Pipe network maintenance multi-dimensional weighted evaluation algorithm for third-party supervision

Abstract

The application discloses a kind of multi-dimensional weighting examination algorithm for third-party supervision-oriented pipe network maintenance, belong to municipal pipe network operation digital evaluation technical field, steps include: build four layers of self-defined layered evaluation index system;Supervision personnel configures index original weight and completes normalization conversion;System automatically captures business data, distinguishes private, SaaS deployment differentiated access side station data source;Monthly / quarterly comprehensive evaluation total score is obtained by layered weighting operation;Rely on evaluation score conversion maintenance service cost reference amount;Full evaluation data supports multidimensional statistical review.The application is designed for government procurement third-party neutral supervision scene, solves the problems of traditional evaluation index solidification, large artificial statistical workload, serious subjective bias, cannot be compatible with double deployment, lack of standardized mathematical model, evaluation result cannot support cost settlement, full-process automatic operation, complete mathematical calculation model is recorded in specification, compatible with private, cloud SaaS double deployment, greatly reduces artificial evaluation cost, provides objective quantitative basis for pipe network maintenance service settlement.

Description

Technical Field

[0001] This invention belongs to the field of digital assessment algorithm technology for municipal pipeline network operation and maintenance, and is suitable for quantitative assessment scenarios of third-party digital supervision services for government procurement. Background Technology

[0002] Existing publicly available patents for pipeline operation and maintenance assessment, including CN120297544A, CN116485606A, CN111798007A, CN115564068A, CN102915474A, CN119338140A, CN117540897A, CN120258780A, CN119624148A, CN119067466A, CN120931067A, and CN120448440A, are all prior patents that are only for internal management of water utilities and cannot be adapted to the quantitative assessment scenarios of third-party neutral supervision in government procurement. They have several inherent defects: 1. The assessment indicators and weights are all fixed and cannot be adjusted according to the different needs of pipeline network management in different areas, resulting in poor adaptability; 2. The original assessment data relies entirely on manual entry and scoring, resulting in a huge workload and serious subjective evaluation bias. 3. It fails to differentiate between two business scenarios: private deployment with on-site recording enabled and cloud SaaS deployment with on-site recording disabled, making it incompatible with the two different data sources. 4. It lacks a standardized weight normalization and hierarchical weighted complete mathematical model, only providing a simple textual description of the scoring logic. The technical solution is not fully disclosed, making it impossible for those skilled in the art to fully reproduce the evaluation process. 5. The evaluation scores lack corresponding cost quantification conversion logic, and cannot output standardized maintenance service cost settlement reference reports. 6. The data statistics dimension is limited, only supporting overall aggregation across the entire domain, and cannot break down and review maintenance weaknesses by pipeline section or maintenance team. 7. The underlying architecture is designed specifically for internal enterprise management and control, lacking the underlying logic for neutral evaluation by third-party digital supervision in government procurement. Summary of the Invention

[0003] (1) Technical problems to be solved To address the shortcomings of existing pipeline maintenance assessment algorithms, such as rigid indicators, high manual statistical costs, large subjective scoring biases, incompatibility with dual-deployment data sources, lack of complete standardized mathematical models, inability to support cost settlement with assessment results, and applicability only to enterprise self-use, this paper provides a multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision. This algorithm enables neutral, automated, and quantifiable pipeline maintenance assessments in government procurement scenarios.

