A power tower operation and inspection strategy making method based on real-time monitoring

Abstract

The application discloses a kind of power tower operation and inspection strategy making method based on real-time monitoring, it is related to power tower maintenance technical field, and its technical solution points include the following steps: collecting the historical deformation detection data of power tower, historical operation state data, historical environmental monitoring data and corresponding historical operation and inspection maintenance scheme and implementation effect data, establish the operation and inspection foundation database of power tower in severe cold area;Screening fault characteristics in operation and inspection foundation database, determine the correlation threshold of tower deformation, abnormal operation and environmental factors in severe cold area according to fault characteristics, establish the abnormal early warning threshold set of tower state;Based on multi-source monitoring terminal, obtain the real-time deformation data, real-time operation parameter and real-time environmental data of power tower in severe cold area, the effect is to realize the real-time perception of tower deformation, operation state and severe cold environment, greatly improve data integrity and availability, provide reliable basis for accurate state determination.

Description

Technical Field

[0001] This invention relates to the field of power pole maintenance technology, and more specifically, to a method for formulating power pole operation and maintenance strategies based on real-time monitoring. Background Technology

[0002] The harsh climate of frigid regions, with extreme conditions such as low-temperature freeze-thaw cycles, ice loads, and strong winds and snow accumulation, can easily cause structural and operational faults in power poles, such as foundation settlement, tower tilting, and conductor galloping, seriously threatening the safety, stability, and reliability of power transmission lines and power supply.

[0003] However, existing power pole operation and maintenance strategies generally rely on periodic inspections, manual checks, or post-event maintenance. Data sources are limited, lacking multi-source real-time monitoring capabilities for pole deformation, operational status, and extreme cold environments. Offline manual inspections alone are insufficient to capture short-term, sudden anomalies coupled with environmental influences. Furthermore, the early warning mechanisms are rigid, often relying on fixed empirical thresholds to determine status without dynamic modeling considering different cold levels, environmental factors, and pole structural characteristics. This leads to delayed warnings or misjudgments. Moreover, the solutions depend on manual experience, lacking simulation and extrapolation of resource conditions and real-time environmental changes across multiple scenarios, resulting in unreasonable allocation of operation and maintenance resources, low operational efficiency, and high safety risks. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for formulating operation and maintenance strategies for power poles based on real-time monitoring.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for formulating operation and maintenance strategies for power poles based on real-time monitoring, the method comprising the following steps: Collect historical deformation detection data, historical operating status data, historical environmental monitoring data, and corresponding historical operation and maintenance plans and implementation effect data of power poles to establish a basic database for the operation and maintenance of power poles in frigid regions; Screen fault characteristics from the operation and maintenance database, determine the correlation thresholds between tower deformation, operational anomalies and environmental factors in cold regions based on the fault characteristics, and establish a set of abnormal early warning thresholds for tower status. Based on the real-time deformation data, real-time operating parameters and real-time environmental data of the power poles obtained by the multi-source monitoring terminal, the real-time comprehensive status data of the poles is obtained by fusing the multi-source real-time monitoring data. The real-time integrated status data of the tower is compared with the abnormal early warning threshold set to determine whether the tower has abnormal deformation, abnormal operation, or environmental impact exceeding the threshold, and to generate the real-time status abnormality judgment result of the tower. Based on the real-time anomaly determination results of the tower, a preliminary operation and maintenance solution library adapted to multiple scenarios is developed; The system simulates and evaluates each scheme in the preliminary operation and maintenance scheme library, selects the optimal operation and maintenance scheme and executes it, and transmits the scheme execution process and final effect data back to the operation and maintenance basic database to realize the dynamic updating of the threshold system and strategy library.

[0006] Preferably, the fault characteristics in the operation and maintenance database are screened, and a set of correlation thresholds between tower deformation, operational anomalies, and environmental factors in cold regions is determined based on the fault characteristics. An early warning threshold for tower condition anomalies is then established, specifically including the following steps: The correlation patterns between tower deformation, operating parameters and environmental factors in the operation and maintenance basic database were screened to determine the correlation coefficient of each influencing factor; Based on the standard for safe operation of towers, and combined with the correlation coefficient and the influence coefficient of extreme environment in frigid regions, the critical safety values ​​of tower deformation and operating parameters under different environmental conditions are calculated. The safety thresholds are divided into low-level warning thresholds, medium-level warning thresholds, high-level warning thresholds, and emergency warning thresholds according to the degree of risk. Corresponding anomaly judgment rules are matched for each level of warning threshold to form an anomaly warning threshold set for tower status.

[0007] Preferably, the real-time comprehensive status data of the tower is obtained by fusing and verifying multi-source real-time monitoring data, specifically including the following steps: Acquire real-time raw data from multiple sources, including deformation monitoring terminals, operational status monitoring terminals, and environmental monitoring terminals, and perform time synchronization and coordinate calibration on the raw data from each terminal. Multi-source terminal data is obtained by fusing data from various terminals within the same monitoring dimension. Outlier detection and screening of multi-source terminal data are performed to obtain valid multi-source terminal data. After calibration, fusion and verification of the valid multi-source terminal data, real-time comprehensive status data of the tower is formed.

[0008] Preferably, the real-time integrated status data of the tower is compared with the anomaly warning threshold set to determine whether the tower has abnormal deformation, abnormal operation, or environmental impact exceeding the threshold, and a real-time status anomaly judgment result of the tower is generated. This specifically includes the following steps: The deformation data, operating parameters, and environmental data of the tower's real-time integrated status data are compared one by one with the corresponding indicators and warning thresholds under the corresponding environmental conditions in the anomaly warning threshold system to obtain the comparison and judgment results. If the monitoring data does not exceed the warning threshold, the tower is considered to be in normal condition. If the monitoring data exceeds the warning threshold, it is determined to be an abnormal status of the corresponding level, and the abnormal indicator type, abnormal value and the warning level are marked. By integrating the comparison and judgment results of all indicators, a real-time status anomaly judgment result for the tower is formed, which includes the anomaly level, anomaly indicator, and anomaly degree.

