Dynamic risk early warning method, system and equipment for natural disasters in power industry

Abstract

The invention relates to the technical field of natural disaster multi-data fusion, in particular to a natural disaster dynamic risk early warning method, system and equipment in the power industry, and the method comprises the following steps: S1, constructing a disaster early warning electronic map which comprises the position information and feature information of power facilities and equipment; s2, when disaster risk early warning is received or a disaster occurs, generating a corresponding initial disaster risk influence range on the disaster early warning electronic map according to disaster early warning information; and S3, according to the position information and the feature information of the electric power facility equipment, correcting the initial disaster risk influence range to obtain the risk influence range of the electric power facility equipment, and sending out early warning information. Special disaster early warning information for electric power facilities and equipment is formed, and the problems of many false alarms of natural disaster early warning, inaccurate targeted release of early warning information and the like in the electric power industry are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural disaster multi-data fusion, and in particular to a natural disaster dynamic risk early warning method, system and device for the power industry. BACKGROUND

[0002] With the continuous expansion of China's power network and the in-depth promotion of smart grid construction, a large number of power facilities and equipment, such as power transmission lines, substations, towers, etc., are widely distributed in mountainous, hilly, coastal and complex and variable outdoor environments. These areas are often prone to natural disasters, and common disasters include landslides, mudslides, earthquakes, heavy rains, floods, lightning, hail, etc. The occurrence of natural disasters not only seriously threatens the safe and stable operation of power facilities and equipment and the safety of personnel in natural disasters, but also easily causes power grid failures, large-scale power outages and other accidents, which have a major impact on the national economy and people's livelihood.

[0003] At present, for the monitoring and early warning of natural disasters, meteorological, geological, water conservancy and other departments have developed relevant disaster early warning systems. Through satellite remote sensing, ground observation stations, radar monitoring and other technical means, the system can realize risk prediction and warning of various natural disasters within a certain spatio-temporal range. The power department has an urgent need for natural disaster early warning information, and hopes to access multi-disaster early warning data to obtain information such as disaster risk or disaster occurrence time, location, intensity, etc. in advance, so as to timely start emergency plans and deploy maintenance resources, thereby reducing the losses that disasters may bring.

[0004] However, in actual application, the existing multi-disaster early warning technology system and the safety monitoring system of power facilities and equipment lack effective connection and deep integration, and there is a obvious "information island" problem. Mainly manifested in the following aspects: first, the existing early warning data is mostly for macro regional or public general early warning, and the accuracy in time and space is lacking, lacking fine analysis and customized early warning for specific power facilities and equipment geographical location, structural characteristics, operating state and other elements; second, the early warning information and real-time monitoring data of power equipment (such as video monitoring, stress monitoring, insulation state, etc.) cannot realize linkage analysis and fusion processing, resulting in insufficient accuracy and pertinence of early warning; third, after the early warning information is transmitted to the power department, manual research and decision-making are usually required, and the response chain is long, which is difficult to meet the real-time requirements of disaster emergency response; fourth, there is a lack of dynamic assessment and early warning capability for multi-disaster coupling or chain disaster risks faced by power facilities and equipment, which cannot accurately reflect the cumulative and superimposed effects on power equipment in the disaster evolution process.

[0005] Therefore, the existing technical mode cannot provide timely, accurate and targeted natural disaster risk early warning for the safe operation of power facility equipment, and it is difficult to support the power department to realize the transition from passive response to active defense. An effective method and system for integrating multi-source disaster early warning data and power facility equipment ontology state information to realize dynamic risk assessment and accurate early warning are urgently needed to improve the resilience and reliability of the power system in response to natural disasters. SUMMARY

[0006] The present application aims to adaptively improve the natural disaster multi-disaster early warning technology according to the particularity of power facility equipment and the particularity of power industry workers, and proposes a natural disaster dynamic risk early warning method, system and device for the power industry, so that the multi-disaster early warning information of natural disasters can actually play a role in the disaster prevention work of the power department, and the natural disaster multi-disaster early warning system can provide guidance for power disaster prevention work, reduce secondary disasters and economic losses.

[0007] In order to achieve the above-mentioned application purposes, the present application provides the following technical solutions:

[0008] The natural disaster dynamic risk early warning method for the power industry comprises the following steps: S1, constructing a disaster early warning electronic map, wherein the disaster early warning electronic map comprises position information and characteristic information of power facility equipment; S2, when receiving a disaster risk early warning or a disaster occurrence, generating a corresponding initial disaster risk influence range on the disaster early warning electronic map according to the disaster early warning information; S3, correcting the initial disaster risk influence range according to the position information and characteristic information of the power facility equipment to obtain a power facility equipment risk influence range, and issuing an early warning information; The correction of the initial disaster risk influence range comprises: deducing a plurality of power lines involved in the disaster risk influence according to the power facility equipment falling within the initial disaster risk influence range, fusing the areas in the plurality of power lines involved in the disaster risk influence which are not included in the initial disaster risk influence range with the initial disaster risk influence range to obtain the power facility equipment risk influence range.

