the multi-agent execution interaction protocol based on the improved contract network; modeling the interaction behavior based on the Markov process; and processing the task sequence and interaction task by expanding the communication primitives and communication mechanisms. Modeling the multi-agent execution interaction protocol based on the improved contract network, modeling the interaction behavior based on the Markov process, and processing the task sequence and interaction task by expanding the communication primitives and communication mechanisms.

[0100] Step 108: Generate an executable simulation model of the Agent execution graph, and combine the executable simulation model with the cloud simulation model.

[0101] After completing the construction of the Agent execution graph, the logic and behavior logic of the Agent execution graph can be dynamically tested to generate an executable simulation model.

[0102] Among them, the logic and behavior logic of the Agent execution graph are dynamically checked to ensure its correctness.

[0103] The executable simulation model can be run on the simulation platform, and in actual emergency management, the emergency strategy can be optimized through simulation results.

[0104] The invention firstly constructs an emergency system by raising emergency roles and nested execution pairs, which can accurately identify and define key roles and behaviors in emergency scenarios, improve the accuracy and adaptability of the emergency system model, and enable it to better reflect the complex situations and role interactions in actual emergency handling; then, based on the system requirement extraction and generation of the emergency system analysis template of the nested execution pairs, it is helpful to systematically and structuredly analyze the emergency requirements, ensure the comprehensiveness and accuracy of the emergency system requirements, and increase the planning and design capabilities of the emergency simulation system; then, the emergency tasks are decomposed, and the specific content and execution sequence of each emergency task can be refined and clarified; the operability and execution efficiency of the emergency tasks are improved; then, based on the task decomposition results, interactive logic modeling is performed to form a logical process, and an Agent execution diagram is generated according to the logical process, which can intuitively display the execution process and interactive relationship of each Agent, improve the visualization and ease of use of the model, and facilitate the verification and optimization of the model; finally, the Agent is compiled into an executable simulation model, which is dynamically combined with the cloud simulation model to realize the automatic generation and deployment of the simulation model, enhance the flexibility and scalability of the system, improve the development efficiency and operation performance of the simulation model, and enable it to respond to actual emergency needs more quickly.

[0105] For ease of understanding, let's take a large-scale fire in a city as an example and explain the need for emergency management:

[0106] 1. Define emergency problems: extract emergency roles such as fire brigades, medical teams, and traffic management departments, and analyze historical fire data, extinguishing strategies, and rescue rules.

[0107] Data reception and processing: Extract emergency knowledge and emergency data from the emergency management database. Emergency knowledge: fire extinguishing strategies, emergency command rules, emergency confrontation experience. Emergency data: historical fire data, traffic rules data, emergency simulation data.

[0108] Data processing: Emergency knowledge analysis: convert fire extinguishing strategies, emergency command rules and emergency confrontation experience into structured data. Emergency data analysis: analyze historical fire data to determine the frequency, cause and treatment of fires. Role determination: determine emergency roles based on data, including fire brigade, medical team and traffic management department.

[0109] Emergency roles: Fire brigade, responsible for extinguishing fires. Medical team, responsible for treating the wounded. Traffic management department, responsible for traffic diversion.

[0110] 2. Propose emergency system requirements: Generate an emergency system analysis template, including fire truck dispatch, medical resource allocation and traffic diversion requirements.

[0111] Requirements extraction: Analyze the role relationships and interaction logic in the nested execution pairs. Extract the specific requirements of each role: Fire brigade: 10 fire trucks and high-pressure water guns are needed. Medical team: 20 emergency personnel, emergency medicines and stretchers are needed. Traffic management department: The roads around the fire scene need to be closed and diversion routes need to be set up.

[0112] Generate analysis template: Based on the above requirements, generate an emergency system analysis template.

[0113] 3. Task decomposition: Using the emergency system analysis template, regional scheduling task decomposition is performed based on strategic game, and time scheduling task decomposition is performed based on the task hierarchy network.

