A power supply command guarantee data integration and emergency disposal method and system

Abstract

The present application relates to the technical field of data processing, and more particularly to a power supply command guarantee data integration and emergency event disposal method and system, the method collects target multidimensional data through step S1, also integrates multi-source data and spatialized visualization through step S2, also accurately geographically correlates and maps multi-source heterogeneous data and the power grid one map through step S3, also upgrades the static command view to the dynamic calibrated intelligent operation map through step S4, also realizes the automatic identification and intelligent filtering of the power grid emergency event through step S5, also realizes the accurate matching of the emergency event and the surrounding emergency resources through step S6, also realizes the intelligent generation of the disposal strategy through step S7, also constructs the self-adaptive closed-loop optimization mechanism through step S8, and also continuously optimizes the resource matching and the scheduling rule through step S9.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for integrating power supply command and control data and handling emergencies. Background Technology

[0002] In the current power supply command and support system, various key information is scattered across independent business systems, forming serious "information silos." Multi-dimensional data such as fault information, emergency repair resources, service work orders, and topic data lack effective integration, making it difficult for command personnel to grasp the complete fault situation. Fault location and repair work orders cannot be quickly linked, affecting judgment efficiency. Emergency repair force dispatch relies on manual telephone confirmation, resulting in delayed response. This situation of data isolation and decision-making reliance on manual processes leads to obvious defects in power supply command when facing sudden faults, such as low information acquisition efficiency, unscientific resource allocation, and lack of closed-loop optimization mechanisms, which seriously affect the speed of fault recovery and power supply reliability.

[0003] Chinese patent application CN113128902A discloses an emergency incident reporting and handling system, including a data source, an instant reporting data processing system connected to the data source, and an instant reporting management system connected to the data processing system. The instant reporting data processing system processes data collected from the data source and transmits it to the instant reporting management system. The instant reporting management system includes interconnected hotspot generation terminals, distribution management terminals, verification and allocation terminals, and verification terminals. The permissions of the hotspot generation terminal include receiving hotspot information transmitted from the data source or manual alarms and creating hotspot events. The permissions of the distribution management terminal include preliminary verification and hotspot verification allocation. The permissions of the verification terminal include verification and feedback of verification results. Therefore, this solution still suffers from low efficiency in emergency power supply command and response due to a lack of multi-dimensional data integration and fault location. Summary of the Invention

[0004] Therefore, the present invention provides a method and system for power supply command and support data integration and emergency response, in order to overcome the problem of low efficiency in emergency power supply command and response caused by the lack of multi-dimensional data integration and fault location in the prior art.

[0005] To achieve the above objectives, on the one hand, the present invention provides a method for integrating power supply command and support data and handling emergencies, the method comprising: Step S1: Collect multi-dimensional data of the target; Step S2: Spatialize the target multidimensional data to obtain spatialized multidimensional data; Step S3: Perform geographic correlation mapping on the spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; Step S4: Perform data calibration on the preliminary command and support visualization layer to obtain the command and support visualization layer; Step S5: Obtain information on power grid emergencies based on spatialized multi-dimensional data; Step S6: Perform spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results; Step S7: Obtain emergency response strategies based on spatial matching analysis results; Step S8: Obtain the fault handling status and optimize the process of obtaining the emergency handling strategy based on the fault handling status. Step S9: Obtain the fault recovery time and adjust the strategy optimization process based on the fault recovery time.

[0006] Furthermore, in step S2, the target multi-dimensional data is spatialized by converting it into latitude and longitude related data through geocoding. Spatialized multidimensional data is generated based on latitude and longitude related data using a spatial density statistical algorithm.

[0007] Furthermore, in step S3, when performing geographic association mapping on the spatialized multi-dimensional data to obtain the preliminary command and support visualization layer, the spatial connection algorithm of the spatial database is used to associate the spatialized multi-dimensional data with the lines in the power grid map. Geographical entities are extracted from spatialized multi-dimensional data using named entity recognition algorithms and associated with administrative regions in a power grid map to obtain a preliminary command and support visualization layer.

