Intelligent planning method and system for municipal drainage facilities

Abstract

The invention discloses an intelligent planning method and system for municipal drainage facilities, and relates to the technical field of municipal drainage planning. An intelligent planning system for municipal drainage facilities comprises a data acquisition module, a drainage modeling module, a settlement analysis module, an optimization modeling module, a cost performance evaluation module, a dynamic feedback module and a result output module. According to the method, a multi-objective optimization function including drainage capacity improvement, settlement risk control and transformation cost control is set, a non-dominated sorting genetic algorithm is introduced, an optimal solution set is efficiently generated, and urban planning requirements of different budget and performance requirements are met; by constructing a cost performance scoring mechanism, quantitative evaluation and graded output are performed on the generated scheme, basic-enhanced-optimal multi-grade reconstruction suggestions are provided for urban managers, and the flexibility and scientificity of decision making are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal drainage planning, and particularly to an intelligent planning method and system for municipal drainage facilities. Background Art

[0002] With the continuous acceleration of the urbanization process, the urban drainage system, as an important infrastructure for ensuring the normal operation of the city and the lives of residents, faces a series of problems such as insufficient drainage capacity, aging facilities, and frequent urban waterlogging. Especially in the context of increasingly frequent extreme weather, the traditional drainage facility planning method has been difficult to meet the rapidly changing urban drainage needs.

[0003] The planning of existing municipal drainage facilities mostly relies on empirical design and static data evaluation, lacking systematic consideration of urban geological settlement factors. In fact, ground settlement, as an important factor affecting the stability and efficiency of the drainage system, has been ignored. In some cities, problems such as drainage pipe fractures and reverse slope waterlogging frequently occur due to ground subsidence, seriously affecting the urban flood control and drainage capacity and the service life of facilities.

[0004] At the same time, the current drainage planning scheme usually has difficulty in forming a scientific cost-performance evaluation mechanism. The decision-making process lacks a quantitative balance between the renovation budget and benefits, and it is difficult to provide hierarchical and multi-objective drainage facility optimization suggestions for cities under different development stages and different regional budget conditions. In addition, most existing systems are one-time output models, lacking a response mechanism for real-time monitoring data and unable to dynamically update and optimize the results according to urban changes. Summary of the Invention

[0005] The present invention proposes an intelligent planning method and system for municipal drainage facilities that integrates drainage capacity simulation, geological settlement analysis, and cost optimization evaluation, which can automatically generate high-cost-performance and adaptable drainage renovation plans under various scenarios and has the ability of dynamic feedback and self-update to achieve refined and intelligent management of the urban drainage system.

[0006] An intelligent planning method for municipal drainage facilities includes: Collect basic data of the target urban area, including municipal drainage facility information, settlement monitoring information, and hydrological information; Based on the municipal drainage facility information and hydrological information, construct a digital twin model of the drainage facility, called the drainage model, and simulate the model under different precipitation scenarios to obtain the regional drainage capacity evaluation results; Based on the settlement monitoring information, quantitatively process the settlement impact of each region through a settlement weight calculation model to obtain the settlement impact weight, and generate a regional spatial weight map according to this weight; Construct an optimization objective function for drainage facilities, which comprehensively considers three factors: improving drainage capacity, reducing the penalty for settlement impact, and minimizing the transformation cost; and based on this objective function, the evaluation results of the regional drainage capacity and the regional spatial weight map, use a drainage planning model constructed based on the non-dominated sorting genetic algorithm to generate a planning scheme for drainage facilities. Perform a cost-performance score on the planning scheme, and output three levels of transformation schemes according to the score results, corresponding to different drainage transformation levels and budget ranges respectively. When the monitoring data changes, trigger the planning process again, dynamically update the input of the drainage model and the objective function, and generate a new planning scheme.