[0114] Spatial domain task decomposition: Fire brigade: assigned to different areas to extinguish fires, ensuring coverage of the entire fire area. Medical team: assigned to areas where the wounded are concentrated, ensuring rapid treatment. Traffic management department: plan the closure and diversion tasks of different roads to ensure smooth roads.

[0115] Time domain task decomposition: Fire brigade: first control the spread of the fire, then gradually put it out. Medical team: first rescue the seriously injured, then deal with the lightly injured. Traffic management department: first close the main roads, then set up diversion routes.

[0116] For example: Fire brigade: first control the fire in area A, then gradually put out the fire in area B. Medical team: first rescue the seriously injured in area A, then deal with the lightly injured in area B. Traffic management department: first close the main road X, then set up diversion route Y.

[0117] 4. Interaction logic modeling:

[0118] Variable and relationship judgment:

[0119] Fire brigade and command center: vehicle location, fire extinguishing progress.

[0120] Medical teams and hospitals: number of casualties and allocation of medical resources.

[0121] Traffic management departments and citizens: traffic conditions and diversion routes.

[0122] Emergency entity interaction logic modeling:

[0123] Fire brigade and medical team: The progress of fire extinguishing affects the treatment of the wounded.

[0124] Traffic management department and fire department: Traffic diversion affects the passage of fire trucks.

[0125] Build semantic representation: form a logical process based on a custom symbolic rule system.

[0126] For example: Fire brigade and command center: After the fire truck arrives at the fire scene, it reports the progress of fire fighting in real time. Medical team and hospital: Real-time report on the condition of the injured and dispatch emergency resources. Traffic management department and citizens: Release real-time traffic information to guide citizens to avoid the fire area.

[0127] 5. Generate execution diagrams for the fire brigade, medical team and traffic management department, including attribute diagrams and process diagrams.

[0128] Based on the logical process, single-agent execution graph and multi-agent execution graph are generated.

[0129] Single-Agent Execution Graph: describes the actions and interactions of a single emergency entity.

[0130] Multi-agent execution graph: describes the coordinated actions and interactions of multiple emergency entities.

[0131] For example: Single Agent execution graph: describes the path planning and fire-fighting actions of fire truck A.

[0132] Multi-agent execution diagram: describes the collaborative actions of fire truck A and medical team B to ensure the coordination of fire extinguishing and wounded treatment.

[0133] Attribute graph and process graph generation: Extract basic information of emergency entities and their actions and interactions to generate attribute graphs and process graphs.

[0134] Attribute graph: describes the static attributes of emergency entities, such as roles and resources.

[0135] Process diagram: describes the dynamic behavior of emergency entities, such as actions and decisions.

[0136] Design Agent execution attribute graph: Based on psychological attributes, thinking attributes and emergency command and dispatch algorithm modules. Design Agent execution process graph: Based on behavior modeling, reasoning and planning mechanism, dynamic strategy optimization and action optimization strategy of deep reinforcement learning based on generative adversarial network.

[0137] Specific implementation: Generate agent execution graphs, including attribute graphs and process graphs, using generative adversarial networks (GAN) and deep reinforcement learning (DRL) algorithms. Generate and optimize execution graphs using cloud computing platforms. In this embodiment, GAN and DRL algorithms are used to generate single agent execution graphs for fire truck path planning and firefighting actions.

[0138] 6. Dynamically check and compile the execution graph to generate an executable simulation model and integrate it with the cloud simulation model.

[0139] Dynamic verification: Dynamically verify the logic and behavior logic of the Agent execution graph to ensure its correctness.

[0140] Load model code embedding: embed the load model code of the Agent general structure into the Agent execution graph.

[0141] Code compilation: compile Agent code to generate an executable simulation model. Simulation model collection: dynamically combine the executable simulation model with the cloud simulation model to achieve distributed simulation.

[0142] For example: Fire truck simulation model: simulates the action path and fire-fighting process of fire trucks. Medical team simulation model: simulates the medical team's treatment of the wounded and resource dispatch. Traffic management simulation model: simulates traffic diversion and citizen evacuation process.

