An electric power emergency command simulation and deduction method and system

Abstract

The application relates to the fields of power emergency technology and command and control technology, and discloses a power emergency command simulation deduction method and system, which comprises the following steps: extracting an initial strategy of emergency command and a scenario assumption, and analyzing and generating an executable simulation script; after the script is loaded, the development process of a disaster is gradually deduced by driving simulation in discrete time steps; the state update, task progress and environmental change of each entity are calculated in parallel in each step; when a preset condition is triggered, a rescheduling request is generated, an adjustment instruction is generated by calling an optimization rule, a task sequence is updated, and then the process is continued, and finally a simulation result data packet is generated; index data in each dimension is extracted from the data packet, an evaluation weight scheme is called, weighted summation is calculated, and a strategy comprehensive score is obtained; and the initial strategy of emergency command and the scenario assumption are simulated and deduced, so that the problem that a traditional technology cannot review historical event emergency command strategies and evaluate expected strategies in the face of disaster emergency disposal is solved.

Description

Technical Field

[0001] This invention relates to the fields of power emergency technology and command and control technology, specifically to a power emergency command simulation method and system. Background Technology

[0002] Due to global climate change, extreme disasters are becoming more frequent and severe, posing a serious threat to power grid systems and causing extreme scenarios such as power outages, grid disruptions, and blackouts, significantly impacting people's lives and livelihoods. Emergency response to power grid disasters requires substantial human and material resources, especially for extreme disasters. Managing and dispatching these massive emergency resources often presents challenges, including initial downtime, mid-stage chaos, and a last-minute rush to complete tasks. Current technical solutions primarily focus on disaster impact analysis and emergency resource planning, lacking robust technical support for evaluating and optimizing emergency command strategies. Summary of the Invention

[0003] This invention provides a power emergency command simulation method and system to solve the problem that traditional technologies cannot review historical emergency command strategies and evaluate anticipated strategies in disaster emergency response.

[0004] The technical solution proposed in this invention is as follows: In a first aspect, the present invention provides a power emergency command simulation method, the power emergency command simulation method comprising: Extract the initial strategy and scenario of emergency command, parse and process the initial strategy and scenario to generate an executable simulation script file, the simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model; The simulation script file is loaded, and the simulation calculation is driven by discrete time steps to gradually deduce the development process of power grid disaster events. In each time step, the state updates, task execution progress and dynamic changes of environmental parameters of multiple simulation entity models are calculated in parallel. When a preset condition is triggered, a rescheduling request is generated. Based on the current simulation state, optimization rules are invoked to generate adjustment instructions. Based on the adjustment instructions, the task sequence of the corresponding simulation entity model is updated and the simulation continues to advance. Simulation result data packets are generated. The indicator data for each dimension is extracted from the simulation result data package, and the evaluation weight scheme is called to perform weighted summation of the indicators for each dimension to calculate the overall strategy score.

[0005] In one optional implementation, the initial strategy and scenario of emergency command are extracted, the initial strategy and scenario are parsed and processed to generate an executable simulation script file, the simulation script file containing initial state data of the simulation environment model, simulation entity model and simulation task model, including: The hypothetical scenario and the initial strategy are constructed based on historical disaster events, or the hypothetical scenario and the initial strategy are constructed using parameterized templates. Based on the simulation objectives, the hypothetical scenario and the initial strategy are extracted from the construction results. The scenario and initial strategy data are converted into a simulation script file that can be executed by the simulation platform using a standardized parser.

[0006] In one optional implementation, when a preset condition is triggered, a rescheduling request is generated, an adjustment instruction is generated by invoking optimization rules according to the current simulation state, the task sequence of the corresponding simulation entity model is updated according to the adjustment instruction and the simulation continues, and a simulation result data packet is generated, including: During the simulation process, key state changes are captured in real time; When a preset condition is triggered, a rescheduling request is generated, and an adjustment instruction is generated by calling the optimization rules based on the current simulation state. The task sequence of the corresponding simulation entity model is updated according to the adjustment instructions, and the simulation continues until the simulation reaches the preset end time or all emergency repair tasks are completed, generating a simulation result data package.

