A method and related apparatus for trenchless rehabilitation design of a municipal sewer pipe

Abstract

The application belongs to the technical field of municipal engineering, and discloses a kind of municipal drainage pipeline trenchless repair design method and related equipment, by obtaining the environmental data of construction site, pipeline disease information and the operation data of trenchless repair equipment, data is preprocessed, and the applicability rule and material performance curve in knowledge base are compared according to the preprocessed data, the applicability of current trenchless repair scheme is evaluated, according to the applicability evaluation result, the preset adjustment rule in knowledge base is triggered, and optimization calculation is carried out in combination with parameter-effect model, and adjustment scheme is generated;It realizes the automation and intelligentization from field data to scheme adjustment, reduces the dependence on artificial experience, improves the accuracy and timeliness of scheme adjustment, so as to improve the quality and efficiency of municipal drainage pipeline trenchless repair engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of municipal engineering, in particular to a municipal drainage pipeline trenchless repair design method and related equipment. BACKGROUND

[0002] As an important infrastructure of the city, the municipal drainage pipeline system will inevitably produce various structural or functional diseases after long-term operation, such as circumferential or longitudinal cracks of the pipe wall, local damage, misalignment or disconnection of the pipe interface, corrosion of the pipe material, deformation (such as ovalization) of the whole or part of the pipe, and internal sediment accumulation, etc. These diseases seriously affect the normal drainage function of the pipeline, reduce the flow capacity, cause leakage, and even may lead to the loss of surrounding soil, and further cause ground subsidence or collapse and other serious consequences, which pose a threat to the city operation and public safety. Therefore, the damaged pipeline must be repaired in time and effectively. Compared with the traditional excavation repair method which needs to break the road surface in a large area, 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 pipeline repair due to its small impact on the surrounding environment, relatively fast construction speed and other advantages.

[0003] The design of the trenchless repair scheme is a key link of the repair work, which directly determines the final effect, economic cost and construction efficiency of the repair project. At present, the design process of the trenchless repair scheme is usually responsible by engineers, who will comprehensively analyze the pipeline detection report (such as high-definition CCTV detection video, laser cross-section scanning data, sonar detection results), evaluate the type, location, degree of the disease and the basic information of the pipeline (pipe material, pipe diameter, buried depth, service life), and refer to the current design specifications and technical standards of the state and industry, select appropriate trenchless repair technology (such as CIPP lining repair, spiral winding method, spraying method, local repair method, etc.) and repair materials (such as different models of resin, lining pipe, winding belt, sprayed mortar, etc.), and preliminarily determine the initial setting range of the key construction process parameters (such as temperature control curve, curing time, steam pressure of CIPP curing; winding tension, overlap width of spiral winding; spraying thickness, curing time of spraying). After the design is completed, the scheme is handed over to the construction unit for execution.

[0004] However, the actual construction site environment of municipal sewer pipelines is complex and variable, with many uncertain factors that may not be consistent with the information obtained through limited survey during the design phase, and may change dynamically during the construction process. For example, the groundwater level determined by historical data or a small number of measurement points during the design phase may be moderate, but during the actual construction period, due to seasonal rainfall, upstream reservoir water release, or changes in groundwater pumping activities in the 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 flow into the pipe through the damaged part of the pipe. This high groundwater level or in-pipe water inflow significantly changes the construction environment. At the same time, preliminary geological survey may not fully reveal the geological details along the pipe section, such as the presence of local weak 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 pipe, bringing additional challenges to the implementation of the repair scheme. In addition, the ground traffic load in the construction area may increase due to temporary traffic control or emergencies, or other ongoing underground engineering (such as pipeline laying, foundation pit excavation) in the surrounding area may cause ground vibration or underground stress field changes, which may also affect the repair process.

[0005] After more thorough cleaning and pretreatment of the pipeline during the construction preparation phase, or during the final inspection before the repair material enters the pipeline, it may be found that the actual situation of the pipeline defects is more complex or severe than described in the preliminary detection report. For example, the preliminary report may only record cracks in the pipe wall, but after cleaning, it is found that there is local pipe wall detachment or structural looseness below the cracks; or it is found that the preliminary detection has missed more serious defects that have a greater impact on the structure, such as severe interface separation, misalignment of overlaps, etc. This more detailed or more severe defect information may mean that the original design of the repair technique or material is insufficient.

[0006] During the construction process, trenchless repair equipment and auxiliary systems generate a large amount of real-time operation data. These real-time data are important basis for evaluating the execution status of the scheme and predicting the final repair effect. Different trenchless repair techniques and selected repair materials exhibit different sensitivities to the above dynamic changes in environmental conditions, complex defect conditions, and parameter fluctuations during the construction process. For example, the curing reaction speed and final strength of certain types of CIPP resin are very sensitive to environmental temperature and water, and if a large amount of low-temperature groundwater inflow is encountered during the curing process, it may result in incomplete curing of the resin or the strength of the cured repair layer not meeting the design requirements. Certain wrapping materials may not be able to ensure uniform overlap when encountering local pipe deformation or obstacles, and may form weak links. Certain sprayed materials have very high requirements for the adhesion conditions of the pipe wall surface, and groundwater or residual sludge may severely affect their performance.

[0007] Due to the deviation of actual situation from design assumption, and the sensitivity of repair technology and material to these deviations, the original design preliminary scheme may not be successfully implemented in the current actual construction environment, or even if it is completed, it may lead to the repair effect not meeting the expected (for example, the leakage rate after repair exceeds the standard, the structural bearing capacity is insufficient, the service life is shortened) or a higher construction risk (for example, construction interruption, equipment damage, material waste, the need for rework, and even safety accidents). Therefore, it is necessary to adjust the original design scheme quickly and scientifically according to the actual situation obtained in real time on the construction site.

[0008] However, when the on-site engineer makes such real-time scheme adjustment, he faces great challenges. He needs to analyze the multi-source heterogeneous real-time data (environmental sensor data, equipment operation data, personnel operation records) in a short time, combine the updated disease 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 effect, cost, construction period and risk. Such multi-factor, high-dimensional complex trade-offs are difficult to complete efficiently and accurately by relying on personal experience and limited computing tools. Manual decision-making may have a lag and fail to respond to changes in the field; different engineers' adjustment strategies may differ, affecting the stability of repair quality. This real-time adjustment method relying on manual experience limits the ability of trenchless repair engineering to achieve the best results in complex environments and increases the uncertainty and risk of engineering implementation.

[0009] In view of the above problems, the prior art needs to be improved. SUMMARY

[0010] The purpose of the present application is to provide a municipal drainage pipeline trenchless repair design method and related equipment, which realizes the automation and intelligentization 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 municipal drainage pipeline trenchless repair engineering.