[0008] Furthermore, in step S4, when calibrating the data of the preliminary command and support visualization layer, a data interface with the meteorological early warning platform is established. When extreme weather affects a specific area, the border of the preliminary command and support visualization layer is highlighted to obtain the command and support visualization layer.

[0009] Furthermore, in step S5, when acquiring information on power grid emergencies based on spatialized multi-dimensional data, a spatial clustering algorithm is used to identify the density Sp of repair work orders based on the spatialized multi-dimensional data. The reported work order density Sp is compared with the preset reported work order density Sp0. Based on the comparison result, the status of the reported work order density is determined, and based on the determination result, information on power grid emergencies is obtained, including: When Sp≤Sp0, the state of the repair work order density is determined to be stable, and no information on power grid emergencies is acquired. When Sp > Sp0, the state of the repair work order density is determined to be unstable, and information on power grid emergencies is acquired, including geographical location, impact range, and estimated level.

[0010] Furthermore, in step S6, when performing spatial matching analysis between the power grid emergency information and the command and support visualization layer, a target search circle is generated based on the spatial buffer analysis algorithm, with the geographical location of the emergency as the center. In the command and support visualization layer, spatial queries and attribute filtering are performed on all target objects within the target search circle. The Euclidean distance between the target object and the geographic center of the emergency is calculated, and a list of available emergency resources sorted by distance and resource status is generated as the spatial matching analysis result.

[0011] Furthermore, in step S7, when acquiring emergency response strategies based on spatial matching analysis results, the spatial matching analysis results are input into a preset rule engine. This preset rule engine has a built-in matching rule library for "fault type - resource type - distance". The disposal strategy options are obtained, including: directional dispatch instructions, preset paths, and work order information pushed to the mobile terminals of designated emergency repair teams via message middleware.

[0012] Furthermore, in step S8, when acquiring the fault handling status and optimizing the acquisition process of the emergency response strategy based on the fault handling status, the system receives on-site status information reported by the repair personnel through the mobile terminal App. The on-site status information includes: "work order received", "arrived on-site", "handling", "reinforcement required", and "restored". When the "reinforcement required" status is received, the system automatically triggers the rule engine to perform strategy rematching. Based on the latest resource location and status, the system re-executes spatial matching analysis and strategy acquisition to generate a reinforcement strategy and adds the reinforcement strategy to the emergency response strategy.

[0013] Furthermore, in step S9, when the fault recovery time is acquired and the strategy is adjusted according to the fault recovery time, the system records the fault occurrence time and fault recovery time of each sudden event and calculates the fault recovery time. Establish a strategy effectiveness evaluation model. When the average recovery time of similar faults is consistently higher than the historical baseline, adjust the relevant parameters in the preset rule engine.

[0014] Specifically, the relevant parameters in the preset rule engine refer to parameters related to "resource scheduling priority".