[0009] Further, in step S1, the construction of the disaster early warning electronic map specifically comprises the following steps: S11, acquiring basic geographic information data and power facility equipment data; S12, constructing a disaster early warning basic electronic map according to the basic geographic information data; S13, dynamically fusing the power facility equipment data and the disaster early warning electronic map under a unified spatial reference; Step S13 specifically comprises: The power facility equipment data is added to the disaster early warning basic electronic map through a data layer to generate a disaster early warning electronic map.

[0010] Further, the step S2 specifically comprises the following steps: The various types of disaster early warning information are parsed and standardized processed. According to the physical characteristics and early warning data format of the disaster early warning information, a model is generated according to preset spatialization rules and influence range to generate an initial disaster risk influence range.

[0011] Further, in the step S3, the position information of the power facility equipment includes tower number, equipment name, voltage level, longitude and latitude, address, model, belonging line, nature of the power facility equipment, height, commissioning date. It also includes line length, overhead line length, cable line length, belonging city, altitude, whether same pole, tower height, physical pole commissioning date, substation position information, power plant position information and transformer position information.

[0012] Further, in the step S3, according to the power facility equipment falling into the initial disaster risk influence range, the disaster risk influence involved several power lines are deduced, and the area not included in the initial disaster risk influence range is fused with the initial disaster risk influence range to obtain the power facility equipment risk influence range, which specifically comprises the following steps: The physical tower and the virtual tower are associated in advance; The influence on the virtual tower is analyzed in combination with the influence of the disaster early warning on the physical tower; The line information affected by the disaster is deduced by using the relationship between the virtual tower and the power line; According to the line information affected by the disaster and the preset rules, a derived influence range is generated, which is fused with the initial disaster risk influence range to obtain the power facility equipment risk influence range.

[0013] Further, the association of the physical tower and the virtual tower in advance comprises physically associating the physical tower and the virtual tower through the same ID and the same spatial coordinates; and logically associating the physical tower and the virtual tower with the power lines they belong to through the "same tower and parallel line" information recorded in the account.

[0014] Further, the generation of the derived influence range according to the line information affected by the disaster and the preset rules specifically comprises: Real-time load data of the line affected by the disaster is obtained, and it is judged whether there is a standby line for load transfer; According to the real-time load data of the line affected by the disaster and the backup route judgment result, the expected loss load data is calculated; According to the expected loss load data, the logical relationship of the power facility equipment and the area, the derived influence range is determined. If there is no real-time load data, the "historical average load x number of users" of the line in a period is used to estimate the load loss, and the expected loss load data is determined.

[0015] Further, the derived influence range is generated according to the line information affected by the disaster and the preset rules, specifically including: The information of the line affected by the disaster is combined with the power grid flow diagram to calculate the expected load loss data; the power grid flow diagram reflects the line trend, voltage and power; if temporary replacement line is involved, the temporary replacement line is combined with the power grid flow diagram to calculate the expected load loss data; according to the expected loss load data, the logical relationship of the power facility equipment and the area, the derived influence range is determined. Or, According to the information of the line affected by the disaster, the fault line affected by the disaster is extended to the load side, the distribution of downstream substations, distribution transformers and low-voltage users is analyzed, and the derived influence range is expanded.

[0016] Based on the same concept, a natural disaster dynamic risk early warning system for the power industry is also proposed, including a data acquisition module, a data processing module and a data correction module. The data acquisition module is used to acquire power facility equipment data, which includes location information and characteristic information of the power facility equipment. The data processing module is used to construct a disaster early warning electronic map. When receiving a disaster risk early warning or a disaster, an initial disaster risk influence range is generated on the disaster early warning electronic map according to the disaster early warning information. The data correction module is used to correct the initial disaster risk influence range according to the location information and characteristic information of the power facility equipment, obtain the power facility equipment risk influence range, and issue a warning information. The correction of the initial disaster influence range includes: deducing a number of power lines involved in the disaster risk influence according to the power facility equipment falling into the initial disaster risk influence range, fusing the area not included in the initial disaster risk influence range in the number of power lines involved in the disaster risk influence with the initial disaster risk influence range, and obtaining the power facility equipment risk influence range.

[0017] Further, the data acquisition module comprises a disaster monitoring device, the monitoring device is used for acquiring monitoring parameters for generating the disaster early warning information, and the monitoring device comprises but is not limited to a GNSS (Global Navigation Satellite System) surveying machine, a rain gauge, a crack gauge, a mud level gauge, a visual monitoring and control integrated RTU (Remote Terminal Unit), and a multi-sensor and visual integrated GNSS.