[0007] As a preferred technical solution of the present invention, the obtaining of the regional drainage capacity evaluation results includes: Introduce different precipitation intensity scenario parameters into the constructed drainage model. For each precipitation scenario, simulate the operation state of the drainage facilities, and obtain the maximum water level, overflow times, and waterlogging duration at each node in the drainage system; based on the simulation results, use a weighted calculation method to generate a comprehensive drainage capacity score, which considers the node flow capacity, the degree of water level overrun, and the waterlogging risk index.

[0008] As a preferred technical solution of the present invention, the settlement weight calculation model includes: Obtain the historical or real-time settlement monitoring data of the target area, and extract the average settlement rate of each sub-area ; combine the geological soil layer type of the area, and assign a soil sensitivity coefficient , which is determined by the hardness of the soil; introduce a regional function influence factor , and this factor is assigned values according to the regional use classification; calculate the settlement impact weight of each area , using the following model: ; for each sub-area corresponding to the numerical value is mapped to generate a regional spatial weight map.

[0009] As a preferred technical solution of the present invention, the regional spatial weight map includes: Based on the grid division of the geographic information system of the target urban area, divide the city into several sub-area units; map the settlement impact weight of each sub-area to the corresponding grid unit, and represent it with different colors or numerical levels to form a two-dimensional or three-dimensional spatial visualization layer; the regional spatial weight map includes the settlement rate level, soil sensitivity category identifier, functional importance level, and comprehensive settlement risk score of each sub-area.

[0010] As a preferred technical solution of the present invention, the optimization objective function of the drainage facilities includes the following content: Target items for drainage capacity improvement , which is used to measure the improvement amplitude of the flow - through capacity and waterlogging control capacity of the transformed drainage system; Subsidence - impact penalty item , which is based on the subsidence - impact weights corresponding in the spatial weight map of each sub - region and is accumulated by weighting, representing the drainage planning pressure cost in high - risk areas; Reconstruction cost item , which represents the budget expenditure required for implementing each planning scheme; They are combined to form the following multi - objective optimization function: , where is a preset target weight coefficient, and satisfies that the sum is 1, which is used to adjust the priority targets under different planning requirements.

[0011] As a preferred technical solution of the present invention, the drainage planning model includes the following structure: Input processing layer: Receiving the drainage capacity evaluation result, regional spatial weight map and budget limit parameters; Standardizing the above - mentioned data to obtain the input vector for multi - objective optimization; Objective function layer: Constructing a multi - objective optimization function including drainage capacity improvement, subsidence - impact weight and reconstruction cost; Supporting users to customize the target weight configuration; Optimization and solution layer: Solving based on the non - dominated sorting genetic algorithm; The improvements include introducing a regional priority guiding factor to enhance the convergence efficiency of the high - risk area scheme; Adopting a hybrid elite crossover and local search mechanism to enhance the convergence accuracy and population diversity; Using a crowding degree regulation function to avoid local optimal traps; Solution set evaluation and screening layer: Conducting a cost - performance score for multiple groups of non - dominated solutions; Combining the drainage effect, subsidence improvement and budget proportion to screen representative high - cost - performance solution sets; Output generation layer: Dividing the solution set into three grades of transformation level schemes; Each grade of scheme corresponds to clear engineering suggestions, input budgets and expected effect indicators.

[0012] As a preferred technical solution of the present invention, the cost - performance score includes the following contents: Calculating the drainage capacity improvement rate of the planning scheme of each drainage facility , the subsidence risk reduction value and the actual reconstruction cost ; Constructing a cost - performance score function , which is defined as follows: , where are the relative weight coefficients of drainage capacity and subsidence risk respectively; The higher the score value , the better the comprehensive benefit brought by the transformation scheme per unit cost; Based on the cost - performance score value, the planning schemes are divided into three grades: the first grade is the basic type, the second grade is the enhanced type, and the third grade is the optimal type.

