A multi-source information fusion power industry multi-disaster early warning method and system

Abstract

The present application relates to the technical field of natural disaster early warning, in particular to a multi-source information fusion power industry multi-disaster early warning method and system, the method comprising the following steps: S1, obtaining the position information and characteristic information of power facility equipment; S2, adding the position information and characteristic information of the power facility equipment to the data layer of the disaster early warning electronic map; S3, generating different levels of disaster risk influence range on the disaster early warning electronic map according to the disaster early warning information; S4, according to the position information of the power facility equipment, the power facility equipment falling into the disaster risk influence range is marked; S5, according to the marked information, different types of early warning influence analysis report are generated, and the early warning influence analysis report is pushed to the preset crowd. Realize the intelligent linkage of early warning information and business process, and realize the accurate positioning and information pushing of disaster early warning risk.

Description

Technical Field

[0001] This invention relates to the field of natural disaster early warning technology, specifically to a multi-hazard early warning method and system for the power industry that integrates multi-source information. Background Technology

[0002] As a critical national infrastructure and the cornerstone of modern society, the safe and stable operation of the power system is directly related to national welfare, economic development, and social stability. Traditional disaster prevention relies heavily on static risk zoning maps and experience-based early warnings, which are insufficient to address the refined prevention and control needs arising from the dynamic evolution of disasters. On the one hand, power facilities are numerous and widely distributed across extremely complex geographical areas, ranging from underground cables in urban centers to transmission lines traversing mountains, exhibiting vastly different vulnerabilities to various disaster environments. On the other hand, existing early warning information is mostly macro-level geographical area warnings, failing to accurately correlate with specific power equipment assets, resulting in unclear and inaccurate risk assessments and slow and inefficient emergency resource dispatch. Therefore, how to achieve spatiotemporal fusion and intelligent correlation between macro-level, dynamically changing natural disaster early warning information and micro-level, statically distributed power facility assets, realizing a breakthrough from "regional early warning" to "equipment early warning," thereby generating decision-making basis that can guide precise prevention and efficient emergency repairs, has become a core bottleneck issue in improving the power industry's disaster prevention, mitigation, and relief capabilities. Developing a disaster dynamic risk early warning method that integrates geographic information technology, dynamic risk updates, and business intelligence analysis is an urgent need to improve the resilience of power systems.

[0003] Based on the above background, the existing technology mainly has the following problems:

[0004] 1. Insufficient accuracy in risk assessment: Existing early warning models are unable to perform real-time spatial overlay analysis of dynamically changing disaster impact ranges (such as the movement of the radius of a typhoon's 7-level wind circle and the evolution of flood-inundated areas) with precise coordinates of power facilities. This results in risk assessments remaining at the administrative division or general line level, failing to pinpoint specific towers, substations, or sections of the line, and thus providing weak guidance for early warning information.

[0005] 2. Static and isolated impact assessment: Traditional assessment methods are mostly based on static parameters and historical experience, which cannot integrate the characteristic information of the power facilities themselves (such as equipment type, voltage level, importance level). Therefore, they cannot assess the differentiated risks caused by disasters to equipment with different characteristics, and the early warning lacks pertinence and hierarchy.

[0006] 3. Disconnect between early warning and business operations: After early warning information is generated, it is often released in the form of general text or simple diagrams, failing to automatically link with the production management system and failing to quickly generate customized analysis reports for different professional roles (such as scheduling, operation and maintenance, and emergency repair). This results in a long and inefficient conversion chain from risk information to prevention and control instructions. Summary of the Invention

[0007] This invention aims to combine multi-hazard early warning technology for natural disasters with data from power facilities and equipment, forming an application method that closely integrates disaster early warning information with power business. It proposes a multi-source information fusion method and system for multi-hazard early warning in the power industry, realizing intelligent linkage between early warning information and business processes, and enabling precise disaster early warning for power facilities from regional early warning to equipment-level early warning.

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: A multi-hazard early warning method for the power industry based on multi-source information fusion includes the following steps: S1, Obtain the location and feature information of power facilities and equipment; S2, add the location and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; S3 generates different levels of disaster risk impact range on the disaster early warning electronic map based on disaster early warning information; S4, Based on the location information of the power facilities and equipment, mark the power facilities and equipment that fall within the disaster risk impact range; S5. Based on the labeled information, generate different types of early warning impact analysis reports and push the early warning impact analysis reports to the preset population.

[0009] Preferably, the steps further include integrating the self-configured temporary base information into the data layer of the disaster early warning electronic map, and pushing the affected disaster early warning information to the temporary base.

[0010] Preferably, the self-configured temporary base information is integrated into the data layer of the disaster early warning electronic map. Specific implementation methods include: Automatic data entry: Receives temporary base information through a data interface and updates the status of temporary bases in real time, including whether a temporary base exists or has been withdrawn; Manual entry: Directly select or input latitude and longitude coordinates on the electronic map to determine the location of the station, and fill in the relevant attribute information; the attribute information includes, but is not limited to, station name, purpose, duration of temporary station, location information, designated responsible person and their contact information, disaster type and corresponding set threshold.

[0011] Preferably, for temporary bases, the step of sending the early warning impact analysis report includes: The dynamically generated impact ranges of various disaster risks are overlaid with the geographical locations of all temporary camps in time and space. When the analysis finds that a certain camp is located within the impact range of a disaster risk, and the warning level of the impact range of the disaster risk reaches or exceeds the threshold set by the temporary camp for the corresponding disaster type in the configuration, the warning condition is triggered.

[0012] Preferably, the steps further include adding the transformer area data to the data layer of the disaster early warning electronic map, identifying transformer areas that fall within the disaster risk impact range and meet the early warning conditions, and issuing an early warning. Specific steps include: Obtain data from the power company's distribution areas; Data association is performed by linking the relationships between the transformers at the end of the distribution network lines in the integrated map of the distribution area and the power grid. After a disaster warning is issued, power facilities and equipment within the disaster risk area are used to calculate the data of the main grid or distribution network lines that may be affected. Based on the data of the affected main network or distribution network lines, analyze the data of the distribution network lines that may be affected; Based on the data association and the data of the distribution network lines that may be affected, a multi-level association relationship is formed between poles, main grid, distribution network, transformers, and distribution areas. After an early warning is issued, the transformer substations that meet the early warning conditions are determined based on the multi-level correlation, the scope of disaster risk impact, and the early warning level.

[0013] Preferably, after correcting the scope of the disaster risk impact, the power facilities and equipment falling within the scope of the disaster risk impact are marked. Specific steps include: Based on the power facilities and equipment falling within the disaster risk impact range, several transmission lines involved in the disaster risk impact are deduced. The areas of these transmission lines not included in the disaster risk impact range are then merged with the disaster risk impact range to obtain the disaster impact range of the power facilities and equipment.

[0014] Preferably, the power facilities and equipment include, but are not limited to, power poles, transmission lines, substations, production and living areas surrounding the power facilities and equipment, and power plants.

[0015] Based on the same concept, a multi-source information fusion power industry multi-hazard early warning system is also proposed, including a data interface, a data fusion module, an early warning information display module, and a data push module. Data interface, used to obtain location and feature information of power facilities and equipment from the power system's management system; The data fusion module is used to add the location and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; The early warning information display module is used to generate different levels of disaster risk impact range on the disaster early warning electronic map based on disaster early warning information, and to mark the power facilities and equipment that fall within the disaster risk impact range based on the location information of the power facilities and equipment; The data push module is used to generate different types of early warning impact analysis reports based on the labeled information, and push the early warning impact analysis reports to the preset population.

[0016] Based on the same concept, a program product is also proposed, which implements the multi-source information fusion method for early warning of multiple disasters in the power industry as described above when the program product is run on a computer.

