Metering box safety protection strategy generation method and system

Abstract

The application provides a metering box safety protection strategy generation method and system, and relates to the technical field of power systems.The method comprises the following steps: step 1, collecting real-time risk data of a target metering box and an associated area; integrating the real-time risk data and historical risk data to construct a multi-dimensional space-time feature matrix; step 2, calculating risk association topology data of the area to which the target metering box belongs through the multi-dimensional space-time feature matrix, and obtaining a regionalized dynamic risk assessment result; and step 3, selecting a free telegraph pole, a distribution transformer and a building corner as a risk reference benchmark based on the geographical range covered by the regionalized dynamic risk assessment result, and delimiting a risk analysis area.The application realizes accurate assessment and dynamic generation of a metering box risk and targeted protection strategy generation.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for generating security protection strategies for metering boxes. Background Technology

[0002] In power distribution network operation, metering boxes, as the core terminals for energy metering and data acquisition, are often deployed in complex outdoor environments. They are susceptible to environmental disturbances, external intrusion, and the transmission of risks from surrounding facilities. Their safe operation is directly related to the accuracy of distribution network metering and the reliability of power supply, making the generation of protection strategies a critical aspect of distribution network operation and maintenance. Existing technologies mostly rely on single-dimensional real-time monitoring data for static risk assessment and combine it with human experience to formulate uniform protection measures, without considering the risk coupling and transmission effects between different physical entities in the area associated with the metering box.

[0003] For example, in older distribution network areas, street-side metering boxes monitor temperature, humidity, and vibration using a single threshold. They employ a fixed strategy of uniformly installing metal protective covers and conducting monthly manual inspections. This approach fails to integrate historical fault data and spatiotemporal risk characteristics of the area, and does not analyze the risk transmission relationship between the rigid entity of the metering box and the surrounding flexible entities such as cables and overhead conductors. During a severe convective weather event, the stress generated by the wind deformation of surrounding cables pulled on the metering box terminals. Static monitoring did not trigger an alarm, and manual inspections failed to identify the risk evolution trend of this rigid-flexible coupling in advance. Ultimately, this resulted in loose terminals, box damage, metering deviations, and potential power supply safety hazards. Existing methods suffer from problems such as a single risk assessment dimension, lack of spatiotemporal dynamic correlation analysis, and neglect of the coupling effect between rigid and flexible entities. Furthermore, the protective measures are not differentiated and are difficult to adapt to the refined metering box requirements in complex distribution network scenarios. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for generating safety protection strategies for metering boxes, so as to realize accurate risk assessment of metering boxes and dynamic and targeted generation of protection strategies.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for generating a safety protection strategy for a metering box, the method comprising: Step 1: Collect real-time risk data of the target metering box and related areas; integrate real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix; Step 2: Calculate the risk-related topology data of the region to which the target metering box belongs using a multi-dimensional spatiotemporal feature matrix, and obtain the regional dynamic risk assessment results. Step 3: Based on the geographical scope covered by the regional dynamic risk assessment results, select free utility poles, distribution transformers, and building corners as risk reference benchmarks, and delineate a risk analysis area; divide the risk analysis area into risk analysis sub-areas, and obtain the risk characteristics of related physical entities within each risk analysis sub-area and between adjacent sub-areas. Related physical entities include rigid entities and flexible entities. Step 4: By performing targeted kinematic analysis on the risk characteristics of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions, and simultaneously coordinating the risk transmission relationship between rigid and flexible entities, the kinematic co-analysis processing under rigid-flexible coupling is completed, and the risk characteristic data of rigid-flexible coupling is obtained; based on the risk characteristic data of rigid-flexible coupling, the risk distribution adjustment value is calculated. Step 5: Correct the regional dynamic risk assessment results by adjusting the risk distribution value to obtain the corrected assessment results. Based on the corrected assessment results, obtain the metering box safety protection strategy with differentiated protection measures combination and implementation nodes.

[0006] Furthermore, real-time risk data of the target metering box and related areas are collected; real-time risk data and historical risk data are integrated to construct a multi-dimensional spatiotemporal feature matrix, including: By deploying edge sensing nodes in the target metering box and related areas, raw real-time risk data characterizing environmental disturbances, structural deformations, and external force intrusions are collected; edge-side noise filtering and time-series alignment are performed on the raw real-time risk data to obtain pre-processed real-time risk data; Based on the preprocessed real-time risk data, historical risk data matching the geographical location and equipment type of the target metering box is retrieved, and the historical risk data is processed by unifying the time base and normalizing the time series scale to obtain standardized historical risk data. Standardized real-time risk data and standardized historical risk data are aligned in a spatiotemporal coordinate system and fused to obtain spatiotemporally aligned fused risk data. Based on the spatiotemporally aligned fused risk data, the risk evolution trend features in the time dimension, the risk diffusion gradient features in the spatial dimension, and the multi-source heterogeneous features in the risk source type dimension are extracted in sequence to obtain a multi-dimensional risk feature set. The multi-dimensional risk feature set is matrix-mapped and encapsulated according to the preset time granularity and spatial grid units to construct a multi-dimensional spatiotemporal feature matrix with temporal continuity, spatial correlation and risk type differentiation.

[0007] Furthermore, by using a multi-dimensional spatiotemporal feature matrix, the risk-related topology data of the region to which the target metering box belongs is calculated, and regional dynamic risk assessment results are obtained, including: Based on the constructed multi-dimensional spatiotemporal feature matrix, and based on the risk diffusion gradient features of the spatial dimension in the matrix, high-risk nodes and potential risk propagation paths within the region are identified to obtain initial risk association data. The initial risk association data is optimized in terms of topology. Based on the risk evolution trend characteristics in the time dimension, each association edge is assigned a weight value. The node attributes are labeled in combination with the multi-source heterogeneous features in the risk type dimension to obtain the risk association topology data. Based on the risk-related topology data, the risk propagation intensity and risk accumulation effect of each spatial grid in the topology network are calculated to obtain the basic risk distribution field; based on the basic risk distribution field, the risk propagation intensity is modified by spatiotemporal attenuation and enhancement to obtain the dynamically modified risk distribution field. The dynamically corrected risk distribution field is mapped to the region to which the target metering box belongs by geographical coordinates, risk level intervals are divided and risk hotspot areas are marked, and regional dynamic risk assessment results of risk level distribution data and risk evolution trends are obtained.

[0008] Furthermore, based on the geographical scope covered by the regional dynamic risk assessment results, free-floating utility poles, distribution transformers, and building corners are selected as risk reference benchmarks, and a risk analysis area is delineated. The risk analysis area is then divided into risk analysis sub-regions to obtain the risk characteristics of associated physical entities within each sub-region and between adjacent sub-regions. These associated physical entities include rigid and flexible entities, including: Receive regional dynamic risk assessment results, extract the boundary coordinates of the covered geographical area and the risk hotspot areas marked inside; within the geographical area, based on geographic information data, identify and select infrastructure of a preset type as a risk spatial reference benchmark, the benchmark including at least free utility poles, distribution transformers and building corners; Using the selected risk reference benchmark as the spatial anchor point, and based on the topological connection relationship between each anchor point and the preset radiation radius, a continuous closed polygonal geographical range covering at least all risk hotspot areas is generated by calculation and defined as the risk analysis area. Based on the distribution density of risk reference benchmarks, the gradient changes in risk level distribution data, and geographical barrier characteristics within the risk analysis area, multiple overlapping or adjacent risk analysis sub-areas are formed by automatically subdividing the area into grids. For each risk analysis sub-region, the structured data of physical entities within the risk analysis sub-region and the pre-set buffer zone at the boundary of adjacent sub-regions are retrieved. Based on the mechanical and functional properties of the physical entities, the physical entities are classified into rigid entities and flexible entities, and the risk characteristics corresponding to each type of entity in the risk assessment results are extracted. The risk characteristics include at least the entity's current risk exposure value, vulnerability coefficient in the risk field, and spatial correlation strength with the entity.

[0009] Furthermore, by performing targeted kinematic analysis on the risk characteristics of rigid and flexible entities within and between adjacent risk analysis sub-regions, and simultaneously coordinating the risk transmission relationship between rigid and flexible entities, kinematic co-analysis processing under rigid-flexible coupling is completed, yielding risk characteristic data associated with rigid-flexible coupling. Based on this risk characteristic data, risk distribution adjustment values ​​are calculated, including: Based on the risk characteristic data of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions; for rigid entities, the displacement constraint and stress concentration characteristics under external risk are analyzed based on mechanical properties to obtain rigid entity risk kinematic analysis data; for flexible entities, the deformation propagation and energy dissipation characteristics in the risk field are analyzed to obtain flexible entity risk kinematic analysis data. Based on the spatial proximity of rigid and flexible entities, a risk transmission coupling relationship is constructed between them. Based on this relationship, the stress change characteristics in the kinematic analysis data of the rigid entity and the deformation propagation characteristics in the kinematic analysis data of the flexible entity are co-mapped and coupled to simulate the transmission path and intensity evolution of risk in the rigid-flexible coupled structure, thus obtaining risk co-evolution data under the rigid-flexible coupled state. Based on risk co-evolution data under rigid-flexible coupling, key indicators characterizing risk transmission efficiency, energy redistribution, and vulnerability of coupling nodes are extracted and fused to form risk characteristic data of rigid-flexible coupling association. Based on the risk characteristic data of rigid-flexible coupling, the risk intensity increment and attenuation caused by the rigid-flexible entity coupling effect within each risk analysis sub-region and at the boundary of adjacent sub-regions are calculated, and the risk intensity increment and attenuation are quantified into risk distribution adjustment values.

[0010] Furthermore, based on the spatial proximity of the rigid and flexible entities, a risk transmission coupling relationship is constructed between them. Based on this relationship, the stress variation characteristics in the kinematic analysis data of the rigid entity and the deformation propagation characteristics in the kinematic analysis data of the flexible entity are co-mapped and coupled for calculation. This simulates the transmission path and intensity evolution of risk in the rigid-flexible coupled structure, yielding risk co-evolution data under rigid-flexible coupling conditions, including: By analyzing the kinematics of risk of rigid entities and flexible entities, and based on the spatial location markers of the entities in the analysis data, combined with the preset spatial connection thresholds, the topological connection relationship and spatial proximity relationship between rigid entities and flexible entities are determined and established. Based on topological connectivity and spatial proximity, we define the rules for the transmission of stress change characteristics from rigid entities to flexible entities, and the rules for the influence of deformation propagation characteristics of flexible entities on the reaction of rigid entities, thus obtaining the collaborative mapping relationship of risk interaction behavior between rigid and flexible entities. Based on the cooperative mapping relationship, key stress nodes in the kinematic analysis data of rigid entities and key deformation bands in the kinematic analysis data of flexible entities are feature-aligned and coupled, and coupled calculations are performed to dynamically simulate the risk energy transfer path, rate and intensity evolution process. By integrating dynamic simulations of risk energy transfer paths, rates, and intensity evolution processes, risk co-evolution data under rigid-flexible coupling states are obtained.

[0011] Furthermore, the regional dynamic risk assessment results are corrected using risk distribution adjustment values ​​to obtain corrected assessment results. Based on the corrected assessment results, a differentiated protection measure combination and implementation node-based metering box safety protection strategy is derived, including: By using the risk distribution adjustment value and the regional dynamic risk assessment results; based on the spatial location corresponding to the risk distribution adjustment value and the basic risk distribution field and risk level distribution data in the regional dynamic risk assessment results, spatial alignment and numerical superposition are performed to complete the dynamic correction of the risk value, and the corrected dynamic risk distribution field and risk level distribution data are obtained as the corrected assessment results. By analyzing the revised assessment results, we identified the final risk hotspots, risk transmission paths, and the set of target metering boxes that require key protection within each risk level range; and by combining information on rigid and flexible entities, we located the critical physical entity nodes that are highly vulnerable. Based on the risk type, intensity, and attributes of key physical entities in risk hotspot areas, various measures from a pre-set protection measures library, including physical reinforcement, environmental isolation, status monitoring, and early warning notifications, are matched and combined to form differentiated protection measures combinations for different risk areas and key nodes. Based on risk evolution trend data, specific implementation time nodes and execution sequences are planned for various protective measures combinations. At the same time, according to the type of measure and geographical location, corresponding protective resources and execution units are scheduled and allocated to generate the final metering box security protection strategy, which includes specific measures, implementation nodes, responsible units and resource lists.