[0013] An intelligent planning system for municipal drainage facilities includes: Data acquisition module: used to collect the basic data of the target urban area; Drainage modeling module: used to build a digital twin model of drainage facilities based on drainage facility information and hydrological information, simulate different precipitation scenarios, and output the evaluation results of regional drainage capacity; Settlement analysis module: used to calculate the settlement influence weight of each region through a settlement weight calculation model based on settlement monitoring data, and generate a regional spatial weight map; Optimization modeling module: used to construct an optimization objective function for drainage facilities and generate a planning scheme for drainage facilities through a drainage planning model; Cost - performance evaluation module: used to score the cost - performance of the said planning scheme and output three grades of drainage renovation schemes according to the scoring results; Dynamic feedback module: when the monitoring data changes, trigger the planning process again, dynamically update the input of the drainage model and the objective function, and generate a new planning scheme; Result output module: used to output the planning scheme, cost - performance scoring results and visual graphic display, including the drainage capacity improvement diagram, settlement risk distribution diagram and renovation grading recommendation letter.

[0014] The present invention has the following advantages: The present invention collaboratively models drainage facility data, hydrological information and settlement monitoring data, overcomes the problem that traditional drainage planning ignores geological risks, and improves the risk perception ability; by establishing a settlement weight calculation model and generating a regional spatial weight map, it realizes the hierarchical evaluation and zonal optimization of different settlement risk regions, and improves the geographical adaptability and pertinence of drainage planning.

[0015] The present invention sets a multi - objective optimization function including drainage capacity improvement, settlement risk control and renovation cost control, introduces a non - dominated sorting genetic algorithm, and efficiently generates an optimization solution set to meet the urban planning requirements of different budgets and performance requirements; by constructing a cost - performance scoring mechanism, it quantitatively evaluates and hierarchically outputs the generated schemes, provides multi - grade renovation suggestions of "basic type - enhanced type - optimal type" for urban managers, and enhances the flexibility and scientificity of decision - making.

[0016] The present invention automatically triggers the reconstruction of the model and regenerates the scheme when significant changes occur in the operating conditions of drainage facilities, rainfall data or geological monitoring data, has the ability of continuous optimization and self - evolution, and improves the long - term stability and practicability of the system. Brief Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only schematic diagrams of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings; Figure 1 It is a schematic structural diagram of an intelligent planning system for municipal drainage facilities adopted in an embodiment of the present invention. Specific embodiments

[0018] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe the present invention in detail with reference to the drawings. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0019] Embodiment 1, an intelligent planning method for municipal drainage facilities, includes the following steps: Step S1: Collect the basic data of the target urban area, including municipal drainage facility information, settlement monitoring information, and hydrological information; The municipal drainage facility information at least includes the spatial layout, pipe diameter, material, slope, node position of the drainage pipeline, structural parameters of the ancillary facilities (such as inspection wells, rainwater inlets, and pumping stations), as well as the operation years, maintenance frequency, and historical fault records of the drainage system, which are used for constructing the structural input of the drainage model; The settlement monitoring information includes historical settlement rate data and real-time ground settlement monitoring data (such as GNSS, level, or InSAR remote sensing monitoring results) in the area, and optionally includes soil compressibility, foundation soil layer structure, and groundwater level changes; The hydrological information includes storm intensity parameters for different return periods (2-year, 5-year, 10-year, 20-year design storm intensity curves), surface runoff coefficients, historical waterlogging point data, rainfall and water level monitoring data, and the underlying surface type in the area (such as the distribution of permeable or impermeable ground); The collected data should be standardized and formatted, and the projection coordinate system should be unified to support docking with the GIS system and subsequent digital twin modeling work.

[0020] Step S2: Based on the municipal drainage facility information and hydrological information, construct a digital twin model of the drainage facility, called the drainage model, and simulate the model under different precipitation scenarios to obtain the regional drainage capacity evaluation results; The obtaining of the regional drainage capacity evaluation results includes: In the constructed drainage model, different precipitation intensity scenario parameters are introduced, including the design storm intensities with return periods of 2 years, 5 years, 10 years, 20 years and above; for each precipitation scenario, the operating state of the drainage facilities is simulated to obtain the maximum water levels, overflow times and ponding duration at each node in the drainage system; based on the simulation results, a comprehensive drainage capacity score is generated by using a weighted calculation method, and the score takes into account the node flow capacity, the degree of water level overrun and the ponding risk index.

