Municipal engineering design data optimization processing method

Abstract

The invention relates to the technical field of data optimization, in particular to a municipal engineering design data optimization processing method which comprises the following steps: classifying design scenes based on municipal engineering project types and specific requirements, including road extension, underground pipe network layout and drainage system optimization; s2, in combination with a design scene classification result in S1, a target decomposition technology is used to deeply analyze the design demand of each scene, and a relative optimization target is automatically deduced; key parameters in municipal engineering design are deduced through a parameter inversion algorithm, preliminary parameter calculation is carried out, and the upper and lower limit range of the parameters is set according to actual requirements; with the implementation progress of a municipal engineering project, design parameters are dynamically adjusted in the early stage, the middle stage and the later stage of the project through a multi-stage optimization algorithm in combination with data feedback monitored in real time. According to the method, it is ensured that the optimization strategy can be flexibly adjusted in different stages of the project under different requirements, and the accuracy and applicability of the design scheme are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data optimization, and in particular to a method for optimizing and processing municipal engineering design data. Background Art

[0002] Municipal engineering design is a key link in urban infrastructure construction, covering a variety of projects such as road expansion, underground pipeline laying, drainage system optimization, etc. These projects usually require the processing of a large amount of complex data, including geological conditions, environmental impacts, construction progress, etc. The design process needs to balance multiple goals of safety, cost and construction period. However, traditional municipal engineering design methods often rely on experience and a single static data processing method, which makes it difficult to cope with the dynamic changes and uncertainties that occur in actual construction.

[0003] Most current design processes lack a feedback mechanism for real-time data, which means that once the design parameters are determined, it is difficult to flexibly adjust them based on real-time feedback during the actual construction process. In addition, traditional optimization methods are usually designed for a single goal, such as focusing only on cost control or safety, but are unable to adjust weights in multi-objective optimization and balance the multiple needs of the project. This limitation can easily lead to project cost overruns, extended construction periods, or increased safety risks.

[0004] With the development of big data, artificial intelligence and real-time monitoring technology, municipal engineering design is gradually shifting towards intelligence and dynamism. Therefore, how to combine multi-objective optimization, parameter inversion and real-time feedback to improve the flexibility, adaptability and overall optimization level of the design scheme has become an important technical bottleneck in the current municipal engineering design. New solutions are urgently needed to break through these limitations. Summary of the invention

[0005] The invention provides a method for optimizing and processing municipal engineering design data.

[0006] A municipal engineering design data optimization processing method comprises the following steps:

[0007] S1, design scenario identification: Based on the types of municipal engineering projects and specific needs, the design scenarios are classified, including road expansion, underground pipe network layout, and drainage system optimization. The scenario classification results provide basic data for the derivation of optimization goals and provide a reference for the initial setting of design parameters;

[0008] S2, scenario-specific optimization target derivation: Combined with the design scenario classification results in S1, the target decomposition technology is used to conduct an in-depth analysis of the design requirements of each scenario, and the relative optimization targets are automatically derived. The optimization targets include project safety, cost control, and construction period. Through the multi-objective optimization weight adaptive adjustment mechanism, weights are assigned to different optimization targets, conflicts between different targets are balanced, and optimization strategies suitable for the current design scenario are generated;

[0009] S3, design parameter inversion: Based on the optimization strategy generated in S2, the key parameters in municipal engineering design, including road width, foundation strength, and pipeline size, are derived through the parameter inversion algorithm. The parameter inversion process will combine the initial data of the project to perform preliminary parameter calculations and set the upper and lower limits of the parameters according to actual needs to ensure that the initial design parameters meet the multi-objective optimization requirements of the project;

[0010] S4, multi-stage design parameter optimization: As the implementation of the municipal engineering project progresses, combined with real-time monitoring data feedback, the initial design parameters in S3 are coupled with the actual progress of the project. Through a multi-stage optimization algorithm, the design parameters are dynamically adjusted in the early, middle and late stages of the project.

