Non-excavation repair design method for municipal drainage pipeline and related equipment

Abstract

The invention belongs to the technical field of municipal engineering, and discloses a municipal drainage pipeline trenchless repair design method and related device.The method comprises the steps that environment data of a construction site, pipeline disease information and operation data of trenchless repair equipment are obtained, and the data are preprocessed; comparing an applicability rule and a material performance curve in the knowledge base according to the preprocessed data, evaluating the applicability of the current trenchless repair scheme, triggering a preset adjustment rule in the knowledge base according to an applicability evaluation result, and performing optimization calculation in combination with a parameter-effect model to generate an adjustment scheme; according to the technical scheme, automation and intelligentization from field data to scheme adjustment are achieved, dependence on artificial experience is reduced, the accuracy and timeliness of scheme adjustment are improved, and therefore the quality and efficiency of non-excavation repair engineering of municipal drainage pipelines are improved.

Description

Technical Field

[0001] This application relates to the technical field of municipal engineering. Specifically, it relates to a trenchless repair design method for municipal drainage pipes and related equipment. Background Art

[0002] As an important urban infrastructure, the municipal drainage pipe system will inevitably develop various structural or functional diseases after long-term operation. For example, circumferential or longitudinal cracks in the pipe wall, local damage, misalignment or disconnection of pipe joints, corrosion of pipe materials, overall or local deformation of the pipe (such as ovalization), and accumulation of internal sediments. These diseases seriously affect the normal drainage function of the pipe, reduce the flow capacity, cause leakage, and may even lead to the loss of surrounding soil, and then cause serious consequences such as ground settlement or collapse, threatening urban operation and public safety. Therefore, it is necessary to repair the diseased pipes in a timely and effective manner. Compared with the traditional excavation repair method that requires large-scale road surface breaking, affects traffic and the surrounding environment, has a long construction period and high cost, the trenchless repair technology has become the mainstream choice for current municipal drainage pipe repair due to its small impact on the surrounding area and relatively fast construction speed.

[0003] The design of trenchless repair solutions is a key link in the repair work, directly determining the final effect, economic cost and construction efficiency of the repair project. At present, the design process of trenchless repair solutions is usually responsible by engineers. They will comprehensively analyze pipe inspection reports (such as high-definition CCTV inspection videos, laser cross-section scanning data, sonar detection results), evaluate the type, location, degree of diseases and the basic information of the pipe (pipe material, pipe diameter, burial depth, service life), and refer to the current design specifications and technical standards of the country and the industry to select appropriate trenchless repair technologies (such as CIPP lining repair, spiral winding method, spraying method, local repair method, etc.) and repair materials (such as resins of different models, lining pipes, winding tapes, spraying mortar, etc.), and initially determine the initial setting range of key construction process parameters (such as the temperature control curve, curing time, steam pressure during CIPP curing; winding tension, overlap width during spiral winding; spraying thickness, curing time during spraying). After the design is completed, the solution will be handed over to the construction unit for implementation.

[0004] However, the actual construction site environment of municipal drainage pipelines is complex and changeable, with many uncertain factors. These factors may not be completely consistent with the information obtained through limited surveys during the design stage and may change dynamically during the construction process. For example, the groundwater level judged based on historical data or a small number of measuring points during the design stage may be moderate. However, during actual construction, due to seasonal rainfall, upstream reservoir water release, or changes in groundwater pumping activities in surrounding plots, the groundwater level in the construction area may rise significantly, forming a high external hydrostatic pressure, and even a large amount of groundwater may pour into the pipe through the damaged part of the pipe. This high groundwater level or water inrush into the pipe significantly changes the construction environment. At the same time, the preliminary geological survey may not fully reveal the geological details along the pipe section. For example, there may be local soft soil layers, water-rich sand layers, or even unexplored underground cavities or obstacles. These geological anomalies may affect the advancement of construction equipment or change the stress state of the pipeline, posing additional challenges to the implementation of the repair plan. In addition, the ground traffic load in the construction area may increase due to temporary traffic control or emergencies, or other ongoing underground projects in the vicinity (such as pipeline laying, foundation pit excavation) may cause ground vibration or changes in the underground stress field. These external interferences may also affect the repair process.

[0005] After more thorough dredging and pretreatment of the pipeline during the construction preparation stage, or during the final inspection before the repair material enters the pipeline, it may be found that the actual situation of the pipeline diseases is more complex or serious than described in the preliminary inspection report. For example, the preliminary report may only record the cracks on the pipe wall, but after cleaning, it is found that there is local pipe wall shedding or loose structure under the cracks; or diseases that are more influential to the structure and were missed in the preliminary inspection are found, such as severe joint disconnection, lap dislocation, etc. These more detailed or more serious disease information may mean that the originally designed repair technology or materials are insufficient to cope with.

[0006] During the construction process, the trenchless repair equipment and auxiliary systems generate a large amount of real-time operation data. These real-time data are important bases for evaluating the implementation status of the plan and predicting the final repair effect. Different trenchless repair technologies and selected repair materials show different sensitivities to the above-mentioned dynamically changing environmental conditions, complex disease conditions, and parameter fluctuations during the construction process. For example, for certain types of CIPP resins, their curing reaction speed and final strength are very sensitive to environmental temperature and the presence of water bodies. If a large amount of low-temperature groundwater rushes in during the curing process, it may cause incomplete curing of the resin or the strength of the cured repair layer not meeting the design requirements. Some winding materials are difficult to ensure uniform lap when encountering local pipe deformation or obstacles, and it is easy to form weak links. Some spraying materials have extremely high requirements for the adhesion conditions on the pipe wall surface, and groundwater or residual sludge will seriously affect their performance.

[0007] Due to deviations between actual conditions and design assumptions, and the sensitivity of repair technologies and materials to these deviations, the original design plan may not be successfully implemented under the current actual construction environment. Even if it is barely completed, it may result in unsatisfactory repair results (e.g., excessive leakage rates, insufficient improvement in structural bearing capacity, shortened service life), or generate high construction risks (e.g., construction interruption, equipment damage, material waste, rework requirements, and even safety accidents). Therefore, it is necessary to make rapid and scientific adjustments to the original design plan based on real-time actual conditions at the construction site.

[0008] However, field engineers face significant challenges in making these real-time adjustments. They must rapidly analyze heterogeneous real-time data from multiple sources (environmental sensor data, equipment operating data, and personnel operation records), integrate updated damage information, consider the performance of different repair technologies and materials in the current environment, and evaluate the potential impact of different adjustment strategies on repair effectiveness, cost, schedule, and risk. This complex, multi-factor, high-dimensional trade-off is difficult to efficiently and accurately address using only individual experience and limited computing tools. Manual decision-making can be delayed, failing to respond promptly to site changes; and adjustment strategies may differ between different engineers, impacting the stability of repair quality. This reliance on manual experience in real-time adjustments limits the ability of trenchless repair projects to achieve optimal results in complex environments and increases the uncertainty and risk of project implementation.

[0009] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the invention

[0010] The purpose of this application is to provide a design method for trenchless repair of municipal drainage pipes and related equipment, which realizes the automation and intelligence from field data to scheme adjustment, reduces the dependence on manual experience, improves the accuracy and timeliness of scheme adjustment, and thus improves the quality and efficiency of trenchless repair projects of municipal drainage pipes.