[0021] The digital twin model is constructed based on the three-dimensional spatial geographic information system (GIS) and the drainage network topology structure, accurately reflecting the geographical location, structural parameters and dynamic operating state of the drainage system; During the simulation process, using the hydraulic calculation model of the drainage system (based on the dynamic wave method), combined with the input rainfall scenario data, the sequential calculation of the dynamic flow and water level change process of the drainage pipeline system is carried out; During the drainage capacity assessment process, weight factors of different nodes or sub-areas are introduced to reflect their importance in the overall drainage system. The scoring result not only considers the overall drainage efficiency of the system, but also considers whether the drainage capacity of key areas (such as main roads and flood-prone points) meets the design requirements; The simulation results are visually output as a ponding risk heat map, a water level distribution curve and a drainage load analysis map of key sections, which serve as an important basis for the subsequent optimization and comparison of planning schemes.

[0022] Step S3: Based on the settlement monitoring information, the settlement influence of each area is quantitatively processed through a settlement weight calculation model to obtain the settlement influence weight, and a regional spatial weight map is generated according to this weight; The settlement weight calculation model includes: Obtain the historical or real-time settlement monitoring data of the target area, and extract the average settlement rate of each sub-area ; Combine the geological soil layer type of the area to assign a soil sensitivity coefficient , which is determined by the hardness of the soil; introduce a regional function influence factor , and this factor is graded and assigned according to the regional use (such as residential, commercial, transportation hub); calculate the settlement influence weight of each area , using the following model: ; Map the values corresponding to each sub-area to generate a regional spatial weight map, which is used to represent the spatial distribution degree of settlement influence.

[0023] The regional spatial weight map includes: Based on the grid division of the geographic information system (GIS) of the target urban area, the city is divided into several sub-area units; the settlement influence weights of each sub-area Map to the corresponding grid cells, and represent them with different colors or numerical levels to form a two-dimensional or three-dimensional spatial visualization layer; the regional spatial weight map includes the settlement rate level, soil sensitivity category identification, functional importance level, and comprehensive settlement risk score of each sub-region.

[0024] The spatial weight map is used to impose spatial risk constraints or regional priority control in the drainage planning model, and is used to guide the priority improvement of the drainage capacity or the upgrade of the facility renovation level in high-risk areas.

[0025] The settlement rate is extracted according to the annual average or moving average calculation method, and combined with data at different depth levels (shallow settlement and deep foundation settlement) for decentralized fusion to enhance the fine description of the settlement trend; The function impact factor automatically extracts the regional use information according to the urban planning layer, and combines the population flow, building density, and traffic load to reflect the comprehensive impact on the functional area after settlement occurs; The regional spatial weight map has a time evolution function, updates the spatial distribution based on annual or quarterly settlement data, and realizes the dynamic update of the settlement risk map; In actual planning applications, this spatial weight map will be used as a constraint input and embedded into the drainage optimization model to improve the priority of facility configuration and the strengthening standard of structural parameters in high-risk areas.

[0026] Step S4: Construct an objective function for optimizing drainage facilities. The objective function comprehensively considers three factors: improving drainage capacity, reducing settlement impact penalties, and minimizing renovation costs; and based on this objective function, the regional drainage capacity evaluation results, and the regional spatial weight map, a drainage planning model constructed using the non-dominated sorting genetic algorithm is used to generate a planning scheme for drainage facilities. The objective function for optimizing drainage facilities includes the following: Objective item for improving drainage capacity , which is used to measure the improvement amplitude of the flow capacity and waterlogging control capacity of the drainage system after renovation; Settlement impact penalty item , based on the settlement impact weights corresponding in the spatial weight map of each sub-region for weighted accumulation, representing the drainage planning pressure cost in high-risk areas; Renovation cost item , representing the budget expenditure required for implementing each planning scheme; Combined to form the following multi-objective optimization function: , where is a preset target weight coefficient, and satisfies the sum of 1, which is used to adjust the priority targets under different planning requirements.