[0009] Preferably, based on the real-time anomaly determination results of the tower, a preliminary operation and maintenance solution library adaptable to multiple scenarios is formulated, specifically including the following steps: Based on the abnormal characteristic parameters of the real-time abnormal status judgment results of the tower and the historical matching data of the operation and maintenance basic database, the causes of abnormalities are traced to obtain the core causes of tower abnormalities and the degree of influence of the causes. Based on the core causes, the degree of impact of the causes, and the geographical climate of the frigid region and the distribution characteristics of tower maintenance resources, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

[0010] Preferably, if an anomaly is determined, the core causes of the anomaly and their degree of influence are obtained by tracing the anomaly's origin based on the anomaly characteristic parameters of the tower's real-time status anomaly determination result and historical matching data from the operation and maintenance basic database. This specifically includes the following steps: Extract the abnormal indicator types from the abnormal status judgment results, and search for historical data with similar abnormal characteristics in the operation and maintenance basic database based on the abnormal indicator types to obtain the historical matching dataset; By analyzing historical matching datasets, current environmental characteristics of the frigid zone, the service life of the tower, and its structural features, a set of potential causes for the tower's current anomaly is obtained. Calculate the matching degree between each trigger in the potential trigger set and the abnormal feature parameters, mark the trigger with the highest matching degree as the core trigger, and obtain the degree of influence of the trigger based on the influence of the core trigger on the abnormal indicators.

[0011] Preferably, based on the core causes, the degree of impact of the causes, and the geographical climate and tower maintenance resource distribution characteristics of the frigid region, a preliminary operation and maintenance solution library adapted to multiple scenarios is formulated, specifically including the following steps: Based on the core causes, the degree of influence of the causes, and the geographical and climatic characteristics of the frigid zone, the core operation content, operation timing, and protection requirements for operation and maintenance in the frigid zone are determined. Different operation and maintenance resource scenarios are obtained based on the distribution characteristics of tower operation and maintenance resources. The distribution characteristics of operation and maintenance resources include the allocation of operation and maintenance personnel, the reserve of maintenance equipment, and traffic conditions. Based on the core operation content, operation timing, cold region protection requirements, and operation and maintenance resource scenarios, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

[0012] Preferably, the various schemes in the preliminary operation, maintenance, and repair plan are simulated and evaluated to select the optimal operation, maintenance, and repair plan and implement it. This specifically includes the following steps: Establish an evaluation index scheme for operation and maintenance strategies. The evaluation index scheme includes operation and maintenance timeliness index, on-site operation difficulty index, resource consumption index, maintenance effect expectation index, and operation safety index in extremely cold regions. Assign weights to each indicator in the evaluation indicator scheme and formulate quantitative evaluation standards for each indicator; Based on quantitative evaluation criteria, each scheme in the preliminary operation and maintenance scheme library is quantitatively scored. The scheme implementation is simulated and deduced in combination with real-time environmental changes in the frigid zone. The strategy with the highest comprehensive score and the best simulation implementation effect is selected as the optimal operation and maintenance scheme.

[0013] Compared with existing technologies, this invention has the following advantages: By integrating multi-source data from deformation monitoring terminals, operational status monitoring terminals, and environmental monitoring terminals, time synchronization, coordinate calibration, and outlier screening are achieved, enabling real-time perception of tower deformation, operational status, and frigid environments. This significantly improves data integrity and availability, providing a reliable foundation for accurate status determination. Based on historical data mining, the correlation between environmental factors and tower anomalies is analyzed. Combined with the frigid region level and real-time environmental characteristics, a dynamic early warning threshold set is constructed to achieve early warnings under different frigid levels and operating conditions, effectively avoiding delayed or misjudged warnings and improving the timeliness and accuracy of tower status anomaly identification. Based on core causes, impact levels, and the distribution characteristics of operation and maintenance resources, a multi-scenario adaptable operation and maintenance solution library is constructed. Through quantitative evaluation and simulation, the optimal solution is selected, achieving reasonable allocation of operation and maintenance resources, improving operational efficiency, reducing safety risks in frigid regions, and solving the problems of unreasonable resource allocation, low efficiency, and high risk in existing solutions. Attached Figure Description

[0014] Figure 1 This invention provides a schematic diagram illustrating the steps of a method for formulating an operation and maintenance strategy for power poles based on real-time monitoring, as provided in an embodiment of the invention. Figure 2 This is a schematic diagram illustrating the steps in generating real-time anomaly determination results for power poles in a method for formulating operation and maintenance strategies based on real-time monitoring, as provided in an embodiment of the present invention. Detailed Implementation

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0018] Reference Figures 1-2 As shown.

[0019] This embodiment further illustrates the method for formulating operation and maintenance strategies for power poles based on real-time monitoring proposed in this invention.

[0020] A method for formulating operation and maintenance strategies for power poles based on real-time monitoring, the method comprising the following steps: Collect historical deformation detection data, historical operating status data, historical environmental monitoring data, and corresponding historical operation and maintenance plans and implementation effect data of power poles to establish a basic database for the operation and maintenance of power poles in frigid regions; Screen fault characteristics from the operation and maintenance database, determine the correlation thresholds between tower deformation, operational anomalies and environmental factors in cold regions based on the fault characteristics, and establish a set of abnormal early warning thresholds for tower status. Based on the real-time deformation data, real-time operating parameters and real-time environmental data of the power poles obtained by the multi-source monitoring terminal, the real-time comprehensive status data of the poles is obtained by fusing the multi-source real-time monitoring data. The real-time integrated status data of the tower is compared with the abnormal early warning threshold set to determine whether the tower has abnormal deformation, abnormal operation, or environmental impact exceeding the threshold, and to generate the real-time status abnormality judgment result of the tower. Based on the real-time anomaly determination results of the tower, a preliminary operation and maintenance solution library adapted to multiple scenarios is developed; The system simulates and evaluates each scheme in the preliminary operation and maintenance scheme library, selects the optimal operation and maintenance scheme and executes it, and transmits the strategy execution process and final effect data back to the operation and maintenance basic database to realize the dynamic updating of the threshold system and strategy library.