[0011] In a first aspect, the present application provides a method for designing a trenchless repair of a municipal drainage pipeline, which is used to design an adjustment plan for a trenchless repair operation of a municipal drainage pipeline, comprising the following steps: S1. Obtain construction site environmental data, pipeline disease information, and trenchless repair equipment operating data; S2 preprocesses the acquired data; the preprocessing includes cleaning and integration processing; S3. Based on the pre-processed environmental data, pre-processed pipeline disease information, and pre-processed operational data, compare the applicability rules in the knowledge base with the material performance curves to evaluate the applicability of the current trenchless repair solution. S4. Trigger the preset adjustment rules in the knowledge base according to the applicability evaluation results, and perform optimization calculations in combination with the parameter-effect model to generate an adjustment plan.

[0012] Preferably, the environmental data includes the groundwater level and soil temperature; The pipeline disease information includes the crack length, crack width, and corrosion depth; The operation data includes the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment.

[0013] Preferably, the knowledge base includes an applicability rule base and a material property curve base; The applicability rule base includes environmental applicability rules, disease applicability rules, and equipment applicability rules. The environmental applicability rules are used to limit the applicable range of repair materials under different groundwater levels and soil temperatures. The disease applicability rules are used to limit the selection of repair methods under different crack lengths, crack widths, and corrosion depths. The equipment applicability rules are used to limit the repair effect under different propulsion speeds, grouting pressures, and temperatures of the trenchless repair equipment; The material property curve base includes temperature-strength curves, pressure-deformation curves, and time-durability curves of different repair materials; Step S3 includes: S301. Read the applicability rule base and the material property curve base in the knowledge base; S302. According to the preprocessed environmental data, preprocessed pipeline disease information, and preprocessed operation data, query the applicability rule base and the material property curve base, and calculate the environmental adaptability score, disease matching score, and material property score; S303. According to the environmental adaptability score, disease matching score, and material property score, calculate the comprehensive applicability score, judge whether the current trenchless repair plan is applicable, and obtain the applicability evaluation result.

[0014] Preferably, step S302 includes: Based on the fuzzy logic algorithm, convert the preprocessed environmental data into membership degrees, and calculate the environmental adaptability score according to the preset weights in the environmental applicability rule base; Based on the cosine similarity algorithm, calculate the similarity between the disease feature vector composed of the preprocessed pipeline disease information and the standard disease feature vector in the disease applicability rule base to obtain the disease matching score; According to the preprocessed operation data, query the material property curve base, use the linear interpolation method to calculate the material strength index, deformation index, and durability index under the current working conditions, and calculate the material property score of the currently used repair material in combination with the preset index thresholds in the equipment applicability rule base.

[0015] Preferably, step S303 includes: According to the environmental adaptability score, disease matching score, and material performance score, combined with the current repair method used, determine the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor; Using the analytic hierarchy process, according to the determined sensitivity factors, determine the weight coefficients of the environmental adaptability score, disease matching score, and material performance score; According to the environmental adaptability score, disease matching score, material performance score, and their corresponding weight coefficients, calculate the comprehensive applicability score; Compare the comprehensive applicability score with the preset applicability threshold to determine whether the current trenchless repair plan is applicable and obtain the applicability evaluation result.

[0016] Preferably, the knowledge base includes an adjustment rule base, and the adjustment rule base includes repair plan adjustment rules, repair material adjustment rules, and repair equipment adjustment rules; the repair plan adjustment rules are used to adjust the type of repair method according to the applicability evaluation result; the repair material adjustment rules are used to adjust the type of repair material according to the material performance score; the repair equipment adjustment rules are used to adjust the trenchless repair equipment model according to the disease matching score; The parameter-effect model is a mapping relationship established between repair parameters and repair effects based on the BP neural network algorithm; the input of the parameter-effect model is the repair parameter, and the output is the repair effect; the repair parameters include the type of repair material, the propulsion speed of the repair equipment, and the grouting pressure, and the repair effects include the strength, deformation, and durability of the repaired pipeline.

[0017] Preferably, step S4 includes: S401. If the current trenchless repair plan is applicable, set the adjustment plan to be empty; S401. If the current trenchless repair plan is not applicable, then perform the following steps: A1. Read the adjustment rule base in the knowledge base; A2. According to the applicability evaluation result, query the adjustment rule base to determine the triggered adjustment rules and the corresponding adjustment results; A3. According to the adjustment results, combined with the parameter-effect model, use the genetic algorithm to optimize the repair parameters to obtain the optimized repair parameters and generate an adjustment plan; among them, the parameter-effect model is used to calculate the fitness value during the iterative optimization process.

[0018] Preferably, step A3 includes: A301. Construct a multi-objective fitness function involving the strength, deformation, and durability of the repaired pipeline; among them, the weight coefficients of the strength, deformation, and durability of the repaired pipeline are determined according to their respective degrees of importance; A302. According to the adjustment result, combined with the parameter-effect model and the multi-objective fitness function, the genetic algorithm is used to iteratively optimize the repair parameters to obtain the optimized repair parameters and generate an adjustment plan. Among them, in the genetic algorithm iteration, the fitness value of each individual is calculated according to the objective fitness function and the parameter-effect model, and selection, crossover, and mutation operations are performed according to the fitness value to generate a new population. An adaptive mutation strategy is introduced to increase the mutation probability and the population diversity when the population diversity decreases, so as to jump out of the local optimal solution.

[0019] In a second aspect, the present application provides an electronic device, including a processor and a memory. The memory stores a computer program executable by the processor. When the processor executes the computer program, it runs the steps in the trenchless repair design method for municipal drainage pipes as described above.

[0020] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the trenchless repair design method for municipal drainage pipes as described above.

[0021] Beneficial effects: The trenchless repair design method for municipal drainage pipes and related devices provided by the present application obtain the environmental data, pipeline disease information, and operation data of trenchless repair equipment at the construction site, preprocess the data, compare the applicability rules and material performance curves in the knowledge base according to the preprocessed data, evaluate the applicability of the current trenchless repair plan, trigger the preset adjustment rules in the knowledge base according to the applicability evaluation result, and perform optimization calculations in combination with the parameter-effect model to generate an adjustment plan. It realizes the automation and intelligence from on-site data to plan adjustment, reduces the dependence on manual experience, improves the accuracy and timeliness of plan adjustment, and thus improves the quality and efficiency of the trenchless repair project for municipal drainage pipes. Description of the Drawings

[0022] Figure 1 It is a flowchart of the trenchless repair design method for municipal drainage pipes provided by an embodiment of the present application.

[0023] Figure 2 It is a schematic structural diagram of the electronic device provided by an embodiment of the present application.

[0024] Label description: 301, processor; 302, memory; 303, communication bus. Detailed Embodiments

[0025] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] refer to Figure 1 This application proposes a design method for trenchless repair of municipal drainage pipelines, which is used to design an adjustment plan for trenchless repair operations of municipal drainage pipelines, including the following steps: S1. Obtain construction site environmental data, pipeline disease information, and trenchless repair equipment operating data; S2 preprocesses the acquired data; the preprocessing includes cleaning and integration processing; S3. Based on the pre-processed environmental data, pre-processed pipeline disease information, and pre-processed operational data, compare the applicability rules in the knowledge base with the material performance curves to evaluate the applicability of the current trenchless repair solution. S4. Based on the applicability assessment results, trigger the preset adjustment rules in the knowledge base, and combine the parameter-effect model to perform optimization calculations to generate an adjustment plan.

[0028] Among them, this method obtains and processes actual data from the construction site, evaluates the applicability of the current repair plan, and automatically generates an adjustment plan based on the evaluation results, thus solving the limitations of relying on manual experience for on-site plan adjustments.

