Long-distance transmission line inspection path optimization algorithm
By fusing multi-source data and mining spatiotemporal association rules using an improved Apriori algorithm, a fault occurrence probability prediction model is constructed, and the inspection path for long-distance transmission lines is optimized. This solves the problems of low efficiency and insufficient intelligent analysis in traditional inspection technologies, and achieves dynamic updates and efficient inspection.
Patent Information
- Application Number
- CN202511924240.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-19
- Publication Date
- 2026-03-20
AI Technical Summary
Traditional long-distance transmission line inspection technologies suffer from low efficiency, lack of intelligent analysis, insufficient data fusion and mining capabilities, and lack of dynamic update capabilities, resulting in low inspection efficiency and an inability to achieve all-weather, full-coverage, and accurate identification of high-risk sections.
By employing multi-source data fusion and preprocessing, spatiotemporal association rule mining based on the improved Apriori algorithm, construction of a fault occurrence probability prediction model, and predictive inspection path planning, a dynamically optimized inspection path is generated, and the model is updated to adapt to changes in equipment and environment.
It significantly improves inspection efficiency and accuracy, reduces inspection costs and safety risks, and provides a guarantee for the safe and stable operation of long-distance transmission lines.
Smart Images
Figure CN121707451A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of long-distance transmission line inspection technology, specifically to a long-distance transmission line inspection path optimization algorithm. Background Technology
[0002] With the increasing demand for energy transmission, the scale and complexity of long-distance transmission lines are constantly increasing. Their safe and stable operation has become the key to ensuring energy supply. Long-distance transmission lines traverse complex and changeable geographical environments and are susceptible to multiple factors such as natural disasters, equipment aging, and human sabotage, leading to frequent failures.
[0003] Traditional long-distance transmission line inspection technologies mainly rely on manual periodic inspections or automated inspection systems based on simple rules, which have the following significant drawbacks: First, manual inspections are limited by the experience and physical strength of the inspectors, making it difficult to achieve all-weather, full-coverage inspections and easily overlooking potential fault points; second, traditional automated inspection systems often lack intelligent analysis capabilities, only able to inspect according to preset routes, unable to dynamically adjust inspection strategies based on the actual conditions of the line, resulting in low inspection efficiency; third, traditional methods lack effective data fusion and mining techniques when processing multi-source heterogeneous data, making it difficult to accurately identify high-risk sections and the probability of fault occurrence, thus affecting the targeting and effectiveness of inspections; in addition, traditional inspection technologies also lack dynamic update capabilities, unable to adapt to changes in long-distance transmission line equipment and environment, causing inspection paths and strategies to gradually become ineffective.
[0004] In view of the problems of low efficiency, lack of intelligent analysis, insufficient data fusion and mining capabilities, and lack of dynamic update capabilities in traditional long-distance transmission line inspection technology, the proposed long-distance transmission line inspection path optimization algorithm is particularly important. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a long-distance transmission line inspection path optimization algorithm. This algorithm can achieve dynamic optimization and adaptive updating of long-distance transmission line inspection paths through multi-source data fusion and preprocessing, spatiotemporal association rule mining based on the improved Apriori algorithm, construction of a fault occurrence probability prediction model, and predictive inspection path planning. This algorithm not only significantly improves inspection efficiency and accuracy, but also effectively reduces inspection costs and safety risks, providing a strong guarantee for the safe and stable operation of long-distance transmission lines.
[0006] To solve the above-mentioned technical problems, this invention provides the following technical solution: a long-distance transmission line inspection path optimization algorithm, the specific steps of which are as follows: Multi-source data fusion and preprocessing steps: Collect historical fault data of long-distance transmission line equipment, meteorological time series data and geographic information data, clean, standardize and feature-encode the multi-source data to form a structured fusion dataset; The steps for mining spatiotemporal association rules based on the improved Apriori algorithm are as follows: Time interval, spatial segment, meteorological conditions, and equipment failure type are used as itemset elements of association rules. By setting a custom minimum support threshold and minimum confidence threshold, the fused dataset is traversed to generate frequent itemsets and extract spatiotemporal association rules between equipment failure and time, space, and meteorological factors. Fault occurrence probability prediction model construction steps: Based on the spatiotemporal correlation rules, construct the fault occurrence probability prediction model, input the current or predicted time and weather conditions, calculate the probability of various faults occurring in each section of the long-distance transmission line, and form a high-risk section probability matrix. Predictive inspection path planning steps: Prioritize the inspection sections according to the high-risk section probability matrix, adjust the inspection coverage frequency of each section, and generate a dynamically optimized inspection path by combining the inspection start point, end point and driving constraints using an improved path generation algorithm.
