Intelligent emergency command system and method

Abstract

This invention relates to an intelligent emergency command system and method, wherein the system includes: a disaster event data management and control module, used to collect disaster data after a disaster occurs and generate disaster information based on the disaster data; an emergency team and equipment management and control module, used to dispatch emergency teams and emergency equipment based on the disaster information; an emergency repair task management and control module, used to create emergency repair tasks based on the disaster information, match emergency teams and emergency equipment for emergency repair tasks, and generate dispatch instructions; and an emergency response card management module, used to push corresponding emergency response cards to dispatched personnel based on dispatch instructions, use a feature matching algorithm to verify the matching between dispatched personnel and emergency response cards, and execute repair tasks after verification.

Description

Technical Field

[0001] This invention relates to the field of power grid emergency rescue, and mainly to an intelligent emergency command system and method. Background Technology

[0002] With the increasing frequency of extreme weather, natural disasters, and power grid failures, the risk of damage to power grid facilities continues to rise, placing higher demands on the real-time, collaborative, intelligent, and closed-loop management of emergency command. Traditional emergency command models suffer from problems such as fragmented data, inefficient team and equipment management, poor matching of repair tasks and resources, difficulty in implementing emergency response plans, and weak oversight. Against this backdrop, existing emergency mobile platforms and other systems remain at the level of plan display, information retrieval, and basic mobile office functions, failing to meet the practical needs of intelligent assessment, precise dispatch, personnel-card matching, full-process traceability, and multi-level collaboration in power grid disaster scenarios.

[0003] Chinese invention patent application CN 108764664A discloses a mobile platform system for emergency response plans and solutions. This technical solution includes an interface display layer for processing user data input and output, including mobile emergency equipment; a business application layer for calling relevant interfaces of various classes according to different business requests, generating SQL statements to retrieve or update the database based on user requests, and returning the results to the interface display layer; a database for actual data storage and retrieval; and a basic support layer, including servers, storage backup systems, and a comprehensive command system. This basic support layer is connected to the database, business application layer, and interface display layer, providing basic support for the operation of the entire system. However, the above technical solution only provides access to the text of the emergency response plan. The system lacks the ability to integrate and process disaster event, power outage / restoration, and disaster damage data, making it unable to automatically generate core decision-making information such as the affected area, power outage / restoration sections, and disaster damage location. Furthermore, it lacks refined management of team records, intelligent information verification, and multi-dimensional weighted matching. Additionally, the aforementioned technical solutions fail to decouple tasks from power lines, making it impossible to create emergency repair tasks for lines without power outages. There is no unified microservice scheduling, and no spatial analysis to accurately match tasks with surrounding resources, resulting in low dispatch efficiency and poor fault tolerance. Moreover, the aforementioned technical solutions only passively push out disposal plans without verifying the characteristics of personnel ID cards, job qualifications, and disposal cards; there is no anomaly identification, multi-level analysis, or visual reports, making safety and compliance impossible to guarantee.

[0004] Therefore, there is an urgent need for an intelligent emergency command system that can achieve intelligent analysis of disaster data, precise dispatch of teams and equipment, efficient creation of emergency repair tasks, safety verification of personnel and cards, closed-loop management of early warning plans, and online collaboration throughout the entire process. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes an intelligent emergency command system and method.

[0006] The technical solution of the present invention is as follows: On one hand, the present invention proposes an intelligent emergency command system, the system comprising: The disaster event data management module is used to collect disaster data after a disaster occurs and generate disaster information based on the disaster data; The emergency response team and equipment management module is used to dispatch emergency response teams and equipment based on disaster information; The emergency repair task management module is used to create emergency repair tasks based on disaster information, match emergency teams and emergency equipment for emergency repair tasks, and generate dispatch instructions. The emergency response card management module is used to push the corresponding emergency response card to the dispatched personnel based on the dispatch instruction. It uses a feature matching algorithm to verify the matching between the dispatched personnel and the emergency response card. Once the verification is successful, the repair task is executed.