[0017] Based on the same concept, a computer device is also proposed, which may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the multi-source information fusion method for multi-hazard early warning in the power industry described above.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: Achieving intelligent linkage between early warning information and business processes: By automatically generating and pushing customized early warning impact analysis reports based on labeled information, the system establishes a closed-loop business process integrating risk warning, emergency response, and production command. According to preset rules, the system can push potentially affected power grid flow analysis to dispatchers, lists of equipment to be inspected to maintenance personnel, and optimal routes and material lists to repair teams. This significantly shortens the reaction time from risk perception to prevention and control actions, and improves the intelligence level of emergency decision-making and the efficiency of collaborative handling.

[0019] Achieving precise risk location and dynamic push: By spatially overlaying and real-time analyzing the precise location of power facilities with the dynamically evolving disaster risk impact range on an electronic map, it is possible to automatically and accurately identify specific power equipment directly threatened, and graphically and dynamically mark the risk level, greatly improving the spatiotemporal accuracy of risk assessment, and realizing "wherever the disaster goes, the risk is seen." Attached Figure Description

[0020] Figure 1 This is a flowchart of a multi-source information fusion method for early warning of multiple disasters in the power industry, as shown in Example 1. Figure 2 Example of a list of early warning targets for poles in Example 3; Figure 3 Example of a list of early warning targets for a substation in Example 3; Figure 4 Example of a list of early warning targets for power plants in Example 3; Figure 5 Example of a warning target list for production / non-production sites in Example 3; Figure 6 This is an example of an interface for real-time viewing of multi-hazard early warnings in Embodiment 3; Figure 7 This is a schematic diagram of the early warning map in Example 3; Figure 8 This is a statistical chart of the warning areas, warning levels, and types in Example 3; Figure 9 This is a statistical diagram of the affected locations of facilities and equipment in Example 3; Figure 10 This is a schematic diagram of a multi-source information fusion power industry multi-hazard early warning system in Example 5. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0022] To achieve spatiotemporal fusion and intelligent correlation between macroscopic, dynamically changing natural disaster early warning information and microscopic, statically distributed power facility assets, forming a penetration from "regional early warning" to "equipment early warning," thereby generating precise decision-making basis for prevention and emergency repair, and realizing intelligent linkage between early warning information and business processes, this invention proposes a multi-source information fusion method and system for multi-hazard early warning in the power industry. The method includes the following steps: S1, acquiring the location and feature information of power facilities and equipment; S2, adding the location and feature information of the power facilities and equipment to the data layer of a disaster early warning electronic map; S3, generating different levels of disaster risk impact ranges on the disaster early warning electronic map based on the disaster early warning information; S4, marking power facilities and equipment falling within the disaster risk impact range based on the location information of the power facilities and equipment; S5, generating different types of early warning impact analysis reports based on the marked information, and pushing the early warning impact analysis reports to a preset group of people. By integrating power facility asset information with natural disaster early warning information from multiple sources, power facilities and equipment falling within the scope of disaster risk can be marked and accurately pushed based on the dynamic changes in natural disaster early warnings. This significantly shortens the response time from risk perception to prevention and control actions, and improves the intelligence level of emergency decision-making and the efficiency of collaborative handling.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0024] Example 1 A multi-source information fusion method for early warning of multiple disasters in the power industry, flowchart as follows: Figure 1 As shown, the specific steps include: S1, Obtain the location and feature information of power facilities and equipment; S2, add the location and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; S3 generates different levels of disaster risk impact range on the disaster early warning electronic map based on disaster early warning information; S4, Based on the location information of the power facilities and equipment, mark the power facilities and equipment that fall within the disaster risk impact range; S5. Based on the labeled information, generate different types of early warning impact analysis reports and push the early warning impact analysis reports to the preset population.

[0025] Furthermore, obtaining the location and feature information of power facilities and equipment in step S1 specifically includes the following steps: Location and characteristic information of power facilities and equipment are obtained from the power grid map component of the power system. These power facilities and equipment mainly include: power poles, transmission lines, substations, surrounding production and living areas, power plants, etc. Temporary camps specifically refer to non-permanent work and living camps established by power companies (power grid companies, power construction companies, etc.) near project sites or task areas for specific engineering projects or emergency tasks. Location information for power facilities and equipment includes at least: location, latitude and longitude, and altitude; characteristic information includes, but is not limited to, pole number, equipment name, voltage level, model, associated line, nature of power facilities and equipment, height, commissioning date, line length, overhead line length, cable line length, city / prefecture, whether it is on the same pole, pole height, and physical pole commissioning date.

[0026] Furthermore, step S2 specifically includes: S21, Constructing electronic maps based on basic geographic information data.

[0027] As a specific implementation, the electronic map can be a two-dimensional map or a three-dimensional map. If presented as a two-dimensional map, it can be constructed using geodetic coordinate systems, rectangular coordinate systems, or planar projected coordinate systems. Subsequent positioning of power facilities and equipment is also based on the same coordinates for data fusion. If presented as a three-dimensional map, it also includes elevation information and uses three-dimensional coordinates for positioning. For example, using a high-precision DEM and remote sensing imagery as a base, a three-dimensional terrain scene with realistic topographic relief and land cover can be generated. This invention uses a two-dimensional map as an example to illustrate the technical solution; however, this does not limit the scope of protection of this invention to two-dimensional maps. Based on the concept of this invention, adding elevation information to a two-dimensional map and converting it into a three-dimensional map is still within the scope of protection of this invention.

[0028] Furthermore, commonly used three-dimensional coordinate systems in the power industry include: WGS-84 coordinate system, CGCS2000 coordinate system, and WebMercator projected coordinate system; commonly used two-dimensional coordinate systems include: Gauss-Kruger projected coordinate system, local independent coordinate system, and power grid coordinate system. Three-dimensional coordinates are projected into two-dimensional coordinates, and two-dimensional coordinates can be converted into three-dimensional coordinates (e.g., converting (longitude, latitude) into (longitude, latitude, altitude)). All of the aforementioned coordinate systems can be applied to the method of this invention and are within the scope of protection of this invention. However, the scope of protection of this invention should not be limited to the aforementioned coordinate systems. Other coordinate systems used based on the concept of this invention are still within the scope of protection of this invention.

[0029] S22, The power facility and equipment data are added to the basic electronic map for disaster early warning through a data layer.

[0030] During the addition process, the latitude and longitude of the power facilities and equipment are known. Latitude and longitude are precise location information. The electronic map and the location of the power facilities and equipment use the same coordinate system (if the coordinate systems are different, coordinate system conversion is performed, and they are presented in the same coordinate system), realizing integration and visualization under a unified spatial benchmark and spatiotemporal framework. The electronic map can be a static scene, preferably, or a non-static scene, serving as a digital twin foundation that carries spatial geography and asset entities.

[0031] Furthermore, for key equipment, such as typical power poles, transformers, and circuit breakers of different voltage levels, a parametric method is used to generate target objects and integrate them into the disaster early warning electronic map. As a specific implementation, the data from the power grid map component is directly read and integrated with the electronic map. This integration can be understood as adding the data from the power grid map component to a data layer. Clicking on any power facility or equipment on the electronic map allows users to query its ID, model, voltage level, nature, commissioning time, historical fault records, and other information.

[0032] Furthermore, step S3 specifically includes the following steps: Different disaster types have different physical characteristics and early warning data formats. Therefore, corresponding spatialization rules and risk impact range generation models are preset for each disaster type. The specific implementation process is as follows: Earthquake: First, obtain rapid earthquake intensity information. This information is usually generated within minutes of an earthquake, providing measured or rapidly estimated earthquake intensity values ​​at different locations throughout the affected area in the form of geographic raster, contour lines, or intensity distribution (usually using the Chinese Earthquake Intensity Scale or the MMI Intensity Scale, divided into VI to IX and above, with 12 intensity levels represented by Roman numerals I, II, III, IV, V, VI, VII, VIII, IX, X, XI, and XII). Second, analyze this data to obtain a digital intensity distribution field containing spatial location (latitude and longitude) and intensity values. Third, map the analyzed rapid intensity data onto a disaster early warning electronic map in real time. The mapping steps mainly include intensity distribution area generation, that is, generating a continuous earthquake intensity impact area layer on the disaster early warning electronic map based on the spatial distribution of intensity values. Different intensity levels are clearly distinguished by different colors (for example, yellow for intensity VI, orange for intensity VII, and red for intensity VIII and above), forming a dynamic intensity distribution map covering the ground surface.