[0004] (2) Technical solution A multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision includes the following steps: S1. Establishing a hierarchical maintenance assessment indicator system, dividing it into four primary indicators: inspection execution, hazard rectification, safety compliance, and construction quality. Each primary indicator has multiple independently activating and deactivating quantitative secondary indicators, constructing a standardized quantitative evaluation framework; S2. Supervisory personnel customize the original weights of each level of indicators according to the area control standards, and the system performs weight normalization conversion; Weight normalization calculation formula: Parameter explanation: For the first The original weights for each secondary indicator are manually configured. This refers to the total number of all secondary indicators at the same level. To ensure normalized effective weights, the sum of all weights at the same level is always equal to 1. S3 and the system backend are configured with scheduled tasks to automatically retrieve basic business data sources such as inspection work orders, hazard receipts, and confined space approval records. Data is read differently based on the system deployment mode: in private deployment mode, side-site video recordings are automatically retrieved and included in the calculation of safety compliance indicators; in cloud-based SaaS multi-tenant deployment mode, side-site data sources are automatically blocked. The system automatically calculates the original scores of each secondary indicator based on business data. The score ranges from 0 to 100, and no manual data entry is required throughout the process; S4, hierarchical weighted calculation of evaluation scores: Single-level indicator score calculation formula: Parameter explanation: For the first The original scores of each secondary indicator The normalized weights are assigned to the corresponding secondary indicators; the formula for calculating the overall score is as follows: In the formula: , , , The scores are, in order, the primary indicators for inspection execution, hazard rectification, safety compliance, and construction quality; , , , Normalized weights for the corresponding primary indicators; S5. Calculate the monthly / quarterly comprehensive evaluation score for the area, ranging from 0 to 100; Generate a detailed reference list for maintenance service cost calculation using the cost conversion formula; Cost conversion calculation formula: Parameter explanation: This serves as the monthly standard maintenance service benchmark for the area. The grading reference coefficient is used to match the total assessment score. This serves as a reference amount for maintenance service fees that can be used by both parties for settlement; S6, the database permanently stores all historical evaluation data, supporting multi-dimensional filtering, statistics, and review by pipeline section, maintenance team, month, and quarter to pinpoint weak links in maintenance operations; the entire set of weighted calculation logic can be fully reused in both private local server deployment and cloud SaaS multi-tenant deployment architectures, with the indicator system and mathematical calculation logic of the two deployment types being completely unified.

[0005] (3) Beneficial effects 1. A four-tiered evaluation indicator framework, coupled with customizable weights and normalization calculation formulas, flexibly adapts to different regional network supervision standards, offering strong versatility. 2. The system automatically collects all original business data, eliminating manual data entry and scoring processes, reducing monthly evaluation workload by over 90%, and eliminating subjective scoring bias. 3. The same algorithm is compatible with both private and cloud-based SaaS deployments, automatically adapting to scenarios where side-site data is enabled / disabled, reducing the cost of repetitive development across multiple versions. 4. The instruction manual fully discloses the complete set of mathematical formulas for weight normalization, tiered weighting, and cost conversion, providing comprehensive technical information so that ordinary technical personnel can fully reproduce the evaluation process. 5. The total evaluation score is linked to the grading coefficient, automatically generating a maintenance cost settlement reference report, providing objective quantitative evidence for the settlement of maintenance services between the government procurement parties. 6. Supports multi-dimensional data statistics and review at the road section, work group, and cycle levels, facilitating regulatory units to accurately locate weak maintenance areas. 7. The underlying architecture is adapted to the neutral evaluation requirements of third-party digital supervision in government procurement, and a single algorithm can be adapted to private, cloud and other scenarios. Attached Figure Description Figure 1 is a hierarchical architecture diagram of the assessment indicators of this invention, showing the tree-like hierarchical structure of the first-level and second-level assessment indicators; Figure 2 Figure 3 is a flowchart of the data collection and weighted calculation process of the algorithm of this invention, which fully shows the business chain of data collection, weight normalization, hierarchical weighting, total score calculation and cost report output; Figure 4 is a bar chart comparing the working hours of manual statistics and the evaluation of this system, which is used to reflect the cost reduction and efficiency improvement effect of this algorithm. Detailed Implementation This algorithm was implemented in a scenario involving the supervision of third-party pipeline maintenance procured by the urban government: Step 1: A four-layer evaluation indicator framework was established, enabling all primary indicators for inspection execution, hazard rectification, safety compliance, and construction quality, and secondary quantitative indicators at each level were enabled as needed; Step 2: Based on the management needs of the area with a high proportion of old pipelines and high frequency of underground operations, the supervisors increased the original weights of safety compliance indicators, and the system automatically unified the weight range of all indicators through a normalization formula; Step 3: The project adopted a private server deployment, and the system automatically captured four types of data daily: inspection work orders, hazard rectification receipts, confined space approvals, and on-site video, and automatically converted the original scores of each secondary indicator in the range of 0-100; Step 4: The system first calculated the scores of the four primary indicators separately using a layered weighting formula, and then substituted them into the comprehensive total score formula to output the monthly comprehensive maintenance evaluation score for the area; Step 5: The graded reference coefficients corresponding to the evaluation total score were matched and substituted into the cost conversion formula to generate a detailed reference for the monthly maintenance service cost calculation, which was directly used for settlement and reconciliation between the client and the contractor; Step 6: Supervisory personnel screen historical assessment data from the past three months by pipeline section and maintenance team, compare the work scores of different teams, identify weak teams with lagging hazard rectification, and adjust the supervision requirements accordingly.

