Electric power disaster intelligent decision-making and mobile cooperation system and device based on multi-source data fusion, and storage medium

Abstract

The invention relates to the technical field of electric power system management, and discloses an electric power disaster intelligent decision-making and mobile cooperation system and device based on multi-source data fusion, and a storage medium. The system comprises a rule management module, an intelligent decision-making module and a mobile cooperation module. The rule management module is configured with a multi-disaster dynamic rule base supporting icing, forest fire and flood prevention and is associated with a sensor threshold value and a disposal strategy. The intelligent decision-making module accesses sensor data streams such as weather, icing thickness and water level in real time, and generates a disposal work order through a rule engine matching rule; and the mobile cooperation module pushes the work order to the terminal, integrates a geographic information visual interface, receives an on-site processing image and progress feedback, and realizes closed-loop tracking of the work order state. According to the system, high-concurrency data is processed by adopting a distributed architecture, data security is guaranteed in combination with role permission control, the problems that traditional disaster response is lagged, disposal is fragmented and the collaboration efficiency is low are solved, and whole-process mobile collaboration management of disaster sensing, decision making, execution and verification is achieved.

Description

Technical Field

[0001] This invention relates to the field of power system management, and specifically provides a power disaster intelligent decision-making and mobile collaborative system, device and storage medium based on multi-source data fusion. Background Technology

[0002] Traditional power disaster management systems rely on manual on-site inspections and hierarchical reporting mechanisms, resulting in a lengthy disaster information transmission chain. Delays of several hours often occur between on-site discovery and command center decision-making, leading to severe delays in disaster response. In existing technologies, handling rules are typically scattered across paper documents or independent business systems, making it difficult for on-site personnel to access the latest handling plans in a timely manner. This can easily lead to operational errors or resource scheduling conflicts due to information asymmetry. Existing platforms lack deep integration with mobile devices, preventing real-time interaction between disaster data, handling instructions, and on-site feedback, creating information silos. Mobile terminals only support basic data viewing functions and lack automatic work order distribution, geographic navigation, and process verification, leading to loss of control during the handling process. The database architecture cannot support the concurrent alarm volumes during peak periods such as the flood season, posing risks of query delays and system crashes. Furthermore, traditional systems lack closed-loop verification mechanisms; work order execution status relies on manual reporting, making it prone to falsification of handling results or loss of progress monitoring, and the completeness of historical data analysis is insufficient. These shortcomings collectively restrict the timeliness, accuracy, and collaborative efficiency of power disaster management. Summary of the Invention

[0003] To address the technical problem that traditional power disaster management systems rely on manual on-site inspections and hierarchical reporting mechanisms, resulting in a lengthy disaster information transmission chain and a delay of several hours from on-site discovery to command center decision-making, leading to severely delayed disaster response, this invention provides a power disaster intelligent decision-making and mobile collaborative system, equipment, and storage medium based on multi-source data fusion.

[0004] This invention provides a power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion, characterized by the following features:

[0005] The rule management module is used to configure a dynamic rule base that supports multiple disaster types. The rule base contains enableable disaster response rules and multi-condition combination logic.

[0006] The intelligent decision-making module communicates with the rule management module to access power disaster monitoring sensor data in real time and generate disposal instructions and work order processes by matching dynamic rule bases through the rule engine;

[0007] The mobile collaboration module interacts with the intelligent decision-making module to push work orders to mobile terminals, integrate geographic information visualization interfaces, and receive on-site feedback data.

[0008] Specifically, the configuration operation of the dynamic rule base includes: when defining icing disaster rules through the management backend, the logical conditions for associating the icing thickness threshold of the conductor with the de-icing disposal strategy.

[0009] Specifically, the dynamic rule base further supports the configuration of wildfire disaster rules, specifically linking the combination of meteorological station temperature data, humidity data, and firebreak development and disposal strategies.

[0010] Specifically, the sensor data accessed by the intelligent decision-making module includes real-time river water level information collected by water level sensors, and the water level information is matched with flood control and disaster response rules.

[0011] Specifically, when the rule engine performs the matching operation, it compares the real-time data stream collected by the sensor with the enabled rule conditions in the dynamic rule base one by one.

[0012] Specifically, when the real-time data stream meets the rule conditions, the rule engine automatically generates a work order process instance containing the processing instruction code.

[0013] Specifically, the geographic information visualization interface of the mobile collaboration module overlays and displays the coordinates of disaster sites, the location markers of monitoring equipment, and the topology map of work order navigation paths.

[0014] Specifically, the on-site feedback data received by the mobile collaboration module includes images of the handling process taken by on-site personnel through mobile terminals and manually entered work order progress status indicators.