[0018] Based on the same concept, a computer device is also provided, which can comprise a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the power industry dynamic disaster risk early warning method according to any one of the above when executing the computer program.

[0019] Preferably, the computer device comprises an early warning receiving terminal and an early warning broadcasting terminal.

[0020] Compared with the prior art, the present application has the beneficial effects that the information of the power facility device is fused with the multi-disaster early warning information, the data and the processing flow are adaptively improved according to the particularity of the power facility device, the multi-disaster early warning information is optimized to form the disaster early warning information for the precise point of the power facility device, and the problems of insufficient early warning precision, insufficient pertinence, and inaccurate target release of the early warning information in the power industry are solved. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A power industry dynamic disaster risk early warning method flowchart in embodiment 1; Figure 2 A statistical chart of early warning areas, early warning levels, and types of an interface for real-time viewing of multi-disaster early warning in embodiment 1; Figure 3 A facility device affected point statistical chart of an interface for real-time viewing of multi-disaster early warning in embodiment 1; Figure 4 A schematic diagram of a power industry dynamic disaster risk early warning system in embodiment 5; Figure 5 A schematic diagram of a multi-disaster early warning receiving terminal in embodiment 5; Figure 6 A disaster early warning broadcasting terminal in embodiment 5. DETAILED DESCRIPTION

[0022] The present application will be further described below in conjunction with specific embodiments. However, it should not be understood that the above-mentioned subject matter of the present application is limited to the following embodiments only, and any technology realized based on the content of the present application falls within the scope of the present application.

[0023] To address the issue that existing natural disaster early warning data is mostly general-purpose warnings targeting macro-regions or the general public, lacking refined analysis and customized early warnings based on specific factors such as the geographical location, structural characteristics, and operational status of power facilities and equipment, this invention proposes a dynamic risk early warning method for natural disasters in the power industry. Specifically, this method includes: constructing a disaster early warning electronic map, which includes the location and characteristic information of power facilities and equipment; when a disaster risk warning is received or a disaster occurs, generating a corresponding initial disaster risk impact range on the disaster early warning electronic map based on the disaster warning information; correcting the initial disaster risk impact range based on the location and characteristic information of the power facilities and equipment to obtain the power facility and equipment risk impact range, and issuing an early warning; the correction of the initial disaster risk impact range includes: deducing several power lines involved in the disaster risk impact based on power facilities and equipment such as substations and towers falling within the initial disaster risk impact range, and merging areas of these power lines not included in the initial disaster risk impact range with the initial disaster risk impact range to obtain the power facility and equipment risk impact range. Power transmission lines, substations, power poles, and other power facilities and equipment are widely distributed in complex and variable climates such as mountainous areas, hilly regions, and coastal areas, presenting challenges such as wide coverage and large spans. In the event of a natural disaster, these facilities and equipment may involve multiple power lines. Simply reflecting the current disaster risk of these facilities and equipment cannot objectively and comprehensively reflect the overall impact of the natural disaster on the power system. Therefore, it is necessary to reassess the impact of disaster risks on the affected power lines to accurately determine the scope of disaster risk impact on power facilities and equipment, enabling more timely and accurate targeted dissemination of disaster early warning information to the power industry.

[0024] 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.

[0025] Example 1 The flowchart of the dynamic risk early warning method for natural disasters in the power industry is as follows: Figure 1 As shown, the specific steps include: S1, Construct a disaster early warning electronic map, which includes the location and feature information of power facilities and equipment; S2, When a disaster risk warning is received or a disaster occurs, the corresponding initial disaster risk impact range is generated on the disaster warning electronic map based on the disaster warning information; S3, based on the location and characteristic information of power facilities and equipment, corrects the initial disaster risk impact range, obtains the risk impact range of power facilities and equipment, and issues early warning information; The modification of the initial disaster risk impact range includes: deducing several power lines involved in the disaster risk impact based on the power facilities and equipment falling within the initial disaster risk impact range, and merging the areas of the several power lines involved in the disaster risk impact that were not included in the initial disaster risk impact range with the initial disaster risk impact range to obtain the power facility and equipment risk impact range.

[0026] Furthermore, the construction of an electronic disaster early warning map specifically includes the following steps: S11 aggregates basic geographic information data and power facility and equipment data from multiple data sources both internally and externally within the power system's integrated power grid map component. Basic geographic information data includes, but is not limited to: administrative divisions, high-precision digital elevation models, remote sensing imagery, water systems, roads, and residential areas. Power facility and equipment data includes location and characteristic information of power facilities and equipment. This data originates from the power sector's asset management system, production management system, and online monitoring system, specifically including: pole / tower number, equipment name, voltage level, latitude and longitude, address, model, associated line, nature of the power facility / equipment, height, and commissioning date. It also includes line length, overhead line length, cable line length, city / prefecture, altitude, whether it is on the same pole, pole / tower height, physical pole commissioning date, substation location information, power plant location information, and transformer location information.

