A method and system for determining a design scheme of an urban engineering project

Abstract

This invention discloses a method and system for determining design schemes for urban engineering projects. The method involves acquiring design information for each scheme and calculating its information density index; obtaining subjective evaluation indicators and objective performance indicators; determining subjective weights using fuzzy hierarchical analysis and extracting objective weights using CRITIC; and performing a game-theoretic equilibrium fusion of the subjective and objective weights to obtain a weight decision matrix for each design scheme. When the information density index is not less than a preset information density threshold, the method calculates annual photovoltaic capacity indicators and annual building global warming potential, updating the weight decision matrix. Based on the weight decision matrix, the TOPSIS method is used to rank the design schemes. Using this invention, adaptive adjustments to decisions can be achieved during the design phase evolution, enabling an automatic switch from a subjective-objective integrated decision structure to a life-cycle objective performance-driven decision structure.

Description

Technical Field

[0001] This invention relates to the field of engineering decision-making technology, and in particular to a method and system for determining the design scheme of urban engineering projects. Background Technology

[0002] Large-scale and complex urban engineering projects face unprecedented challenges in performance complexity during the conceptual design phase. Unlike the relatively singular engineering indicator system of the past, real-world projects often involve multiple performance objectives across scales, dimensions, and disciplines. These objectives can be mutually reinforcing, but also present significant conflicts and constraints.

[0003] In the early stages of conceptual design, design data is often incomplete, containing only limited geometric information, functional assumptions, and empirical judgments, making it difficult to support objective decision-making based on complete lifecycle data. As the design deepens, a large amount of quantifiable performance data is gradually introduced, and the original decision-making logic, which is mainly based on subjective experience, is no longer sufficient to meet the requirements of high-precision performance control. Existing technologies generally use fixed decision models or single-weight generation methods, which cannot dynamically adjust the decision structure itself. This leads to early decisions relying excessively on immature objective data, or later decisions neglecting refined performance analysis. Summary of the Invention

[0004] The present invention aims to provide a method and equipment for determining the design scheme of urban engineering projects, which can achieve adaptive adjustment of decision-making during the evolution of the design stage and realize the automatic switching from a decision-making structure that integrates subjective and objective factors to a decision-making structure dominated by objective performance throughout the life cycle.

[0005] To address the above technical problems, embodiments of the present invention provide a method for determining the design scheme of urban engineering projects, comprising: The design information of each design scheme of the urban engineering project at the current design stage is obtained. Based on the design information, the information completeness of each design scheme is calculated to obtain the information density index. The subjective evaluation indicators and objective performance indicators of each design scheme are obtained. The subjective weights of each subjective evaluation indicator are determined by fuzzy hierarchical analysis. The objective weights of the objective performance indicators are extracted by CRITIC. The subjective weights and objective weights are then fused through game equilibrium to obtain the weight decision matrix of each design scheme. When the information density index is not less than the preset information density threshold, the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme are obtained, the annual photovoltaic capacity index and annual building global warming potential of each design scheme are calculated, and the weight decision matrix is ​​updated according to the annual photovoltaic capacity index and annual building global warming potential. Based on the weight decision matrix, the TOPSIS method is used to rank the design schemes.

[0006] As an improvement to the above solution, the step of obtaining design information for each design scheme of an urban engineering project at the current design stage, and calculating the information completeness of each design scheme based on the design information to obtain an information density index, includes: Obtain geometric information, functional information, performance simulation information, and life cycle data of various design schemes for urban engineering projects at the current design stage to obtain design information; Based on the design information, the number of acquired indicators is obtained, and combined with the predefined total number of indicators, the indicator coverage rate of each design scheme is calculated. Based on the data acquisition method of the design information and the preset reliability grading table, the data reliability of each design scheme is obtained; Based on the performance simulation information, the number of performance modules that have completed the simulation and the total number of simulation modules required are obtained, and the simulation completion rate of each design scheme is calculated. Based on the indicator coverage, the data reliability, and the simulation completeness, the information completeness of each design scheme is calculated and used as the information density index.

[0007] As an improvement to the above scheme, the subjective evaluation indicators and objective performance indicators of each design scheme are obtained. Fuzzy hierarchical analysis is used to determine the subjective weights of each subjective evaluation indicator, and CRITIC is used to extract the objective weights of the objective performance indicators. Game equilibrium fusion is performed on the subjective weights and the objective weights to obtain the weight decision matrix for each design scheme, including: Based on the pre-constructed subjective and objective indicator system of urban engineering projects, obtain the subjective evaluation indicators and objective performance indicators of each design scheme; Based on the experts' scores for the subjective evaluation indicators of each design scheme, a triangular fuzzy number matrix is ​​constructed; The fuzzy weight of each index element in the triangular fuzzy number matrix is ​​calculated using the fuzzy hierarchical analysis method to obtain the subjective weight of each subjective evaluation index. Based on the type of objective performance indicators, the objective performance indicators are normalized to obtain a standardized performance matrix; the types include performance indicators and cost indicators. The information content of each objective performance index in the standardized performance matrix is ​​extracted using CRITIC to obtain the objective weights; A game theory-based combination weighting algorithm is used to calculate the optimal combination weights with the goal of minimizing the deviation between the subjective weights and the objective weights, thereby obtaining the weight decision matrix for each design scheme.

[0008] As an improvement to the above scheme, the fuzzy hierarchical analysis method is used to calculate the fuzzy weight of each index element in the triangular fuzzy number matrix to obtain the subjective weight of each subjective evaluation index, including: Based on the upper limit, lower limit and best estimate of each index element in the triangular fuzzy number matrix, calculate the cumulative fuzzy weight of each row in the triangular fuzzy number matrix. Based on the accumulated fuzzy weights, the triangular fuzzy number matrix is ​​normalized to obtain a normalized fuzzy weight vector. The normalized fuzzy weights are defuzzified to obtain the subjective weights of each subjective evaluation index.

[0009] As an improvement to the above scheme, the step of normalizing the objective performance indicators according to their types to obtain a standardized performance matrix includes: When the objective performance indicator is a performance-type indicator, the objective performance indicator is positively normalized. When the objective performance indicator is a cost-type indicator, the objective performance indicator is negatively normalized. Based on the normalized objective performance indicators, a standardized performance matrix is ​​obtained.

