Digital plan construction method for emergency command decision

Abstract

The invention relates to the technical field of emergency command decisions, in particular to a digital plan construction method for emergency command decisions, comprising the following steps: S1, collecting a data establishment scheme, defining a data source, a data collection method and scheme construction from data arrangement and preprocessing to the final; s2, determining an emergency scene; s3, making an emergency command scheme; s4, implementing an emergency command decision, and enabling command to be more scientific through decision execution, dynamic adjustment and enhancement of joint command and coordination; s5, effect evaluation and feedback: providing improvement measures and suggestions through effect evaluation and experience feedback after emergency disposal is finished, and optimizing an emergency command decision system; and S6, revising and perfecting the digital plan according to an emergency command decision implementation effect evaluation result, calculating through a plan revising and perfecting formula, and then issuing.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency command decision-making, and in particular to a digital emergency plan construction method oriented to emergency command decision-making. Background Art

[0002] Emergency command, or simply command, is commonly used to refer to military organizational command. It is the specialized organizational and leadership activities undertaken by military commanders and their agencies over combat and other military operations. In ancient law, "command" was an abbreviation for "batch command," a legal form dating back to the Song Dynasty. Today, the concept of command is widely applied to management levels across all sectors of society, encompassing the organizational and leadership activities of superiors over the various activities of their subordinates.

[0003] Traditional emergency command and decision-making processes rely primarily on manual experience and on-site command. This approach has numerous limitations. First, information collection is incomplete and untimely, making it difficult to integrate data from multiple channels, resulting in decision-makers unable to fully grasp the on-site situation. Second, emergency plan formulation and adjustments are often based on subjective judgment and lack scientific quantitative evaluation methods, making it difficult to ensure the scientific nature and reliability of the plans. Third, coordinated command is inefficient, with departments lacking information sharing and coordinated actions, which can easily lead to wasted resources and delayed action. Finally, the revision and improvement of emergency plans often lag behind actual conditions, making them unable to adapt to the complexity and variability of emergencies. Summary of the Invention

[0004] Technical issues addressed: Incomplete and untimely information collection, making it difficult to integrate data from multiple channels, prevents decision-makers from fully understanding the on-site situation. Furthermore, emergency plan formulation and adjustments are often based on subjective judgments, lacking scientific quantitative evaluation methods, making it difficult to ensure the scientific nature and reliability of the plans. Furthermore, coordinated command is inefficient, with departments lacking information sharing and coordinated actions, leading to wasted resources and delayed action. Finally, the revision and improvement of emergency plans often lag behind actual conditions, failing to adapt to the complexity and variability of emergencies.

[0005] In view of the shortcomings of the existing technology, the present invention provides a digital plan construction method for emergency command decision-making, thereby solving the technical problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for constructing a digital emergency plan for emergency command decision-making, comprising the following steps:

[0007] The establishment of S1 database and scenario library, by clarifying the data source, data collection method, data collation and preprocessing to form a complete database, as well as the establishment of scenario library;

[0008] S2 determines the emergency scenario and further establishes the scenario through scenario identification, scenario refinement and scenario verification;

[0009] S3 formulates an emergency command plan. Based on the emergency scenario determined in S2, the corresponding handling process template and resource scheduling model are called from the emergency plan database. In combination with real-time data and geographic information, a preliminary emergency command plan is generated. The plan should clearly define the task objectives, responsible departments and personnel, resource requirements, and action steps for each emergency response stage, including command structure and responsibilities, standardized emergency response processes and resource information and scheduling mechanisms, and preset emergency response measures and strategies.

[0010] S4 implements emergency command decisions, rehearses and improves the plans generated in S3, and finally makes the command more scientific through decision execution, dynamic adjustment, and strengthening of joint command and coordination;

[0011] S5 Effect Evaluation and Feedback: After the emergency response is completed, improvement measures and suggestions are proposed through effect evaluation and experience feedback to continuously optimize the emergency command and decision-making system;

[0012] S6 plan revision and improvement: revise and improve the digital plan based on the evaluation results of the implementation effect of emergency command decisions, calculate through the plan revision and improvement formula, and then publish it.

[0013] In one possible implementation, the dynamic adjustment describes the problems and processes of adjusting plans in emergency decision-making based on the scenario evolution characteristics of the emergency, analyzes the correlation between different plans, and considers comprehensive factors: considering factors such as emergency response effects, response losses, and conversion costs between different plans to generate the optimal adjustment plan.