[0011] Optionally, the derivation of the scenario optimization target in S2 specifically includes:

[0012] S21, based on the design scenario classification results in S1, determine the design requirements for each scenario, which cover the project's safety, cost control, and construction period;

[0013] S22, through the goal decomposition technology, the overall design requirements are broken down into different optimization goal subsets, each of which corresponds to a specific design requirement. For example, in the road expansion scenario, the sub-goals may include roadbed stability (safety), material cost (cost control) and construction progress (construction cycle);

[0014] S23, establish the objective function f1(x), f2(x), ..., f for each sub-goal n (x), where each objective function represents a specific design requirement dimension;

[0015] S24 uses a multi-objective weight adaptive adjustment mechanism to dynamically adjust the weights w1, w2, ..., w of each objective function according to the project priority and actual needs. n , the objective function of the multi-objective optimization problem is expressed as:

[0016] where w i is the weight factor, representing the importance of each optimization objective, weight w iAdaptive adjustment is dynamically allocated according to the actual constraints and priorities of the current project. For example, when the budget is limited, the weight will be tilted towards cost control, while when the project security risk is high, the weight of the security goal will be increased;

[0017] S25, through the optimization solution algorithm, finds the balance point between multiple objectives and generates an optimization strategy suitable for the current design scenario to ensure the global optimal solution under the constraints of safety, cost and construction period.

[0018] Optionally, the target decomposition technology analyzes the overall design requirements of the current design scenario and identifies its core requirements, which include safety, cost control, and construction period. For the "road expansion" design scenario, the core requirements are defined as:

[0019] Safety subset: involves roadbed strength, road slope design, and geological condition analysis;

[0020] Cost control subset: involves material costs, labor costs, and equipment rental costs;

[0021] Construction cycle subset: involves construction schedule scheduling, critical path identification, and construction period estimation;

[0022] Using the target decomposition technique, the core requirements are decomposed into a set of specific optimization target subsets. Each subset can be expressed as the safety O1 target, the cost control O2 target, and the construction period O3 target. The target function can be expressed as:

[0023] O1 = maximize Safety (x); Safety is safety;

[0024] O2=minimize Cost(x); Cost is cost control;

[0025] O3=minimize Time(x); Time is the construction period;

[0026] Among them, x represents the decision variable, including design parameters such as road width, material selection, etc. The core of target decomposition is to transform each requirement into a specific quantifiable objective function to form an optimization target subset.

[0027] Optionally, in the multi-objective weight adaptive adjustment mechanism, the weights w1, w2, w3 corresponding to each objective function are dynamically allocated through the adaptive adjustment mechanism. During the optimization process, the actual constraints of the project (such as budget constraints, changes in security priorities) affect the weight allocation, specifically including:

[0028] When the budget constraint is tight, the weight w2 of the cost control objective O2 will increase;

[0029] If the project is in a high-risk environment, the weight w1 of the security goal O1 is increased;

[0030] If the construction period is tight, the weight w3 of the construction period target O3 increases;

[0031] The objective function update of the multi-objective optimization problem is expressed as:

[0032] minimizeZ=w1·O1(x)+w2·O2(x)+w3·O3(x), where w1+w2+w3=1, and weight w i Adaptive adjustment based on project needs.

[0033] Optionally, the optimization solution algorithm gradually finds a balance between safety, cost and construction period objectives through iterative calculation and constraint processing to ensure that the optimal solution meets the multi-dimensional requirements of the project. The solution process includes:

[0034] Initialize population: generate multiple initial design parameter combinations;

[0035] Evaluate fitness: Calculate the fitness of each design combination based on the objective function;

[0036] Selection, crossover and mutation: select the optimal solution according to fitness and perform crossover and mutation to generate a new generation of population;

[0037] Repeat the iteration until the optimal solution is found. The optimal solution is the optimization strategy suitable for the current design scenario.

[0038] Optionally, the design parameter inversion in S3 specifically includes:

[0039] S31, based on the multi-objective optimization strategy generated in S2, determines the road width, foundation strength, and pipeline size in the current municipal engineering design, and combines the initial design data of the project through the parameter inversion algorithm. initial ,The optimization strategy is used to derive the preliminary estimates of the road width, ,foundation strength, and pipeline size. The inversion process is based on the ,following formula:

[0040] P i =f(O i ,D initial ), where P i are key parameters, including road width, foundation strength, i is the target value generated by the optimization strategy (the balance result of safety, cost control, and construction period), f is the mapping function of parameter inversion, and the parameter estimation value is derived using the optimization target and initial data;

[0041] S32, during the parameter inversion process, set the upper and lower limits for each key parameter [P min ,Pmax ], the upper and lower limits are set according to actual needs and project environment conditions:

[0042] Road width P width Set according to traffic demand, urban planning standards, etc.