[0010] As an improvement to the above scheme, the step of using CRITIC to extract the information content of each objective performance index in the standardized performance matrix to obtain objective weights includes: Calculate the standard deviation of each objective performance index in the standardized performance matrix; Calculate the correlation coefficients among the objective performance indicators in the standardized performance matrix to obtain the correlation coefficient matrix; Calculate the information content of each objective performance indicator based on the standard deviation and the correlation coefficient matrix; The information is normalized, and the objective weights of each objective performance index are calculated.

[0011] As an improvement to the above scheme, when the information density index is not less than a preset information density threshold, the photovoltaic capacity index, energy use intensity index, and current building global warming potential of each design scheme are obtained; the annual photovoltaic capacity index and annual building global warming potential of each design scheme are calculated; and the weight decision matrix is ​​updated based on the annual photovoltaic capacity index and annual building global warming potential, including: When the information density index is not less than the preset information density threshold, the photovoltaic capacity index of each design scheme is obtained; Based on the photovoltaic capacity indicators and the preset photovoltaic degradation rate, calculate the annual photovoltaic capacity indicators for each design scheme; Obtain the energy intensity index and current global warming potential of each design scheme, and calculate the annual global warming potential of each design scheme by combining the annual photovoltaic capacity index. Based on the annual photovoltaic capacity index and the annual building global warming potential, calculate the dynamic weights of the photovoltaic capacity index and the building global warming potential in each year; The weight decision matrix is ​​updated based on the dynamic weights.

[0012] As an improvement to the above scheme, the step of ranking the design schemes using the TOPSIS method based on the weight decision matrix includes: Based on the weighted decision matrix, determine the positive and negative ideal solutions for the urban engineering project at the current design stage; Calculate the first Euclidean distance from each design scheme to the positive ideal solution, and the second Euclidean distance from each design scheme to the negative ideal solution; Calculate the overall performance of each design scheme based on the first Euclidean distance and the second Euclidean distance; Based on the overall performance, the design schemes are ranked.

[0013] As an improvement to the above scheme, after ranking the design schemes using the TOPSIS method based on the weight decision matrix, the method for determining the design scheme of the urban engineering project further includes: Based on the preset perturbation range, several sets of perturbation weight vectors are generated. The weight decision matrix is ​​perturbed using the perturbation weight vectors to obtain the ranking stability of the design schemes under each set of perturbations. Based on the ranking of each design scheme, calculate the minimum weight change that would flip the ranking of the first and second design schemes, and obtain the ranking flip threshold. Calculate the decision confidence level based on the sorting stability and the sorting flip threshold.

[0014] This invention also provides a system for determining design schemes for urban engineering projects, comprising: The information density index calculation module is used to obtain the design information of each design scheme of the urban engineering project at the current design stage, and calculate the information completeness of each design scheme based on the design information to obtain the information density index. The weight decision matrix calculation module is used to obtain the subjective evaluation index and objective performance index of each design scheme. It uses fuzzy hierarchical analysis to determine the subjective weight of each subjective evaluation index and uses CRITIC to extract the objective weight of the objective performance index. It then performs game equilibrium fusion on the subjective weight and the objective weight to obtain the weight decision matrix of each design scheme. The weight decision matrix update module is used to obtain the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme when the information density index is not less than the preset information density threshold, calculate the annual photovoltaic capacity index and annual building global warming potential of each design scheme, and update the weight decision matrix according to the annual photovoltaic capacity index and annual building global warming potential. The design scheme ranking module is used to rank the design schemes according to the weight decision matrix using the TOPSIS method.

[0015] Compared with existing technologies, the present invention discloses a method and system for determining design schemes for urban engineering projects. This method involves acquiring design information for each design scheme at the current design stage of the urban engineering project; calculating the information completeness of each design scheme based on the design information to obtain an information density index; acquiring subjective evaluation indicators and objective performance indicators for each design scheme; determining the subjective weights of each subjective evaluation indicator using fuzzy hierarchical analysis; extracting the objective weights of the objective performance indicators using CRITIC; and performing game equilibrium fusion on the subjective and objective weights to obtain a weight decision matrix for each design scheme. When the information density index is not less than a preset information density threshold, acquiring the photovoltaic capacity index, energy intensity index, and current global warming potential of buildings for each design scheme; calculating the annual photovoltaic capacity index and annual global warming potential of buildings for each design scheme; updating the weight decision matrix based on the annual photovoltaic capacity index and annual global warming potential of buildings; and ranking each design scheme using the TOPSIS method based on the weight decision matrix. By employing embodiments of the present invention, adaptive adjustments to decisions can be achieved during the evolution of the design phase, enabling an automatic switch from a decision-making structure that integrates subjective and objective factors to a decision-making structure dominated by objective performance throughout the lifecycle. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a method for determining a design scheme for an urban engineering project, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a system for determining the design scheme of an urban engineering project provided by an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description and claims, it should be understood that the terms "first," "second," etc., used in the description and claims are only for the purpose of distinguishing the description of the same technical features, and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated, nor necessarily the order of description or chronological order. The terms are interchangeable where appropriate. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature.

[0019] This invention provides a method for determining the design scheme of urban engineering projects. Please refer to [link / reference]. Figure 1 In this embodiment, the method for determining the design scheme of the urban engineering project is specifically executed through steps S1 to S4: S1. Obtain the design information of each design scheme of the urban engineering project at the current design stage, and calculate the information completeness of each design scheme based on the design information to obtain the information density index; S2. Obtain the subjective evaluation index and objective performance index of each design scheme, use the fuzzy hierarchical analysis method to determine the subjective weight of each subjective evaluation index, and use CRITIC to extract the objective weight of the objective performance index. Perform game equilibrium fusion on the subjective weight and the objective weight to obtain the weight decision matrix of each design scheme. S3. When the information density index is not less than the preset information density threshold, obtain the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme, calculate the annual photovoltaic capacity index and annual building global warming potential of each design scheme, and update the weight decision matrix according to the annual photovoltaic capacity index and annual building global warming potential. S4. Based on the weight decision matrix, the TOPSIS method is used to rank the design schemes.