[0021] Screening fault characteristics from the operation and maintenance database, determining the correlation threshold set between tower deformation, operational anomalies, and environmental factors in frigid regions based on these fault characteristics, and establishing early warning thresholds for tower condition anomalies, specifically includes the following steps: The correlation patterns between tower deformation, operating parameters and environmental factors in the operation and maintenance basic database were screened to determine the correlation coefficient of each influencing factor; Based on the standard for safe operation of towers, and combined with the correlation coefficient and the influence coefficient of extreme environment in frigid regions, the critical safety values ​​of tower deformation and operating parameters under different environmental conditions are calculated. The safety thresholds are divided into low-level warning thresholds, medium-level warning thresholds, high-level warning thresholds, and emergency warning thresholds according to the degree of risk. Corresponding anomaly judgment rules are matched for each level of warning threshold to form an anomaly warning threshold set for tower status.

[0022] Fault characteristics were screened and correlation coefficients were determined. Using tower deformation variables, operating parameters, and environmental factors stored in the operation and maintenance database as the analysis objects, correlation analysis was conducted to uncover the inherent patterns among these three factors. Specifically, the Pearson correlation coefficient method was used to quantify the correlation strength of each factor; the calculation formula is as follows: r ij This represents the correlation coefficient between variables i and j, ranging from -1 to 1. The closer the absolute value is to 1, the stronger the correlation. i,k Let i be the k-th observation of variable i. x is the mean of variable i; j,k Let j be the k-th observation. Let j be the mean of variable j; n be the total number of observed samples. For example, when analyzing the correlation between ice thickness and tower tilt, a positive correlation can be found that for every 1 mm increase in ice thickness, the tower tilt increases by an average of 0.02 degrees, with a correlation coefficient of 0.87. At the same time, core features strongly correlated with faults, such as extreme low temperature, foundation settlement, and conductor overload current, are screened out, while weak influencing factors with correlation coefficients below 0.3 are eliminated to ensure the accuracy of subsequent threshold calculations.

[0023] The critical safety values ​​under different environmental conditions were calculated using the tower safety operation standard as a benchmark, combined with established correlation coefficients and the extreme environmental impact coefficient of frigid regions. The safety operation standard specifies that, without extreme environmental impacts, the upper limit for tower tilt is 3 degrees and the upper limit for foundation settlement is 5 millimeters. The extreme environmental impact coefficient is determined according to the frigid region classification: 1.5 for Level 1 frigid regions, 1.2 for Level 2, and 1.0 for Level 3. The calculation formula is as follows: C represents the safety threshold, which is the upper limit of safety for tower parameters affected by extreme cold environments; S represents the baseline safety value, which refers to the industry standard safety threshold for tower parameters when there are no extreme environmental influences; r represents the correlation coefficient, which represents the correlation strength between environmental factors and tower deformation or operating parameters; λ represents the extreme environmental influence coefficient, which is 1.5 for Level 1 extreme cold regions, 1.2 for Level 2 extreme cold regions, and 1.0 for Level 3 extreme cold regions. For example, in Level 1 extreme cold regions, the correlation coefficient between ice thickness and tower tilt is 0.87, and the baseline safety value for tilt is 3 degrees. Therefore, the corresponding safety threshold is 3 × (1 + 0.87 × 1.5) = 6.915 degrees. The safety threshold values ​​for each parameter under different environments can be obtained by analogy, realizing the quantitative representation of the impact of environmental factors on tower safety.

[0024] The calculated safety threshold values ​​are divided into four levels of early warning thresholds based on risk level: low-level warning threshold is 60% of the safety threshold value, medium-level warning threshold is 75% of the safety threshold value, high-level warning threshold is 90% of the safety threshold value, and emergency warning threshold is 100% of the safety threshold value. Each level of threshold is matched with corresponding anomaly judgment rules. For example, the low-level warning threshold for tower tilt is 4.15 degrees, which is considered a minor anomaly and requires routine inspection; the medium-level warning threshold is 5.19 degrees, which is considered a moderate anomaly and requires shortening the inspection cycle; the high-level warning threshold is 6.22 degrees, which is considered a severe anomaly and requires specialized testing; and the emergency warning threshold is 6.92 degrees, which is considered a critical anomaly and requires immediate emergency repairs. This hierarchical approach transforms abstract safety standards into directly applicable quantitative early warning rules, ultimately forming a complete set of early warning thresholds covering tower deformation, operational anomalies, and environmental impacts, providing a standardized basis for subsequent real-time anomaly judgments.

[0025] The real-time integrated status data of the tower is obtained by fusing and verifying multi-source real-time monitoring data, specifically including the following steps: Acquire real-time raw data from multiple sources, including deformation monitoring terminals, operational status monitoring terminals, and environmental monitoring terminals, and perform time synchronization and coordinate calibration on the raw data from each terminal. Multi-source terminal data is obtained by fusing data from various terminals within the same monitoring dimension. Outlier detection and screening of multi-source terminal data are performed to obtain valid multi-source terminal data. After calibration, fusion and verification of the valid multi-source terminal data, real-time comprehensive status data of the tower is formed.