[0043] Foundation strength P strength Set based on geological conditions and structural safety requirements;

[0044] Pipe size P pipe_size Set according to displacement requirements and hydraulic conditions;

[0045] S33, the inversion algorithm uses an iterative inversion method to adjust the initial estimate P according to the upper and lower limits. i , ensuring that it meets the multi-objective optimization requirements of the project and continuously approaches the optimal parameter value. The final output key parameter set P = {P1, P2, ..., P n}Meet the multi-objective requirements under the optimization strategy and ensure that the engineering design plan meets the requirements of safety, cost, construction period and other aspects.

[0046] Optionally, the iterative inversion method adjusts the initial estimate P i , to ensure that it meets the multi-objective optimization requirements of the project, including:

[0047] Initialization parameter estimation: Generate preliminary estimates of key design parameters based on the optimization strategy. The estimates are generated through existing initial project data and optimization goals as the starting point of the inversion process;

[0048] Multi-objective optimization error function: An error function is defined for each key parameter to measure the gap between the current parameter and the optimization target (safety, cost control and construction period). The error function integrates all aspects of multi-objective optimization.

[0049] Calculate error gradient: Calculate the gradient of the error function relative to the key parameters to determine how to adjust the parameters to get closer to the optimal solution. The size and direction of the gradient determine the magnitude and direction of each adjustment.

[0050] Iterative update of parameters: Through the iterative inversion method, the values ​​of key parameters are gradually adjusted. Each iteration updates the parameters according to the error gradient so that they gradually approach the optimal value.

[0051] Check parameter range: After each iteration, make sure that the adjusted parameter value is within the preset upper and lower limits. If the parameter is out of the reasonable range, the error function needs to be redefined.

[0052] Convergence condition: When the error value or the change in the parameter is less than the preset threshold, the iteration stops and the parameter is considered to have converged to the optimal solution;

[0053] Output optimal parameters: After multiple iterations, a set of optimal parameters that meet multi-objective optimization requirements are finally obtained. These parameters ensure that the project achieves the best balance between safety, cost and construction period requirements.

[0054] Optionally, the multi-stage design parameter optimization in S4 specifically includes:

[0055] S41, as the implementation of municipal engineering projects progresses, combined with the data feedback from real-time on-site monitoring, including construction progress, material usage, and environmental condition changes, the actual execution status of the project is dynamically obtained;

[0056] The real-time monitoring data is coupled with the initial design parameters in S3, the deviations in actual progress are compared with the initial design parameters, and the parameters and ranges that need to be adjusted are identified;

[0057] S42 uses a multi-stage optimization algorithm to dynamically adjust design parameters according to different stages of the project, including early, mid and late stages:

[0058] Early stage of the project: focus on whether the initial setting of key parameters is consistent with the actual construction environment;

[0059] Mid-term project: adjust parameters based on real-time feedback, including changes in material usage and construction progress delays during construction, optimize costs and construction cycles, and ensure the flexibility of mid-term design solutions;

[0060] Late stage of the project: In the final stage of the project, the remaining construction time and cost are optimized to ensure that the final design meets safety requirements and is completed within a reasonable time.

[0061] Optionally, the multi-stage optimization algorithm also performs iterative adjustments through feedback from real-time monitoring data, dynamically updates design parameters, ensures that the optimization decisions made at each stage meet the project's safety, cost and construction cycle requirements, and ultimately outputs optimized design parameters that meet the actual needs of the project, ensuring that the design scheme can be adaptively adjusted as the project progresses.

[0062] Beneficial effects of the present invention:

[0063] The present invention, after clearly classifying the types of municipal engineering projects, uses the target decomposition technology to deeply analyze the specific design requirements of each scenario, and automatically derives the optimization objectives such as the project's safety, cost control and construction period. The scenario-based multi-objective optimization not only avoids the limitations of a single objective in traditional methods, but also dynamically allocates the importance of each objective through a multi-objective weight adaptive adjustment mechanism, thereby ensuring that the optimization strategy can be flexibly adjusted at different stages and under different requirements of the project, thereby improving the accuracy and applicability of the design solution.