[0007] Furthermore, in the multi-source data fusion and preprocessing steps, the historical fault data of the equipment includes fault type, occurrence time, geographical location, and fault cause; the meteorological time series data includes temperature, humidity, wind speed, rainfall, and extreme weather types; the geographic information data includes geographic coordinates, terrain, and surrounding environment; the data cleaning includes removing null values and outliers; the standardization includes unifying time data into a timestamp format and converting geographic coordinates into a unified coordinate system; and the feature encoding includes classifying and encoding fault types and terrain categories.
[0008] Furthermore, the formula for the improved Apriori algorithm in the spatiotemporal association rule mining step based on the improved Apriori algorithm is: ,in For itemsets Overall support Representation Itemset In the dataset Frequency of occurrence in For the number of datasets, Total number of itemsets , These are the weighting coefficients. , The value range is 0.6-0.8. The value ranges from 0.2 to 0.4, and it was determined through multiple rounds of regression analysis on historical fault data to achieve the highest good fit between the overall support and the actual frequency of fault occurrence. For itemsets The corresponding time interval length, This represents the total time span.
[0009] Furthermore, in the spatiotemporal association rule mining step based on the improved Apriori algorithm, the minimum support threshold ranges from 3% to 8%, and the minimum confidence threshold ranges from 55% to 70%. These thresholds are determined by statistical analysis of fault data from long-distance transmission lines over the past five years, combined with expert experience, to ensure that the mined association rules have practical fault prediction value.
[0010] Furthermore, the formula for calculating the probability of failure in the failure occurrence prediction model construction step is as follows: ,in In time Spatial Section Meteorological conditions The following failure occurred The probability, The number of matched association rules. For the first The coverage coefficient of each association rule ranges from 0.7 to 1.0, and is determined based on the historical fault coverage range corresponding to the rule. For the first The confidence level of the association rule. For the first The weight of each association rule ranges from 0.5 to 1.5, and is determined based on the rule's support and the expert's assessment of its importance. The higher the support and the more important the expert assessment, the greater the weight.
[0011] Furthermore, the formula for calculating the segment priority in the predictive inspection path planning step is as follows: ,in For section priority, Number of fault types For section Malfunction The probability, For fault The severity coefficient ranges from 1 to 5. It is determined by experts based on the degree of impact of the fault on the operation of the long-distance transmission line. The greater the impact, the higher the coefficient.
[0012] Furthermore, the formula for calculating the inspection coverage frequency in the predictive inspection path planning step is as follows: ,in For section The frequency of inspection coverage, Basic inspection frequency, , These are the lowest and highest priorities for all segments, respectively. This is a frequency adjustment factor, ranging from 1 to 3, determined based on the inspection resources and maintenance requirements of the long-distance transmission line. More abundant resources and higher maintenance requirements generally correlate with higher frequency adjustments. The larger.
[0013] Furthermore, the improved path generation algorithm in the predictive inspection path planning step is a priority-based genetic algorithm with the following fitness function: ,in For path fitness For path Number of covered segments For path Total length, The distance weighting coefficient ranges from 0.3 to 0.5. It is determined by analyzing the length of multiple historical inspection paths and the fault discovery status, so that the fitness function can balance priority coverage and path length.
[0014] Furthermore, the method also includes a model update step: periodically collecting new equipment fault data, meteorological data, and geographic information data to update the fused dataset, and re-executing the steps of spatiotemporal association rule mining based on the improved Apriori algorithm, fault occurrence probability prediction model construction, and predictive inspection path planning to achieve dynamic model updates in order to adapt to changes in long-distance transmission line equipment and environment.