[0015] On the other hand, the present invention also provides a power supply command and support data integration and emergency response system, the system comprising: The multi-dimensional acquisition module is used to collect multi-dimensional data of the target. The spatial processing module is used to perform spatial processing on the target multi-dimensional data to obtain spatial multi-dimensional data; The association mapping module is used to perform geographic association mapping on spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; The layer calibration module performs data calibration on the initial command and support visualization layer to obtain the command and support visualization layer. The information acquisition module acquires information on power grid emergencies based on spatialized multi-dimensional data. The spatial analysis module performs spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results. The strategy acquisition module acquires emergency response strategies based on spatial matching analysis results. The strategy optimization module acquires the fault handling status and optimizes the acquisition process of emergency handling strategies based on the fault handling status. The strategy adjustment module acquires the fault recovery time and adjusts the strategy optimization process based on the fault recovery time.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: the method collects multi-dimensional data of the target in step S1; the method integrates multi-source data and spatial visualization in step S2 to achieve fault situation awareness, precise emergency resource scheduling, and proactive risk warning; the method accurately maps multi-source heterogeneous data to a single power grid map in step S3, effectively breaking down information silos; the method upgrades the static command view to a dynamically calibrated intelligent operational map in step S4, improving the accuracy and predictability of emergency repair resource scheduling and effectively avoiding delays caused by traffic congestion and severe weather; and the method automatically identifies and intelligently manages power grid emergencies in step S5. The method can filter and effectively avoid invalid alarms caused by sporadic repair reports. In step S6, the method achieves accurate and rapid matching of emergencies with surrounding emergency resources, greatly improving the scientific nature and response speed of emergency repair force dispatch. In step S7, the method achieves intelligent generation of handling strategies, reducing the time delay of traditional manual judgment and layer-by-layer transmission, ensuring the scientific nature and accuracy of dispatch decisions, and greatly improving the efficiency of emergency handling. In step S8, the method constructs an adaptive closed-loop optimization mechanism to improve the ability to respond to complex faults and the success rate of handling them. In step S9, the method continuously optimizes resource matching and dispatch rules to improve the self-evolution capability and long-term emergency response efficiency of the command system. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for integrating multi-dimensional data and efficiently handling emergencies in power supply command and control in this embodiment. Figure 2 This is a schematic diagram of the power supply command and control system for multi-dimensional data integration and efficient emergency response in this embodiment. Detailed Implementation

[0018] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0019] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0020] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0021] Furthermore, it should be noted that, in the description of this invention, 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; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0022] Please see Figure 1 As shown, this is a flowchart illustrating the multi-dimensional data integration and efficient emergency response method for power supply command and support in this embodiment. The method includes: Step S1: Collect multi-dimensional data of the target; Step S2: Spatialize the target multidimensional data to obtain spatialized multidimensional data; Step S3: Perform geographic correlation mapping on the spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; Step S4: Perform data calibration on the preliminary command and support visualization layer to obtain the command and support visualization layer; Step S5: Obtain information on power grid emergencies based on spatialized multi-dimensional data; Step S6: Perform spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results; Step S7: Obtain emergency response strategies based on spatial matching analysis results; Step S8: Obtain the fault handling status and optimize the process of obtaining the emergency handling strategy based on the fault handling status. Step S9: Obtain the fault recovery time and adjust the strategy optimization process based on the fault recovery time.

[0023] Specifically, the method for integrating multi-dimensional data and efficiently handling emergencies in power supply command and support is applied to power supply command and support equipment terminals, such as mobile devices used by power supply maintenance personnel. The method collects multi-dimensional data of the target in step S1. Step S2 integrates multi-source data with spatial visualization to achieve fault situation awareness, precise emergency resource scheduling, and proactive risk warning. Step S3 precisely maps multi-source heterogeneous data to a single power grid map, effectively breaking down information silos. Step S4 upgrades the static command view to a dynamically calibrated intelligent operational map, improving the accuracy and predictability of emergency repair resource scheduling and effectively mitigating delays caused by traffic congestion and severe weather. The method also... Step S5 enables automatic identification and intelligent filtering of power grid emergencies, effectively avoiding invalid alarms caused by sporadic repair reports. Step S6 further enables accurate and rapid matching of emergencies with surrounding emergency resources, greatly improving the scientific nature and response speed of emergency repair force dispatch. Step S7 enables intelligent generation of handling strategies, reducing the time delay of traditional manual judgment and layer-by-layer transmission, ensuring the scientific nature and accuracy of dispatch decisions, and greatly improving the efficiency of emergency response. Step S8 constructs an adaptive closed-loop optimization mechanism to improve the ability to respond to complex faults and the success rate of handling them. Step S9 continuously optimizes resource matching and dispatch rules, improving the self-evolution capability of the command system and the long-term emergency response efficiency.

[0024] Specifically, in step S1, multi-dimensional data of the target are collected.