[0020] Existing multi-criteria decision-making methods (such as simple weighted TOPSIS, analytic hierarchy process, etc.) usually adopt a fixed weight generation mechanism, which cannot automatically adjust the integration strategy of subjective and objective weights according to the completeness of information in the design stage. This leads to early decision-making relying too much on immature objective data, or later decision-making neglecting refined performance analysis.

[0021] In this embodiment of the invention, the design stage of an urban engineering project is determined by calculating the information completeness of each design scheme, and different decision-making methods are adaptively designed for different design stages.

[0022] When the information density index is less than a preset information density threshold, the design scheme is considered to have incomplete information, making accurate full lifecycle performance simulation difficult. Therefore, a fuzzy hierarchical method is used to incorporate expert subjective judgment, while the CRITIC method is used to initially quantify existing objective data. Finally, a game-theoretic equilibrium is achieved by merging subjective and objective weights. This ensures that, even with limited information, decisions can consider the value demands of multiple parties and remain true to the public nature of urban projects.

[0023] Subjective evaluation indicators rely on experts' experience and linguistic judgment, making them difficult to measure precisely with numerical values, and subjective judgment itself is inherently ambiguous. Therefore, this invention employs a fuzzy hierarchy method, allowing experts to use fuzzy language to identify subjective evaluation indicators, and then using mathematical methods to defuzzify and obtain precise weights, effectively reducing the arbitrariness and uncertainty in subjective judgment.

[0024] Objective performance indicators have clear numerical values. The CRITIC method can simultaneously consider the comparative strength and conflict of indicators, thus assigning higher weights to indicators with large data fluctuations and those that provide unique information. It avoids subjective assumptions and reveals the inherent importance of each indicator in differentiating design schemes purely based on the statistical characteristics of the objective data itself, ensuring the fairness of objective weights and data-driven nature.

[0025] Unlike conventional information entropy weight fusion methods, the embodiments of this invention employ a game theory-based combined weighting method. The technical idea is to seek a Nash equilibrium between subjective and objective weights by minimizing the deviation between subjective and objective weights through an optimization model, thereby achieving a game-theoretic equilibrium fusion of subjective and objective information and avoiding subjective bias caused by artificially setting the fusion ratio.

[0026] When the information density index is not less than the preset information density threshold, the design scheme is considered to be sufficiently detailed and is judged to be in a high information density state, which can support reliable performance analysis. Therefore, the decision structure is automatically switched, and the decision is deepened by introducing annual dynamic data. The decision center is shifted from the integration of subjective and objective weights to long-term objective performance indicators composed of photovoltaic capacity, energy use intensity and global warming potential, so as to substantially enhance the influence of objective performance indicators in the final evaluation.

[0027] It should also be noted that when the information density index is not less than the preset information density threshold, the embodiments of the present invention do not simply replace the subjective evaluation weight with the objective performance weight, but rebalance the original game equilibrium result based on the calculated year-by-year data, so that the weight decision matrix tilts towards the objective performance index, thereby retaining a small number of necessary subjective dimensions.

[0028] TOPSIS is a commonly used comprehensive evaluation method. It calculates the distance between each scheme and the ideal optimal solution and the ideal worst solution, and uses the relative proximity to the worst solution as the ranking criterion. It can make full use of the information in the weight decision matrix to accurately reflect the gap between the evaluated schemes.

[0029] In the above scheme, by objectively quantifying the completeness of information in the design phase and calculating the information density index, it is possible to automatically switch from a decision-making structure that integrates subjective and objective factors to a decision-making structure that is dominated by objective performance throughout the life cycle, and to achieve adaptive adjustment of decisions during the evolution of the design phase.

[0030] As a preferred implementation, step S1 involves obtaining design information for each design scheme of an urban engineering project at the current design stage, and calculating the information completeness of each design scheme based on the design information to obtain an information density index, including: Obtain geometric information, functional information, performance simulation information, and life cycle data of various design schemes for urban engineering projects at the current design stage to obtain design information; Based on the design information, the number of acquired indicators is obtained, and combined with the predefined total number of indicators, the indicator coverage rate of each design scheme is calculated. Based on the data acquisition method of the design information and the preset reliability grading table, the data reliability of each design scheme is obtained; Based on the performance simulation information, the number of performance modules that have completed the simulation and the total number of simulation modules required are obtained, and the simulation completion rate of each design scheme is calculated. Based on the indicator coverage, the data reliability, and the simulation completeness, the information completeness of each design scheme is calculated and used as the information density index.

[0031] Different design phases correspond to different information completeness requirements. By calculating the coverage of indicators, we can effectively monitor whether the design progress meets the phase expectations and measure the completeness of design information.

[0032] The reliability of data is affected by its source and the preprocessing methods used after acquisition. In this embodiment of the invention, a reliability grading table for different data acquisition methods under typical scenarios is constructed, and the data reliability corresponding to different data acquisition methods can be obtained by looking up the table.

[0033] In some preferred embodiments, for objective performance indicators, in typical scenarios where data is compared and inferred from similar project data, the data acquisition method is empirical estimation, and the corresponding data reliability is 0.2, as can be seen from the table; in typical scenarios where boundary conditions such as EnergyPlus / Radiance / Karamba are set according to specifications or experience, the data acquisition method is parameterized simplified calculation, and the corresponding data reliability is 0.5; in typical scenarios where boundary conditions are detailed, meteorological data is accurate, and fine grids or actual measurements are used for verification, the data acquisition method is high-precision simulation, and the corresponding data reliability is 1.0.

[0034] In other preferred embodiments, for subjective evaluation indicators, in the typical scenario of self-judgment in the early stage of the solution, the data acquisition method is a single designer's self-evaluation, and the corresponding data reliability is 0.4; in the typical scenario of three or more people scoring back-to-back, the data acquisition method is multi-expert independent evaluation, and the corresponding data reliability is 0.7; in the typical scenario of FuzzyAHP with CR verification, the data acquisition method is multi-expert independent evaluation combined with consistency verification, and the corresponding data reliability is 1.0.

[0035] Preferably, the simulation completeness rate of the design scheme is obtained by calculating the ratio of the number of performance modules that have been simulated to the total number of required simulation modules. The simulation completeness rate can reflect the work completion degree of the urban design project and the technical depth of the design information.