[0026] The system acquires real-time raw data from multiple sources and performs time synchronization and coordinate calibration. Raw data is collected from three types of terminals: deformation monitoring terminals, operational status monitoring terminals, and environmental monitoring terminals. Deformation monitoring terminals collect data on tower tilt and foundation settlement; operational status monitoring terminals collect conductor current and equipment operating condition data; and environmental monitoring terminals collect data on icing thickness, extreme low temperatures, and wind speeds in frigid environments. Due to differences in sampling frequency and deployment location among the terminals, all raw data must first be time-synchronized. Using a unified timestamp as a benchmark, the sampling data from different terminals are aligned to the same time series. For example, deformation data sampled at a 1-minute sampling frequency and environmental data sampled at a 5-minute sampling frequency are aligned to 5-minute time nodes to ensure data consistency across time. Simultaneously, coordinate calibration is performed. A three-dimensional coordinate system is established with the tower foundation center as the origin. The coordinates of each terminal monitoring point are uniformly mapped to this coordinate system. For example, tilt monitoring data at a height of 10 meters on the tower and settlement monitoring data on the side of the foundation abutment are both calibrated to the same coordinate system to eliminate data deviations caused by spatial differences.

[0027] Multi-source terminal data is obtained by fusing data from various terminals within the same monitoring dimension. For multiple sets of data within the same monitoring dimension, a weighted average method is used for fusion; the calculation formula is as follows: , This refers to the fusion of data, specifically the final result of fusing data from multiple terminals under the same monitoring dimension; x k The raw data collected by the kth single terminal; w k Let be the weight corresponding to the k-th single terminal, determined by factors such as terminal accuracy and reliability; n is the total number of terminals participating in the fusion, with weights set according to terminal accuracy and reliability, and higher-accuracy terminals assigned higher weights. For example, in the dimension of tower tilt monitoring, high-precision tilt sensors and ordinary tilt sensors are deployed simultaneously. The former has an accuracy of 0.01 degrees and a weight of 0.7, while the latter has an accuracy of 0.05 degrees and a weight of 0.3. If at a certain moment the former collects data of 2.15 degrees and the latter collects data of 2.23 degrees, then the fused tilt data = (2.15 × 0.7 + 2.23 × 0.3) / (0.7 + 0.3) = 2.174 degrees. This method improves the accuracy and stability of data in the same dimension.

[0028] Outlier detection and screening, along with multi-dimensional integration and verification, are performed on multi-source terminal data. Outlier detection employs the 3σ principle, calculated as: Outlier Threshold = Data Mean ± 3 × Data Standard Deviation. Data exceeding this range is considered outlier and removed. For example, if the mean of the icing thickness data sequence collected by an environmental monitoring terminal at a given moment is 8 mm and the standard deviation is 1.2 mm, the outlier threshold is 8 ± 3.6 mm. Extreme data of 13 mm is considered outlier and removed, yielding valid multi-source terminal data. Subsequently, the valid data undergoes multi-dimensional calibration, fusion, and verification. The deformation, operational status, and environmental data are re-aligned along time and coordinate dimensions. A feature fusion algorithm is used to construct real-time comprehensive status data for the towers. For instance, calibrated data such as tilt, conductor current, and icing thickness are integrated into structured data containing timestamps, coordinate information, and various monitoring indicators. After all verifications are completed, the final real-time comprehensive status data for the towers is formed, providing a comprehensive and reliable real-time data foundation for subsequent anomaly detection.

[0029] The real-time integrated status data of the tower is compared with the anomaly warning threshold set to determine whether the tower has abnormal deformation, operational abnormalities, or environmental impact exceeding the threshold, and a real-time anomaly judgment result for the tower is generated. The specific steps include: The deformation data, operating parameters, and environmental data of the tower's real-time integrated status data are compared one by one with the corresponding indicators and warning thresholds under the corresponding environmental conditions in the anomaly warning threshold system to obtain the comparison and judgment results. If the monitoring data does not exceed the warning threshold, the tower is considered to be in normal condition. If the monitoring data exceeds the warning threshold, it is determined to be an abnormal status of the corresponding level, and the abnormal indicator type, abnormal value and the warning level are marked. By integrating the comparison and judgment results of all indicators, a real-time status anomaly judgment result for the tower is formed, which includes the anomaly level, anomaly indicator, and anomaly degree.

[0030] A multi-dimensional benchmarking process was conducted, breaking down the real-time comprehensive status data of the towers into three core categories: deformation data, operational parameters, and environmental data. These were then matched against corresponding indicators in the anomaly warning threshold system and the warning thresholds under the corresponding environmental conditions. Deformation data included tower tilt and foundation settlement; operational parameters included conductor current; and environmental data included icing thickness and extreme low temperatures. Each category of indicator corresponded to a pre-established four-level warning threshold, which was dynamically adjusted based on the severity of the cold region where the tower was located. For example, for a tower tilt in a Level 1 cold region, the corresponding warning thresholds were 4.203 degrees for low-level, 5.254 degrees for medium-level, 6.305 degrees for high-level, and 7.005 degrees for emergency. Real-time tilt data was directly compared against these thresholds one by one to ensure environmental adaptability and accuracy of judgment.

[0031] Based on threshold comparison results, state classification is determined. If the real-time monitoring data of a certain indicator does not exceed the low-level warning threshold for the corresponding environment, the indicator is considered normal. If the monitoring data exceeds the warning threshold for the corresponding level, it is considered an abnormal state for that level, and the abnormal indicator type, abnormal value, and warning level are simultaneously marked. For example, if the real-time tilt of a certain tower is 5.5 degrees, this value exceeds the intermediate warning threshold of 5.254 degrees for tilt in a first-level severe cold region, but does not reach the high-level warning threshold of 6.305 degrees, therefore the tower tilt indicator is determined to be an intermediate-level abnormality, the abnormality type is marked as tower deformation tilt abnormality, the abnormal value is 5.5 degrees, and the warning level is intermediate. As another example, if the real-time icing thickness of a certain tower is 12 mm, exceeding the high-level warning threshold of 10 mm for icing thickness in a first-level severe cold region, then this environmental indicator is determined to be a high-level abnormality, the abnormality type is marked as environmental icing thickness abnormality, the abnormal value is 12 mm, and the warning level is high.