[0064] The present invention adopts a parameter inversion algorithm to dynamically adjust the design parameters (road width, foundation strength, pipeline size) according to the real-time monitoring data during the project progress. By coupling the actual monitoring data with the initial design parameters, a multi-stage optimization algorithm is used to dynamically adjust the early, mid-term and late stages of the project, so that the design scheme can adapt to the actual progress of the project in real time. This multi-stage dynamic optimization inversion technology effectively solves the problem of unforeseen variables that arise during project implementation, ensures the feasibility and reliability of the design scheme, and improves the flexibility and stability of project execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0068] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0069] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0070] like Figure 1 As shown, a method for optimizing and processing municipal engineering design data includes the following steps:

[0071] S1, design scenario identification: Based on the types of municipal engineering projects and specific needs, the design scenarios are classified, including road expansion, underground pipe network layout, and drainage system optimization. The scenario classification results provide basic data for the derivation of optimization goals and provide a reference for the initial setting of design parameters;

[0072] S2, scenario-specific optimization target derivation: Combined with the design scenario classification results in S1, the target decomposition technology is used to conduct an in-depth analysis of the design requirements of each scenario, and the relative optimization targets are automatically derived. The optimization targets include project safety, cost control, and construction period. Through the multi-objective optimization weight adaptive adjustment mechanism, weights are assigned to different optimization targets, conflicts between different targets are balanced, and optimization strategies suitable for the current design scenario are generated;

[0073] S3, design parameter inversion: Based on the optimization strategy generated in S2, the key parameters in municipal engineering design, including road width, foundation strength, and pipeline size, are derived through the parameter inversion algorithm. The parameter inversion process will combine the initial data of the project to perform preliminary parameter calculations and set the upper and lower limits of the parameters according to actual needs to ensure that the initial design parameters meet the multi-objective optimization requirements of the project;

[0074] S4, multi-stage design parameter optimization: As the implementation of the municipal engineering project progresses, the initial design parameters in S3 are coupled with the actual progress of the project in combination with real-time monitoring data feedback. Through a multi-stage optimization algorithm, the design parameters are dynamically adjusted in the early, mid, and late stages of the project. This step is based on actual feedback and scenario-specific optimization goals to ensure that the design parameters can maintain the optimal state throughout the life cycle of the project and meet the needs of different stages of the project.

[0075] The derivation of the scenario optimization objectives in S2 specifically includes:

[0076] S21, based on the design scenario classification results in S1, determine the design requirements for each scenario, which cover the project's safety, cost control, and construction period;

[0077] S22, through the goal decomposition technology, the overall design requirements are broken down into different optimization goal subsets, each of which corresponds to a specific design requirement. For example, in the road expansion scenario, the sub-goals may include roadbed stability (safety), material cost (cost control) and construction progress (construction cycle);

[0078] S23, establish the objective function f1(x), f2(x), ..., f for each sub-goal n(x), where each objective function represents a specific design requirement dimension;

[0079] S24 uses a multi-objective weight adaptive adjustment mechanism to dynamically adjust the weights w1, w2, ..., w of each objective function according to the project priority and actual needs. n , the objective function of the multi-objective optimization problem is expressed as:

[0080] where w i is the weight factor, representing the importance of each optimization objective, weight w i Adaptive adjustment is dynamically allocated according to the actual constraints and priorities of the current project. For example, when the budget is limited, the weight will be tilted towards cost control, while when the project security risk is high, the weight of the security goal will be increased;

[0081] S25, through the optimization solution algorithm, finds the balance point between multiple objectives and generates an optimization strategy suitable for the current design scenario to ensure the global optimal solution under the constraints of safety, cost and construction period.

[0082] The target decomposition technology analyzes the overall design requirements of the current design scenario and identifies its core requirements, which include safety, cost control, and construction period. For the "road expansion" design scenario, the core requirements are defined as:

[0083] Safety subset: involves roadbed strength, road slope design, and geological condition analysis;

[0084] Cost control subset: involves material costs, labor costs, and equipment rental costs;

[0085] Construction cycle subset: involves construction schedule scheduling, critical path identification, and construction period estimation;

[0086] Using the target decomposition technique, the core requirements are decomposed into a set of specific optimization target subsets. Each subset can be expressed as the safety O1 target, the cost control O2 target, and the construction period O3 target. The target function can be expressed as:

[0087] O1 = maximize Safety (x); Safety is safety;

[0088] O2=minimize Cost(x); Cost is cost control;

[0089] O3=minimize Time(x); Time is the construction period;

[0090] Among them, x represents the decision variable, including design parameters such as road width, material selection, etc. The core of target decomposition is to transform each requirement into a specific quantifiable objective function to form an optimization target subset.