[0027] The nature of power facilities and equipment varies depending on the specific facility or equipment, including the nature of poles, power stations, and substations. For poles, their nature is primarily defined by four dimensions: load-bearing function, manufacturing materials, structural form, and number of circuits. For example, According to their load-bearing function, towers are classified as follows: Straight-line towers: They only support the conductors and do not bear tension along the direction of the line.

[0028] Tension towers: bear the tension of the conductor, segment the line, and limit the scope of accidents. Angle towers: used at line corners to bear the resultant force of the angle.

[0029] Terminal towers: used at the start and end points of the line, bearing all the tension on one side.

[0030] According to structural materials, towers are classified as follows: Reinforced concrete poles: standard type and prestressed type.

[0031] Angle steel tower / steel pipe tower: a steel structure used for high-voltage and above power lines.

[0032] Steel pipe poles: commonly used in urban landscape roads and other areas where aesthetics are important.

[0033] Based on their structural form, they are divided into self-supporting towers: which rely on their own foundation for stability.

[0034] Guyed pole tower: Stabilized by guy wires, saving materials but occupying a large area.

[0035] Classified by the number of circuits: Single-circuit, double-circuit, and multi-circuit pole towers.

[0036] Classified by voltage level and geographical environment: Voltage level: Such as 10 kV, 110 kV, 500 kV, etc., which determines insulation and size requirements.

[0037] Geographical environment: Flat land, mountainous areas, river networks, polluted areas (industrial, coastal), lightning-prone areas, icing areas, etc.

[0038] Other properties of the pole tower also include: Grounding resistance: Directly affects the lightning withstand level of the line.

[0039] Commissioning years: Reflects the degree of equipment aging.

[0040] Tower type: Such as wine glass type, cat head type, dry character type, etc.

[0041] These dimensions provide a basis for refined management and risk analysis.

[0042] As a preferred solution of the present invention, the power facility equipment data at least includes: overhead line length, pole tower number, pole tower height, commissioning date of the physical pole, pole tower property (tension), whether on the same pole, equipment model, phase sequence level, altitude, location of the pole tower, commissioning time of the operating pole, voltage level, equipment responsible person, maintenance team, operation and maintenance unit, affiliated area, the line. Among them, the location information of the power facility equipment at least includes: altitude, location of the pole tower. The characteristic information at least includes: overhead line length, pole tower number, pole tower height, commissioning date of the physical pole, pole tower property (tension), whether on the same pole, equipment model, phase sequence level, commissioning time of the operating pole, voltage level, equipment responsible person, maintenance team, operation and maintenance unit, affiliated area, the line.

[0043] S12, construct an electronic map based on the basic geographic information data.

[0044] 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.

[0045] 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)).

[0046] S13 involves dynamically fusing power facility and equipment data with disaster early warning electronic maps within a unified spatial benchmark and spatiotemporal framework. Step S13 specifically includes: The power facility and equipment data are added to the disaster early warning electronic map through a data layer to generate the disaster early warning electronic map.

[0047] As a specific implementation, power facilities and equipment can be overlaid as layers based on a disaster early warning electronic map. During the overlay process, the latitude and longitude of the power facilities and equipment are known, which constitutes 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), achieving integration and visualization under a unified spatial benchmark and spatiotemporal framework. The electronic map is not a static scene, but a digital twin foundation that carries spatial geography and asset entities.

[0048] Furthermore, integrating power facility and equipment data into the disaster early warning electronic map includes refined linking of power facility and equipment data. For critical equipment, such as typical power poles, transformers, and circuit breakers at 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 of the power system is directly read and integrated with the electronic map. This integration can be understood as adding a data layer. Clicking on any power facility or equipment in the electronic map allows users to query its ID, model, commissioning time, historical fault records, real-time monitoring data stream, and other information.

[0049] The resulting electronic disaster early warning map successfully achieves deep integration, unification, and visualization of "power facility entities (where they are and what their status is)" and "power grid topology (how they are connected)" in a unified three-dimensional space. Power grid dispatchers or maintenance personnel can intuitively see the impact range of various natural disasters on power facilities and equipment from the electronic map. More importantly, the affected area is precisely located on specific equipment, providing direct and accurate spatial input for subsequent risk assessment and targeted early warning.

[0050] Furthermore, step S2, generating the corresponding initial disaster risk impact range on the disaster early warning electronic map based on the disaster early warning information, specifically includes the following steps: After receiving various types of disaster warning information, the early warning platform first analyzes and standardizes them. Different disaster types have different physical characteristics and warning data formats; therefore, the system pre-defines corresponding spatialization rules and risk impact range generation models 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.