[0033] Flash floods: For flash flood warnings, information such as catchment area, spillway point, warning level, and warning release time is received. This catchment area and spillway point are combined with a disaster warning electronic map to display the inundated area (e.g., defining the impact range as 3km around the spillway point), thus generating a "flash flood risk warning impact range" and visually presenting the relevant data.

[0034] Landslides, subsidence, and debris flows: Early warning systems for geological hazards such as landslides, subsidence, and debris flows are based on risk warning information from different hazard points (hazard susceptibility levels are classified as high, medium, and low). The regions with different hazard warning risk information are overlaid onto an electronic disaster warning map to form the risk impact range for different hazard types. In particular, geographical information such as topographic slope, aspect, and lithology can be combined to locally correct the hazard warning risk impact area, making it more consistent with the actual situation.

[0035] After generating the disaster risk impact range, not only is the "macro" multi-hazard early warning information intuitively mapped on the disaster early warning electronic map, but more importantly, it provides accurate regional information for the next step of marking the power facilities and equipment that fall within the disaster risk impact range based on the location information of the power facilities and equipment.

[0036] Step S4 specifically includes the following steps: First, based on the disaster risk impact range obtained in step S3, the impact range layer is determined. It is in the same spatial reference system as the power facility and equipment layer. Therefore, through efficient spatial relationship calculation, it can automatically identify specific power facilities and equipment (such as poles, line sections, and substations) located within the impact range of various disaster risks, or automatically identify specific power facilities and equipment that may fall into the impact range of various disaster risks as the disaster risk spreads further. This transforms the "macro" natural disaster early warning into risk early warning prompts for specific power facilities and equipment and transmission lines, realizing the precise transmission of early warning information from the original "area" warning to "points" and "lines".

[0037] The labeling includes the equipment type, characteristics, nature, and disaster risk level of the power facilities and equipment.

[0038] Furthermore, the labeled information also includes: risk labeling information, equipment asset association information, impact analysis and decision support information, and visualization and interactive information.

[0039] The risk labeling information includes: 1. Risk Status Identification Risk level: usually corresponds to the warning level, such as red (very serious), orange (serious), yellow (relatively serious), and blue (moderate).

[0040] Disaster type: Identify the causative factors, such as "typhoon impact", "flood inundation", "wildfire threat", "geological disaster (landslide / mudslide)", etc.

[0041] Predicted impact time: When the equipment is expected to begin to be affected (e.g., expected to enter the 10-level wind circle in 3 hours).

[0042] 2. Spatial Association Information Distance from disaster risk points: such as XX kilometers from the typhoon center, or XX meters below the flood inundation line.

[0043] Areas affected by early warning risks include: areas within the "6-degree intensity influence circle", "7-level wind circle influence area", "red rainstorm warning area", and "high-risk area for geological disasters".

[0044] The equipment asset association information includes: 1. Basic device identification information Unique device code: such as asset ID, RFID number.

[0045] Equipment name and type: such as "220kV XX line #053 tower" or "110kV XX substation No. 1 main transformer".

[0046] System / Line: Which transmission line, substation, or distribution network does it belong to?

[0047] 2. Key Equipment Attributes (for Impact Assessment) Voltage levels: 500kV / 220kV / 10kV, etc.

[0048] Design disaster resistance standards: wind resistance level (e.g., 35 m / s), flood control elevation, and seismic fortification intensity.

[0049] Importance level: Whether it is a "lifeline" facility, supplies power to important users (hospitals, governments), or a hub node.

[0050] Service life and health status: Older equipment or equipment with existing defects is at higher risk.

[0051] The impact analysis and decision support information includes: 1. Predicting the impact and consequences Expected impacts of the fault: such as "potential tower collapse", "potential flooding and shutdown", "potential discharge caused by tree obstruction".

[0052] Impact scope: If the equipment fails, how many users are expected to experience power outages (number of households), the size of the affected load (kW / MW), and whether it will cause a complete substation shutdown or grid disconnection.

[0053] 2. Pre-emptive response strategies and resource allocation Emergency response plan link: Linked to the on-site response plan for this equipment or this type of risk.

[0054] Responsible unit and contact person: The equipment operation and maintenance unit, on-site person in charge and contact information.

[0055] Surrounding emergency resources: the nearest repair teams, material warehouses, and the location of readily available generator trucks.

[0056] The visual and interactive information includes: 1. Map graphic annotation Highlight: Highlight the risk on the map with an icon or halo of the corresponding risk color.

[0057] The tooltip displays key risk information when the mouse hovers over the device.

[0058] Clustered display: When devices are densely packed, the number of risk points is automatically clustered and displayed.

[0059] 2. Status and Process Tracking Warning release time: The timestamp when this label was generated.

[0060] Confirmation status: Whether it has been signed and confirmed by on-site or dispatch personnel.

[0061] Status tracking: It can be updated to "Inspected", "Reinforcement measures have been taken", "Power outage for safety", "Repaired", etc.

[0062] The purpose of labeling information is to establish a temporal and spatial link between natural disaster early warning information and power facilities and equipment, providing a data foundation for the classification of subsequent information dissemination.

[0063] S5. Based on the labeled information, generate different types of early warning impact analysis reports, and push the early warning impact analysis reports to the preset population, specifically including the following: When analysis reveals that a power facility is located within the impact range of a disaster risk, and the disaster warning level reaches or exceeds the corresponding disaster warning threshold, the warning conditions are triggered. Once triggered, a warning notification is automatically and concurrently initiated through an integrated communication gateway or communication link, based on the responsible person and contact information configured for that facility. The warning information is then sent via platform, warning terminal, SMS, APP, outbound calls, etc. Notification methods include: SMS: Send concise and clear warning text, including key information such as disaster type, warning level, period of impact, names of affected locations and power facilities, and recommended actions.

[0064] Mobile App Push Notifications: Send more detailed warning messages to the dedicated mobile application installed by the responsible person, and link to an electronic map to view the risk situation around the site and the affected power facilities and equipment.

[0065] Intelligent voice outbound calling: Automatically dials the responsible person's phone number and plays the warning content through voice synthesis technology, ensuring that users can receive emergency reminders even when they do not check their phones in time.

[0066] Early warning terminal: Early warning information is automatically sent to the multi-hazard early warning terminal at the corresponding early warning location, and the personnel at the location are reminded to take shelter through the form of images, lights, loudspeakers and other means.

[0067] Warning Records and Feedback: All issued warning messages are logged completely on the platform, including trigger time, recipient, notification method, and sending status, which can be queried and traced. Responsible personnel can provide feedback such as "received" via the app or SMS link.

[0068] Through the above implementation methods, the previously decentralized, passive risk warning system, which relied on individual experience for judgment, has been transformed into a centralized, proactive, and data-driven intelligent early warning management model, thus creating a closed-loop business process that integrates risk warning with emergency response and production command. The system can, according to preset rules, push potentially affected power grid flow analysis to dispatchers, lists of equipment to be inspected to maintenance personnel, and optimal routes and material lists to repair teams. This significantly shortens the reaction time from risk perception to prevention and control actions, and improves the intelligence level of emergency decision-making and the efficiency of collaborative handling.

[0069] The method of this invention further enables differentiated risk assessment and tiered early warning: by integrating the characteristic information of power facilities (such as equipment type and importance), the system can perform differentiated risk analysis on facilities falling into the same risk area. For example, it can identify key hub substations or important user power supply lines on disaster risk paths, thereby achieving risk grading (red, orange, yellow, blue) and highlighting early warning of key targets, so that limited defense and emergency resources can be prioritized for the most critical and vulnerable links.