[0091] In the multi-objective weight adaptive adjustment mechanism, the weights w1, w2, w3 corresponding to each objective function are dynamically allocated through the adaptive adjustment mechanism. During the optimization process, the actual constraints of the project (such as budget constraints and changes in security priorities) affect the weight allocation, including:

[0092] When the budget constraint is tight, the weight w2 of the cost control objective O2 will increase;

[0093] If the project is in a high-risk environment, the weight w1 of the security goal O1 is increased;

[0094] If the construction period is tight, the weight w3 of the construction period target O3 increases;

[0095] The objective function update of the multi-objective optimization problem is expressed as:

[0096] minimizeZ=w1·O1(x)+w2·O2(x)+w3·O3(x), where w1+w2+w3=1, and weight w i Adaptive adjustment based on project needs.

[0097] The optimization algorithm gradually finds a balance between safety, cost, and construction period through iterative calculation and constraint processing to ensure that the optimal solution meets the multi-dimensional requirements of the project. The solution process includes:

[0098] Initialize population: generate multiple initial design parameter combinations;

[0099] Evaluate fitness: Calculate the fitness of each design combination based on the objective function;

[0100] Selection, crossover and mutation: select the optimal solution according to fitness and perform crossover and mutation to generate a new generation of population;

[0101] Repeat the iteration until the optimal solution is found. The optimal solution is the optimization strategy suitable for the current design scenario.

[0102] Finally, an optimization algorithm is used to generate a global optimal solution that meets the constraints of safety, cost control, and construction period. This optimization strategy can adapt to the current design scenario and ensure the best execution plan for the project under multi-objective constraints.

[0103] The design parameter inversion in S3 specifically includes:

[0104] S31, based on the multi-objective optimization strategy generated in S2, determines the road width, foundation strength, and pipeline size in the current municipal engineering design, and combines the initial design data of the project through the parameter inversion algorithm. initial ,The optimization strategy is used to derive the preliminary estimates of the road width, ,foundation strength, and pipeline size. The inversion process is based on the ,following formula:

[0105] P i =f(O i ,D initial ), where P i are key parameters, including road width, foundation strength, i is the target value generated by the optimization strategy (the balance result of safety, cost control, and construction period), f is the mapping function of parameter inversion, and the parameter estimation value is derived using the optimization target and initial data;

[0106] S32, during the parameter inversion process, set the upper and lower limits for each key parameter [P min ,P max ], the upper and lower limits are set according to actual needs and project environment conditions:

[0107] Road width P width Set according to traffic demand, urban planning standards, etc.

[0108] Foundation strength P strength Set based on geological conditions and structural safety requirements;

[0109] Pipe size P pipe_size Set according to displacement requirements and hydraulic conditions;

[0110] S33, the inversion algorithm uses an iterative inversion method to adjust the initial estimate P according to the upper and lower limits. i , ensuring that it meets the multi-objective optimization requirements of the project and continuously approaches the optimal parameter value. The final output key parameter set P = {P1, P2, ..., P n}Meet the multi-objective requirements under the optimization strategy and ensure that the engineering design plan meets the requirements of safety, cost, construction period and other aspects.

[0111] Iterative inversion method adjusts the initial estimate P i , to ensure that it meets the multi-objective optimization requirements of the project, including:

[0112] Initialization parameter estimation: Generate preliminary estimates of key design parameters based on the optimization strategy. The estimates are generated through existing initial project data and optimization goals as the starting point of the inversion process;

[0113] Multi-objective optimization error function: An error function is defined for each key parameter to measure the gap between the current parameter and the optimization target (safety, cost control and construction period). The error function integrates all aspects of multi-objective optimization.

[0114] Calculate error gradient: Calculate the gradient of the error function relative to the key parameters to determine how to adjust the parameters to get closer to the optimal solution. The size and direction of the gradient determine the magnitude and direction of each adjustment.