[0007] In one optional implementation, indicator data for each dimension is extracted from the simulation result data package, and an evaluation weighting scheme is invoked to perform a weighted summation of the indicators for each dimension to calculate the overall strategy score, including: The simulation result data package contains indicator data for each dimension, including timeliness data, economic data, safety data, and social impact data. The range transformation method is used to map the index data of each dimension to a preset interval to form a standardized index set; The evaluation weighting scheme is invoked, and the overall strategy score is calculated using the linear weighted summation method.

[0008] In one optional implementation, the indicator data for each dimension is extracted from the simulation result data package, including: Extract the recovery timestamps of key nodes from the simulation result data package, compare the recovery timestamps with the preset target time series to calculate the deviation rate, and obtain the timeliness data; Extract the cost of calling emergency resources and the economic loss caused by power outage during the simulation process, and sum the cost of calling resources and the economic loss to obtain the economic data; Extract the number of operations that violate safety procedures in the simulation and the probability of secondary disaster risks triggered by the environmental model. Statistically analyze the number of operations and the probability of secondary disaster risks, and calculate the safety deduction value through a preset risk matrix to obtain the safety data. The weighted classification results of the power outage areas are extracted, and the social impact index is calculated by combining the power outage duration to obtain the social impact data.

[0009] In one optional implementation, the power emergency command simulation method further includes: A three-dimensional model is constructed based on actual power grid equipment to obtain a simulation entity model; The execution logic of the emergency response task is written using the scripting language built into the simulation platform to obtain the simulation task model; Import environmental data using the scene editor to obtain a simulation environment model.

[0010] In an optional implementation, after loading the simulation script file, the power emergency command simulation method further includes: The simulation entity model, the simulation task model, and the simulation environment model are initialized.

[0011] Secondly, the present invention provides a power emergency command simulation system, the power emergency command simulation system comprising: The parsing module is used to extract the initial strategy and scenario of emergency command, parse the initial strategy and scenario and generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model. The processing module is used to load the simulation script file and drive the simulation calculation step by step to deduce the development process of the power grid disaster event. In each time step, it calculates the state update, task execution progress and dynamic changes of environmental parameters of multiple simulation entity models in parallel. The simulation module generates a rescheduling request when a preset condition is triggered, calls optimization rules to generate adjustment instructions based on the current simulation state, updates the task sequence of the corresponding simulation entity model according to the adjustment instructions and continues to advance the simulation, and generates a simulation result data packet. The calculation module extracts the indicator data of each dimension from the simulation result data package, calls the evaluation weight scheme to perform weighted summation of the indicators of each dimension, and calculates the comprehensive strategy score.

[0012] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the power emergency command simulation and deduction method of the first aspect or any corresponding embodiment described above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the power emergency command simulation method described in the first aspect or any corresponding embodiment thereof.

[0014] The technical solution provided by this invention has at least the following beneficial effects: The power emergency command simulation and deduction method and system provided in this invention, by simulating and deducing the initial strategies and hypothetical scenarios of emergency command, can provide scientific and quantifiable decision support for verifying emergency command strategies under power grid disaster scenarios, effectively filling the technical gap of lacking strategy simulation and deduction tools in relevant power grid disaster emergency command systems. The system possesses dual capabilities for historical event review and future scenario rehearsal. It can both reproduce and retrospectively analyze the emergency command process that has already occurred, and also perform deduction, evaluation, and effect prediction of anticipated strategy solutions. Therefore, it provides a complete methodological system and system platform support for the review, effect evaluation, and optimization and improvement of power grid disaster emergency command strategies, helping to comprehensively improve the scientificity and rationality of power grid disaster emergency command strategy formulation. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a power emergency command simulation method according to an embodiment of the present invention; Figure 2 This is a flowchart of the initial strategy setting process according to an embodiment of the present invention; Figure 3 This is a schematic editing flowchart of an embodiment of the present invention; Figure 4 This is a simulation and deduction flowchart of an embodiment of the present invention; Figure 5 This is a flowchart of the strategy evaluation process according to an embodiment of the present invention; Figure 6 This is a simulation model architecture diagram of an embodiment of the present invention; Figure 7 This is a structural block diagram of a power emergency command simulation system according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] According to an embodiment of the present invention, a power emergency command simulation method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] This embodiment provides a power emergency command simulation method, which can be used in power emergency command simulation systems. Figure 1This is a flowchart of a power emergency command simulation method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S11: Extract the initial strategy and scenario of emergency command, parse and process the initial strategy and scenario and generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model.