[0007] Preferably, the emergency response team and equipment management module includes an emergency response team management submodule and an emergency equipment management submodule, wherein the emergency response team management submodule includes: The ledger-based refined management unit is used to store the ledger information of emergency repair teams with preset management fields in a relational database, and supports real-time updates and queries of the information; The information intelligent verification unit is used to automatically verify and identify team ledger information from multiple dimensions based on preset integrity verification rules, logical consistency verification rules, and business compliance verification rules. It marks abnormal information that is missing, conflicting, or does not conform to the specifications, and pushes rectification reminders to abnormal information. The multi-dimensional matching unit is used to perform weighted calculations on disaster information and corresponding weights to generate a comprehensive team matching index; emergency teams are matched based on the comprehensive team matching index.

[0008] Preferably, the emergency equipment control submodule includes: The equipment classification sub-unit is used to classify emergency equipment using a tree-like classification structure and generate a unique number for each piece of emergency equipment. The full-process traceability sub-unit is used to record information about emergency equipment from demand, approval, allocation, outbound, use, return to warehousing, and realize full life-cycle traceability of equipment. The online allocation sub-unit is used to build an online collaborative allocation platform for emergency equipment based on a microservice architecture. It supports disaster-stricken units to initiate equipment demand applications online, and higher-level units to complete approval and allocation instructions online. It also supports equipment lending units and borrowing units to complete handover confirmation online.

[0009] Preferably, the emergency repair task management module includes: The task-line decoupling control unit is used to remove the strong correlation between emergency repair tasks and power outage / restoration lines using data decoupling technology, so as to realize the creation of emergency repair tasks without corresponding power outage lines. The multi-type task management unit is used to break down various types of emergency repair tasks into independent microservices using a microservice architecture, so as to achieve centralized management and unified scheduling of all repair tasks. The resource association and matching unit is used to acquire emergency teams and equipment within a preset range for emergency repair tasks using spatial analysis technology, so as to achieve precise matching of emergency repair tasks and resources.

[0010] Preferably, the emergency response card management module includes: The person-card matching and recognition unit is used to calculate the matching similarity by using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs using a feature matching algorithm; and to determine whether the emergency response card and the dispatched personnel have passed the verification based on the matching similarity. The emergency response card anomaly verification unit is used to identify anomalies in emergency response card information based on preset verification rules, identify abnormal situations, and mark the anomaly type. Hierarchical data analysis units are used to generate visual analysis reports based on the established multi-level data analysis model and preset core analysis indicators.

[0011] Preferably, it also includes an early warning response control module for collecting early warning data, obtaining the early warning level and the area affected by the early warning based on the data, and triggering the corresponding early warning response process, including: The regional and graded alarm unit is used to map early warning data to the built-in power grid early warning classification standard to obtain the degree of disaster impact; and uses geographic information system technology to delineate the early warning impact area according to the degree of disaster impact. The early warning process self-running unit is used to map early warning data to preset early warning classification rules to obtain early warning levels; based on the early warning level and the area affected by the early warning, the corresponding early warning response process is triggered to generate a list of pre-disaster defense tasks; The online approval and control unit is used to build a standardized online approval platform based on a microservice architecture. It automatically records the operation time, approval opinions and release scope of the entire process, and forms an early warning and response timeline.

[0012] On the other hand, the present invention also provides an intelligent emergency command method, the method comprising: After a disaster occurs, disaster data is collected, including disaster event data, power outage and restoration data, and damage data. Based on the disaster data, disaster information is obtained, including the affected area, power outage and restoration section data, and damage location information. Based on disaster information, emergency teams and equipment are dispatched, and emergency repair tasks are created; spatial analysis technology is used to obtain emergency teams and equipment within the preset range of emergency repair tasks, so as to match emergency repair tasks with emergency resources, generate dispatch instructions, and issue them to repair personnel. Based on the dispatch instruction, the corresponding emergency response card is pushed to the dispatched personnel. The feature matching algorithm is used to verify the matching between the dispatched personnel and the emergency response card. Once the verification is successful, the emergency repair task is executed.