[0012] Secondly, the metering box safety protection strategy generation system includes: The acquisition module is used to collect real-time risk data of the target metering box and related areas; it integrates real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix. The assessment module is used to calculate the risk-related topology data of the area to which the target metering box belongs through a multi-dimensional spatiotemporal feature matrix, and obtain regional dynamic risk assessment results. The delineation module is used to select free utility poles, distribution transformers, and building corners as risk reference benchmarks based on the geographical range covered by the regional dynamic risk assessment results, and delineate a risk analysis area; the risk analysis area is divided into risk analysis sub-areas, and the risk characteristics of the associated physical entities within each risk analysis sub-area and between adjacent sub-areas are obtained. The associated physical entities include rigid entities and flexible entities. The calculation module is used to perform targeted kinematic analysis on the risk characteristics of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions. At the same time, it coordinates the risk transmission relationship between rigid and flexible entities to complete the kinematic collaborative analysis processing under rigid-flexible coupling state and obtain risk characteristic data of rigid-flexible coupling. Based on the risk characteristic data of rigid-flexible coupling, the risk distribution adjustment value is calculated. The adjustment module is used to correct the regional dynamic risk assessment results by adjusting the risk distribution value, so as to obtain the corrected assessment results. Based on the corrected assessment results, the metering box safety protection strategy with differentiated protection measures and implementation nodes is obtained.

[0013] Thirdly, a computing device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0014] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0015] The above-described solution of the present invention has at least the following beneficial effects: Because it employs technical means such as collecting real-time risk data of target metering boxes and related areas and integrating historical risk data to construct a multi-dimensional spatiotemporal feature matrix, calculating risk-related topology data based on this matrix to achieve regional dynamic risk assessment, delineating and subdividing risk analysis areas based on free utility poles, distribution transformers, and building corners and extracting rigid and flexible entity risk characteristics, and calculating risk distribution adjustment values ​​through rigid-flexible coupling kinematic analysis to correct assessment results and match differentiated protection measures combinations and planning implementation nodes, it overcomes the technical problems of existing metering box protection strategy generation methods, such as single risk assessment dimensions, lack of spatiotemporal dynamic correlation analysis, neglect of the risk coupling and transmission effect of rigid and flexible entities in related areas, lack of differentiated protection measures, and difficulty in adapting to the refined protection needs of complex distribution network scenarios. Therefore, it achieves accurate, comprehensive, and dynamic risk assessment of metering boxes, fills the technical gap in risk coupling analysis between entities, generates highly targeted and executable refined safety protection strategies, improves the scientificity and effectiveness of metering box safety protection, and ensures the accuracy of power distribution network metering and the reliability of power supply. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for generating a metering box safety protection strategy according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a metering box safety protection strategy generation system provided in an embodiment of the present invention.

[0018] Figure 3 This is a statistical diagram illustrating the risk-related topology and level distribution.

[0019] Figure 4 This is a statistical diagram of risk distribution adjustment values.

[0020] Figure 5 This is a diagram showing the comparison of the effectiveness of the protection strategies. Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art.

[0022] like Figure 1 As shown, an embodiment of the present invention proposes a method for generating a metering box safety protection strategy, the method comprising the following steps: Step 1: Collect real-time risk data of the target metering box and related areas; integrate real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix; Step 2: Calculate the risk-related topology data of the region to which the target metering box belongs using a multi-dimensional spatiotemporal feature matrix, and obtain the regional dynamic risk assessment results. Step 3: Based on the geographical scope covered by the regional dynamic risk assessment results, select free utility poles, distribution transformers, and building corners as risk reference benchmarks, and delineate a risk analysis area; divide the risk analysis area into risk analysis sub-areas, and obtain the risk characteristics of related physical entities within each risk analysis sub-area and between adjacent sub-areas. Related physical entities include rigid entities and flexible entities. Step 4: By performing targeted kinematic analysis on the risk characteristics of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions, and simultaneously coordinating the risk transmission relationship between rigid and flexible entities, the kinematic co-analysis processing under rigid-flexible coupling is completed, and the risk characteristic data of rigid-flexible coupling is obtained; based on the risk characteristic data of rigid-flexible coupling, the risk distribution adjustment value is calculated. Step 5: Correct the regional dynamic risk assessment results by adjusting the risk distribution value to obtain the corrected assessment results. Based on the corrected assessment results, obtain the metering box safety protection strategy with differentiated protection measures combination and implementation nodes.

[0023] In this embodiment of the invention, the following steps are employed: Real-time risk data of the target metering box and associated areas are collected sequentially and historical risk data is fused to construct a multi-dimensional spatiotemporal feature matrix. Based on this matrix, risk-related topology data is calculated to obtain regionalized dynamic risk assessment results. Risk analysis areas are delineated and subdivided using free utility poles, distribution transformers, and building corners as risk reference benchmarks. Risk characteristics of rigid and flexible entities within each sub-area and between adjacent sub-areas are extracted. Targeted kinematic analysis of the rigid and flexible entity risk characteristics is performed, and their risk transmission relationships are coordinated to complete rigid-flexible coupling kinematic collaborative analysis to calculate risk distribution adjustment values. These adjustment values ​​are used to correct the regionalized dynamic risk assessment results, and differentiated protection is generated accordingly. The technical means of combining measures and implementing nodes for protection strategies overcome the technical problems of existing metering box protection strategy generation, such as a single risk assessment dimension, lack of spatiotemporal dynamic correlation analysis, neglect of the risk coupling and transmission effect between rigid and flexible entities in the associated area, and lack of differentiation in protection measures, making it difficult to adapt to the refined protection needs of complex distribution network scenarios. Thus, it achieves a comprehensive, dynamic, and accurate assessment of metering box risks, fills the technical gap in risk coupling analysis between rigid and flexible entities, and generates highly targeted and executable protection strategies. This effectively improves the scientific nature and effectiveness of metering box security protection, and ensures the safe and stable operation of target metering boxes and the accuracy of power distribution network metering and the reliability of power supply.

[0024] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: By deploying edge sensing nodes in the target metering box and related areas, raw real-time risk data characterizing environmental disturbances, structural deformations, and external force intrusions are collected. The raw real-time risk data is then processed by edge-side noise filtering and time-series alignment to obtain pre-processed real-time risk data. Specifically, in the area where the old distribution network metering boxes are located, edge sensing nodes are deployed on the surface of the target metering box, at the terminal locations, and at the mounting brackets. Simultaneously, edge sensing nodes are deployed in the surrounding cable laying sections, overhead conductor suspension points, and on the ground and building walls within a 50-meter radius of the metering box in the area associated with the metering box. Various edge sensing nodes include temperature and humidity sensors, structural deformation sensors, vibration sensors, wind speed sensors, and infrared intrusion sensors. These sensors simultaneously collect temperature and humidity values, wind speed values, and rainfall values ​​characterizing environmental disturbances; metering box deformation, terminal offset, cable deformation, and overhead conductor deformation characterizing structural deformation; and infrared sensing of moving object distance and vibration amplitude generated by external force impacts characterizing external force intrusion. All types of sensors continuously collect raw real-time risk data at a frequency of once per second. Noise filtering is performed on the raw real-time risk data collected at the edge. A moving average method is used to smooth 60 seconds of continuously collected raw real-time risk data, and abnormal data that deviates from the average value of the data for this time period by more than 20% is removed to complete the noise filtering. Then, the noise-filtered risk data is time-series aligned. The timestamps of the data collected by all edge sensing nodes are all calibrated to Beijing time, with milliseconds as the time unit. The timestamp data collected by edge sensing nodes at different deployment locations are matched and integrated one by one to form preprocessed real-time risk data with unified data dimensions and consistent time series.

[0025] Step 1.2: Based on the preprocessed real-time risk data, retrieve historical risk data that matches the geographical location and equipment type of the target metering box. Perform time benchmark unification and time series scale normalization on the historical risk data to obtain standardized historical risk data. Specifically, this includes: retrieving historical risk data from the power distribution network operation and maintenance database for the past five years for similar outdoor wall-mounted electricity metering boxes in the same area of ​​the old distribution network, based on the geographical location information of the target metering box (e.g., the street-side area of ​​the old distribution network) and equipment type information (e.g., outdoor wall-mounted electricity metering box) marked on the preprocessed real-time risk data. The retrieved historical risk data includes the specific time of the historical fault, the fault type, the corresponding environmental disturbance data, structural deformation data, external force intrusion data, and various risk data monitored daily. Perform time benchmark unification processing on the retrieved historical risk data, calibrating all timestamps of the historical risk data to Beijing time to maintain complete consistency with the time benchmark of the preprocessed real-time risk data. Next, the historical risk data after the time base is unified is subjected to time-series scale normalization processing, and the collection scale of the historical risk data is unified to once per second. For historical risk data whose original collection scale is not once per second, linear interpolation is performed to fill in the missing data in the time series according to the chronological order. After the time-series scale normalization is completed, the validity of the data is verified, and invalid data segments with missing values ​​exceeding 10% are removed, and the complete and valid historical risk data is retained, finally obtaining standardized historical risk data.

[0026] Step 1.3 involves aligning and fusing standardized real-time risk data with standardized historical risk data in a spatiotemporal coordinate system to obtain spatiotemporally aligned fused risk data. Specifically, this includes: first, aligning the standardized real-time risk data and standardized historical risk data in a spatiotemporal coordinate system, establishing a unified geographic spatiotemporal coordinate system with the latitude and longitude of the target metering box location as the origin, with east longitude as the positive x-axis, north latitude as the positive y-axis, and time as the positive z-axis. Then, mapping all data points from the standardized real-time and standardized historical risk data one by one to this unified geographic spatiotemporal coordinate system according to their actual geographical location and collection time, ensuring that each data point... Each point has corresponding three-dimensional coordinate information, achieving spatiotemporal coordinate system alignment between the two types of data. After coordinate system alignment is completed, the two types of data are fused using a point-by-point fusion method. In a unified geographic spatiotemporal coordinate system, standardized real-time risk data and standardized historical risk data at the same coordinate location and time dimension are numerically integrated. Among them, environmental disturbance data is taken as the arithmetic mean of the two types of data, while structural deformation and external intrusion data are taken as the weighted average. The weight of standardized real-time risk data is 70%, and the weight of standardized historical risk data is 30%. The fusion calculation of all data points is completed according to this rule, and finally spatiotemporally aligned fused risk data is obtained.

[0027] Step 1.4: Based on the spatiotemporally aligned fused risk data, extract risk evolution trend features in the time dimension, risk diffusion gradient features in the spatial dimension, and multi-source heterogeneous features in the risk source type dimension to obtain a multi-dimensional risk feature set. Specifically, this includes: Based on the spatiotemporally aligned fused risk data, extract risk features in three dimensions sequentially. First, extract risk evolution trend features in the time dimension, using 24 hours as a time period, segmenting the fused risk data by hour, calculating the rate of change and cumulative change of various risk data such as environmental disturbance, structural deformation, and external intrusion in each hour, analyzing the evolutionary patterns of various risk data over time, such as rising, stabilizing, and falling, and extracting specific risk evolution trend features such as the slope of change, peak occurrence time, trough occurrence time, and duration of continuous change for various data. Next, extract risk diffusion gradient features in the spatial dimension, using the origin of the target metering box as... The system is divided into spatial concentric circles at 5-meter intervals. The mean values ​​of various risk data, including environmental disturbances, structural deformations, and external force intrusions, are calculated within each concentric circle. The diffusion speed and numerical attenuation of these risk data from the origin to the surrounding space are analyzed, extracting specific risk diffusion gradient characteristics such as diffusion radius, numerical attenuation rate, gradient change direction, and maximum diffusion range. Finally, multi-source heterogeneous features are extracted along the risk source type dimension, classifying risk sources into three categories: environmental disturbance, structural deformation, and external force intrusion. Specific data features are extracted for each category: for environmental disturbance, extreme values ​​of temperature and humidity, wind speed variation range, and maximum rainfall; for structural deformation, maximum deformation, deformation duration, and deformation recovery rate; and for external force intrusion, minimum intrusion distance, peak vibration amplitude, and duration of external force action. All specific features of the three risk sources are integrated to obtain a multi-dimensional risk feature set.