[0070] Furthermore, the method also includes integrating self-configured temporary work site information into the data layer of the disaster early warning electronic map, and pushing disaster early warning information to the temporary work sites. Power operations (such as line construction, equipment maintenance, and emergency rescue) often require the establishment of temporary work sites in the field or disaster-prone areas, where personnel and equipment face direct threats from sudden natural disasters. In traditional models, proactive early warning capabilities for such mobile and temporary risks are almost nonexistent. This embodiment, by constructing a user-customizable temporary work site early warning function, extends the coverage of disaster prevention early warning from fixed power facilities to dynamic work sites and personnel, greatly enhancing the safety assurance capabilities of on-site operations.

[0071] Temporary camps specifically refer to non-permanent work and living camps established by power companies (power grid companies, power construction companies, etc.) near project sites or mission areas to execute specific engineering projects or emergency tasks. Temporary camps are characterized by their temporary nature, highly integrated functions, high vulnerability, and high risk. Temporariness primarily means that temporary camps are established with the start of a project or emergency task and dismantled upon its completion, with a duration ranging from a few days to several years. In disaster risk early warning schemes, they are dynamically updated and monitored, unlike long-term fixed power facilities and equipment. High functional integration means that the temporary camp densely integrates command, production, living, and logistical functions within a limited space. High vulnerability and high risk refer to the fact that temporary camps are mostly located in the field, with significant site selection risks. Due to urgency or limited conditions, camps may have to be located in high-risk areas such as geological hazard sites, flood channels, low-lying areas, and windy areas, making them inherently susceptible to disasters. Temporary buildings often fail to meet the wind resistance, flood prevention, and earthquake resistance standards of fixed buildings. To ensure the safety of personnel and equipment, special attention needs to be paid to the key monitoring of the disaster risk early warning system.

[0072] Before sending out early warning information, the status of temporary outposts should be updated periodically. If a temporary outpost is removed, there is no need to send out early warning information to avoid wasting early warning resources; if a temporary outpost exists, it should be included in the scope of early warning information.

[0073] First, configure the temporary base information.

[0074] On the one hand, the party issuing the disaster risk warning integrates the self-configured temporary base information into the data layer of the disaster warning electronic map. Specific implementation methods include: Automatic data entry: Receives temporary base information through a data interface and updates the status of temporary bases in real time, including whether a temporary base exists or has been withdrawn.

[0075] Manual entry: Directly select or input latitude and longitude coordinates on the electronic map to determine the location of the station, and fill in the relevant attribute information; the attribute information includes, but is not limited to, station name, purpose, duration of temporary station, location information, designated responsible person and their contact information, disaster type and corresponding set threshold.

[0076] On the other hand, power users can also proactively request temporary site alerts. Power users can independently configure and input temporary site information through the data interface. The system provides a dedicated configuration interface for authorized users (such as construction project departments, inspection teams, and emergency command centers). Users can input temporary site information into the alert platform through one of the following methods: Manual entry: Directly select or enter latitude and longitude coordinates on the electronic map to determine the location of the base, and fill in the relevant attribute information, including but not limited to: Basic information about the site: site name and purpose (e.g., "XX Line Relocation Project Department", "XX Substation Maintenance Camp").

[0077] Spatiotemporal attributes: warning release time and disaster risk point location information. The system will only issue warnings to the station during the valid warning period.

[0078] Responsibility Information: Designated responsible person and their contact information (mobile phone number).

[0079] Early Warning Strategy: Users can customize early warning reception strategies based on the nature of their work and their sensitivity to different disaster warnings. For example, for "Construction Site A," it can be set to receive "Red Flash Flood Warning" and "Orange or Higher Debris Flow Warning"; for "Temporary Inspection Rest Point B," it can be set to only receive "Orange or Higher Landslide Warning." Strategies can be refined to the level of disaster type and warning level threshold.

[0080] Batch Import: Supports users to import complete information from multiple temporary bases in batches according to preset templates (such as Excel spreadsheets), improving configuration efficiency. The system performs format validation and spatial coordinate standardization on the entered or imported data, and includes it as a special type of "risk concern" in the system monitoring database.

[0081] Secondly, the system features intelligent early warning notifications triggered by thresholds. The core early warning engine, while processing multi-hazard early warning data in real time, simultaneously monitors all temporary bases within their validity period. Its workflow is as follows: Spatial and Condition Matching: The system performs real-time spatial overlay analysis of dynamically generated disaster risk impact ranges (such as flash floods, landslides, and debris flows) with the geographical locations of all temporary camps. When the analysis finds that a camp is located within the disaster risk impact range, and the disaster warning level reaches or exceeds the threshold set for that type of disaster in the camp's configuration, the warning condition is triggered.

[0082] Multi-channel early warning dissemination: Once triggered, the system immediately initiates early warning notifications automatically and in parallel through the integrated communication gateway, based on the responsible person and contact information configured for that location. After an early warning is generated, the system determines which locations meet the early warning criteria based on the scope and level of the warning, and then sends the warning information through early warning terminals, SMS, APP, outbound calls, etc. Notification methods include: SMS: Send concise and clear warning text, including key information such as disaster type, warning level, period of impact, names of affected locations and power facilities, and recommended actions.

[0083] Mobile App Push Notifications: Send more detailed warning messages to the dedicated mobile application installed by the responsible person, and link to an electronic map to view the risk situation around the site and the affected power facilities and equipment.

[0084] Intelligent voice outbound calling: Automatically dials the responsible person's phone number and plays the warning content through voice synthesis technology, ensuring that users can receive emergency reminders even when they do not check their phones in time.

[0085] Early warning terminal: Early warning information is automatically sent to the multi-hazard early warning terminal at the corresponding early warning location, and the personnel at the location are reminded to take shelter through the form of images, lights, loudspeakers and other means.

[0086] Warning Records and Feedback: All issued warning messages are logged completely on the platform, including trigger time, recipient, notification method, and sending status, which can be queried and traced. Responsible personnel can provide feedback such as "received" via the app or SMS link.

[0087] Through the above implementation methods, the safety risks of field operations that were previously scattered, passive, and relied on personal experience are transformed into a centralized, proactive, and data-driven intelligent early warning management model. This achieves closed-loop control of safety risks of "mobile units" and "temporary locations" in power production activities, effectively filling the gap in the existing early warning system in terms of dynamic personnel safety protection, and significantly improving the overall safety production level and emergency response foresight of the power industry.

[0088] Example 2 The power facilities also include transformer substations. Based on Example 1, the system further combines transformer substation information with multi-hazard early warning information to achieve disaster risk early warning for the transformer substations. The specific steps include: (1) Obtain power company distribution area data; distribution area data includes the number of users in the distribution area, user account number, distribution area management responsible department and contact information, and real-time power consumption of the rated capacity of the distribution area transformer.

[0089] (2) Data association is achieved by linking the distribution network line (10KV) end transformers in the distribution area and the power grid map; specifically, the power grid map of the power department has end 10KV transformer data, and the distribution area also has transformer data. In the process of information fusion, the power facilities and equipment use the same ID; to achieve data association.

[0090] (3) After a disaster warning is issued, the power facilities and equipment within the scope of the disaster warning risk are used to automatically calculate the data of the main grid (lines above 10KV) / distribution network lines that may be affected.

[0091] (4) Then analyze the data of the distribution network lines that may be affected based on the data of the affected main grid lines. For example, the main grid and the distribution network are physically branches. The correlation can be queried from the database that stores data of power facilities and equipment to obtain the data of the distribution network lines that may be affected.

[0092] (5) Using the relationship in (2) and the branch relationship between the main network and the distribution network in (4), a multi-level relationship of pole-tower-main network-distribution network-transformer-transformer area is formed. After the warning is generated, the transformer area that meets the warning conditions is determined according to the multi-level relationship, the scope of the warning and the level of the disaster warning. Then, the warning information is sent to the transformer area users and management users who meet the warning conditions through the warning receiving terminal, SMS, APP, outbound call, etc.