[0013] Preferably, obtaining disaster information based on disaster data specifically involves: Input disaster event data into a geographic information system, delineate the boundaries of the disaster area by hand-drawing polygons or selecting coordinate points, and bind the start and end times of the disaster to each disaster area to generate a disaster area containing the regional boundary coordinates, disaster level, and start and end times; The power outage and restoration data are input into the cross-section extraction algorithm. Based on any time point selected by the user, the algorithm queries the power outage equipment or users whose power outage time is no greater than the selected time point and whose restoration time is greater than the selected time point or whose restoration time is empty. These are the objects that are still in power outage. The algorithm also counts the number of households with power outages, the number of households with power restoration, and the restoration rate according to the preset hierarchy, and generates the power outage and restoration cross-section data for the selected time point. The fuzzy matching algorithm is used to match the equipment number or equipment name in the disaster damage data with the equipment ledger of the power grid PMS system and calculate the matching similarity. If the matching similarity is greater than the preset matching similarity threshold, the matching is successful. After the matching is successful, the latitude and longitude coordinates of the equipment, the line to which it belongs and the power supply station information are automatically read to generate disaster damage location information.

[0014] Preferably, emergency teams and equipment are dispatched based on disaster information. The specific steps are as follows: the disaster information is weighted and calculated with corresponding weights to obtain the team matching degree; emergency teams are matched based on the team matching degree.

[0015] Preferably, a feature matching algorithm is used to verify the match between the dispatched personnel and the emergency response card. The specific steps are as follows: The system calculates the matching similarity using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs; based on the matching similarity, it determines whether the emergency response card and the dispatched personnel pass the verification.

