Power transmission line inspection method combining meteorological data and dynamic confidence weighting

Abstract

The invention relates to the technical field of data processing, in particular to a power transmission line inspection method combining meteorological data and dynamic confidence weighting, and the method comprises the steps: obtaining multi-source meteorological data, executing time-space reference alignment processing to generate a standardized data set, and carrying out the spatial overlay analysis of the standardized data set and power grid GIS topological data; constructing a meteorological and equipment association topological graph to identify threatened equipment; distributing a data source weight based on the dynamic confidence score table, generating an optimized meteorological prediction result through weighted fusion, and reversely updating a data interface layer; and in combination with field measured data and user correction records, a geofence matching rule and a weight parameter are dynamically adjusted to realize closed-loop calibration. According to the method, accurate association of meteorological data and power grid equipment space risks is realized, the extreme weather prediction reliability is improved through multi-source data fusion and adaptive optimization, the emergency resource allocation efficiency is optimized, the risk that a patrol plan is interfered by weather is reduced, and intelligent decision making of power operation and maintenance business is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a transmission line inspection method combining meteorological data with dynamic confidence weighting. Background Art

[0002] In the field of power production operation control, the existing technology mainly relies on transmission inspection mobile operation, substation operation and inspection mobile operation and inspection control system to realize basic operation management, but there is a significant defect of separation between meteorological data and business scenarios. Specifically, the real-time meteorological parameters and future weather trend forecasts are not integrated in the planning of inspection tasks, which makes the inspection and maintenance plan vulnerable to sudden severe weather interference and forced interruption. It is impossible to dynamically adjust the operation sequence through meteorological correlation, and it is difficult to trigger equipment hidden danger warning in time. For extreme meteorological events such as heavy rain and strong wind, there is a lack of linkage analysis mechanism with the geographic spatial data of the power grid, and it is impossible to accurately locate the substations, transmission lines and maintenance operation points within the range affected by the weather, resulting in blindness in emergency resource allocation and risk disposal. In addition, the traditional system does not integrate the cross-validation function of multi-source meteorological data (such as numerical forecast, wildfire monitoring, ice cover warning), and the flow of meteorological warning work orders relies on manual distribution and lacks closed-loop management, resulting in the decision-making support value of meteorological information in the power operation and inspection business chain not being effectively released. Summary of the invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a transmission line inspection method that combines meteorological data with dynamic confidence weighting to solve the technical problems that the inspection and maintenance plan is easily disturbed by bad weather due to the lack of deep integration of meteorological data and power operation and inspection business scenarios, the emergency resource allocation is blind under extreme meteorological events, and the efficiency of multi-source meteorological warning work order circulation is low.

[0004] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides a transmission line inspection method combining meteorological data with dynamic confidence weighting, comprising: Acquire multi-source meteorological data, the multi-source meteorological data including numerical forecast data, real-time monitoring data and forecast data, perform spatiotemporal benchmark alignment processing on the multi-source meteorological data through a protocol parser, and generate a standardized meteorological data set, the spatiotemporal benchmark alignment processing including unifying timestamps of different data sources to minute-level accuracy and eliminating spatial benchmark differences through geographic coordinate interpolation; Perform spatial overlay analysis on the standardized meteorological data set and the power grid GIS topology data, generate a dynamic buffer based on the meteorological impact radius, identify the transmission equipment threatened by the meteorological weather through spatial relationship calculation, and construct a meteorological and equipment related topology map. The power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridor; According to the risk level mapping relationship in the meteorological and equipment-related topological map, an event-driven engine is triggered by a predefined meteorological event threshold to generate an early warning work order, which encapsulates the identification of the threatened power transmission equipment and disposal suggestions, and is pushed to the operation and inspection control system through a wireless communication network for closed-loop management of work order issuance, execution and feedback; For the multi-source meteorological forecast data of the same geographical area in the meteorological data interface layer, a dynamic confidence score table is generated based on historical error statistical analysis, a weight of each data source is assigned according to the confidence score table, an optimized meteorological forecast result is generated through weighted fusion, and the optimized meteorological forecast result is reversely updated to the meteorological data interface layer to cover the original single-source data; Based on the on-site measured meteorological data and user correction records, the regional deviation between the optimized meteorological forecast result and the measured value is calculated, and the geographic fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion are dynamically adjusted according to the regional deviation to form a closed-loop calibration of meteorological data and equipment risk analysis.

[0005] Furthermore, in the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the multi-source meteorological data performs spatiotemporal reference alignment processing including: Extract meteorological elements from heterogeneous data sources through a protocol parser, unify the timestamps of the multi-source meteorological data to minute-level accuracy, and eliminate spatial reference differences through a geographic coordinate interpolation algorithm to generate a time-space continuous standardized meteorological data set; The power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridors. The spatial overlay analysis is based on the spatial relationship matching between the standardized meteorological data set and the geographic fences, and the threat radius of the substation is determined by calculating the wind circle range associated with the wind speed.

[0006] Furthermore, in the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the construction of a meteorological and equipment related topological map comprises: Divide the dynamic buffer zone radius according to the meteorological types of heavy rain and strong wind, determine the threatened transmission lines and substations through spatial relationship calculation based on the threat radius output by the spatial overlay analysis, and associate the maintenance tasks in the power grid GIS topology data to generate a risk level mapping table; The early warning work order encapsulates equipment identification, risk type and disposal suggestions, and is pushed to the mobile operation terminal via a wireless communication network, which uses narrowband Internet of Things technology to transmit work order data.

[0007] Furthermore, in the power transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the generation of a dynamic confidence score table based on historical error statistical analysis includes: Based on the historical database of the standardized meteorological data set, the prediction errors of each data source in the heavy rain and strong wind scenes are counted, and a dynamic confidence score table is generated using a sliding window statistical method; The dynamic weight allocation is based on the dynamic confidence score table, initial weights are allocated according to meteorological types, and weight degradation is performed on abnormal forecast values ​​that deviate from the group mean through box plot analysis.

[0008] Furthermore, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention further includes: Perform linear weighted calculation on the multi-source forecast data according to the dynamic weight allocation parameters, and introduce a time decay factor to adjust the weight ratio for short-term heavy rainfall scenarios, wherein the time decay factor is configured to have a prediction weight ratio of 70% for the next 6 hours and decrease to 30% after 24 hours; The optimized meteorological forecast results are written back to the meteorological data interface layer via a wireless communication protocol, overwriting the original single-source data and providing updated meteorological input for the spatial overlay analysis.

[0009] Furthermore, in the power transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the method of dynamically adjusting the geographic fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion according to the regionalized deviation includes: Based on the regional deviation between the optimized meteorological forecast result and the measured meteorological data, for the rainstorm scene, the buffer radius parameter is iteratively optimized by a sliding window statistical method; The adjustment of the spatiotemporal resolution parameters includes increasing the time granularity of the standardized meteorological data set according to meteorological mutation scene markers in the user correction record.

[0010] Furthermore, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention also includes a weather feedback module that collects on-site temperature, humidity and rainfall data in real time and transmits the data to the meteorological data interface layer via the narrowband Internet of Things; The weather feedback module deployed in the mobile operation terminal collects on-site temperature, humidity and rainfall data in real time, and the on-site data is transmitted to the meteorological data interface layer via the narrowband Internet of Things; The user correction record includes manual adjustment information of the risk level in the early warning work order, and the regionalized deviation calculation generates calibration parameters based on the gridded difference analysis between the field data and the optimized meteorological forecast result.