[0011] In a first aspect, the present application provides a municipal drainage pipeline trenchless repair design method for designing an adjustment scheme for municipal drainage pipeline trenchless repair work, comprising the following steps:

[0012] S1. Obtain environmental data, pipeline disease information, and operation data of trenchless repair equipment at the construction site;

[0013] S2. Preprocess the obtained data; the preprocessing includes cleaning and integration processing;

[0014] S3. According to the pre-processed environmental data, the pre-processed pipeline disease information and the pre-processed operation data, compare the applicability rules and the material performance curves in the knowledge base to evaluate the applicability of the current trenchless repair scheme;

[0015] S4. According to the applicability evaluation result, trigger the preset adjustment rules in the knowledge base and combine the parameter-effect model for optimization calculation to generate an adjustment scheme.

[0016] Preferably, the environmental data includes groundwater level and soil temperature;

[0017] The pipeline disease information includes crack length, crack width and corrosion depth;

[0018] The operation data includes the pushing speed of the trenchless repair equipment, the grouting pressure and the temperature.

[0019] Preferably, the knowledge base contains an applicability rule base and a material performance curve base;

[0020] The applicability rule base contains environmental applicability rules, disease applicability rules and equipment applicability rules, the environmental applicability rules are used to define the applicable range 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, and the equipment applicability rules are used to define the repair effect under different pushing speeds, grouting pressures and temperatures of the trenchless repair equipment;

[0021] The material performance curve base contains temperature-strength curves, pressure-deformation curves and time-durability curves of different repair materials;

[0022] Step S3 includes:

[0023] S301. Read the applicability rule base and the material performance curve base in the knowledge base;

[0024] S302. According to the pre-processed environmental data, the pre-processed pipeline disease information and the pre-processed operation data, query the applicability rule base and the material performance curve base, calculate the environmental adaptability score, the disease matching degree score and the material performance score;

[0025] S303. According to the environmental adaptability score, the disease matching degree score and the material performance score, calculate the comprehensive applicability score, judge whether the current trenchless repair scheme is applicable, and obtain the applicability evaluation result.

[0026] Preferably, step S302 includes:

[0027] Based on the fuzzy logic algorithm, the pre-processed environmental data is converted into membership degree, and the environmental adaptability score is calculated according to the preset weight in the environmental applicability rule base.

[0028] Based on the cosine similarity algorithm, 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 library is calculated to obtain the disease matching score;

[0029] Based on the preprocessed operating data, the material performance curve library is queried, and the linear interpolation method is used to calculate the material strength index, deformation index and durability index under the current working conditions. Combined with the preset index thresholds in the equipment applicability rule library, the material performance score of the currently used repair material is calculated.

[0030] Preferably, step S303 includes:

[0031] Based on the environmental adaptability score, disease matching score and material performance score, combined with the currently used restoration methods, determine the environmental sensitivity factor, disease sensitivity factor and material sensitivity factor;

[0032] The analytic hierarchy process is used to determine the weight coefficients of environmental adaptability score, disease matching score and material performance score based on the determined sensitivity factors;

[0033] Calculate the comprehensive applicability score based on the environmental adaptability score, disease matching score, material performance score and the corresponding weight coefficients;

[0034] Compare the comprehensive applicability score with the preset applicability threshold to determine whether the current trenchless repair plan is applicable and obtain the applicability assessment result.

[0035] Preferably, the knowledge base includes an adjustment rule base, which includes repair scheme adjustment rules, repair material adjustment rules, and repair equipment adjustment rules; the repair scheme adjustment rules are used to adjust the type of repair method according to the applicability assessment results; 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 non-excavation repair equipment according to the disease matching score;

[0036] 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 and the propulsion speed and grouting pressure of the repair equipment, and the repair effect includes the strength, deformation and durability of the pipeline after repair.

[0037] Preferably, step S4 includes:

[0038] S401. If the current trenchless repair solution is applicable, set the adjustment solution to empty;

[0039] S401. If the current trenchless repair scheme is not applicable, the following steps are performed:

[0040] A1. Read the adjustment rule base in the knowledge base;

[0041] A2. According to the applicability evaluation result, query the adjustment rule base to determine the triggered adjustment rule and the corresponding adjustment result;

[0042] A3. According to the adjustment result, combine the parameter-effect model, and use the genetic algorithm to optimize the repair parameters to obtain the optimized repair parameters, and generate an adjustment scheme; wherein the parameter-effect model is used to calculate the fitness value in the iterative optimization process.

[0043] Preferably, step A3 comprises:

[0044] A301. Construct a multi-objective fitness function related to 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 the respective attention degrees;

[0045] A302. According to the adjustment result, combine the parameter-effect model and the multi-objective fitness function, and use the genetic algorithm to iteratively optimize the repair parameters to obtain the optimized repair parameters, and generate an adjustment scheme; wherein the fitness value of each individual is calculated according to the target fitness function and the parameter-effect model in the genetic algorithm iteration, and the selection, crossover and mutation operations are performed according to the fitness value to generate a new population; a self-adaptive mutation strategy is introduced to increase the mutation probability and increase the population diversity when the population diversity decreases, so as to jump out of the local optimal solution.

[0046] In a second aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the steps of the trenchless repair design method for municipal drainage pipelines are executed.

[0047] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the trenchless repair design method for municipal drainage pipelines are executed.

[0048] Beneficial effects: The present application provides a method for designing trenchless repair of municipal drainage pipelines and related equipment. By obtaining environmental data of the construction site, pipeline disease information, and operating data of the trenchless repair equipment, the data is pre-processed. The applicability of the current trenchless repair scheme is evaluated by comparing the applicability rules and material performance curves in the knowledge base based on the pre-processed data. Based on the applicability evaluation results, the preset adjustment rules in the knowledge base are triggered, and the parameter-effect model is combined for optimization calculation to generate an adjustment scheme. The method realizes the automation and intelligence of the adjustment from field data to scheme, reduces the dependence on manual experience, improves the accuracy and timeliness of scheme adjustment, and thus improves the quality and efficiency of the trenchless repair project of municipal drainage pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Flowchart of the trenchless repair design method for municipal drainage pipes provided in an embodiment of the present application.

[0050] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0051] Description of reference numerals: 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0052] 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.

[0053] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or 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 should not be understood as indicating or implying relative importance.