[0051] For flash flood warnings, the electronic map receives information such as the catchment area, the spillway point, the warning level, and the warning release time. The system combines the catchment area and spillway point with the disaster warning electronic map, displaying the inundation area on the disaster warning electronic map (for example, defining the 3km radius around the spillway point as the impact range), thereby generating a "flash flood risk warning impact range" and visualizing the relevant data.

[0052] For early warning of geological disasters such as landslides, subsidence, and debris flows, the system is based on risk warning information from different hazard points (landslide hazard points are classified into high, medium, and low risk levels). The system overlays the areas of different disaster warning risk information onto the electronic disaster warning map, forming the risk impact range of different disaster types. In particular, it can combine geographical information such as topographic slope, aspect, and lithology to make local corrections to the warning zones, making them more consistent with the actual situation.

[0053] After generating the initial disaster risk impact range, not only is the "macro" multi-hazard early warning information visually mapped onto the disaster early warning electronic map, but more importantly, it provides precise regional information for the next step of correcting the initial disaster impact range based on the location information and characteristic associations of power facilities, power grids, and risk points. These impact range layers are in the same spatial reference system as the power facility and equipment layers, enabling the automatic identification of specific power facilities and equipment (such as poles, line sections, and substations) located within the impact range of various disaster risks through efficient spatial relationships, or the automatic identification of specific power facilities and equipment that may fall into the impact range of various disaster risks as the disaster risk further spreads. This transforms the "macro" natural disaster early warning into risk warning prompts for specific power facilities, equipment, and power lines, realizing the precise transmission of early warning information from the original "area" warning to "points" and "lines".

[0054] In step S3, during the correction of the initial disaster risk impact range based on the location information of power facilities and equipment, transmission lines often have 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. To address the specific needs of power facilities and equipment, the process of correcting the initial disaster risk impact range primarily involves deducing the number of power lines affected by the disaster risk from the tower locations, and then making the correction accordingly. The specific solution is as follows: (1) Based on the logical relationship between physical poles and virtual poles, data is integrated, and physical associations are established between physical poles and virtual poles. For example, in a power grid diagram component, although physical poles and virtual poles share the same ID, they represent actual physical equipment and logical virtual equipment, respectively. The association mapping between physical poles and virtual poles can be achieved through this ID. In the fields of power facility data obtained from the power grid asset management system or the power intranet database, the "whether it is the same pole" field is used to identify whether there are multiple lines sharing the same physical pole. If the field is "yes", it means that there is a situation where one physical pole carries multiple virtual poles. At this time, these virtual poles and physical poles are identified with the same ID and are uniformly managed in the data processing process. In order to establish an accurate mapping relationship, a mapping database between physical poles and one or more virtual poles they carry is constructed based on preset association rules (such as precise matching based on spatial coordinates, "line with parallel connection 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 operation status of the power grid, which helps to achieve accurate analysis and response to the power grid status.

[0055] (2) Combining the impact of disaster early warning on physical poles, the impact on virtual poles is analyzed. As mentioned above, the initial risk impact range of various disasters 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 risk 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.

[0056] (3) Using the relationship between the virtual pole and the line, the affected line information is deduced.

[0057] After completing the risk labeling of virtual towers, the location and function of the affected virtual towers within their respective lines are further analyzed based on the power grid topology. 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] (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.

[0070] Table 1 Overall Operational Risk Level and Judgment Criteria

[0071] 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.

[0072] Table 2. List of assessment parameters for risk and defect assessment

[0073] 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).

[0074] 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.

[0075] Load and Role: Regional critical communication line, current load rate 75%.

[0076] Redundancy: There is a backup Hanjin 2 line in the same channel, so N-1 is satisfied.

[0077] Environmental factors: Located on a plain, with convenient transportation.

[0078] 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.

[0079] 2. Line L200 (110kV, XX Line 3) Risky tower: #15 (terminal tower, high risk - foundation settlement).

[0080] Topological location: The starting point of the line is the terminal tower, which connects to the substation.

[0081] Load and Role: Single radial dedicated line for important users, no backup, load rate 60%.

[0082] Redundancy: No backup power supply.

[0083] Environmental factors: Located within the factory area, making maintenance convenient.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] (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.

[0088] The final affected area is the superposition area of ​​the affected line itself and the derived area from the above analysis.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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."

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Estimate current load: Based on the date type and time period of the disaster, retrieve the corresponding historical average load (or the load value corrected by the number of users) as the estimated value of the current load (P_estimated).

[0101] 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.

[0102] 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?

[0103] 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.

[0104] 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).

[0105] 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.

[0106] (5) The derived impact range is integrated with the initial disaster risk impact range to obtain the power facility and equipment risk impact range.

[0107] Furthermore, the fusion method for integrating the derived impact range with the initial disaster risk impact range can be 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 temporarily bear the load of the line, the load of other lines can also be calculated in a timely manner to ensure power transmission safety.