[0028] Step 1.5 involves matrix mapping and encapsulating the multi-dimensional risk feature set according to a preset time granularity and spatial grid unit, constructing a multi-dimensional spatiotemporal feature matrix with temporal continuity, spatial correlation, and risk type differentiation. Specifically, this includes: first, setting the preset time granularity to 1 hour and the preset spatial grid unit to a 5m × 5m square grid; dividing the target metering box and its surrounding 50m × 50m associated area into a 10×10 spatial grid array according to the 5m × 5m spatial grid unit; and simultaneously dividing the time dimension into 24 consecutive time units with a 1-hour time granularity; mapping all risk evolution trend features, risk diffusion gradient features, and multi-source heterogeneous feature data in the multi-dimensional risk feature set to their corresponding time units and spatial grid units according to their corresponding time and geographical location attributes; and mapping each time unit... The specific values ​​of the three types of risk features within each spatial grid cell are completely filled into the corresponding matrix positions. After completing the matrix mapping of all feature data, the mapped matrix is ​​encapsulated. The matrix is ​​structured by using the row dimension as the spatial grid, the column dimension as the time unit, and the internal elements as combinations of multi-dimensional risk features. During the encapsulation process, the continuous change values ​​of risk features within each time unit are preserved to ensure that the feature data of adjacent time units continuously show the risk evolution trend. The correlation values ​​of risk features within each spatial grid are preserved to ensure that the feature data of adjacent spatial grids reflect the risk diffusion gradient. At the same time, it is ensured that the multi-source heterogeneous features within each matrix element clearly distinguish the three different risk sources: environmental disturbance type, structural deformation type, and external force intrusion type. Finally, a multi-dimensional spatiotemporal feature matrix with temporal continuity, spatial correlation, and risk type differentiation is constructed.

[0029] In this embodiment of the invention, the technical means of collecting raw real-time risk data of the target metering box and associated area through edge sensing nodes and performing edge noise filtering and time-series alignment preprocessing, retrieving matching historical risk data and performing time base unification and time-series scale normalization processing, aligning and fusing the standardized real-time and historical risk data in spatiotemporal coordinate systems, and extracting multi-source heterogeneous features of time dimension risk evolution trend, spatial dimension risk diffusion gradient and risk source type dimension from the fused data to form a multi-dimensional risk feature set, and then performing matrix mapping and encapsulation according to preset time granularity and spatial grid units to construct a multi-dimensional spatiotemporal feature matrix, overcomes the technical means of the prior art, which has chaotic risk data collection, large noise interference, chaotic time sequence, inability to effectively fuse real-time and historical risk data, single risk feature extraction, lack of time continuity, spatial correlation and risk type differentiation, and difficulty in constructing an accurate and comprehensive risk feature matrix.

[0030] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Based on the constructed multi-dimensional spatiotemporal feature matrix, and based on the risk diffusion gradient features of the spatial dimension in the matrix, identify high-risk nodes and potential risk propagation paths within the region to obtain initial risk association data. Specifically, this includes: retrieving the constructed multi-dimensional spatiotemporal feature matrix and extracting the spatial dimension risk diffusion gradient features corresponding to all 5m×5m spatial grid units from the matrix, including all feature parameters such as the risk diffusion radius, numerical decay rate, gradient change direction, and maximum diffusion range of each grid; performing global statistical analysis on the risk diffusion gradient feature values ​​of all extracted spatial grid units to calculate the global mean of the risk diffusion gradient features within the region to which the target metering box belongs; comparing the risk diffusion gradient feature value of each spatial grid unit with the global mean one by one, marking spatial grid units with feature values ​​exceeding 80% of the global mean as high-risk nodes, and simultaneously recording the actual geographical coordinates and corresponding specific values ​​of the risk diffusion gradient features of each high-risk node. After identifying high-risk nodes, the dominant risk diffusion direction of each node is determined based on the gradient change direction of each node. Adjacent high-risk nodes along the same dominant risk diffusion direction are linearly connected to form potential risk propagation paths. The starting node, ending node, passing grid, and extension direction of each potential risk propagation path are recorded. The basic information and feature data of all marked high-risk nodes are structured and integrated with the association information of all potential risk propagation paths to form initial risk association data containing the correspondence between nodes and paths.

[0031] Step 2.2 involves optimizing the topology of the initial risk association data. Weights are assigned to each associated edge based on the risk evolution trend characteristics over time, and node attributes are labeled using multi-source heterogeneous features of the risk type dimension to obtain the risk association topology data. Specifically, this includes: optimizing the topology of the initial risk association data; screening all high-risk nodes one by one; removing isolated nodes without any potential risk propagation paths; comparing all potential risk propagation paths; merging redundant paths with completely identical paths and highly overlapping grids; and retaining valid nodes and paths in the topology. Furthermore, the time-dimensional risk evolution trend characteristics corresponding to each valid node and path are extracted from the multi-dimensional spatiotemporal feature matrix, including parameters such as risk change slope, peak occurrence time, and duration of continuous change. Weights are then assigned to the associated edges corresponding to each valid path based on these parameters. The basic weights of associated edges with a risk change slope of 0.8 or higher are set to 9, those with a slope between 0.5 and 0.8 to 6, and those with a slope below 0.5 to 3. For associated edges with a risk change duration exceeding 12 hours, the basic weight is increased by 2; for those with a duration between 6 and 12 hours, the weight is increased by 1; and for those with a duration below 6 hours, no weight is added. The final weight value for each associated edge is calculated and assigned according to this rule. Then, multi-source heterogeneous features corresponding to the risk source type dimension of each effective node are extracted from the multi-dimensional spatiotemporal feature matrix. Based on these features, each effective node is labeled with its corresponding node attributes: nodes with a single risk source feature are labeled as environmental disturbance type, structural deformation type, or external intrusion type; nodes containing two or more risk source features are labeled as composite type. The specific risk source composition of each node is also labeled. The optimized topology, the associated edges with final weight values, and the effective nodes with labeled attributes are then fully integrated to obtain the risk-related topology data.

[0032] Step 2.3: Based on the risk-related topology data, calculate the risk propagation intensity and risk accumulation effect of each spatial grid in the topology network to obtain the basic risk distribution field; based on the basic risk distribution field, perform spatiotemporal attenuation and enhancement correction on the risk propagation intensity to obtain the dynamically corrected risk distribution field. Specifically, this includes: retrieving the risk-related topology data, traversing every 5m × 5m spatial grid cell within the area to which the target metering box belongs, and calculating the risk propagation intensity of each spatial grid by combining the node attributes within each grid in the topology data and the final weight value of the associated edges connecting that grid. The risk propagation intensity calculation coefficient for the grid containing structurally deformable nodes is set to 1.2. The calculation coefficient for the grid containing disturbed nodes is 1.0, the calculation coefficient for the grid containing external intrusion nodes is 1.1, and the calculation coefficient for the grid containing composite nodes is matched according to the main risk source type. The final weight value of the associated edges within the grid is multiplied by the corresponding calculation coefficient to obtain the specific value of the risk propagation intensity of each grid. Based on this, the risk accumulation effect of each grid is calculated, and the duration of the risk characteristics in each grid remaining at a high value is statistically analyzed. The risk accumulation coefficient is set as 1.3 for a duration of more than 8 hours, 1.1 for a duration of 4 to 8 hours, and 1.0 for a duration of less than 4 hours. The wind... The risk propagation intensity is multiplied by the corresponding risk accumulation coefficient to obtain the basic risk value for each grid. The basic risk values ​​of all spatial grid units are arranged geographically to form a basic risk distribution field covering the entire area of ​​the target metering box. The risk propagation intensity in the basic risk distribution field is subject to spatiotemporal attenuation and enhancement correction. In the time dimension, grids with a risk propagation duration exceeding 10 hours have their risk propagation intensity attenuated by 5% per hour; grids with a risk propagation duration of less than 10 hours are not attenuated; and grids with sudden risk values ​​increasing by more than 50% within one hour have their risk propagation intensity directly enhanced by 20%. In the spatial dimension… In terms of degree, when a risk spreads from a high-risk node to the surrounding area, the risk propagation intensity decreases by 8% for each 5m×5m spatial grid unit it passes through. If there are rigid entities such as utility poles or meter boxes in the risk propagation path, the risk propagation intensity of that grid decreases by an additional 15%. If there are flexible entities such as cables or overhead wires in the risk propagation path, the risk propagation intensity of that grid increases by 10%. According to the above-mentioned time and space dimension correction rules, the basic risk value of each grid in the basic risk distribution field is calculated and corrected one by one to obtain the dynamic risk value of each grid. All dynamic risk values ​​are rearranged according to geographical location to form a dynamically corrected risk distribution field.

[0033] Step 2.4: Map the dynamically corrected risk distribution field to the area where the target metering box belongs according to geographical coordinates, divide the risk level intervals and mark the risk hotspot areas, and obtain the regionalized dynamic risk assessment results of risk level distribution data and risk evolution trend. Specifically, this includes: retrieving the dynamically corrected risk distribution field, and according to the established unified geographic spatiotemporal coordinate system with the latitude and longitude of the target metering box location as the coordinate origin, accurately mapping the dynamic risk value of each 5m × 5m spatial grid unit in the field to the actual geographical area of ​​the old distribution network along the street where the target metering box belongs, clarifying the location of each grid unit. The corresponding actual latitude and longitude range, surrounding geographical landmarks, and locations of associated power facilities are used to achieve a one-to-one correspondence between the dynamically corrected risk distribution field and the actual geographical area. The dynamic risk values ​​in the mapped dynamically corrected risk distribution field are divided into risk level ranges, with specific numerical thresholds set: a dynamic risk value above 90 is considered Level 1 risk, 70 to 89 is Level 2 risk, 50 to 69 is Level 3 risk, 30 to 49 is Level 4 risk, and 0 to 29 is Level 5 risk. Each mapped actual geographical grid is labeled according to this range standard. The risk levels of all geographic grids are integrated according to their actual geographical locations to obtain the risk level distribution data of the area where the target metering box belongs. Then, risk hotspot areas are marked, with the judgment criteria being a contiguous area consisting of three or more adjacent first-level risk geographic grids, or an independent area with a single first-level risk geographic grid and a dynamic risk value exceeding 95. Based on this standard, eligible areas in the dynamically corrected risk distribution field are identified one by one, accurately marking the actual geographic boundaries, the spatial grid range covered, the corresponding dynamic risk value range, and surrounding key power facilities of each risk hotspot area. Finally, the temporal dimension of risk evolution trend characteristics for each risk level area is extracted from the risk level distribution data, including hourly changes in risk values, the specific time of risk peak occurrence, the direction of risk value increase or decrease, and the estimated duration of sustained high risk. The geographic mapping data of the dynamically corrected risk distribution field, the risk level distribution data, and the marked risk hotspot area information are then structurally integrated with the risk evolution trend characteristics of each area to obtain a regionalized dynamic risk assessment result containing risk level distribution data and risk evolution trends.

[0034] In this embodiment of the invention, because a multi-dimensional spatiotemporal feature matrix is ​​constructed, high-risk nodes and potential risk propagation paths are identified based on spatial dimension risk diffusion gradient features to obtain initial risk association data. The topology structure of the initial association data is optimized, and the association edges are weighted according to the temporal dimension risk evolution trend features. The node attributes are labeled by combining the multi-source heterogeneous features of the risk type dimension to obtain risk association topology data. Based on the topology data, the risk propagation intensity and cumulative effect of each spatial grid are calculated to obtain the basic risk distribution field. The spatiotemporal attenuation and enhancement correction are applied to obtain the dynamically corrected risk distribution field. Finally, the distribution field is mapped according to geographical coordinates and the risk levels are divided and risk hotspot areas are labeled to obtain regionalized dynamic risk assessment results that include risk level distribution and risk evolution trends. Therefore, this technical means overcomes the shortcomings of existing technologies, such as lack of topological association analysis, no weights on association edges, no clear attributes of nodes, static and uncorrected risk distribution fields, inability to conform to the spatiotemporal evolution law of risks, inaccurate risk level division and hotspot area labeling, and difficulty in obtaining comprehensive, dynamic and realistic regionalized risk assessment results.