[0011] Furthermore, in the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the historical database stores the forecast data and corresponding actual data of the past 30 days in the meteorological data interface layer, and the hit rate and mean square error are statistically classified by the rainstorm and strong wind meteorological types; The confidence score table is generated based on the historical data and linked with the field measured data collected by the weather feedback module, and the initial weight ratio in the dynamic weight allocation is reversely corrected through deviation analysis.

[0012] Furthermore, in the power transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the wireless communication protocol uses narrowband Internet of Things technology to transmit the optimized meteorological forecast results to the mobile operation terminal, and the narrowband Internet of Things supports low-power wide-area communication; The time decay factor is configured such that the prediction weight for the next 6 hours in the weighted fusion accounts for 70%, and the weight decreases linearly to 30% after 24 hours. The decreasing logic is associated with the meteorological mutation scene tag.

[0013] Furthermore, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention further includes: Dynamically expand the buffer coverage of the power transmission line corridor in the meteorological and equipment-related topological map according to the measured rainfall distribution of the rainstorm scene; The adjustment of the spatiotemporal resolution parameters triggers the update of the sampling frequency of the meteorological data interface layer, and synchronously updates the dynamic weight allocation parameters based on the regional deviation between the optimized meteorological forecast results and the measured values, forming a closed-loop calibration mechanism for data fusion and equipment risk analysis.

[0014] Beneficial effects of the present invention: The present invention realizes precise positioning of meteorological risks by aligning spatiotemporal benchmarks of multi-source meteorological data with spatial overlay analysis of power grid GIS, constructs meteorological and equipment-related topological maps, combines dynamic confidence scoring with weight allocation to optimize the reliability of prediction results; dynamically adjusts geographic fence rules and fusion weight parameters based on a closed-loop feedback calibration mechanism of field measured data and user correction records, and enhances the model's adaptability to regional meteorological mutations; dynamically associates meteorological warnings with equipment status and maintenance resources through closed-loop management of early warning work orders driven by narrowband Internet of Things, significantly improves the accuracy of emergency resource allocation and the timeliness of work order execution under extreme weather conditions, effectively reduces the inspection interruption rate and equipment failure risk caused by meteorological factors, and realizes full-process intelligent decision-making of electric power operation and inspection services driven by meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0016] Figure 1 A flow chart of a transmission line inspection method combining meteorological data with dynamic confidence weighting provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0018] See also Figure 1 The present invention provides a transmission line inspection method combining meteorological data with dynamic confidence weighting, comprising: Step S101, acquiring multi-source meteorological data in real time through the meteorological data interface layer, the multi-source meteorological data including numerical forecast data, real-time monitoring data and forecast data, performing spatiotemporal benchmark alignment processing on the multi-source meteorological data through a protocol parser to generate a standardized meteorological data set, the spatiotemporal benchmark alignment processing including unifying timestamps of different data sources to minute-level accuracy and eliminating spatial benchmark differences through geographic coordinate interpolation; Step S102, performing spatial overlay analysis on the standardized meteorological data set and the power grid GIS topology data, generating a dynamic buffer based on the meteorological impact radius, identifying the transmission equipment threatened by the meteorological conditions through spatial relationship calculation, and constructing a meteorological and equipment related topology map, wherein the power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridors; Step S103, based on the risk level mapping relationship in the meteorological and equipment-related topological map, an event-driven engine is triggered by a predefined meteorological event threshold to generate an early warning work order, the early warning work order encapsulates the identification of the threatened power transmission equipment and disposal suggestions, and is pushed to the operation and inspection control system through a wireless communication network for closed-loop management of work order issuance, execution and feedback; Step S104, for the multi-source meteorological forecast data of the same geographical area in the meteorological data interface layer, a dynamic confidence score table is generated based on historical error statistical analysis, a weight of each data source is assigned according to the confidence score table, an optimized meteorological forecast result is generated through weighted fusion, and the optimized meteorological forecast result is reversely updated to the meteorological data interface layer to cover the original single-source data; Step S105, based on the on-site measured meteorological data and user correction records, calculate the regional deviation between the optimized meteorological forecast result and the measured value, and dynamically adjust the geographic fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion according to the regional deviation to form a closed-loop calibration of meteorological data and equipment risk analysis.

[0019] The present invention provides a transmission line inspection method combining meteorological data with dynamic confidence weighting, and the specific implementation process of the technical solution is as follows: First, through the meteorological data interface layer, real-time access to heterogeneous data sources such as the numerical forecast center, wildfire monitoring platform, and disaster reduction institute is carried out to obtain multi-source meteorological data including temperature, rainfall, and wind speed. In view of the differences in timestamps between different data sources, a protocol parser is used to extract the time tags of meteorological elements, and the timestamps are unified to minute-level accuracy through GPS time synchronization technology; at the same time, grid alignment processing is performed on data with inconsistent spatial references based on geographic coordinate interpolation algorithms, such as matching the longitude and latitude coordinates of the meteorological station with the coordinate system of the power grid GIS, eliminating spatial position deviations, and generating a standardized meteorological data set that is continuous in time and space. This data set provides a unified time-space reference for subsequent analysis, avoiding analysis errors caused by differences in data sources.

[0020] When the standardized meteorological data set is spatially overlaid with the power grid GIS topology data, the system calls the geographic fence information of the transmission line corridor and the coordinates of the maintenance operation points, and dynamically generates a buffer zone in combination with the meteorological impact radius. For example, for high wind scenarios, the wind circle range is calculated based on real-time wind speed data, and the substations and transmission lines within the coverage of the wind circle are identified through a spatial relationship matching algorithm. At the same time, based on the rainfall intensity distribution predicted by the rainstorm, a circular buffer zone centered on the transmission equipment is generated to determine the list of equipment that may be threatened by waterlogging or mudslides. Through spatial overlay analysis, a meteorological and equipment-related topological map is constructed, which records the spatial location of the threatened equipment, the type of meteorological risk, and the associated maintenance tasks in the form of structured data.

[0021] When the risk level in the meteorological and equipment-related topological map reaches the predefined threshold, the event-driven engine is triggered. For example, in a rainstorm scenario, if the predicted rainfall in a certain area exceeds 50mm / 24h, the system automatically generates an early warning work order, which encapsulates the unique identification of the threatened equipment, the risk type (such as "risk of insulator flashover caused by heavy rainfall") and disposal suggestions (such as "priority inspection of tower numbers T-103 to T-115"). The generated work order is transmitted to the operation and inspection control system through the narrowband Internet of Things. The system automatically allocates maintenance resources according to the priority of the work order, and receives on-site feedback information after the work order is executed, forming a closed-loop management process of issuance, execution, and feedback to ensure the timeliness of risk disposal.

[0022] For multi-source meteorological forecast data in the same geographical area, the system counts the error distribution of each data source based on the historical database. For example, the mean square error between the heavy rain forecast data of the Meteorological Observatory and Xiangji Technology in the past 30 days and the actual rainfall is compared, and the confidence score table is dynamically generated using the sliding window statistical method. Initial weights are assigned to different data sources based on the scoring results, and the abnormal forecast values ​​that deviate from the group mean are identified through the box plot analysis method, and their weights are dynamically downgraded. Subsequently, linear weighted fusion is performed on the multi-source data to generate optimized meteorological forecast results. The results are written back to the meteorological data interface layer through the wireless communication protocol, covering the original single-source data, providing updated input for subsequent spatial overlay analysis, and forming a data quality improvement loop.