[0006] After 6 months of actual operation and verification in the area: Traditional manual summary and evaluation of a single area requires 4 to 6 working hours per month, and the scoring is subject to subjective bias; after using this algorithm for automatic calculation, the manual time consumption is close to 0, the evaluation standard is uniform and objective, and the output evaluation data can be directly used as a quantitative reference for the settlement of maintenance service fees.

Claims

1. A multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision, characterized in that, The process includes the following steps: S1. Establish a hierarchical maintenance assessment indicator system, dividing it into four primary indicators: inspection execution, hazard rectification, safety compliance, and construction quality. Each primary indicator has multiple independently quantifiable secondary indicators that can be enabled or disabled. S2. Supervisors customize the original weights of each level of indicators. The system normalizes all original weights at the same level, ensuring that the sum of all weights at the same level is 1. S3. The system automatically captures basic data sources such as inspection work orders, hazard receipts, and confined space approval records at regular intervals. Data is loaded differently based on the system deployment mode: private deployments synchronously include on-site video recordings for safety compliance indicator calculations, while cloud-based SaaS deployments automatically block on-site data sources. The system automatically calculates the original scores for each secondary indicator based on the collected business data, with scores ranging from 0 to 100. S4. A hierarchical weighted calculation method is used to calculate scores at each level: First, the original scores of the secondary indicators are combined with normalized weights to calculate the scores of the corresponding primary indicators. Then, the scores of the four primary indicators are combined with their respective normalized weights to obtain the total comprehensive evaluation score for the area. The comprehensive maintenance evaluation score for the area is output monthly and quarterly. S5. Based on the total comprehensive evaluation score for the area, corresponding tiered reference coefficients are matched, and the monthly standard maintenance service base for the area is used to generate a detailed reference for calculating maintenance service costs. S6. The system permanently stores all historical evaluation data and supports multi-dimensional filtering, statistics, and review of evaluation data by pipeline section, maintenance service team, month, and quarter.

2. The multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision as described in claim 1, characterized in that, The entire weighted calculation logic is fully reused in both private local server deployment and cloud SaaS multi-tenant deployment architectures. The core logic of the indicator system, weight conversion, hierarchical weighting, and cost conversion is completely consistent for both types of deployments.

3. The multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision as described in claim 1, characterized in that, In step S3, the system is configured to automatically retrieve business data from the database via a background scheduled task, eliminating the need for manual filling and entry of raw evaluation data throughout the entire process.

4. The multi-dimensional weighted assessment algorithm for pipeline maintenance oriented towards third-party supervision as described in claim 1, characterized in that, Step S6 outputs multi-dimensional statistical data for horizontal comparison of maintenance operation levels in different areas and maintenance teams, and to identify weak links in maintenance.

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