[0035] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Receive the regional dynamic risk assessment results, extract the boundary coordinates of the covered geographical area and the internally marked risk hotspot areas; within the geographical area, based on geographic information data, identify and select infrastructure of a preset type as a risk spatial reference benchmark. The benchmark includes at least free utility poles, distribution transformers, and building corners. Specifically, this includes: receiving the regional dynamic risk assessment results of the old distribution network along the street area to which the target metering box belongs, parsing and extracting the complete boundary latitude and longitude coordinates of the assessed geographical area from the results, recording the specific coordinate values ​​of all inflection points of the boundary, and simultaneously extracting the geographical area, center coordinates, and spatial grid information of all marked risk hotspot areas within the results. High-precision geographic information data of the old power distribution network along the street was retrieved. The geographic information data includes detailed mapping information on the distribution of power facilities, the overall layout of buildings, and the direction of roads within the area. Within the extracted assessment geographic range, according to the preset infrastructure type requirements, the entity identifiers and spatial features in the geographic information data were screened one by one to identify all free utility poles, distribution transformers, and building corners. Free utility poles are single-pole power support poles without other auxiliary power equipment in the area. Distribution transformers are power equipment in the area that performs voltage transformation. Building corners are the outer corners of brick-concrete or reinforced concrete structures along the street. The precise geographic coordinates, entity structural attributes, and surrounding related power facility information of all identified infrastructure were recorded one by one. All identified free utility poles, distribution transformers, and building corners were selected as risk spatial reference benchmarks.

[0036] Step 3.2: Using the selected risk reference benchmarks as spatial anchor points, and based on the topological connections between each anchor point and the preset radiation radius, a continuous closed polygonal geographical area covering at least all risk hotspot areas is calculated and defined as the risk analysis area. Specifically, this includes: using each selected risk spatial reference benchmark as an independent spatial anchor point; firstly, retrieving the power distribution network topology connection data for the old distribution network along the street area to clarify the power line connection routes, line laying methods, and geographical adjacency relationships between each spatial anchor point; setting the preset radiation radius of each spatial anchor point to 50 meters; drawing a circular radiation area with a radius of 50 meters centered on the precise geographical coordinates of each spatial anchor point; and verifying the areas marked in the regional dynamic risk assessment results one by one. For risk hotspot areas, confirm whether they are covered by the radiation range of the corresponding spatial anchor point. For risk hotspot areas that are not fully covered, extend the radiation range of the corresponding spatial anchor point to 60 meters until the risk hotspot area is fully covered. After adjusting all radiation ranges, connect the outer intersections of the radiation ranges of each spatial anchor point, and generate a continuous and uninterrupted closed polygon outline through linear fitting calculation of geographic coordinates. Verify one by one whether the closed polygon outline contains all the risk hotspot areas in the old power distribution network along the street, ensuring that no risk hotspot area exceeds the polygon's range. Define the verified closed polygon's geographic range as the risk analysis area, and record all boundary inflection point coordinates and the overall geographic coverage of the risk analysis area.

[0037] Step 3.3: Based on the distribution density of risk reference benchmarks, the gradient change of risk level distribution data, and geographical barrier characteristics within the risk analysis area, the area is automatically subdivided into multiple overlapping or adjacent risk analysis sub-areas through automated grid subdivision. Specifically, this includes: firstly, extracting the distribution density of risk reference benchmarks and the gradient change information of risk level distribution data within the risk analysis area from the regional dynamic risk assessment results; and secondly, identifying geographical barrier characteristics within the risk analysis area from geographic information data. Geographic barrier characteristics include main roads, continuous high walls, rivers, and other geographical entities within the area that cannot directly transmit risk. The distribution density of risk reference benchmarks is statistically calculated per 100 square meters of geographical area, and the gradient change of risk levels is calculated based on the difference in dynamic risk values ​​between adjacent spatial grids. The basic unit for automated grid subdivision is set as a 20m × 20m square grid, targeting the risk reference benchmarks... For areas with a distribution density of more than 3 per 100 square meters, the basic grid is subdivided into 10m x 10m denser grids. For areas where the risk level distribution data gradient change value exceeds 30, adjacent basic or denser grids are set with a 5m wide overlap. For identified geographical barriers, their geographical boundaries are used as natural boundaries for grid subdivision, ensuring that the subdivided grids do not cross any geographical barrier entities. The risk analysis area is then subjected to full-area automated grid subdivision according to the above rules. After subdivision, adjacent denser or basic grids are integrated based on geographical features and risk distribution features to form multiple risk analysis sub-areas with shapes adapted to the geographical features of the area. Each risk analysis sub-area is basically adjacent to the others, and sub-areas in areas with large risk level gradient changes retain a 5m wide overlap. Finally, multiple overlapping or adjacent risk analysis sub-areas covering the entire risk analysis area are formed.

[0038] Step 3.4: For each risk analysis sub-region, retrieve the structured data of physical entities within the sub-region and the pre-set buffer zones at the boundaries of adjacent sub-regions. Based on the mechanical and functional properties of the physical entities, classify them into rigid and flexible entities, and extract the risk characteristics corresponding to each type of entity in the risk assessment results. These risk characteristics include at least the entity's current risk exposure value, vulnerability coefficient in the risk field, and spatial correlation strength with the entity. Specifically, this includes: first, setting the pre-set buffer zone width at the boundaries of adjacent sub-regions to 3 meters; for each risk analysis sub-region that has completed identification and coding, retrieving the structured data of all physical entities within that sub-region from the power facility structured database and the geographic entity structured database. The system retrieves structured data of all physical entities within a 3-meter buffer zone between the current sub-region and adjacent sub-regions. This structured data includes the entity's precise geographic coordinates, material composition, mechanical properties, functional attributes, and detailed information on associated power facilities. Based on the retrieved structured data, all physical entities are classified according to their material mechanical and functional properties. Entities with high material hardness, low deformation capacity, and functions as power support, metering cores, or geographically fixed components are classified as rigid entities, including metering boxes, free-standing utility poles, distribution transformers, building corners, concrete mounting brackets, and metal junction boxes. Entities with low material hardness, high deformation capacity, and functions as power transmission, flexible connections, or insulation protection components are classified as flexible entities. This includes overhead conductors, cable lines, protective sleeves, conductor connection sleeves, and rubber insulation layers. After completing entity classification, the risk characteristics corresponding to each type of entity in the regional dynamic risk assessment results are extracted. First, the current risk exposure value of the entity is extracted, directly determined based on the dynamic risk value of the geographical grid where the entity is located. The risk exposure value of entities in the first-level risk grid is 95, the risk exposure value of entities in the second-level risk grid is 80, the risk exposure value of entities in the third-level risk grid is 65, the risk exposure value of entities in the fourth-level risk grid is 45, and the risk exposure value of entities in the fifth-level risk grid is 25. Second, the vulnerability coefficient of the entity in the risk field is extracted, set according to the material mechanical properties and electrical function importance of the entity. For rigid entities, the metering box is included. The vulnerability coefficients for the following entities are calculated: 0.9 for the main body, 0.8 for free utility poles, 0.85 for distribution transformers, 0.7 for building corners, 0.75 for concrete mounting brackets, and 0.82 for metal junction boxes. For flexible entities, the vulnerability coefficients for overhead conductors are 0.95, 0.92 for cable lines, 0.88 for protective sleeves, 0.98 for conductor connection sleeves, and 0.9 for rubber insulation layers. Finally, the spatial correlation strength between the entity and the target metering box is extracted. This is determined based on the straight-line spatial distance and power topology connection between the entity and the target metering box. The spatial correlation strength is 1.0 for entities within a straight-line spatial distance of 5 meters, 0.8 for 5 to 10 meters, 0.6 for 10 to 20 meters, 0.4 for 20 to 30 meters, and 0 for distances above 30 meters.2. For entities with a direct power topology connection to the target metering box, increase the spatial association strength by 0.2; integrate and record the risk exposure value, vulnerability coefficient, and spatial association strength of each entity to form complete risk characteristic data for each type of entity.

[0039] In this embodiment of the invention, by receiving regional dynamic risk assessment results and extracting the geographical boundary coordinates and risk hotspot areas covered by them, and selecting free utility poles, distribution transformers, and building corners as risk spatial reference benchmarks based on geographic information data, a closed risk analysis area covering all risk hotspot areas is delineated using these benchmarks as spatial anchor points, combined with the topological connection relationship of the anchor points and a preset radiation radius. Then, based on the distribution density of the risk reference benchmarks, the gradient change of risk levels, and geographical barrier characteristics, the risk analysis sub-regions that overlap or are adjacent are automatically subdivided. Finally, the structured data of entities within the boundary buffer zones of each sub-region and adjacent sub-regions are retrieved, and rigid and flexible entities are classified according to the mechanical and functional properties of the entity materials, and risk characteristics including risk exposure values, vulnerability coefficients, and spatial correlation strength are extracted. Therefore, this invention overcomes the shortcomings of existing technologies, such as the lack of clear reference benchmarks for risk analysis area delineation, inaccurate scope and inability to accurately cover risk hotspots, lack of reasonable basis for risk sub-region division, low level of refinement, failure to classify physical entities, and failure to extract targeted risk characteristics. Thus, it achieves accurate delineation of risk analysis areas and refined sub-region division, clarifies the classification standards for rigid and flexible entities, and comprehensively extracts risk characteristics.

[0040] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Based on the risk characteristic data of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions; for rigid entities, analyze the displacement constraint and stress concentration characteristics under external risk based on mechanical properties to obtain rigid entity risk kinematic analysis data; for flexible entities, analyze the deformation propagation and energy dissipation characteristics in the risk field to obtain flexible entity risk kinematic analysis data. Specifically, this includes: retrieving complete risk characteristic data of all rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions, and simultaneously retrieving material mechanical property data of various entities. The mechanical properties of rigid entities include material hardness, deformation resistance coefficient, etc. For fixed connection methods and displacement constraint boundary conditions, the mechanical properties of flexible entities include material elastic modulus, deformation recovery coefficient, damping coefficient, and energy conduction efficiency. For rigid entities, based on their inherent mechanical properties and combined with the types and intensity of external risks in the regional dynamic risk assessment results, the displacement constraint characteristics and stress concentration characteristics of each rigid entity under the action of external risks are analyzed one by one. External risks include wind disturbance, tension of flexible entities, and ground settlement. When analyzing displacement constraint characteristics, the displacement limits of each rigid entity in both horizontal and vertical directions are defined. The horizontal displacement constraint value of the metering box body is 5 mm, and the vertical displacement constraint value is 3 mm. The horizontal displacement of the free utility pole is approximately... The constraint value is 8 mm, the vertical displacement constraint value is 4 mm, and the horizontal and vertical displacement constraints of the distribution transformer are both 2 mm. Simultaneously, the structural deformation trend of each rigid entity after exceeding the displacement limit is recorded. When analyzing stress concentration characteristics, the key stress concentration points of each rigid entity are identified: for the metering box, the connection point between the terminal and the box body; for the mounting bracket and the box body, the connection point; for the free utility pole, the connection point between the middle and bottom of the pole body; and for the distribution transformer, the connection point between the bushing and the main body. The real-time stress values, stress change rates, and physical extent of stress concentration areas of each key point under different external risks are calculated. The displacement constraint limits, deformation trends, and stress concentration points of all rigid entities are recorded. Real-time stress values, stress change rates, and other data are integrated to form kinematic analysis data for rigid entities under risk. For flexible entities, the deformation propagation characteristics and energy dissipation characteristics of each flexible entity in the risk field are analyzed by combining the risk level distribution of the risk field and the intensity of external risk. When analyzing the deformation propagation characteristics, the deformation amount, deformation wave propagation speed, deformation propagation distance, and deformation amplitude along the propagation path of the flexible entity under the action of external risk are calculated. The deformation limit for overhead conductors is 20 cm and the deformation wave propagation speed is 15 m / s. The deformation limit for cable lines is 15 cm and the deformation wave propagation speed is 10 m / s. At the same time, the dominant direction and duration of deformation propagation are recorded.When analyzing energy dissipation characteristics, the initial energy value, energy dissipation rate, dissipation duration, and remaining energy percentage of the flexible entity during deformation generation and propagation are calculated. The basic energy dissipation rate of the flexible entity is set at 8% per meter, with an additional 5% per meter for wind disturbances and an additional 3% per meter for tensile forces. Simultaneously, the main forms of energy dissipation are recorded as internal friction dissipation and environmental drag dissipation. Data on the deformation, deformation wave propagation parameters, deformation evolution laws, initial energy value, dissipation parameters, and remaining energy percentage of all flexible entities are integrated to form risk kinematic analysis data for the flexible entity.