[0023] Based on the on-site measured meteorological data (such as temperature and humidity) collected by the mobile operation terminal and the user's manual correction records of the warning work order, the system calculates and optimizes the regional deviation between the predicted results and the measured values. For example, the deviation matrix of each geographic unit is generated through grid difference analysis to identify areas with large prediction errors. According to the deviation results, the geographic fence matching rules in the spatial overlay analysis are dynamically adjusted, such as expanding the coverage radius of the transmission line buffer in the rainstorm scenario; at the same time, the dynamic weight allocation parameters in the weighted fusion model are updated to reduce the weight of the data source with large errors. The above adjustments are synchronized to the meteorological data interface layer and the operation and inspection control system through the narrowband Internet of Things to achieve closed-loop calibration of meteorological data and equipment risk analysis, and continuously improve the regional adaptability of the model.

[0024] Each step is closely linked through data flow and feedback mechanisms: standardized meteorological data sets provide input for spatial overlay analysis, the generated topological map drives the automatic triggering of early warning work orders, the optimized forecast data reversely updates the interface layer to improve the accuracy of subsequent analysis, and the measured data and user feedback further calibrate the model parameters, forming a complete technical closed loop from data collection to decision optimization.

[0025] Specifically, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, wherein the multi-source meteorological data performs spatiotemporal reference alignment processing including: Extract meteorological elements from heterogeneous data sources through a protocol parser, unify the timestamps of the multi-source meteorological data to minute-level accuracy, and eliminate spatial reference differences through a geographic coordinate interpolation algorithm to generate a time-space continuous standardized meteorological data set; The power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridors. The spatial overlay analysis is based on the spatial relationship matching between the standardized meteorological data set and the geographic fences, and the threat radius of the substation is determined by calculating the wind circle range associated with the wind speed.

[0026] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of spatiotemporal reference alignment processing and spatial superposition analysis is as follows: Through the protocol parser, the heterogeneous data interfaces of the meteorological station, the disaster reduction institute and the wildfire monitoring platform are connected, and core meteorological elements such as temperature, rainfall, and wind speed are extracted according to the data formats of different data sources (such as JSON and GRIB2). For example, the parser identifies the timestamp field in the meteorological station data, and synchronizes the GPS clock with the time tag of the disaster reduction institute data, unifies the time base to minute-level accuracy, and eliminates the time axis misalignment problem caused by differences in data collection frequency. At the same time, the geographic coordinate interpolation algorithm is used to grid the data with inconsistent spatial references. For example, the WGS84 coordinate system used by the meteorological station is aligned with the CGCS2000 coordinate system of the power grid GIS through a bilinear interpolation algorithm to generate a time-space continuous standardized meteorological data set, providing a unified data input benchmark for subsequent analysis.

[0027] When the standardized meteorological data set is spatially overlaid with the power grid GIS topology data, the system calls the geographic fence coordinates and maintenance operation point information of the transmission line corridor, and generates a dynamic buffer zone based on the spatial distribution characteristics of meteorological elements. For example, for high wind scenarios, based on the real-time wind speed data in the standardized data set, the wind speed attenuation model centered on the substation is calculated, and the fan-shaped wind circle range associated with the wind speed level is dynamically generated. The transmission towers and line sections within the coverage of the wind circle are identified through the spatial relationship matching algorithm. For heavy rain scenarios, a radial buffer zone centered on the transmission equipment is generated based on the rainfall intensity forecast data, and the spatial boundaries of the waterlogging risk area are corrected in combination with the terrain elevation data.

[0028] The spatial overlay analysis further determines the threat radius of the substation through the wind speed correlation model. Specifically, a wind speed-equipment failure rate correlation model is established based on the historical meteorological disaster case library. When the real-time wind speed exceeds the preset threshold, the threat radius is dynamically calculated based on the equipment type (such as insulators, conductors) and service life. For example, for old transmission lines that have been in service for more than 15 years, the threat radius is expanded to 1.5 times the baseline value when the wind speed reaches level 8. The affected equipment is marked through a spatial topological relationship matching algorithm, and the threat radius data is written into the meteorological and equipment correlation topology map, providing a spatial analysis basis for subsequent risk level mapping.

[0029] In the above processing, the standardized data set generated by the alignment of time and space benchmarks provides calibrated input data for spatial overlay analysis, while the threat radius data output by spatial analysis directly drives the risk level assessment and warning work order generation, forming a technical closed loop from data standardization to spatial decision-making. The coordinated application of geographic coordinate interpolation algorithm and wind speed correlation model solves the problem of equipment positioning deviation caused by inconsistent spatial benchmarks of multi-source data and improves the accuracy of meteorological impact range determination.

[0030] Specifically, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the construction of the meteorological and equipment association topology map includes: Divide the dynamic buffer zone radius according to the meteorological types of heavy rain and strong wind, determine the threatened transmission lines and substations through spatial relationship calculation based on the threat radius output by the spatial overlay analysis, and associate the maintenance tasks in the power grid GIS topology data to generate a risk level mapping table; The early warning work order encapsulates equipment identification, risk type and disposal suggestions, and is pushed to the mobile operation terminal via a wireless communication network, which uses narrowband Internet of Things technology to transmit work order data.

[0031] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of meteorological and equipment-related topological map construction and early warning work order generation is as follows: According to the characteristic differences between heavy rain and strong wind meteorological types, the system dynamically divides the buffer zone generation rules. For heavy rain scenes, based on the historical waterlogging point distribution data and the real-time rainfall intensity forecast value, a radial buffer zone is generated with the transmission line as the center. The radius of the buffer zone expands nonlinearly with the increase of rainfall level. For example, when the predicted hourly rainfall exceeds 30mm, the radius of the buffer zone expands to twice the baseline value; for strong wind scenes, based on the wind speed monitoring data and the tower structure strength parameters, the fluid mechanics model is used to calculate the wind pressure influence range, and a fan-shaped buffer zone associated with the wind direction is generated. The radius of the buffer zone is positively correlated with the square of the wind speed.

[0032] Based on the threat radius data output by spatial overlay analysis, the threatened equipment is determined through spatial topological relationship calculation. The system calls the tower coordinates, line direction and substation location data in the power grid GIS, performs spatial intersection operations with the meteorological buffer zone, and identifies the equipment in the overlapping area. For example, when a rainstorm buffer zone covers a certain section of the transmission line, the historical defect records of the line (such as the degree of aging of the insulator) and the maintenance plan are associated to generate a risk level mapping table. The table records the equipment code, risk factor (such as high risk, medium risk), recommended disposal measures (such as completing insulation testing within 72 hours) and related maintenance team information, forming a dynamic matching relationship between equipment risk and operation and maintenance resources.

[0033] When an early warning work order is generated, the system extracts a list of high-priority equipment from the risk level mapping table, and encapsulates the equipment's unique identification code, risk type code, and standardized handling instructions. For example, in response to the risk of conductor dancing caused by strong winds, the work order embeds equipment location coordinates, predicted values ​​of dancing amplitude, and fastening hardware inspection specifications; for the risk of tower foundation scour caused by heavy rain, a foundation settlement detection process and emergency reinforcement plan are attached. The encapsulated work order data is transmitted to the mobile operation terminal via the narrowband Internet of Things. The wide coverage characteristics of NB-IoT technology are used to maintain stable transmission of work order data in mountainous areas, outdoor areas, and other areas with weak signals. After receiving the data, the terminal automatically triggers the inspection navigation path planning and operation process reminder functions.

