Rail transit maintenance quality evaluation method
Patent Information
- Application Number
- CN202611137942.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-29
- Publication Date
- 2026-09-11
AI Technical Summary
但当前维修质量评价技术存在明显缺陷：其一，评价方式高度依赖人工对比分析，需要专业人员逐一核对维修前后检测报告，人工成本高、分析效率低，且评价结果受人员经验、主观判断影响较大，缺乏统一、标准化的评价准则，一致性差；其二，现有评价手段仅能统计线路整体报警数量变化，无法建立同一空间区段、同一故障类型的前后对应关系，难以精准定位局部区段的维修缺陷，无法有效识别维修后新发病害及残留病害；其三，现有轨道状态评估系统仅实现病害统计、状态展示、趋势分析基础功能，未形成维修质量自动评价、效果验证、问题复盘的闭环体系，存在显著技术空白
[0013]本发明提供了一种轨道交通维修质量评估方法，具备以下有益效果：本发明全程实现检测数据自动抓取、时空对齐、统计分析、质量评价、结果输出，无需人工对比研判，彻底摆脱对专业人员经验的依赖，大幅减少人工工作量，显著提升维修质量评价效率；
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Abstract
Description
Technical Field
[0001] This invention relates to the fields of intelligent operation and maintenance of rail transit, condition monitoring of rail equipment, and maintenance quality evaluation, specifically a method for evaluating the maintenance quality of rail transit. Background Technology
[0002] With the continuous expansion of my country's urban rail transit network and the year-on-year increase in operating mileage, the pressure on routine monitoring, maintenance, and quality control of core track equipment continues to increase. Currently, rail transit track condition detection mainly relies on the detection equipment carried by track inspection vehicles and comprehensive inspection vehicles, combined with manual inspection methods. This can comprehensively collect track geometric condition data and generate detection alarm data including various track defects such as gauge anomalies, elevation irregularities, horizontal irregularities, track alignment anomalies, and triangular pits.
[0003] Under the current operation and maintenance model, maintenance units formulate maintenance plans and carry out line maintenance work based on detection alarm data. After maintenance is completed, they conduct another inspection and verify the maintenance effect by comparing the before and after inspection results. However, current maintenance quality evaluation technology has obvious defects: First, the evaluation method relies heavily on manual comparison and analysis, requiring professionals to check the before and after inspection reports one by one. This results in high labor costs, low analysis efficiency, and the evaluation results are greatly affected by personnel experience and subjective judgment. There is a lack of unified and standardized evaluation criteria, leading to poor consistency. Second, existing evaluation methods can only count the overall changes in the number of alarms on the line. They cannot establish a correlation between before and after the same spatial section and the same fault type, making it difficult to accurately locate maintenance defects in local sections and effectively identify new and residual defects after maintenance. Third, existing track condition assessment systems only realize basic functions such as defect statistics, status display, and trend analysis. They have not formed a closed-loop system for automatic maintenance quality evaluation, effect verification, and problem review, resulting in significant technological gaps. Summary of the Invention
[0004] The purpose of this invention is to provide a method for assessing the maintenance quality of rail transit systems, in order to solve the problems mentioned in the background section.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the maintenance quality of rail transit, comprising the following steps: S1. Data Acquisition: Acquire track inspection alarm data for two inspection cycles before and after maintenance of the rail transit line. The alarm data shall include at least kilometer marker information, mileage information, alarm type information and inspection time information. S2. Spatiotemporal alignment: Using line number and up / down direction as filtering conditions, match the detection data before and after maintenance, distinguish the pre-maintenance dataset from the post-maintenance dataset by detection time, and complete the spatial alignment of the two datasets using the kilometer marker as the spatial reference to establish a unified spatiotemporal correspondence. S3. Line segment division: Determine the total length of the line based on the starting and ending kilometer markers. Divide the line into several continuous, non-overlapping standardized line segments according to the preset fixed segment lengths. Establish a unique segment index and spatial location information for each segment. S4. Fault Statistics Matrix Construction: Map the alarm data before and after maintenance to the corresponding standardized line sections, classify and count the number of faults in each section according to fault type, and construct the fault statistics matrix before maintenance and the fault statistics matrix after maintenance respectively. S5. Maintenance quality status determination: Compare the number of faults before and after maintenance for the same section and the same type of fault, and assign corresponding maintenance status codes. The maintenance status includes four types: normal status, new fault, repaired, and poor maintenance quality. A maintenance quality evaluation matrix is constructed based on the sum of fault status codes for each section. S6. Maintenance and repair rate calculation: For each type of track fault, the total number of faults before and after maintenance is counted, the maintenance and repair elimination rate of each type of fault is calculated, and a fault maintenance effect statistical table is formed. S7. Key Problem Section Identification: Traverse the maintenance quality evaluation matrix, extract spatial information, fault types and changes in the number of faults before and after maintenance for each section, classify and identify and generate a list of newly occurring fault sections and a list of sections with poor maintenance quality. S8. Output of comprehensive evaluation results: Integrate fault statistics, repair rate, maintenance quality evaluation matrix and key problem section information, calculate the overall maintenance quality score of the line, generate and output standardized multi-dimensional maintenance quality comprehensive evaluation results.