[0041] Step 4.2: Based on the spatial proximity of rigid and flexible entities, construct the risk transmission coupling relationship between them. Based on this relationship, perform co-mapping and coupling calculations on the stress variation characteristics in the kinematic analysis data of the rigid entity and the deformation propagation characteristics in the kinematic analysis data of the flexible entity to simulate the transmission path and intensity evolution of risk in the rigid-flexible coupled structure. This yields risk co-evolution data under the rigid-flexible coupled state. Specifically, this includes: first, retrieving the precise geographic coordinates of all rigid and flexible entities; using a spatial straight-line distance of 10 meters as the proximity criterion; and then checking each flexible entity within a 10-meter radius of the rigid entity to identify those with spatial proximity to the rigid entity. The system constructs a combination of rigid and flexible entities, recording the specific types, spatial relative positions, and linear distances of the rigid and flexible entities within each combination. For each spatially adjacent combination of rigid and flexible entities, it establishes a risk transmission coupling relationship between the rigid and flexible entities, clarifying that stress changes in the rigid entity are transmitted to the flexible entity through contact points, triggering deformation propagation. The deformation propagation of the flexible entity generates tensile stress, which reacts on the stress concentration points of the rigid entity. Simultaneously, it determines the direction of action, contact points, and transmission medium of the coupling relationship, forming a global network of rigid-flexible entity risk transmission coupling relationships. Based on the constructed risk transmission coupling relationships, it analyzes the stress change characteristics in the kinematic analysis data of the rigid entity and the deformation characteristics in the kinematic analysis data of the flexible entity. The propagation characteristics are processed through co-mapping, calibrating the temporal dimension of the two types of feature data to a unified minute-level scale and the spatial dimension to a local coordinate system with the coupling contact point as the origin. This ensures a precise spatiotemporal match between the stress change rate of the rigid entity and the deformation propagation speed of the flexible entity, while spatially corresponding the stress concentration points of the rigid entity to the deformation propagation initiation points of the flexible entity. After co-mapping, coupling calculations are performed. The tensile force on the flexible entity is calculated based on the real-time stress value of the rigid entity, thereby correcting the deformation and deformation propagation speed of the flexible entity. For every 10-unit increase in stress value, the deformation of the flexible entity increases by 2 centimeters, and the deformation propagation speed increases by 1 meter per second. The reaction force on the rigid entity is calculated based on the deformation of the flexible entity. The tensile stress is applied to correct the stress value at the stress concentration point of the rigid entity. For every 5 cm increase in deformation, the stress value at the stress concentration point of the rigid entity increases by 3 units. Through continuous coupling calculations, the transmission path of risk in each group of rigid-flexible coupled structures is dynamically simulated. The specific path and location of risk transmission from the deformation initiation point of the flexible entity to the stress concentration point of the rigid entity are clarified. At the same time, the evolution of risk intensity during the transmission process is simulated, and the enhancement, attenuation or stable change law of risk intensity at different transmission stages is recorded. The risk transmission path, transmission direction, location, risk intensity evolution law and specific value of risk intensity at each stage of all rigid-flexible coupled structures in the entire domain are integrated to obtain risk co-evolution data under rigid-flexible coupling state.

[0042] Step 4.3: Based on the risk co-evolution data under rigid-flexible coupling, extract key indicators characterizing risk transmission efficiency, energy redistribution, and vulnerability of coupling nodes, and fuse them to form risk characteristic data related to rigid-flexible coupling. Specifically, this includes: retrieving risk co-evolution data under rigid-flexible coupling; extracting key indicators characterizing risk transmission efficiency, energy redistribution, and vulnerability of coupling nodes from the risk co-evolution data; and characterizing risk transmission efficiency, which includes the transmission time, transmission completion rate, and transmission rate of risk in the rigid-flexible coupling structure. Transmission time is the actual time it takes for the risk to travel from the starting point to the ending point of the coupling; transmission completion rate is the ratio of the actual transmitted risk intensity to the theoretically transmittable risk intensity; and transmission rate is the distance the risk is transmitted per unit time. Key indicators characterizing energy redistribution include... In a rigid-flexible coupled structure, the energy reception ratio, energy transfer ratio, and energy loss ratio of the coupled node are defined as follows: the energy reception ratio is the proportion of energy received by the coupled node from the associated entity to the total transferred energy; the energy transfer ratio is the proportion of energy transferred by the coupled node to other entities to the received energy; and the energy loss ratio is the proportion of energy dissipated by the coupled node during energy transfer to the received energy. Key indicators characterizing the vulnerability of the coupled node include the real-time stress tolerance value, deformation tolerance value, current damage level, and risk accumulation time. The real-time stress tolerance value is the maximum stress value that the coupled node can withstand; the deformation tolerance value is the maximum deformation value that the coupled node can withstand; the current damage level is the ratio of the actual damage to the ultimate damage caused by the risk to the coupled node; and the risk accumulation time is the time that the coupled node remains under high risk. The three types of key indicators extracted were standardized, and all indicator values ​​were uniformly mapped to a range of 0 to 100. A transmission completion rate below 30 was considered inefficient, 30 to 70 medium efficient, and above 70 high efficient. Energy loss ratio above 60% was considered high loss, 30 to 60 medium loss, and below 30 low loss. Current damage level above 70 was considered highly vulnerable, 40 to 70 medium vulnerable, and below 40 low vulnerable. The standardized key indicators were categorized and integrated according to the spatial location and rigid-flexible entity combination type of the rigid-flexible coupling structure. Risk transmission efficiency indicators, energy redistribution indicators, and coupling node vulnerability indicators for the same rigid-flexible coupling structure and the same coupling node were correlated and matched to form risk characteristic data of rigid-flexible coupling associations, with coupling nodes as the basic unit and containing complete data of the three types of key indicators. At the same time, each group of rigid-flexible coupling structures was labeled with the corresponding coupling association risk characteristic data identifier.

[0043] Step 4.4: Based on the risk characteristic data of rigid-flexible coupling, calculate the risk intensity increment and attenuation caused by rigid-flexible entity coupling within each risk analysis sub-region and at the boundary of adjacent sub-regions, and quantify the risk intensity increment and attenuation into risk distribution adjustment values. Specifically, this includes: retrieving the risk characteristic data of rigid-flexible coupling, and simultaneously retrieving the spatial range information and rigid-flexible coupling structure distribution information of each risk analysis sub-region and the boundary of adjacent sub-regions; for each risk analysis sub-region and the boundary of adjacent sub-regions, statistically analyze the coupling-related risk characteristic data of all rigid-flexible coupling structures within the region, and calculate the risk intensity increment and attenuation caused by rigid-flexible entity coupling based on the key indicator values. Attenuation amount; When calculating the risk intensity increment, the vulnerability of the coupled nodes and the risk transmission efficiency are the core criteria. For a rigid-flexible coupled structure with a highly vulnerable coupled node and a highly efficient risk transmission efficiency, a single structure generates a risk intensity increment of 20. For a rigid-flexible coupled structure with a highly vulnerable coupled node and a moderately efficient risk transmission efficiency, or a moderately vulnerable coupled node and a highly efficient risk transmission efficiency, a single structure generates a risk intensity increment of 12. For a rigid-flexible coupled structure with a moderately vulnerable coupled node and a moderately efficient risk transmission efficiency, a single structure generates a risk intensity increment of 5. Other coupled structures do not generate a risk intensity increment. The total risk intensity increment of the region is obtained by summing the risk intensity increments generated by all rigid-flexible coupled structures in the region. When calculating the risk intensity attenuation, the proportion of energy loss in energy redistribution is the core basis. A rigid-flexible coupled structure with a high energy loss proportion generates a risk intensity attenuation of 18% per structure; a rigid-flexible coupled structure with a medium energy loss proportion generates 9% per structure; and a rigid-flexible coupled structure with a low energy loss proportion generates 3% per structure. Other coupled structures do not generate any risk intensity attenuation. The total risk intensity attenuation of the region is obtained by summing the risk intensity attenuation of all rigid-flexible coupled structures within the region. After calculating the total risk intensity increment and total risk intensity attenuation for each region, the two types of values ​​are... The risk distribution adjustment value is directly quantified, where the total risk intensity increment corresponds to the risk distribution increment adjustment value, and the total risk intensity decay corresponds to the risk distribution decay adjustment value. A unique geographic identifier is marked within each risk analysis sub-region and at the boundary of adjacent sub-regions. This geographic identifier is precisely associated with the corresponding risk distribution increment adjustment value and risk distribution decay adjustment value. At the same time, the adjustment value is secondary-distributed according to a 5m × 5m spatial grid within the region. The proportion of the number of rigid-flexible coupling structures in the grid to the total number in the region is the proportion of the adjustment value allocated to that grid. Finally, a risk distribution adjustment value is formed that covers all risk analysis sub-regions and their boundaries and is accurate to each spatial grid.

[0044] In this embodiment of the invention, because it employs the method of analyzing the kinematic characteristics of rigid and flexible entities in each risk analysis sub-region and adjacent sub-regions to obtain corresponding risk kinematic analysis data, constructing the risk transmission coupling relationship of rigid and flexible entities based on spatial proximity, co-mapping and coupling the stress change characteristics of rigid entities with the deformation propagation characteristics of flexible entities to simulate the transmission path and intensity evolution process of risk in rigid-flexible coupled structures, and then extracting key indicators from the rigid-flexible coupled risk co-evolution data and fusing them into rigid-flexible coupled associated risk characteristic data, and finally calculating the risk intensity increment and attenuation generated by the coupling effect of rigid and flexible entities and quantifying them into risk distribution adjustment values, it overcomes the technical means of... Existing technologies lack targeted kinematic analysis of rigid-flexible entities, ignore the risk transmission coupling effect between rigid-flexible entities, cannot simulate the evolution of risk in rigid-flexible coupled structures, cannot quantify the risk intensity changes brought about by coupling, and lack numerical basis for accurate coupling correction of initial risk assessment results, resulting in significant deviations between risk assessment results and actual risk scenarios. This technology achieves targeted risk kinematic analysis of rigid-flexible entities, accurately constructs the risk transmission coupling relationship between the two, dynamically simulates the evolution of risk in coupled structures, and quantifies the risk distribution adjustment value under rigid-flexible coupling, filling the technical gap in risk analysis of rigid-flexible entity coupling.

[0045] In a preferred embodiment of the present invention, step 4.2 above may include: Step 4.21: Using the kinematic analysis data of rigid entities and flexible entities, based on the spatial location identifiers of the entities attached to the analysis data and combined with the preset spatial connection threshold, determine and establish the topological connection relationship and spatial proximity relationship between rigid and flexible entities. Specifically, this includes: retrieving all the kinematic analysis data of rigid and flexible entities, analyzing the spatial location identifiers of the entities attached to both types of analysis data one by one. These identifiers contain the precise geographical coordinates, installation location details, and relative position descriptions of each entity to surrounding entities. Completely extract the spatial location information of each rigid entity and each flexible entity, and record the specific latitude and longitude coordinates, installation height, and fixed points of rigid entities such as metering boxes, free utility poles, and distribution transformers. At the same time, record the laying path coordinates, start and end connection points, and relative distances to rigid entities such as overhead conductors and cable lines. Set the preset spatial connection threshold to 10 meters, and use the precise geographical coordinates of each rigid entity as a benchmark to calculate the topological connection relationship and spatial proximity relationship of each rigid entity. The system calculates the straight-line spatial distance between the rigid entity and all flexible entities. Flexible entities with a straight-line spatial distance ≤ 10 meters are considered to have spatial connection conditions with the rigid entity, and the specific distance values ​​are recorded. For rigid-flexible entity combinations that are determined to have spatial connection conditions, the actual physical connection relationship between the two is further verified. If the end of the flexible entity is directly connected to the rigid entity, or the two are indirectly connected through connectors, a topological connection relationship is established between the two, specifying whether the topological connection is direct or indirect, the connection location, and the connection method. If the two have no actual physical connection but the straight-line spatial distance is ≤ 10 meters, a spatial proximity relationship is established between the two, specifying the spatial proximity distance, relative orientation, and whether there are geographical barriers. The spatial location information of all rigid and flexible entities, the results of the straight-line spatial distance calculation, and the established topological connection and spatial proximity relationships are structurally integrated to form complete association data containing the correspondence, connection, and proximity attributes of rigid and flexible entities, thus completing the judgment and establishment of the topological connection and spatial proximity relationships between rigid and flexible entities.