[0093] Example 3 Based on Example 1, instead of directly labeling the power facilities and equipment falling within the disaster risk impact range based on their location information, the disaster risk impact range is modified according to the characteristics of the power facilities and equipment. After modification, the disaster risk impact is labeled. The modification of the disaster risk impact range based on the characteristics of the power facilities and equipment specifically includes: deducing several transmission lines involved in the disaster risk impact based on the power facilities and equipment falling within the disaster risk impact range, and merging the areas of the several transmission lines involved in the disaster risk impact that are not included in the disaster risk impact range with the disaster risk impact range to obtain the disaster impact range of the power facilities and equipment.

[0094] As a specific implementation, this method addresses the large number and wide distribution of power facility towers, adjusting the scope of disaster risk impact based on the tower characteristics. Transmission lines often involve a single tower supporting multiple transmission lines; that is, the same physical tower may appear as two different towers (virtual towers) on the power grid map, indicating that the same tower belongs to different transmission lines. This is what the power industry commonly refers to as "double / multi-circuit lines on the same tower." Physical towers refer to towers that actually exist in the physical world, while virtual towers refer to towers that exist logically, forming a mapping relationship. Based on the specific needs of power facility equipment, the method deduces the transmission lines involved in disaster risk impact based on the towers, and then makes the necessary adjustments. The specific solution is as follows: (1) Based on the logical relationship between physical and virtual poles, the data is integrated, and physical associations are established between physical and virtual poles. For example, in the power grid diagram component, although physical poles and virtual poles share the same ID, they represent actual physical equipment and logically virtual equipment, respectively. The association mapping between physical poles and virtual poles can be realized through this ID. The system obtains power facility data from the power grid asset management system or the power intranet database, including: pole number, equipment name, voltage level, latitude and longitude, address, model, line to which it belongs, nature of power facility equipment, height, commissioning date, line length, overhead line length, cable line length, city, altitude, whether it is a bend, whether it is on the same pole, pole nature (such as tension), pole height, physical pole commissioning date, substation location information, power plant location information, and transformer location information, etc. Among them, the "whether it is on the same pole" field is used to identify whether multiple lines share the same physical pole. If this field is "Yes", it indicates that there is a situation where one physical tower supports multiple virtual towers. In this case, these virtual towers and physical towers are identified using the same ID and are managed uniformly during data processing. To establish accurate mapping relationships, the system constructs a mapping database between physical towers and one or more virtual towers they support based on preset association rules (such as precise matching based on spatial coordinates, "parallel lines on the same tower" information in the ledger, or equipment ID association). This database is a key bridge connecting the impact of physical disasters in the real world with the logical operating status of the power grid, helping to achieve accurate analysis and response to the power grid status.

[0095] (2) Combining the impact of disaster early warning on physical poles, the impact on virtual poles is analyzed. As mentioned above, the impact range of various disaster risks has been generated on the disaster early warning electronic map, and specific physical poles located within the impact range have been identified through spatial analysis. When a physical pole is identified as a high-risk point, the aforementioned power facility equipment data is immediately queried to obtain all virtual poles with the same ID as the physical pole and their corresponding line information. The threat information of disaster risks to the physical pole (such as the risk of tower collapse caused by mudslides, the risk of erosion caused by flash floods, etc.) will be simultaneously and equally associated with all corresponding virtual poles. For example, if physical pole "T001" carries virtual poles for line "L100" and line "L200", then once "T001" is marked as high risk due to a flash flood warning, the system will automatically synchronize this risk information to all virtual poles associated with "T001", including the virtual poles corresponding to lines "L100" and "L200", so that their risk levels are consistent with those of the physical poles, thus achieving synchronous transmission of risk information.

[0096] (3) By using the relationship between the virtual pole and the line, the information of the affected line can be inferred.

[0097] After completing the risk labeling of virtual towers, the location and function of affected virtual towers within their respective lines are further analyzed based on the power grid topology model. By traversing the topological connections of the lines, the affected lines are statistically analyzed: a list of all transmission lines containing at least one high-risk virtual tower is clearly compiled, such as lines L100 and L200 mentioned above. The degree of impact on the lines is assessed: combining the line topology and the location of the affected virtual towers (e.g., whether they are located in tension sections, important crossing points, etc.), the overall operational risk level of the lines is analyzed.

[0098] Furthermore, by utilizing the relationship between physical and virtual poles, and the relationship between pole / tower properties and power lines, information about affected power lines can be deduced. The main idea is as follows: after a real or simulated early warning is generated, the power equipment and facility analysis module in the system analyzes information such as geographical location, disaster type, early warning level, and disaster warning impact range to obtain the impact assessment results for all affected physical poles / towers within the service area.

[0099] Based on the analysis of the characteristics of the poles and towers, it is determined that the relevant lines will be affected. Then, the power facility and equipment ledger database is queried to find the correspondence between the virtual pole and the line, thereby deducing the information of the affected lines.

[0100] In real-world scenarios, different power lines may intersect or share poles, meaning a single physical pole may have multiple virtual poles. Since these poles belong to different power lines and have different functions, properties, and levels of importance, they will have varying degrees of impact. By combining the relationship between physical and virtual poles, and considering factors such as the type of poles on different lines and their location on the lines, we can calculate and analyze the impact of disaster warnings on the entire power infrastructure and power grid, rather than just analyzing the impact on physical poles.

[0101] The types of poles and towers involve their functions, structures, and usage scenarios. When a virtual pole is affected by disaster risks, the system combines the pole's own disaster resistance capabilities and performs impact assessment calculations on power grid facilities, equipment, and lines from multiple perspectives, including physical impact, functional damage, and performance stability.

[0102] As a specific implementation, the impact can be analyzed based on the characteristics of the towers. Based on the tower number, the tension section number, starting and ending tower numbers, and corresponding line name of the tower can be directly correlated. Alternatively, by using line design data or databases and based on the distribution patterns of tension towers, the tension towers before and after the target tower can be located, thereby determining the range of the tension section and the line to which it belongs.

[0103] If the affected tower is a straight tower in a tension section, the warning range is limited to that tension section and generally will not affect the power supply of the line. As "load-bearing nodes" of the line, the impact of tension towers and corner towers on the line depends primarily on their segmenting role within the line. Tension towers typically divide the line into several tension sections, each ranging from several hundred meters to several kilometers in length. Therefore, the failure of a single tension tower or corner tower can lead to conductor slack, breakage, or even tower collapse throughout the entire tension section, with the impact area being the length of that section. If a tower at a critical corner is affected, it may connect to multiple tension sections simultaneously, resulting in a larger impact area. The specific impact also depends on the line voltage level; high-voltage lines typically have longer tension sections, leading to a wider impact area. Generally, the tension section length for 110kV lines is approximately 1-3 kilometers, for 220kV it's about 2-5 kilometers, and for 500kV and above ultra-high-voltage lines it can reach 5-10 kilometers or even longer. The length of the tension section where a corner tower is located is also affected by terrain; it may be shorter in mountainous areas than in plains.

[0104] As a specific example, if a critical tower in a tension section (such as an angle tower or terminal tower) is affected by disaster warning risks, the stress on the angle tower is more complex at the corner, and the superimposed effect of reasonable cornering and related disaster risks needs to be carefully considered. Adjacent towers may tilt or even collapse successively due to sudden changes in stress, expanding the power outage area, and the line will be affected in this case.

[0105] Based on the disaster warning impact area and tower location information, tension and angle towers within the warning zone were analyzed and selected. According to the attributes of these towers, their associated tension sections and related lines were determined. Then, the impact was analyzed in conjunction with warning information such as disaster type and warning level. For example, during earthquake early warning, it is important to determine whether the predicted intensity exceeds the seismic resistance level of the tower. The key stress points for tension towers and angle towers are at the connection between the foundation and the tower body. The horizontal acceleration generated by the earthquake will amplify the inertial force of the tower. Due to the existence of conductor angular tension, angle towers bear a greater torque, which may lead to tower deformation or foundation pull-out.

[0106] Geological disaster warnings, such as landslides and collapses, can directly damage the foundations of towers. Tension towers and corner towers have deeper foundations, but if the warning indicates that the disaster risk is near the tower, soil sliding will cause uneven settlement of the foundation. Corner towers, due to bidirectional stress, have a higher risk of foundation instability than straight towers.