[0006] Furthermore, in step S3, the preset segment length is 50m, 100m or 200m, preferably 100m; for the divided line segments, the starting kilometer marker, ending kilometer marker and relative mileage information of each segment are stored, and a fixed segment index system is established to ensure the spatial comparability of data from different detection cycles.
[0007] Furthermore, in step S4, the fault types include abnormal track gauge, unevenness in elevation, unevenness in level, abnormal track alignment, and typical defects of triangular pit tracks.
[0008] Furthermore, the maintenance status coding rules in step S5 are as follows: status value 0 indicates that the section is normal, with no residual faults or new faults in the section; status value 1 indicates a new fault, with no corresponding fault before maintenance and a fault appearing after maintenance; status value 2 indicates that the fault has been repaired, with a fault existing before maintenance and a fault completely eliminated after maintenance; status value 3 indicates poor maintenance quality, with a fault existing before maintenance and a fault remaining after maintenance.
[0009] Furthermore, in step S6, the fault repair rate is calculated as follows: Repair rate = (Total number of corresponding faults before repair - Total number of corresponding faults after repair) / Total number of corresponding faults before repair × 100%. The higher the repair rate, the better the repair and management effect of the corresponding fault.
[0010] Furthermore, in step S7, the fault change information is recorded in a standardized format of "number of alarms before maintenance → number of alarms after maintenance" to clearly represent the trend of fault changes.
[0011] Furthermore, in step S8, the comprehensive evaluation results of maintenance quality specifically include: comprehensive score of line maintenance quality, statistical data of various fault alarms before and after maintenance, maintenance handling rate of various faults, maintenance quality evaluation matrix, list of newly occurring fault sections, and list of sections with poor maintenance quality.
[0012] Furthermore, the quality assessment method is based on a unified kilometer marker spatial coordinate system.
[0013] This invention provides a method for assessing the maintenance quality of rail transit, which has the following advantages: This invention realizes automatic data acquisition, spatiotemporal alignment, statistical analysis, quality evaluation, and result output throughout the entire process, without the need for manual comparison and judgment, completely eliminating the dependence on the experience of professional personnel, greatly reducing the workload of manual labor, and significantly improving the efficiency of maintenance quality assessment. This invention establishes standardized section division rules, fault statistics rules, maintenance status judgment rules, and a quantitative scoring system, unifying the overall evaluation standards and solving the problems of strong subjectivity, chaotic standards, and inconsistent results in traditional manual evaluation. The evaluation results are objective, accurate, and traceable. This invention constructs a two-dimensional matrix of "section-fault type" to achieve precise evaluation of each section and each type of defect. It can accurately identify repaired defects, new defects, and residual defects, accurately locate sections with weak maintenance quality, and achieve refined operation and maintenance management. This invention provides core data support for subsequent maintenance plan formulation, maintenance quality assessment, and track health status evaluation through quantitative evaluation results, thereby achieving continuous optimization of track operation and maintenance work. Attached Figure Description
[0014] Figure 1 This is a flowchart illustrating the overall technical process of the present invention. Figure 2 This is a schematic diagram illustrating the construction of the fault statistics matrix of the present invention; Figure 3 This is a schematic diagram showing the correspondence between maintenance status types and codes in this invention; Figure 4 This is a schematic diagram of the maintenance quality evaluation matrix structure of the present invention; Figure 5This is a flowchart illustrating the key problem segment identification process of this invention; Figure 6 This is a flowchart illustrating the process of generating the comprehensive evaluation results of maintenance quality in this invention. Detailed Implementation
[0015] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0016] This embodiment discloses a method for assessing the maintenance quality of rail transit. It selects urban subway lines as the assessment object and divides the lines using a standard 100m section length. The specific implementation steps are as follows: The first step is data acquisition: Select the target subway line in the up direction, extract the track detection alarm data from the previous inspection cycle and the next inspection cycle before the current maintenance operation, and filter the valid data fields: line number, up and down direction, inspection time, kilometer marker, fault type, and number of alarms. Remove invalid, missing, and abnormal data to form two sets of standardized raw datasets.