[0036] In some preferred embodiments, the information density index The following formula is used to calculate: ; in, This represents the number of indicators already acquired. This represents the total number of indicators; Let be the reliability coefficient of the i-th index; Let be the reliability importance weight of the i-th indicator; This represents the number of performance modules that have completed the simulation. This represents the total number of simulation modules required.

[0037] The above scheme integrates the breadth of information coverage, data credibility, and technical analysis depth into a single quantitative indicator to truly reflect the actual utility of information. This ensures that only design schemes with sufficient information density can enter the subsequent life cycle performance in-depth analysis, guaranteeing the scientific nature and reliability of decision-making.

[0038] As a preferred implementation, step S2 involves obtaining the subjective evaluation indicators and objective performance indicators of each design scheme, determining the subjective weights of each subjective evaluation indicator using fuzzy hierarchical analysis, extracting the objective weights of the objective performance indicators using CRITIC, and performing game equilibrium fusion on the subjective weights and objective weights to obtain the weight decision matrix for each design scheme. This is then executed through steps S21-S26. S21. Based on the pre-constructed subjective and objective indicator system of urban engineering projects, obtain the subjective evaluation indicators and objective performance indicators of each design scheme; S22. Construct a triangular fuzzy number matrix based on the experts' scores for the subjective evaluation indicators of each design scheme; S23. Using the fuzzy hierarchical analysis method, calculate the fuzzy weight of each index element in the triangular fuzzy number matrix to obtain the subjective weight of each subjective evaluation index. S24. Based on the type of objective performance indicators, the objective performance indicators are normalized to obtain a standardized performance matrix; the types include performance indicators and cost indicators. S25. Use CRITIC to extract the information content of each objective performance index in the standardized performance matrix to obtain the objective weights; S26. Using a game theory-based combined weighting algorithm, with the goal of minimizing the deviation between the subjective weights and the objective weights, calculate the optimal combined weights to obtain the weight decision matrix for each design scheme.

[0039] In some preferred embodiments, the pre-constructed subjective and objective indicator system is a two-level indicator system. Specifically, the first-level indicator system is divided into nine categories: urban space and function integration, morphological expression, functional rationality, structural rationality, indoor natural light environment, energy consumption, life cycle carbon emissions, and energy production.

[0040] The secondary indicators under the integration of urban space and function include urban fabric echo (the alignment relationship between building axis and urban historical axis / main and secondary roads, the adaptability of building volume to the scale of surrounding blocks, and the coordination of roof form with the urban skyline), urban interface penetration (the coordination of building setback and line-to-line ratio, the green space ratio of venues and the formation of a continuous slow-moving network with urban parks / water systems, and the visual connectivity between open squares and streets) and transportation resilience (walking distance between main entrances and bus stops / subway stations, redundant design of emergency evacuation channels, the proportion of non-motorized vehicle parking spaces, and the coupling degree between underground space and urban traffic nodes).

[0041] The secondary indicators under the form expression include architectural form expression (the uniqueness and recognizability of the architectural image, such as morphological symbols and innovation of surface materials; metaphors of cultural symbolic meaning, such as the extraction of regional symbols and the embodiment of spirit), structural expression (the selection of large-span structures and their relevance to urban imagery) and architectural interface interaction (the adaptation of facade materials to regional climate).

[0042] The secondary indicators under functional rationality include functional group topology (circulation separation and circulation rationality, accessibility of buildings and public service facilities), functional adaptation and social services (multifunctional collaborative layout, integration of community service functions, functional flexibility) and site and grandstand functional adaptation (site, grandstand scale, form and functional adaptation, flexibility).

[0043] The secondary indicators under structural rationality include structural feasibility (whether the structure is feasible and reasonable) and steel consumption level (the structural lines are extracted through design and modeling, the cross-sectional dimensions are obtained by structural calculation through Karamba, and the steel consumption is further roughly calculated through Grasshopper).

[0044] The secondary indicators for indoor natural light environments include natural light potential (calculated through effective natural light), glare probability (calculated through glare autonomy), and illuminance uniformity (calculated through spatial illuminance testing).

[0045] Secondary indicators under energy consumption include cooling energy consumption, heating energy consumption, and lighting energy consumption. Secondary indicators under life-cycle carbon emissions include the global warming potential of buildings. Secondary indicators under energy production include total energy production (calculated from the total electricity actually generated by photovoltaic systems in one year) and energy production efficiency (calculated from electricity generation per unit area).

[0046] Among them, the integration of urban space and function, form expression, functional rationality and structural feasibility under structural rationality are subjective evaluation indicators, while the steel consumption level, indoor natural light environment, energy consumption, life cycle carbon emissions and energy production under structural rationality are objective performance indicators.

[0047] The values ​​of the subjective evaluation indicators are obtained by expert scoring. In some preferred embodiments, a five-level fuzzy language scoring table as shown in Table 1 is used. Based on the five-level fuzzy language scoring table and expert scoring, a triangular fuzzy number matrix covering all subjective evaluation indicators is constructed.

[0048] Table 1. Five-level Fuzzy Language Scoring Table Further, preferably, step S23, employing fuzzy hierarchical analysis, calculates the fuzzy weight of each index element in the triangular fuzzy number matrix to obtain the subjective weight of each subjective evaluation index, including: Based on the upper limit, lower limit and best estimate of each index element in the triangular fuzzy number matrix, calculate the cumulative fuzzy weight of each row in the triangular fuzzy number matrix. Based on the accumulated fuzzy weights, the triangular fuzzy number matrix is ​​normalized to obtain a normalized fuzzy weight vector. The normalized fuzzy weights are defuzzified to obtain the subjective weights of each subjective evaluation index.

[0049] In some preferred embodiments, the cumulative fuzzy weight in the i-th row of the triangular fuzzy number matrix can be expressed as: ; in, Let be the lower bound of the j-th element in the i-th row; This is the best estimate of the j-th element in the i-th row; It is the upper limit of the j-th element in the i-th row.

[0050] The unnormalized triangular fuzzy number matrix represents the absolute fuzzy importance of each subjective evaluation indicator, but the numerical ranges of different indicators may differ, making it unsuitable for direct multi-attribute decision-making. Therefore, the triangular fuzzy number matrix is ​​further normalized based on the cumulative fuzzy weights.