[0029] Step S1 acquires data reflecting the actual construction site conditions, including environmental data, pipeline defect information, and equipment operating data. This data forms the basis for design adjustments and provides information on the actual site conditions, which may differ from the initial design. Environmental data can be acquired using on-site sensors, pipeline defect information can be acquired using testing equipment, and equipment operating data can be acquired using the equipment's built-in monitoring system. This allows for the acquisition of multi-source data reflecting the actual site conditions.

[0030] In step S2, the obtained raw data is preprocessed, including cleaning and integration. The cleaning process removes errors or anomalies in the data, and the integration process unifies and aligns data from different sources. Data preprocessing ensures the accuracy and reliability of the data used in subsequent evaluations and calculations. The cleaning process can adopt outlier detection and missing value filling techniques. The integration process can adopt timestamp alignment and data format unification techniques. Thus, the raw data is transformed into usable preprocessed data.

[0031] In step S3, using the preprocessed on-site data, combined with the applicability rules and material performance curves in the knowledge base, the applicability of the current trenchless repair plan under the current actual conditions is evaluated. This step quantifies the matching degree between the plan and the actual situation by comparing the rules and curves, providing an objective basis for whether adjustment is needed. The knowledge base can store preset condition judgment rules and material performance data. The comparison process can involve query matching or calculation of scores. Thus, the applicability evaluation result of the current plan is obtained.

[0032] In step S4, according to the applicability evaluation result of step S3, the preset adjustment rules in the knowledge base are triggered, and an optimization calculation is performed using the parameter-effect model, and finally a specific adjustment plan is generated. This step automatically starts the adjustment process according to the evaluation result, uses the model to calculate the optimized repair parameters or plan details, provides specific and executable adjustment suggestions, and improves the efficiency and scientificity of plan adjustment. The adjustment rules can define the adjustment direction to be taken under specific evaluation results. The parameter-effect model can predict the repair effects under different parameter combinations. The optimization calculation can find the best parameter combination. Thus, an adjustment plan for the actual on-site situation is generated.

[0033] Specifically, the working principle of this method is as follows. First, real-time or near-real-time data of the construction site are obtained through step S1. These data reflect the actual environmental conditions, the status of pipeline diseases, and the operating conditions of the repair equipment. These actual situations may deviate from the assumptions made during the initial design. Then, in step S2, these raw data are cleaned and integrated to remove noise and errors, forming a structured and reliable dataset. Next, in step S3, the preprocessed data are used for comparative analysis with the applicability rules and material performance curves stored in the knowledge base. The applicability rules define the applicable ranges of solutions or materials under different environmental, disease, and equipment conditions, and the material performance curves describe the performance of materials under different conditions. Through this comparison, the method evaluates the applicability of the trenchless repair solution currently being implemented or planned to be implemented under the current actual site conditions, and determines whether it can achieve the expected repair effect or whether there are implementation risks. Finally, in step S4, if the evaluation result shows that the current solution is not applicable or there is room for optimization, the preset adjustment rules in the knowledge base are triggered. These rules point to specific adjustment directions according to the evaluation results, such as adjusting the type of repair material, adjusting the operating parameters of the equipment, or adjusting the construction process. At the same time, combined with the parameter-effect model, the method performs optimized calculations on the repair parameters related to the adjustment direction. The parameter-effect model predicts the impact of different parameter settings on the repair effect. The optimization calculation process searches for the parameter combination that can maximize the repair effect or minimize the risk. Finally, a solution containing specific adjustment suggestions is generated to guide the on-site construction personnel in their operations. The whole process realizes the automation and intelligence from on-site data to solution adjustment, reduces the dependence on manual experience, improves the accuracy and timeliness of solution adjustment, and thus enhances the quality and efficiency of the trenchless repair project of municipal drainage pipelines.

[0034] Specifically, the environmental data include the groundwater level and soil temperature; The pipeline disease information includes the crack length, crack width, and corrosion depth; The operating data include the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment.

[0035] Environmental data includes groundwater level and soil temperature. Groundwater level can be obtained using groundwater level monitoring sensors installed in the construction area, while soil temperature can be obtained using soil temperature sensors. Pipeline defect information includes crack length, crack width, and corrosion depth. Crack length and width can be obtained through video analysis of pipeline CCTV (Closed-Circuit Television) inspections or processing of laser cross-sectional scanning data. Corrosion depth can be determined using non-destructive testing methods such as ultrasonic testing or eddy current testing. Operational data includes the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment. These data can be directly read from the equipment's built-in sensors and control systems.

[0036] Specifically, by limiting environmental data to groundwater level and soil temperature, the system captures key environmental parameters that influence the curing process of the repair material and the external forces acting on the pipeline. The groundwater level directly affects the water pressure differential between the inside and outside of the pipeline and the presence of groundwater influx. Soil temperature influences the material's curing reaction rate and ultimate strength. By limiting pipeline disease information to crack length, crack width, and corrosion depth, the system captures specific indicators that quantify the extent of damage to the pipeline structure. These indicators are directly linked to the severity of the disease and the selection of repair methods and materials. By limiting operational data to the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment, the system captures key parameters reflecting the execution status of the repair process and the level of process control. These parameters directly affect the material's placement quality, density, adhesion to the pipe wall, and curing effect. This specific, quantifiable data provides an accurate input foundation for subsequent data preprocessing, scheme applicability assessment, and adjustment scheme generation. Obtaining this data enables the system to accurately evaluate the applicability of existing solutions under the current environment, disease and construction conditions, and predict the repair effect based on actual operating parameters. This can trigger more targeted adjustment rules and generate adjustment plans that better meet the actual needs of the site through optimization calculations, thereby improving the scientific nature and reliability of the design and adjustment of trenchless repair plans.

[0037] In some embodiments, step S2 includes: S201. For the acquired raw data, use an outlier detection method based on a boxplot to identify and remove outliers that exceed the upper and lower limits, and use Lagrange interpolation to fill missing values to obtain cleaned data; S202. Time-align the cleaned data and uniformly store them in CSV format to obtain pre-processed data.

[0038] Among them, in step S201, the outlier detection method based on the box plot calculates the quartiles (Q1, Q3) and the interquartile range (IQR = Q3 - Q1) of the data to determine the upper and lower limits of the outliers (usually Q1 - 1.5 * IQR and Q3 + 1.5 * IQR), and the data points outside this range are marked as outliers. The Lagrange interpolation method constructs a polynomial passing through these known data points and then uses this polynomial to estimate the values at the missing positions. In step S202, the cleaned data is time-aligned to ensure that different types of data have consistent timestamps and are uniformly stored as a CSV format file.

[0039] Specifically, this method first receives the original data streams from the environmental sensors, pipeline inspection reports, and trenchless repair equipment at the construction site. These original data may contain outliers (such as temperature readings outside the physical range) and missing values (such as data loss at a certain time point) due to sensor failures, transmission errors, or incomplete records. To address these defects in the original data, a cleaning process is performed. In the cleaning process, the box plot-based algorithm analyzes each type of data (such as groundwater level, soil temperature, propulsion speed, etc.) independently, identifies and removes the outliers that significantly deviate from the main data distribution, thereby eliminating the interference of incorrect data on subsequent calculations. Then, for the missing points in the data sequence, the Lagrange interpolation method is used to mathematically estimate according to the trend of the known data before and after the missing points to fill in the missing values and ensure the integrity and continuity of the data set. After the cleaning process, the obtained data set does not contain obvious outliers and the missing values have been filled. Subsequently, an integration process is performed. The integration process synchronizes and aligns different types of cleaned data (environmental data, disease information, operation data) according to their timestamps to ensure that the data obtained at the same time point can be correlated and analyzed. Finally, the time-aligned data is uniformly saved in the standard CSV file format for easy reading and processing by the subsequent solution evaluation module. Through these steps, the original data is transformed into high-quality, complete, and uniformly formatted preprocessed data, providing a reliable data basis for the subsequent solution applicability evaluation.