[0032] The comparison and judgment results of all indicators are integrated to form standardized real-time anomaly judgment results for towers. The integration process aggregates all abnormal indicators, using the highest warning level as the overall anomaly level for the tower. It also summarizes the type, value, and level of all abnormal indicators to clarify the degree of anomaly. For example, if a tower simultaneously exhibits a medium-level tilt anomaly and a high-level icing thickness anomaly, the overall anomaly level is judged as high-level. The anomaly result will clearly indicate the high-level anomaly level, and the abnormal indicators will include tower tilt and icing thickness, specifying the abnormal values ​​and warning levels for each indicator, clearly presenting the current risk status and problem distribution of the tower. Through this hierarchical judgment and result integration method, complex multi-source real-time data is transformed into intuitive and actionable anomaly judgment conclusions, providing accurate data support and risk basis for the subsequent development of multi-scenario operation and maintenance plans.

[0033] Based on the real-time anomaly determination results of the tower status, a preliminary operation and maintenance solution library adaptable to multiple scenarios is developed, which includes the following steps: Based on the abnormal characteristic parameters of the real-time abnormal status judgment results of the tower and the historical matching data of the operation and maintenance basic database, the causes of abnormalities are traced to obtain the core causes of tower abnormalities and the degree of influence of the causes. Based on the core causes, the degree of impact of the causes, and the geographical climate of the frigid region and the distribution characteristics of tower maintenance resources, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

[0034] The classification of tower status is based on real-time anomaly assessment results, categorizing tower status into normal and abnormal. If the assessment results show that all monitored indicators do not exceed the lower-level warning threshold, the tower status is directly determined to be normal, and a routine operation and maintenance plan is formulated accordingly. This includes basic operation and maintenance work such as daily inspections and equipment status spot checks carried out according to established cycles, without needing to initiate anomaly handling procedures. If the assessment results show that any indicator exceeds the corresponding warning threshold, the tower status is determined to be abnormal, and the process proceeds to the anomaly cause tracing and analysis stage.

[0035] For anomaly cause tracing analysis, when the tower condition is determined to be abnormal, the following steps are taken: First, extract the abnormal characteristic parameters, including the type of abnormal indicator, abnormal value, warning level, and information on the severity of the extreme cold region. Then, compare these characteristic parameters with historical matching data in the operation and maintenance database. The historical matching data contains records of the causes of similar anomalies in the past, operation and maintenance plans, and their implementation effects. Through feature matching, the core cause of the anomaly can be quickly located, and the influence of each cause can be quantified. For example, if the tower tilt index is determined to be a high-level anomaly, after extracting the abnormal characteristics as Level 1 extreme cold region, tilt of 6.5 degrees, and high-level warning, and matching with historical data in the database, it is found that this type of anomaly is mostly caused by a combination of icing overload and foundation frost heave. Among them, the influence of icing overload accounts for 60%, and the influence of foundation frost heave accounts for 40%. Thus, the core cause is clearly identified as icing overload, and the influence weight of each cause is determined.

[0036] A preliminary maintenance and operation solution library adaptable to multiple scenarios has been established. Based on the core causes and their impact, combined with the geographical and climatic characteristics of the severe cold region and the distribution characteristics of tower maintenance resources, candidate solutions adapted to different scenarios are generated. The geographical and climatic characteristics of the severe cold region include the regional cold level, terrain complexity, and transportation accessibility. The distribution characteristics of maintenance resources include the location of maintenance teams, the number of personnel, and equipment reserves. Taking the above-mentioned high tilt anomaly as an example, the core cause is icing overload, with an impact level of 60%. The area is a level 1 severe cold region with moderate transportation accessibility. The maintenance team is 30 kilometers away from the tower. Three types of candidate solutions can be generated: the first type is an emergency de-icing and foundation reinforcement solution, suitable for scenarios with high risk levels and rapid resource allocation; the second type is a step-by-step handling solution, which first carries out de-icing operations to alleviate risks, and then carries out foundation reinforcement when the opportunity arises, suitable for scenarios with limited resource allocation; the third type is a remote monitoring and temporary inspection solution, suitable for scenarios where extreme weather makes it impossible for maintenance personnel to arrive in time. Through this scenario-based matching approach, a preliminary operation and maintenance solution library is ultimately formed, covering different risk levels, environmental conditions, and resource configurations, providing a rich set of candidates for subsequent simulation and selection of the optimal solution.

[0037] If an anomaly is determined, the core causes and their impact on the tower anomaly are traced based on the anomaly characteristic parameters of the tower's real-time status anomaly determination results and historical matching data from the operation and maintenance database. This process includes the following steps: Extract the abnormal indicator types from the abnormal status judgment results, and search for historical data with similar abnormal characteristics in the operation and maintenance basic database based on the abnormal indicator types to obtain the historical matching dataset; By analyzing historical matching datasets, current environmental characteristics of the frigid zone, the service life of the tower, and its structural features, a set of potential causes for the tower's current anomaly is obtained. Calculate the matching degree between each trigger in the potential trigger set and the abnormal feature parameters, mark the trigger with the highest matching degree as the core trigger, and obtain the degree of influence of the trigger based on the influence of the core trigger on the abnormal indicators.

[0038] Anomaly feature parameters are extracted and historical matching datasets are retrieved. These parameters, including anomaly type, anomaly value, warning level, severity of cold region, and core information of the monitoring point, are extracted from the real-time anomaly determination results of the towers. Then, based on the anomaly type, a similarity search is performed in the operation and maintenance database to select historical data with highly similar anomaly features, forming a historical matching dataset. For example, if the tower tilt index is determined to be a high-level anomaly, the extracted feature parameters are: Level 1 severe cold region, tilt of 6.5 degrees, high-level warning, and a 15-meter monitoring point on the tower. Subsequently, historical tilt anomaly data from the same region, warning level, and monitoring point are retrieved from the database, resulting in a historical matching dataset containing 20 similar cases, providing a data foundation for subsequent causal analysis.