[0027] The drainage planning model includes the following structure: Input processing layer: Receive the drainage capacity evaluation results, regional spatial weight map, and budget limit parameters; perform normalization processing on the above data to obtain the input vector for multi-objective optimization; Objective function layer: Construct a multi-objective optimization function that includes drainage capacity improvement, settlement impact weight, and transformation cost; support users to customize the target weight configuration for adapting to multi-scenario planning requirements; Optimization and solution layer: Solve based on the non-dominated sorting genetic algorithm; the improvements include introducing a regional priority guiding factor to enhance the convergence efficiency of solutions in high-risk areas; adopting a hybrid elite crossover and local search mechanism to enhance convergence accuracy and population diversity; using a crowding degree regulation function to avoid local optimal traps; Solution set evaluation and screening layer: Score the cost performance of multiple groups of non-dominated solutions; combine drainage effect, settlement improvement, and budget proportion to screen representative high-cost-performance solution sets; Output generation layer: Divide the solution set into three levels of transformation plan (basic type, enhanced type, optimized type); each level of plan corresponds to clear engineering suggestions, investment budgets, and expected effect indicators.

[0028] The training of the drainage planning model includes the following steps: Based on historical drainage facility operation data, settlement evolution trend data, and existing project transformation cases, construct a training sample set; each sample includes input features (regional drainage capacity parameters, settlement impact parameters, transformation budget) and output labels (actual transformation effect or scoring results); Convert the samples into individual encodings based on gene structure, including drainage network adjustment strategies, regional transformation priorities, and budget allocation ratios; In the non-dominated sorting genetic algorithm, initialize the initial population that combines historical high-performance samples and random samples to improve the population quality; Use the scoring results in the training set as the target value of the fitness function to evaluate the performance of each encoded individual in the objective function and improve the model convergence speed; In each generation of evolution, perform operations such as selection, crossover, mutation, and local search; and by introducing a population guiding mechanism driven by regional risks, increase the priority search probability of transformation strategies in high-risk areas; Retain the non-dominated optimal solution set as a candidate for recommended transformation plans; evaluate the model generalization performance in combination with the validation set, and update the genetic parameters or fitness weight configuration.

[0029] The drainage capacity improvement target item is calculated from the drainage simulation results in step S2, and can be comprehensively evaluated based on dimensions such as the shortening rate of the system's flood drainage response time, the decrease ratio of the maximum water accumulation depth at nodes, and the reduction in the number of nodes with over-limit water levels; The settlement impact penalty term realizes the quantitative coupling between regional risks and design schemes by assigning risk weight values to each region in the spatial weight map and associating them with the drainage facility change strategies in that region; The transformation cost item comprehensively considers engineering costs such as material replacement, civil construction, underground obstacle relocation, and road restoration. The cost library can be preset as standardized or automatically matched according to historical engineering data; During the execution of the optimization algorithm, fitness sorting, non-dominated rank stratification, and crowding distance screening strategies are adopted to ensure the diversity and global convergence of the population solution set; and convergence stop conditions such as the maximum number of iterations and the minimum generational convergence difference are set; After each round of iteration, several solutions with the highest scores in the current Pareto optimal solution set are used as candidate outputs, and their strategy codes and corresponding index values are recorded for subsequent scoring and visualization display.