[0015] Specifically, the system also includes a work order status tracking module, which updates the work order status to "completed" based on the feedback data uploaded by the mobile collaboration module and stores the processing data.

[0016] Specifically, when updating the work order status, the work order status tracking module simultaneously records the time of the disposal instruction generation, the on-site feedback time, and the operator's identification.

[0017] Secondly, the present invention also provides a power disaster intelligent decision-making and mobile collaborative device based on multi-source data fusion, comprising: one or more processors; and a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the power disaster intelligent decision-making and mobile collaborative systems based on multi-source data fusion.

[0018] Thirdly, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute any of the aforementioned intelligent decision-making and mobile collaborative systems for power disasters based on multi-source data fusion.

[0019] Beneficial effects

[0020] This invention provides a power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion. This system significantly improves the efficiency and response speed of power disaster management by integrating a dynamic rule base, intelligent decision-making, and mobile collaborative technologies. The system utilizes multi-source sensors to collect real-time multi-dimensional data such as ice thickness, wildfire meteorological parameters, and river water levels. Through a rule engine, it automatically matches preset response strategies, achieving full automation from disaster monitoring to the generation of response instructions. This reduces the response delay of traditional manual inspections and hierarchical reporting, which can take hours, to minutes or even seconds. The dynamic rule base supports differentiated configurations for multiple disaster types, including ice accumulation, wildfires, and flood control. It achieves precise decision-making through multi-condition combination logic, such as intelligently associating conductor ice thickness thresholds with ice melting strategies and meteorological data with firebreak opening strategies, greatly improving the targeting of disaster response. The mobile collaborative module provides intuitive disaster location navigation and response guidance to on-site personnel through a geographic information visualization interface and real-time work order push. It also enables two-way information exchange between the command center and the front line through image feedback and status update mechanisms, forming a closed-loop management system. The work order status tracking module further enhances the traceability of the handling process by recording the instruction generation time, feedback time, and operator information, providing data support for subsequent analysis and evaluation. This system not only solves the problem of delayed response in traditional power disaster management but also optimizes resource allocation efficiency through data fusion and intelligent decision-making, reduces the risk of human error, and comprehensively improves the resilience and reliability of the power system in the face of natural disasters, providing innovative technical means to ensure the safe and stable operation of the power grid. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of a power disaster intelligent decision-making and mobile collaborative system module based on multi-source data fusion provided in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of a power disaster intelligent decision-making and mobile collaborative device based on multi-source data fusion, provided in an embodiment of the present invention. Detailed Implementation

[0024] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0026] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text implies three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied. Furthermore, the technical solutions of the various embodiments can be combined, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0027] Traditional power disaster management systems rely on manual on-site inspections and hierarchical reporting mechanisms, resulting in a lengthy disaster information transmission chain. Delays of several hours often occur between on-site discovery and command center decision-making, leading to severe delays in disaster response. In existing technologies, handling rules are typically scattered across paper documents or independent business systems, making it difficult for on-site personnel to access the latest handling plans in a timely manner. This can easily lead to operational errors or resource scheduling conflicts due to information asymmetry. Existing platforms lack deep integration with mobile devices, preventing real-time interaction between disaster data, handling instructions, and on-site feedback, creating information silos. Mobile terminals only support basic data viewing functions and lack automatic work order distribution, geographic navigation, and process verification, leading to loss of control during the handling process. The database architecture cannot support the concurrent alarm volumes during peak periods such as the flood season, posing risks of query delays and system crashes. Furthermore, traditional systems lack closed-loop verification mechanisms; work order execution status relies on manual reporting, making it prone to falsification of handling results or loss of progress monitoring, and the completeness of historical data analysis is insufficient. These shortcomings collectively restrict the timeliness, accuracy, and collaborative efficiency of power disaster management.

[0028] This implementation relates to a power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion. Its core lies in achieving intelligent and mobile management of the entire power disaster process, from monitoring to response, through the collaborative work of a rule management module, an intelligent decision-making module, and a mobile collaboration module. The system's overall architecture is driven by a dynamic rule engine, establishing a link between real-time access to multi-source data, intelligent analysis, and mobile collaboration, thus constructing a comprehensive disaster management platform with high concurrency processing capabilities and data security.

[0029] The system adopts a three-tier architecture, with each layer being independent yet closely collaborative in terms of physical deployment and logical functionality. The rule management module, serving as the system's policy core, is deployed on a cloud server cluster. Its hardware configuration is based on high-performance servers and runs on a Linux operating system to ensure stability. This layer contains a built-in disaster response rule library, using a relational database to store rule data. Rules are described in a structured language, including the relationship between triggering conditions, threshold ranges, and response strategies. The rule management module provides a visual operation interface through a web management backend. Administrators can enable or disable rules through this interface, supporting multi-condition combinations based on logical operators (such as AND, OR, and NOT) to achieve flexible rule definition in complex disaster scenarios.