[0014] In one possible implementation, the plan is to conduct a drill and prepare drill materials, including a drill site, equipment, props, etc., organize the implementation of the drill: carry out actual operations according to the established plan to ensure that the drill process is safe and orderly, and finally evaluate and summarize the process, evaluate the drill process, summarize the lessons learned, and put forward improvement suggestions.

[0015] In one possible implementation, the formula for revising and improving the plan is: comprehensive plan score = α×real-time data matching degree + β×historical case fit + γ×expert evaluation score + δ×technical advancement score + ε×operability score + ζ×risk assessment score.

[0016] In a possible implementation, α, β, γ, δ, ε, and ζ are weight coefficients of each factor, and satisfy 0<α, β, γ, δ, ε, ζ<1 and α+β+γ+δ+ε+ζ=1.

[0017] Beneficial effects compared with existing technologies:

[0018] 1. This plan uses a digital plan construction method for emergency command decision-making. By clarifying data sources and collection methods, it comprehensively collects multi-source information such as historical emergency event data, geographic information, real-time monitoring data, internal organizational documents, and external agency data, and conducts strict screening and preprocessing to ensure that the data used for emergency decision-making is comprehensive, accurate, and reliable, providing a solid foundation for scientific decision-making. It uses machine learning algorithms to identify and refine scenarios, and combines expert knowledge to construct emergency scenarios, making the determination of emergency scenarios more scientific and comprehensive. A decision support system is used to evaluate and optimize emergency command plans, and the feasibility, effectiveness, and risks of the plans are analyzed based on a quantitative evaluation indicator system. This improves the scientific nature and reliability of the plans and ensures that the emergency command plans can accurately respond to various complex emergency events.

[0019] 2. In this plan, a digital plan construction method for emergency command decision-making is adopted. By clarifying the dynamic adjustment process and decision-making mechanism, the emergency command plan can be quickly adjusted according to on-site feedback and real-time monitoring data, and the backup plan can be activated or re-formulated in time, thereby enhancing the flexibility and adaptability of emergency command decision-making, ensuring that emergency response work can proceed smoothly, and effectively respond to the complexity and uncertainty of emergencies; building a cross-departmental collaborative linkage platform, breaking down the information barriers between departments, realizing real-time information sharing and interaction, and improving the efficiency of collaborative work. Through the joint command and coordination mechanism, it ensures that all departments act in unison, form a working synergy, and jointly respond to emergency events, thereby improving the overall effectiveness of emergency response.

[0020] 3. In this plan, a digital plan construction method for emergency command decision-making is used to establish a plan revision and improvement mechanism based on effect evaluation and feedback. The revision and improvement of the plan are scientifically evaluated through a quantitative evaluation formula, so that the plan can be updated and optimized in a timely manner according to the actual situation to maintain its effectiveness, adaptability and advancement. The comprehensive plan score = α×real-time data matching degree + β×historical case fit + γ×expert evaluation score + δ×technical advancement score + ε×operability score + ζ×risk assessment score is used to make a decision on the optimized plan again, and then the plan is released for subsequent reference and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0022] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various forms, so the present invention is not limited to the embodiments described below.

[0023] The technical solution in the embodiments of the present application is to solve the problems of the above-mentioned background technology, and the overall idea is as follows:

[0024] Example 1:

[0025] Please refer to Figure 1 As shown, this embodiment introduces a digital plan construction method for emergency command decision-making, including a digital plan construction method for emergency command decision-making, including the following steps:

[0026] Establishment of S1 database and scene library

[0027] In this step, a complete database is formed by clarifying the data source, data collection method, data collation and preprocessing, as well as the establishment of a scenario library.

[0028] Clarify the data sources: including historical emergency event data, geographic information system (GIS) data, real-time monitoring data (such as sensor data), internal organizational documents (such as emergency resource lists, organizational charts, etc.) and data from external relevant agencies (such as data provided by meteorological departments, transportation departments, etc.). When identifying these data sources, they need to be screened. Data richness cannot be ignored. If the data is not screened, some false data will affect the overall data collection, resulting in irreversible effects.