[0054] 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:

[0055] S1. Obtain construction site environmental data, pipeline disease information, and trenchless repair equipment operating data;

[0056] S2 preprocesses the acquired data; the preprocessing includes cleaning and integration processing;

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] Step S2 preprocesses the acquired raw data, including cleaning and consolidation. Cleaning removes errors or anomalies, while consolidation unifies and aligns data from different sources. Data preprocessing ensures the accuracy and reliability of the data used in subsequent evaluations and calculations. Cleaning can utilize outlier detection and missing value filling techniques. Consolidation can utilize timestamp alignment and data format unification techniques. This transforms the raw data into usable preprocessed data.

[0062] Step S3 uses preprocessed field data, combined with applicability rules and material performance curves from a knowledge base, to assess the applicability of the current trenchless remediation solution under current conditions. This step quantifies the degree of compatibility between the solution and the actual situation by comparing the rules and curves, providing objective evidence for determining whether adjustments are necessary. The knowledge base can store pre-set condition judgment rules and material performance data. This comparison process can involve query matching or score calculation. This results in an assessment of the applicability of the current solution.

[0063] Step S4 triggers the preset adjustment rules in the knowledge base according to the suitability evaluation results of step S3, and performs optimization calculation using the parameter-effect model, to finally generate a specific adjustment scheme. This step automatically starts the adjustment process according to the evaluation results, calculates the optimized repair parameters or scheme details using the model, provides specific and executable adjustment suggestions, and improves the efficiency and scientificity of scheme adjustment. The adjustment rules can define the adjustment direction to be taken under certain evaluation results. The parameter-effect model can predict the repair effect under different parameter combinations. Optimization calculation can find the best parameter combination. Thus, an adjustment scheme is generated for the actual situation on site.

[0064] Specifically, the working principle of the method is that, first, real-time or near real-time data of the construction site are obtained through step S1, which reflect the actual environmental conditions, pipe disease states and operation conditions of repair equipment. These actual conditions may deviate from the assumptions at the initial design. Then, step S2 cleans and integrates these raw data to remove noise and errors, forming a structured and reliable data set. Then, step S3 compares and analyzes these preprocessed data with the suitability rules and material performance curves stored in the knowledge base. The suitability rules define the applicable range of schemes 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 scheme being implemented or planned to be implemented under the current actual site conditions, judges whether it can achieve the expected repair effect or whether there is an implementation risk. Finally, in step S4, if the evaluation results show that the current scheme is not applicable or there is optimization space, 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 operation parameters of equipment or adjusting the construction process. At the same time, combined with the parameter-effect model, the method performs optimization calculation on the repair parameters related to the adjustment direction. The parameter-effect model predicts the influence of different parameter settings on the repair effect. The optimization calculation process finds the parameter combination that can maximize the repair effect or minimize the risk. Finally, a scheme containing specific adjustment suggestions is generated to guide the on-site construction personnel to operate. The whole process realizes the automation and intelligentization from site 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 municipal drainage pipeline trenchless repair projects.

[0065] Specifically, the environmental data includes groundwater level and soil temperature;

[0066] The pipe disease information includes crack length, crack width and corrosion depth;

[0067] The operation data includes the pushing speed, grouting pressure and temperature of the trenchless repair equipment.

[0068] wherein the environmental data includes groundwater level and soil temperature, the groundwater level can be obtained by a groundwater level monitoring sensor installed in the construction area, and the soil temperature can be obtained by a soil temperature sensor. The pipe disease information includes crack length, crack width and corrosion depth, the crack length and the crack width can be obtained by analyzing a pipe CCTV (Closed-Circuit Television) detection video or processing laser section scanning data, and the corrosion depth can be obtained by a non-destructive detection method such as ultrasonic detection or eddy current detection. The operation data includes the pushing speed of the trenchless repair equipment, the grouting pressure and the temperature, which can be directly read from the sensors and control systems of the trenchless repair equipment.

[0069] Specifically, by limiting the environmental data to be the groundwater level and the soil temperature, the system obtains key environmental parameters that affect the curing process of the repair material and the external force of the pipe. The groundwater level directly affects the water pressure difference inside and outside the pipe and whether groundwater flows into the pipe, and the soil temperature affects the curing reaction rate and the final strength of the material. By limiting the pipe disease information to be the crack length, the crack width and the corrosion depth, the system obtains specific indicators that quantify the damage degree of the pipe structure, which are directly related to the severity of the disease and the selection of repair methods and materials. By limiting the operation data to be the pushing speed of the trenchless repair equipment, the grouting pressure and the temperature, the system obtains key parameters reflecting the execution state and process control level of the repair process, which directly affect the laying quality, density, adhesion to the pipe wall and curing effect of the material. These specific and quantifiable data provide accurate input basis for subsequent data preprocessing, scheme applicability evaluation and adjustment scheme generation. Obtaining these data enables the system to accurately evaluate the applicability of the existing scheme under the current environment, disease and construction conditions, and predict the repair effect based on the actual operation parameters, thereby triggering more targeted adjustment rules and generating adjustment schemes that better meet the actual needs of the site through optimization calculation, improving the scientificity and reliability of the design and adjustment of the trenchless repair scheme.

[0070] In some embodiments, step S2 comprises:

[0071] S201. For the obtained raw data, an outlier detection method based on a box plot is used to identify and eliminate abnormal data beyond the upper and lower limit ranges, and a Lagrange interpolation method is used to fill in the missing values to obtain cleaned data;

[0072] S202. The cleaned data is time-aligned and stored uniformly in CSV format to obtain preprocessed data.

[0073] In step S201, the boxplot-based outlier detection method calculates the data's quartiles (Q1, Q3) and interquartile range (IQR = Q3-Q1) to determine the upper and lower limits of outliers (typically Q1-1.5*IQR and Q3+1.5*IQR). Data points outside this range are marked as outliers. The Lagrange interpolation method constructs a polynomial passing through known data points, then uses this polynomial to estimate the values ​​at the missing locations. In step S202, the cleaned data is time-aligned to ensure consistent timestamps for different data types, and then stored uniformly in a CSV format file.