[0017] The second step is spatiotemporal alignment: confirm that the line numbers and uplink / downlink directions of the two sets of datasets are completely consistent. Distinguish between the pre-maintenance dataset and the post-maintenance dataset by detection time. Use the continuous kilometer markers of the line as a spatial reference to perform position calibration on the discrete alarm data to ensure that the data before and after maintenance at the same kilometer marker location are accurately correlated, thus completing the unified spatiotemporal alignment.
[0018] The third step is to divide the route into sections: determine the starting and ending kilometer markers of the target route, calculate the total operating mileage of the route, divide the route into several continuous and non-overlapping standardized sections with a fixed length of 100m, assign a unique serial number to each section, record the starting and ending kilometer markers and relative mileage information of each section, and establish a section index database.
[0019] The fourth step is to construct a fault statistics matrix: Match all alarm data before and after maintenance to the corresponding 100m standardized sections, and count the number of fault alarms for each section according to five core defects: abnormal gauge, uneven elevation, uneven horizontality, abnormal track alignment, and triangular pit. Construct two-dimensional fault statistics matrices of "section-fault type" before and after maintenance. The matrix rows correspond to the section number, the columns correspond to the fault type, and the cell values are the number of alarms for the corresponding section and the corresponding fault.
[0020] The fifth step is to determine the maintenance status and construct an evaluation matrix: Compare the two sets of fault statistics matrix data row by row and column by column, and perform status coding judgment: Status 0 (normal) indicates no faults in the section; Status 1 (new fault) indicates no faults before maintenance but faults appear after maintenance; Status 2 (repaired) indicates faults before maintenance but faults are cleared after maintenance; Status 3 (poor maintenance quality) indicates faults before maintenance but faults still exist after maintenance. Summarize the judgment results of all fault types for each section, determine the final maintenance status of each section, and generate a comprehensive maintenance quality evaluation matrix.
[0021] Step 6: Calculate the fault repair and handling rate: Count the total number of alarms before and after repair for each of the five types of faults. Using the formula: Repair and handling rate = (Total number of faults before repair - Total number of faults after repair) / Total number of faults before repair × 100%, calculate the repair and handling rate for each type of fault and create a statistical table of fault repair effectiveness. A higher handling rate indicates a better repair and treatment effect for that type of fault.
[0022] Step 7: Identify key problem sections: Traverse the maintenance quality evaluation matrix, filter out problem sections with status values of 1 and 3, record the start and end mileage markers, fault type, and fault change data (number before maintenance → number after maintenance) for each section, and compile and generate lists of newly occurring fault sections and poor maintenance quality sections respectively, and identify key sections for review and secondary maintenance.
[0023] Step 8: Generate comprehensive evaluation results: Based on the repair and handling rates of various faults and the maintenance status of each section, calculate the overall comprehensive score of the line maintenance quality by weighting; integrate fault statistics, repair and handling rates, maintenance quality evaluation matrix, and list of key problem sections to generate a standardized and visualized comprehensive maintenance quality evaluation report, providing a basis for operation and maintenance management, quality assessment, and maintenance optimization.
[0024] In this embodiment, the above method realizes fully automated and refined assessment of the maintenance quality of subway lines, effectively avoids errors in manual evaluation, accurately locates defective sections, forms a complete operation and maintenance closed loop, and significantly improves the level of intelligent operation and maintenance management of rail transit.
[0025] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0026] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only for the purpose of helping to understand the method and core ideas of the present invention. The above descriptions are only preferred embodiments of the present invention. It should be noted that due to the limitations of textual expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of the present invention.