[0029] Methods of data collection: Use web crawlers to obtain public data, establish data sharing agreements with relevant institutions to obtain real-time data, collect expert experience data through questionnaires, etc. In the collection of data, it is necessary to ensure the accuracy of the data and the relevance of the data source. At the same time, the time span of historical data should be considered to ensure its reference value for current emergency decision-making. It is necessary to confirm that the data source is authoritative and a credible institution or recording system. In the process of collecting data, from the massive amount of network data, filter out useful information related to emergency command decision-making, verify the authenticity of Internet data through multi-channel cross-verification, and avoid using unverified information. At the same time, social media data also needs to be identified. It is necessary to filter real information through official rumor refutation, multi-user verification, etc. At the same time, it is necessary to pay attention to hot topics and high-profile information, explore potential emergency needs and public opinion trends, to ensure the reliability of information sources, make the overall information processing more reasonable, and facilitate information screening in the next step, reducing unnecessary work processes.

[0030] Data collation and preprocessing: Clean the collected data to remove duplicate, erroneous, and incomplete information; standardize different types of data and unify the data format; establish a data warehouse, classify and store the data, and index it for subsequent quick query and call; in the process of data collation, if the data is large and there are few data records with missing values, or the entire feature (variable) corresponding to the missing value is not very useful for the analysis task, these record features can be directly deleted. If the data records are relatively important and the missing ratio is not high, the missing values ​​can be filled. For numerical data, the mean, median, or mode can be used to fill. When data comes from multiple data sources (such as different databases, files, etc.), they need to be merged together. During the merging process, attention should be paid to data consistency. If different data sources may provide different information for the same attribute, causing conflicts, rule-based methods (such as taking a higher credit rating, taking an average, etc.) or data fusion algorithms can be used to resolve data conflicts.

[0031] Finally, this data is summarized, and the subset of features most useful for the analysis task is selected from the numerous features of the original data. This reduces data dimensionality and improves model performance and interpretability. Common methods include filter-based methods (such as calculating feature information gain and chi-square tests), wrapper-based methods (such as using specific machine learning algorithms for feature selection), and embedded methods (such as automatically performing feature selection during model training). The original high-dimensional data is converted to a low-dimensional space through functions or mappings while preserving the data's key information and structure. Common methods include principal component analysis (PCA), linear discriminant analysis (LDA), and singular value decomposition (SVD). Data compression techniques, such as Huffman coding and LZW coding, are used to reduce data storage and improve data processing efficiency. In this way, a database and a scenario library will be established. The scenario library will be classified by event type: such as natural disasters (earthquakes, floods, typhoons, etc.), accident disasters (fires, explosions, traffic accidents, etc.), public health events (infectious disease epidemics, food safety incidents, etc.), social security events (attacks, mass incidents, etc.), and classified by severity: such as minor incidents, general incidents, major incidents, and major incidents; classified in chronological order: such as early, middle, and late stages, so that responses can be made quickly and efficiently in subsequent scheduling.

[0032] S2 determines the emergency scenario and further confirms the scenario through scenario identification, scenario refinement and scenario verification.

[0033] Based on the scenario library established in S1, the collected historical data and real-time data are analyzed through scenario recognition using machine learning algorithms (such as cluster analysis and classification algorithms) to identify potential emergency scenario patterns. At the same time, combined with expert knowledge and experience, scenarios for possible new emergency events are constructed.

[0034] Scenario refinement: For identified emergency scenarios, further refine scenario elements, including event type (such as fire, earthquake, flood, public health incident, etc.), location (such as urban center, industrial park, mountainous area, etc.), impact range (such as the area involved, population size, etc.), severity (such as the number of casualties, estimated property losses, etc.) and possible secondary disasters (such as environmental pollution caused by fire, landslides caused by earthquake, etc.).

[0035] Scenario verification: Verify the determined emergency scenarios through simulation exercises, historical emergency event data in S1 data, etc., evaluate the rationality and completeness of the scenarios, and ensure that they can truly reflect possible emergency events.

[0036] S3 Develop emergency command plan

[0037] Based on the identified emergency scenario, the corresponding response process template and resource scheduling model are retrieved from the emergency plan database, combined with real-time data and geographic information to generate a preliminary emergency command plan. The plan should clearly define the mission objectives, responsible departments and personnel, resource requirements, and action steps for each emergency response phase, including command structure and responsibilities, standardized emergency response processes (response levels, information reporting, and pre-emptive measures), resource information and scheduling mechanisms, and pre-set emergency response measures and strategies.