[0074] Specifically, the method first receives raw data streams from construction site environmental sensors, pipeline inspection reports, and trenchless repair equipment. This raw data may contain outliers (such as temperature readings outside the physical range) and missing values ​​(such as missing data at a certain time point) due to sensor failure, transmission errors, or incomplete recording. To address these flaws in the raw data, a cleansing process is performed. During the cleansing process, a boxplot-based algorithm independently analyzes each data type (such as groundwater level, soil temperature, and advance speed), identifying and removing outliers that significantly deviate from the main data distribution, thereby eliminating interference from erroneous data in subsequent calculations. Next, for missing points in the data series, Lagrange interpolation is used to mathematically estimate the trends of known data before and after the missing point, filling in the missing values ​​and ensuring the integrity and continuity of the dataset. After the cleansing process, the resulting dataset contains no obvious outliers and has filled in missing values. Subsequently, an integration process is performed. This process synchronizes the cleaned data types (environmental data, disease information, and operational data) according to their timestamps, ensuring that data acquired at the same time can be analyzed in a correlated manner. Finally, the time-aligned data is saved in a standard CSV file format for easy reading and processing by the subsequent solution evaluation module. Through these steps, the raw data is converted into high-quality, complete, and uniformly formatted pre-processed data, providing a reliable data foundation for subsequent solution applicability evaluation.

[0075] In some embodiments, the knowledge base includes an applicability rule library and a material property curve library;

[0076] The applicability rule library includes environmental applicability rules, disease applicability rules, and equipment applicability rules. Environmental applicability rules are used to limit the applicability of repair materials under different groundwater levels and soil temperatures. Disease applicability rules are used to limit the selection of repair methods under different crack lengths, crack widths, and corrosion depths. Equipment applicability rules are used to limit the repair effects under the advancement speed, grouting pressure, and temperature of different trenchless repair equipment.

[0077] The material performance curve library includes temperature-strength curves, pressure-deformation curves and time-durability curves of different repair materials.

[0078] The step S3 comprises:

[0079] S301. Read the applicability rule library and the material performance curve library in the knowledge base.

[0080] S302. According to the pre-processed environmental data, the pre-processed pipeline disease information and the pre-processed operation data, query the applicability rule library and the material performance curve library, calculate the environmental adaptability score, the disease matching degree score and the material performance score.

[0081] S303. According to the environmental adaptability score, the disease matching degree score and the material performance score, calculate the comprehensive applicability score, judge whether the current trenchless repair scheme is applicable, and obtain the applicability evaluation result.

[0082] The knowledge base refers to a collection of professional knowledge and data for evaluating the applicability of the trenchless repair scheme, which can be implemented by a database, a file system or a memory structure, and is used to store the applicability rule library and the material performance curve library.

[0083] The applicability rule library refers to a rule set containing a series of logical judgments or condition settings, which is used to guide how to evaluate the applicability of the scheme according to the input data, and can be implemented by a rule engine, an expert system or a lookup table, and contains environmental applicability rules, disease applicability rules and equipment applicability rules.

[0084] The environmental applicability rules refer to rules that limit the applicability of different repair materials according to environmental parameters such as groundwater level and soil temperature, which are combined with the pre-processed environmental data to evaluate the influence of the environment on the implementation of the scheme. The disease applicability rules refer to rules that limit the selection of different repair methods according to pipeline disease parameters such as crack length, crack width and corrosion depth, which are used to evaluate the matching degree of the scheme to the disease. The equipment applicability rules refer to rules that limit or predict the repair effect according to the running parameters of the trenchless repair equipment such as the pushing speed, the grouting pressure and the temperature, which are used to evaluate the influence of the equipment running state on the repair quality.

[0085] The material performance curve library refers to a collection of performance data of different repair materials under different working conditions, which can be implemented by a lookup table, a function model or a data file, and contains temperature-strength curves, pressure-deformation curves and time-durability curves of different repair materials.

[0086] The temperature-strength curve refers to a curve or data set describing the change in strength performance of a specific repair material at different temperatures, which can be realized by connecting discrete data points or mathematical function fitting, and is used to evaluate the strength performance of the material at actual environmental temperature or curing temperature. The pressure-deformation curve refers to a curve or data set describing the change in deformation performance of a specific repair material under different pressure, which can be realized by connecting discrete data points or mathematical function fitting, 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 describing the decay or retention trend of the performance of a specific repair material over time, which can be realized by connecting discrete data points or mathematical function fitting, and is used to evaluate the long-term service life and performance stability of the material.

[0087] In step S301, the applicability rule base and the material performance curve base in the knowledge base are read, which means that the required rules and curve data are loaded into the evaluation system before the applicability evaluation is performed, which can be realized by file reading, database query or memory loading, etc., to provide data basis for subsequent query and calculation.

[0088] In step S302, the data actually obtained and cleaned and integrated are matched and calculated against the rules and curves in the knowledge base, which can be realized by rule matching algorithm, similarity calculation algorithm or curve interpolation calculation. The preprocessed environmental data is used to query the environmental applicability rule base and calculate the environmental adaptability score; the preprocessed pipeline disease information is used to query the disease applicability rule base and calculate the disease matching degree score; the preprocessed operation data is used to query the material performance curve base and equipment applicability rule base and calculate the material performance score. These scores convert the qualitative rules and curves into quantitative indicators.

[0089] The environmental adaptability score refers to an index quantifying the degree of friendliness or influence of the current environmental conditions on the implementation of the trenchless repair scheme, which can be represented by a value between 0 and 1, with a higher value indicating a more adaptable environment. The disease matching degree score refers to an index quantifying the matching degree of the current trenchless repair scheme to the type and degree of pipeline disease, which can be represented by a value between 0 and 1, with a higher value indicating a higher matching degree. The material performance score refers to an index quantifying whether the current repair material can meet the expected performance requirements under actual working conditions, which can be represented by a value between 0 and 1, with a higher value indicating a higher performance.

[0090] In step S303, the scores are combined and weighted to obtain a total suitability index, which is compared with a preset threshold. Weighted summation, analytic hierarchy process, or fuzzy comprehensive evaluation can be used to achieve this. The comprehensive suitability score reflects the overall suitability of the scheme in terms of environment, disease, and material and equipment performance. To determine whether the scheme is suitable, for example, if the comprehensive suitability score is higher than a certain threshold (such as 0.6), the scheme is considered suitable; otherwise, the scheme is considered unsuitable. The suitability evaluation result is obtained, which is used to guide subsequent scheme adjustment.