[0051] In some preferred embodiments, the normalized fuzzy weights Deblurring is represented as follows: .

[0052] In the above scheme, the uncertainty of language is effectively quantified by using a triangular fuzzy number matrix and solved by fuzzy hierarchical analysis. This can output definite subjective weights while preserving the fuzziness of judgment, thereby improving the scientific nature of subjective weighting.

[0053] Objective performance indicators are obtained through simulation analysis of the design scheme. For example, energy consumption indicators are calculated using EnergyPlus simulation software, light environment indicators are calculated using the Radiance light environment analysis engine, and structural indicators are calculated using the Karamba3D structural analysis plugin.

[0054] Preferably, step S24, according to the type of objective performance index, normalizes the objective performance index to obtain a standardized performance matrix, including: When the objective performance indicator is a performance-type indicator, the objective performance indicator is positively normalized. When the objective performance indicator is a cost-type indicator, the objective performance indicator is negatively normalized. Based on the normalized objective performance indicators, a standardized performance matrix is ​​obtained.

[0055] For performance indicators, a larger value indicates better performance and better benefits. In this embodiment of the invention, the performance indicators are positively normalized, mapping the original values ​​to the [0,1] interval, with the maximum value mapped to 1 and the minimum value mapped to 0.

[0056] For cost-related indicators, smaller values ​​generally indicate better performance and lower costs or negative effects. In this embodiment of the invention, cost-related indicators are reverse-normalized, mapping the original values ​​to the [0,1] interval, with the minimum value mapped to 1 and the maximum value mapped to 0.

[0057] In objective data, different indicators may have opposite implications for the merits of a solution. In the above solution, by distinguishing between performance-based and cost-based indicators, the correctness of the decision-making logic can be ensured.

[0058] Preferably, step S25, extracting the information content of each objective performance index in the standardized performance matrix using CRITIC to obtain objective weights, includes: Calculate the standard deviation of each objective performance index in the standardized performance matrix; Calculate the correlation coefficients among the objective performance indicators in the standardized performance matrix to obtain the correlation coefficient matrix; Calculate the information content of each objective performance indicator based on the standard deviation and the correlation coefficient matrix; The information is normalized, and the objective weights of each objective performance index are calculated.

[0059] It should be noted that if a certain objective performance index has similar values ​​across all design schemes, it indicates that its impact on the decision is minimal, and therefore its weight should be reduced. In the CRITIC algorithm, the standard deviation reflects the dispersion of the standardized values ​​of each objective performance index across all design schemes. The larger the standard deviation, the more significant the differences in performance of each design scheme on that index.

[0060] When multiple indicators are highly correlated, they are very likely making decisions on the same or similar dimensions. In this case, assigning high weights to multiple indicators would result in repeated weighting of the same trait, leading to information redundancy. In the CRITIC algorithm, the correlation coefficient is used to measure the independence between indicators.

[0061] Furthermore, by combining the standard deviation with the correlation analysis results, a numerical value representing the information carrying capacity, or information content, is calculated for each indicator. The greater the information content of an objective performance indicator, the more important that indicator is in decision-making.

[0062] In some preferred embodiments, the information content of the j-th objective performance index is represented as: ; in, Let be the standard deviation of the j-th objective performance index; Let be the correlation coefficient between the j-th objective performance indicator and the k-th objective performance indicator.

[0063] In the above scheme, the standard deviation and correlation coefficient between indicators in the standardized matrix are analyzed based on the CRITIC method, which can automatically identify the inherent information content and independence of the data and avoid human interference.

[0064] In some preferred embodiments, the objective function of the game-theoretic combinatorial weighting algorithm is expressed as: ; in, For subjective weight vectors, For objective weight vectors, and Let be the coefficients of the linear combination to be solved. This represents the optimal combination of weights.

[0065] By solving the above optimization objective function, we can obtain: ; in, For the reason and The weight matrix is ​​formed.

[0066] The final calculated optimal combination weights can be expressed as: .

[0067] In the above scheme, the total deviation between subjective and objective weights is minimized by using a game theory-based combined weighting algorithm, which can effectively balance the conflict between subjective and objective perspectives and seek the optimal solution in uncertainty and multiple conflicts.

[0068] As a preferred implementation, step S3 involves obtaining the photovoltaic capacity index, energy intensity index, and current building global warming potential of each design scheme when the information density index is not less than a preset information density threshold; calculating the annual photovoltaic capacity index and annual building global warming potential of each design scheme; and updating the weight decision matrix based on the annual photovoltaic capacity index and annual building global warming potential, including: When the information density index is not less than the preset information density threshold, the photovoltaic capacity index of each design scheme is obtained; Based on the photovoltaic capacity indicators and the preset photovoltaic degradation rate, calculate the annual photovoltaic capacity indicators for each design scheme; Obtain the energy intensity index and current global warming potential of each design scheme, and calculate the annual global warming potential of each design scheme by combining the annual photovoltaic capacity index. Based on the annual photovoltaic capacity index and the annual building global warming potential, calculate the dynamic weights of the photovoltaic capacity index and the building global warming potential in each year; The weight decision matrix is ​​updated based on the dynamic weights.

[0069] It should be noted that the natural light environment index and energy consumption index remain constant year by year, while the photovoltaic capacity index changes with photovoltaic degradation. Therefore, in this embodiment of the invention, in order to shift the decision-making of the design scheme to objective performance indicators when the information density index is high, the annual photovoltaic capacity index is calculated.

[0070] In some preferred embodiments, the photovoltaic capacity index of the i-th design scheme in year t is expressed as: ; in, This represents the initial photovoltaic production capacity target; This represents the photovoltaic degradation rate.

[0071] Building carbon emissions are dynamic, and photovoltaic (PV) capacity directly affects carbon offsetting during the operational phase. In this embodiment of the invention, the static GWP (Building Global Warming Potential) index is transformed into a dynamic sequence that evolves over time and is influenced by PV power generation.

[0072] Preferably, when performing dynamic weight calculations, for the two indicator sequences of annual photovoltaic capacity and annual building global warming potential, a similar idea to the CRITIC algorithm is used to recalculate their importance in the current year's plan for each year of the project's life cycle.

[0073] In some preferred embodiments, the dynamic weights are represented as: ; in, Let be the standard deviation of the j-th indicator in year t; Let be the degree of conflict of the j-th indicator in year t.