[0108] 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 in the table below: Table 3 Example of a list of early warning targets for towers

[0109] Table 4 Example of a list of early warning targets for substations

[0110] Table 5 Example of a list of early warning targets for power plants

[0111] Table 6 Example of an early warning target list for production / non-production sites

[0112] 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 problem of risk transmission analysis in scenarios where multiple power lines share a single power pole. 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.

[0113] 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.

[0114] 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.

[0115] 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 of "physical space-logical topology-power grid system," providing efficient and reliable decision support for ensuring the safe and stable operation of the power grid in complex environments, and has significant technological advancement, practicality, and wide application value. A statistical chart of the early warning area, early warning level, and type in a real-time interface for viewing multi-hazard early warnings is shown below. Figure 2 As shown, a statistical chart of affected locations of facilities and equipment is displayed on an interface for real-time viewing of multi-hazard early warnings. Figure 3 As shown.

[0116] Example 2 In addition to power poles and towers, power facilities also include transformer substations. Based on Example 1, this method further combines transformer substation information with multi-hazard early warning information, specifically including the following steps: (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 electricity consumption of the rated capacity of the distribution area transformer.

[0117] (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.

[0118] (3) After a disaster warning is issued, the power facilities and equipment within the scope of the disaster warning are used to automatically calculate the data of the main grid (lines above 10KV) / distribution network lines that may be affected.

[0119] (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.

[0120] (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 issued, the transformer area that meets the warning conditions is determined according to the multi-level relationship, the scope of the warning and the disaster level. Then, the warning information is sent to the transformer area users and management users who meet the warning conditions through the warning receiving terminal, broadcast terminal, platform, SMS, APP, outbound call, etc.

[0121] Example 3 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. Traditionally, proactive early warning capabilities for such mobile and temporary risks are virtually nonexistent. This embodiment addresses this by constructing a user-customizable temporary work site early warning function, extending disaster prevention and early warning coverage from fixed power facilities to dynamic work sites and personnel, significantly enhancing the safety assurance capabilities of on-site operations.

[0122] First, power users can independently configure and input temporary site information. The system provides a dedicated configuration interface for authorized users (such as construction project teams, inspection teams, and emergency command centers). Users can input temporary site information into the early warning 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").

[0123] Spatiotemporal attributes: the time of the early warning and the location information of the disaster risk point. The system will only monitor and issue early warnings for the station within this time period.

[0124] Responsibility Information: Designated responsible person and their contact information (mobile phone number).

[0125] Early warning strategy: Users can customize early warning reception strategies based on the nature of the operation and sensitivity to different disasters. For example, for "Construction Site A", it can be set to receive "Red Flash Flood Warning" and "Orange or Above Debris Flow Warning"; for "Temporary Inspection Rest Point B", it can be set to only receive "Orange or Above Landslide Warning". The strategy can be refined to the disaster type, warning level threshold, etc.

[0126] 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.

[0127] 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: The system performs real-time spatial overlay analysis of dynamically generated initial risk impact ranges for various disasters (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 impact range of a disaster, and the warning level of the disaster reaches or exceeds the threshold set for that type of disaster in the configuration of that camp, the warning condition is triggered.

[0128] Through the above implementation methods, this invention transforms the previously scattered, passive, and experience-based judgment of field operation safety risks into a centralized, proactive, data-driven intelligent early warning management model. It realizes closed-loop control of safety risks of "mobile units" and "temporary locations" in power production activities, effectively fills the gap in the existing early warning system in terms of personnel dynamic safety protection, and significantly improves the overall safety production level and emergency response foresight of the power industry.

[0129] Example 4 This embodiment expands upon the content of embodiments 1 and 2.

[0130] As a preferred embodiment, this embodiment also includes prioritizing the affected power facilities and equipment according to rules such as voltage level and distance.

[0131] 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.

[0132] 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 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.

[0133] Example 5 Please refer to Figure 4 , Figure 4 This is a schematic diagram of a dynamic risk early warning system for natural disasters in the power industry, provided as an embodiment of this application.

[0134] The dynamic risk early warning system for natural disasters in the power industry can include: The data acquisition module 91 is used to acquire power facility and equipment data, which includes the location information and feature information of the power facility and equipment.

[0135] The data processing module 92 is used to construct an electronic map for disaster early warning. When a disaster risk warning is received or a disaster occurs, the module generates the corresponding initial disaster risk impact range on the electronic map according to the type of disaster warning.

[0136] The data correction module 93 is used to correct the initial disaster risk impact range based on the location and characteristic information of power facilities and equipment, obtain the risk impact range of power facilities and equipment, and issue early warning information. The modification of the initial disaster risk impact range includes: deduce several power lines involved in the disaster risk impact based on the substations and towers that fall within the disaster risk impact range of the power facilities and equipment, and merge the areas of the several power lines involved in the disaster risk impact that are not included in the initial disaster risk impact range with the initial disaster risk impact range to obtain the disaster risk impact range of the power facilities and equipment.