[0040] In some embodiments, the knowledge base includes an applicability rule base and a material property curve base; The applicability rule base includes environmental applicability rules, disease applicability rules, and equipment applicability rules. The environmental applicability rules are used to define the applicable ranges of repair materials under different groundwater levels and soil temperatures. The disease applicability rules are used to define the selection of repair methods under different crack lengths, crack widths, and corrosion depths. The equipment applicability rules are used to define the repair effects under different propulsion speeds, grouting pressures, and temperatures of trenchless repair equipment; The material property curve base includes temperature-strength curves, pressure-deformation curves, and time-durability curves of different repair materials; Step S3 includes: S301. Read the applicability rule library and material property curve library in the knowledge base; S302. Query the applicability rule library and material property curve library according to the pre-processed environmental data, pre-processed pipeline disease information, and pre-processed operation data, and calculate the environmental adaptability score, disease matching score, and material property score; S303. Calculate the comprehensive applicability score according to the environmental adaptability score, disease matching score, and material property score, determine whether the current trenchless repair plan is applicable, and obtain the applicability evaluation result.

[0041] Among them, the knowledge base refers to a collection of professional knowledge and data used to evaluate the applicability of trenchless repair plans, and can be specifically implemented using a database, file system, or memory structure, and is used to store the applicability rule library and material property curve library.

[0042] Among them, the applicability rule library refers to a collection of rules containing a series of logical judgments or condition settings, which are used to guide how to evaluate the applicability of the plan according to the input data, and can be specifically implemented using a rule engine, expert system, or lookup table, and includes environmental applicability rules, disease applicability rules, and equipment applicability rules.

[0043] Among them, the environmental applicability rules refer to the rules that limit the applicability of different repair materials according to environmental parameters such as groundwater level and soil temperature. These rules are combined with the pre-processed environmental data to evaluate the impact of the environment on the implementation of the plan. The disease applicability rules refer to the rules that limit the selection of different repair methods according to pipeline disease parameters such as crack length, crack width, and corrosion depth, and are used to evaluate the matching degree of the plan to the disease. The equipment applicability rules refer to the rules that limit or predict the repair effect according to operation parameters such as the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment, and are used to evaluate the impact of the equipment operation status on the repair quality.

[0044] Among them, the material property curve library refers to a collection of performance data of different repair materials under different working conditions, and can be specifically implemented using a lookup table, function model, or data file, and includes temperature-strength curves, pressure-deformation curves, and time-durability curves of different repair materials.

[0045] Among them, the temperature-strength curve refers to a curve or data set that describes the strength performance changes of a specific repair material at different temperatures. Specifically, it can be implemented by connecting discrete data points or fitting a mathematical function, and is used to evaluate the strength performance of the material at the actual environmental temperature or curing temperature. The pressure-deformation curve refers to a curve or data set that describes the deformation performance changes of a specific repair material under different pressures. Specifically, it can be implemented by connecting discrete data points or fitting a mathematical function, and is used to evaluate the deformation resistance of the material under external hydrostatic pressure or soil pressure. The time-durability curve refers to a curve or data set that describes the attenuation or retention trend of the performance of a specific repair material over time. Specifically, it can be implemented by connecting discrete data points or fitting a mathematical function, and is used to evaluate the long-term service life and performance stability of the material.

[0046] Among them, step S301 reads the applicability rule library and material performance curve library in the knowledge base, which means that before the applicability evaluation, the required rule and curve data are loaded into the evaluation system. Specifically, it can be implemented by file reading, database query, memory loading, etc., to provide a data basis for subsequent queries and calculations.

[0047] Among them, in step S302, the actually obtained and cleaned and integrated data are used to match and calculate against the rules and curves in the knowledge base. Specifically, it can be implemented by rule matching algorithms, similarity calculation algorithms, or curve interpolation calculations. The preprocessed environmental data are used to query the environmental applicability rule library to calculate the environmental adaptability score; the preprocessed pipeline disease information is used to query the disease applicability rule library to calculate the disease matching degree score; the preprocessed operation data are used to query the material performance curve library and the equipment applicability rule library to calculate the material performance score. These scores convert qualitative rules and curves into quantitative indicators.

[0048] Among them, the environmental adaptability score refers to an indicator that quantifies the friendliness or influence degree of the current environmental conditions on the implementation of the trenchless repair plan. Specifically, it can be represented by a value between 0 and 1, and the higher the value, the more adaptable the environment is to the plan. The disease matching degree score refers to an indicator that quantifies the matching degree of the current trenchless repair plan to the type and degree of pipeline diseases. Specifically, it can be represented by a value between 0 and 1, and the higher the value, the higher the matching degree. The material performance score refers to an indicator that quantifies whether the currently used repair material can meet the expected performance requirements under actual working conditions. Specifically, it can be represented by a value between 0 and 1, and the higher the value, the more the performance meets the requirements.

[0049] Among them, in step S303, various scores are comprehensively weighted and calculated to obtain an overall applicability index, which is compared with a preset threshold. Specifically, methods such as weighted summation, analytic hierarchy process, or fuzzy comprehensive evaluation can be used to achieve this. The comprehensive applicability score reflects the overall applicability of the solution in multiple aspects such as the environment, diseases, and material and equipment performance. To determine whether the solution is applicable, for example, if the comprehensive applicability score is higher than a certain threshold (such as 0.6), the solution is considered applicable; otherwise, it is considered inapplicable. Thus, the applicability evaluation result is obtained, and this result is used to guide subsequent solution adjustments.

[0050] Specifically, this solution provides a quantitative evaluation method based on a knowledge base and multi-source data for the applicability evaluation problem of the trenchless repair solution for municipal drainage pipelines at the actual construction site. First, a knowledge base containing an applicability rule base and a material performance curve base is constructed. The applicability rule base is further refined into an environmental applicability rule, a disease applicability rule, and an equipment applicability rule, which respectively limit the applicable ranges of repair materials, repair methods, or repair effects for key parameters such as the groundwater level, soil temperature, crack length, crack width, corrosion depth, equipment propulsion speed, grouting pressure, and temperature. The material performance curve base stores performance data such as the strength, deformation, and durability of different repair materials under different temperatures, pressures, and times. During the evaluation process, first, the rules and curves in the knowledge base are read. Then, using the preprocessed construction site environment data, pipeline disease information, and the operation data of trenchless repair equipment, the knowledge base is queried. According to the environment data, the environmental applicability rule is queried to calculate the environmental adaptability score and quantify the impact of the environment on the solution. According to the disease information, the disease applicability rule is queried to calculate the disease matching degree score and quantify the pertinence of the solution to the disease. According to the operation data, the material performance curve and the equipment applicability rule are queried to calculate the material performance score and quantify the performance of the materials and equipment under the current working conditions. Finally, considering the environmental adaptability score, the disease matching degree score, and the material performance score comprehensively, a comprehensive applicability score is calculated and compared with a preset threshold to determine whether the current trenchless repair solution is applicable under the actual site conditions. This method transforms qualitative empirical judgment into data-based quantitative evaluation, making the evaluation process more objective, specific, and traceable, and can more accurately reflect the actual feasibility and expected effect of the solution in a complex site environment, overcoming the limitations of traditional methods that rely on experience and have a fuzzy evaluation process. By comprehensively considering and quantifying multi-dimensional factors such as the environment, diseases, and material and equipment performance, this solution can effectively identify the problems that may be encountered during the actual implementation of the solution and provide a clear basis for subsequent solution adjustments.