[0115] Iterative update of parameters: Through the iterative inversion method, the values ​​of key parameters are gradually adjusted. Each iteration updates the parameters according to the error gradient so that they gradually approach the optimal value.

[0116] Check parameter range: After each iteration, make sure that the adjusted parameter value is within the preset upper and lower limits. If the parameter is out of the reasonable range, the error function needs to be redefined.

[0117] Convergence condition: When the error value or the change in the parameter is less than the preset threshold, the iteration stops and the parameter is considered to have converged to the optimal solution;

[0118] Output optimal parameters: After multiple iterations, a set of optimal parameters that meet multi-objective optimization requirements are finally obtained. These parameters ensure that the project achieves the best balance between safety, cost and construction period requirements.

[0119] 1. Based on the optimization strategy in S2, first generate the key parameter P by optimizing the target i Assume that the initial parameters are Right now:

[0120] Among them, f is the mapping function between the target and the initial data, O i is the result of the optimization strategy, D initial This is initial data.

[0121] 2. For each parameter P i , define a multi-objective optimization error function E(P i ), which is used to measure the gap between the current parameters and the optimization target. The error function can be defined based on the deviation of each target:

[0122] E(P i )=w4·E Safety (P i )+w5·E Cost (P i )+w6·E Time (P i ), where w4, w5, w6 are the weights of different objectives, E Safety ,E Cost ,ETime They are the error terms of safety, cost control and construction period respectively.

[0123] 3. Calculate the error function E(P i ) for parameter P i Gradient The gradient indicates the direction and magnitude of the current parameter adjustment. The gradient is expressed as: The gradient indicates the rate at which the deviation of the optimization target changes with the parameters and is used to guide the next parameter adjustment.

[0124] 4. Use gradient descent or Newton iteration to update parameters. Common update formulas are as follows: in, represents the parameter value of the kth iteration, α is the learning rate, which controls the step size of each iteration adjustment. is the gradient of the kth iteration, which is used to indicate the adjustment direction;

[0125] If Newton's iteration method is used, the second-order derivative, namely the Hessian matrix H, can also be used to accelerate convergence: Newton's method takes into account curvature information and can converge to the optimal parameter value more quickly.

[0126] 5. Check parameter range constraints: After each iteration update, check Is it within the preset upper and lower limits? min ,P max ]Inside: If the parameter is out of range, it is necessary to adjust the parameter value or redefine the error function to ensure that the parameter meets the actual requirements of the project.

[0127] 6. When the error function Less than the preset value ∈, that is:

[0128] Or parameter change When it is less than a certain threshold, the iteration is terminated and the final optimal parameter is obtained.

[0129] 7. Final output of optimal parameters: After multiple iterations and adjustments, the final output is the optimal parameter set that meets the multi-objective optimization requirements These parameters ensure the optimal balance between project safety, cost control and construction period.

[0130] The multi-stage design parameter optimization in S4 specifically includes:

[0131] S41, as the implementation of municipal engineering projects progresses, combined with the data feedback from real-time on-site monitoring, including construction progress, material usage, and environmental condition changes, the actual execution status of the project is dynamically obtained;

[0132] The real-time monitoring data is coupled with the initial design parameters in S3, the deviations in actual progress are compared with the initial design parameters, and the parameters and ranges that need to be adjusted are identified;

[0133] S42 uses a multi-stage optimization algorithm to dynamically adjust design parameters according to different stages of the project, including early, mid and late stages:

[0134] Early stage of the project: Pay attention to whether the initial setting of key parameters is consistent with the actual construction environment, such as foundation strength and road width, to ensure the rationality of the foundation design;

[0135] Mid-term project: adjust parameters based on real-time feedback, including changes in material usage and construction progress delays during construction, optimize costs and construction cycles, and ensure the flexibility of mid-term design solutions;

[0136] Late stage of the project: In the final stage of the project, the remaining construction time and cost are optimized to ensure that the final design meets safety requirements and is completed within a reasonable time.

[0137] The multi-stage optimization algorithm also makes iterative adjustments through feedback from real-time monitoring data and dynamically updates design parameters to ensure that the optimization decisions made at each stage meet the project's safety, cost, and construction cycle requirements. It ultimately outputs optimized design parameters that meet the actual needs of the project, ensuring that the design solution can be adaptively adjusted as the project progresses, thereby achieving dynamic optimization throughout the project's life cycle.