[0023] Specifically, the emergency simulation engine obtains the initial emergency command strategy and scenario, which are then standardized and parsed to generate an executable simulation script. Based on this, the system completes the simulation initialization and loads all model data and initial states. The engine automatically parses the "scenario + strategy" data to generate the executable simulation script, drives the synchronous evolution of multiple entities, tasks, and environments in a time-series manner, and uniformly schedules computing resources to complete the entire simulation process.

[0024] Step S12: Load the simulation script file and drive the simulation calculation to gradually deduce the development process of the power grid disaster event with discrete time steps. In each time step, the state update, task execution progress and dynamic changes of environmental parameters of multiple simulation entity models are calculated in parallel.

[0025] Specifically, the simulation control component schedules the entire simulation process using a discrete event-driven mechanism, gradually simulating the complete process of a power grid disaster event from its occurrence to its evolution. After loading the script, the simulation engine sequentially initializes the simulation environment model, the simulation entity model, and the simulation task model. Subsequently, the simulation control component drives the simulation calculation engine forward with discrete time steps. Within each time step, the engine calculates in parallel the state updates of all entities, the progress of task execution, and the dynamic changes in environmental conditions. The simulation time step can be set to 1 minute, and the dynamic changes in the environment are reflected in the decline in road traffic capacity caused by the continuous deterioration of weather conditions. This design enables the simulation system to not only accurately depict the continuous evolution of disaster events but also efficiently support the synchronous calculation of large-scale power grid entities and complex environmental models through parallel computing mechanisms.

[0026] Step S13: When the preset conditions are triggered, a rescheduling request is generated, an adjustment instruction is generated by calling the optimization rules according to the current simulation state, the task sequence of the corresponding simulation entity model is updated according to the adjustment instruction and the simulation continues to advance, and a simulation result data packet is generated.

[0027] Specifically, during the simulation, the monitoring agent captures key status changes in real time. Situations such as a substation completing repairs and restoring power, or a road section being disrupted due to a secondary disaster preventing the repair team from reaching it, are promptly captured. When predefined triggering conditions such as insufficient repair resources, task timeouts, or sudden secondary faults are activated, the simulation engine sends a rescheduling request to the strategy management system. The strategy management system, based on the current simulation state, invokes built-in heuristic rules or optimization algorithms to fine-tune resource allocation and repair priorities, generating adjustment instructions that are returned to the simulation engine. Upon receiving the adjustment instructions, the simulation engine updates the task queue of the corresponding entity and continues the simulation. Through this closed loop of simulation progression, status awareness, strategy fine-tuning, and simulation continuation, the system simulates the process of dynamically adjusting strategies based on on-site feedback in actual command, until the simulation reaches its preset end time or all repair tasks are completed. The entire simulation process generates a structured result data packet containing multi-dimensional information such as time, status, and events.

[0028] Step S14: Extract the indicator data of each dimension from the simulation result data package, call the evaluation weight scheme to perform weighted summation of the indicators of each dimension, and calculate the comprehensive strategy score.