[0051] Preferably, step S302 may include: Based on the fuzzy logic algorithm, convert the preprocessed environmental data into membership degrees, and calculate the environmental adaptability score according to the preset weights in the environmental applicability rule base; Based on the cosine similarity algorithm, calculate the similarity between the disease feature vector composed of the preprocessed pipeline disease information and the standard disease feature vector in the disease applicability rule base to obtain the disease matching degree score; According to the preprocessed operation data, query the material performance curve library, use the linear interpolation method to calculate the material strength index, deformation index and durability index under the current working condition, and combine the preset index thresholds in the equipment applicability rule base to calculate the material performance score of the currently used repair material.

[0052] Among them, converting the preprocessed environmental data into membership degrees based on the fuzzy logic algorithm can be specifically realized by using the fuzzy set theory. For example, for the groundwater level data, fuzzy sets such as "low", "medium", "high", etc. can be defined, and membership functions such as trigonometric functions, trapezoidal functions or Gaussian functions can be used to map the specific groundwater level values to membership degree values between 0 and 1. For the soil temperature data, fuzzy sets and membership functions can be defined similarly. Calculating the environmental adaptability score according to the preset weights in the environmental applicability rule base can be specifically realized by using the weighted average method or the fuzzy inference method. For example, multiply the membership degrees of the environmental data by their corresponding weights and then sum to obtain the environmental adaptability score. The preset weights in the environmental applicability rule base can be determined according to the sensitivity of different environmental factors to specific repair technologies.

[0053] Among them, calculating the similarity between the disease feature vector composed of the preprocessed pipeline disease information and the standard disease feature vector in the disease applicability rule base based on the cosine similarity algorithm can be specifically realized by using the vector space model. The preprocessed pipeline disease information (such as crack length, crack width, corrosion depth, etc.) is constructed into a multi-dimensional disease feature vector. The disease applicability rule base stores the standard disease feature vectors representing the applicable ranges of different repair methods. According to the currently used repair method, the corresponding standard disease feature vector is extracted from the disease applicability rule base to calculate the cosine similarity with the disease feature vector composed of the preprocessed pipeline disease information. The cosine similarity calculation method is used to measure the cosine value of the angle between two vectors, and this value reflects the similarity degree in the directions of the two vectors, that is, the matching degree between the current disease feature and the standard disease feature of the currently used repair method. The higher the similarity value, the more the current disease meets the applicable conditions of the currently used repair method, and thus the disease matching degree score is obtained; for example, the similarity value can be directly used as the disease matching degree score, or through a preset mapping method, the similarity value is mapped to the disease matching degree score.

[0054] Among them, according to the preprocessed operation data, query the material property curve library. For the material strength index, according to the temperature in the operation data, in the temperature-strength curve corresponding to the currently used repair material, the linear interpolation method can be used to determine the material strength index under the current working conditions; for the deformation index, according to the pressure in the operation data, in the pressure-deformation curve corresponding to the currently used repair material, the linear interpolation method can be used to determine the deformation index under the current working conditions; for the durability index, according to the expected service life, query the time-durability curve corresponding to the currently used repair material to obtain the durability at the corresponding time point, and obtain the durability index. Combine the index thresholds preset in the equipment applicability rule library to calculate the material property score of the currently used repair material. Specifically, the calculated material property index value can be compared with the performance thresholds preset in the equipment applicability rule library for the current repair material and equipment. For example, judge whether the material strength meets the minimum requirements, whether the deformation amount exceeds the allowable range, and whether the durability at the end of the expected life is not less than the preset durability lower limit. According to the comparison results, the performance performance of the material under the current working conditions can be quantified to obtain the material property score.

[0055] Specifically, in the steps of evaluating the applicability of trenchless repair solutions, this solution provides specific methods for calculating the environmental adaptability score, disease matching score, and material performance score. First, for the preprocessed environmental data, such as the groundwater level and soil temperature, the fuzzy logic algorithm is used to convert them into membership degrees. For example, the groundwater level is divided into three fuzzy sets: low, medium, and high, and the corresponding membership functions are defined. A specific groundwater level value will correspond to different membership degrees of these three sets. The soil temperature data is processed similarly. Then, according to the weights preset for different environmental factors in the environmental applicability rule base, such as the groundwater level weight is 0.6 and the soil temperature weight is 0.4, the membership degrees of each environmental data are weighted and summed to obtain the environmental adaptability score. This score reflects the degree of adaptation of the current environmental conditions to the repair solution. Secondly, for the preprocessed pipeline disease information, such as crack length, crack width, and corrosion depth, they are constructed into a disease feature vector. For example, the vector can be expressed as [crack length, crack width, corrosion depth]. At the same time, the standard disease feature vectors for different repair methods (such as CIPP, spiral winding, spraying, etc.) are stored in the disease applicability rule base. The cosine similarity is calculated by selecting the standard disease feature vector of the currently used repair method. The cosine similarity algorithm is used to calculate the similarity between the current disease feature vector and the standard disease feature vector of the currently used repair method. The higher the similarity value, the closer the current disease is to the applicable disease type of this repair method, and thus the disease matching score is obtained. This score reflects the matching degree between the current disease type and the current repair method. Finally, for the preprocessed operation data, such as the propulsion speed, grouting pressure, and temperature, the material performance curve library is queried according to these data. For example, according to the current grouting pressure and temperature, the pressure-deformation curve and temperature-strength curve of the currently used repair material are found in the material performance curve library. Using the linear interpolation method, according to the current specific pressure value and temperature value, the deformation index and strength index of the material under the current working conditions are estimated on these two curves. According to the expected service life, the time-durability curve corresponding to the currently used repair material is queried to obtain the durability at the corresponding time point (i.e., the end point of the expected service life), and the durability index is obtained. Then, combined with the strength, deformation, and durability thresholds preset for the current repair material in the equipment applicability rule base, the material performance score is calculated. For example, if the calculated material strength is higher than the strength threshold, the strength score is higher; if the deformation amount is lower than the deformation amount threshold, the deformation score is higher, and the durability at the end point of the expected service life is higher than the durability threshold, the durability score is higher. By synthesizing these indicators (such as weighted average calculation), the material performance score is obtained. This score reflects the performance of the currently used repair material under the actual construction working conditions.Through these specific algorithm calculations, the problem of relying solely on experience for fuzzy judgment is overcome, providing a quantitative and data-based basis for subsequent calculation of the comprehensive applicability score, and improving the accuracy and reliability of the applicability assessment.

[0056] Preferably, step S303 may include: Based on the environmental adaptability score, disease matching score, and material performance score, and in combination with the current repair method in use, determine the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor; Using the analytic hierarchy process, based on the determined sensitivity factors, determine the weight coefficients of the environmental adaptability score, disease matching score, and material performance score; Based on the environmental adaptability score, disease matching score, material performance score, and their corresponding weight coefficients, calculate the comprehensive applicability score; Compare the comprehensive applicability score with a preset applicability threshold to determine whether the current trenchless repair plan is applicable and obtain the applicability assessment result.