[0046] Step 4.22: Based on topological connectivity and spatial proximity, define the rules governing the transmission of stress change characteristics from rigid entities to flexible entities, and the rules governing the influence of deformation propagation characteristics of flexible entities on the reaction of rigid entities. This yields a collaborative mapping relationship for the risk interaction behavior between rigid and flexible entities. Specifically, based on the established topological connectivity and spatial proximity, classify and organize all rigid-flexible entity combinations. For different types of relationships, define the rules governing the transmission of stress change characteristics from rigid entities to flexible entities, and the rules governing the influence of deformation propagation characteristics of flexible entities on the reaction of rigid entities. For rigid-flexible entity combinations with topological connectivity, define the rule for the transmission of stress change characteristics from rigid entities as follows: for every 10 units increase in the real-time stress value at the stress concentration point of the rigid entity, a corresponding strength of force will be transmitted to the flexible entity through the connection point, causing the flexible entity... For every 2 cm increase in deformation, the deformation wave propagation speed increases by 1 m / s. The transmission efficiency is determined by the connection method: 90% for direct connections and 70% for indirect connections. The stress transmission delay is defined as 10 seconds, meaning that the flexible entity exhibits a corresponding deformation response 10 seconds after the rigid entity's stress changes. The reaction rule for the deformation propagation characteristics of the flexible entity is defined as follows: for every 5 cm increase in deformation of the flexible entity, a reaction tensile stress is generated at the stress concentration point of the rigid entity through the connection, increasing the stress value of the rigid entity by 3 units. The reaction strength is determined by the deformation recovery coefficient of the flexible entity: the reaction strength increases by 20% for a deformation recovery coefficient > 0.8, remains unchanged for a deformation recovery coefficient between 0.5 and 0.8, and decreases by 10% for a deformation recovery coefficient less than 0.5. For rigid-flexible entity combinations that only have spatial proximity, the stress change characteristic transmission rule for the rigid entity is defined as follows: when the stress change rate of the rigid entity is greater than 0.5 units per second, it will transmit a weak stress influence to the flexible entity within a 10-meter radius. The transmission intensity decreases with increasing distance, decreasing by 10% for every 1-meter increase in distance. The deformation propagation characteristic reaction rule for the flexible entity is defined as follows: when the deformation of the flexible entity is greater than 10 centimeters, it will have a slight tensile influence on the rigid entity within a 10-meter radius. The influence intensity decreases with increasing distance, decreasing by 8% for every 1-meter increase in distance. All defined transmission and reaction influence rules are integrated to clarify the rule parameters corresponding to different relationships and entity types, forming a collaborative mapping relationship for risk interaction behaviors between rigid and flexible entities, ensuring that each rigid-flexible entity combination corresponds to a specific interaction rule.

[0047] Step 4.23: Based on the cooperative mapping relationship, feature alignment and data coupling are performed on the key stress nodes in the rigid entity risk kinematics analysis data and the key deformation bands in the flexible entity risk kinematics. Coupled calculations are then performed to dynamically simulate the risk energy transfer path, rate, and intensity evolution process. Specifically, this includes: retrieving the cooperative mapping relationship of the risk interaction behavior between the rigid and flexible entities; simultaneously retrieving the rigid entity risk kinematics analysis data and the flexible entity risk kinematics analysis data again; and extracting the key stress nodes of each rigid entity from the rigid entity risk kinematics analysis data. The key stress nodes are the stress sets of the rigid entities. The most obvious and vulnerable parts, such as the connection between the metering box and the box body, the bottom fixing part of the free utility pole, and the bushing part of the distribution transformer, are the focus of data extraction. Real-time stress values, stress change rates, stress peak values, and occurrence times are extracted for each key stress node. Key deformation bands for each flexible entity are extracted from the kinematic analysis data of the flexible entity. The key deformation bands are the deformation data corresponding to the period when the deformation of the flexible entity is the largest and the deformation change is the most intense. The deformation, deformation wave propagation speed, deformation amplitude, deformation duration, and propagation direction of each key deformation band are extracted.Based on the collaborative mapping relationship, feature alignment processing is performed on the extracted key stress node feature data and key deformation band feature data. In the time dimension, the timestamps of both types of feature data are calibrated to a unified minute-level scale to ensure that the stress changes of key stress nodes in the rigid entity and the deformation changes of key deformation bands in the flexible entity are synchronously correlated in time. In the spatial dimension, a local spatial coordinate system is established with the connection point or nearest neighbor of the rigid and flexible entities as the origin. The spatial positions corresponding to the key stress nodes and key deformation bands are calibrated into this local coordinate system to ensure accurate spatial correspondence. After feature alignment, data coupling is performed on the key stress node feature data and key deformation band feature data according to the interaction rules corresponding to the collaborative mapping relationship. This involves associating and matching the real-time stress change data of key stress nodes in the rigid entity with the deformation change data of key deformation bands in the flexible entity. The system employs coupled calculations to determine the magnitude of the force exerted on the flexible entity based on the stress change of the rigid entity, thereby correcting the deformation propagation parameters of the flexible entity. Simultaneously, it calculates the magnitude of the reaction stress on the rigid entity based on the deformation of the flexible entity, further correcting the stress change parameters of the rigid entity. Through continuous coupled calculations, the system dynamically simulates the transmission path of risk energy in each set of rigid-flexible coupled structures. It clarifies the specific path, location, and transmission medium of risk energy from the starting point of the key deformation band of the flexible entity to the key stress node of the rigid entity through interaction. Simultaneously, it simulates the rate change of risk energy transmission, records the rate values ​​at different transmission stages, and the evolution process of risk energy intensity. It clarifies whether the risk energy increases, decreases, or remains stable during transmission, recording the specific values ​​of risk energy intensity at different times and locations, thus fully reconstructing the entire process of risk energy transmission and evolution in the rigid-flexible coupled structure.

[0048] Step 4.24: Integrate the dynamic simulation of risk energy transfer paths, rates, and intensity evolution processes to obtain risk co-evolution data under rigid-flexible coupling conditions. This includes: retrieving all data obtained from the dynamic simulation, including detailed records of risk energy transfer paths, rates, and intensity evolution processes; classifying and integrating this simulation data across the entire domain; firstly, classifying data according to the type of rigid-flexible entity combination, grouping simulation data of the same rigid entity and its corresponding flexible entity combination into one category; systematically reviewing the risk energy transfer path information for each category, clarifying the starting point, ending point, entity parts traversed, and geographical coordinates of each transfer path, and fully recording the extension direction and length of the path; then, organizing the risk energy transfer rate data for each type of rigid-flexible entity combination, recording the real-time changes in the transfer rate in chronological order, analyzing the patterns of rate changes, identifying key nodes and corresponding causes for rate increases or decreases, such as those caused by the flexible entity... Increased volume deformation leads to a higher transmission rate, while geographical barriers cause a decrease in the transmission rate. Next, data on the evolution of risk energy intensity are integrated, recording intensity changes in both time and space dimensions. Temporally, the data records the increase or decrease in risk energy intensity over time, the time of peak occurrence, and the duration. Spatially, it records the attenuation or enhancement patterns of risk energy intensity along the transmission path and the intensity values ​​at different locations. Simultaneously, corrected rigid entity stress data and flexible entity deformation data obtained during the simulation, as well as key parameters from the coupling calculation process, are also included in the integration. All integrated data undergoes structured processing, recording the specific value, corresponding entity, corresponding time period, and spatial location of each data point according to a unified format to ensure data integrity and relevance. Ultimately, this results in risk co-evolution data under rigid-flexible coupling states, covering all rigid-flexible coupled structures and containing full information on risk energy transmission and evolution.

[0049] In this embodiment of the invention, by combining spatial location identifiers of rigid-flexible entity risk kinematic analysis data with preset spatial connection thresholds to establish their topological connections and spatial proximity relationships, and then defining rules for stress transmission in the rigid entity and deformation reaction in the flexible entity to obtain a risk interaction and collaborative mapping relationship, the invention overcomes the limitations of existing technologies in rigid-flexible entity kinematic analysis. This is achieved by combining spatial location identifiers with preset spatial connection thresholds to establish topological connections and spatial proximity relationships between the two entities. Furthermore, rules for stress transmission in the rigid entity and deformation reaction in the flexible entity are defined accordingly to obtain risk interaction and collaborative evolution data under rigid-flexible coupling. The lack of a clear basis for constructing risk interaction relationships between rigid and flexible entities, and the failure to accurately align and couple the core risk characteristics of the two entities, make it impossible to dynamically and accurately simulate the entire process of risk energy transmission and evolution in rigid-flexible coupled structures. This results in a lack of systematicity and accuracy in rigid-flexible coupling risk evolution analysis, and significant deviations between the obtained data and actual risk scenarios. This research aims to achieve the scientific and accurate construction of risk interaction relationships between rigid and flexible entities, complete the precise matching and coupling calculation of their core risk characteristics, dynamically and comprehensively restore the transmission and evolution laws of risk energy in rigid-flexible coupled structures, and obtain risk co-evolution data that accurately matches actual risk scenarios.

[0050] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Using the risk distribution adjustment values ​​and the regional dynamic risk assessment results; based on the spatial location corresponding to the risk distribution adjustment values ​​and the basic risk distribution field and risk level distribution data in the regional dynamic risk assessment results, spatial alignment and numerical overlay are performed to complete the dynamic correction of the risk values, obtaining the corrected dynamic risk distribution field and risk level distribution data as the corrected assessment results. Specifically, this includes: all obtained risk distribution adjustment values, which contain the risk distribution increment adjustment value and risk distribution decay value of each 5m × 5m spatial grid within each risk analysis sub-region and at the boundary of adjacent sub-regions. The adjustment value is reduced, and the regional dynamic risk assessment results are retrieved simultaneously. The basic risk distribution field, risk level distribution data, and corresponding spatial location information are extracted from the results. First, the risk distribution adjustment value and the regional dynamic risk assessment results are spatially aligned. Using the previously established unified geographic spatiotemporal coordinate system with the latitude and longitude of the target metering box location as the origin, the spatial grid coordinates corresponding to each risk distribution adjustment value are matched one by one with the coordinates of the corresponding spatial grids in the basic risk distribution field and risk level distribution data. This ensures that the adjustment value and the basic risk value and risk level of the corresponding grid are spatially aligned. The data is precisely aligned without misalignment or omission. Simultaneously, the time dimension of both types of data is calibrated to an hourly scale, ensuring complete synchronization across time and space. After spatial alignment, numerical overlay processing is performed. For each spatial grid, the base risk value in the basic risk distribution field is added to the corresponding risk distribution increment adjustment value, and then the corresponding risk distribution decay adjustment value is subtracted to obtain the corrected real-time dynamic risk value for each spatial grid. During numerical overlay, if the calculation result is negative, it is treated as 0; if the calculation result exceeds 100, it is treated as 100. Numerical overlay of all spatial grids is then completed. After correction, all real-time dynamic risk values ​​are rearranged according to their corresponding spatial grid coordinates to form a corrected dynamic risk distribution field covering the entire risk analysis area. Then, based on the corrected dynamic risk values, risk level intervals are redefined, using the previously set risk level thresholds: dynamic risk values ​​above 90 are classified as Level 1 risk, 70-89 as Level 2 risk, 50-69 as Level 3 risk, 30-49 as Level 4 risk, and 0-29 as Level 5 risk. Each spatial grid is re-labeled with its corresponding risk level, and the risk level information from all grids is integrated to obtain the corrected risk level distribution data. The corrected dynamic risk distribution field and the corrected risk level distribution data are then structurally integrated, supplementing the preliminary judgment of the risk evolution trend corresponding to each grid, to form a complete corrected assessment result.