[0030] The intelligent decision-making module, serving as the central hub for data processing and decision generation, is deployed on computing nodes within the same cluster as the rule management module, enabling data interaction via a high-speed local area network. The data access unit at this layer employs a distributed message queue architecture, receiving real-time data streams from multiple sources, including temperature and humidity sensors from weather stations, conductor ice thickness sensors from power transmission lines, and river water level sensors. The data access protocol supports multiple standard protocols such as MQTT and HTTP, ensuring compatibility with devices from different manufacturers. The rule engine unit is built upon forward chain reasoning technology and utilizes the Rete algorithm to optimize rule matching efficiency. It can perform real-time pattern matching between the received sensor data and the disaster response rules of the rule management module, generating standardized response instructions and corresponding work order process data structures.

[0031] The mobile collaboration module consists of a mobile application and a backend service module. The mobile application is built on a cross-platform development framework, supporting both Android and iOS systems, and interacts with the intelligent decision-making module via API interfaces. The work order push module uses long-connection technology to ensure that work orders are pushed to designated on-site personnel accounts in real time. The geographic information integration module integrates a professional GIS engine, loading a power grid-specific map service to overlay spatial data such as disaster locations and monitoring station locations in layers, supporting users to zoom, pan, and query location information on their mobile devices. The on-site feedback module provides photo, video, and text input functions, allowing operators to transmit the handling process and results back to the system backend in multimedia format.

[0032] The disaster response rule base of the rule management module adopts a classification and hierarchical management mechanism, establishing different rule subsets according to disaster type (such as icing, wildfire, flood control, etc.), and the rules within each subset are divided into different levels according to severity. The rule storage structure includes fields such as rule ID, disaster type, trigger condition, response strategy, and effective time. The trigger condition adopts a parameterized design, allowing administrators to configure threshold parameters according to actual needs. The rule management module provides full lifecycle management functions for rules, including rule creation, editing, deletion, version control, and audit trail. During rule enabling or disabling operations, the system performs rule conflict detection to avoid abnormal decision-making caused by simultaneously enabling contradictory rules. Multi-condition combination configuration supports dragging and dropping condition nodes through a graphical interface to set the logical relationship between conditions and generate complex rule trigger expressions.

[0033] The data access unit of the intelligent decision-making module adopts a distributed deployment model, setting up edge computing nodes in each data collection area to achieve local data preprocessing and caching, reducing the pressure on cloud servers. The data preprocessing process includes data cleaning (removing duplicate data and filling missing values), format conversion (unifying the data format of different devices to the JSON standard format), and outlier filtering (identifying and removing obvious abnormal data points based on statistical methods). The core of the rule engine unit is the rule matching engine, whose workflow is as follows: first, real-time data is converted into fact objects; then, the fact objects are matched against rules in the rule base; when a fact meets the triggering condition of a rule, the corresponding handling strategy is activated. The rule matching process uses an incremental matching algorithm, only re-matching changed data to improve processing efficiency. The handling instruction generation module automatically generates standardized work orders containing information such as handling tasks, responsible personnel, and time requirements based on the matching results, and determines the workflow nodes of the work order.

[0034] The mobile collaboration module integrates with the system's user management module to automatically filter and push relevant work orders based on the operator's role and permissions. Push methods support in-app messages, SMS, and voice notifications to ensure timely task reception by on-site personnel. The geographic information integration module combines offline maps with online services, supporting offline map loading in areas with poor network signal to ensure the continuity of on-site operations. The GIS layer overlay function supports real-time display of disaster location data (such as ice thickness and temperature), and uses different colors to indicate the severity of the disaster. The on-site feedback module adopts a form-based design, dynamically generating feedback forms based on the type of task. Operators can complete feedback by uploading photos, recording videos, or filling in text information. The system automatically adds timestamps and geographic location information to the feedback data to ensure data traceability.