[0034] In the above process, the dynamic buffer division rules provide meteorological impact range input for spatial overlay analysis, and the spatial topological relationship calculation results drive the generation of risk level mapping tables. The structured data in the mapping table supports the precise packaging and targeted push of early warning work orders. The application of narrowband Internet of Things technology converts meteorological analysis results into executable on-site work instructions, forming a technical closed loop from risk identification to disposal execution, solving the problems of low efficiency and high information attenuation of traditional manual dispatching. The combination of differentiated processing of meteorological types and equipment status correlation analysis improves the matching accuracy of risk warnings and operation and maintenance decisions. Specifically, the power transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the generation of a dynamic confidence score table based on historical error statistical analysis includes: Based on the historical database of the standardized meteorological data set, the prediction errors of each data source in the rainstorm and strong wind scenes are counted. The window period of the sliding window statistics method is 7 days, and the hit rate and mean square error of each data source in the window are updated daily. The hit rate is defined as the proportion of data with a prediction error within the range of ±2°C or ±5mm; The dynamic weight allocation is based on the dynamic confidence score table, initial weights are allocated according to meteorological types, and weight degradation is performed on abnormal forecast values ​​that deviate from the group mean through box plot analysis.

[0035] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of confidence evaluation and dynamic weight allocation is as follows: Based on the historical meteorological data in the standardized meteorological data set, the system stores the forecast records and corresponding real-time data of each data source according to the types of heavy rain and strong wind. For example, the heavy rain forecast data of the past 30 days from the Meteorological Observatory, the Disaster Reduction Institute and the third-party meteorological service provider are extracted and compared with the actual rainfall monitoring values ​​hour by hour, and the absolute error distribution of each data source in the 24-hour, 48-hour and 72-hour forecast periods is counted. The sliding window statistical method is used to dynamically calculate the error index. The window period is set to 7 days. The hit rate (such as the proportion of data within the forecast error ±2℃) and the mean square error of each data source in the window are updated daily to generate a dynamically updated confidence score table. In the score table, priority labels are assigned to each data source according to the meteorological type.

[0036] The dynamic weight allocation module assigns initial weights to data sources in different meteorological scenarios according to the confidence score table. For example, in a rainstorm scenario, if the historical hit rate of the meteorological station data is 85% and the disaster reduction rate is 72%, the initial weights are set to 55% and 30% respectively, and the remaining weights are allocated to other data sources. For multi-source forecast data received in real time, the box plot analysis method is used to identify outliers that deviate from the group mean. For example, when the predicted rainfall value of a data source exceeds the upper and lower interquartile range of the predicted value distribution of other data sources in the same area by 1.5 times, it is judged as an abnormal forecast, triggering the weight downgrade mechanism, and temporarily adjusting the real-time weight of the data source to 50% of the initial value until its predicted value returns to the normal fluctuation range.

[0037] The update results of the above confidence score table and dynamic weight allocation parameters act in real time on the weighted fusion process of multi-source meteorological data. For example, in a short-term heavy rainfall scenario, the meteorological station data obtains a higher weight due to its low recent error, and its prediction results dominate the fusion calculation; when an abnormally high value prediction appears in the third-party data source, the box plot analysis automatically reduces its weight ratio to avoid the interference of abnormal values ​​on the fusion results. The optimized meteorological forecast results are written back to the meteorological data interface layer, replacing the original single-source data, providing quality-calibrated input for subsequent spatial overlay analysis, and updating the forecast records in the historical database at the same time, forming a data quality closed loop from historical error analysis to real-time weight adjustment.

[0038] The synergy between confidence assessment and dynamic weight allocation is reflected in the following aspects: historical error statistics provide a benchmark for weight allocation, real-time anomaly detection dynamically corrects weight parameters, and the optimized forecast results continue to iterate the historical database, forming a technical closed loop for self-optimization of data quality. This design solves the problem of unstable forecast results caused by fluctuations in the accuracy of multi-source meteorological data, and improves the reliability of risk warnings in extreme weather scenarios.

[0039] Specifically, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention also includes: Perform linear weighted calculation on the multi-source forecast data according to the dynamic weight allocation parameters, and introduce a time decay factor to adjust the weight ratio for short-term heavy rainfall scenarios, wherein the time decay factor is configured to have a prediction weight ratio of 70% for the next 6 hours and decrease to 30% after 24 hours; The optimized meteorological forecast results are written back to the meteorological data interface layer via a wireless communication protocol, overwriting the original single-source data and providing updated meteorological input for the spatial overlay analysis.

[0040] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of weighted fusion and data write-back is as follows: perform linear weighted calculation on multi-source meteorological forecast data based on dynamic weight allocation parameters, wherein the weight parameters are derived from the historical confidence score table and the real-time anomaly detection results. For example, in a rainstorm scenario, the meteorological station data is assigned a 55% weight due to its low recent error, and the third-party data source is downgraded to 15% due to abnormal box plot detection. The remaining weight is allocated to other data sources, and the multi-source rainfall forecast values ​​are weighted and summed in proportion to generate a regionalized meteorological forecast result. For short-term heavy rainfall scenarios, a time attenuation factor is introduced to adjust the weight distribution, and the weight of the forecast data for the next 6 hours is set to 70%, and the weight of the forecast data after 24 hours is linearly reduced to 30%, so as to strengthen the decision-making influence of recent forecast data.

[0041] The optimized meteorological forecast results are written back to the meteorological data interface layer through narrowband Internet of Things technology to replace the original single-source data. For example, the fused 72-hour rainfall forecast value is overwritten with the original forecast data of the meteorological station, and the data timestamp and spatial identifier are updated to form the latest version of the standardized meteorological data set. The updated data set is used as input to re-execute the spatial overlay analysis, driving the real-time refresh of the meteorological and equipment-related topological maps, ensuring that the risk warning work order is generated based on the latest meteorological information.

[0042] During the data write-back process, the wireless communication protocol uses low-power, wide-area coverage narrowband Internet of Things technology to maintain data transmission stability in signal-weak areas along the transmission lines. The write-back operation triggers the version management mechanism of the meteorological data interface layer, retains historical version data for deviation backtracking analysis, and provides an iterative optimization data basis for the dynamic weight allocation module. The spatial overlay analysis module calls the updated meteorological data to recalculate the buffer range and risk level of the threatened equipment, forming a positive feedback link from data fusion to decision optimization.

[0043] The synergy of dynamic weights and time decay factors effectively avoids prediction deviations caused by anomalies in a single data source, while the data write-back mechanism enables dynamic replacement of original data with multi-source fusion results. The continuous optimization of meteorological forecast results improves the accuracy of spatial overlay analysis, thereby enhancing the effectiveness of handling early warning work orders, forming a two-way closed loop of meteorological data quality and business decision-making accuracy.

[0044] Specifically, the transmission line inspection method combining meteorological data and dynamic confidence weighting described in the present invention, the dynamic adjustment of the geographic fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion according to the regionalized deviation includes: Based on the regional deviation between the optimized meteorological forecast result and the measured meteorological data, for the rainstorm scene, the buffer radius parameter is iteratively optimized by a sliding window statistical method; The adjustment of the spatiotemporal resolution parameters includes increasing the time granularity of the standardized meteorological data set according to meteorological mutation scene markers in the user correction record.

[0045] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of the geographic fence matching rules and the spatiotemporal resolution adjustment is as follows: Based on the optimized meteorological forecast results and the measured meteorological data reported by the mobile operation terminal, the system performs gridded regional deviation analysis. For example, the transmission line corridor is divided into 1km×1km geographical units, and the absolute deviation between the predicted rainfall and the measured value in each unit is compared. The sliding window statistical method is used to calculate the average deviation rate of the past 7 days. When the deviation rate exceeds 15%, the iterative optimization of the buffer radius parameter is triggered. The optimization process is based on the historical disaster data correction model. For example, in a rainstorm scenario, if the measured rainfall in a certain area continues to be higher than the predicted value, the buffer radius is expanded by 5% per day, and the spatial threat range in the meteorological and equipment-related topological maps is updated.