[0029] Specifically, after receiving the simulation result data packet and emergency command strategy, the command strategy management process parses, cleans, and standardizes it. Then, it calls the evaluation scheme to read the corresponding evaluation indicator system and evaluation weight scheme, configures the indicator operators, retrieves the appropriate evaluation algorithm and report template, performs evaluation calculations, and generates an evaluation report. The evaluation dimensions include timeliness, economic efficiency, safety, and social impact. This process transforms the dispersed simulation output and strategy input into a unified, standardized data format, enabling subsequent evaluation stages to perform calculations based on standardized data, avoiding evaluation biases caused by inconsistent data formats. Simultaneously, through the flexible combination and configuration of preset evaluation indicator system weight schemes and indicator operators, multi-dimensional quantitative evaluation of different types of emergency strategies is achieved. The mechanism for calling evaluation algorithms and report templates ensures the standardization of the evaluation process and the consistency of result presentation. The final generated evaluation report provides commanders with clear feedback on the strategy's effectiveness.

[0030] In one optional implementation, step S11 specifically includes the following process: Step S111: Construct a hypothetical scenario and initial strategy based on historical disaster events, or construct a hypothetical scenario and initial strategy using a parameterized template based on a hypothetical scenario.

[0031] Specifically, Figure 2 This is a flowchart illustrating the initial strategy setting process according to an embodiment of the present invention. Figure 2As shown, the initial emergency command strategy for the simulation is set, which includes power restoration timing strategy, emergency repair timing strategy, resource allocation strategy, and emergency support strategy. Two initial strategy setting modes are available: First, strategy setting based on historical events, where the user selects a historical disaster event, and the system automatically imports real data such as emergency resource allocation records, power restoration timing, and repair and recovery times to quickly generate a command strategy. Second, hypothetical command strategy setting, where the user sets the initial strategy through manual configuration or parameterized templates, used for forward-looking strategy research and resource allocation optimization. The raw data from both sources undergoes three processing steps: information data extraction, data standardization, and data completion, before entering the strategy file construction stage and ultimately generating the initial strategy. The output strategy content is divided into four categories: power restoration timing, emergency repair timing, resource allocation, and emergency support. These four categories together constitute the complete initial command strategy set for the simulation, providing standardized strategy input for subsequent simulation calculations.

[0032] Figure 3 This is a hypothetical editing flowchart of an embodiment of the present invention. Figure 3 As shown, the initial conditions, boundary constraints, evolution rules, and start and end nodes for the simulation are set. Two scenario editing modes are available: First, scenario construction based on historical events. Users select historical disaster events, and the system automatically imports real data such as the event's timeline, disaster conditions, and power grid damage to quickly generate a scenario. Second, scenario construction based on hypothetical scenarios. Users manually configure or parameterize templates to set initial conditions such as disaster type, impact range, fault list, and available resources to construct virtual scenario scenarios for strategy simulation and optimization in extreme disaster scenarios. The raw data from both sources undergoes three processing steps: information data extraction, data standardization, and data completion, before entering the scenario file construction stage and ultimately generating the initial simulation state. The output target data after processing is divided into four categories: equipment status data, emergency resource data, disaster environment data, and user status data. These four types of data together constitute a complete set of initial conditions for the simulation, providing standardized input for subsequent simulation calculations.

[0033] In this embodiment, the scenario supports the creation of full-scenario scenarios based on historical disaster cases and extreme disaster predictions, and has functions such as historical case scenario extraction, personalized scenario preset, and scenario file management. It can also directly import public meteorological data and power grid enterprise production control system data to update meteorological and power grid fault data in real time, realize dynamic updating of simulation scenarios, and use them for online simulation during disasters, rather than offline preset scenarios.

[0034] The initial strategy supports the setting of full-element strategies based on historical disaster cases and extreme disaster scenarios, and has functions such as historical case strategy extraction, personalized strategy settings, strategy parameter adjustment, evaluation indicator system editing, evaluation template management, and evaluation generation.

[0035] Step S112: Extract the hypothetical scenario and initial strategy from the construction results based on the simulation objectives.