[0035] The workflow module, based on a state machine model, enables full-process management of power disaster response. During the rule configuration phase, administrators define various disaster response rules visually through the rule configuration interface on the PC management backend. Rules must be reviewed and approved before taking effect. In the disaster triggering phase, sensor data is matched in real-time by the rule engine of the intelligent decision-making module. When data meets the rule triggering conditions, a disaster event is automatically generated, and the response process is initiated. In the decision-making phase, the system generates a response work order based on the rules corresponding to the disaster event and pushes it to relevant personnel through the message center. The work order includes disaster details, response steps, and safety precautions. In the on-site execution phase, operators receive the work order via mobile devices, use GIS navigation to navigate to the disaster site, perform the response operation, and submit a process record through the on-site feedback module. In the closed-loop verification phase, the system automatically verifies the feedback data, checking whether the response results meet the rule requirements and whether the feedback information is complete. Based on the verification results, the system updates the work order status to include three states: pending review, processing, and completed. Historical data is archived and stored to support subsequent statistical analysis and experience summarization.

[0036] The system's high concurrency processing capability is achieved through a Kubernetes (K8s) cluster deployment. Core services such as the rule management module and intelligent decision-making module are containerized, leveraging K8s' automatic scaling mechanism to dynamically adjust the number of cluster nodes based on real-time load, ensuring system stability even under billions of concurrent alarms. Service mesh technology is used to manage communication between containers, enabling functions such as traffic control, service discovery, and fault recovery. Database optimization employs a strategy combining database sharding and index optimization. Databases are sharded based on data type (e.g., real-time data, historical data), and frequently accessed tables such as work order tables and rule tables are sharded, with sharding keys set according to timestamps or business types. Index optimization includes creating composite indexes and covering indexes to improve the efficiency of complex queries. The caching layer uses a Redis cluster to cache hot data (e.g., frequently used rules, active work orders), reducing database access pressure.

[0037] The security mechanism module is built on the RBAC (Role-Based Access Control) model. The system includes multiple roles such as administrator, operations and maintenance personnel, and field operators, each with different data access and operation permissions. Access management is implemented through Access Control Lists (ACLs), setting detailed permission rules for each functional module and data object. Data transmission uses the TLS encryption protocol to ensure the security of data interaction between the mobile terminal and the backend. At the data storage level, sensitive information (such as user passwords and permission data) is encrypted and stored, and regular data backups and recovery drills are performed. The system also includes a security audit function, recording logs of critical operations (such as rule modifications and data deletion), supporting the tracing and analysis of security incidents.

[0038] The preprocessing module of the data access unit adopts a plug-in architecture, supporting flexible expansion with different data processing plugins. The data cleaning plugin automatically identifies and handles duplicate data and missing values ​​by defining cleaning rules. Missing value handling methods include mean imputation, median imputation, and machine learning-based predictive imputation. The format conversion plugin converts data to standard formats for different sensor devices, ensuring the rule engine can process data uniformly. The outlier filtering plugin uses a combination of statistical methods and machine learning algorithms to identify and remove data points that significantly deviate from the normal range. The processing results of outlier data can be recorded in the audit log for administrator review.

[0039] The GIS engine in the geographic information integration module supports 2D and 3D map display, and can customize map styles according to power grid business needs, highlighting key facilities such as transmission lines, substations, and monitoring stations. Disaster locations and monitoring station locations are visualized using symbols, with different types of disasters identified by different icons, and disaster severity distinguished by icon color or size. The map interaction function supports multi-touch operation; users can zoom and pan the map using gestures, and clicking on a point on the map will pop up an information window displaying real-time monitoring data, historical disaster records, and other information for that point. Map layer management allows users to customize the displayed layer content, such as displaying only the monitoring station layer related to the current disaster, improving information retrieval efficiency.

[0040] The closed-loop verification module comprises an automatic verification engine and a work order status management module. The automatic verification engine pre-sets verification rules based on different disaster types and response tasks. For example, verification rules for icing response tasks may include whether the post-response icing thickness is below a threshold and whether the response time meets requirements. The verification process uses rule matching, comparing the on-site feedback (text and images) with the verification rules to generate verification results. The work order status management module updates the work order status based on the verification results. When verification passes, the work order status is updated to "Completed," and a notification mechanism is triggered to inform relevant personnel. When verification fails, the work order status is updated to "Processing," and a rework task is automatically generated and pushed to the operators. The historical data storage module archives and stores the entire lifecycle data of work orders (including data from rule configuration, disaster triggering, decision issuance, on-site execution, closed-loop verification, etc.) in a time-series format. It supports multi-dimensional retrieval, including by disaster type, time range, and response results, for historical data query and analysis, providing data support for disaster management.

[0041] In actual deployment, the system can be flexibly configured according to the scale of the power grid and business needs. For large power grid enterprises, a distributed multi-cluster deployment mode can be adopted, setting up sub-clusters in different regions and achieving unified global data management through a data synchronization mechanism. For small and medium-sized power grid enterprises, a single-cluster deployment can be adopted to reduce hardware investment costs. The system's scalability is achieved through a microservice architecture. When adding new disaster types or functional modules, corresponding microservice components can be developed and registered with the service registry center to achieve seamless functional expansion. The user interface design follows ergonomic principles, providing customized operation interfaces for different roles (administrators, field operators, etc.) to improve user experience and work efficiency.