[0091] Specifically, the present scheme provides a quantitative evaluation method based on knowledge base and multi-source data for the suitability evaluation of municipal drainage pipeline trenchless repair schemes in actual construction sites. First, a knowledge base containing a suitability rule base and a material performance curve base is constructed. The suitability rule base is further refined into environmental suitability rules, disease suitability rules, and equipment suitability rules, which are respectively for key parameters such as groundwater level, soil temperature, crack length, crack width, corrosion depth, equipment advancing speed, grouting pressure, and temperature, to limit the applicable range of repair materials, repair methods, or repair effects. The material performance curve base stores performance data such as strength, deformation, and durability of different repair materials under different temperatures, pressures, and times. In the evaluation process, the rules and curves in the knowledge base are first read. Then, the pre-processed construction site environmental data, pipeline disease information, and non-excavation repair equipment operation data are used to query the knowledge base. The environmental suitability rules are queried according to the environmental data, the environmental adaptability score is calculated, and the influence of the environment on the scheme is quantified. The disease suitability rules are queried according to the disease information, the disease matching degree score is calculated, and the pertinence of the scheme to the disease is quantified. The material performance curves and equipment suitability rules are queried according to the operation data, the material performance score is calculated, and the performance of the materials and equipment under the current working conditions is quantified. Finally, the environmental adaptability score, the disease matching degree score, and the material performance score are considered comprehensively to calculate a comprehensive suitability score, which is compared with a preset threshold to determine whether the current non-excavation repair scheme is suitable for the actual site conditions. This method converts qualitative experience judgment into data-based quantitative evaluation, making the evaluation process more objective, specific, and traceable, and more accurately reflecting the actual feasibility and expected effect of the scheme in complex site environments, overcoming the limitations of traditional methods that rely on experience and have a fuzzy evaluation process. By considering and quantifying multiple factors such as environment, disease, and material and equipment performance, the present scheme can effectively identify potential problems in the actual implementation of the scheme and provide clear basis for subsequent scheme adjustment.

[0092] Preferably, step S302 can include:

[0093] The preprocessed environmental data is converted into membership degrees based on a fuzzy logic algorithm, and an environmental adaptability score is calculated according to preset weights in the environmental adaptability rule base;

[0094] A disease matching degree score is obtained by calculating the similarity between the disease feature vector composed of the preprocessed pipeline disease information and the standard disease feature vector in the disease adaptability rule base based on a cosine similarity algorithm.

[0095] According to the preprocessed operation data, the material performance curve library is queried, the material strength index, deformation index and durability index under the current working condition are calculated by using a linear interpolation method, and the material performance score of the repair material currently used is calculated in combination with the preset index threshold in the equipment adaptability rule base.

[0096] The preprocessed environmental data is converted into membership degrees based on a fuzzy logic algorithm, and an environmental adaptability score is calculated according to preset weights in the environmental adaptability rule base;

[0097] The preprocessed environmental data is converted into membership degrees based on a fuzzy logic algorithm, and an environmental adaptability score is calculated according to preset weights in the environmental adaptability rule base; The preprocessed environmental data is converted into membership degrees based on a fuzzy logic algorithm, and an environmental adaptability score is calculated according to preset weights in the environmental adaptability rule base;

[0098] According to the pre-processed operation data, the material performance curve library is queried, for the material strength index, the temperature in the operation data is used in the temperature-strength curve corresponding to the current used repair material, and a linear interpolation method is used to determine the material strength index under the current working condition; for the deformation index, the pressure in the operation data is used in the pressure-deformation curve corresponding to the current used repair material, and a linear interpolation method is used to determine the deformation index under the current working condition; for the durability index, the expected service life is used to query the time-durability curve corresponding to the current used repair material, to obtain the durability at the corresponding time point, and to obtain the durability index. The material performance score of the current used repair material is calculated in combination with the preset index threshold in the equipment applicability rule library, and specifically, the calculated material performance index value can be compared with the performance threshold preset for the current repair material and the equipment in the equipment applicability rule library, for example, whether the material strength meets the minimum requirement, whether the deformation exceeds the allowed range, and whether the durability at the end of the expected service life is not less than the preset lower limit of the durability. According to the comparison result, the performance of the material under the current working condition can be quantified, and the material performance score is obtained.

[0099] Specifically, in the step of evaluating the applicability of the trenchless repair scheme, the present solution provides specific methods for calculating the environmental adaptability score, the disease matching score, and the material performance score. First, for the pre-processed environmental data, such as groundwater level and soil temperature, 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 in these three sets. Similarly, the soil temperature data is processed. Then, according to the weights preset in the environmental applicability rule base for different environmental factors, such as the groundwater level weight of 0.6 and the soil temperature weight of 0.4, the membership degrees of each environmental data are weighted and summed to obtain the environmental adaptability score. This score reflects the adaptability of the current environmental conditions to the repair scheme. Second, for the pre-processed pipeline disease information, such as crack length, crack width, and corrosion depth, it is constructed into a disease feature vector. For example, the vector can be represented as [crack length, crack width, corrosion depth]. At the same time, the disease applicability rule base stores standard disease feature vectors for different repair methods (such as CIPP, spiral wrapping, spraying, etc.), and the standard disease feature vector of the currently used repair method is selected for cosine similarity calculation. 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 of the current disease type and the current repair method. Finally, for the pre-processed operation data, such as pushing 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 linear interpolation method, the deformation index and strength index of the material under the current working condition are estimated on these two curves according to the specific pressure value and temperature value. 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 of the expected service life), and the durability index is obtained. Then, combined with the strength, deformation, and durability thresholds preset in the equipment applicability rule base for the current repair material, 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 is lower than the deformation threshold, the deformation score is higher, and the durability at the end of the expected service life is higher than the durability threshold, the durability score is higher. By integrating these indicators (for example, by weighted average calculation), the material performance score is obtained. This score reflects the performance of the currently used repair material under actual construction conditions.Through these specific algorithm calculations, the problem of relying only on experience for fuzzy judgment is overcome, providing a quantitative, data-based basis for subsequent calculation of comprehensive applicability scores, and improving the accuracy and reliability of applicability evaluation.

[0100] Preferably, step S303 can include:

[0101] According to the environmental adaptability score, the disease matching degree score and the material performance score, and in combination with the current repair method being used, an environmental sensitivity factor, a disease sensitivity factor and a material sensitivity factor are determined;

[0102] According to the determined sensitivity factors, the weight coefficients of the environmental adaptability score, the disease matching degree score and the material performance score are determined using the analytic hierarchy process;

[0103] According to the environmental adaptability score, the disease matching degree score and the material performance score and the corresponding weight coefficients, a comprehensive applicability score is calculated;

[0104] The comprehensive applicability score is compared with a preset applicability threshold to determine whether the current trenchless repair scheme is applicable, and an applicability evaluation result is obtained.

[0105] The environmental sensitivity factor, the disease sensitivity factor and the material sensitivity factor are determined by quantifying the importance or influence of the environment, the disease and the material performance on the successful implementation and final effect of the current repair method according to the already calculated environmental adaptability score, disease matching degree score and material performance score, and taking into account the specific repair method currently being used (such as CIPP, spiral winding, spraying, etc.). Specifically, a pre-set rule table or lookup table can be used to achieve this, which sets corresponding sensitivity factor values for different ranges of environmental adaptability scores, disease matching degree 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 influence on this technology. These sensitivity factors reflect the relative importance of each evaluation dimension to the applicability of the scheme under specific technology and specific conditions.