[0074] In the above scheme, the weight decision matrix is ​​adjusted based on the calculated dynamic weights of photovoltaic capacity and GWP. This quantitatively injects the importance of long-term, dynamic objective performance data into the decision framework that was originally composed of subjective and objective static data, thus realizing a substantial shift from the integration of subjective and objective data to the dominance of objective performance throughout the life cycle.

[0075] As a preferred implementation, step S4, ranking the design schemes using the TOPSIS method according to the weight decision matrix, includes: Based on the weighted decision matrix, determine the positive and negative ideal solutions for the urban engineering project at the current design stage; Calculate the first Euclidean distance from each design scheme to the positive ideal solution, and the second Euclidean distance from each design scheme to the negative ideal solution; Calculate the overall performance of each design scheme based on the first Euclidean distance and the second Euclidean distance; Based on the overall performance, the design schemes are ranked.

[0076] In the TOPSIS method, both the positive ideal solution and the negative ideal solution are virtual solutions. The positive ideal solution represents the optimal value among all design options for each metric, while the negative ideal solution represents the worst value among all design options for each metric.

[0077] By solving for the first Euclidean distance from the design scheme to the positive ideal solution and the second Euclidean distance from the design scheme to the negative ideal solution, a comprehensive geometric characterization of the merits and demerits of the design schemes can be obtained, thus obtaining a clear order of merits and demerits for all design schemes and providing decision-makers with a clear basis for scheme selection.

[0078] In some preferred embodiments, the overall performance of the i-th design scheme is expressed as: ; in, Let be the first Euclidean distance from the i-th design scheme to the positive ideal solution; Let be the second Euclidean distance from the i-th design scheme to the negative ideal solution. The larger the value, the closer the solution is to the ideal solution and the better the overall performance.

[0079] In the above scheme, the complex multi-attribute decision problem is transformed into a one-dimensional ranking problem by using the TOPSIS method, and both the positive and negative ideal solutions are derived from the actual data of the design scheme, which ensures the adaptability of the evaluation benchmark to the overall quality level of the design scheme.

[0080] As a preferred embodiment, after performing step S4, the method for determining the design scheme of the urban engineering project further includes: Based on the preset perturbation range, several sets of perturbation weight vectors are generated. The weight decision matrix is ​​perturbed using the perturbation weight vectors to obtain the ranking stability of the design schemes under each set of perturbations. Based on the ranking of each design scheme, calculate the minimum weight change that would flip the ranking of the first and second design schemes, and obtain the ranking flip threshold. Calculate the decision confidence level based on the sorting stability and the sorting flip threshold.

[0081] The weight decision matrix is ​​estimated through a series of models and algorithms, and it is inherently uncertain. Therefore, in this embodiment of the invention, based on the optimal combination of weights, a random perturbation weight vector that conforms to a preset perturbation range is superimposed on each component. If the weight decision matrix is ​​stable, then the TOPSIS ranking is re-executed under each group of perturbations. The more stable the fluctuation of the ranking of the design schemes obtained by statistics, the more robust the decision is and the higher the confidence level.

[0082] In some preferred embodiments, the sorting stability is expressed as: ; in, RS represents the number of perturbations that keep the sorting result unchanged; N is the total number of groups of perturbation weight vectors. The closer RS ​​is to 1, the more stable the sorting result is.

[0083] The smaller the minimum weight change that causes the first and second ranked design schemes to be reversed, the more intense the competition for ranking among the design schemes, and the more fragile the decision result.

[0084] Furthermore, the sorting flip threshold is expressed as: ; in, This is currently the top-ranked solution. This is the currently ranked second solution. A larger θ value indicates a more robust ranking result.

[0085] In some preferred embodiments, the decision confidence level is expressed as: ; in, , These are the weighting coefficients (each is set to 0.5 by default). The sorting flip threshold (default value is 0.1). The closer DC is to 1, the more reliable the decision result is.

[0086] In the above scheme, by introducing sensitivity analysis and ranking flip threshold calculation, a decision confidence index is output, enabling decision-makers to evaluate the stability and credibility of the ranking results.

[0087] The method for determining the design scheme of urban engineering projects provided by the embodiments of the present invention can achieve automatic switching from a decision-making structure that integrates subjective and objective factors to a decision-making structure dominated by objective performance throughout the life cycle by objectively quantifying the completeness of information in the design stage and calculating the information density index, thereby enabling adaptive adjustment of decisions during the evolution of the design stage.

[0088] This invention provides a system for determining design schemes for urban engineering projects. Please refer to [link / reference]. Figure 2 The urban engineering project design scheme determination system includes an information density index calculation module 11, a weight decision matrix calculation module 12, a weight decision matrix update module 13, and a design scheme ranking module 14, wherein: The information density index calculation module 11 is used to obtain the design information of each design scheme of the urban engineering project at the current design stage, calculate the information completeness of each design scheme based on the design information, and obtain the information density index. The weight decision matrix calculation module 12 is used to obtain the subjective evaluation index and objective performance index of each design scheme, use the fuzzy hierarchical analysis method to determine the subjective weight of each subjective evaluation index, use CRITIC to extract the objective weight of the objective performance index, and perform game equilibrium fusion on the subjective weight and the objective weight to obtain the weight decision matrix of each design scheme. The weight decision matrix update module 13 is used to obtain the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme when the information density index is not less than the preset information density threshold, calculate the annual photovoltaic capacity index and annual building global warming potential of each design scheme, and update the weight decision matrix according to the annual photovoltaic capacity index and annual building global warming potential. The design scheme ranking module 14 is used to rank the design schemes according to the weight decision matrix using the TOPSIS method.

[0089] In a preferred embodiment, the information density index calculation module 11 is specifically used for: Obtain geometric information, functional information, performance simulation information, and life cycle data of various design schemes for urban engineering projects at the current design stage to obtain design information; Based on the design information, the number of acquired indicators is obtained, and combined with the predefined total number of indicators, the indicator coverage rate of each design scheme is calculated. Based on the data acquisition method of the design information and the preset reliability grading table, the data reliability of each design scheme is obtained; Based on the performance simulation information, the number of performance modules that have completed the simulation and the total number of simulation modules required are obtained, and the simulation completion rate of each design scheme is calculated. Based on the indicator coverage, the data reliability, and the simulation completeness, the information completeness of each design scheme is calculated and used as the information density index.