[0057] Among them, determining the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor means, based on the already calculated environmental adaptability score, disease matching score, and material performance score, and considering the specific repair method currently in use (such as CIPP, spiral winding, spraying, etc.), to quantify the importance or influence degree of the three factors of environment, disease, and material performance on the successful implementation and final effect of the current repair method. Specifically, it can be implemented using a preset rule table or lookup table, which sets corresponding sensitivity factor values for different ranges of environmental adaptability scores, disease matching scores, and material performance scores according to different repair methods. For example, for a repair method that is highly sensitive to environmental temperature and humidity, when the environmental adaptability score is low, the corresponding environmental sensitivity factor will be set to a higher value, indicating that the environmental factor has a great impact on this technology. These sensitivity factors reflect the relative importance of each evaluation dimension to the applicability of the plan under specific technologies and specific conditions.

[0058] Using the Analytic Hierarchy Process (AHP), based on the determined sensitivity factors, to determine the weight coefficients of the environmental adaptability score, disease matching score, and material property score means using the AHP, a decision-making analysis method. Taking the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor determined in the previous step as inputs or references, constructing a judgment matrix, and determining the relative importance of the environmental adaptability score, disease matching score, and material property score in calculating the comprehensive applicability score through pairwise comparison, and calculating the corresponding weight coefficients. Specifically, it can be achieved by constructing a three-layer hierarchical structure, with the top layer being the goal (evaluating the applicability of the solution), the middle layer being the criteria (environmental adaptability, disease matching, material properties), and the bottom layer being the specific evaluation indicators or solutions. According to the sensitivity factors, the pairwise comparison values between the middle-layer criteria can be adjusted. For example, if the environmental sensitivity factor is high, a higher value will be assigned to the importance of environmental adaptability when comparing environmental adaptability and disease matching. By calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix and performing a consistency test, the normalized weight coefficients are finally obtained. These weight coefficients are dynamically determined and can reflect the relative importance of each evaluation dimension under different repair methods and different site conditions.

[0059] Calculating the comprehensive applicability score based on the environmental adaptability score, disease matching score, material property score, and their corresponding weight coefficients means multiplying the environmental adaptability score, disease matching score, and material property score obtained in the previous step by the corresponding weight coefficients determined by the Analytic Hierarchy Process, and then adding the weighted scores to obtain a single value. This value represents the overall applicability of the current trenchless repair solution under the current environmental, disease, and material conditions. This calculation process combines the evaluation results and their importance of each dimension, providing a quantitative comprehensive evaluation index.

[0060] Compare the calculated comprehensive applicability score with a pre-set threshold. If the comprehensive applicability score is higher than or equal to the pre-set applicability threshold, it is judged that the current trenchless repair solution is applicable; otherwise, it is judged that the solution is not applicable.

[0061] Specifically, when evaluating the applicability of trenchless repair solutions for municipal drainage pipelines, environmental data, pipeline disease information, and operating data of trenchless repair equipment at the construction site are first obtained and preprocessed, such as cleaning and integration. Then, based on the preprocessed data, the applicability rules and material performance curves in the knowledge base are queried to calculate the environmental adaptability score, disease matching score, and material performance score of the current solution. These scores respectively quantify the performance of the solution in terms of environment, disease, and material performance. To obtain a comprehensive evaluation result, these three scores need to be combined. On this basis, the current actually used repair method is further considered. Different repair methods have different sensitivities to environment, disease, and material performance. For example, one method may be very sensitive to changes in the groundwater level, while another is more sensitive to pipeline deformation. Therefore, according to the currently used technology type and the calculated scores, the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor are determined. These sensitivity factors reflect the relative importance of each factor to the applicability of the solution in the current context. Then, using the analytic hierarchy process, based on the sensitivity factors determined in the previous step, the weight coefficients of the environmental adaptability score, disease matching score, and material performance score in calculating the comprehensive score are systematically determined. The analytic hierarchy process ensures the objectivity and rationality of the weight determination through a structured comparison process, avoiding subjective assumptions or simple averaging. After the weights are determined, each score is multiplied by its corresponding weight coefficient and summed to obtain the comprehensive applicability score. This comprehensive score is a single value that comprehensively reflects the overall applicability of the current solution after considering the differences in the importance of each factor. Finally, the calculated comprehensive applicability score is compared with a preset applicability threshold. If the comprehensive score reaches or exceeds the threshold, the solution is considered applicable; otherwise, it is considered inapplicable. Thus, the final applicability evaluation result is obtained. Through the introduction of sensitivity factors and the analytic hierarchy process, the entire process makes the comprehensive evaluation process more refined and intelligent, can dynamically adjust the evaluation focus according to different repair methods and site conditions, improves the accuracy and reliability of the evaluation results, and provides a more solid foundation for subsequent solution adjustment.

[0062] In some possible implementation manners, the knowledge base includes an adjustment rule base, and the adjustment rule base includes repair solution adjustment rules, repair material adjustment rules, and repair equipment adjustment rules; the repair solution adjustment rules are used to adjust the type of repair method according to the applicability evaluation result; the repair material adjustment rules are used to adjust the type of repair material according to the material performance score; the repair equipment adjustment rules are used to adjust the model of the trenchless repair equipment according to the disease matching score; The parameter-effect model is a mapping relationship between repair parameters and repair effects established based on the BP neural network algorithm. The input of the parameter-effect model is the repair parameters, and the output is the repair effect. The repair parameters include the type of repair material, the propulsion speed of the repair equipment, and the grouting pressure. The repair effects include the strength, deformation, and durability of the repaired pipeline.

[0063] Among them, the adjustment rule library set in the knowledge base provides guidance for adjustment according to the applicability evaluation results. The repair plan adjustment rule determines whether to change the type of repair plan according to the applicability evaluation results. The repair material adjustment rule guides the selection of a more suitable repair material type according to the environmental adaptability score. The repair equipment adjustment rule guides the selection of a more suitable trenchless repair equipment model for dealing with the current disease type according to the disease matching degree score. These rules transform the evaluation results into specific adjustment directions, solving the problem of how to make preliminary adjustments according to the evaluation results.

[0064] The parameter-effect model, established using the BP neural network algorithm, provides the ability to predict repair effects under different combinations of repair parameters. Repair parameters are variables that can be controlled during the construction process, and repair effects are indicators to measure the repair quality. This model establishes a non-linear mapping relationship between parameters and effects by learning historical data. This solves the problem of predicting the impact of different parameter settings on the final repair effect.

[0065] In some embodiments, step S4 includes: S401. If the current trenchless repair plan is applicable, set the adjustment plan to be empty; S401. If the current trenchless repair plan is not applicable, perform the following steps: A1. Read the adjustment rule library in the knowledge base; A2. According to the applicability evaluation results, query the adjustment rule library to determine the triggered adjustment rules and the corresponding adjustment results; A3. According to the adjustment results, combined with the parameter-effect model, use the genetic algorithm to optimize the repair parameters, obtain the optimized repair parameters, and generate an adjustment plan; among them, the parameter-effect model is used to calculate the fitness value during the iterative optimization process.

[0066] Among them, the method first determines whether the current trenchless repair plan is applicable. The applicability evaluation results are provided by the previous steps. If the evaluation result is applicable, no adjustment is required, and the adjustment plan is set to have no content. If the evaluation result is not applicable, the system enters the adjustment process.

[0067] Furthermore, the first step (step A1) of the adjustment process is to access and obtain the set of adjustment rules stored in the knowledge base. These rules preset the adjustment directions to be taken in different inapplicable situations.