[0025] Specifically, the target multi-dimensional data includes internal data from the power grid dispatching system, equipment management system, and marketing service system, as well as external data from social media, news media, and meteorological departments. This data includes fault location, repair work orders, service personnel location, important user information, and topic information. The fault location refers to the geographical and logical location of an abnormal tripping or signal distortion of a circuit breaker, line, or distribution transformer with a unique equipment code in the power grid topology map, uploaded through the power grid dispatching system, distribution automation system, or fault indicator. The repair work order refers to a report submitted by a user through channels such as call centers, mobile apps, and social media, and generated by the power supply service command system, containing the user's address, contact information, and fault details. The system includes structured data records of the phenomenon, reporting time, and unique work order number; the location of the service personnel refers to the latitude and longitude coordinates obtained and uploaded by the smart terminals of field personnel such as community managers and customer service personnel through location services; the important user information refers to the electricity consumption information of important guarantee units such as hospitals, government agencies, transportation hubs, and large commercial districts that are pre-entered into the system knowledge base, including their geographical location, power supply lines, load levels, and contact information of dedicated customer managers; and the topic information refers to text, image, or video information that is negative, urgent, or of high concern related to power failures, collected from public channels such as social media, news websites, and forums through web crawling technology and judged to be related to power failures after sentiment analysis using natural language processing technology.

[0026] Specifically, in step S2, the multi-dimensional data of the target is spatialized by converting it into latitude and longitude related data through geocoding. Spatialized multidimensional data is generated based on latitude and longitude related data using a spatial density statistical algorithm.

[0027] Specifically, geocoding refers to the technical process of converting text addresses or geographic location entities into latitude and longitude coordinates using a geocoding service interface or terminal positioning module with a built-in address rule base. The spatial density statistical algorithm refers to a data processing algorithm that calculates the distribution density of point features in geographic space based on latitude and longitude coordinates and using kernel density estimation or grid aggregation methods to generate a heat map.

[0028] Specifically, in step S2, by integrating multi-source data and spatial visualization, fault situation awareness, precise scheduling of emergency resources, and proactive early warning of topic risks are achieved.

[0029] Specifically, in step S3, when performing geographic association mapping on the spatialized multi-dimensional data to obtain the preliminary command and support visualization layer, the spatial connection algorithm of the spatial database is used to associate the spatialized multi-dimensional data with the lines in the power grid map. Geographical entities are extracted from spatialized multi-dimensional data using named entity recognition algorithms and associated with administrative regions in a power grid map to obtain a preliminary command and support visualization layer.

[0030] Specifically, the "power grid map" refers to a visualization platform that integrates geographic information, power grid topology, and resource locations, providing a unified spatiotemporal background and operating interface for command and support. The "spatial database spatial connection algorithm" refers to a database query operation that associates and merges attributes of two or more spatial data layers based on geometric positional relationships, such as intersection, containment, and proximity. The "named entity recognition algorithm" refers to a natural language processing technique used to automatically identify and extract entities with specific meanings from text, such as place names and organization names, and classify them into predefined categories.

[0031] Specifically, in step S3, spatial connectivity and named entity recognition algorithms are used to accurately map multi-source heterogeneous data to a single power grid map, effectively breaking down information silos and spatially associating fault points, resource locations, and topic information, laying a solid foundation for subsequent precise spatial analysis and collaborative command.

[0032] Specifically, in step S4, when calibrating the data of the preliminary command and support visualization layer, a data interface with the meteorological early warning platform is established. When extreme weather affects a specific area, the border of the preliminary command and support visualization layer is highlighted to obtain the command and support visualization layer.

[0033] Specifically, the data interface of the meteorological early warning platform refers to a standardized data communication channel, which is used to automatically receive weather data issued by meteorological departments, containing structured information such as early warning type, geographical impact range, intensity level, and effective time.

[0034] Specifically, in step S4, by introducing meteorological data, the static command view is upgraded to a dynamically calibrated intelligent operational map, which improves the accuracy and predictability of emergency repair resource scheduling, effectively avoids the risk of delays caused by traffic congestion and severe weather, and thus provides key decision support for power supply command and support.