[0090] In a preferred embodiment, the weight decision matrix calculation module 12 includes: The indicator acquisition unit is used to acquire subjective evaluation indicators and objective performance indicators of each design scheme based on the pre-constructed subjective and objective indicator system of urban engineering projects. The triangular fuzzy number matrix construction unit is used to construct a triangular fuzzy number matrix based on the subjective evaluation index scores given by experts for each design scheme. The subjective weight calculation unit is used to calculate the fuzzy weight of each index element in the triangular fuzzy number matrix using the fuzzy hierarchical analysis method, so as to obtain the subjective weight of each subjective evaluation index. The indicator normalization unit is used to normalize the objective performance indicators according to their types to obtain a standardized performance matrix; the types include performance indicators and cost indicators. An objective weight calculation unit is used to extract the information content of each objective performance indicator in the standardized performance matrix using CRITIC to obtain the objective weights. The optimal combination weight calculation unit is used to calculate the optimal combination weight by using a game theory combination weighting algorithm, with the goal of minimizing the deviation between the subjective weight and the objective weight, and to obtain the weight decision matrix of each design scheme.

[0091] Further, preferably, the subjective weight calculation unit is specifically used for: Based on the upper limit, lower limit and best estimate of each index element in the triangular fuzzy number matrix, calculate the cumulative fuzzy weight of each row in the triangular fuzzy number matrix. Based on the accumulated fuzzy weights, the triangular fuzzy number matrix is ​​normalized to obtain a normalized fuzzy weight vector. The normalized fuzzy weights are defuzzified to obtain the subjective weights of each subjective evaluation index.

[0092] Preferably, the index normalization unit is specifically used for: When the objective performance indicator is a performance-type indicator, the objective performance indicator is positively normalized. When the objective performance indicator is a cost-type indicator, the objective performance indicator is negatively normalized. Based on the normalized objective performance indicators, a standardized performance matrix is ​​obtained.

[0093] Preferably, the objective weight calculation unit is specifically used for: Calculate the standard deviation of each objective performance index in the standardized performance matrix; Calculate the correlation coefficients among the objective performance indicators in the standardized performance matrix to obtain the correlation coefficient matrix; Calculate the information content of each objective performance indicator based on the standard deviation and the correlation coefficient matrix; The information is normalized, and the objective weights of each objective performance index are calculated.

[0094] In a preferred embodiment, the weight decision matrix update module 13 is specifically used for: When the information density index is not less than the preset information density threshold, the photovoltaic capacity index of each design scheme is obtained; Based on the photovoltaic capacity indicators and the preset photovoltaic degradation rate, calculate the annual photovoltaic capacity indicators for each design scheme; Obtain the energy intensity index and current global warming potential of each design scheme, and calculate the annual global warming potential of each design scheme by combining the annual photovoltaic capacity index. Based on the annual photovoltaic capacity index and the annual building global warming potential, calculate the dynamic weights of the photovoltaic capacity index and the building global warming potential in each year; The weight decision matrix is ​​updated based on the dynamic weights.

[0095] In a preferred embodiment, the design scheme sorting module 14 is specifically used for: Based on the weighted decision matrix, determine the positive and negative ideal solutions for the urban engineering project at the current design stage; Calculate the first Euclidean distance from each design scheme to the positive ideal solution, and the second Euclidean distance from each design scheme to the negative ideal solution; Calculate the overall performance of each design scheme based on the first Euclidean distance and the second Euclidean distance; Based on the overall performance, the design schemes are ranked.

[0096] In a preferred embodiment, the urban engineering project design scheme determination system further includes a confidence calculation module, used for: Based on the preset perturbation range, several sets of perturbation weight vectors are generated. The weight decision matrix is ​​perturbed using the perturbation weight vectors to obtain the ranking stability of the design schemes under each set of perturbations. Based on the ranking of each design scheme, calculate the minimum weight change that would flip the ranking of the first and second design schemes, and obtain the ranking flip threshold. Calculate the decision confidence level based on the sorting stability and the sorting flip threshold.

[0097] The design scheme determination system for urban engineering projects provided by this invention can automatically switch from a subjective-objective integrated decision-making structure to a life-cycle objective performance-driven decision-making structure by objectively quantifying the completeness of information in the design stage and calculating the information density index, thus enabling adaptive adjustment of decisions during the evolution of the design stage.

[0098] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0099] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for determining the design scheme of an urban engineering project, characterized in that, include: The design information of each design scheme of the urban engineering project at the current design stage is obtained. Based on the design information, the information completeness of each design scheme is calculated to obtain the information density index. The subjective evaluation indicators and objective performance indicators of each design scheme are obtained. The subjective weights of each subjective evaluation indicator are determined by fuzzy hierarchical analysis. The objective weights of the objective performance indicators are extracted by CRITIC. The subjective weights and objective weights are then fused through game equilibrium to obtain the weight decision matrix of each design scheme. When the information density index is not less than the preset information density threshold, the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme are obtained, the annual photovoltaic capacity index and annual building global warming potential of each design scheme are calculated, and the weight decision matrix is ​​updated according to the annual photovoltaic capacity index and annual building global warming potential. Based on the weight decision matrix, the TOPSIS method is used to rank the design schemes.

2. The method for determining the design scheme of an urban engineering project as described in claim 1, characterized in that, The process involves acquiring design information for each design scheme of an urban engineering project at the current design stage, calculating the information completeness of each design scheme based on the design information, and obtaining an information density index, including: Obtain geometric information, functional information, performance simulation information, and life cycle data of various design schemes for urban engineering projects at the current design stage to obtain design information; Based on the design information, the number of acquired indicators is obtained, and combined with the predefined total number of indicators, the indicator coverage rate of each design scheme is calculated. Based on the data acquisition method of the design information and the preset reliability grading table, the data reliability of each design scheme is obtained; Based on the performance simulation information, the number of performance modules that have completed the simulation and the total number of simulation modules required are obtained, and the simulation completion rate of each design scheme is calculated. Based on the indicator coverage, the data reliability, and the simulation completeness, the information completeness of each design scheme is calculated and used as the information density index.