[0068] Subsequently (step A2), the system uses the previously obtained applicability evaluation results as input and searches in the read set of adjustment rules. By matching the evaluation results with the conditions set by the rules, the system identifies which specific adjustment rules are activated (for example, if the environmental adaptability score is lower than the preset environmental adaptability score threshold, the repair plan adjustment rule is activated; if the disease matching score is lower than the preset disease matching score threshold, the repair equipment adjustment rule is activated; if the material performance score is lower than the preset material performance score threshold, the repair material adjustment rule is activated), and obtains the adjustment results pointed to by these rules. The adjustment results indicate the aspects that need to be adjusted, such as suggesting to change the type of repair material or adjust the equipment operation parameters.

[0069] Finally (step A3), according to the adjustment direction determined in step A2, the system combines the parameter-effect model and the genetic algorithm to generate a specific adjustment plan. The parameter-effect model is a prediction tool that can predict the repair effect indicators (such as strength, deformation, durability) based on the input repair parameters (such as material type, equipment speed, pressure). The genetic algorithm is a search technique that uses the parameter-effect model as an evaluation function and performs iterative search within the possible parameter range to find the parameter combination that can optimize the repair effect. The result of the optimization process is a set of specific and computationally determined repair parameter values. Combining these optimized parameters with the adjustment direction determined in step A2 forms the final adjustment plan.

[0070] Specifically, this method solves the problem of how to quickly and scientifically generate an adjustment plan in the trenchless repair operation of municipal drainage pipes when the original design plan is no longer applicable due to actual site conditions, disease changes, or equipment operating status inconsistencies.

[0071] First, the method receives an applicability evaluation result, which judges whether the current plan can achieve the expected effect under actual conditions.

[0072] If the evaluation result indicates that the plan is applicable, it is directly determined that no adjustment is required, avoiding unnecessary calculations and operations and improving efficiency.

[0073] If the evaluation result indicates that the plan is not applicable, the method starts an automated adjustment process. It first consults the preset adjustment rule library, which is established based on domain knowledge and historical experience and can indicate the general direction of adjustment (such as the need to adjust materials, equipment, or process parameters) according to the reasons for inapplicability (such as environmental factors, disease types, equipment performance).

[0074] Next, the method utilizes a parameter-effect model that quantifies the relationship between the repair parameters and the final repair effect. This model allows the system to simulate the repair results under different parameter combinations in a virtual environment.

[0075] Meanwhile, the method introduces a genetic algorithm. The genetic algorithm is a powerful optimization tool that can efficiently search for the optimal solution in a complex parameter space. During the adjustment process, the genetic algorithm uses the prediction results of the parameter-effect model as a guide, iteratively tries different combinations of repair parameters, and evaluates the impact of these combinations on the repair effect. The goal of the algorithm is to find a set of parameters that make the predicted repair effect optimal or meet specific requirements, while following the adjustment direction determined in step A2.

[0076] Thus, by combining the rule guidance of the knowledge base, the prediction ability of the parameter-effect model, and the optimization ability of the genetic algorithm, the method can systematically and data-driven generate an adjustment plan containing specific optimization parameters, such as recommending replacing a specific type of repair material and giving new values for the equipment propulsion speed and grouting pressure. This overcomes the limitations of relying solely on manual experience for on-site adjustment, improves the scientificity, accuracy, and response speed of the adjustment plan, and thus enhances the quality and reliability of the repair project.

[0077] Preferably, step A3 may include: A301. Construct a multi-objective fitness function involving the strength, deformation, and durability of the repaired pipeline; wherein, the weight coefficients of the strength, deformation, and durability of the repaired pipeline are determined according to their respective degrees of importance. A302. According to the adjustment results, combining the parameter-effect model and the multi-objective fitness function, use the genetic algorithm to iteratively optimize the repair parameters, obtain the optimized repair parameters, and generate an adjustment plan; wherein, in the genetic algorithm iteration, calculate the fitness value of each individual according to the objective fitness function and the parameter-effect model, and perform selection, crossover, and mutation operations according to the fitness value to generate a new population; introduce an adaptive mutation strategy to increase the mutation probability and population diversity when the population diversity decreases, and jump out of the local optimal solution.

[0078] Among them, when constructing the multi-objective fitness function, performance indicators such as the strength, deformation, and durability of the repaired pipeline can be normalized, and then these normalized indicators can be combined by weighted summation to form a single fitness value. The determination of the weight coefficients can be based on expert experience, historical data analysis, or user-set priorities. For example, in areas where high bearing capacity is required, the weight of strength can be set higher.

[0079] Among them, the iterative optimization process of the genetic algorithm includes initializing a population of repair parameters, where each individual represents a set of parameter combinations. In each generation, the parameter-effect model is used to predict the repair effect under each individual's parameter combination, and then its fitness value is calculated through a multi-objective fitness function. Based on the fitness value, methods such as roulette wheel selection and tournament selection are used to select excellent individuals to enter the next generation. Through crossover operations (such as single-point crossover and multi-point crossover), the parameter information between individuals is exchanged to generate new parameter combinations. Through mutation operations (such as Gaussian mutation and uniform mutation), the individual parameter values are randomly changed to increase the population diversity. The implementation of the adaptive mutation strategy can monitor indicators such as the average fitness, variance of the population, or the distance between individuals to evaluate the diversity level. When the diversity is lower than the preset threshold, the mutation probability is dynamically increased to prompt the algorithm to explore new solution spaces.

[0080] Specifically, this technical solution solves the problem of how to efficiently and accurately determine the optimal repair parameters to simultaneously meet the performance requirements in multiple aspects such as strength, deformation, and durability during the adjustment process of the trenchless repair plan for municipal drainage pipelines, and avoid the optimization process falling into local optimal solutions. First, by constructing a multi-objective fitness function, multiple originally interrelated and potentially conflicting repair objectives (strength, deformation, durability) are integrated into a quantitative evaluation criterion. This makes it possible to uniformly compare the comprehensive repair effects generated by different parameter combinations. The introduction of weight coefficients further allows adjusting the relative importance of each objective according to the actual engineering requirements. Then, the parameter-effect model is used as a bridge to associate the abstract repair parameters with the specific repair effects. This model can predict the performance level that the repaired pipeline will reach under given parameters. Then, the multi-objective fitness function and the parameter-effect model are combined into the framework of the genetic algorithm. As a global optimization algorithm, the genetic algorithm can search in the complex parameter space by simulating natural selection and genetic mechanisms to find parameter combinations with higher fitness (i.e., better repair effects). During the iterative process of the genetic algorithm, the parameter-effect model provides the necessary performance prediction data for the calculation of the fitness function, while the fitness function guides the selection, crossover, and mutation operations of the genetic algorithm, driving the population to evolve towards better solutions. To overcome the problem that the standard genetic algorithm is prone to falling into local optima, an adaptive mutation strategy is introduced. This strategy can sense the diversity state of the population and increase the intensity of mutation when the diversity is insufficient, thereby helping the algorithm jump out of local optima and increasing the probability of finding the global optimum or near-optimum solution. Thus, through the iterative optimization of the genetic algorithm, a set of repair parameters that can achieve better comprehensive repair effects in the current environment can be obtained, and a specific adjustment plan can be generated based on this.