[0106] Determination of weight coefficients of environmental adaptability score, disease matching degree score and material performance score according to sensitivity factors is to use the analytic hierarchy process (AHP) decision analysis method, with the environmental sensitivity factor, disease sensitivity factor and material sensitivity factor determined in the previous step as input or reference, to construct a judgment matrix, determine the relative importance of environmental adaptability score, disease matching degree score and material performance score in calculating the comprehensive applicability score through pair-wise comparison, and calculate the corresponding weight coefficients. Specifically, a three-level hierarchical structure can be used to achieve this, with the top layer being the target (evaluation scheme applicability), the middle layer being the criteria (environmental adaptability, disease matching degree, material performance), and the bottom layer being the specific evaluation indicators or schemes. According to the sensitivity factors, the pair-wise comparison values between the intermediate layer criteria can be adjusted, for example, if the environmental sensitivity factor is high, the importance of environmental adaptability will be given a higher value when comparing environmental adaptability with disease matching degree. By calculating the maximum eigenvalue of the judgment matrix and the corresponding eigenvector, and conducting consistency check, 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.

[0107] Calculation of comprehensive applicability score according to environmental adaptability score, disease matching degree score and material performance score and corresponding weight coefficients is to multiply the environmental adaptability score, disease matching degree score and material performance score obtained in the previous step by the corresponding weight coefficients determined by the analytic hierarchy process, then add the weighted scores to get a single value, which represents the overall applicability of the current trenchless repair scheme under the current environmental, disease and material conditions. This calculation process combines the evaluation results of each dimension and their importance to provide a quantitative comprehensive evaluation index.

[0108] Comparison of the calculated comprehensive applicability score with a pre-set threshold value. If the comprehensive applicability score is higher than or equal to the pre-set applicability threshold value, the current trenchless repair scheme is judged to be applicable; otherwise, the scheme is judged to be not applicable.

[0109] Specifically, in the process of evaluating the applicability of trenchless repair schemes for municipal sewer pipes, firstly, the environmental data of the construction site, the pipe disease information, and the operation data of the trenchless repair equipment are obtained, and preprocessing such as cleaning and integration is performed. Then, according to the preprocessed data, the applicability rules and material performance curves in the knowledge base are queried, and the environmental adaptability score, disease matching degree score, and material performance score of the current scheme are calculated. These scores quantify the performance of the scheme in terms of environment, disease, and material performance. In order to obtain a comprehensive evaluation result, the three scores need to be combined. On this basis, the scheme further considers the actual repair method currently used. Different repair methods have different sensitivities to environment, disease, and material performance. For example, a certain method may be very sensitive to changes in groundwater level, while another method may be more sensitive to pipe deformation. Therefore, according to the type of technology currently used and the scores calculated, 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 scheme in the current situation. Then, using the analytic hierarchy process, the weight coefficients of the environmental adaptability score, disease matching degree score, and material performance score in calculating the comprehensive score are determined systematically based on the sensitivity factors determined in the previous step. The analytic hierarchy process ensures the objectivity and rationality of the weight determination through a structured comparison process, avoiding subjective speculation or simple averaging. After the weights are determined, the scores are multiplied by their corresponding weight coefficients and summed to obtain the comprehensive applicability score. This comprehensive score is a single numerical value that comprehensively reflects the overall applicability of the current scheme after considering the importance of each factor. Finally, the calculated comprehensive applicability score is compared with a pre-set applicability threshold. If the comprehensive score reaches or exceeds the threshold, the scheme is considered applicable; otherwise, the scheme is considered not applicable. Thus, the final applicability evaluation result is obtained. The entire process introduces sensitivity factors and the analytic hierarchy process, making the comprehensive evaluation process more refined and intelligent, dynamically adjusting the evaluation focus according to different repair methods and site conditions, and improving the accuracy and reliability of the evaluation results, providing a more solid foundation for subsequent scheme adjustment.

[0110] In some possible implementations, the knowledge base comprises an adjustment rule base, the adjustment rule base comprising a repair scheme adjustment rule, a repair material adjustment rule, and a repair equipment adjustment rule; the repair scheme adjustment rule is used to adjust the type of repair method according to the applicability evaluation result; the repair material adjustment rule is used to adjust the type of repair material according to the material performance score; and the repair equipment adjustment rule is used to adjust the model of trenchless repair equipment according to the disease matching degree score.

[0111] The parameter-effect model is a mapping relationship between the repair parameters and the repair effect based on a BP neural network algorithm; the input of the parameter-effect model is the repair parameter, and the output is the repair effect; the repair parameter includes the type of repair material and the propulsion speed and grouting pressure of the repair equipment, and the repair effect includes the strength, deformation and durability of the repaired pipeline.

[0112] The adjustment rule library set in the knowledge base provides guidance for adjustment according to the applicability evaluation result. The repair scheme adjustment rule decides whether to change the type of the repair scheme according to the applicability evaluation result. The repair material adjustment rule guides the selection of a repair material type that is more suitable for the current environmental conditions according to the environmental adaptability score. The repair equipment adjustment rule guides the selection of a trenchless repair equipment model that is more suitable for processing the current disease type according to the disease matching degree score. These rules convert the evaluation results into specific adjustment directions, solving the problem of how to make preliminary adjustments according to the evaluation results.

[0113] The parameter-effect model is established by using a BP neural network algorithm, providing the ability to predict the repair effect under different combinations of repair parameters. The repair parameter is a variable that can be controlled during construction, and the repair effect is an index for measuring the repair quality. The model establishes a nonlinear mapping relationship between the parameters and the effects by learning historical data. This solves the problem of predicting the impact of different parameter settings on the final repair effect.

[0114] In some embodiments, step S4 comprises:

[0115] S401. If the current trenchless repair scheme is applicable, set the adjustment scheme to be empty;

[0116] S401. If the current trenchless repair scheme is not applicable, perform the following steps:

[0117] A1. Read the adjustment rule library in the knowledge base;

[0118] A2. According to the applicability evaluation result, query the adjustment rule library to determine the triggered adjustment rule and the corresponding adjustment result;

[0119] A3. According to the adjustment result, combine the parameter-effect model, and use a genetic algorithm to optimize the repair parameters to obtain optimized repair parameters, and generate an adjustment scheme; wherein the parameter-effect model is used to calculate the fitness value in the iterative optimization process.