[0036] Step S113: The scenario and initial strategy data are converted into simulation script files that can be executed by the simulation platform using a standardized parser.

[0037] Specifically, the simulation engine first extracts the "scenario" and "initial command strategy" data, and then uses a built-in standardized parser to convert them into .scr format simulation script files executable by the AFSim platform. The script content covers the initial state of all participating entities, task sequences, time triggering conditions, and dynamic change curves of environmental parameters.

[0038] In one optional implementation, step S13 specifically includes the following process: Step S131: During the simulation process, key state changes are captured in real time.

[0039] Step S132: When the preset conditions are triggered, a rescheduling request is generated, and an adjustment instruction is generated by calling the optimization rules according to the current simulation state.

[0040] Step S133: Update the task sequence of the corresponding simulation entity model according to the adjustment instruction and continue to advance the simulation until the simulation reaches the preset end time or all emergency repair tasks are completed, and generate a simulation result data package.

[0041] Specifically, during the simulation, the monitoring agent captures key status changes in real time, such as a substation completing emergency repairs and restoring power, or a road section experiencing a secondary disaster causing a repair team to be unable to reach it. When triggers occur, such as insufficient repair resources, task timeouts, or sudden secondary failures, the engine sends a "rescheduling request" to the policy management system. Based on the current simulation status, it invokes built-in heuristic rules or optimization algorithms to fine-tune resource allocation and repair priorities, generating an "adjustment instruction" which is returned to the simulation engine. Upon receiving the adjustment instruction, the simulation engine updates the task queue of the corresponding entity and continues the simulation until the simulation reaches the preset end time or all repair tasks are completed. The entire simulation process generates a structured result data packet containing multiple dimensions such as time, status, and events.

[0042] Figure 4 This is a simulation and deduction flowchart of an embodiment of the present invention. For example... Figure 4As shown, the initial scenario and initial command strategy serve as inputs. The simulation script generation stage transforms the input data into an executable simulation script. Under the scheduling and driving force of simulation control, the simulation calculations are completed, and the simulation results are output. During the simulation, the strategy can be adjusted based on intermediate simulation states. The adjusted strategy is fed back to the simulation to update subsequent simulation logic. Simulation management coordinates and controls the overall simulation process, while the simulation data extraction stage extracts key data from the simulation process for subsequent analysis. The entire process embodies a closed-loop interaction mechanism between simulation and strategy adjustment.

[0043] In this embodiment, the simulation engine is a computational component that drives simulation progress with discrete time steps. It loads standardized script files and calculates the state changes, task execution progress, and environmental parameter evolution of each entity in parallel according to time sequence. It supports simulation of all types, all elements, and all scenarios of power grid disasters, and has functions such as simulation data processing, simulation script generation, model interactive simulation, simulation process control, and simulation result generation. It connects the entire simulation computation link of "environment-entity-task-command strategy" to achieve multi-element coupled parallel simulation.

[0044] In one optional implementation, step S14 specifically includes the following process: Step S141: Extract indicator data for each dimension from the simulation result data package. The indicator data for each dimension includes timeliness data, economic data, safety data, and social impact data.

[0045] Step S142: The range transformation method is used to map the indicator data of each dimension to a preset interval to form a standardized indicator set.

[0046] Step S143: Invoke the evaluation weight scheme and use the linear weighted summation method to calculate the overall strategy score.

[0047] Specifically, Figure 5 This is a flowchart of the strategy evaluation process according to an embodiment of the present invention. Figure 5 As shown, the strategy evaluation takes the simulation results as input, calls the evaluation scheme to standardize the simulation result data, and then reads the corresponding evaluation index system, which includes four evaluation dimensions: timeliness, economy, safety, and social impact. After the index is read, the corresponding index operators are configured, and then the evaluation algorithm and model are called to perform the evaluation calculation. The range transformation method is used to map the index values ​​of the above dimensions to the interval [0, 1] to eliminate the difference in dimensions and form a standardized index set; the user-configured evaluation weight scheme (w1, w2, w3, w4) is read, and the comprehensive score of the strategy S=w1×E+w2×M+w3×S is calculated using the linear weighted summation method. f +w4×I, where E is the timeliness score, M is the economic score, and Sf I represents the safety score, and I represents the social impact score.