[0107] In flash flood warnings, it is important to analyze whether the area is in a flash flood danger zone. Flood erosion will erode the soil around the tower foundation. If the foundation of a tension tower is located near a river, the erosion will cause the foundation to be exposed, reducing its bearing capacity. If a corner tower is located at a bend in a valley, the flash flood flow will be faster and the erosion force will be stronger, which may cause the foundation to collapse.

[0108] For these warnings, it is necessary to consider the specific location of the tower, the type of foundation, and the intensity of the warning to determine whether an emergency shutdown of the line or temporary reinforcement is required. For towers confirmed to be affected, the line risk in the tension section where they are located should be assessed. If the risk is high, power can be switched to a backup line in advance.

[0109] (4) Generate the derivative impact range based on the affected line information, and divide the derivative impact range into different risk levels according to the preset risk level classification rules. An example of a preset rule is shown in Table 1.

[0110] Table 1 Overall Operational Risk Level and Judgment Criteria

[0111] Table 1 shows the assessment parameters for risk and defect judgment, which consider key parameters and their corresponding relationships. Table 2 shows a list of assessment parameters for risk and defect judgment.

[0112] Table 2. List of assessment parameters for risk and defect assessment

[0113] After the analysis is complete, the following list can be generated: List of affected routes and risk assessment 1. Line L100 (220kV, XX Line 1) Risky towers: #32 (tension tower, high risk - structural deformation), #78 (straight-line tower, medium risk - insulator contamination).

[0114] Topological location: #32 is located in the tension section in the middle of the line, controlling two straight sections; #78 is located at a general crossing point.

[0115] Load and Role: Regional critical communication line, current load rate 75%.

[0116] Redundancy: There is a backup Hanjin 2 line in the same channel, so N-1 is satisfied.

[0117] Environmental factors: Located on a plain, with convenient transportation.

[0118] Comprehensive analysis: Tower #32 has an extremely high risk, but because there is a backup line, the overall operational risk level of the line is assessed as Level II (high risk), and a planned power outage should be arranged as soon as possible.

[0119] 2. Line L200 (110kV, XX Third Line) Risky tower: #15 (terminal tower, high risk - foundation settlement).

[0120] Topological location: The starting point of the line is the terminal tower, which connects to the substation.

[0121] Load and Role: Single radial dedicated line for important users, no backup, load rate 60%.

[0122] Redundancy: No backup power supply.

[0123] Environmental factors: Located within the factory area, making maintenance convenient.

[0124] Comprehensive analysis: The critical location has a high-risk defect with no redundancy; a failure would result in a complete user outage. The overall operational risk level of the line is assessed as Level I (extremely high risk), requiring immediate activation of the emergency plan and handling.

[0125] Furthermore, the impact of natural disaster risks on the power grid is not limited to poles and their associated lines, but may also affect the lines and equipment associated with the power grid structure. These lines, equipment, and their associated transformer substations are also within the scope of the impact assessment.

[0126] Once a line is determined to be affected, the system iterates through and analyzes all important nodes on that line, including substations, branch lines, and transformers, and analyzes each of these important nodes one by one: (1) For a substation, if multiple lines are connected to the substation and the affected lines can perform load transfer operations, the downstream lines are determined to be unaffected; if only the line is connected to the substation or multiple lines are connected but the affected lines cannot perform load transfer, all downstream lines of the substation are determined to be within the scope of the derivative impact.

[0127] (2) For branch lines and transformers, when the line is affected, all downstream branch lines and transformers are automatically calculated based on the topological relationship between the equipment and determined to be affected. When a transformer is affected, the transformer area information of the affected transformer is extracted by obtaining the transformer area data of the business system and used as the derived impact range.

[0128] The final affected area is the superposition area of ​​the affected line itself and the derived area from the above analysis.

[0129] As another specific implementation, the derived impact range generated based on the affected line information can also be analyzed from the perspective of power industry business. For example, for a line connecting multiple substations or power plants, the power grid flow model can be combined to preliminarily infer the load loss, power flow shift, and even cascading failure risks that may be caused by the line being out of service due to a disaster.

[0130] After completing the statistics of affected lines, multi-scenario simulation switching analysis is conducted by combining the respective commissioning logic models and power grid flow models to accurately define the scope of the power outage. Information such as outage equipment, lost load, and lost power generation within the outage area is also collected, and a list of important users and their impact levels are identified. Based on the above analysis results, an intuitive power flow diagram of the outage area can be automatically generated.

[0131] The following detailed explanation, using specific implementation methods, outlines the detailed process for conducting power supply risk assessment, rapid load loss estimation, and adjustment support decisions for affected transmission lines after a disaster warning is triggered. The basic concept is as follows: obtain real-time load data for the affected lines, determine if there are backup lines available for load transfer, and calculate the expected load loss data ("affected line load - transferred load"). When real-time load data is unavailable, use the line's "historical average load × number of users" for that time period to quickly estimate load loss and assist in adjusting the power supply lines.

[0132] Once a disaster early warning system identifies a high-risk transmission line, its ultimate impact needs to be quantified at the level of power supply security. By integrating real-time operational data and historical data, a rapid assessment and decision support mechanism has been constructed, moving from "line risk" to "power supply impact."

[0133] A. Integration of information on affected power lines and power grid operation data After identifying affected power lines (e.g., through conduction analysis via physical poles → virtual poles → lines), the system immediately initiates power supply risk analysis. The core of this analysis process is secure data interaction with dispatch automation systems (such as energy management systems, EMS). Through standard interfaces (such as IEC 61970), the system acquires a real-time snapshot of the current power grid, particularly real-time operational data of affected lines, grid topology and backup path information, and information on relevant load points, thereby integrating information on affected lines with grid operational data.

[0134] Real-time operational data for the affected lines includes active power, reactive power, current, and switch status (on / off). Grid topology and backup path information includes connections to electrically adjacent or supporting lines and transformers, current load rates, and transferable capacity. Information on relevant load points includes load data for substations, distribution transformers, or critical users downstream of the affected lines.

[0135] B. Refined estimation method for load loss Based on the acquired data, the system employs a tiered strategy to estimate load loss: Scenario 1: Precise calculation with real-time data When the real-time load (active power P_line) of the affected line can be reliably obtained from the dispatch automation system, the system combines topology analysis to determine the backup transfer path, which specifically includes the following steps: Backup capacity assessment: The system analyzes whether there are any normally operating backup lines or tie lines that can take over the transferred load. If so, the current remaining available transmission capacity of the backup path is assessed (rated capacity minus current load).

[0136] Transferable load calculation: The remaining available capacity of the backup path is compared with the real-time load of the affected line, and the smaller value is taken as the theoretical maximum transferable load value.

[0137] Estimated load loss calculation: Estimated load loss = MAX(0, Real-time load of affected lines - Maximum transferable load). This value represents the unavoidable load loss if the line were immediately shut down under optimized operation. The system also records the estimated load that can be successfully transferred.

[0138] Scenario 2: Rapid estimation when real-time data is missing When scheduling data is unavailable, communication is interrupted, or during the initial system startup phase, the system employs a rapid estimation method based on historical data, which includes the following steps: Historical load baseline establishment: The system presets or dynamically learns the historical average load level of each line on different date types (weekdays, weekends, holidays) and different time periods (24 hours) to form a load curve baseline.

[0139] User-weighted correction: To improve estimation accuracy, the system correlates with marketing system data to obtain the effective number of users supplied by the line. When the line structure changes (e.g., new users are added), the estimation is corrected using "the average load per user on the same day and time period of the same type in history × the current number of users". The load per user can be updated periodically.

[0140] Estimate current load: Based on the date type and time period to which the disaster occurred or the disaster warning was issued, retrieve the corresponding historical average load (or the load value corrected for the number of users) as the estimated value of the current load (P_estimated).