[0120] The method first determines whether the current trenchless repair scheme is applicable. The applicability evaluation result is provided by the previous steps. If the evaluation result is applicable, no adjustment is needed, and the adjustment scheme is set to be empty. If the evaluation result is not applicable, the system enters the adjustment process.

[0121] Further, the first step (step A1) of the adjustment procedure is to access and obtain the set of adjustment rules stored in the knowledge base. These rules predefine the adjustment direction that should be taken under different inapplicable conditions.

[0122] Subsequently (step A2), the system uses the previously obtained applicability assessment result as input to search in the read set of adjustment rules. By matching the assessment result with the conditions set in the rules, the system identifies which specific adjustment rules are activated (e.g., if the environmental suitability score is below a pre-set environmental suitability score threshold, then the repair scheme adjustment rule is activated; if the disease matching score is below a pre-set disease matching score threshold, then the repair equipment adjustment rule is activated; if the material performance score is below a pre-set material performance score threshold, then the repair material adjustment rule is activated) and obtains the adjustment results pointed by these rules. The adjustment results indicate the aspects that need to be adjusted, such as suggesting to change the repair material type or adjusting the equipment operation parameters.

[0123] Finally (step A3), according to the adjustment direction determined in step A2, the system generates a specific adjustment scheme by combining the parameter-effect model and the genetic algorithm. The parameter-effect model is a prediction tool that can predict the repair effect indicators (such as strength, deformation, durability) according to 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 to iteratively search for the parameter combination that can optimize the repair effect within the possible parameter range. The result of the optimization process is a set of specific, calculated repair parameter values. Combining these optimized parameters with the adjustment direction determined in step A2 forms the final adjustment scheme.

[0124] Specifically, the method solves the problem of how to quickly and scientifically generate an adjustment scheme when the original design scheme is no longer applicable due to actual site conditions, disease changes, or equipment operation state inconsistencies in non-excavation repair operations of municipal drainage pipelines.

[0125] First, the method receives an applicability assessment result that judges whether the current scheme can achieve the expected effect under actual conditions.

[0126] If the assessment result shows that the scheme is applicable, it is directly determined that no adjustment is needed, avoiding unnecessary calculations and operations and improving efficiency.

[0127] If the assessment result shows that the scheme is not applicable, the method starts the automated adjustment procedure. It first consults the pre-set adjustment rule library, which is established based on domain knowledge and historical experience and can indicate the general direction of adjustment (such as needing to adjust the material, equipment, or process parameters) according to the inapplicable reasons (such as environmental factors, disease types, equipment performance).

[0128] Next, the method utilizes a parameter-effect model that quantifies the relationship between repair parameters and the final repair effect. This model allows the system to simulate repair results in a virtual environment under different parameter combinations.

[0129] Meanwhile, the method introduces a genetic algorithm. Genetic algorithms are powerful optimization tools that can efficiently search for optimal solutions in complex parameter spaces. During the adjustment process, the genetic algorithm uses the predictions of the parameter-effect model as guidance, iteratively trying different combinations of repair parameters and evaluating their impact on the repair effect. The goal of the algorithm is to find a set of parameters that result in an optimal or satisfactory predicted repair effect, while following the adjustment direction determined in step A2.

[0130] In this way, by combining the rule guidance of the knowledge base, the predictive power of the parameter-effect model, and the optimization capabilities of the genetic algorithm, the method can systematically and data-drivenly generate an adjustment plan containing specific optimized parameters, such as recommending replacing a specific type of repair material and providing new device propulsion speed and grouting pressure values. This overcomes the limitations of relying solely on manual experience for on-site adjustments, improves the scientificity, accuracy and response speed of the adjustment plan, and thus improves the quality and reliability of the repair project.

[0131] Preferably, step A3 can include:

[0132] A301. Construct a multi-objective fitness function related to 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 attention levels;

[0133] A302. According to the adjustment result, combining 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 an adjustment plan is generated; wherein in the genetic algorithm iteration, the fitness value of each individual is calculated according to the target 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; a self-adaptive mutation strategy is introduced to increase the mutation probability and increase the population diversity when the population diversity decreases, and to jump out of the local optimal solution.

[0134] Wherein, when constructing the multi-objective fitness function, the performance indicators such as the strength, deformation and durability of the repaired pipeline can be normalized, and then these normalized indicators are 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 requiring high bearing capacity, the weight of strength can be set higher.

[0135] wherein the iterative optimization process of the genetic algorithm includes initializing a population of repair parameters, each individual representing a combination of parameters. In each generation, the repair effect under each individual parameter combination is predicted using the parameter-effect model, and then its fitness value is calculated by the multi-objective fitness function. Based on the fitness value, methods such as roulette selection, tournament selection, etc. are used to select excellent individuals into the next generation. Through crossover operations (such as single-point crossover, multi-point crossover), the parameter information between individuals is exchanged to generate new parameter combinations. Through mutation operations (such as Gaussian mutation, uniform mutation), the individual parameter values are randomly changed to increase the diversity of the population. The implementation of the adaptive mutation strategy can monitor indicators such as the average fitness, variance or distance between individuals of the population to evaluate the diversity level. When the diversity is lower than the preset threshold, the mutation probability is dynamically increased to encourage the algorithm to explore new solution space.

[0136] Specifically, the technical solution solves the problem of how to efficiently and accurately determine the optimal repair parameters to meet the requirements of strength, deformation and durability, etc. at the same time, and avoid the optimization process from falling into a local optimal solution in the process of adjusting the non-excavation repair scheme of municipal drainage pipelines. First, by constructing a multi-objective fitness function, multiple repair targets (strength, deformation, durability) that are related to each other and may conflict with each other are integrated into a quantitative evaluation standard. This makes it possible to compare the comprehensive repair effect produced by different parameter combinations uniformly. The introduction of weight coefficients further allows the relative importance of each target to be adjusted according to actual engineering needs. Then, the parameter-effect model is used as a bridge to associate abstract repair parameters with specific repair effects. This model can predict the performance level that the repaired pipeline will achieve 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 for parameter combinations with higher fitness (i.e. better repair effect) in complex parameter space by simulating natural selection and genetic mechanisms. In 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, and promotes the evolution of the population towards better solutions. In order to overcome the problem that the standard genetic algorithm is prone to fall into a local optimum, an adaptive mutation strategy is introduced. This strategy can sense the diversity state of the population and increase the strength of mutation when the diversity is insufficient, thereby helping the algorithm to jump out of the local optimum and improving the probability of finding the global optimum or near-optimal solution. Thus, through the iterative optimization of the genetic algorithm, a set of repair parameters that can achieve better comprehensive repair effect under the current environment can be obtained, and based on this, a specific adjustment scheme can be generated.