[0030] Step S5: Conduct a cost-performance score on the planning scheme, and output three levels of transformation schemes according to the scoring results, corresponding to different drainage transformation levels and budget ranges; The cost-performance score includes the following: Calculate the drainage capacity improvement rate of the planning scheme for each drainage facility , the settlement risk reduction value and the actual transformation cost ; Construct a cost-performance scoring function , defined as follows: , where are the relative weight coefficients of drainage capacity and settlement risk respectively; the scoring value The higher it is, the better the comprehensive benefits brought by the unit cost of the transformation scheme; Based on the cost-performance scoring value, the planning schemes are divided into three levels: The first level is the basic type, applicable to schemes with low budgets but reasonable transformation benefits; the second level is the enhanced type, applicable to schemes with medium budgets and taking into account both drainage capacity and risk control; the third level is the optimized type, applicable to scenarios with sufficient budgets and pursuing the best transformation effects.

[0031] The drainage capacity improvement rate is obtained by comparing the results before and after the output of the digital twin model, and is comprehensively weighted and calculated using the percentage increase in the average flow rate, the number of reduced waterlogging points, and the proportion of shortened drainage time; The settlement risk reduction value is obtained by comparing the transformation behavior of the target area in the planning scheme with its corresponding settlement impact weight, reflecting the degree of mitigation of the settlement impact in high-risk areas after intervention; The actual transformation cost adopts a refined estimation method, calculates the unit project quantity price according to the specific transformation types (pipe diameter replacement, new pumping station addition, node rearrangement), and considers the construction coefficients in different areas and the floating prices of materials; The three-level transformation plan is further refined into: Basic plan: Only conduct low-cost optimization on key waterlogging points or main drainage facilities, without adjusting the overall network structure, applicable to projects with tight funds or emergency projects; Enhanced plan: On the basis of the basic plan, cover medium-risk areas, optimize part of the network structure and replace old pipelines to improve the overall operation stability of the system; Preferred plan: Conduct a systematic transformation plan for the whole area, strengthen the capacity redundancy of key nodes and potential risk areas, and conduct forward-looking design in combination with future rain scenarios.

[0032] The output results can be provided with a plan scoring comparison table, a transformation scope map, a cost composition analysis, and an expected benefit prediction to provide multi-dimensional decision-making references for urban managers.

[0033] Step S6: When the monitoring data changes, re-trigger the planning process, dynamically update the input of the drainage model and the objective function, and generate a new planning plan.

[0034] Real-time collect the changes in the monitoring data of the urban area, including the operation status data of drainage facilities, rainfall and waterlogging data, and the updated values of settlement monitoring information; Judge whether the data change exceeds the set threshold conditions, including at least: the cumulative duration of the over-standard water level at the drainage node exceeds the preset threshold; the average annual growth rate of the settlement rate exceeds the set percentage; the rainfall intensity exceeds the historical maximum recurrence period level; When any triggering condition is met, automatically reactivate the intelligent planning process of drainage facilities, including: updating the simulation results of drainage capacity assessment; updating the settlement influence weight and the regional space weight map; recalculating the optimization objective function; starting the drainage planning model to regenerate and score the plan; Compare and evaluate the updated recommended plan with the historical plan, and output the difference change indicators for assisting the dynamic management and iterative planning of urban infrastructure.

[0035] The real-time monitoring data can be sourced from urban Internet of Things infrastructure, including at least rain sensors, liquid level monitors, ground settlement sensors, water quality and flow monitors, supporting the cloud data synchronization mode of collecting while uploading; The above-mentioned threshold conditions support dynamic adjustment on the platform side. The urban management department can customize and configure the trigger conditions according to the safety level, emergency requirements, or seasonal climate characteristics in different periods. For example, the trigger threshold can be automatically lowered during the flood season to enhance response sensitivity; the settlement change threshold can be increased during the construction season to avoid frequent triggering of ineffective recalculation. The re-planning process supports two modes: "local update" and "global update": The local update is for the situation where the risk in a local area rises rapidly. Only the drainage model and optimization objectives of the involved area are updated, shortening the calculation time; The global update is triggered when there are continuous extreme weather conditions or the overall network operation performance deteriorates, and the whole-region plan is recalculated; The comparison and evaluation of the above-mentioned plans include index difference analysis (changes in drainage improvement rate, cost, and risk level) and visual comparison layers (superposition map of the transformed areas of the new and old plans, comparison of performance heat maps), assisting management personnel in trend judgment and decision-making on the priority of engineering arrangements; This dynamic update mechanism can be deployed as a scheduled task or an event-driven mode, and integrated with the urban intelligent drainage platform to realize a closed-loop intelligent response system of "intelligent monitoring - autonomous perception - active planning - real-time update".