[0016] The present invention has the following beneficial effects: 1. This invention utilizes the principles of multi-source disaster data fusion, GIS geographic analysis, and cross-section extraction to automatically generate accurate disaster information, improve the efficiency and accuracy of disaster assessment, enhance the intelligence level of command and decision-making, and strengthen the real-time performance and reliability of emergency response. 2. This invention utilizes the principles of multi-dimensional weighted matching, spatial analysis, and data decoupling to achieve intelligent scheduling of team equipment, flexible creation of emergency repair tasks, and precise matching of resources, thereby improving the utilization rate of emergency resources, increasing the efficiency of emergency repair dispatch, and enhancing cross-departmental collaborative response capabilities. 3. This invention utilizes feature matching algorithms and the principle of node-based full lifecycle management to achieve full traceability of the entire process of personnel card security verification, early warning plan closed-loop management and handling, improve on-site operation compliance, enhance the standardization of emergency management, and strengthen the overall capability of power grid disaster prevention and emergency repair. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0020] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0022] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0023] Example 1: See Figure 1 This invention provides an intelligent emergency command system, the system comprising: S1, Disaster Event Data Management Module, is used to collect disaster data through multiple channels such as IoT monitoring terminals, business system interfaces and on-site reporting terminals after a disaster occurs, and generate disaster information based on the disaster data; The spatiotemporal data fusion algorithm is used to deduplicatize, complete and normalize disaster data. Combined with the preset disaster impact assessment model, it generates structured disaster information including the affected area, damage level, key hidden danger points and the scope of impact. The disaster information is updated and dynamically labeled in real time. For example, the line damage data includes the district and county where it is located, the line name, the damaged transformer, the power outage time, the number of users affected, etc. S2, Emergency Team and Equipment Management Module, is used to dispatch emergency teams and equipment based on disaster information. The dispatch content includes emergency team ledger data, including the district / county where the team is located, team name, personnel data, team leader data, team equipment data, team status, location data, etc., and equipment ledger data, including equipment type, equipment owner, equipment quantity, equipment parameters, equipment status, contact person, etc. The emergency response team and equipment management module includes an emergency response team management sub-module and an emergency equipment management sub-module; S21. The emergency team management submodule includes: The ledger-based refined management unit is used to store the ledger information of emergency repair teams using a relational database with preset management fields, and to realize real-time updates, accurate queries and version tracking of ledger information through an incremental synchronization mechanism; the preset management fields include basic team information, professional qualifications, staffing, duty status and historical repair history, etc. The information intelligent verification unit is used to automatically verify and identify team ledger information from multiple dimensions based on preset integrity verification rules, logical consistency verification rules, and business compliance verification rules (e.g., the number of emergency repair teams must not be less than 20 people, and the emergency repair equipment must include at least one emergency repair vehicle). It marks missing, conflicting, or non-compliant abnormal information and pushes rectification reminders to abnormal information. The multi-dimensional matching unit is used to extract disaster type, repair difficulty, geographical scope and task priority features from disaster information, and perform weighted calculation with corresponding weights to obtain the team matching degree; based on the team matching degree, emergency teams are matched, including the team's professional type (variable), team location (distance from the current location), team's current status (idle or busy), team's qualifications (whether it is a senior repair team), team's workload (whether it has completed multiple tasks under the current disaster situation), etc. S22, The emergency equipment control submodule includes: The equipment classification sub-unit is used to construct a multi-level tree-shaped classification system to standardize the classification of emergency equipment. It establishes a classification catalog by combining equipment type, purpose, specifications and professional dimensions, and generates a globally unique identification code for each piece of emergency equipment through the snowflake algorithm to achieve one-item-one-code accurate identification of equipment. The full-process traceability sub-unit is used to record information about emergency equipment from demand, approval, allocation, outbound, use, return to warehousing, and realize full life-cycle traceability of equipment. The online allocation subunit is used to build an online collaborative allocation platform for emergency equipment based on a microservice architecture. It supports disaster-stricken units to initiate equipment demand applications online, and superior units to complete approval and allocation instructions online. It also supports equipment lending units and borrowing units to complete handover confirmation online. S3, the emergency repair task management module, is used to create emergency repair tasks based on disaster information. It employs a task allocation model based on an improved Hungarian algorithm to match the optimal combination of emergency teams and equipment for each emergency repair task, generating standardized dispatch instructions containing task information, personnel information, equipment information, and operational requirements, as well as repair task data. This repair task data includes the task location, the associated power line, line status, power outage / restoration time, repair team, team leader's phone number, team leader's name, repair progress status, equipment information, and time points for each stage of the repair process. Specifically, it includes: The task-line decoupling control unit is used to remove the strong correlation between emergency repair tasks and power outage / restoration lines using data decoupling technology, so as to realize the creation of emergency repair tasks without corresponding power outage lines. The multi-type task management unit is used to break down various types of emergency repair tasks into independent microservices using a microservice architecture, so as to achieve centralized management and unified scheduling of all repair tasks. The resource association and matching unit is used to obtain emergency teams and equipment within a preset range for emergency repair tasks using spatial analysis technology, so as to achieve accurate matching of emergency repair tasks and resources. The task pool unit is used to standardize the data related to power outages and restorations based on disaster event data and the event's initial data acquisition timestamp, using data cleaning algorithms. Following the principles of "merging data within the same region, classifying data of the same type, and merging short-term outages for statistical purposes," it integrates and normalizes invalid or redundant data such as duplicate trips and short-term power outages, automatically converting them into standardized emergency repair tasks. Employing data decoupling and field mapping technology, it breaks the strong correlation between emergency repair tasks and power outage / restoration lines, supporting the independent creation of general emergency response repair tasks as needed in scenarios without associated power outage lines. This further enhances the flexibility of task creation and its adaptability to multiple scenarios, ensuring the standardization and practicality of tasks within the task pool. The resource association matching unit combines GIS spatial analysis technology with buffer analysis algorithms to set an emergency repair task operation radius threshold and automatically retrieve available emergency teams and equipment resources within that threshold range. By constructing a multi-dimensional matching evaluation model, it comprehensively considers the professional matching degree of emergency teams and tasks (power transmission, substation, distribution transformer), the distance priority between the team's geographical location and the work site, the type compatibility of emergency equipment, and its real-time availability status, and performs dynamic weighted matching to ultimately achieve precise docking and optimal allocation of emergency repair tasks and emergency resources, thereby improving resource scheduling efficiency. S4, Emergency Response Card Management Module, is used