[0038] The command structure consists of the following: The Command Center: Serving as the core of emergency command, it is typically headed by a senior government or organizational leader, responsible for overall command and decision-making. Specialized Teams: These include rescue teams, medical teams, logistics support teams, communications teams, and information teams, each responsible for specific tasks. External Support Units: These include professional rescue teams such as firefighters, police, and medical rescue teams, as well as volunteer organizations. Division of Responsibilities: The Commander-in-Chief: Responsible for overall command and decision-making, determining the level and strategy of the emergency response. Coordinates the work of various teams and external units. Reports to superiors and requests support. Specialized Teams: Rescue Team: Responsible for on-site rescue operations, including search and rescue, incident response, and more. Medical Team: Responsible for medical care and casualty transport. Logistics Support Team: Responsible for material supply and equipment deployment. Communications Team: Responsible for communications, ensuring smooth communication between the command center and the scene. Information Team: Responsible for collecting, organizing, and reporting information, ensuring its accuracy and timeliness. External Support Units: Carry out specific rescue tasks in accordance with the command center's instructions and provide professional technical and equipment support.

[0039] Standardized emergency response process: Response levels are divided into Level 1 response: the most serious incident, requiring the mobilization of all available resources and involving multiple departments and external units. Level 2 response: a more serious incident, requiring the mobilization of some resources and involving multiple specialized teams. Level 3 response: a less serious incident, usually handled by a single specialized team.

[0040] Information processing utilizes a reporting model, with the information team responsible for collecting on-site information, casualties, and the scope of impact. This information is verified and collated to ensure accuracy and completeness, and then submitted to higher-level authorities and the command center within the prescribed timeframe and format. Preliminary reports are typically completed within 15 minutes of an incident, and detailed reports within 30 minutes.

[0041] Preliminary Disposal and Rapid Response: Immediately activate the emergency plan after an incident occurs, organizing personnel for preliminary disposal. Initial Rescue: The rescue team quickly arrives at the scene to conduct search and rescue operations. On-site Control: Establish a cordon, evacuate unauthorized personnel, and ensure safety at the scene. Information Feedback: Promptly provide feedback on the on-site situation to the command center to provide a basis for subsequent decision-making.

[0042] Resource Information and Scheduling Mechanism: Resource information management and resource inventory: Create a detailed resource inventory, including manpower, material resources, equipment, and supplies. Resource distribution: Clarify the distribution of resources to ensure rapid deployment when needed. Regularly update resource information to ensure the accuracy and timeliness of the resource inventory. The command center will centrally allocate resources based on the severity and needs of the incident.

[0043] Preset emergency response measures and strategies: Develop detailed personnel search and rescue measures, including rescue methods, equipment use, etc. Medical rescue: Clarify the process and measures of medical rescue, including on-site first aid, transfer of casualties, and allocation of medical resources. On-site disposal: Develop specific on-site disposal measures based on the type of incident, such as fire fighting, leak treatment, building collapse rescue, etc. Secondary disaster prevention: Develop measures to prevent secondary disasters, such as preventing explosions caused by fires, preventing mudslides caused by floods, etc. Initiate the corresponding response level and plan based on the severity of the incident. Clarify the collaborative combat methods between various professional teams and external units to ensure coordinated and consistent actions. Resource optimization strategy: Ensure the efficient use of resources through reasonable allocation of resources. Information sharing strategy: Establish an information sharing mechanism to ensure that various teams and units can obtain and share information in a timely manner. Dynamic adjustment strategy: Dynamically adjust emergency response measures and strategies based on the development of the incident and the on-site situation.

[0044] S4 implements emergency command decision-making

[0045] Begin by conducting simulations and rehearsals of the developed plan, developing detailed implementation steps, and preparing drill materials: including a drill site, equipment, and props. Organize and implement the drill: Follow the developed plan and conduct actual operations to ensure a safe and orderly process. Finally, conduct an evaluation and summary, assessing the drill process, summarizing lessons learned, and providing suggestions for improvement. Through continuous rehearsals, the plan is refined to prepare for actual emergency events, enabling accurate judgment and response. Once an emergency occurs, immediate decision-making and implementation will be initiated.

[0046] Decision-making and Execution: Emergency command organizations and personnel at all levels will promptly carry out emergency response work in accordance with the requirements of the emergency command plan. The emergency command system will be used to monitor the on-site situation in real time to keep abreast of the development of the incident and resource utilization.

[0047] Dynamic Adjustment: Based on on-site feedback and real-time monitoring data, the emergency command plan is dynamically adjusted. In the event of an emergency or the original plan is inapplicable, a backup plan is quickly activated or a new emergency command plan is formulated to ensure the smooth progress of emergency response. Dynamic Adjustment describes the issues and processes of plan adjustment in emergency decision-making based on the evolving characteristics of the emergency scenario, and analyzes the correlation between different plans. Comprehensive Factor Consideration: When adjusting the plan, factors such as emergency response effectiveness, response losses, and conversion costs between different plans are considered to generate the optimal adjustment plan.