[0036] Embodiment 2, an intelligent planning system for municipal drainage facilities, as shown in Figure 1 shown, includes the following modules: Data acquisition module: used to acquire the basic data of the target urban area; Drainage modeling module: used to construct a digital twin model of drainage facilities based on drainage facility information and hydrological information, simulate different precipitation scenarios, and output the evaluation results of regional drainage capacity; Settlement analysis module: used to calculate the settlement influence weight of each area through a settlement weight calculation model based on settlement monitoring data, and generate a regional spatial weight map; Optimization modeling module: used to construct an optimization objective function for drainage facilities and generate a planning scheme for drainage facilities through a drainage planning model; Cost-performance evaluation module: used to score the cost-performance of the above-mentioned planning scheme and output a three-level drainage renovation scheme according to the scoring results; Dynamic feedback module: when the monitoring data changes, re-trigger the planning process, dynamically update the input of the drainage model and the objective function, and generate a new planning scheme; Result output module: used to output the planning scheme, cost-performance scoring results, and visual graphic displays, including drainage capacity improvement map, settlement risk distribution map, and renovation grading recommendation letter.

[0037] The specific embodiments described above further elaborate on the object, technical solution and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent planning method for municipal drainage facilities, characterized in that, Including: Collecting the basic data of the target urban area, including municipal drainage facility information, settlement monitoring information, and hydrological information; Constructing a digital twin model of the drainage facilities based on the municipal drainage facility information and hydrological information, called the drainage model, and simulating the model under different precipitation scenarios to obtain the regional drainage capacity evaluation results; Based on the settlement monitoring information, quantitatively processing the settlement impact of each region through the settlement weight calculation model to obtain the settlement impact weight, and generating a regional spatial weight map according to this weight; Constructing an optimization objective function for drainage facilities, which comprehensively considers three factors: improving drainage capacity, reducing settlement impact penalty, and minimizing renovation costs; and based on this objective function, the regional drainage capacity evaluation results, and the regional spatial weight map, using a drainage planning model constructed based on the non-dominated sorting genetic algorithm to generate a planning scheme for drainage facilities; Performing a cost-performance score on the planning scheme, and outputting three levels of renovation schemes according to the score results, corresponding to different drainage renovation levels and budget ranges; When the monitoring data changes, re-trigger the planning process, dynamically update the input of the drainage model and the objective function, and generate a new planning scheme.

2. The intelligent planning method for municipal drainage facilities according to claim 1, characterized in that The obtaining of the regional drainage capacity evaluation results includes: Introducing different precipitation intensity scenario parameters into the constructed drainage model, for each precipitation scenario, simulating the operation state of the drainage facilities to obtain the maximum water level, overflow times, and waterlogging duration of each node in the drainage system; based on the simulation results, generating a comprehensive drainage capacity score using a weighted calculation method, and the score considers the node flow capacity, water level overrun degree, and waterlogging risk index.

3. The intelligent planning method for municipal drainage facilities according to claim 1, characterized in that, The settlement weight calculation model includes: Obtain historical or real-time settlement monitoring data of the target area, and extract the average settlement rate of each sub-area ; Combine the geological soil layer types of the area and assign soil sensitivity coefficients , which is determined by the hardness of the soil; Introduce the regional function influence factor , and this factor is assigned values according to the regional use classification; Calculate the settlement influence weight of each area , using the following model: ; Map the values corresponding to each sub-area to generate a regional spatial weight map.