to automatically retrieve the corresponding emergency response card based on the dispatch instruction and push it to the dispatched personnel. It uses multi-dimensional feature vector matching to verify the matching between the dispatched personnel and the emergency response card based on personnel qualification characteristics, task scenario characteristics, and operational risk characteristics. After the verification is successful, the emergency response card is pushed to the corresponding personnel's mobile operation terminal to execute the repair task. It also records the card viewing status, task execution nodes, and on-site feedback information simultaneously, forming a closed-loop management of the entire process. The emergency response card data includes response card ledger data, personnel response card associated data, and response push data. The response card ledger data includes the type of disaster, personnel qualification characteristics, unit / department, response method, and corresponding scenario. The personnel response card associated data includes personnel number, response card number, whether verification is required, and the personnel who performed the verification. The response push data includes event number, response card number, personnel number, push time, and whether the response has been viewed. The core principle of matching emergency response cards with personnel is "one card per post, one card per person," which means that each person undertaking emergency response tasks is equipped with a corresponding exclusive emergency response card based on the specific risks and responsibilities of their post. Specifically, it includes: The person-card matching and recognition unit is used to calculate the matching similarity by using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs using a feature matching algorithm; and to determine whether the emergency response card and the dispatched personnel have passed the verification based on the matching similarity. The emergency response card anomaly verification unit is used to identify anomalies in the emergency response card information based on preset verification rules, identify abnormal situations and mark the anomaly type; the anomaly identification includes the emergency response card's response process, operational risks, applicable scenarios, etc.; the abnormal situations include missing information, process conflicts, incorrect risk labeling, etc. The hierarchical data analysis unit, based on a multi-level data analysis model and preset core analysis indicators, supports refined statistical analysis by level, time period, and region, generates visual analysis reports, and completes the entire process control of online preparation, approval, analysis, summary, and reminders for handling cards, ensuring the accuracy of handling card information. The multi-level data analysis model specifically consists of a four-level data analysis model at the provincial, municipal, county, and power supply station levels; the preset core analysis indicators include preparation completion rate, approval rate, anomaly rectification rate, and personnel matching. For example, by unit / department level, the number and types of disposal cards at each level of the organization can be displayed; by disposal card type level, the personnel data for each type of disposal card can be displayed; by disaster task type level, the disposal data of disposal cards at each level of the organization can be displayed, etc. S5 also includes an early warning response control module for collecting early warning data such as typhoons, rainstorms, and wildfires. Based on the early warning data, it obtains the early warning level (e.g., divided into levels I-IV, corresponding to different degrees of disaster impact) and the affected area, triggering the corresponding early warning response process, including: The regional and graded alarm unit is used to map early warning data (such as disaster intensity, impact range, and development trend) to the built-in power grid early warning grading standard, match the built-in power grid early warning grading threshold, and determine the degree of impact of the disaster on the power grid. Relying on the GIS geographic information system, combined with the power grid topology and regional power supply range, spatial overlay analysis technology is used to accurately delineate the early warning impact areas corresponding to different impact levels, realize the visualization and graded labeling of the early warning areas, and generate early warning response data (including the unit to which it belongs, disaster type, early warning or response level, impact range, impact time, emergency measures, etc.). The early warning process self-running unit is used to map early warning data to preset early warning classification rules to obtain early warning levels; based on the early warning level and the area affected by the early warning, the corresponding early warning response process is triggered to generate a list of pre-disaster defense tasks; The online approval and control unit is used to build a standardized online approval platform based on a microservice architecture. It presets approval nodes for the entire process, such as early warning release, response initiation, and task assignment. It automatically records the operation time, operators, approval opinions, and early warning release scope of each approval node, generates a traceable and queryable early warning response timeline, and simultaneously retains an electronic ledger of the approval process to ensure that the early warning approval process is standardized and fully traceable. S6 also includes an emergency plan lifecycle management module, with built-in process node management unit, online platform operation unit, real-time progress tracking unit and intelligent reminder unit; The process node management unit adopts process node management technology and online platform construction technology. Specifically, it breaks down the entire life cycle of the emergency plan into 7 core nodes: preparation, internal review, expert review, release, periodic review, dynamic update, and archiving. For each node, a unique identifier code, a list of responsible positions and permissions, a completion time limit threshold (preparation node ≤ 15 working days, review node ≤ 7 working days), a standard template for deliverables, and a multi-level approval chain are preset. The online platform operation unit is built using a B / S architecture, supporting login for four levels of accounts: provincial, municipal, county, and power supply station. Operation permissions are allocated based on the role-based access control (RBAC) model, enabling online operations, opinion signing, and document upload and download for personnel at all levels corresponding to their respective nodes. The real-time progress tracking unit and intelligent reminder unit adopt a timed triggering mechanism. Expiration reminders are triggered via platform messages and SMS 30 days, 15 days, and 3 days before the node completion deadline, respectively. Update reminders are automatically pushed before the expiration of the plan review cycle (once a year). This achieves node-based online management of the entire lifecycle of emergency plan preparation, review, updating, and archiving. It enables real-time recording and traceable management of detailed information throughout the entire plan process (operator, operation time, modification content, approval opinions, and deliverables), and implements node-based management of the regular preparation and dynamic updating of the plan, ensuring real-time progress tracking and timely warnings of anomalies. S7 also includes a pre-disaster defense task management module, which has a built-in task lifecycle process management unit and adopts task process management technology. Specifically, it establishes a task process management system for the early warning stage, covering the entire lifecycle of task generation, allocation, execution, feedback, review, and rectification. Task generation uses rule-driven technology, pre-setting standardized defense task lists for different early warning scenarios (such as station reinforcement and line special inspections corresponding to typhoon warnings) based on the early warning level and impact range, automatically generating tasks and assigning unique task IDs. Task allocation uses precise positioning technology, combined with GIS geographic information, to assign tasks... Tasks are precisely assigned to the corresponding city and county companies and power supply stations, clearly defining the responsible persons, completion deadlines, and execution standards. Task execution personnel provide feedback on task execution through the online platform, uploading supporting materials such as on-site photos and inspection records (supporting JPG and other formats, with a single file size ≤10MB). Review personnel complete the review online (review time ≤2 working days), and push rectification requirements to unqualified tasks, achieving closed-loop management and abandoning the traditional simple text-based task assignment and response mode. This enables refined online management and control of the entire process of pre-disaster prevention work, including station duty, pre-disaster special inspections and protection, and investigation of potential hazards for important users.