[0046] The adjustment of spatiotemporal resolution parameters is based on the meteorological mutation marks in the user's correction records. When the user manually marks a region with short-term heavy rainfall or sudden strong winds through a mobile operation terminal, the system automatically extracts the meteorological data stream of the region and increases the time granularity from the hourly level to the 10-minute level. For example, for meteorological mutation scenarios in the marked area, the timestamp interval of the standardized meteorological data set is compressed from 60 minutes to 10 minutes, and the data sampling frequency of the spatial overlay analysis module is adjusted synchronously to enhance the ability to capture rapid fluctuations in meteorological parameters.

[0047] The optimized buffer radius parameters and time granularity parameters are transmitted back to the meteorological data interface layer through the narrowband Internet of Things, driving the real-time update of spatial overlay analysis. The updated meteorological and equipment-related topological map recalculates the list of threatened equipment and generates a revised warning work order that is pushed to the terminal. At the same time, the adjusted time granularity data provides high-precision input for the dynamic weight allocation module, optimizing the timeliness of multi-source data fusion. The interaction between the user's correction mark and the system's automatic optimization results forms a two-way calibration mechanism for meteorological data analysis and business decision-making.

[0048] The dynamic expansion of geo-fence parameters and the scenario-based adjustment of time granularity work together to solve the problem that traditional static rules are difficult to adapt to regional meteorological changes. Buffer optimization driven by measured data reduces the risk of underreporting caused by forecast bias, and the improvement of time granularity enhances the response speed to short-term extreme weather. The two continuously improve the system's adaptability to complex meteorological conditions through closed-loop feedback.

[0049] Specifically, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention also includes a weather feedback module that collects on-site temperature, humidity and rainfall data in real time and transmits them to the meteorological data interface layer through the narrowband Internet of Things; The weather feedback module deployed in the mobile operation terminal collects on-site temperature, humidity and rainfall data in real time, and the on-site data is transmitted to the meteorological data interface layer via the narrowband Internet of Things; The user correction record includes manual adjustment information of the risk level in the early warning work order, and the regionalized deviation calculation generates calibration parameters based on the gridded difference analysis between the field data and the optimized meteorological forecast result.

[0050] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of weather feedback module data collection and regional deviation calibration is as follows: the mobile operation terminal integrates temperature, humidity and rainfall sensors to collect on-site meteorological data of the transmission line corridor in real time. The sensor data is format-standardized by the edge computing node and converted into a structured data format compatible with the meteorological data interface layer, such as converting the analog signal into a JSON data packet with a timestamp and attaching the device geographic location code. The standardized field data is transmitted to the meteorological data interface layer via the narrowband Internet of Things, and the low power consumption characteristics of NB-IoT are used to realize long-period data transmission in the field environment. The transmission interval is dynamically adjusted according to the network signal strength, and the minimum data reporting frequency supported is once every 5 minutes.

[0051] The user correction record module receives manual risk level adjustment information submitted by the mobile operation terminal. For example, the inspection personnel found that the actual wind speed around a tower was lower than the predicted value in the early warning work order. The risk level was lowered from "high risk" to "medium risk" through the terminal interface. The correction record contains the equipment code, correction time and correction basis text. The system extracts the geographic location information in the correction record, associates and optimizes the predicted value of the corresponding grid in the meteorological forecast result, and performs grid difference analysis.

[0052] The regional deviation calculation module matches the field measured data with the optimized prediction results according to the 1km×1km grid unit, and calculates the absolute deviation values ​​of temperature, humidity and rainfall in each unit. For grid cells with user correction records, a 3-fold weight coefficient is assigned to strengthen the deviation impact, and a calibration parameter matrix with weight distribution is generated. The calibration parameters are synchronized to the dynamic weight allocation module through the meteorological data interface layer, triggering real-time adjustment of the multi-source data fusion weights, such as reducing the initial weight proportion of data sources with large recent deviations.

[0053] The calibration parameters also act on the iterative optimization of geo-fence matching rules. For example, when the measured rainfall value of a grid unit is continuously higher than the predicted value and there is a user correction record, the coverage radius of the transmission line buffer in the area is expanded, and the spatial threat range in the meteorological and equipment-related topological map is updated. The real-time data transmitted by the narrowband Internet of Things and the user correction information form a two-way data stream, supporting the continuous optimization of meteorological forecast models and business decision rules, and building a closed-loop feedback mechanism from on-site perception to system calibration.

[0054] The synergy between on-site data collection and user correction is reflected in: sensor data provides an objective monitoring basis, and manual correction injects business experience judgment. The two are converted into quantitative calibration parameters through grid difference analysis, driving the adaptive adjustment of meteorological forecast models and risk warning rules, and improving the system's regional adaptability under complex terrain conditions.

[0055] Specifically, in the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the historical database stores the forecast data and corresponding actual data of the past 30 days in the meteorological data interface layer, and the hit rate and mean square error are statistically classified according to the rainstorm and strong wind meteorological types; The confidence score table is generated based on the historical data and linked with the field measured data collected by the weather feedback module, and the initial weight ratio in the dynamic weight allocation is reversely corrected through deviation analysis.

[0056] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of the linkage between the historical database and the confidence score is as follows: the historical database stores the forecast data and corresponding actual data of the past 30 days in the meteorological data interface layer according to the meteorological type classification. For heavy rain scenes, the 24-hour cumulative rainfall forecast values ​​from data sources such as the Meteorological Observatory and the Disaster Reduction Institute are extracted, and compared with the measured values ​​of the meteorological monitoring station hour by hour, and the hit rate indicators of each data source are counted, such as the proportion of data with a prediction error within ±5mm; for high wind scenes, the mean square error of the wind speed prediction of each data source is counted, and the window period is set to a rolling update of 7 days. The system generates a dynamic confidence score table based on the hit rate and mean square error. The real-time score value under each meteorological type is recorded in the score table according to the data source identifier, and the score fluctuation trend in the past 3 days is marked.

[0057] A linkage mechanism is established between the confidence score table and the field measured data of the weather feedback module. When the mobile operation terminal reports the measured rainfall data of a certain area, the system extracts the optimized meteorological forecast results corresponding to the area and calculates the gridded deviation rate between the predicted value and the measured value. If the deviation rate of the same data source exceeds the threshold within three consecutive window periods, the downgrade operation of the dynamic weight allocation parameter is triggered. For example, the historical hit rate of a third-party data source in heavy rain scenarios is 78%, but because the measured deviation rate has risen to 12% recently, its initial weight is dynamically reduced from 25% to 15%, and the weight ratio of data sources with lower deviation rates is increased.

[0058] The dynamic weight adjustment results are updated in real time to the multi-source data weighted fusion module, and the optimized meteorological forecast data is written back to the historical database, forming a closed loop of iterative optimization of data quality. The historical database simultaneously records the weight adjustment log, including the data source identifier that triggers the downgrade, the adjustment time and the adjustment range, providing a traceability basis for the subsequent update of the confidence score table. The linkage mechanism between the confidence score table and the weight allocation parameters enables the parameter adjustment of the meteorological forecast model to refer to both long-term historical performance and short-term real-time deviations, enhancing the model's adaptability to different meteorological scenarios.

[0059] The classified storage of historical data provides a statistical basis for confidence scoring, and the real-time deviation analysis injects dynamic correction basis into the weight allocation, while the weight adjustment results continuously iterate the content of the historical database. The three form a technical closed loop through the data flow and parameter update mechanism, solving the problem that the traditional static weight allocation method is difficult to adapt to the fluctuation of the accuracy of meteorological data sources, and improving the regional accuracy of multi-source data fusion results.