[0051] Step 5.2: Analyze the revised assessment results to identify the final determined risk hotspot areas, risk transmission paths, and the set of target metering boxes requiring key protection within each risk level interval. Combining rigid and flexible entity information, locate critical physical entity nodes with high vulnerability. Specifically, this includes retrieving the revised assessment results and conducting a comprehensive and detailed analysis. First, identify the final determined risk hotspot areas. The criteria for determining risk hotspot areas are defined as contiguous areas consisting of three or more adjacent first-level risk spatial grids, or independent areas with a single first-level risk spatial grid and a revised dynamic risk value exceeding 95. Screen all spatial grids in the revised dynamic risk distribution field according to this standard, marking eligible areas. Record the complete boundary latitude and longitude coordinates, the number of covered spatial grids, the revised dynamic risk value range, risk level, and the main types of risk concentration for each risk hotspot area to ensure accurate and complete identification. Next, identify risk transmission paths. Based on the revised risk level distribution data, find spatial grid sequences that extend continuously from high to low risk levels, prioritizing grid sequences extending from first-level risk grids to second- and third-level risk grids. Simultaneously, combine rigid and flexible... The distribution of entities is coupled with the risk transmission relationship. This confirms that the grid sequence represents the actual risk transmission path. The starting and ending points, the spatial grids traversed, the direction of extension, the length, and the types of rigid and flexible entities along each risk transmission path are recorded. The dominant direction and intensity variation of risk transmission along each path are clarified. Then, the set of target metering boxes requiring key protection within each risk level interval is identified. All target metering boxes located in the first-level risk area are included in the key protection set after screening the corrected risk level distribution data. Those located in the second-level risk area, close to the risk transmission path, and within 10 meters of the risk hotspot area are also included. Target metering boxes are included in the key protection set. Target metering boxes located in level-three risk areas and corresponding to key power supply nodes within the area are also included in the key protection set. The specific number, geographical coordinates, risk level area, surrounding rigid and flexible entity distribution, and current risk exposure value of each key protected metering box are recorded to form a complete set of key protected target metering boxes. Finally, combined with the previously extracted risk characteristic data of rigid and flexible entities, highly vulnerable key physical entity nodes are located. The criteria for judging highly vulnerable nodes are a vulnerability coefficient higher than 0.85, a risk exposure value higher than 80, and a spatial correlation strength higher than 0.7. Screen all rigid and flexible entities one by one according to this standard, and select the entity nodes that meet the conditions. For rigid entities, focus on screening the metering box terminals, the fixed parts at the bottom of free utility poles, and the wiring bushings of distribution transformers. For flexible entities, focus on screening the connection ends of overhead conductors and the connection points between cable lines and rigid entities. Record the specific type, geographical coordinates, risk analysis sub-region, vulnerability coefficient, risk exposure value, spatial correlation strength, and surrounding risk situation for each highly vulnerable critical physical entity node, thus completing the location of the highly vulnerable critical physical entity nodes.

[0052] Step 5.3: Based on the risk type, intensity, and attributes of key physical entities in the risk hotspot areas, match and combine various measures from the pre-set protective measures library, including physical reinforcement, environmental isolation, status monitoring, and early warning notifications, to form differentiated protective measures combinations for different risk areas and key nodes. Specifically, this includes: pre-establishing a pre-set protective measures library containing four main categories: physical reinforcement, environmental isolation, status monitoring, and early warning notifications. Each category includes various specific protective methods. Physical reinforcement includes measures such as reinforcing the metal supports of metering boxes, tightening the terminals to prevent loosening, reinforcing the base of free-floating utility poles with concrete, reinforcing the protective casing of distribution transformers, adjusting the tension of overhead conductors, and strengthening cable lines. Reinforcement with protective covers; environmental isolation measures include installing rain and dust covers on metering boxes, windproof railings on outdoor metering boxes, anti-collision baffles on metering boxes near buildings, and installing anti-rollover protective pipes for cable laying; status monitoring measures include installing stress sensors, deformation sensors, temperature and humidity sensors, and vibration sensors, increasing the monitoring frequency to once every 5 minutes, and collecting real-time physical operation data; early warning and notification measures include installing audible and visual warning devices, setting up outdoor early warning signs, sending SMS warnings to maintenance personnel in the area, and establishing a 24-hour early warning duty mechanism; retrieving information on identified risk hotspot areas, risk transmission paths, key protected metering box sets, and highly vulnerable key physical entity nodes, and analyzing each risk hotspot area one by one. Risk types and intensities are categorized as follows: environmental disturbance, structural deformation, external force intrusion, and composite. Risk intensities are classified according to modified dynamic risk values: extremely high (90-100), high (70-89), medium (50-69), low (30-49), and extremely low (0-29). For risk hotspots of different risk types and intensities, corresponding protective measures are matched and combined from a protective measure library based on the attributes of highly vulnerable key physical entity nodes to form differentiated protective measure combinations. For extremely high-intensity structural deformation risk hotspots, physical reinforcement measures include anti-loosening and tightening of connection terminals, reinforcement of the base of utility poles by pouring concrete, and reinforcement of cable line protective sleeves, combined with stress sensors installed in condition monitoring. Combined with deformation sensors, audible and visual warning devices, and SMS alerts in early warning notices, this forms a combined measure. For high-intensity environmental disturbance risk hotspots, rainproof and dustproof covers and windproof guardrails in environmental isolation are matched with temperature and humidity sensors and wind speed sensors in status monitoring, along with outdoor warning signs in early warning notices, forming a combined measure. For medium-intensity external force intrusion risk hotspots, anti-collision barriers and anti-crushing protective pipes in environmental isolation are matched with vibration sensors in status monitoring, forming a combined measure. For highly vulnerable key physical nodes, specific protective measures are matched according to the node type. For rigid physical nodes such as metering box terminals, anti-loosening fastening in physical reinforcement and stress sensors in status monitoring are matched.Flexible physical nodes, such as overhead conductor connection ends, are reinforced with tension adjustment and protective sleeves in physical reinforcement, combined with deformation sensors in condition monitoring. For key protected metering boxes, corresponding protective measures are supplemented based on the risk level of the metering box's location and the surrounding risk situation. Metering boxes in Level 1 risk areas receive additional encrypted monitoring and 24-hour early warning duty, while those in Level 2 and 3 risk areas receive regular inspection and reinforcement measures. Simultaneously, it is ensured that the combinations of protective measures for different risk areas and key nodes are significantly differentiated to avoid uniform protection. Each combination of protective measures clearly defines its applicable area, applicable nodes, specific measures, and implementation objectives, forming a differentiated protective measure combination system covering all risk hotspots, risk transmission paths, key protected metering boxes, and highly vulnerable key nodes.

[0053] Step 5.4: Based on risk evolution trend data, plan specific implementation time nodes and execution sequences for each combination of protective measures. Simultaneously, according to the measure type and geographical location, allocate corresponding protective resources and execution units to generate the final metering box security protection strategy, which includes specific measures, implementation nodes, responsible units, and resource lists. Specifically, this includes: risk evolution trend data from the obtained regional dynamic risk assessment results. This data includes hourly changes in risk values ​​for each risk hotspot area, each risk level area, and each key node; the specific time of risk peak occurrence; the direction of risk value increase or decrease; and the estimated duration of sustained high risk. Combined with the resulting differentiated protective measure combinations, this strategy is used for each... The plan outlines specific implementation timelines and sequences for each set of protective measures. Implementation timelines are prioritized based on risk intensity. Protective measures for extremely high-risk hotspots and highly vulnerable critical nodes are planned to be initiated within 24 hours, with emergency reinforcement measures such as securing wiring terminals and temporary windproofing completed within 12 hours, and routine monitoring and early warning measures deployed within 24 hours. Protective measures for high-risk hotspots are planned to be initiated within 48 hours and fully deployed within 72 hours. Protective measures for medium- and lower-risk areas are planned to be initiated within 72 hours and fully deployed within one week. Furthermore, the plan considers risk evolution trends; if the risk value shows an upward trend and... If the risk value is estimated to peak within 48 hours, the corresponding protective measures should be implemented 12 hours in advance. If the risk value shows a downward trend, the measures can be implemented as originally planned, provided that safety is ensured, but risk monitoring during implementation should be intensified. The execution sequence should be planned according to the principle of emergency priority and gradual implementation. The execution sequence for each protective measure combination is as follows: emergency reinforcement measures are implemented first, followed by environmental isolation measures, then status monitoring and early warning notification measures, and finally routine inspection and maintenance measures. For example, for the combination measures for high-strength structural deformation risk hotspots, emergency measures such as tightening of wiring terminals and temporary reinforcement of utility poles should be implemented first, followed by environmental isolation measures such as installing windproof guardrails and protective covers, and then installing sensors and deploying early warning systems. After the equipment is inspected, a daily inspection plan is developed. Once the implementation timeline and execution sequence are planned, corresponding protective resources and execution units are allocated based on the specific type of each protective measure combination and its corresponding geographical location. Protective resources are categorized and allocated according to measure type: physical reinforcement resources, including metal supports, fastening bolts, concrete, and protective sleeves, are allocated to the corresponding risk area's material storage points to ensure timely retrieval during implementation; status monitoring resources, including various sensors and data acquisition terminals, are allocated to the equipment installation team; and early warning and notification resources, including audible and visual warning devices and warning signs, are allocated to the on-site deployment team. The quantity, timing, transportation route, and receiving personnel for each type of resource are clearly defined.The execution units are divided according to geographical location and measure type. The old power distribution network along the street is divided into three operation and maintenance areas, with each area assigned a dedicated execution unit. A professional construction team is responsible for implementing physical reinforcement and environmental isolation measures; an equipment operation and maintenance team is responsible for the installation and commissioning of status monitoring equipment; and an early warning operation and maintenance team is responsible for the deployment and monitoring of early warning notification measures. The person in charge, number of members, job responsibilities, implementation area, and completion deadline for each execution unit are clearly defined. All differentiated protection measures, corresponding implementation time nodes, execution sequence, protection resource scheduling information, and execution unit allocation information are structurally integrated. Detailed information is provided for each protection measure, including its specific content, implementation time, execution team, required resource quantity, resource specifications, responsible personnel, and acceptance standards. This forms a complete and implementable final metering box safety protection strategy, encompassing specific measures, implementation nodes, responsible units, and resource lists.

[0054] In this embodiment of the invention, because it employs a technical approach that spatially aligns and numerically superimposes risk distribution adjustment values ​​with regional dynamic risk assessment results to achieve dynamic correction of risk values, analyzes the corrected assessment results to accurately identify risk hotspots, transmission paths, key protected metering boxes, and locate highly vulnerable critical physical entity nodes, combines risk type, intensity, and entity node attributes to match and combine differentiated protective measures from a protective measure library, and then plans the implementation time nodes and execution sequence of measures based on risk evolution trends, schedules protective resources according to measure type and geographical location, and generates a complete protective strategy containing specific measures, implementation nodes, responsible units, and resource lists, it overcomes the limitations of existing technologies that do not address initial risk... The technical problems of incomplete identification of key risk elements, homogenized protective measures, lack of scientific implementation node planning and reasonable protection resource scheduling, insufficient targeting of protection strategies, low executability and lack of complete implementation elements make it difficult to adapt to the refined protection needs of complex power distribution network scenarios. In order to achieve precise correction of regional dynamic risk assessment results, the risk assessment results are highly consistent with the actual risk scenarios, accurately identify the protection focus and key nodes, and the generated protection measure combination has strong spatial and node differentiation. The implementation nodes and resource scheduling planning of the protection strategy are scientific and reasonable and the implementation elements are complete, which improves the targeting and executability of the protection strategy.

[0055] like Figure 2 As shown, embodiments of the present invention also provide a metering box security protection strategy generation system, including: The acquisition module is used to collect real-time risk data of the target metering box and related areas; it integrates real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix. The assessment module is used to calculate the risk-related topology data of the area to which the target metering box belongs through a multi-dimensional spatiotemporal feature matrix, and obtain regional dynamic risk assessment results. The delineation module is used to select free utility poles, distribution transformers, and building corners as risk reference benchmarks based on the geographical range covered by the regional dynamic risk assessment results, and delineate a risk analysis area; the risk analysis area is divided into risk analysis sub-areas, and the risk characteristics of the associated physical entities within each risk analysis sub-area and between adjacent sub-areas are obtained. The associated physical entities include rigid entities and flexible entities. The calculation module is used to perform targeted kinematic analysis on the risk characteristics of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions. At the same time, it coordinates the risk transmission relationship between rigid and flexible entities to complete the kinematic collaborative analysis processing under rigid-flexible coupling state and obtain risk characteristic data of rigid-flexible coupling. Based on the risk characteristic data of rigid-flexible coupling, the risk distribution adjustment value is calculated. The adjustment module is used to correct the regional dynamic risk assessment results by adjusting the risk distribution value, so as to obtain the corrected assessment results. Based on the corrected assessment results, the metering box safety protection strategy with differentiated protection measures and implementation nodes is obtained.

[0056] Figure 3 The statistical results of risk level distribution after the risk association topology construction are presented. In the 100 5m×5m spatial grids of the area where the target metering box is located, there are 18 grids in the Level 1 risk area (risk value ≥ 90), accounting for 18.0%; 28 grids in the Level 2 risk area (risk value 70-89), accounting for 28.0%; 35 grids in the Level 3 risk area (risk value 50-69), accounting for 35.0%; 42 grids in the Level 4 risk area (risk value 30-49), accounting for 42.0%; and 37 grids in the Level 5 risk area (risk value 0-29), accounting for 37.0%. This distribution reflects the spatial unevenness of risk, providing a data foundation for subsequent identification of risk hotspots and the formulation of differentiated protection strategies.