[0024] Example 2: This embodiment provides an intelligent emergency command method, the method including: After a disaster occurs, disaster data is collected, including disaster event data, power outage and restoration data, and damage data. Based on the disaster data, disaster information is obtained, including the affected area, power outage and restoration section data, and damage location information. Based on disaster information, emergency teams and equipment are dispatched, and emergency repair tasks are created; spatial analysis technology is used to obtain emergency teams and equipment within the preset range of emergency repair tasks, so as to match emergency repair tasks with emergency resources, generate dispatch instructions, and issue them to repair personnel. Based on the dispatch instruction, the corresponding emergency response card is pushed to the dispatched personnel. The feature matching algorithm is used to verify the matching between the dispatched personnel and the emergency response card. Once the verification is successful, the emergency repair task is executed.

[0025] Preferably, obtaining disaster information based on disaster data specifically involves: Input disaster event data into a geographic information system, delineate the boundaries of the disaster area by hand-drawing polygons or selecting coordinate points, and bind the start and end times of the disaster to each disaster area to generate a disaster area containing the regional boundary coordinates, disaster level, and start and end times; The power outage and restoration data are input into the cross-sectional extraction algorithm. Based on any time point selected by the user, the algorithm queries for power outage equipment or users whose power outage time is no greater than the selected time point and whose restoration time is greater than the selected time point or whose restoration time is empty. These are considered as objects that are still experiencing power outages. The algorithm also counts the number of households experiencing power outages, the number of households with restored power, and the restoration rate according to preset hierarchical statistics, generating power outage and restoration cross-sectional data for the selected time point. Every hour, the algorithm automatically stores the current power outage and restoration data statistics and details into the cross-sectional history table for subsequent statistical analysis of the overall power outage and restoration trend. The fuzzy matching algorithm is used to match the equipment number or equipment name in the disaster damage data with the equipment ledger of the power grid PMS system and calculate the matching similarity. If the matching similarity is greater than the preset matching similarity threshold, the matching is successful. After the matching is successful, the latitude and longitude coordinates of the equipment, the line to which it belongs and the power supply station information are automatically read to generate disaster damage location information.