[0039] To determine the potential causes of this anomaly, a causal breakdown analysis was performed on the historical matching dataset. All possible causes of similar anomalies in the past were identified, such as icing overload, foundation frost heave, abnormal conductor tension, and tower aging. These causes were then further refined and supplemented based on the current environmental characteristics of the frigid region, the tower's service life, and its structural features, forming the current set of potential causes for this anomaly. For example, in the case of a high-level tilt anomaly, the main causes in the historical matching dataset were icing overload and foundation frost heave. Considering the current environmental characteristics of continuous low temperatures and icing, the tower's 12-year service life, and the angle steel tower structure, conductor wind-induced vibration was added as a potential cause. This resulted in a final set of potential causes including icing overload, foundation frost heave, and conductor wind-induced vibration.

[0040] The matching degree and contribution value of each trigger are calculated to quantify the core triggers and their degree of influence. First, the matching degree between each trigger in the potential trigger set and the current anomaly is calculated. The matching degree is calculated by the proportion of similar anomalies caused by that trigger in historical cases. The formula is: Matching degree = Number of similar cases corresponding to that trigger / Total number of cases in the historical matching dataset. The trigger with the highest matching degree is then marked as the core trigger. Next, the contribution value of each trigger to the anomaly indicator is calculated using the formula: Contribution value = Matching degree of that trigger / Sum of matching degrees of all triggers. This serves as the quantitative result of the trigger's influence. For example, in the historical matching dataset, there are 12 cases of icing overload, 6 cases of foundation frost heave, and 2 cases of conductor wind-induced vibration, for a total of 20 cases. The matching degree of icing overload is 12 / 20=0.6, the matching degree of foundation frost heave is 6 / 20=0.3, and the matching degree of conductor wind-induced vibration is 2 / 20=0.1. The core cause is icing overload. The contribution values ​​of each cause are 0.6 / (0.6+0.3+0.1)=0.6, 0.3 / (0.6+0.3+0.1)=0.3, and 0.1 / (0.6+0.3+0.1)=0.1, respectively. That is, the influence of icing overload on this tilt anomaly is 60%, foundation frost heave is 30%, and conductor wind-induced vibration is 10%, thus completing the accurate tracing and quantification of the cause of the anomaly.

[0041] Based on the core causes, the degree of impact of the causes, and the geographical and climatic characteristics of the frigid region, as well as the distribution characteristics of tower maintenance resources, a preliminary operation and maintenance solution library adapted to multiple scenarios is formulated, which specifically includes the following steps: Based on the core causes, the degree of influence of the causes, and the geographical and climatic characteristics of the frigid zone, the core operation content, operation timing, and protection requirements for operation and maintenance in the frigid zone are determined. Different operation and maintenance resource scenarios are obtained based on the distribution characteristics of tower operation and maintenance resources. The distribution characteristics of operation and maintenance resources include the allocation of operation and maintenance personnel, the reserve of maintenance equipment, and traffic conditions. Based on the core operation content, operation timing, cold region protection requirements, and operation and maintenance resource scenarios, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

[0042] The core work content, timing, and specific protective requirements are determined, with the core trigger as the guiding principle, and key elements of operation and maintenance work are formulated in conjunction with the geographical and climatic characteristics of the frigid region. Targeted work content is matched to different types of core triggers. For example, when the core trigger is icing overload, the core work content includes clearing ice from conductors, re-measuring the stress on towers, and checking the tightness; when the core trigger is foundation frost heave, the core work content includes foundation thawing, soil reinforcement, and settlement monitoring. The timing of work is determined in accordance with the climatic patterns of the frigid region. For freezing weather in Level 1 frigid regions, outdoor work is prioritized during the relatively warmer midday hours to reduce the impact of low temperatures on worker safety and equipment condition. Specific protective requirements are also formulated based on the environmental characteristics of the frigid region, including workers wearing anti-freezing gear, activating anti-freezing and insulation modes on equipment, and setting up anti-slip safety facilities at the work site to ensure the safety and effectiveness of operations in harsh environments.

[0043] Maintenance resource scenarios are categorized based on the distribution characteristics of maintenance resources. According to the distribution characteristics of maintenance resources in the area where the tower is located, scenarios are divided into three dimensions: maintenance personnel allocation, maintenance equipment reserves, and traffic conditions. The maintenance personnel allocation dimension is categorized according to the response time for personnel to arrive on-site, such as a rapid response scenario reachable within 30 minutes, a regular response scenario reachable within 1 to 2 hours, and a remote area scenario reachable within 2 hours. The maintenance equipment reserves dimension is categorized according to the number of dedicated equipment available on-site, such as a scenario with sufficient equipment and a scenario with limited equipment. The traffic conditions dimension is categorized according to road conditions and accessibility, such as a scenario with convenient access, a scenario with restricted access, and a scenario with closed access. By combining these dimensions, different types of maintenance resource scenarios are formed, such as a rapid and sufficient scenario in a Level 1 cold region with convenient access and personnel reachable within 30 minutes, and a remote and restricted scenario in a Level 3 cold region with restricted access and limited equipment reserves.

[0044] Differentiated solutions were developed and integrated into a solution library for different early warning levels and resource scenarios. For different early warning levels, and combined with different operation and maintenance resource scenarios, corresponding operation and maintenance work processes, technical parameters, and personnel and equipment configuration plans were developed. Early warning levels are divided into four levels: low, medium, high, and emergency. Operation and maintenance resource scenarios are divided into rapid and sufficient scenarios, routine response scenarios, and remote and restricted scenarios, forming multi-dimensional combined scenarios. Taking a high-level anomaly in a rapid and sufficient resource scenario as an example, the developed work process includes three stages: rapid mobilization, emergency repair, and on-site retesting. The technical parameters clearly define the concrete strength grade for foundation reinforcement and the standards for tower tilt repair. The personnel and equipment configuration arranges for 5 experienced repair personnel to arrive on-site within 30 minutes with a full set of de-icing and reinforcement equipment. Taking a low-level anomaly in a remote and restricted resource scenario as an example, the developed work process includes three stages: remote monitoring, regular inspection, and slow handling. The technical parameters reduce the required inspection frequency, and the personnel and equipment configuration arranges for 2 inspection personnel to conduct periodic inspections with portable equipment. The preliminary operation and maintenance solutions for all combined scenarios are integrated, classified and archived according to warning level and resource scenario, and finally formed a preliminary operation and maintenance solution library adapted to multiple scenarios, providing a comprehensive and hierarchical candidate strategy foundation for the subsequent simulation and selection of the optimal solution.