[0137] Please refer to Figure 2 , Figure 2A structural schematic diagram of an electronic device provided by an embodiment of the present application, the present application provides an electronic device, comprising: 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 mechanism (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 municipal drainage pipeline trenchless repair design method in any optional implementation manner of the above-mentioned embodiment, to realize the following functions: obtaining the environmental data of the construction site, the pipeline disease information and the operation data of the trenchless repair equipment; preprocessing the obtained data; the preprocessing includes cleaning processing and integration processing; comparing the applicability rules and the material performance curve in the knowledge base according to the preprocessed environmental data, the preprocessed pipeline disease information and the preprocessed operation data, evaluating the applicability of the current trenchless repair scheme; according to the applicability evaluation result, triggering the preset adjustment rule in the knowledge base, and combining the parameter-effect model to perform optimization calculation, and generating an adjustment scheme.

[0138] The present application provides a computer readable storage medium, which stores a computer program, when the computer program is executed by a processor, the municipal drainage pipeline trenchless repair design method in any optional implementation manner of the above-mentioned embodiment is executed, to realize the following functions: obtaining the environmental data of the construction site, the pipeline disease information and the operation data of the trenchless repair equipment; preprocessing the obtained data; the preprocessing includes cleaning processing and integration processing; comparing the applicability rules and the material performance curve in the knowledge base according to the preprocessed environmental data, the preprocessed pipeline disease information and the preprocessed operation data, evaluating the applicability of the current trenchless repair scheme; according to the applicability evaluation result, triggering the preset adjustment rule in the knowledge base, and combining the parameter-effect model to perform optimization calculation, and generating an adjustment scheme.

[0139] Among them, the computer readable storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0140] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for designing a trenchless repair of a municipal drainage pipeline, which is used to design an adjustment plan for a trenchless repair of a municipal drainage pipeline, characterized in that: The following steps are involved: 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; The environmental data include groundwater level and soil temperature; The pipeline disease information includes crack length, crack width and corrosion depth; The operating data includes the advancement speed, grouting pressure and temperature of the trenchless repair equipment; The knowledge base includes an applicability rule base and a material performance curve base; The applicability rule library includes environmental applicability rules, disease applicability rules, and equipment applicability rules. Environmental applicability rules are used to limit the applicability of repair materials under different groundwater levels and soil temperatures. Disease applicability rules are used to limit the selection of repair methods under different crack lengths, crack widths, and corrosion depths. Equipment applicability rules are used to limit the repair effects under the advancement speed, grouting pressure, and temperature of different trenchless repair equipment. The material performance curve library 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 performance curve library in the knowledge base; S302. Based on the pre-processed environmental data, pre-processed pipeline disease information and pre-processed operation data, query the applicability rule library and material performance curve library to calculate the environmental adaptability score, disease matching score and material performance score; S303. Calculate a comprehensive applicability score based on the environmental adaptability score, the disease matching score, and the material performance score to determine whether the current trenchless remediation solution is applicable and obtain a suitability assessment result.

2. A trenchless repair design method for municipal drainage pipes according to claim 1, characterized in that: Step S302 includes: Based on the fuzzy logic algorithm, the pre-processed environmental data are converted into membership degrees, and the environmental adaptability score is calculated according to the preset weights in the environmental suitability rule library; Based on the cosine similarity algorithm, 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 library is calculated to obtain the disease matching score; Based on the preprocessed operating data, the material performance curve library is queried, and the linear interpolation method is used to calculate the material strength index, deformation index and durability index under the current working conditions. Combined with the preset index thresholds in the equipment applicability rule library, the material performance score of the currently used repair material is calculated.

3. A trenchless repair design method for municipal drainage pipes according to claim 1, characterized in that: Step S303 includes: Based on the environmental adaptability score, disease matching score and material performance score, combined with the currently used restoration methods, determine the environmental sensitivity factor, disease sensitivity factor and material sensitivity factor; The weight coefficients of environmental adaptability score, disease matching score and material performance score are determined by using the analytic hierarchy process according to the determined sensitivity factors; Calculate the comprehensive applicability score based on the environmental adaptability score, disease matching score, material performance score and the corresponding weight coefficients; Compare the comprehensive applicability score with the preset applicability threshold to determine whether the current trenchless repair plan is applicable and obtain the applicability assessment result.

4. A trenchless repair design method for a municipal drainage pipeline according to claim 1, characterized in that: The knowledge base includes an adjustment rule base, which includes repair scheme adjustment rules, repair material adjustment rules, and repair equipment adjustment rules; the repair scheme adjustment rules are used to adjust the type of repair method according to the applicability assessment results; the repair material adjustment rules are used to adjust the type of repair material according to the material performance score; and the repair equipment adjustment rules are used to adjust the model of non-excavation 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 effect includes the strength, deformation, and durability of the repaired pipeline.

5. A trenchless repair design method for a municipal drainage pipeline according to claim 4, characterized in that: Step S4 includes: S401. If the current trenchless repair solution is applicable, set the adjustment solution to empty; S401. If the current trenchless repair solution is not applicable, perform the following steps: A1. Read the adjustment rule library in the knowledge base; A2. Based on the applicability assessment results, query the adjustment rule library to determine the triggered adjustment rules and the corresponding adjustment results; A3. Based on the adjustment results and in combination with a parameter-effect model, a genetic algorithm is used to optimize the repair parameters. The optimized repair parameters are then generated and an adjustment plan is generated. The parameter-effect model is used to calculate the fitness value during the iterative optimization process.

6. A trenchless repair design method for a municipal drainage pipeline according to claim 5, characterized in that: Step A3 includes: A301. Construct a multi-objective fitness function involving the strength, deformation, and durability of the repaired pipeline; the weight coefficients of the strength, deformation, and durability of the repaired pipeline are determined based on their respective importance. A302. Based on the adjustment results, combined with the parameter-effect model and the multi-objective fitness function, a genetic algorithm is used to iteratively optimize the repair parameters to obtain the optimized repair parameters and generate an adjustment plan. Specifically, during the genetic algorithm iteration, the fitness value of each individual is calculated based on the target fitness function and the parameter-effect model, and selection, crossover, and mutation operations are performed based on the fitness value to generate a new population. An adaptive mutation strategy is introduced to increase the mutation probability when the population diversity decreases, thereby increasing the population diversity and escaping the local optimal solution.

7. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps of the non-excavation repair design method for municipal drainage pipelines as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program runs the steps of the method for designing trenchless repair of a municipal drainage pipeline according to any one of claims 1 to 6.

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