[0026] Preferably, emergency teams and equipment are dispatched based on disaster information. The specific steps are as follows: the disaster information is weighted and calculated with corresponding weights to obtain the team matching degree; emergency teams are matched based on the team matching degree.

[0027] Preferably, a feature matching algorithm is used to verify the match between the dispatched personnel and the emergency response card. The specific steps are as follows: The system calculates the matching similarity using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs; based on the matching similarity, it determines whether the emergency response card and the dispatched personnel pass the verification.

[0028] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0029] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0030] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0031] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An intelligent emergency command system, characterized in that, The system includes: The disaster event data management module is used to collect disaster data after a disaster occurs and generate disaster information based on the disaster data; The emergency response team and equipment management module is used to dispatch emergency response teams and equipment based on disaster information; The emergency repair task management module is used to create emergency repair tasks based on disaster information, match emergency teams and emergency equipment for emergency repair tasks, and generate dispatch instructions. The emergency response card management module is used to push the corresponding emergency response card to the dispatched personnel based on the dispatch instruction. It uses a feature matching algorithm to verify the matching between the dispatched personnel and the emergency response card. Once the verification is successful, the repair task is executed.

2. The intelligent emergency command system according to claim 1, characterized in that, The emergency response team and equipment management module includes an emergency response team management submodule and an emergency equipment management submodule, wherein the emergency response team management submodule includes: The ledger-based refined management unit is used to store the ledger information of emergency repair teams with preset management fields in a relational database, and supports real-time updates and queries of the information; The information intelligent verification unit is used to automatically verify and identify team ledger information from multiple dimensions based on preset integrity verification rules, logical consistency verification rules, and business compliance verification rules. It marks abnormal information that is missing, conflicting, or does not conform to the specifications, and pushes rectification reminders to abnormal information. The multi-dimensional matching unit is used to perform weighted calculations on disaster information and corresponding weights to generate a comprehensive team matching index; emergency teams are matched based on the comprehensive team matching index.

3. The intelligent emergency command system according to claim 2, characterized in that, The emergency equipment management submodule includes: The equipment classification sub-unit is used to classify emergency equipment using a tree-like classification structure and generate a unique number for each piece of emergency equipment. The full-process traceability sub-unit is used to record information about emergency equipment from demand, approval, allocation, outbound, use, return to warehousing, and realize full life-cycle traceability of equipment. The online allocation sub-unit is used to build an online collaborative allocation platform for emergency equipment based on a microservice architecture. It supports disaster-stricken units to initiate equipment demand applications online, and higher-level units to complete approval and allocation instructions online. It also supports equipment lending units and borrowing units to complete handover confirmation online.

4. The intelligent emergency command system according to claim 1, characterized in that, The emergency repair task management module includes: The task-line decoupling control unit is used to remove the strong correlation between emergency repair tasks and power outage / restoration lines using data decoupling technology, so as to realize the creation of emergency repair tasks without corresponding power outage lines. The multi-type task management unit is used to break down various types of emergency repair tasks into independent microservices using a microservice architecture, so as to achieve centralized management and unified scheduling of all repair tasks. The resource association and matching unit is used to acquire emergency teams and equipment within a preset range for emergency repair tasks using spatial analysis technology, so as to achieve precise matching of emergency repair tasks and resources.