4. The intelligent planning method for municipal drainage facilities according to claim 3, characterized in that The regional spatial weight map includes: Based on the grid division of the geographic information system for the target urban area, the city is divided into several sub-region units; the settlement influence weights of each sub-region are mapped to the corresponding grid cells and represented by different colors or numerical levels to form a two-dimensional or three-dimensional spatial visualization layer; the regional spatial weight map includes the settlement rate level, soil sensitivity category identifier, functional importance level, and comprehensive settlement risk score of each sub-region.

5. The intelligent planning method for municipal drainage facilities according to claim 1, wherein The optimization objective function for drainage facilities includes the following content: Target items for drainage capacity improvement , used to measure the improvement amplitude of the flow-through capacity and waterlogging control capacity of the drainage system after transformation; Settlement impact penalty item , based on the settlement impact weights corresponding in the spatial weight maps of each sub-region for weighted accumulation, indicating the drainage planning pressure cost in high-risk areas; Reconstruction cost item , indicating the budget expenditure required for implementing each planning scheme; The combination forms the following multi-objective optimization function: , where is a preset target weight coefficient, and satisfies the sum of 1, which is used to adjust the priority targets under different planning requirements.

6. The intelligent planning method for municipal drainage facilities according to claim 1, characterized in that The drainage planning model includes the following structure: Input processing layer: Receiving the drainage capacity evaluation results, regional spatial weight map, and budget limit parameters; performing standardization processing on the above data to obtain the input vector for multi-objective optimization; Objective function layer: Constructing a multi-objective optimization function including improving drainage capacity, settlement impact weight, and renovation cost; Supporting user-defined target weight configuration; Optimization solution layer: Solving based on the non-dominated sorting genetic algorithm; the improvements include introducing a regional priority guiding factor to enhance the convergence efficiency of the high-risk area scheme; adopting a hybrid elite crossover and local search mechanism to enhance the convergence accuracy and population diversity; using a crowding degree regulation function to avoid local optimal traps; Solution set evaluation and screening layer: Performing a cost-performance score on multiple groups of non-dominated solutions; combining drainage effect, settlement improvement, and budget proportion to screen representative high-cost-performance solution sets; Output generation layer: Dividing the solution set into three levels of renovation grade schemes; each level of scheme corresponds to clear engineering suggestions, investment budgets, and expected effect indicators.

7. The intelligent planning method for municipal drainage facilities according to claim 6, characterized in that, The cost-performance score includes the following content: Calculate the drainage capacity improvement rate of the planning scheme for each drainage facility , the settlement risk reduction value and the actual transformation cost ; Construct a cost-performance scoring function , which is defined as follows: , where are the relative weight coefficients of drainage capacity and settlement risk respectively; the higher the scoring value , the better the comprehensive benefit brought by the unit cost of the renovation plan; Based on the cost-performance score value, dividing the planning scheme into three levels: the first level is the basic type, the second level is the enhanced type, and the third level is the preferred type.

8. An intelligent planning system for municipal drainage facilities, characterized in that, The system applies the intelligent planning method for a municipal drainage facility according to any one of claims 1 to 7 above, including: Data acquisition module: used to acquire the basic data of the target urban area; Drainage modeling module: used to build a digital twin model of drainage facilities based on drainage facility information and hydrological information, simulate different precipitation scenarios, and output the evaluation results of regional drainage capacity; Settlement analysis module: used to calculate the settlement influence weight of each region through the settlement weight calculation model based on the settlement monitoring data, and generate a regional spatial weight map; Optimization modeling module: used to build an optimization objective function for drainage facilities and generate a planning scheme for drainage facilities through a drainage planning model; Cost-performance evaluation module: used to score the cost-performance of the planning scheme and output a three-level drainage renovation scheme according to the scoring results; Dynamic feedback module: when the monitoring data changes, re-trigger the planning process, dynamically update the input of the drainage model and the objective function, and generate a new planning scheme; Result output module: used to output the planning scheme, cost-performance scoring results and visual graphic display, including the drainage capacity improvement map, settlement risk distribution map and renovation grading recommendation letter.

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