[0141] Backup path and loss estimation: Follow the logic of scenario one, but use P_estimated as the load value of the affected line, and combine it with the rated capacity of the backup path (since there is no real-time load data, we conservatively assume that its load is 0 or use a typical load rate) to estimate the transferable load and the expected loss load.

[0142] C. Preliminary assessment of the risk of cascading failures While calculating load losses, the system also makes a preliminary assessment of the broader power grid risks that may result: Power Flow Transfer Path Analysis: When load is transferred via a backup path, the system quickly assesses whether the transfer will lead to overload of other critical sections or equipment, based on a simplified DC power flow or a pre-calculated sensitivity matrix. For example, will the load rate of the backup line exceed the short-term allowable limit, or will it trigger a cascading overload of other lines?

[0143] Voltage stability risk warning: For outages of lines containing a large amount of reactive load or located at the end of the power grid, based on the topology, a warning is issued regarding the potential risk of related bus voltage exceeding limits.

[0144] Comprehensive risk level assessment: Combining the "estimated load loss value" (directly reflecting the impact on social electricity consumption) and the "possibility of triggering cascading overload / voltage problems" (reflecting the threat to the safety and stability of the power grid), the system conducts a comprehensive risk level assessment for this line outage (e.g., low, medium, high, severe).

[0145] By combining disaster early warning information, real-time / historical power grid operating data, topology analysis, and rapid electrical calculations, a rapid and quantitative transformation from physical disaster risk to power supply security risk has been achieved. This enables decision-makers not only to know "which lines may be damaged," but also to predict in advance "how much electricity will be affected if this line goes out, whether there will be cascading risks to the power grid, and what we should do in advance." This greatly enhances the power grid's predictive defense capabilities and precise emergency response level in the face of disasters, shifting from "passive repair" to "proactive control," maximizing the reliability of power supply.

[0146] (5) The scope of the derived impact is integrated with the scope of the disaster risk to obtain the scope of the risk impact of power facilities and equipment.

[0147] Furthermore, one way to integrate the scope of the derived impact with the scope of the disaster risk is to organically combine the information of the affected transmission lines with the power flow diagram (the power flow diagram mainly reflects the line trend, voltage, power, etc.) to directly calculate the expected load loss. If other lines are temporarily taking over the load of the affected line, the load of other lines can also be calculated in a timely manner to ensure power transmission safety.

[0148] Furthermore, the integration can also involve extending the analysis along the affected faulty line towards the load side, examining the distribution of downstream substations, distribution transformers, and low-voltage users. For example, a high-voltage line (e.g., 220kV) fault may affect multiple 35kV substations, subsequently impacting several towns and villages. A distribution line (e.g., 10kV) fault directly affects the users in the transformer substations under that line. By combining this with a user information management system (e.g., a marketing system), a list of all transformer substations and users under the faulty line can be queried, down to the individual user level. After obtaining the scope of the risk impact on power facilities and equipment, a list of affected early warning targets is generated based on the location information of the power facilities and equipment. An example of an affected early warning target list is shown below. Figures 2-5 As shown in the figure, Figure 2 This is an example of a list of warning targets for poles; Figure 3 This is an example of a list of early warning targets for a substation; Figure 4 This is an example of a list of early warning targets for power plants; Figure 5 This is an example of a list of warning targets for production / non-production sites.

[0149] In summary, by constructing an electronic disaster early warning map, linking physical and virtual power poles, and enabling precise transmission of risks from physical entities to the logical units of the power grid, a three-dimensional, refined, and topological upgrade of natural disaster risk early warning for power facilities has been achieved. Specific beneficial effects include: 1. Solved the challenge of risk transmission analysis in scenarios where multiple transmission lines share a single tower. Traditional early warning systems typically assess risks based on physical location, failing to automatically distinguish the logical risks of multiple different lines connected to virtual poles on the same physical tower. This can easily lead to missed risk assessments or overloaded warnings. This invention establishes a mapping chain between physical towers, virtual towers, and transmission lines, enabling precise logical risk transmission. Once a physical tower is identified as a risk point, all associated virtual towers (i.e., the corresponding nodes on each line) are automatically marked as risk. It achieves traceability of the impact range: all affected transmission lines can be immediately traced and listed, clearly defining the risk level of each line. It also quantifies the impact of natural disasters; by combining the location of affected virtual towers in the line topology (e.g., whether they are located in tension sections or at important crossings), the degree of threat to the overall safe operation of the line can be assessed.

[0150] 2. A leap has been achieved from "area-based early warning" to "point-based early warning" and then to "network-based early warning". The shift from "area-based early warning" to "point-based early warning" specifically refers to moving from macroscopic, area-based early warnings to disaster early warnings based on precise locations (such as the impact zone of a landslide). This involves using spatial analysis to pinpoint specific physical power poles, thus concretizing the risk object. The shift from "point-based early warning" to "network-based early warning" specifically refers to mapping the risk of a single physical point to multiple logical nodes (virtual power poles) in the power grid topology network through the association between physical and virtual power poles. This allows for topology analysis to infer the impact on the operational safety of the entire power line or even a local power grid. This extends risk early warning from individual equipment to the power grid system level, supporting global power grid safety risk assessment. For example, area-based early warnings are officially issued with a wide scope and long time span (e.g., a yellow alert for a geological disaster in a specific district or county, or a rainstorm warning for a specific township), while point-based early warnings can pinpoint the affected power facilities and equipment, generating a list of affected facilities and equipment.

[0151] 3. Improved the foresight and decision-making efficiency of emergency response. In the traditional model, maintenance personnel must manually identify potentially affected equipment based on early warning information and then check records to determine line ownership, a cumbersome and time-consuming process. This invention, through automated risk correlation and topology inference, automatically generates a report upon receiving a disaster risk warning, clearly indicating: a list of affected key equipment (physical / virtual poles), threatened transmission lines and their levels, and potential grid operation risks (such as possible cascading failures). This enables dispatching and maintenance departments to allocate repair resources, adjust operating modes, and issue inspection instructions in advance and accurately, transforming passive emergency response into proactive defense, significantly shortening emergency response time, and minimizing potential disaster losses.

[0152] The correlation analysis and risk transmission analysis based on this scenario can not only be used for pre-disaster early warning, but also provide a unified data platform and analysis tools for disaster situation assessment and post-disaster loss evaluation, becoming a key support module for intelligent operation and maintenance and resilience enhancement of the power grid. It effectively solves the core pain points of poor connection between current power facility safety monitoring and multi-hazard early warning systems, and insufficient targeted early warning. It achieves accurate, automatic, and visualized assessment of disaster risks at multiple levels—physical space, logical topology, and power grid system—providing efficient and reliable decision support for ensuring the safe and stable operation of the power grid in complex environments, demonstrating significant technological advancement, practicality, and broad application value. Figure 6 This is an example of an interface for real-time viewing of multi-hazard early warnings, allowing users to intuitively view nearby power facilities and equipment that may be affected. Figure 6 The document is divided into three parts: a schematic diagram of the early warning map in the middle; a statistical chart of warning areas, warning levels, and types on the left; and a statistical chart of affected facilities and equipment locations on the right. The schematic diagram of the early warning map is shown below. Figure 7 As shown in the figure, the statistical chart of warning areas, warning levels and types is as follows: Figure 8 As shown in the diagram, the locations of affected facilities and equipment are statistically analyzed. Figure 9 As shown.

[0153] Example 4 This embodiment expands upon the content of embodiments 1 and 2.

[0154] As a preferred embodiment, this embodiment also includes: sorting the affected power facilities and equipment according to voltage level, distance from the disaster center risk point, and degree of impact of the disaster risk on the power facilities and equipment, to obtain the disaster risk level of the power facilities and equipment, and sending early warning information according to the risk level.

[0155] As a preferred embodiment, this embodiment also includes: for a single power facility or equipment, supporting the query of past early warning data statistics, including: the total number of warnings for each disaster type, the number of warnings generated for each risk level, assisting power companies in assessing the comprehensive risks of power facilities and equipment and formulating line inspection plans.