5. The intelligent emergency command system according to claim 1, characterized in that, The emergency response card management module includes: The person-card matching and recognition unit is used to calculate the matching similarity by using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs using a feature matching algorithm; and to determine whether the emergency response card and the dispatched personnel have passed the verification based on the matching similarity. The emergency response card anomaly verification unit is used to identify anomalies in emergency response card information based on preset verification rules, identify abnormal situations, and mark the anomaly type. Hierarchical data analysis units are used to generate visual analysis reports based on the established multi-level data analysis model and preset core analysis indicators.

6. The intelligent emergency command system according to claim 1, characterized in that, It also includes an early warning response control module for collecting early warning data, determining the early warning level and affected area based on the data, and triggering the corresponding early warning response process, including: The regional and graded alarm unit is used to map early warning data to the built-in power grid early warning classification standard to obtain the degree of disaster impact; and uses geographic information system technology to delineate the early warning impact area according to the degree of disaster impact. The early warning process self-running unit is used to map early warning data to preset early warning classification rules to obtain early warning levels; based on the early warning level and the area affected by the early warning, the corresponding early warning response process is triggered to generate a list of pre-disaster defense tasks; The online approval and control unit is used to build a standardized online approval platform based on a microservice architecture. It automatically records the operation time, approval opinions and release scope of the entire process, and forms an early warning and response timeline.

7. An intelligent emergency command method, characterized in that, The method includes: After a disaster occurs, disaster data is collected, including disaster event data, power outage and restoration data, and damage data. Based on the disaster data, disaster information is obtained, including the affected area, power outage and restoration section data, and damage location information. Based on disaster information, emergency teams and equipment are dispatched, and emergency repair tasks are created; spatial analysis technology is used to obtain emergency teams and equipment within the preset range of emergency repair tasks, so as to match emergency repair tasks with emergency resources, generate dispatch instructions, and issue them to repair personnel. Based on the dispatch instruction, the corresponding emergency response card is pushed to the dispatched personnel. The feature matching algorithm is used to verify the matching between the dispatched personnel and the emergency response card. Once the verification is successful, the emergency repair task is executed.

8. The intelligent emergency command method according to claim 7, characterized in that, The disaster information obtained based on disaster data specifically includes: Input disaster event data into a geographic information system, delineate the boundaries of the disaster area by hand-drawing polygons or selecting coordinate points, and bind the start and end times of the disaster to each disaster area to generate a disaster area containing the regional boundary coordinates, disaster level, and start and end times; The power outage and restoration data are input into the cross-section extraction algorithm. Based on any time point selected by the user, the algorithm queries the power outage equipment or users whose power outage time is no greater than the selected time point and whose restoration time is greater than the selected time point or whose restoration time is empty. These are the objects that are still in power outage. The algorithm also counts the number of households with power outages, the number of households with power restoration, and the restoration rate according to the preset hierarchy, and generates the power outage and restoration cross-section data for the selected time point. The fuzzy matching algorithm is used to match the equipment number or equipment name in the disaster damage data with the equipment ledger of the power grid PMS system and calculate the matching similarity. If the matching similarity is greater than the preset matching similarity threshold, the matching is successful. After the matching is successful, the latitude and longitude coordinates of the equipment, the line to which it belongs and the power supply station information are automatically read to generate disaster damage location information.

9. The intelligent emergency command method according to claim 7, characterized in that, The dispatch of emergency teams and equipment based on disaster information involves the following steps: weighting the disaster information with corresponding weights to obtain the team matching degree; and matching emergency teams based on the team matching degree.

10. The intelligent emergency command method according to claim 7, characterized in that, The feature matching algorithm is used to verify the match between the dispatched personnel and the emergency response card. The specific steps are as follows: The system calculates the matching similarity using the QR code information of the emergency response card, the ID card information of the dispatched personnel, and the job qualification information of the dispatched personnel as inputs; based on the matching similarity, it determines whether the emergency response card and the dispatched personnel pass the verification.

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