[0015] Compared with existing technologies, this long-distance transmission line inspection path optimization algorithm has the following advantages: I. This algorithm, through multi-source data fusion and preprocessing, combined with spatiotemporal association rule mining based on the improved Apriori algorithm, can accurately identify the probability of fault occurrence in each section of long-distance transmission lines under different time, space and meteorological conditions. This helps inspection personnel to prioritize high-risk sections and dynamically adjust the inspection coverage frequency, thereby significantly improving inspection efficiency and reducing unnecessary inspection workload while ensuring inspection quality.
[0016] Second, this algorithm constructs a fault occurrence probability prediction model and combines the inspection start point, end point, and driving constraints with an improved path generation algorithm to generate dynamically optimized inspection paths. In addition, the algorithm also includes a model update step, which periodically collects new data to update the fused dataset and re-executes the association rule mining, prediction model construction, and path planning steps to ensure that the inspection path can adapt to changes in long-distance transmission line equipment and environment and maintain optimal status. This dynamic optimization and adaptive update capability greatly improves the flexibility and effectiveness of inspection work.
[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0019] Figure 1 A flowchart of an algorithm for optimizing inspection routes on long-distance transmission lines; Figure 2 This is a schematic diagram of the core process of an algorithm for optimizing inspection paths on long-distance transmission lines. Detailed Implementation
[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0021] Example 1: Multi-source data fusion and preprocessing steps The project collected historical equipment failure data, meteorological time-series data, and geographic information data for the past five years from the long-distance natural gas pipeline in the mountainous area. The historical equipment failure data covered failure types such as pipeline corrosion and leakage, valve failure, and loose interfaces, as well as the occurrence time, precise geographical location, and identified cause of each failure. The meteorological time-series data included daily temperature, humidity, wind speed, and rainfall records, as well as extreme weather types such as heavy rain, heavy snow, and lightning, and the time periods of their occurrence. The geographic information data involved the latitude and longitude coordinates of the pipeline route, terrain types such as mountains, canyons, and forests, and information on the surrounding environment such as whether there were construction areas or residential areas nearby. The collected multi-source data were processed to remove null values and obvious outliers, convert all failure occurrence times into a unified timestamp format, convert geographic coordinates from different coordinate systems into a unified coordinate system, and classify and encode failure types and terrain categories, ultimately forming a structured fused dataset.
[0022] Spatiotemporal association rule mining steps based on the improved Apriori algorithm The time intervals are divided into quarterly, monthly, and daily time periods, and the spatial segments are divided into 5-kilometer segments along the pipeline. Meteorological conditions include temperature ranges, humidity ranges, wind speed levels, rainfall levels, and extreme weather types. Equipment failure types include known corrosion leaks and valve failures. Using the above items as itemset elements for association rules, statistical analysis of the pipeline's failure data over the past five years is conducted. Combined with the experience of experts in the field of natural gas pipeline inspection, the minimum support threshold is determined to be 5%, and the minimum confidence threshold is determined to be 60%. Based on the above itemset elements and the determined thresholds, the structured fusion dataset is traversed to generate frequent itemsets, and then the spatiotemporal association rules between equipment failures and time, space, and meteorological factors are extracted. For example, the association rules between summer, mountainous canyon sections, heavy rain, and pipeline corrosion leak failures are obtained every year.
[0023] Steps for building a failure probability prediction model Based on the mined spatiotemporal correlation rules, a fault occurrence probability prediction model is constructed. Inputs include the current season, the mountainous and canyon section of the target inspection area, and the 7-day heavy rain forecast. The model matches the corresponding correlation rules and uses the fault occurrence probability calculation formula, which is: ,in In time Spatial Section Meteorological conditions The following failure occurred The probability, The number of matched association rules. For the first The coverage coefficient of the association rules. For the first The confidence level of the association rule. For the first The weights of the association rules are combined with the coverage coefficient, confidence level, and weight of each association rule to calculate the probability of various faults such as corrosion leakage and valve failure in the section, and finally form the probability matrix of high-risk sections of each section of the long-distance natural gas pipeline in the mountainous area.