[0048] Furthermore, based on the overall score and the scores for each dimension, the system retrieves the evaluation report template and automatically generates an evaluation report containing radar charts, trend comparison charts, and textual analysis. If the overall score is lower than a preset threshold or a specific indicator fails to meet the standard, the system will mark the weak links and generate strategy optimization suggestions to feed back to strategy management. For example, if there is insufficient timeliness, it is recommended to increase the emergency repair force in key areas to assist in subsequent decision-making optimization.

[0049] In one optional implementation, the indicator data for each dimension is extracted from the simulation result data package, specifically including the following process: Step S1411: Extract the recovery timestamps of key nodes in the simulation result data package, compare the recovery timestamps with the preset target time series to calculate the deviation rate, and obtain the timeliness data.

[0050] Specifically, the recovery timestamps of key nodes in the simulation log are extracted. These key nodes include important loads and substations. The average outage duration (SAIDI) and average power availability (ASAI) of the system are calculated based on the extracted timestamps. Then, the calculated actual values ​​are compared with the preset target time series and the deviation rate is calculated.

[0051] Step S1412: Extract the cost of calling emergency resources and the economic loss caused by power outage during the simulation process. Add up the cost of calling resources and the economic loss to obtain economic data.

[0052] Specifically, the cumulative cost C of calling up all emergency resources during the simulation process. resource The cost of this resource allocation includes manpower hours, equipment shifts, and material consumption costs, plus the economic losses (C) caused by the power outage. loss The economic loss is calculated based on the number of users experiencing power outages, load importance, and the weight of loss per unit time.

[0053] Step S1413: Extract the number of operations that violate safety procedures and the probability of secondary disaster risks triggered by the environmental model in the simulation. Statistically analyze the number of operations and the probability of secondary disaster risks, and calculate the safety deduction value through a preset risk matrix to obtain safety data.

[0054] Specifically, the number of operations that violated safety procedures during the simulation process was statistically analyzed. These operations included violations such as forcibly operating under adverse weather conditions. Simultaneously, the probability P of secondary disasters triggered by the environmental model was statistically analyzed. risk Then, the safety deduction value is calculated using a preset risk matrix.

[0055] Step S1414: Extract the weighted classification results of the power outage area, and calculate the social impact index by combining the power outage duration to obtain social impact data.

[0056] Specifically, the power outage areas are weighted and categorized, with hospitals and command centers designated as high-weight areas and residential areas as medium-weight areas. Then, the social impact index is calculated based on the power outage duration for each area. The calculation formula is as follows: W i N represents the weight. i T represents the number of users. i This indicates the duration of the power outage.

[0057] In one optional implementation, the power emergency command simulation method further includes: Step S151: Construct a three-dimensional model based on the actual power grid equipment to obtain the simulation entity model.

[0058] Step S152: Write the execution logic of the emergency response task using the scripting language built into the simulation platform to obtain the simulation task model.

[0059] Step S153: Import environmental data through the scene editor to obtain the simulation environment model.

[0060] Specifically, model libraries are built using AFSim, where simulation entity models are constructed by connecting to 3D models built by simulation software such as Ansys via API interfaces; simulation task models are implemented using the WSF scripting language built into AFSim; and simulation environment model libraries import environmental data such as terrain and weather data through the Wizard scene editor. Figure 6 This is a simulation model architecture diagram according to an embodiment of the present invention. Figure 6 As shown, the simulation entity model includes power grid equipment models, emergency personnel models, emergency equipment models, emergency material models, and power supply user models; the simulation task models include disaster reconnaissance models, mobilization models, equipment repair models, emergency power supply models, and emergency support models; and the simulation environment models include meteorological environment models, terrain environment models, traffic accessibility models, and communication coverage models. Furthermore, it achieves layered construction of three standardized models: emergency simulation entity models, emergency response simulation task models, and disaster simulation environment models, covering power grid equipment, emergency resources, various types of repair tasks, and complex environmental constraints such as roads, communications, meteorology, and geography, achieving accurate modeling of extreme "three-disruption" scenarios.