[0138] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A municipal engineering design data optimization processing method, characterized in that: The following steps are involved: S1, design scenario identification: based on the type of municipal engineering projects and specific needs, the design scenarios are classified, including road expansion, underground pipe network layout, and drainage system optimization; S2, scenario-specific optimization target derivation: Combined with the design scenario classification results in S1, the target decomposition technology is used to conduct an in-depth analysis of the design requirements of each scenario, and the relative optimization targets are automatically derived. The optimization targets include project safety, cost control, and construction period. Through the multi-objective optimization weight adaptive adjustment mechanism, weights are assigned to different optimization targets, conflicts between different targets are balanced, and optimization strategies suitable for the current design scenario are generated; S3, design parameter inversion: Based on the optimization strategy generated in S2, the key parameters in municipal engineering design, including road width, foundation strength, and pipeline size, are derived through the parameter inversion algorithm. The parameter inversion process will combine the initial data of the project to perform preliminary parameter calculations and set the upper and lower limits of the parameters according to actual needs to ensure that the initial design parameters meet the multi-objective optimization requirements of the project; S4, multi-stage design parameter optimization: As the implementation of the municipal engineering project progresses, combined with real-time monitoring data feedback, the initial design parameters in S3 are coupled with the actual progress of the project. Through a multi-stage optimization algorithm, the design parameters are dynamically adjusted in the early, middle and late stages of the project.

2. A municipal engineering design data optimization processing method according to claim 1, characterized in that: The derivation of the scenario optimization target in S2 specifically includes: S21, based on the design scenario classification results in S1, determine the design requirements for each scenario, which cover the project's safety, cost control, and construction period; S22, through the target decomposition technology, the overall design requirements are decomposed into different optimization target subsets, each subset corresponds to a specific design requirement; S23, establish the objective function f1(x), f2(x), ..., f for each sub-goal n (x), where each objective function represents a specific design requirement dimension; S24 uses a multi-objective weight adaptive adjustment mechanism to dynamically adjust the weights w1, w2, ..., w of each objective function according to the project priority and actual needs. n , the objective function of the multi-objective optimization problem is expressed as: where w i is the weight factor, representing the importance of each optimization objective, weight w i Adaptive adjustment is dynamically allocated according to the actual constraints and priorities of the current project; S25, through the optimization solution algorithm, finds the balance point between multiple objectives and generates an optimization strategy suitable for the current design scenario to ensure the global optimal solution under the constraints of safety, cost and construction period.

3. A municipal engineering design data optimization processing method according to claim 2, characterized in that: The target decomposition technology analyzes the overall design requirements of the current design scenario and identifies its core requirements, which include safety, cost control, and construction period. For the "road expansion" design scenario, the core requirements are defined as: Safety subset: involves roadbed strength, road slope design, and geological condition analysis; Cost control subset: involves material costs, labor costs, and equipment rental costs; Construction cycle subset: involves construction schedule scheduling, critical path identification, and construction period estimation; Using the goal decomposition technique, the core requirements are decomposed into a set of specific optimization goal subsets. Each subset can be expressed as the safety O1 goal, the cost control O2 goal, and the construction period O3 goal.

4. A municipal engineering design data optimization processing method according to claim 3, characterized in that: In the multi-objective weight adaptive adjustment mechanism, the weights w1, w2, w3 corresponding to each objective function are dynamically allocated through the adaptive adjustment mechanism. During the optimization process, the actual constraints of the project affect the weight allocation, including: When the budget constraint is tight, the weight w2 of the cost control objective O2 will increase; If the project is in a high-risk environment, the weight w1 of the security goal O1 is increased; If the construction period is tight, the weight w3 of the construction period target O3 increases; The objective function update of the multi-objective optimization problem is expressed as: minimizeZ=w1·O1(x)+w2·O2(x)+w3·O3(x), where w1+w2+w3=1, and weight w i Adaptive adjustment based on project needs.

5. A municipal engineering design data optimization processing method according to claim 2, characterized in that: The optimization algorithm gradually finds a balance between safety, cost and construction period through iterative calculation and constraint processing to ensure that the optimal solution meets the multi-dimensional requirements of the project. The solution process includes: Initialize population: generate multiple initial design parameter combinations; Evaluate fitness: Calculate the fitness of each design combination based on the objective function; Selection, crossover and mutation: select the optimal solution according to fitness and perform crossover and mutation to generate a new generation of population; Repeat the iteration until the optimal solution is found. The optimal solution is the optimization strategy suitable for the current design scenario.