[0024] Predictive inspection path planning steps Based on the probability matrix of high-risk segments, and using the segment priority calculation formula, the fitness function is: ,in For path fitness For path Number of covered segments For path Total length, Using distance as the weighting factor and combining it with the severity coefficients of various faults, all pipeline inspection sections are prioritized. Among them, mountainous canyon sections under summer rainstorms are listed as the highest priority due to their high probability of failure and severe consequences. According to the inspection coverage frequency calculation formula, the inspection coverage frequency of high-priority sections is increased and the inspection frequency of low-risk sections is reduced on the basis of the basic inspection frequency. Taking into account the inspection start and end points, as well as driving constraints such as mountain road traffic restrictions and inspection vehicle range, a genetic algorithm combined with priority is used to evaluate and optimize the path through a fitness function to generate dynamically optimized inspection paths, ensuring that high-risk sections are inspected first and inspection resources are reasonably allocated.
[0025] Model update steps Every quarter, new equipment failure data, meteorological data, and geographic information data are collected, such as newly added pipeline maintenance records, quarterly meteorological summary data, and information on newly emerging construction areas along the pipeline. The fused dataset is then updated. Based on the updated dataset, the spatiotemporal association rule mining, failure probability prediction model construction, and predictive inspection path planning steps are re-executed to ensure that the model and inspection path always fit the actual situation and maintain the optimization effect.
[0026] Example 2: Multi-source data fusion and preprocessing steps Historical equipment failure data, meteorological time-series data, and geographic information data for the past five years were collected for the long-distance crude oil pipeline in the plain. The historical equipment failure data includes failure types such as pipeline rupture, pump failure, and instrument failure, as well as the specific time, geographical location, and cause of the failure. The meteorological time-series data includes temperature, humidity, wind speed, rainfall data, and extreme weather information such as high temperatures and cold waves. The geographic information data covers the geographical coordinates of the pipeline, the terrain around the plain, farmland, and highways, and the surrounding environment. The data was cleaned to remove null values and outliers, the time data was unified into a timestamp format, the geographic coordinates were converted into a unified coordinate system, and the failure types and terrain categories were classified and coded to construct a structured fused dataset.
[0027] Spatiotemporal association rule mining steps based on the improved Apriori algorithm Using time intervals, spatial segments, meteorological conditions, and equipment failure types as itemset elements for association rules, and by analyzing failure data from the past five years and combining expert experience, a minimum support threshold of 3% and a minimum confidence threshold of 55% are set. The dataset is traversed and merged to generate frequent itemsets, and spatiotemporal association rules such as high temperature weather + highway surrounding sections + summer afternoon and pipeline rupture failures are extracted.
[0028] Steps for building a failure probability prediction model Based on the extracted spatiotemporal association rules, a fault occurrence probability prediction model is built. Input information such as the current time, each pipeline section, and the high temperature weather forecast for the next week. The model matches the corresponding association rules, uses the fault occurrence probability calculation formula, and combines the coverage coefficient, confidence and weight of the association rules to calculate the probability of various faults occurring in each section, forming a high-risk section probability matrix. Among them, the fault probability of the highway surrounding sections under high temperature weather is significantly higher than that of other sections.
[0029] Predictive inspection path planning steps Using the segment priority calculation formula and combining the severity coefficients of various faults, all inspection segments are prioritized. Sections around highways in high-temperature weather are designated as high priority. According to the inspection coverage frequency calculation formula, the inspection coverage frequency of each segment is adjusted, and the inspection frequency of high-priority segments is significantly increased compared to the base frequency. Taking into account the inspection start and end points, as well as road traffic conditions in plain areas and the working hours of inspection personnel, a genetic algorithm combined with priority is used to optimize the path through the fitness function, generating dynamic inspection paths to ensure that high-risk segments receive key inspections while also taking into account inspection efficiency.