[0057] Figure 4 The statistical results of risk distribution adjustment values ​​for each risk analysis sub-region and boundary region are presented. The diagonally filled bars represent the risk intensity increment (generated by rigid-flexible coupling), and the dotted bars represent the risk intensity attenuation (caused by energy dissipation). Sub-region C has the highest risk intensity increment (52), followed by sub-region A (45) and sub-region B (38), which is consistent with the actual situation of high density of flexible entities (overhead conductors) and complex risk transmission paths within sub-region C. In the boundary region, the attenuation at boundary AB is the highest (32), indicating significant energy dissipation in this region; the increment (35) at boundary BC is higher than the attenuation (20), suggesting a strong risk coupling effect in this region, requiring close attention.

[0058] Figure 5The comparison of the effects before and after the implementation of the protection strategy is shown. White bars represent indicator values ​​before implementation, and black bars represent indicator values ​​after implementation. Implementation effect data shows: the number of primary risk grids decreased from 18 to 8, a reduction of 55.6%; the number of secondary risk grids decreased from 28 to 15, a reduction of 46.4%; the number of high-risk metering boxes decreased from 12 to 5, a reduction of 58.3%; the number of risk transmission paths decreased from 8 to 3, a reduction of 62.5%; and the average risk value decreased from 68.5 to 42.3, a reduction of 38.2%. These results indicate that the differentiated protection measure combination and implementation node planning generated by this invention can effectively reduce the security risks of metering boxes and improve the safe operation level of the distribution network.

[0059] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for generating a safety protection strategy for a metering box, characterized in that, The method includes: Step 1: Collect real-time risk data of the target metering box and related areas; integrate real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix; Step 2: Calculate the risk-related topology data of the region to which the target metering box belongs using a multi-dimensional spatiotemporal feature matrix, and obtain the regional dynamic risk assessment results. Step 3: Based on the geographical scope covered by the regional dynamic risk assessment results, select free utility poles, distribution transformers, and building corners as risk reference benchmarks, and delineate a risk analysis area; divide the risk analysis area into risk analysis sub-areas, and obtain the risk characteristics of related physical entities within each risk analysis sub-area and between adjacent sub-areas. Related physical entities include rigid entities and flexible entities. Step 4: By performing targeted kinematic analysis on the risk characteristics of rigid and flexible entities within and between adjacent risk analysis sub-regions, and simultaneously coordinating the risk transmission relationship between rigid and flexible entities, kinematic co-analysis processing under rigid-flexible coupling is completed, yielding risk characteristic data associated with rigid-flexible coupling. Based on the risk characteristic data associated with rigid-flexible coupling, the risk distribution adjustment value is calculated, including: Based on the risk characteristic data of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions; for rigid entities, the displacement constraint and stress concentration characteristics under external risk are analyzed based on mechanical properties to obtain rigid entity risk kinematic analysis data; for flexible entities, the deformation propagation and energy dissipation characteristics in the risk field are analyzed to obtain flexible entity risk kinematic analysis data. Based on the spatial proximity of rigid and flexible entities, a risk transmission coupling relationship is constructed between them. Based on this relationship, the stress change characteristics in the kinematic analysis data of the rigid entity and the deformation propagation characteristics in the kinematic analysis data of the flexible entity are co-mapped and coupled to simulate the transmission path and intensity evolution of risk in the rigid-flexible coupling structure, thus obtaining risk co-evolution data under the rigid-flexible coupling state. Based on risk co-evolution data under rigid-flexible coupling, key indicators characterizing risk transmission efficiency, energy redistribution, and vulnerability of coupling nodes are extracted and fused to form risk characteristic data of rigid-flexible coupling association. Based on the risk characteristic data of rigid-flexible coupling, the risk intensity increment and attenuation caused by the rigid-flexible entity coupling effect are calculated within each risk analysis sub-region and at the boundary of adjacent sub-regions, and the risk intensity increment and attenuation are quantified into risk distribution adjustment values. Step 5: Correct the regional dynamic risk assessment results by adjusting the risk distribution value to obtain the corrected assessment results. Based on the corrected assessment results, obtain the metering box safety protection strategy with differentiated protection measures combination and implementation nodes.

2. The method for generating a safety protection strategy for a metering box according to claim 1, characterized in that, Collect real-time risk data of the target metering box and related areas; By integrating real-time risk data with historical risk data, a multi-dimensional spatiotemporal feature matrix is ​​constructed, including: By deploying edge sensing nodes in the target metering box and related areas, raw real-time risk data characterizing environmental disturbances, structural deformations, and external force intrusions are collected; edge-side noise filtering and time-series alignment are performed on the raw real-time risk data to obtain pre-processed real-time risk data; Based on the preprocessed real-time risk data, historical risk data matching the geographical location and equipment type of the target metering box is retrieved, and the historical risk data is processed by unifying the time base and normalizing the time series scale to obtain standardized historical risk data. Standardized real-time risk data and standardized historical risk data are aligned in a spatiotemporal coordinate system and fused to obtain spatiotemporally aligned fused risk data. Based on the spatiotemporally aligned fused risk data, the risk evolution trend features in the time dimension, the risk diffusion gradient features in the spatial dimension, and the multi-source heterogeneous features in the risk source type dimension are extracted in sequence to obtain a multi-dimensional risk feature set. The multi-dimensional risk feature set is matrix-mapped and encapsulated according to the preset time granularity and spatial grid units to construct a multi-dimensional spatiotemporal feature matrix with temporal continuity, spatial correlation and risk type differentiation.

3. The method for generating a safety protection strategy for a metering box according to claim 2, characterized in that, By using a multi-dimensional spatiotemporal feature matrix, the risk-related topology data of the area to which the target metering box belongs is calculated, and the regional dynamic risk assessment results are obtained, including: Based on the constructed multi-dimensional spatiotemporal feature matrix, and based on the risk diffusion gradient features of the spatial dimension in the matrix, high-risk nodes and potential risk propagation paths within the region are identified to obtain initial risk association data. The initial risk association data is optimized in terms of topology. Based on the risk evolution trend characteristics in the time dimension, each association edge is assigned a weight value. The node attributes are labeled in combination with the multi-source heterogeneous features in the risk type dimension to obtain the risk association topology data. Based on the risk-related topology data, the risk propagation intensity and risk accumulation effect of each spatial grid in the topology network are calculated to obtain the basic risk distribution field; based on the basic risk distribution field, the risk propagation intensity is modified by spatiotemporal attenuation and enhancement to obtain the dynamically modified risk distribution field. The dynamically corrected risk distribution field is mapped to the region to which the target metering box belongs by geographical coordinates, risk level intervals are divided and risk hotspot areas are marked, and regional dynamic risk assessment results of risk level distribution data and risk evolution trends are obtained.

4. The method for generating a safety protection strategy for a metering box according to claim 3, characterized in that, Step 3 includes: Receive regional dynamic risk assessment results, extract the boundary coordinates of the covered geographical area and the risk hotspot areas marked inside; within the geographical area, based on geographic information data, identify and select infrastructure of a preset type as a risk spatial reference benchmark, the benchmark including at least free utility poles, distribution transformers and building corners; Using the selected risk reference benchmark as the spatial anchor point, and based on the topological connection relationship between each anchor point and the preset radiation radius, a continuous closed polygonal geographical range covering at least all risk hotspot areas is generated by calculation and defined as the risk analysis area. Based on the distribution density of risk reference benchmarks, the gradient changes in risk level distribution data, and geographical barrier characteristics within the risk analysis area, multiple overlapping or adjacent risk analysis sub-areas are formed by automatically subdividing the area into grids. For each risk analysis sub-region, the structured data of physical entities within the risk analysis sub-region and the pre-set buffer zone at the boundary of adjacent sub-regions are retrieved. Based on the mechanical and functional properties of the physical entities, the physical entities are classified into rigid entities and flexible entities, and the risk characteristics corresponding to each type of entity in the risk assessment results are extracted. The risk characteristics include at least the entity's current risk exposure value, vulnerability coefficient in the risk field, and spatial correlation strength with the entity.

5. The method for generating a metering box safety protection strategy according to claim 4, characterized in that, Based on the spatial proximity between rigid and flexible entities, a risk transmission coupling relationship is constructed between the two entities. Based on the risk transmission coupling relationship, the stress change characteristics in the kinematic analysis data of a rigid entity and the deformation propagation characteristics in the kinematic analysis data of a flexible entity are co-mapped and coupled for calculation. This simulates the transmission path and intensity evolution process of risk in a rigid-flexible coupled structure, yielding risk co-evolution data under rigid-flexible coupling conditions, including: By analyzing the kinematics of risk of rigid entities and flexible entities, and based on the spatial location markers of the entities in the analysis data, combined with the preset spatial connection thresholds, the topological connection relationship and spatial proximity relationship between rigid entities and flexible entities are determined and established. Based on topological connectivity and spatial proximity, we define the rules for the transmission of stress change characteristics from rigid entities to flexible entities, and the rules for the influence of deformation propagation characteristics of flexible entities on the reaction of rigid entities, thus obtaining the collaborative mapping relationship of risk interaction behavior between rigid and flexible entities. Based on the cooperative mapping relationship, key stress nodes in the kinematic analysis data of rigid entities and key deformation bands in the kinematic analysis data of flexible entities are feature-aligned and coupled, and coupled calculations are performed to dynamically simulate the risk energy transfer path, rate and intensity evolution process. By integrating dynamic simulations of risk energy transfer paths, rates, and intensity evolution processes, data on risk co-evolution under rigid-flexible coupling states are obtained.

6. The method for generating a metering box safety protection strategy according to claim 5, characterized in that, Step 5 includes: By using the risk distribution adjustment value and the regional dynamic risk assessment results; based on the spatial location corresponding to the risk distribution adjustment value and the basic risk distribution field and risk level distribution data in the regional dynamic risk assessment results, spatial alignment and numerical superposition are performed to complete the dynamic correction of the risk value, and the corrected dynamic risk distribution field and risk level distribution data are obtained as the corrected assessment results. By analyzing the revised assessment results, we identified the final risk hotspots, risk transmission paths, and the set of target metering boxes that require key protection within each risk level range; and by combining information on rigid and flexible entities, we located the critical physical entity nodes that are highly vulnerable. Based on the risk type, intensity, and attributes of key physical entities in risk hotspot areas, various measures from a pre-set protection measures library, including physical reinforcement, environmental isolation, status monitoring, and early warning notifications, are matched and combined to form differentiated protection measures combinations for different risk areas and key nodes. Based on risk evolution trend data, specific implementation time nodes and execution sequences are planned for various protective measures combinations. At the same time, according to the type of measure and geographical location, corresponding protective resources and execution units are scheduled and allocated to generate the final metering box security protection strategy, which includes specific measures, implementation nodes, responsible units and resource lists.

7. A metering box safety protection strategy generation system, which implements the method as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to collect real-time risk data of the target metering box and related areas; Integrate real-time risk data with historical risk data to construct a multi-dimensional spatiotemporal feature matrix; The assessment module is used to calculate the risk-related topology data of the area to which the target metering box belongs through a multi-dimensional spatiotemporal feature matrix, and obtain regional dynamic risk assessment results. The delineation module is used to select free utility poles, distribution transformers, and building corners as risk reference benchmarks based on the geographical area covered by the regional dynamic risk assessment results, and delineate a risk analysis area. The risk analysis area is divided into risk analysis sub-regions to obtain the risk characteristics of the associated physical entities within each risk analysis sub-region and between adjacent sub-regions. The associated physical entities include rigid entities and flexible entities. The calculation module is used to perform targeted kinematic analysis on the risk characteristics of rigid and flexible entities within each risk analysis sub-region and between adjacent sub-regions. At the same time, it coordinates the risk transmission relationship between rigid and flexible entities to complete the kinematic collaborative analysis processing under rigid-flexible coupling state and obtain risk characteristic data of rigid-flexible coupling. Based on the risk characteristic data associated with rigid-flexible coupling, the risk distribution adjustment value is calculated; The adjustment module is used to correct the regional dynamic risk assessment results by adjusting the risk distribution value, so as to obtain the corrected assessment results. Based on the corrected assessment results, the metering box safety protection strategy with differentiated protection measures and implementation nodes is obtained.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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