[0035] Specifically, in step S5, when acquiring information on power grid emergencies based on spatialized multi-dimensional data, a spatial clustering algorithm is used to identify the density Sp of repair work orders based on the spatialized multi-dimensional data. The reported work order density Sp is compared with the preset reported work order density Sp0. Based on the comparison result, the status of the reported work order density is determined, and based on the determination result, information on power grid emergencies is obtained, including: When Sp≤Sp0, the state of the repair work order density is determined to be stable, and no information on power grid emergencies is acquired. When Sp > Sp0, the state of the repair work order density is determined to be unstable, and information on power grid emergencies is acquired, including geographical location, impact range, and estimated level.

[0036] Specifically, the preset repair order density refers to a preset value for judging the state of the repair order density. The state of the repair order density includes stable and unstable. The geographical location refers to the geographical entity jointly determined by the coordinates of the power grid equipment, the latitude and longitude of the user's repair address, or the topic information. The precise latitude and longitude coordinates and the power supply area corresponding to the sudden event in the spatial database and the power grid map. The impact range refers to the line segment, distribution transformer, and the final set of users affected by the sudden event and the geographical area automatically calculated based on power grid topology analysis through upstream power point tracing and downstream load point search. The estimated level refers to the classification level set according to the preset rule base, used to identify the urgency of the event and the priority of resource allocation, such as level one, level two, and level three.

[0037] Specifically, in step S5, spatial clustering and threshold comparison are used to achieve automatic identification and intelligent filtering of power grid emergencies, effectively avoiding invalid alarms caused by sporadic repairs, and triggering precise event location only when the repair density is abnormal, which greatly improves the accuracy and efficiency of emergency response and reduces information interference for command personnel.

[0038] Specifically, in step S6, when performing spatial matching analysis between the power grid emergency information and the command and support visualization layer, a target search circle is generated based on the spatial buffer analysis algorithm, with the geographical location of the emergency as the center. In the command and support visualization layer, spatial queries and attribute filtering are performed on all target objects within the target search circle. The Euclidean distance between the target object and the geographic center of the emergency is calculated, and a list of available emergency resources sorted by distance and resource status is generated as the spatial matching analysis result.

[0039] Specifically, the target objects refer to repair vehicles, generators, and service personnel. The spatial query refers to a technology that uses spatial relationship functions of a spatial database to quickly filter out all potential resources within the emergency radius. The attribute filtering refers to filtering the non-spatial attributes of the target objects based on the spatial query results and according to preset business rules, such as selecting only repair vehicles with a status of "standby" or "current task completed". The available emergency resource list refers to a structured list containing resource IDs and types, generated according to multi-dimensional sorting rules such as Euclidean distance from near to far and resource status from best to worst.

[0040] Specifically, through spatial buffer analysis and multi-condition screening, the system achieves accurate and rapid matching of emergencies with surrounding emergency resources, greatly improving the scientific nature and response speed of emergency repair force dispatch.

[0041] Specifically, in step S7, when acquiring emergency response strategies based on spatial matching analysis results, the spatial matching analysis results are input into a preset rule engine. This preset rule engine has a built-in matching rule library for "fault type - resource type - distance". The disposal strategy options are obtained, including: directional dispatch instructions, preset paths, and work order information pushed to the mobile terminals of designated emergency repair teams via message middleware.

[0042] Specifically, the directional scheduling instruction refers to a standardized instruction automatically generated by the rules engine that includes a specific execution object, task objective, and time limit, and the preset path refers to a preset vehicle driving route.

[0043] Specifically, in step S7, the spatial matching results are automatically converted into executable scheduling instructions through the rule engine, realizing the intelligent generation of handling strategies, reducing the time delay of traditional manual judgment and layer-by-layer communication, ensuring the scientific and accurate nature of scheduling decisions, and greatly improving the efficiency of handling emergencies.

[0044] Specifically, in step S8, when acquiring the fault handling status and optimizing the acquisition process of the emergency response strategy based on the fault handling status, the system receives on-site status information reported by the repair personnel through a mobile terminal App. The on-site status information includes: "work order received", "arrived on-site", "handling", "reinforcement needed", and "restored". When the "reinforcement needed" status is received, the system automatically triggers the rule engine to perform strategy rematching. Based on the latest resource location and status, the system re-executes spatial matching analysis and strategy acquisition to generate a reinforcement strategy and adds the reinforcement strategy to the emergency response strategy.