[0030] Model update steps Every six months, new equipment fault data, meteorological data, and geographic information data are collected, such as newly added fault repair records, semi-annual meteorological statistics, and information on changes in the surrounding environment along the route. The merged dataset is then updated. Based on the updated dataset, spatiotemporal association rule mining, fault occurrence probability prediction model construction, and predictive inspection path planning are carried out again to keep the inspection path continuously optimized and adapt to changes in actual conditions.
[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A long-distance transmission line inspection path optimization algorithm, characterized in that, The specific steps of this method are as follows: Multi-source data fusion and preprocessing steps: Collect historical fault data of long-distance transmission line equipment, meteorological time series data and geographic information data, clean, standardize and feature-encode the multi-source data to form a structured fusion dataset; The steps for mining spatiotemporal association rules based on the improved Apriori algorithm are as follows: Time interval, spatial segment, meteorological conditions, and equipment failure type are used as itemset elements of association rules. By setting a custom minimum support threshold and minimum confidence threshold, the fused dataset is traversed to generate frequent itemsets and extract spatiotemporal association rules between equipment failure and time, space, and meteorological factors. Fault occurrence probability prediction model construction steps: Based on the spatiotemporal correlation rules, construct the fault occurrence probability prediction model, input the current or predicted time and weather conditions, calculate the probability of various faults occurring in each section of the long-distance transmission line, and form a high-risk section probability matrix. Predictive inspection path planning steps: Prioritize the inspection sections according to the high-risk section probability matrix, adjust the inspection coverage frequency of each section, and generate a dynamically optimized inspection path by combining the inspection start point, end point and driving constraints using an improved path generation algorithm.
2. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The equipment historical fault data in the multi-source data fusion and preprocessing steps include fault type, occurrence time, geographical location, and fault cause; the meteorological time series data includes temperature, humidity, wind speed, rainfall, and extreme weather types; the geographic information data includes geographic coordinates, terrain, and surrounding environment; the data cleaning includes removing null values and outliers; the standardization includes unifying time data into a timestamp format and converting geographic coordinates into a unified coordinate system; and the feature encoding includes classifying and encoding fault types and terrain categories.
3. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The formula for the improved Apriori algorithm in the spatiotemporal association rule mining step based on the improved Apriori algorithm is: ,in For itemsets Overall support Representation Itemset In the dataset Frequency of occurrence in For the number of datasets, The total number of itemsets , These are the weighting coefficients. For itemsets The corresponding time interval length, This represents the total time span.
4. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, In the spatiotemporal association rule mining step based on the improved Apriori algorithm, the minimum support threshold ranges from 3% to 8%, and the minimum confidence threshold ranges from 55% to 70%. These thresholds are determined by statistical analysis of fault data from long-distance transmission lines over the past five years, combined with expert experience, to ensure that the mined association rules have practical fault prediction value.
5. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The formula for calculating the probability of failure in the failure occurrence prediction model construction step is as follows: ,in In time Spatial Section Meteorological conditions The following malfunction occurred The probability, The number of matched association rules. For the first The coverage coefficient of the association rules. For the first The confidence level of the association rule. For the first The weight of each association rule.
6. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The formula for calculating the segment priority in the predictive inspection path planning step is as follows: ,in For section priority, Number of fault types For section Malfunction The probability, For fault The severity coefficient.
7. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The formula for calculating the inspection coverage frequency in the predictive inspection path planning step is as follows: ,in For section The frequency of inspection coverage, Basic inspection frequency, , These are the lowest and highest priorities for all segments, respectively. This is the frequency adjustment factor.
8. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The improved path generation algorithm in the predictive inspection path planning step is a priority-based genetic algorithm with the following fitness function: ,in For path Adaptability, For path Number of covered segments For path Total length, This is the distance weighting coefficient.
9. The long-distance transmission line inspection path optimization algorithm according to claim 1, characterized in that, The method also includes a model update step: periodically collecting new equipment fault data, meteorological data, and geographic information data, updating the fused dataset, and re-executing the steps of spatiotemporal association rule mining based on the improved Apriori algorithm, fault occurrence probability prediction model construction, and predictive inspection path planning.