[0061] In this embodiment, model management supports the full lifecycle management of emergency entity models, task models, and environment models, and has functions such as model resource library construction, model standardization review, model application parameter setting, model calling, and maintenance. A lightweight cloud-based model library can also be used, where models are texture-compressed, parameter-simplified, and layered to achieve lightweighting and storage on a cloud server. Models can be called online as needed, replacing locally deployed model libraries and adapting to remote simulations across multiple terminals.

[0062] In one optional implementation, after loading the simulation script file, the power emergency command simulation method further includes: Step S161: Initialize the simulation entity model, simulation task model, and simulation environment model.

[0063] Specifically, the initialization of complete data loading supports the synchronous readiness of power grid equipment status, environmental parameters, and task sequences, enabling the simulation engine to drive subsequent calculations within a unified initial framework, ensuring logical coherence and data integrity throughout the simulation process from start to finish.

[0064] This embodiment also provides a power emergency command simulation system, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0065] like Figure 7 As shown, this embodiment of the invention provides a power emergency command simulation system, which includes: The parsing module 11 is used to extract the initial strategy and scenario of emergency command, parse and process the initial strategy and scenario and generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model.

[0066] The processing module 12 is used to load simulation script files and drive simulation calculations to gradually deduce the development process of power grid disaster events in discrete time steps. In each time step, it calculates in parallel the state updates, task execution progress, and dynamic changes of environmental parameters of multiple simulation entity models.

[0067] Simulation module 13 generates a rescheduling request when a preset condition is triggered, calls optimization rules to generate adjustment instructions based on the current simulation state, updates the task sequence of the corresponding simulation entity model according to the adjustment instructions and continues to advance the simulation, and generates a simulation result data packet.

[0068] The calculation module 14 extracts the indicator data of each dimension from the simulation result data package, calls the evaluation weight scheme to perform weighted summation of the indicators of each dimension, and calculates the comprehensive strategy score.

[0069] The power emergency command simulation system provided in this embodiment of the invention can execute the power emergency command simulation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0070] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0071] The following is a detailed reference. Figure 8 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from memory 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0072] Typically, the following devices can be connected to I / O port 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 8 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0073] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a memory 808, or installed from a ROM 802. When the computer program is executed by the processor 801, it performs the functions defined in the power emergency command simulation method of the embodiments of the present invention.

[0074] Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0075] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the power emergency command simulation method shown in the above embodiments is implemented.

[0076] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A power emergency command simulation method, characterized in that, The power emergency command simulation method includes: Extract the initial strategy and scenario of emergency command, parse and process the initial strategy and scenario and generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model. The simulation script file is loaded, and the simulation calculation is driven by discrete time steps to gradually deduce the development process of power grid disaster events. In each time step, the state updates, task execution progress and dynamic changes of environmental parameters of multiple simulation entity models are calculated in parallel. When a preset condition is triggered, a rescheduling request is generated. Based on the current simulation state, optimization rules are invoked to generate adjustment instructions. Based on the adjustment instructions, the task sequence of the corresponding simulation entity model is updated and the simulation continues to advance. Simulation result data packets are generated. The indicator data for each dimension is extracted from the simulation result data package, and the evaluation weight scheme is called to perform weighted summation of the indicators for each dimension to calculate the overall strategy score.