[0045] The preliminary operation, maintenance and repair plan involves simulation and evaluation of each option, selection of the optimal operation, maintenance and repair plan, and its implementation. This process includes the following steps: Establish an evaluation index scheme for operation and maintenance strategies. The evaluation index scheme includes operation and maintenance timeliness index, on-site operation difficulty index, resource consumption index, maintenance effect expectation index, and operation safety index in extremely cold regions. Assign weights to each indicator in the evaluation indicator scheme and formulate quantitative evaluation standards for each indicator; Based on quantitative evaluation standards, each scheme in the preliminary operation and maintenance scheme library is quantitatively scored. Combined with real-time environmental changes in the frigid zone, the scheme implementation is simulated and deduced. The strategy with the highest comprehensive score and the best simulation implementation effect is selected as the optimal operation and maintenance strategy for the power poles in the frigid zone in this anomaly.

[0046] An evaluation index scheme for operation and maintenance strategies was established, covering five core dimensions: operation and maintenance timeliness, on-site operation difficulty, resource consumption, expected maintenance results, and safety of operations in frigid regions. The operation and maintenance timeliness index measures the time cycle from response to completion, including response time and total operation time. The on-site operation difficulty index assesses the ease of operation based on the climate of frigid regions, tower terrain, and operational complexity. The resource consumption index calculates the input costs of manpower, equipment, and materials. The expected maintenance results index predicts the anomaly elimination rate, tower condition recovery time, and the probability of subsequent fault recurrence. The safety of operations in frigid regions focuses on assessing the operational risks and the effectiveness of protection in environments such as low temperatures and snowstorms, comprehensively covering the key considerations of the operation and maintenance plan.

[0047] Weights were assigned to the evaluation indicators, and quantitative evaluation standards were established. Based on the core needs of operation and maintenance in frigid regions, differentiated weights were allocated to each indicator. For example, the weight of the expected maintenance effect indicator was set at 0.3, the weights of operation and maintenance timeliness and operational safety indicators were each set at 0.25, and the weights of resource consumption and operational difficulty indicators were each set at 0.1, emphasizing the priority of safety and effectiveness. Simultaneously, quantitative scoring standards were established for each indicator, with a maximum score of 100 points. The operation and maintenance timeliness indicator had a maximum score based on a response time of 30 minutes, with 5 points deducted for every 10-minute delay; the on-site operational difficulty indicator had a maximum score based on routine operations on flat ground, with 10 points deducted for each additional limitation imposed by frigid conditions or terrain; the resource consumption indicator had a maximum score based on the lowest cost solution, with 5 points deducted for every 10% increase in cost; the expected maintenance effect indicator had a maximum score based on 100% elimination of anomalies and no recurrence within 30 days, with 10 points deducted for every 10% decrease in the elimination rate or shortening of the recurrence cycle; and the operational safety indicator had a maximum score based on a zero-risk contingency plan, with 15 points deducted for each existing safety hazard.

[0048] The optimal solution was selected through scoring and simulation based on quantitative standards. First, each solution in the preliminary operation and maintenance solution library was scored item by item according to the quantitative evaluation standards. Then, a weighted summation was used to calculate the comprehensive score, calculated as follows: Comprehensive Score = Operation and Maintenance Timeliness Score × 0.25 + Operation Difficulty Score × 0.1 + Resource Consumption Score × 0.1 + Maintenance Effectiveness Score × 0.3 + Operation Safety Score × 0.25. Subsequently, simulations were conducted on each solution in light of real-time environmental changes in the frigid region, such as temperature fluctuations and snowstorm warnings, to verify the feasibility and effectiveness of the solutions in the actual environment. The simulation content included the adaptability of the work process, the smoothness of resource allocation, and the success rate of anomaly handling. For example, in an advanced anomaly scenario, there were two candidate solutions: Solution A had a comprehensive score of 88 points, and the simulation showed a high risk of operational safety under the current snowstorm warning; Solution B had a comprehensive score of 85 points, and the simulation showed strong environmental adaptability and stable implementation results. Ultimately, Solution B was selected as the optimal operation and maintenance strategy. Through dual verification of quantitative scoring and simulation, the selected solutions are ensured to meet the indicator requirements and adapt to the complex real-time environment of frigid regions, providing a scientific and reliable basis for handling tower anomalies.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for formulating operation and maintenance strategies for power poles based on real-time monitoring, characterized in that, The method includes the following steps: Collect historical deformation detection data, historical operating status data, historical environmental monitoring data, and corresponding historical operation and maintenance plans and implementation effect data of power poles to establish a basic database for the operation and maintenance of power poles in frigid regions; Screen fault characteristics from the operation and maintenance database, determine the correlation thresholds between tower deformation, operational anomalies and environmental factors in cold regions based on the fault characteristics, and establish a set of abnormal early warning thresholds for tower status. Real-time deformation data, real-time operating parameters, and real-time environmental data of power poles are obtained to obtain multi-source real-time monitoring data. The multi-source real-time monitoring data are then fused to obtain real-time comprehensive status data of the poles. The real-time integrated status data of the tower is compared with the abnormal early warning threshold set to determine whether the tower has abnormal deformation, abnormal operation, or environmental impact exceeding the threshold, and to generate the real-time status abnormality judgment result of the tower. Based on the real-time anomaly determination results of the tower, a preliminary operation and maintenance solution library adapted to multiple scenarios is developed; The solutions in the preliminary operation, maintenance and repair solution library are simulated and evaluated to select the optimal operation, maintenance and repair solution and implement it.

2. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 1, characterized in that, Screening fault characteristics from the operation and maintenance database, determining the correlation threshold set between tower deformation, operational anomalies, and environmental factors in frigid regions based on these fault characteristics, and establishing early warning thresholds for tower condition anomalies, specifically includes the following steps: The correlation patterns between tower deformation, operating parameters and environmental factors in the operation and maintenance basic database were screened to determine the correlation coefficient of each influencing factor; Based on the standard for safe operation of towers, and combined with the correlation coefficient and the influence coefficient of extreme environment in frigid regions, the critical safety values ​​of tower deformation and operating parameters under different environmental conditions are calculated. The safety thresholds are divided into low-level warning thresholds, medium-level warning thresholds, high-level warning thresholds, and emergency warning thresholds according to the degree of risk. Corresponding anomaly judgment rules are matched for each level of warning threshold to form an anomaly warning threshold set for tower status.

3. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 2, characterized in that, The real-time integrated status data of the tower is obtained by fusing and verifying multi-source real-time monitoring data, specifically including the following steps: Acquire real-time raw data from multiple sources, including deformation monitoring terminals, operational status monitoring terminals, and environmental monitoring terminals, and perform time synchronization and coordinate calibration on the raw data from each terminal. Multi-source terminal data is obtained by fusing data from various terminals within the same monitoring dimension. Outlier detection and screening of multi-source terminal data are performed to obtain valid multi-source terminal data. After calibration, fusion and verification of the valid multi-source terminal data, real-time comprehensive status data of the tower is formed.

4. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 3, characterized in that, The real-time integrated status data of the tower is compared with the anomaly warning threshold set to determine whether the tower has abnormal deformation, operational abnormalities, or environmental impact exceeding the threshold, and a real-time anomaly judgment result for the tower is generated. The specific steps include: The deformation data, operating parameters, and environmental data of the tower's real-time integrated status data are compared one by one with the corresponding indicators and warning thresholds under the corresponding environmental conditions in the anomaly warning threshold system to obtain the comparison and judgment results. If the monitoring data does not exceed the warning threshold, the tower is considered to be in normal condition. If the monitoring data exceeds the warning threshold, it is determined to be an abnormal status of the corresponding level, and the abnormal indicator type, abnormal value and the warning level are marked. By integrating the comparison and judgment results of all indicators, a real-time status anomaly judgment result for the tower is formed, which includes the anomaly level, anomaly indicator, and anomaly degree.

5. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 4, characterized in that, Based on the real-time anomaly determination results of the tower status, a preliminary operation and maintenance solution library adaptable to multiple scenarios is developed, which includes the following steps: Based on the abnormal characteristic parameters of the real-time abnormal status judgment results of the tower and the historical matching data of the operation and maintenance basic database, the causes of abnormalities are traced to obtain the core causes of tower abnormalities and the degree of influence of the causes. Based on the core causes, the degree of impact of the causes, and the geographical climate of the frigid region and the distribution characteristics of tower maintenance resources, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

6. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 5, characterized in that, If an anomaly is determined, the core causes and their impact on the tower anomaly are traced based on the anomaly characteristic parameters of the tower's real-time status anomaly determination results and historical matching data from the operation and maintenance database. This process includes the following steps: Extract the abnormal indicator types from the abnormal status judgment results, and search for historical data with similar abnormal characteristics in the operation and maintenance basic database based on the abnormal indicator types to obtain the historical matching dataset; By analyzing historical matching datasets, current environmental characteristics of the frigid zone, the service life of the tower, and its structural features, a set of potential causes for the tower's current anomaly is obtained. Calculate the matching degree between each trigger in the potential trigger set and the abnormal feature parameters, mark the trigger with the highest matching degree as the core trigger, and obtain the degree of influence of the trigger based on the influence of the core trigger on the abnormal indicators.

7. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 6, characterized in that, Based on the core causes, the degree of impact of the causes, and the geographical and climatic characteristics of the frigid region, as well as the distribution characteristics of tower maintenance resources, a preliminary operation and maintenance solution library adaptable to multiple scenarios is formulated, which specifically includes the following steps: Based on the core causes, the degree of influence of the causes, and the geographical and climatic characteristics of the frigid zone, the core operation content, operation timing, and protection requirements for operation and maintenance in the frigid zone are determined. Different operation and maintenance resource scenarios are obtained based on the distribution characteristics of tower operation and maintenance resources. The distribution characteristics of operation and maintenance resources include the allocation of operation and maintenance personnel, the reserve of maintenance equipment, and traffic conditions. Based on the core operation content, operation timing, cold region protection requirements, and operation and maintenance resource scenarios, a preliminary operation and maintenance solution library adapted to multiple scenarios was developed.

8. The method for formulating operation and maintenance strategies for power poles based on real-time monitoring according to claim 7, characterized in that, The preliminary operation, maintenance and repair plan involves simulation and evaluation of each option, selection of the optimal operation, maintenance and repair plan, and its implementation. This process includes the following steps: Establish an evaluation index scheme for operation and maintenance strategies. The evaluation index scheme includes operation and maintenance timeliness index, on-site operation difficulty index, resource consumption index, maintenance effect expectation index, and operation safety index in extremely cold regions. Assign weights to each indicator in the evaluation indicator scheme and formulate quantitative evaluation standards for each indicator; Based on quantitative evaluation criteria, each scheme in the preliminary operation and maintenance scheme library is quantitatively scored. The scheme implementation is simulated and deduced in combination with real-time environmental changes in the frigid zone. The strategy with the highest comprehensive score and the best simulation implementation effect is selected as the optimal operation and maintenance scheme.

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