[0081] Please refer to Figure 2 , Figure 2A schematic structural diagram of an electronic device provided by an embodiment of the present application. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores a computer program executable by the processor 301. When the electronic device runs, the processor 301 executes the computer program to execute the non-excavation repair design method for municipal drainage pipelines in any optional implementation manner of the above embodiment to implement the following functions: obtaining environmental data, pipeline disease information, and operation data of non-excavation repair equipment at the construction site; preprocessing the obtained data; the preprocessing includes cleaning processing and integration processing; comparing the applicability rules and material performance curves in the knowledge base according to the preprocessed environmental data, preprocessed pipeline disease information, and preprocessed operation data, and evaluating the applicability of the current non-excavation repair plan; according to the applicability evaluation result, triggering the preset adjustment rules in the knowledge base, and combining with the parameter-effect model for optimization calculation to generate an adjustment plan.

[0082] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the non-excavation repair design method for municipal drainage pipelines in any optional implementation manner of the above embodiment to implement the following functions: obtaining environmental data, pipeline disease information, and operation data of non-excavation repair equipment at the construction site; preprocessing the obtained data; the preprocessing includes cleaning processing and integration processing; comparing the applicability rules and material performance curves in the knowledge base according to the preprocessed environmental data, preprocessed pipeline disease information, and preprocessed operation data, and evaluating the applicability of the current non-excavation repair plan; according to the applicability evaluation result, triggering the preset adjustment rules in the knowledge base, and combining with the parameter-effect model for optimization calculation to generate an adjustment plan.

[0083] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (abbreviated as SRAM), electrically erasable programmable read-only memory (abbreviated as EEPROM), erasable programmable read-only memory (abbreviated as EPROM), programmable read-only memory (abbreviated as PROM), read-only memory (abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0084] The above are only the embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A trenchless repair design method for municipal drainage pipelines, which is used to design an adjustment plan for trenchless repair operations of municipal drainage pipelines, is characterized in that, It includes the following steps: S1. Obtain the environmental data, pipeline disease information, and operation data of the trenchless repair equipment at the construction site; S2. Preprocess the obtained data; the preprocessing includes cleaning and integration; S3. According to the preprocessed environmental data, preprocessed pipeline disease information, and preprocessed operation data, compare the applicability rules and material performance curves in the knowledge base, and evaluate the applicability of the current trenchless repair plan; S4. According to the applicability evaluation result, trigger the preset adjustment rules in the knowledge base, and perform optimization calculations in combination with the parameter-effect model to generate an adjustment plan.

2. The trenchless repair design method for a municipal drainage pipeline according to claim 1, characterized in that, The environmental data includes the groundwater level and soil temperature; The pipeline disease information includes the crack length, crack width, and corrosion depth; The operation data includes the propulsion speed, grouting pressure, and temperature of the trenchless repair equipment.

3. The trenchless repair design method for a municipal drainage pipeline according to claim 2, characterized in that, The knowledge base includes an applicability rule base and a material performance curve base; The applicability rule base includes environmental applicability rules, disease applicability rules, and equipment applicability rules. The environmental applicability rules are used to limit the applicable range of repair materials under different groundwater levels and soil temperatures. The disease applicability rules are used to limit the selection of repair methods under different crack lengths, crack widths, and corrosion depths. The equipment applicability rules are used to limit the repair effect under different propulsion speeds, grouting pressures, and temperatures of the trenchless repair equipment; The material performance curve base includes temperature-strength curves, pressure-deformation curves, and time-durability curves of different repair materials; Step S3 includes: S301. Read the applicability rule base and material performance curve base in the knowledge base; S302. According to the preprocessed environmental data, preprocessed pipeline disease information, and preprocessed operation data, query the applicability rule base and material performance curve base, and calculate the environmental adaptability score, disease matching score, and material performance score; S303. According to the environmental adaptability score, disease matching score, and material performance score, calculate the comprehensive applicability score, judge whether the current trenchless repair plan is applicable, and obtain the applicability evaluation result.

4. A trenchless repair design method for municipal drainage pipelines according to claim 3, characterized in that Step S302 includes: Based on the fuzzy logic algorithm, convert each preprocessed environmental data into a membership degree, and calculate the environmental adaptability score according to the preset weights in the environmental applicability rule base; Based on the cosine similarity algorithm, calculate the similarity between the disease feature vector composed of each preprocessed pipeline disease information and the standard disease feature vector in the disease applicability rule base to obtain the disease matching score; According to the preprocessed operation data, query the material performance curve base, and use the linear interpolation method to calculate the material strength index, deformation index, and durability index under the current working conditions. Combine the preset index thresholds in the equipment applicability rule base to calculate the material performance score of the currently used repair material.

5. A trenchless repair design method for municipal drainage pipes according to claim 3, characterized in that Step S303 includes: According to the environmental adaptability score, disease matching score, and material performance score, combined with the currently used repair method, determine the environmental sensitivity factor, disease sensitivity factor, and material sensitivity factor; Using the analytic hierarchy process, according to the determined sensitivity factors, determine the weight coefficients of the environmental adaptability score, the disease matching score, and the material performance score; According to the environmental adaptability score, the disease matching score, the material performance score, and the corresponding weight coefficients, calculate the comprehensive applicability score; Compare the comprehensive applicability score with the preset applicability threshold to determine whether the current trenchless repair plan is applicable, and obtain the applicability evaluation result.

6. A trenchless repair design method for municipal drainage pipelines according to claim 3, characterized in that, The knowledge base includes an adjustment rule base, and the adjustment rule base includes repair plan adjustment rules, repair material adjustment rules, and repair equipment adjustment rules; the repair plan adjustment rules are used to adjust the type of repair method according to the applicability evaluation result; the repair material adjustment rules are used to adjust the type of repair material according to the material performance score; the repair equipment adjustment rules are used to adjust the trenchless repair equipment model according to the disease matching score; The parameter-effect model is a mapping relationship between repair parameters and repair effects established based on the BP neural network algorithm; The input of the parameter-effect model is the repair parameter, and the output is the repair effect; the repair parameters include the type of repair material, the propulsion speed of the repair equipment, and the grouting pressure, and the repair effects include the strength, deformation, and durability of the repaired pipeline.

7. A trenchless repair design method for municipal drainage pipelines according to claim 6, characterized in that, Step S4 includes: S401. If the current trenchless repair plan is applicable, set the adjustment plan to be empty; S401. If the current trenchless repair plan is not applicable, then perform the following steps: A1. Read the adjustment rule base in the knowledge base; A2. According to the applicability evaluation result, query the adjustment rule base to determine the triggered adjustment rule and the corresponding adjustment result; A3. According to the adjustment result, combined with the parameter-effect model, use the genetic algorithm to optimize the repair parameters, obtain the optimized repair parameters, and generate an adjustment plan; among them, the parameter-effect model is used to calculate the fitness value during the iterative optimization process.

8. A trenchless repair design method for municipal drainage pipelines according to claim 7, characterized in that, Step A3 includes: A301. Construct a multi-objective fitness function involving the strength, deformation, and durability of the repaired pipeline; among them, the weight coefficients of the strength, deformation, and durability of the repaired pipeline are determined according to their respective degrees of importance; A302. According to the adjustment result, combined with the parameter-effect model and the multi-objective fitness function, use the genetic algorithm to iteratively optimize the repair parameters, obtain the optimized repair parameters, and generate an adjustment plan; among them, in the genetic algorithm iteration, calculate the fitness value of each individual according to the objective fitness function and the parameter-effect model, and perform selection, crossover, and mutation operations according to the fitness value to generate a new population; introduce an adaptive mutation strategy to increase the mutation probability and population diversity when the population diversity decreases, and jump out of the local optimal solution.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the trenchless repair design method for municipal drainage pipelines according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it runs the steps in the trenchless repair design method for municipal drainage pipelines according to any one of claims 1-8.

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