[0048] Utility analysis: clarify the utility functions of adjustment cost, adjustment loss and disposal effect, determine the decision maker's subjective weight utility function, select the plan with the largest expected utility value as the preferred plan, and evaluate the optimal plan using the utility function. The linear function is: U(x)=ax+b, where x is the decision variable (the cost of coping with losses and switching between different plans), a is the slope, which indicates the sensitivity of utility to the decision variable, and b is the intercept, which indicates the basic utility. The feasibility of this plan can be obtained through calculation.

[0049] At the same time, joint command and coordination will be strengthened during the implementation of the plan: a joint command seat will be established at the emergency command center, with personnel from each department assigned to take charge and jointly participate in emergency decision-making and command and dispatch. Through joint command, we will ensure that all departments act in unison, form a working synergy, and jointly respond to emergency events.

[0050] Coordinated command: Strengthen collaboration with relevant departments (such as public security, fire protection, medical care, transportation, etc.) and external agencies (such as the military, volunteer organizations, etc.) to achieve information sharing, resource allocation and action coordination, and form a joint force for emergency response.

[0051] S5 effect evaluation and feedback

[0052] Effectiveness Evaluation: After the emergency response is completed, various data from the emergency response process are collected and organized, including resource utilization, casualties and property damage statistics, and incident response time. Using an evaluation indicator system (such as emergency response time, resource utilization, and loss control effectiveness), the effectiveness of the emergency command decision-making process is evaluated, the strengths and weaknesses of the emergency response work are analyzed, and the feasibility of the plan is assessed.

[0053] Experience Feedback: Evaluation results are fed back into the emergency plan database as an important basis for revising and improving the digital emergency plan. At the same time, relevant personnel are organized to summarize and reflect on the emergency response process, propose improvement measures and suggestions, and continuously optimize the emergency command and decision-making system.

[0054] S6 Plan Revision and Improvement

[0055] Revision trigger conditions: The trigger conditions for plan revision are determined based on the results of the emergency command decision-making implementation effect evaluation, changes in laws, regulations and policies, the application of new technologies, organizational structure adjustments and changes in the external environment.

[0056] Revision Process: The digital emergency plan will be revised and improved according to the prescribed revision process. Revisions will include updating event classification and grading standards, optimizing emergency response processes, adjusting resource scheduling models, and adding new emergency scenarios and response plans.

[0057] The following is a relatively complete formula for quantitatively evaluating the revision and improvement of emergency plans, thereby making the plans more scientific:

[0058] Comprehensive score of the plan = α × real-time data matching degree + β × historical case fit + γ × expert evaluation score + δ × technological advancement score + ε × operability score + ζ × risk assessment score

[0059] in:

[0060] Real-time data matching (Dmatch): This measures the applicability of the emergency plan to current real-time data (such as meteorological data, casualty data, traffic conditions, etc. at the time of the disaster). This can be determined by calculating the coverage ratio and matching degree of each link in the emergency plan to real-time data. The formula is:

[0061] Dmatch=n∑i=1n The matching degree of real-time data type i in the plan, where n is the number of real-time data types.

[0062] Historical Case Fit (Chistory): Evaluates the similarity and fit between the plan and previous cases of similar emergency response. This can be determined by analyzing historical cases and calculating the similarity ratio between the measures and processes in the plan and historical successful cases. The formula is:

[0063] Chistory=m∑j=1mThe degree of fit between historical case j and the current plan

[0064] Where m is the number of historical cases.

[0065] Expert Evaluation Score (Escore): Experts in the relevant field will rate the plan based on their experience and expertise, with a maximum score of 100. Expert consultation methods such as the Delphi method can be used to collect expert opinions and calculate this score.

[0066] Technology advancement score (Tscore): Evaluate the advancement of the technical means and information systems used in the plan. Scores can be given based on factors such as the novelty of the technology, functional completeness, and stability, with a maximum score of 100. The formula is:

[0067] Tscore = w1 × Tnovelty + w2 × Tstability + w3 × Tfunctionality, where Tnovelty is the novelty score of the technology, Tstability is the stability score of the technology, and Tfunctionality is the functional completeness score of the technology. w1, w2, and w3 are the weights of the corresponding factors, and w1 + w2 + w3 = 1.