[0060] Specifically, in the power transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the wireless communication protocol uses narrowband Internet of Things technology to transmit the optimized meteorological forecast results to the mobile operation terminal, and the narrowband Internet of Things supports low-power wide-area communication; The time decay factor is configured such that the prediction weight for the next 6 hours in the weighted fusion accounts for 70%, and the weight decreases linearly to 30% after 24 hours. The decreasing logic is associated with the meteorological mutation scene tag.

[0061] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of narrowband Internet of Things communication and time attenuation factor configuration is as follows: Narrowband Internet of Things technology uses authorized frequency bands to achieve low-power transmission of optimized meteorological forecast results, and matches the signal strength changes along the transmission line through an adaptive modulation and coding mechanism. For example, in areas with weak signal coverage in mountainous areas, the terminal device switches to BPSK modulation to reduce the transmission rate to maintain connection stability, while QPSK modulation is used in plain areas to improve data transmission efficiency. The optimized meteorological forecast results are encapsulated as lightweight data packets, with timestamps and geographic grid codes attached. After encryption, they are transmitted to the mobile operation terminal through the narrowband Internet of Things. After the terminal device parses the data packet, it triggers local early warning prompts and navigation path updates.

[0062] The configuration of the time attenuation factor is dynamically associated with the meteorological mutation scenario marker. When the user corrects the record or real-time monitoring data to mark a short-term heavy rainfall in a certain area, the weighted fusion module activates the time attenuation factor and sets the weight of the forecast data for the next 6 hours to 70%, and then decreases by 10% every 6 hours until it drops to 30% after 24 hours. For example, in the scenario of sudden rainstorm, the weight of the forecast data for the next 3 hours provided by the meteorological station is increased to 80%, while the weight of the forecast data after 24 hours is reduced to 20%, strengthening the decision-making influence of recent data. The attenuation logic parameters are stored in the rule base of the dynamic weight allocation module, which supports dynamic adjustment of the decreasing gradient according to the meteorological type and regional characteristics.

[0063] The optimized data transmitted by the narrowband Internet of Things is synchronously written back to the meteorological data interface layer, triggering the real-time update of the spatial overlay analysis module. The updated meteorological forecast results and the time decay factor parameters act together in the dynamic weight allocation process, forming a technical closed loop from data transmission to model iteration. The warning information received by the mobile operation terminal contains time decay marks, such as highlighting the high-risk period in the next 6 hours in the interface to assist inspection personnel in giving priority to dealing with nearby meteorological threats.

[0064] The low power consumption characteristics of narrowband IoT and the dynamic configuration of time decay factors work together to solve the problem of balancing long-distance data transmission and prediction timeliness in field environments. The meteorological mutation scene marking triggers the targeted adjustment of the weight decay logic, and the field data returned by IoT reversely calibrates the decay parameters. The two improve the temporal and spatial matching accuracy of meteorological forecasts and business handling through closed-loop feedback.

[0065] Specifically, the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention also includes: Dynamically expand the buffer coverage of the power transmission line corridor in the meteorological and equipment-related topological map according to the measured rainfall distribution of the rainstorm scene; The adjustment of the spatiotemporal resolution parameters triggers the update of the sampling frequency of the meteorological data interface layer, and synchronously updates the dynamic weight allocation parameters based on the regional deviation between the optimized meteorological forecast results and the measured values, forming a closed-loop calibration mechanism for data fusion and equipment risk analysis.

[0066] In the transmission line inspection method combining meteorological data with dynamic confidence weighting described in the present invention, the specific implementation process of geographic fence optimization and closed-loop calibration is as follows: Based on the measured rainfall distribution data of the rainstorm scene, the system performs grid-based waterlogging risk analysis of the transmission line corridor. The measured data is compared with the optimized meteorological forecast results in 1km×1km geographic units, and the areas where the forecast deviation rate exceeds 20% are identified. The buffer zone coverage is dynamically expanded by combining historical tower tilt records and geological settlement data. For example, the measured rainfall in a certain area is 15% higher than the predicted value and there are 3 historical landslide records. The buffer zone radius is expanded from the baseline 500 meters to 800 meters, and the spatial threat boundary in the meteorological and equipment-related topological map is updated, and the maintenance priority mark of the affected tower is associated.

[0067] The adjustment of spatiotemporal resolution parameters is triggered by the meteorological mutation mark in the user correction record. When the mobile operation terminal marks a short-term heavy rainfall in a certain area, the sampling frequency of the meteorological data interface layer is increased from once per hour to once every 10 minutes, and the time granularity of the standardized meteorological data set is simultaneously refined to the minute level. The high-precision data stream is input into the spatial overlay analysis module to drive the real-time iteration of the dynamic buffer model. At the same time, the calibration parameters are transmitted back to the dynamic weight allocation module through the narrowband Internet of Things to reduce the weight of the data source with large recent deviations.

[0068] The synchronous update of calibration parameters and weight allocation parameters forms a cross-step closed loop. For example, in a sudden rainstorm scenario, the data with improved spatiotemporal resolution identifies that the predicted value of a certain data source continues to deviate from the measured value. The dynamic weight allocation module reduces its weight from 30% to 15%, and expands the geographic fence range of the area. The updated parameter set is written into the historical database through the meteorological data interface layer, providing data support for the subsequent iteration of the confidence score table, forming a complete feedback link from on-site perception to model optimization.

[0069] The dynamic expansion of geographic fences and the scenario-based adjustment of spatiotemporal resolution work together to solve the problem that traditional static models are difficult to adapt to regional meteorological changes. Buffer optimization driven by measured data reduces the risk of missed reports, and the increase in sampling frequency enhances the sensitivity of response to short-term extreme weather. The parameter synchronization update mechanism realizes the multi-module linkage of meteorological analysis, weight allocation and risk warning, improving the overall decision-making accuracy of the system under complex meteorological conditions.

[0070] The specific embodiments of the technical solution of the present invention are as follows: Embodiment 1; In the mobile operation application of power transmission inspection, the system calls the next 72 hours of rainfall forecast data in the standardized meteorological data set and performs spatial matching with the GIS coordinates of the transmission line inspection section. The interface displays the rainfall intensity curve and risk level (low / medium / high) of a certain section (such as tower numbers T-103 to T-115) in the next three days, and the inspection personnel adjust the inspection priority according to the forecast results. If the predicted rainfall exceeds 50mm / 24h, the system automatically marks the section as "high risk" and recommends delaying the inspection or enabling the drone inspection plan.

[0071] Example 1 solves the problem of execution interruption of traditional inspection tasks due to sudden weather changes by dynamically associating meteorological data with inspection plans, thereby improving the timeliness of plan adjustments.

[0072] Embodiment 2; In the power grid one-picture interface, the rainstorm warning area covers the transmission line corridor with a red semi-transparent layer, and the system automatically calculates the affected substations (such as substation A) and lines (such as line L-205). Through spatial overlay analysis, a list of affected equipment is generated and associated with maintenance tasks. For example, the rainstorm buffer zone covers three towers of line L-205. The system marks this section of the line as "high risk of foundation scour" and pushes the reinforcement plan to the maintenance team.

[0073] Example 2 accurately locates equipment affected by weather conditions based on spatial overlay analysis, optimizes emergency resource allocation, and reduces the blindness of manual inspections.