[0137] Optionally, the power facility and equipment data comes from the power sector's asset management system, production management system, and online monitoring system. The data acquisition module 91 can be specifically used for: Data is obtained through data interfaces from the power sector's asset management system, production management system, and online monitoring system, including 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 on the same pole, pole / tower height, physical pole commissioning date, substation location information, power plant location information, and transformer location information.

[0138] Optionally, the data processing module 92 may be specifically used for: Upon receiving multi-hazard early warning data from an external early warning platform, the data is first analyzed and standardized. Different hazard types have varying physical characteristics and early warning data formats; therefore, corresponding spatialization rules and impact range generation models are pre-defined for each hazard type.

[0139] Optionally, the data acquisition module 93 may be specifically used for: As a specific embodiment, the data acquisition module 93 includes a disaster monitoring device. The monitoring device is used to acquire monitoring parameters for generating the disaster early warning information. The monitoring device includes, but is not limited to, a GNSS mapping machine, a rain gauge, a crack gauge, a mud level gauge, a visual measurement and control integrated RTU, and a multi-sensor and visual integrated GNSS.

[0140] As a specific embodiment, the data acquisition module 93 can also correct the initial disaster risk impact range based on the location information of the 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 initial disaster risk impact range is mainly based on the tower to deduce the number of power lines involved in the disaster, and then make the correction.

[0141] It should be understood that the various modules of the power facility and equipment natural disaster dynamic risk 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.

[0142] 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.

[0143] Preferably, a dynamic risk early warning system for natural disasters in the power industry also includes a multi-channel early warning dissemination device: after an early warning is issued, the system sends the warning information through early warning receiving terminals, broadcast terminals, platforms, SMS, APP, outbound calls, etc., according to the scope of impact and risk level of the warning. Notification methods include: SMS: Send concise and clear warning text, including key information such as location name, disaster type, warning level, period of impact, and recommended actions.

[0144] 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.

[0145] 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.

[0146] Early warning terminals: Early warning information is automatically sent to the multi-hazard early warning terminals at the corresponding early warning camps, reminding personnel to take evacuation measures through visuals, lights, loudspeakers, and other means. It also includes a multi-hazard early warning broadcast terminal, which uses 4G wireless network transmission technology to independently deploy IoT loudspeakers, automatically receiving early warning information and issuing audible alarms to prompt users to take evacuation measures.

[0147] 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.

[0148] 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 the dynamic risk early warning method for natural disasters in the power industry as described above. Preferably, the computer device may be a dynamic risk early warning terminal for natural disasters in the power industry, which includes an early warning receiving terminal and an early warning broadcasting terminal.

[0149] As a specific embodiment of computer equipment, the early warning receiving terminal can be a multi-hazard early warning receiving terminal, as illustrated in the schematic diagram below. Figure 5 As shown, the multi-hazard early warning receiving terminal maintains a connection with the disaster early warning center, receiving early warning information from the center in real time and providing the latest disaster early warning information. When new early warning information is received, the disaster icon will flash continuously while emitting an early warning sound. This terminal has functions such as real-time multi-hazard early warning, early warning details query, simulation exercises, and viewing and replaying early warning records.

[0150] As a specific embodiment of a computer device, a schematic diagram of an early warning broadcast terminal is shown below. Figure 6 As shown, it includes a receiving terminal and a loudspeaker. When the receiving terminal receives new warning information, it broadcasts the warning through the loudspeaker, emitting a warning tone.

[0151] 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 the dynamic risk early warning method for natural disasters in the power industry as described above.

[0152] 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 dynamic risk early warning method for natural disasters in the power industry, characterized in that, Includes the following steps: S1, Construct a disaster early warning electronic map, which includes the location and feature information of power facilities and equipment; S2, When a disaster risk warning is received or a disaster occurs, the corresponding initial disaster risk impact range is generated on the disaster warning electronic map based on the disaster warning information; S3. Based on the location and feature information of the power facilities and equipment, the initial disaster risk impact range is corrected to obtain the risk impact range of the power facilities and equipment, and an early warning information is issued. The modification of the initial disaster risk impact range includes: deducing several power lines involved in the disaster risk impact based on the power facilities and equipment falling within the initial disaster risk impact range, and merging the areas of the several power lines involved in the disaster risk impact that were not included in the initial disaster risk impact range with the initial disaster risk impact range to obtain the power facility and equipment risk impact range.

2. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 1, characterized in that, In step S1, the construction of the disaster early warning electronic map specifically includes the following steps: S11, acquire basic geographic information data and power facility and equipment data; S12, Construct a basic electronic map for disaster early warning based on the aforementioned basic geographic information data; S13, dynamically integrate the power facility and equipment data and the disaster early warning electronic map under a unified spatial reference; Step S13 specifically includes: The power facility and equipment data are added to the disaster early warning electronic map through a data layer to generate the disaster early warning electronic map.

3. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 1, characterized in that, Step S2 specifically includes the following steps: Analyze and standardize various types of disaster early warning information; Based on the physical characteristics and data format of disaster early warning information, a model is generated according to preset spatialization rules and impact range to generate the initial disaster risk impact range.

4. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 1, characterized in that, In step S3, the location information of the power facilities and equipment includes latitude and longitude, address, altitude, substation location information, power plant location information, and transformer location information; the characteristic information of the power facilities and equipment includes pole number, equipment name, voltage level, model, line to which it belongs, nature of the power facilities and equipment, height, commissioning date, line length, overhead line length, cable line length, city to which it belongs, whether it is on the same pole, pole height, and physical pole commissioning date.

5. The dynamic risk early warning method for natural disasters in the power industry as described in claim 4, characterized in that, In step S3, based on the power facilities and equipment falling within the initial disaster risk impact range, several power lines involved in the disaster risk impact are deduced. Areas of these power lines not included in the initial disaster risk impact range are then merged with the initial disaster risk impact range to obtain the power facility and equipment risk impact range. This specifically includes the following steps: The physical and virtual poles are pre-associated; Based on the impact of disaster early warning on physical poles, the impact on virtual poles is analyzed; By utilizing the relationship between virtual power poles and power lines, information about power lines affected by disasters can be inferred. Based on the information of the lines affected by the disaster and the preset rules, a derived impact range is generated. The derived impact range is a region that is not included in the initial disaster risk impact range and is merged with the initial disaster risk impact range to obtain the power facility and equipment risk impact range.

6. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 5, characterized in that, The pre-association of physical and virtual poles includes: physically associating physical and virtual poles using the same ID and the same spatial coordinates; and logically associating physical and virtual poles with their respective power lines using the "parallel lines on the same tower" information recorded in the ledger.

7. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 5, characterized in that, The process of generating a derived impact range based on information about the affected lines and preset rules specifically includes: Obtain real-time load data of lines affected by disasters, and determine whether there are backup lines available for load transfer; The estimated load loss data is calculated based on the real-time load data of the affected lines and the results of the backup route assessment. The scope of the derived impact is determined based on the expected load loss data, the logical relationships between power facilities and equipment, and transformer substations. If real-time load data is unavailable, the load loss is estimated by using "historical average load × number of users" for a certain period of time on the line, and the expected load loss data is determined.

8. The method for dynamic risk early warning of natural disasters in the power industry as described in claim 5, characterized in that, The process of generating a derived impact range based on information about the affected lines and preset rules specifically includes: Information on lines affected by the disaster is combined with the power flow diagram to calculate the expected load loss data; the power flow diagram reflects the line trend, voltage, and power; if temporary alternative lines are involved, the temporary alternative lines are combined with the power flow diagram to calculate the expected load loss data; the scope of the derived impact is determined based on the expected load loss data, the logical relationship between power facilities and equipment, and the distribution area. or, Based on the information of the lines affected by the disaster, the faulty lines affected by the disaster are extended to the load side, and the distribution of downstream substations, distribution transformers and low-voltage users is analyzed, thereby expanding the scope of the derivative impact.

9. A dynamic risk early warning system for natural disasters in the power industry, characterized in that: It includes a data acquisition module, a data processing module, and a data correction module; The data acquisition module is used to acquire power facility and equipment data, which includes the location information and feature information of the power facility and equipment. The data processing module is used to build an electronic map for disaster early warning. When a disaster risk warning is received or a disaster occurs, the module generates the corresponding initial disaster risk impact range on the electronic map based on the disaster warning information. The data correction module is used to correct the initial disaster risk impact range based on the location and characteristic information of power facilities and equipment, obtain the risk impact range of power facilities and equipment, and issue early warning information. The correction of the initial disaster impact range includes: deduce several power lines involved in the disaster risk impact based on the power facilities and equipment falling within the initial disaster risk impact range, and merge the areas of the several power lines involved in the disaster risk impact that are not included in the initial disaster risk impact range with the initial disaster risk impact range to obtain the power facility and equipment risk impact range.

10. The dynamic risk early warning system for natural disasters in the power industry as described in claim 9, characterized in that, The data acquisition module includes disaster monitoring equipment, which is used to acquire monitoring parameters for generating the disaster early warning information. The disaster monitoring equipment includes a GNSS mapping machine, a rain gauge, a crack gauge, a mud level gauge, a visual measurement and control integrated RTU, and a multi-sensor and visual integrated GNSS.

11. 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 the dynamic risk early warning method for natural disasters in the power industry as described in any one of claims 1 to 8.

12. A computer device as described in claim 11, characterized in that, The computer equipment includes an early warning receiving terminal and an early warning broadcasting terminal.

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