[0156] As a preferred embodiment, this embodiment also includes: generating a power facility and equipment analysis report, utilizing the latitude and longitude data, elevation data, and equipment material of power facilities such as poles, lines, substations, production (non-production) and living sites, and power plants, combined with precise location early warning information (early warning type, latitude and longitude, early warning level), and taking advantage of the different impact ranges of different disaster types and different early warning locations, automatically generating a disaster early warning risk impact range circle, and automatically calculating the power facilities and equipment that may be affected through system calculations, and automatically generating an early warning impact analysis report.

[0157] Example 5 Please refer to Figure 10 , Figure 10 This is a schematic diagram of a multi-source information fusion-based early warning system for the power industry, provided as an embodiment of this application.

[0158] A multi-source information fusion power industry multi-hazard early warning system 90 may include: Data interface 91 is used to obtain the location and feature information of power facilities and equipment from the power system's management system; Data fusion module 92 is used to add the location information and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; The early warning information display module 93 is used to generate different levels of disaster risk impact range on the disaster early warning electronic map based on the disaster early warning information, and to mark the power facilities and equipment that fall within the disaster risk impact range based on the location information of the power facilities and equipment; The data push module 94 is used to generate different types of early warning impact analysis reports based on the labeled information, and push the early warning impact analysis reports to the preset population.

[0159] Data obtained through data interface 91 from the power sector's asset management system, production management system, and online monitoring system includes pole / tower number, equipment name, voltage level, latitude and longitude, address, model, associated line, nature of power facilities and equipment, height, and commissioning date. It also includes line length, overhead line length, cable line length, city / prefecture, altitude, whether it's a bend, whether it's on the same pole, pole / tower type (tension), pole / tower height, physical pole commissioning date, substation location information, power plant location information, and transformer location information.

[0160] Optionally, the data fusion module 92 can be specifically used to: integrate the characteristic information of power facilities and equipment into the data layer of the disaster early warning electronic map. Power facilities and equipment include poles, transmission lines, substations, production and living sites around power facilities and equipment, and power plants.

[0161] Optionally, the warning information display module 93 can be specifically used for: The self-configured temporary base information is integrated into the data layer of the disaster early warning electronic map, and disaster early warning information is pushed to the temporary bases. The specific implementation methods include: Automatic data entry: Receives temporary base information through a data interface and updates the status of temporary base information in real time, including whether a temporary base exists or has been withdrawn; Manual entry: Directly select or input latitude and longitude coordinates on the electronic map to determine the location of the station, and fill in the relevant attribute information; the attribute information includes, but is not limited to, station name, purpose, duration of temporary station, location information, designated responsible person and their contact information, disaster type and corresponding set threshold.

[0162] Optionally, the warning information display module 93 can also be specifically used for: In the process of correcting the scope of disaster risk impact based on the location information of power facilities, power grid, and risk points, transmission lines often have multiple transmission lines supported by the same tower. That is, the same physical pole will be displayed as two different towers (virtual poles) on the power grid map. In view of the special needs of power facilities and equipment, the process of correcting the scope of disaster risk impact is mainly based on the tower to deduce the number of transmission lines involved in the disaster, and then make the correction.

[0163] It should be understood that the various modules of the multi-source information fusion power industry multi-hazard early warning system 90 provided in the above embodiments are only illustrated by the division of each functional module in the above description when performing natural disaster risk early warning. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above.

[0164] The functional modules in the above embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of this application.

[0165] Based on the same concept, embodiments of this application also provide a computer device, which may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a multi-source information fusion method for early warning of multiple disasters in the power industry as described above.

[0166] Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-source information fusion method for early warning of multiple disasters in the power industry as described above.

[0167] Based on the same application concept, this application embodiment also provides a program product, which implements the aforementioned multi-source information fusion method for early warning of multiple disasters in the power industry when the program product is run on a computer.

[0168] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-source information fusion method for early warning of multiple disasters in the power industry, characterized in that, Includes the following steps: S1, Obtain the location and feature information of power facilities and equipment; S2, add the location and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; S3 generates different levels of disaster risk impact range on the disaster early warning electronic map based on disaster early warning information; S4, Based on the location information of the power facilities and equipment, mark the power facilities and equipment that fall within the disaster risk impact range; S5. Based on the labeled information, generate different types of early warning impact analysis reports and push the early warning impact analysis reports to the preset population.

2. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 1, characterized in that, The steps also include integrating the self-configured temporary base information into the data layer of the disaster early warning electronic map, and pushing the affected disaster early warning information to the temporary base.

3. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 2, characterized in that, The information on self-configured temporary bases is integrated into the data layer of the disaster early warning electronic map. Specific implementation methods include: Automatic data entry: Receives temporary base information through a data interface and updates the status of temporary bases in real time, including whether a temporary base exists or has been withdrawn; Manual entry: Directly select or input latitude and longitude coordinates on the electronic map to determine the location of the station, and fill in the relevant attribute information; the attribute information includes, but is not limited to, station name, purpose, duration of temporary station, location information, designated responsible person and their contact information, disaster type and corresponding set threshold.

4. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 3, characterized in that, For temporary bases, the steps for sending the aforementioned early warning impact analysis report include: The dynamically generated impact ranges of various disaster risks are overlaid with the geographical locations of all temporary camps in time and space. When the analysis finds that a certain camp is located within the impact range of a disaster risk, and the warning level of the impact range of the disaster risk reaches or exceeds the threshold set by the temporary camp for the corresponding disaster type in the configuration, the warning condition is triggered.

5. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 1, characterized in that, The steps also include adding the transformer area data to the data layer of the disaster early warning electronic map, identifying transformer areas that fall within the disaster risk impact range and meet the early warning conditions, and issuing early warnings. Specific steps include: Obtain data from the power company's distribution areas; Data association is performed by linking the relationships between the transformers at the end of the distribution network lines in the integrated map of the distribution area and the power grid. After a disaster warning is issued, power facilities and equipment within the disaster risk area are used to calculate the data of the main grid or distribution network lines that may be affected. Based on the data of the affected main network or distribution network lines, analyze the data of the distribution network lines that may be affected; Based on the data association and the data of the distribution network lines that may be affected, a multi-level association relationship is formed between poles, main grid, distribution network, transformers, and distribution areas. After an early warning is issued, the transformer substations that meet the early warning conditions are determined based on the multi-level correlation, the scope of disaster risk impact, and the early warning level.

6. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 1, characterized in that, After correcting the scope of the disaster risk impact, the power facilities and equipment falling within the scope of the disaster risk impact are then marked. The specific steps include: Based on the power facilities and equipment falling within the disaster risk impact range, several transmission lines involved in the disaster risk impact are deduced. The areas of these transmission lines not included in the disaster risk impact range are then merged with the disaster risk impact range to obtain the disaster impact range of the power facilities and equipment.

7. The multi-source information fusion method for early warning of multiple disasters in the power industry as described in claim 1, characterized in that, The power facilities and equipment include, but are not limited to, power poles, transmission lines, substations, production and living areas around the power facilities and equipment, and power plants.

8. A multi-source information fusion power industry multi-hazard early warning system, characterized in that, This includes a data interface, a data fusion module, an early warning information display module, and a data push module. Data interface, used to obtain location and feature information of power facilities and equipment from the power system's management system; The data fusion module is used to add the location and feature information of the power facilities and equipment to the data layer of the disaster early warning electronic map; The early warning information display module is used to generate different levels of disaster risk impact range on the disaster early warning electronic map based on disaster early warning information, and to mark the power facilities and equipment that fall within the disaster risk impact range based on the location information of the power facilities and equipment; The data push module is used to generate different types of early warning impact analysis reports based on the labeled information, and push the early warning impact analysis reports to the preset population.

9. A program product, characterized in that, When the program product is run on a computer, it implements a multi-source information fusion method for early warning of multiple disasters in the power industry as described in any one of claims 1 to 7.

10. A computer device, characterized in that, The computer device may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a multi-source information fusion method for early warning of multiple disasters in the power industry as described in any one of claims 1 to 7.

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