[0074] Embodiment 3; When a high wind warning work order is issued, the mobile operation terminal receives the work order information and displays the threatened substation (such as substation B) and the list of hardware to be inspected. After the inspection personnel confirm on-site that the actual wind speed is lower than the predicted value, they submit correction feedback through the terminal, and the system dynamically reduces the priority of subsequent warning work orders in the area. After the work order is executed, the system automatically archives and generates an analysis report, and updates the confidence score table under the high wind scenario.

[0075] Example 3 forms a closed-loop link from warning issuance to execution optimization through work order feedback and dynamic adjustment of model parameters, thereby improving the matching accuracy of meteorological warnings with actual business scenarios.

[0076] The technical solution of the present invention realizes the deep integration of meteorological data and power operation and inspection business through the following steps: The present invention accesses heterogeneous data sources such as numerical forecast centers, wildfire monitoring platforms, and disaster reduction institutes through a meteorological data interface layer to obtain multi-source meteorological data such as temperature, rainfall, and wind speed. A protocol parser is used to extract meteorological elements from each data source, and the timestamp is unified to minute-level accuracy through GPS time synchronization technology. The spatial reference difference is eliminated based on the geographic coordinate interpolation algorithm (such as aligning the WGS84 coordinate system with the CGCS2000 coordinate system of the power grid GIS), and a standardized meteorological data set that is continuous in time and space is generated. For example, grid interpolation processing is performed on the real-time monitoring data of the meteorological station and the disaster reduction institute to form 5km×5km grid data covering the transmission line corridor, providing a unified time and space reference for subsequent analysis. The geographic coordinate interpolation algorithm uses a bilinear interpolation method to align the meteorological data in the WGS84 coordinate system with the CGCS2000 coordinate system of the power grid GIS.

[0077] The standardized meteorological data set is spatially overlaid with the power grid GIS topology data to dynamically generate a buffer zone based on the meteorological impact radius. For heavy rain scenarios, a radial buffer zone centered on the transmission tower is generated based on the rainfall intensity forecast data, and the boundary of the waterlogging risk area is corrected in combination with the terrain elevation; for high wind scenarios, the wind circle range of the substation is calculated through the wind speed attenuation model to identify the threatened transmission lines. For example, when the real-time wind speed reaches level 8, the system automatically marks the old lines that have been in service for more than 15 years within the threat radius, and constructs a meteorological and equipment-related topological map, which records the spatial coordinates of the threatened equipment, the risk type (such as insulator flashover risk) and the associated maintenance tasks.

[0078] Based on the historical database, the prediction errors (such as hit rate and mean square error) of each data source in heavy rain and strong wind scenarios are counted, and the sliding window statistical method is used to generate a dynamic confidence score table. The initial weight is assigned according to the scoring results, and the abnormal forecast values ​​are detected in real time through box plot analysis, and the weight parameters are adjusted dynamically. For example, the initial weight of the meteorological station data in the heavy rain scenario is set to 55%. If its predicted rainfall deviates from the mean of other data sources by 1.5 times, the weight is downgraded to 30%. Through linear weighted fusion of multi-source data, optimized meteorological forecast results are generated, and the time decay factor is introduced to strengthen the recent forecast weight (70% for the next 6 hours, decreasing to 30% after 24 hours). The optimization results are written back to the meteorological data interface layer through the narrowband Internet of Things to update the original single-source data.

[0079] The mobile operation terminal collects on-site temperature, humidity and rainfall data in real time through sensors, and transmits them to the meteorological data interface layer via the narrowband Internet of Things. The system calculates and optimizes the grid deviation between the predicted results and the measured values, and dynamically adjusts the geographic fence matching rules (such as expanding the radius of the rainstorm buffer zone) and the weight distribution parameters. For example, if the measured rainfall in a certain area continues to be 5% higher than the predicted value, the trigger buffer radius will be expanded from 500 meters to 800 meters. The early warning work order is automatically generated according to the risk level mapping table, encapsulating the equipment identification and disposal suggestions (such as "priority inspection of towers T-103 to T-115"), and is pushed to the terminal through NB-IoT. After the work order is executed, the on-site feedback data triggers the iteration of the model parameters, forming a closed-loop link from data collection to decision optimization.

[0080] The spatiotemporal resolution parameters are dynamically adjusted according to the meteorological mutation marks in the user's correction records. For example, after marking the short-term heavy rainfall area, the time granularity of the standardized data set is increased from the hour level to the 10-minute level, and the spatial overlay analysis module synchronously updates the sampling frequency. The calibration parameters and weight allocation parameters are updated in conjunction with each other, and the historical database stores the weight adjustment log to support the iterative optimization of the confidence score table. Narrowband Internet of Things technology ensures low-power data transmission in the wild environment. For example, BPSK modulation is used to maintain connection stability in mountainous areas, and data packets are optimized and encrypted before transmission to the terminal, triggering navigation path updates and risk warnings. The narrowband Internet of Things adopts NB-IoT technology, supports BPSK / QPSK adaptive modulation, and switches to BPSK mode when the signal strength is lower than -90dBm to maintain transmission stability.

[0081] The above implementation methods achieve accurate positioning of meteorological risks and efficient dispatch of emergency resources through standardized data alignment, dynamic weight allocation and closed-loop feedback mechanism. For example, in heavy rain scenarios, the system predicts high-risk sections 24 hours in advance, and after the work order is issued, maintenance resources are allocated in a targeted manner to reduce the inspection interruption rate; in high wind scenarios, real-time wind speed calibration is used to reduce the false alarm rate. Multi-source data fusion and adaptive optimization technology significantly improve the protection capabilities of power equipment in extreme weather and the intelligent level of operation and inspection decision-making.

[0082] The present invention systematically solves the problem of integrating meteorological data with power operation and inspection business scenarios through the following technical solutions: Through the alignment of time and space benchmarks, the timestamps and spatial benchmarks of unified multi-source meteorological data are processed to generate standardized meteorological data sets, and spatial overlay analysis is performed with the power grid GIS topology data. Buffer zones are dynamically generated based on the meteorological impact radius, transmission equipment threatened by heavy rain and strong winds is identified, and a meteorological and equipment-related topological map is constructed. The map associates equipment status with maintenance tasks through risk level mapping relationships, and pre-defined meteorological event thresholds trigger the automatic generation of warning work orders, achieving accurate matching of meteorological risks and equipment positioning. This mechanism maps meteorological forecast results directly to the geographic space of the power grid, solving the problem of weak anti-interference ability of traditional inspection plans due to the lack of meteorological relevance.

[0083] Generate a dynamic confidence score table based on historical error statistics, dynamically assign data source weights in combination with real-time anomaly detection, and generate optimized meteorological forecast results through weighted fusion. Introduce a time decay factor to strengthen the decision weight of recent forecast data, and reversely update the optimization results to the meteorological data interface layer through the narrowband Internet of Things. The field measured data and user correction records generate calibration parameters through gridded difference analysis, dynamically adjust the geographic fence matching rules and weight allocation parameters, and form a closed-loop calibration of data quality and business rules. This technical path solves the problem of blind emergency resource allocation under extreme meteorological events, and improves decision-making accuracy through multi-source data cross-validation and adaptive optimization.

[0084] The early warning work order encapsulates the identification of the threatened equipment, risk type and disposal suggestions, and is pushed to the mobile operation terminal through the narrowband Internet of Things, supporting real-time data transmission under low-power wide-area communication. The work order execution results and on-site feedback data are transmitted back to the operation and inspection control system to trigger dynamic updates of risk levels and iterations of model parameters. The meteorological mutation scene marking is linked to the spatiotemporal resolution adjustment and weight decay logic to achieve dynamic sorting of work order priorities and targeted resource allocation. This process converts multi-source early warning information into executable closed-loop management instructions, solving the problems of low efficiency and high information attenuation of traditional manual dispatching, and improving the circulation and execution efficiency of meteorological early warning work orders in complex scenarios.