[0045] Specifically, the reinforcement strategy refers to an emergency response plan generated by the rule engine through rematching when the initial repair force is insufficient. This plan includes additional resource scheduling schemes and updated collaborative task instructions, such as dispatching additional vehicles, personnel, or special equipment.

[0046] Specifically, in step S8, an adaptive closed-loop optimization mechanism is constructed by dynamically rematching the status feedback of the mobile terminal with the rule engine, thereby improving the ability to respond to complex faults and the success rate of handling them, and realizing the leap from static prediction to dynamic optimization in command and dispatch.

[0047] Specifically, in step S9, when the fault recovery time is acquired and the strategy is adjusted according to the fault recovery time, the system records the fault occurrence time and fault recovery time of each sudden event and calculates the fault recovery time. Establish a strategy effectiveness evaluation model. When the average recovery time of similar faults is consistently higher than the historical baseline, adjust the relevant parameters in the preset rule engine.

[0048] Specifically, the relevant parameters in the preset rule engine refer to parameters concerning "resource scheduling priority" and "path selection weight".

[0049] Specifically, in step S9, by establishing a strategy effectiveness evaluation model, quantitative analysis of historical response effectiveness and adaptive optimization of the rule engine are achieved, thereby continuously optimizing resource matching and scheduling rules, and improving the self-evolution capability and long-term emergency response efficiency of the command system.

[0050] Please see Figure 2 As shown, this is a structural diagram of the power supply command and support data integration and emergency response system of this embodiment. The system includes: The multi-dimensional acquisition module is used to collect multi-dimensional data of the target. The spatial processing module is used to perform spatial processing on the target multi-dimensional data to obtain spatial multi-dimensional data; The association mapping module is used to perform geographic association mapping on spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; The layer calibration module performs data calibration on the initial command and support visualization layer to obtain the command and support visualization layer. The information acquisition module acquires information on power grid emergencies based on spatialized multi-dimensional data. The spatial analysis module performs spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results. The strategy acquisition module acquires emergency response strategies based on spatial matching analysis results. The strategy optimization module acquires the fault handling status and optimizes the acquisition process of emergency handling strategies based on the fault handling status. The strategy adjustment module acquires the fault recovery time and adjusts the strategy optimization process based on the fault recovery time.

[0051] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for integrating power supply command and control data and handling emergencies, characterized in that, The method includes: Step S1: Collect multi-dimensional data of the target; Step S2: Spatialize the target multidimensional data to obtain spatialized multidimensional data; Step S3: Perform geographic correlation mapping on the spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; Step S4: Perform data calibration on the preliminary command and support visualization layer to obtain the command and support visualization layer; Step S5: Obtain information on power grid emergencies based on spatialized multi-dimensional data; Step S6: Perform spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results; Step S7: Obtain emergency response strategies based on spatial matching analysis results; Step S8: Obtain the fault handling status and optimize the process of obtaining the emergency handling strategy based on the fault handling status. Step S9: Obtain the fault recovery time and adjust the strategy optimization process based on the fault recovery time.

2. The method for power supply command and support data integration and emergency response as described in claim 1, characterized in that: In step S2, the multi-dimensional data of the target is spatialized by converting it into latitude and longitude related data through geocoding. Spatialized multidimensional data is generated based on latitude and longitude related data using a spatial density statistical algorithm.

3. The method for power supply command and support data integration and emergency response as described in claim 2, characterized in that: In step S3, when performing geographic association mapping on the spatialized multi-dimensional data to obtain the preliminary command and support visualization layer, the spatial connection algorithm of the spatial database is used to associate the spatialized multi-dimensional data with the lines in the power grid map. Geographical entities are extracted from spatialized multi-dimensional data using named entity recognition algorithms and associated with administrative regions in a power grid map to obtain a preliminary command and support visualization layer.