[0068] Operability score (Oscore): Evaluates the difficulty and operability of the plan during actual implementation. The plan can be scored based on the clarity of the process, the rationality of resource requirements, and the clarity of personnel responsibilities, with a maximum score of 100. The formula is:

[0069] Oscore = p1 × Oclarity + p2 × Oresources + p3 × Oresponsibilities, where Oclarity is the process clarity score, Oresources is the resource demand rationality score, Oresponsibilities is the personnel responsibility clarity score, p1, p2, and p3 are the weights of the corresponding factors, and p1 + p2 + p3 = 1.

[0070] Risk Assessment Score (Rscore): Quantify the various risks that may be faced during the implementation of the plan (such as insufficient resources, information transmission delays, personnel errors, etc.). The probability and impact of each risk can be determined using methods such as risk matrices, and then a comprehensive risk score can be calculated. The formula is: Rscore = ∑k = 1lPriskk × Iriskk

[0071] Among them, l is the number of risk types, Priskk is the probability of occurrence of the k-th risk, and Iriskk is the impact of the k-th risk.

[0072] α, β, γ, δ, ε, and ζ are the weight coefficients of each factor, and they satisfy 0 < α, β, γ, δ, ε, ζ < 1 and α + β + γ + δ + ε + ζ = 1. These weights can be determined using methods such as the Analytic Hierarchy Process (AHP) and the Entropy Weight Method based on the specific needs and priorities of the plan revision and improvement.

[0073] Review and release: After the plan revision is completed, it will be strictly reviewed to ensure that the revised plan conforms to the actual situation and the requirements of emergency command decision-making. After the review is passed, the revised digital plan will be re-released and relevant personnel will be trained.

[0074] Finally, it should be noted that the above embodiments are merely examples for the purpose of illustrating the present invention and are not intended to limit the embodiments. Those skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. It is not necessary and impossible to provide an exhaustive list of all possible embodiments. However, obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A digital plan construction method for emergency command decision-making, characterized by: The following steps are involved: The establishment of S1 database and scenario library, by clarifying the data source, data collection method, data collation and preprocessing to form a complete database, as well as the establishment of scenario library; S2 determines the emergency scenario and further establishes the scenario through scenario identification, scenario refinement and scenario verification; S3 formulates an emergency command plan. Based on the emergency scenario determined in S2, the corresponding handling process template and resource scheduling model are called from the emergency plan database. In combination with real-time data and geographic information, a preliminary emergency command plan is generated. The plan should clearly define the task objectives, responsible departments and personnel, resource requirements, and action steps for each emergency response stage, including command structure and responsibilities, standardized emergency response processes and resource information and scheduling mechanisms, and preset emergency response measures and strategies. S4 implements emergency command decisions, rehearses and improves the plans generated in S3, and finally makes the command more scientific through decision execution, dynamic adjustment, and strengthening of joint command and coordination; S5 Effect Evaluation and Feedback: After the emergency response is completed, improvement measures and suggestions are proposed through effect evaluation and experience feedback to continuously optimize the emergency command and decision-making system; S6 plan revision and improvement: revise and improve the digital plan based on the evaluation results of the implementation effect of emergency command decisions, calculate through the plan revision and improvement formula, and then publish it.

2. A digital emergency plan construction method for emergency command decision-making according to claim 1, characterized in that: The dynamic adjustment is based on the characteristics of the scenario evolution of the emergency, describes the problems and processes of plan adjustment in emergency decision-making, analyzes the correlation between different plans, and considers comprehensive factors: considering factors such as emergency response effects, response losses, and conversion costs between different plans to generate the optimal adjustment plan.

3. A digital emergency plan construction method for emergency command decision-making according to claim 1, characterized in that: Prepare the drill materials, including the drill site, equipment, props, etc., organize the implementation of the drill: carry out actual operations according to the established plan, ensure the safety and order of the drill process, and finally evaluate and summarize the process, summarize the experience and lessons learned, and put forward suggestions for improvement.

4. A digital emergency plan construction method for emergency command decision-making according to claim 1, characterized in that: The formula for revising and improving the plan is: comprehensive plan score = α×real-time data matching degree + β×historical case fit + γ×expert evaluation score + δ×technical advancement score + ε×operability score + ζ×risk assessment score.

5. A digital emergency plan construction method for emergency command decision-making according to claim 4, characterized in that: The α, β, γ, δ, ε, and ζ are the weight coefficients of each factor, and satisfy 0<α, β, γ, δ, ε, ζ <1 and α+β+γ+δ+ε+ζ=1.

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