[0042] Secondly, the present invention also provides a power disaster intelligent decision-making and mobile collaborative device based on multi-source data fusion, comprising: one or more processors; and a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the power disaster intelligent decision-making and mobile collaborative systems based on multi-source data fusion.

[0043] Thirdly, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute any of the aforementioned power disaster intelligent decision-making and mobile collaborative systems based on multi-source data fusion.

[0044] Figure 2 This is a schematic diagram of the structure of a power disaster intelligent decision-making and mobile collaborative device based on multi-source data fusion, provided in an embodiment of this application. The device includes a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 and the number of memories 32 in the device can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 can be connected via a bus or other means.

[0045] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion in any embodiment of this application. The memory 32 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 32 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0046] The communication device 33 is used for data transmission.

[0047] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 32, thereby realizing the above-mentioned intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion.

[0048] Input device 34 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 35 may include display devices such as a display screen.

[0049] The aforementioned intelligent decision-making and mobile collaborative device for power disasters based on multi-source data fusion can be used to execute the intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion provided in the above embodiments, and has corresponding functions and beneficial effects.

[0050] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion. Specifically, it includes a rule management module for configuring a dynamic rule base that supports multiple disaster types. The rule base includes enableable disaster handling rules and multi-condition combination logic. An intelligent decision-making module is communicatively connected to the rule management module for real-time access to power disaster monitoring sensor data and generating handling instructions and work order processes by matching the dynamic rule base through a rule engine. A mobile collaborative module interacts with the intelligent decision-making module for pushing handling work orders to mobile terminals, integrating a geographic information visualization interface, and receiving on-site feedback data.

[0051] Storage medium – any type of memory device or storage apparatus. The term “storage medium” is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media may reside in a first computer system in which a program is executed, or may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage media may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0052] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the above-mentioned intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion, but can also execute related operations in the intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion provided in any embodiment of this application.

[0053] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A power disaster intelligent decision-making and mobile collaborative system based on multi-source data fusion, characterized in that, include: The rule management module is used to configure a dynamic rule base that supports multiple disaster types. The rule base contains enableable disaster response rules and multi-condition combination logic. The intelligent decision-making module communicates with the rule management module to access power disaster monitoring sensor data in real time and generate disposal instructions and work order processes by matching dynamic rule bases through the rule engine; The mobile collaboration module interacts with the intelligent decision-making module to push work orders to mobile terminals, integrate geographic information visualization interfaces, and receive on-site feedback data.

2. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion as described in claim 1, characterized in that, The configuration operations for the dynamic rule base include: When defining icing disaster rules through the management backend, the logical conditions for linking the conductor icing thickness threshold with the de-icing disposal strategy are defined.

3. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 1, characterized in that, The dynamic rule base further supports the configuration of wildfire disaster rules, specifically linking the combination of meteorological station temperature data, humidity data, and firebreak development and disposal strategies.

4. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 1, characterized in that, The sensor data accessed by the intelligent decision-making module includes real-time river water level information collected by water level sensors, and the water level information is matched with flood control and disaster response rules.

5. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 1, characterized in that, When the rule engine performs a matching operation, it compares the real-time data stream collected by the sensor with the enabled rule conditions in the dynamic rule base one by one.

6. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 5, characterized in that, When the real-time data stream meets the rule conditions, the rule engine automatically generates a work order process instance containing the processing instruction code.

7. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 1, characterized in that, The geographic information visualization interface of the mobile collaboration module overlays and displays the coordinates of disaster sites, the location markers of monitoring equipment, and the topology map of work order navigation paths.

8. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 7, characterized in that, The on-site feedback data received by the mobile collaboration module includes images of the handling process taken by on-site personnel through mobile terminals and manually entered work order progress status indicators.

9. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 1, characterized in that, The system also includes a work order status tracking module, which updates the work order status to "completed" based on the feedback data uploaded by the mobile collaboration module and stores the processing data.

10. The intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion according to claim 9, characterized in that, When updating the work order status, the work order status tracking module simultaneously records the time of the disposal instruction generation, the on-site feedback time, and the operator's identification.

11. A power disaster intelligent decision-making and mobile collaborative device based on multi-source data fusion, characterized in that, include: One or more processors; A memory that stores one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion as described in any one of claims 1-10.

12. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to execute the intelligent decision-making and mobile collaborative system for power disasters based on multi-source data fusion as described in any one of claims 1-10.

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