Claims
1. A method for assessing the maintenance quality of rail transit, characterized in that, Includes the following steps: S1. Data Acquisition: Acquire track inspection alarm data for two inspection cycles before and after maintenance of the rail transit line. The alarm data shall include at least kilometer marker information, mileage information, alarm type information and inspection time information. S2. Spatiotemporal alignment: Using line number and up / down direction as filtering conditions, match the detection data before and after maintenance, distinguish the pre-maintenance dataset from the post-maintenance dataset by detection time, and complete the spatial alignment of the two datasets using the kilometer marker as the spatial reference to establish a unified spatiotemporal correspondence. S3. Line segment division: Determine the total length of the line based on the starting and ending kilometer markers. Divide the line into several continuous, non-overlapping standardized line segments according to the preset fixed segment lengths. Establish a unique segment index and spatial location information for each segment. S4. Fault Statistics Matrix Construction: Map the alarm data before and after maintenance to the corresponding standardized line sections, classify and count the number of faults in each section according to fault type, and construct the fault statistics matrix before maintenance and the fault statistics matrix after maintenance respectively. S5. Maintenance quality status determination: Compare the number of faults before and after maintenance for the same section and the same type of fault, and assign corresponding maintenance status codes. The maintenance status includes four types: normal status, new fault, repaired, and poor maintenance quality. A maintenance quality evaluation matrix is constructed based on the sum of fault status codes for each section. S6. Maintenance and repair rate calculation: For each type of track fault, the total number of faults before and after maintenance is counted, the maintenance and repair elimination rate of each type of fault is calculated, and a fault maintenance effect statistical table is formed. S7. Key Problem Section Identification: Traverse the maintenance quality evaluation matrix, extract spatial information, fault types and changes in the number of faults before and after maintenance for each section, classify and identify and generate a list of newly occurring fault sections and a list of sections with poor maintenance quality. S8. Output of comprehensive evaluation results: Integrate fault statistics, repair rate, maintenance quality evaluation matrix and key problem section information, calculate the overall maintenance quality score of the line, generate and output standardized multi-dimensional maintenance quality comprehensive evaluation results.
2. The method for assessing the maintenance quality of rail transit according to claim 1, characterized in that, In step S3, the preset section length is 50m, 100m or 200m, preferably 100m; for the divided line sections, the starting kilometer marker, ending kilometer marker and relative mileage information of each section are stored, and a fixed section index system is established to ensure the spatial comparability of data from different detection cycles.
3. The method for assessing the maintenance quality of rail transit according to claim 1, characterized in that, In step S4, the fault types include abnormal track gauge, uneven elevation, uneven horizontal alignment, abnormal track orientation, and typical defects of triangular pit tracks.
4. The rail transit maintenance quality assessment method according to claim 3, characterized in that, The maintenance status coding rules in step S5 are as follows: Status value 0 indicates that the section is normal, with no residual faults or new faults in the section; Status value 1 indicates a new fault, with no corresponding faults before maintenance and a fault appearing after maintenance; Status value 2 indicates that the fault has been repaired, with a fault existing before maintenance and a fault completely eliminated after maintenance; Status value 3 indicates poor maintenance quality, with a fault existing before maintenance and a fault still remaining after maintenance.
5. The rail transit maintenance quality assessment method according to claim 4, characterized in that, In step S6, the fault repair rate is calculated as follows: Repair rate = (Total number of corresponding faults before repair - Total number of corresponding faults after repair) / Total number of corresponding faults before repair × 100%. The higher the repair rate, the better the repair and management effect of the corresponding fault.
6. The rail transit maintenance quality assessment method according to claim 4, characterized in that, In step S7, the fault change information is recorded in a standardized format of "number of alarms before maintenance and number of alarms after maintenance" to clearly represent the trend of fault change.
7. The rail transit maintenance quality assessment method according to claim 6, characterized in that, In step S8, the comprehensive evaluation results of maintenance quality specifically include: comprehensive score of line maintenance quality, statistical data of various fault alarms before and after maintenance, maintenance handling rate of various faults, maintenance quality evaluation matrix, list of newly occurring fault sections, and list of sections with poor maintenance quality.
8. The method for assessing the maintenance quality of rail transit according to claim 6, characterized in that, The quality assessment method is based on a unified kilometer marker spatial coordinate system.