6. A municipal engineering design data optimization processing method according to claim 1, characterized in that: The design parameter inversion in S3 specifically includes: S31, based on the multi-objective optimization strategy generated in S2, determines the road width, foundation strength, and pipeline size in the current municipal engineering design, and combines the initial design data of the project through the parameter inversion algorithm. initial ,The optimization strategy is used to derive the preliminary estimates of the road width, ,foundation strength, and pipeline size. The inversion process is based on the ,following formula: P i =f(O i ,D initial ), where P i are key parameters, including road width, foundation strength, i is the target value generated by the optimization strategy, f is the mapping function of parameter inversion, and the parameter estimate is derived using the optimization target and initial data; S32, during the parameter inversion process, set the upper and lower limits for each key parameter [P min ,P max ], the upper and lower limits are set according to actual needs and project environment conditions: Road width P width Set according to traffic demand, urban planning standards, etc. Foundation strength P strength Set based on geological conditions and structural safety requirements; Pipe size P pipe_size Set according to displacement requirements and hydraulic conditions; S33, the inversion algorithm uses an iterative inversion method to adjust the initial estimate P according to the upper and lower limits. i , to ensure that it meets the multi-objective optimization requirements of the project, the final output key parameter set P = {P1, P2, ..., P n }Meet the multi-objective requirements under the optimization strategy.

7. A municipal engineering design data optimization processing method according to claim 6, characterized in that: The iterative inversion method adjusts the initial estimate P i , to ensure that it meets the multi-objective optimization requirements of the project, including: Initialization parameter estimation: Generate preliminary estimates of key design parameters based on the optimization strategy. The estimates are generated through existing initial project data and optimization goals as the starting point of the inversion process; Multi-objective optimization error function: An error function is defined for each key parameter to measure the gap between the current parameter and the optimization target. The error function integrates all aspects of multi-objective optimization. Calculate error gradient: Calculate the gradient of the error function relative to the key parameters to determine how to adjust the parameters to get closer to the optimal solution. The size and direction of the gradient determine the magnitude and direction of each adjustment. Iterative update of parameters: Through the iterative inversion method, the values ​​of key parameters are gradually adjusted. Each iteration updates the parameters according to the error gradient so that they gradually approach the optimal value. Check parameter range: After each iteration, make sure that the adjusted parameter value is within the preset upper and lower limits. If the parameter is out of the reasonable range, the error function needs to be redefined. Convergence condition: When the error value or the change in the parameter is less than the preset threshold, the iteration stops and the parameter is considered to have converged to the optimal solution; Output optimal parameters: Through multiple iterations, a set of optimal parameters that meet the requirements of multi-objective optimization is finally obtained.

8. A municipal engineering design data optimization processing method according to claim 1, characterized in that: The multi-stage design parameter optimization in S4 specifically includes: S41, as the implementation of municipal engineering projects progresses, combined with the data feedback from real-time on-site monitoring, including construction progress, material usage, and environmental condition changes, the actual execution status of the project is dynamically obtained; The real-time monitoring data is coupled with the initial design parameters in S3, the deviations in actual progress are compared with the initial design parameters, and the parameters and ranges that need to be adjusted are identified; S42 uses a multi-stage optimization algorithm to dynamically adjust design parameters according to different stages of the project, including early, mid and late stages: Early stage of the project: focus on whether the initial setting of key parameters is consistent with the actual construction environment; Mid-term project: adjust parameters based on real-time feedback, including changes in material usage and construction progress delays during construction, and optimize costs and construction cycles; Late stage of the project: In the final stage of the project, the remaining construction time and cost are optimized to ensure that the final design meets safety requirements and is completed within a reasonable time.

9. A municipal engineering design data optimization processing method according to claim 8, characterized in that: The multi-stage optimization algorithm also makes iterative adjustments through feedback from real-time monitoring data and dynamically updates design parameters to ensure that the optimization decisions made at each stage meet the project's safety, cost and construction cycle requirements, and ultimately outputs optimized design parameters that meet the actual needs of the project, ensuring that the design solution can be adaptively adjusted as the project progresses.

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