2. The power emergency command simulation method according to claim 1, characterized in that, The initial strategy and scenario of emergency command are extracted, and the initial strategy and scenario are parsed and processed to generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model, and simulation task model, including: The hypothetical scenario and the initial strategy are constructed based on historical disaster events, or the hypothetical scenario and the initial strategy are constructed using parameterized templates. Based on the simulation objectives, the hypothetical scenario and the initial strategy are extracted from the construction results. The hypothetical scenario and the initial strategy are transformed into a simulation script file that can be executed by the simulation platform using a standardized parser.

3. The power emergency command simulation method according to claim 1, characterized in that, When a preset condition is triggered, a rescheduling request is generated. Based on the current simulation state, optimization rules are invoked to generate adjustment instructions. The task sequence of the corresponding simulation entity model is updated according to the adjustment instructions, and the simulation continues. A simulation result data package is generated, including: During the simulation process, key state changes are captured in real time; When a preset condition is triggered, a rescheduling request is generated, and an adjustment instruction is generated by calling the optimization rules based on the current simulation state. The task sequence of the corresponding simulation entity model is updated according to the adjustment instructions, and the simulation continues until the simulation reaches the preset end time or all emergency repair tasks are completed, generating a simulation result data package.

4. The power emergency command simulation method according to claim 1, characterized in that, The indicator data for each dimension is extracted from the simulation result data package. The evaluation weighting scheme is then used to perform a weighted summation of the indicators for each dimension, and the overall strategy score is calculated, including: The simulation result data package contains indicator data for each dimension, including timeliness data, economic data, safety data, and social impact data. The range transformation method is used to map the index data of each dimension to a preset interval to form a standardized index set; The evaluation weighting scheme is invoked, and the overall strategy score is calculated using the linear weighted summation method.

5. The power emergency command simulation method according to claim 4, characterized in that, Extract the indicator data for each dimension from the simulation result data package, including: Extract the recovery timestamps of key nodes from the simulation result data package, compare the recovery timestamps with the preset target time series to calculate the deviation rate, and obtain the timeliness data; Extract the cost of calling emergency resources and the economic loss caused by power outage during the simulation process, and sum the cost of calling resources and the economic loss to obtain the economic data; Extract the number of operations that violate safety procedures in the simulation and the probability of secondary disaster risks triggered by the environmental model. Statistically analyze the number of operations and the probability of secondary disaster risks, and calculate the safety deduction value through a preset risk matrix to obtain the safety data. The weighted classification results of the power outage areas are extracted, and the social impact index is calculated by combining the power outage duration to obtain the social impact data.

6. The power emergency command simulation method according to claim 1, characterized in that, The power emergency command simulation method also includes: A three-dimensional model is constructed based on actual power grid equipment to obtain a simulation entity model; The execution logic of the emergency response task is written using the scripting language built into the simulation platform to obtain the simulation task model; Import environmental data using the scene editor to obtain a simulation environment model.

7. The power emergency command simulation method according to claim 6, characterized in that, After loading the simulation script file, the power emergency command simulation method further includes: The simulation entity model, the simulation task model, and the simulation environment model are initialized.

8. A power emergency command simulation system, characterized in that, The power emergency command simulation system includes: The parsing module is used to extract the initial strategy and scenario of emergency command, parse the initial strategy and scenario and generate an executable simulation script file. The simulation script file contains the initial state data of the simulation environment model, simulation entity model and simulation task model. The processing module is used to load the simulation script file and drive the simulation calculation step by step to deduce the development process of the power grid disaster event. In each time step, it calculates the state update, task execution progress and dynamic changes of environmental parameters of multiple simulation entity models in parallel. The simulation module generates a rescheduling request when a preset condition is triggered, calls optimization rules to generate adjustment instructions based on the current simulation state, updates the task sequence of the corresponding simulation entity model according to the adjustment instructions and continues to advance the simulation, and generates a simulation result data packet. The calculation module extracts the indicator data of each dimension from the simulation result data package, calls the evaluation weight scheme to perform weighted summation of the indicators of each dimension, and calculates the comprehensive strategy score.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the power emergency command simulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which are used to cause the computer to execute the power emergency command simulation method according to any one of claims 1 to 7.

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