3. The method for determining the design scheme of an urban engineering project as described in claim 1, characterized in that, The process involves obtaining subjective evaluation indicators and objective performance indicators for each design scheme, using fuzzy hierarchical analysis to determine the subjective weights of each subjective evaluation indicator, and using CRITIC to extract the objective weights of the objective performance indicators. A game-theoretic equilibrium fusion of the subjective and objective weights is then performed to obtain the weight decision matrix for each design scheme, including: Based on the pre-constructed subjective and objective indicator system of urban engineering projects, obtain the subjective evaluation indicators and objective performance indicators of each design scheme; Based on the experts' scores for the subjective evaluation indicators of each design scheme, a triangular fuzzy number matrix is ​​constructed; The fuzzy weight of each index element in the triangular fuzzy number matrix is ​​calculated using the fuzzy hierarchical analysis method to obtain the subjective weight of each subjective evaluation index. Based on the type of objective performance indicators, the objective performance indicators are normalized to obtain a standardized performance matrix; the types include performance indicators and cost indicators. The information content of each objective performance index in the standardized performance matrix is ​​extracted using CRITIC to obtain the objective weights; A game theory-based combination weighting algorithm is used to calculate the optimal combination weights with the goal of minimizing the deviation between the subjective weights and the objective weights, thereby obtaining the weight decision matrix for each design scheme.

4. The method for determining the design scheme of an urban engineering project as described in claim 3, characterized in that, The method employs fuzzy hierarchical analysis to calculate the fuzzy weight of each index element in the triangular fuzzy number matrix, thereby obtaining the subjective weight of each subjective evaluation index, including: Based on the upper limit, lower limit and best estimate of each index element in the triangular fuzzy number matrix, calculate the cumulative fuzzy weight of each row in the triangular fuzzy number matrix. Based on the accumulated fuzzy weights, the triangular fuzzy number matrix is ​​normalized to obtain a normalized fuzzy weight vector. The normalized fuzzy weights are defuzzified to obtain the subjective weights of each subjective evaluation index.

5. The method for determining the design scheme of an urban engineering project as described in claim 3, characterized in that, The step of normalizing the objective performance indicators according to their types to obtain a standardized performance matrix includes: When the objective performance indicator is a performance-type indicator, the objective performance indicator is positively normalized. When the objective performance indicator is a cost-type indicator, the objective performance indicator is negatively normalized. Based on the normalized objective performance indicators, a standardized performance matrix is ​​obtained.

6. The method for determining the design scheme of an urban engineering project as described in claim 3, characterized in that, The step of extracting the information content of each objective performance index in the standardized performance matrix using CRITIC to obtain objective weights includes: Calculate the standard deviation of each objective performance index in the standardized performance matrix; Calculate the correlation coefficients among the objective performance indicators in the standardized performance matrix to obtain the correlation coefficient matrix; Calculate the information content of each objective performance indicator based on the standard deviation and the correlation coefficient matrix; The information is normalized, and the objective weights of each objective performance index are calculated.

7. The method for determining the design scheme of an urban engineering project as described in claim 1, characterized in that, When the information density index is not less than a preset information density threshold, the photovoltaic capacity index, energy intensity index, and current building global warming potential of each design scheme are obtained. The annual photovoltaic capacity index and annual building global warming potential of each design scheme are calculated. Based on the annual photovoltaic capacity index and annual building global warming potential, the weighted decision matrix is ​​updated, including: When the information density index is not less than the preset information density threshold, the photovoltaic capacity index of each design scheme is obtained; Based on the photovoltaic capacity indicators and the preset photovoltaic degradation rate, calculate the annual photovoltaic capacity indicators for each design scheme; Obtain the energy intensity index and current global warming potential of each design scheme, and calculate the annual global warming potential of each design scheme by combining the annual photovoltaic capacity index. Based on the annual photovoltaic capacity index and the annual building global warming potential, calculate the dynamic weights of the photovoltaic capacity index and the building global warming potential in each year; The weight decision matrix is ​​updated based on the dynamic weights.

8. The method for determining the design scheme of an urban engineering project as described in claim 1, characterized in that, The step of ranking the design schemes using the TOPSIS method based on the weight decision matrix includes: Based on the weighted decision matrix, determine the positive and negative ideal solutions for the urban engineering project at the current design stage; Calculate the first Euclidean distance from each design scheme to the positive ideal solution, and the second Euclidean distance from each design scheme to the negative ideal solution; Calculate the overall performance of each design scheme based on the first Euclidean distance and the second Euclidean distance; Based on the overall performance, the design schemes are ranked.

9. The method for determining the design scheme of an urban engineering project as described in claim 1, characterized in that, After ranking the design schemes using the TOPSIS method based on the weight decision matrix, the method for determining the design scheme of the urban engineering project further includes: Based on the preset perturbation range, several sets of perturbation weight vectors are generated. The weight decision matrix is ​​perturbed using the perturbation weight vectors to obtain the ranking stability of the design schemes under each set of perturbations. Based on the ranking of each design scheme, calculate the minimum weight change that would flip the ranking of the first and second design schemes, and obtain the ranking flip threshold. Calculate the decision confidence level based on the sorting stability and the sorting flip threshold.

10. A system for determining the design scheme of urban engineering projects, characterized in that, include: The information density index calculation module is used to obtain the design information of each design scheme of the urban engineering project at the current design stage, and calculate the information completeness of each design scheme based on the design information to obtain the information density index. The weight decision matrix calculation module is used to obtain the subjective evaluation index and objective performance index of each design scheme. It uses fuzzy hierarchical analysis to determine the subjective weight of each subjective evaluation index and uses CRITIC to extract the objective weight of the objective performance index. It then performs game equilibrium fusion on the subjective weight and the objective weight to obtain the weight decision matrix of each design scheme. The weight decision matrix update module is used to obtain the photovoltaic capacity index, energy use intensity index and current building global warming potential of each design scheme when the information density index is not less than the preset information density threshold, calculate the annual photovoltaic capacity index and annual building global warming potential of each design scheme, and update the weight decision matrix according to the annual photovoltaic capacity index and annual building global warming potential. The design scheme ranking module is used to rank the design schemes according to the weight decision matrix using the TOPSIS method.

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