4. The method for power supply command and support data integration and emergency response as described in claim 3, characterized in that: In step S4, when calibrating the data of the preliminary command and support visualization layer, a data interface with the meteorological early warning platform is established. When extreme weather affects a specific area, the border of the preliminary command and support visualization layer is highlighted to obtain the command and support visualization layer.

5. The method for power supply command and support data integration and emergency response as described in claim 4, characterized in that: In step S5, when acquiring information on power grid emergencies based on spatialized multi-dimensional data, the density Sp of repair work orders is identified using a spatial clustering algorithm based on the spatialized multi-dimensional data. The reported work order density Sp is compared with the preset reported work order density Sp0. Based on the comparison result, the status of the reported work order density is determined, and based on the determination result, information on power grid emergencies is obtained, including: When Sp≤Sp0, the state of the repair work order density is determined to be stable, and no information on power grid emergencies is acquired. When Sp > Sp0, the state of the repair work order density is determined to be unstable, and information on power grid emergencies is acquired, including geographical location, impact range, and estimated level.

6. The method for power supply command and support data integration and emergency response as described in claim 5, characterized in that: In step S6, when performing spatial matching analysis between the power grid emergency information and the command and support visualization layer, a target search circle is generated based on the spatial buffer analysis algorithm, with the geographical location of the emergency as the center. In the command and support visualization layer, spatial queries and attribute filtering are performed on all target objects within the target search circle. The Euclidean distance between the target object and the geographic center of the emergency is calculated, and a list of available emergency resources sorted by distance and resource status is generated as the spatial matching analysis result.

7. The method for power supply command and support data integration and emergency response as described in claim 6, characterized in that: In step S7, when obtaining the emergency response strategy based on the spatial matching analysis results, the spatial matching analysis results are input into a preset rule engine. The preset rule engine has a built-in matching rule library of "fault type - resource type - distance" to obtain response strategy options. The response strategy options include: directional dispatch instructions, preset paths, and work order information pushed to the mobile terminal of the designated emergency repair team through message middleware.

8. The method for power supply command and support data integration and emergency response as described in claim 7, characterized in that: In step S8, when the fault handling status is acquired and the emergency response strategy acquisition process is optimized based on the fault handling status, the on-site status information reported by the repair personnel through the mobile terminal App is received. The on-site status information includes: "work order received", "arrived on site", "handling", "reinforcement required", and "restored". When the "reinforcement required" status is received, the rule engine is automatically triggered to perform strategy rematching. Based on the latest resource location and status, spatial matching analysis and strategy acquisition are re-executed to generate a reinforcement strategy and add the reinforcement strategy to the emergency response strategy.

9. The method for power supply command and support data integration and emergency response as described in claim 8, characterized in that: In step S9, when the fault recovery time is acquired and the strategy is adjusted according to the fault recovery time, the system records the fault occurrence time and fault recovery time of each sudden event and calculates the fault recovery time. Establish a strategy effectiveness evaluation model. When the average recovery time of similar faults is consistently higher than the historical baseline, adjust the relevant parameters in the preset rule engine.

10. A power supply command and control data integration and emergency response system applied to any one of claims 1-9, the system comprising: The multi-dimensional acquisition module is used to collect multi-dimensional data of the target. The spatial processing module is used to perform spatial processing on the target multi-dimensional data to obtain spatial multi-dimensional data; The association mapping module is used to perform geographic association mapping on spatialized multi-dimensional data to obtain a preliminary command and support visualization layer; The layer calibration module performs data calibration on the initial command and support visualization layer to obtain the command and support visualization layer. The information acquisition module acquires information on power grid emergencies based on spatialized multi-dimensional data. The spatial analysis module performs spatial matching analysis between the power grid emergency information and the command and support visualization layer to obtain the spatial matching analysis results. The strategy acquisition module acquires emergency response strategies based on spatial matching analysis results. The strategy optimization module acquires the fault handling status and optimizes the acquisition process of emergency handling strategies based on the fault handling status. The strategy adjustment module acquires the fault recovery time and adjusts the strategy optimization process based on the fault recovery time.

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