[0143] Specific implementation:

[0144] Automatic code generation algorithms and model verification algorithms are applied to code conversion and dynamic verification of Agent execution graphs. Distributed simulation is performed using a cloud simulation platform to ensure real-time and high efficiency. The Agent execution graph is converted into an executable simulation model using an automatic code generation algorithm and run on a cloud simulation platform to simulate the coordinated emergency response of the fire brigade, medical team, and traffic management department.

[0145] Through the above steps, the fire emergency management simulation is completed. The simulation model is run on the simulation platform to simulate the actual emergency process, and the emergency strategy is optimized according to the simulation results to improve the efficiency and accuracy of emergency response.

[0146] See also Figure 2 , Figure 2 A structural block diagram of a city emergency management simulation device provided in an embodiment of the present invention.

[0147] An embodiment of the present invention provides a city emergency management simulation device, comprising:

[0148] The emergency knowledge and emergency data acquisition module 201 is used to acquire the emergency knowledge and emergency data corresponding to the emergency task;

[0149] An emergency role determination module 202, for determining an emergency role based on emergency knowledge and emergency data;

[0150] A nested execution pair construction module 203, used to construct a nested execution pair of an emergency role;

[0151] The emergency system analysis template generation module 204 is used to extract the system requirements of the emergency role according to the nested execution, and generate the emergency system analysis template according to the system requirements;

[0152] The task decomposition module 205 is used to decompose the emergency task using the emergency system analysis template to obtain the task decomposition result;

[0153] A logic flow generation module 206 is used to perform interactive logic modeling using the task decomposition results to form a logic flow;

[0154] Agent execution graph generation module 207, used to generate Agent execution graph using logic flow;

[0155] The combining module 208 is used to generate an executable simulation model of the Agent execution graph and combine the executable simulation model with the cloud simulation model.

[0156] In the embodiment of the present invention, the emergency role determination module 202 includes:

[0157] The parsing submodule is used to parse emergency knowledge and emergency data using a natural language processing algorithm to obtain parsed data;

[0158] The structured data conversion submodule is used to convert the parsed data into structured data;

[0159] The emergency role determination submodule is used to perform cluster analysis on structured data and determine the emergency role corresponding to each clustering result.

[0160] In the embodiment of the present invention, the emergency knowledge includes emergency command logic, emergency command rules and emergency confrontation experience; the emergency data includes emergency history data, constraint rule data and emergency simulation data.

[0161] In the embodiment of the present invention, the emergency system analysis template generation module 204 includes:

[0162] A system requirement extraction submodule is used to extract the system requirements of each emergency role according to the interaction relationship between the emergency roles in the nested execution pair;

[0163] The task decomposition information acquisition submodule is used to obtain the task decomposition information corresponding to the emergency task;

[0164] The emergency system analysis module generates a submodule, which is used to generate an emergency system analysis template using emergency roles, system requirements and task decomposition information.

[0165] In the embodiment of the present invention, the task decomposition module 205 includes:

[0166] The spatial domain task decomposition submodule is used to perform spatial domain task decomposition on emergency tasks using task decomposition information to obtain regional scheduling decomposition results;

[0167] The time domain task decomposition submodule is used to use the task decomposition information to perform time domain task decomposition on the emergency task and obtain the time scheduling task decomposition result.

[0168] In the embodiment of the present invention, the logic flow generation module 206 includes:

[0169] The emergency entity determination submodule is used to determine the emergency entity according to the task decomposition result;

[0170] The interaction relationship determination submodule is used to determine the interaction relationship between various emergency entities;

[0171] The logic flow forming submodule is used to perform interaction logic modeling according to the interaction relationship to form a logic flow.

[0172] In the embodiment of the present invention, the Agent execution graph generation module 207 includes:

[0173] The action and interaction information acquisition submodule is used to acquire the action and interaction information of each emergency role using a logical process;

[0174] The single agent execution graph generation submodule is used to generate the single agent execution graph of each emergency role using action and interaction information;

[0175] The multi-agent execution graph generation submodule is used to generate a multi-agent execution graph using the actions and interaction information of multiple emergency roles.