Claims

1. A transmission line inspection method combining meteorological data with dynamic confidence weighting, characterized in that: include: Acquire multi-source meteorological data, the multi-source meteorological data including numerical forecast data, real-time monitoring data and forecast data, perform spatiotemporal benchmark alignment processing on the multi-source meteorological data through a protocol parser, and generate a standardized meteorological data set, the spatiotemporal benchmark alignment processing including unifying timestamps of different data sources to minute-level accuracy and eliminating spatial benchmark differences through geographic coordinate interpolation; Perform spatial overlay analysis on the standardized meteorological data set and the power grid GIS topology data, generate a dynamic buffer based on the meteorological impact radius, identify the transmission equipment threatened by the meteorological weather through spatial relationship calculation, and construct a meteorological and equipment related topology map. The power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridor; According to the risk level mapping relationship in the meteorological and equipment-related topological map, an event-driven engine is triggered by a predefined meteorological event threshold to generate an early warning work order, which encapsulates the identification of the threatened power transmission equipment and disposal suggestions, and is pushed to the operation and inspection control system through a wireless communication network for closed-loop management of work order issuance, execution and feedback; For the multi-source meteorological forecast data of the same geographical area in the meteorological data interface layer, a dynamic confidence score table is generated based on historical error statistical analysis, a weight of each data source is assigned according to the confidence score table, an optimized meteorological forecast result is generated through weighted fusion, and the optimized meteorological forecast result is reversely updated to the meteorological data interface layer to cover the original single-source data; Based on the on-site measured meteorological data and user correction records, the regional deviation between the optimized meteorological forecast result and the measured value is calculated, and the geographic fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion are dynamically adjusted according to the regional deviation to form a closed-loop calibration of meteorological data and equipment risk analysis.

2. The power transmission line inspection method combining meteorological data and dynamic confidence weighting according to claim 1, characterized in that: The multi-source meteorological data performs time-space reference alignment processing including: Extract meteorological elements from heterogeneous data sources through a protocol parser, the protocol parser supports protocol conversion in JSON, GRIB2 and XML formats, extract the timestamp field of meteorological elements through regular expression matching, and call the GPS clock synchronization module to unify the timestamp to minute-level accuracy, unify the timestamps of the multi-source meteorological data to minute-level accuracy, and eliminate spatial benchmark differences through geographic coordinate interpolation algorithms to generate a time-space continuous standardized meteorological data set; The power grid GIS topology data includes the geographic fences and maintenance operation points of the transmission line corridors. The spatial overlay analysis is based on the spatial relationship matching between the standardized meteorological data set and the geographic fences, and the threat radius of the substation is determined by calculating the wind circle range associated with the wind speed.

3. The power transmission line inspection method combining meteorological data and dynamic confidence weighting according to claim 1, characterized in that: The construction of the meteorological and equipment related topological map includes: Divide the dynamic buffer zone radius according to the meteorological types of heavy rain and strong wind, determine the threatened transmission lines and substations through spatial relationship calculation based on the threat radius output by the spatial overlay analysis, and associate the maintenance tasks in the power grid GIS topology data to generate a risk level mapping table; The early warning work order encapsulates equipment identification, risk type and disposal suggestions, and is pushed to the mobile operation terminal via a wireless communication network, which uses narrowband Internet of Things technology to transmit work order data.

4. The power transmission line inspection method combining meteorological data and dynamic confidence weighting according to claim 1, characterized in that: The generating of a dynamic confidence score table based on historical error statistical analysis includes: Based on the historical database of the standardized meteorological data set, the prediction errors of each data source in the heavy rain and strong wind scenes are counted, and a dynamic confidence score table is generated using a sliding window statistical method; The dynamic weight allocation is based on the dynamic confidence score table, initial weights are allocated according to meteorological types, and weight degradation is performed on abnormal forecast values ​​that deviate from the group mean through box plot analysis.

5. The power transmission line inspection method combining meteorological data with dynamic confidence weighting according to claim 4, characterized in that: Also includes: The multi-source forecast data is linearly weighted according to the dynamic weight allocation parameters, and a time decay factor is introduced to adjust the weight ratio for the short-term heavy rainfall scenario. The time decay factor adopts a linear decreasing logic, and the weight ratio of the next 6 hours is 70%, and the weight ratio decreases by 10% every 6 hours until the weight ratio drops to 30% after 24 hours. The decreasing logic is dynamically adjusted based on the meteorological mutation scene mark. When short-term heavy rainfall occurs in the marked area, the weight ratio of the next 3 hours is increased to 80%; The optimized meteorological forecast results are written back to the meteorological data interface layer via a wireless communication protocol, overwriting the original single-source data and providing updated meteorological input for the spatial overlay analysis.

6. The power transmission line inspection method combining meteorological data and dynamic confidence weighting according to claim 1, characterized in that: The dynamically adjusting the geo-fence matching rules in the spatial overlay analysis and the dynamic weight allocation parameters in the weighted fusion according to the regionalized deviation includes: Based on the regional deviation between the optimized meteorological forecast result and the measured meteorological data, for the rainstorm scene, the buffer radius parameter is iteratively optimized by a sliding window statistical method; The adjustment of the spatiotemporal resolution parameters includes increasing the time granularity of the standardized meteorological data set according to meteorological mutation scene markers in the user correction record.

7. The power transmission line inspection method combining meteorological data with dynamic confidence weighting according to claim 3, characterized in that: It also includes a weather feedback module that collects on-site temperature, humidity and rainfall data in real time and transmits it to the meteorological data interface layer via the narrowband Internet of Things; The weather feedback module deployed in the mobile operation terminal collects on-site temperature, humidity and rainfall data in real time, and the on-site data is transmitted to the meteorological data interface layer via the narrowband Internet of Things; The user correction record includes manual adjustment information of the risk level in the early warning work order, and the regionalized deviation calculation generates calibration parameters based on the gridded difference analysis between the field data and the optimized meteorological forecast result.

8. The power transmission line inspection method combining meteorological data with dynamic confidence weighting according to claim 4, characterized in that: The historical database stores the forecast data and corresponding actual data of the past 30 days in the meteorological data interface layer, and classifies and calculates the hit rate and mean square error according to the rainstorm and strong wind meteorological types; The confidence score table is generated based on the historical data and linked with the field measured data collected by the weather feedback module, and the initial weight ratio in the dynamic weight allocation is reversely corrected through deviation analysis.

9. The power transmission line inspection method combining meteorological data with dynamic confidence weighting according to claim 5, characterized in that: The wireless communication protocol uses narrowband Internet of Things technology to transmit the optimized weather forecast result to the mobile operation terminal, and the narrowband Internet of Things supports low-power wide-area communication; The time decay factor is configured such that the prediction weight for the next 6 hours in the weighted fusion accounts for 70%, and the weight decreases linearly to 30% after 24 hours. The decreasing logic is associated with the meteorological mutation scene tag.

10. The power transmission line inspection method combining meteorological data with dynamic confidence weighting according to claim 6, characterized in that: Also includes: Dynamically expand the buffer coverage of the power transmission line corridor in the meteorological and equipment-related topological map according to the measured rainfall distribution of the rainstorm scene; The adjustment of the spatiotemporal resolution parameters triggers the update of the sampling frequency of the meteorological data interface layer, and synchronously updates the dynamic weight allocation parameters based on the regional deviation between the optimized meteorological forecast results and the measured values, forming a closed-loop calibration mechanism for data fusion and equipment risk analysis.

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