[0176] An embodiment of the present invention further provides an electronic device, characterized in that the device includes a processor and a memory:

[0177] The memory is used to store the program code and transmit the program code to the processor;

[0178] The processor is used to execute the urban emergency management simulation method of the embodiment of the present invention according to the instructions in the program code.

[0179] An embodiment of the present invention further provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store program code, and the program code is used to execute the urban emergency management simulation method of the embodiment of the present invention.

[0180] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0181] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0182] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0183] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable terminal device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0186] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0187] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0188] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A city emergency management simulation method, characterized in that: include: Acquire emergency knowledge and emergency data corresponding to emergency tasks; Determining an emergency role according to the emergency knowledge and the emergency data; constructing a nested execution pair of the emergency roles; Extracting the system requirements of the emergency role according to the nested execution, and generating an emergency system analysis template according to the system requirements; Decomposing the emergency task by using the emergency system analysis template to obtain a task decomposition result; Using the task decomposition results to perform interactive logic modeling to form a logical process; Generate an Agent execution graph using the logic flow; An executable simulation model of the Agent execution graph is generated, and the executable simulation model is combined with a cloud simulation model.

2. The method according to claim 1, characterized in that The step of determining the emergency role according to the emergency knowledge and the emergency data comprises: Using a natural language processing algorithm to parse the emergency knowledge and the emergency data to obtain parsed data; Converting the parsed data into structured data; Cluster analysis is performed on the structured data, and the emergency role corresponding to each clustering result is determined.

3. The method according to claim 1, characterized in that The emergency knowledge includes emergency command logic, emergency command rules and emergency confrontation experience; the emergency data includes emergency history data, constraint rule data and emergency simulation data.

4. The method according to claim 1, characterized in that The step of extracting the system requirements of the emergency role according to the nested execution and generating an emergency system analysis template according to the system requirements includes: Extracting system requirements of each emergency role according to the interaction relationship between the emergency roles in the nested execution pair; Obtaining task decomposition information corresponding to the emergency task; An emergency system analysis template is generated using the emergency role, the system requirements and the task decomposition information.

5. The method according to claim 4, characterized in that The step of using the emergency system analysis template to decompose the emergency task to obtain a task decomposition result includes: Using the task decomposition information, the emergency task is decomposed into a spatial domain to obtain a regional scheduling decomposition result; The task decomposition information is used to perform time domain task decomposition on the emergency task to obtain a time scheduling task decomposition result.

6. The method according to claim 1, characterized in that The step of using the task decomposition result to perform interactive logic modeling to form a logic flow includes: Determine the emergency entity according to the task decomposition result; Determine the interaction between emergency entities; Interaction logic modeling is performed according to the interaction relationship to form a logical process.

7. The method according to claim 1, characterized in that The step of using the logic flow to generate an Agent execution graph includes: The logic flow is used to obtain the actions and interaction information of each emergency role; The actions and interaction information are used to generate a single agent execution graph of each emergency role; The actions and interaction information of multiple emergency roles are used to generate a multi-agent execution graph.

8. A city emergency management simulation device, characterized in that: include: An emergency knowledge and emergency data acquisition module is used to acquire emergency knowledge and emergency data corresponding to emergency tasks; An emergency role determination module, used to determine the emergency role according to the emergency knowledge and the emergency data; A nested execution pair construction module, used to construct a nested execution pair of the emergency role; An emergency system analysis template generation module, used for extracting the system requirements of the emergency role according to the nested execution, and generating an emergency system analysis template according to the system requirements; A task decomposition module, used to decompose the emergency task using the emergency system analysis template to obtain a task decomposition result; A logic flow generation module, used to use the task decomposition results to perform interactive logic modeling to form a logic flow; An Agent execution graph generation module, used to generate an Agent execution graph using the logic flow; The combining module is used to generate an executable simulation model of the Agent execution graph and combine the executable simulation model with the cloud simulation model.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the urban emergency management simulation method described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the urban emergency management simulation method described in any one of claims 1-7.

Images