Method, device, equipment and medium for optimizing cost of three-star green building

Abstract

The application relates to the technical field of architectural design, and provides a three-star green building cost optimization method, device, equipment and medium, which can construct a full-life cycle incremental cost prediction model of a green scheme based on the interaction between technical measures, so as to accurately perform building cost prediction; a plurality of to-be-optimized technical measures are selected from each technical measure included in the green scheme through cost sensitivity analysis, so as to lock the technical measures with high conversion rates for optimization; a target function is constructed according to annual total energy consumption, annual carbon emission and the full-life cycle incremental cost prediction model, and a constraint condition is constructed according to a three-star green building scoring rule, so that multiple optimization targets can be considered; a Pareto front solution set of the optimization model is solved based on an orthogonal table, so that the solving efficiency can be improved; and a target scheme is selected from a plurality of candidate schemes according to a value coefficient and a dynamic benefit balance point, so that an optimal three-star green building cost optimization scheme can be reasonably selected.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, and in particular to a method, apparatus, equipment and medium for optimizing the cost of three-star green buildings. Background Technology

[0002] As an important vehicle for promoting urban and rural development, the control of green building costs and energy consumption throughout its entire life cycle is a crucial issue.

[0003] However, in the current design and cost control practices of three-star green building projects, there are still many technical deficiencies that urgently need to be addressed, which seriously restrict the large-scale promotion and high-quality development of green buildings. These deficiencies include the following:

[0004] (1) The lack of a systematic cost calculation model makes it difficult to accurately quantify the incremental costs of green technologies;

[0005] (2) The interaction between technical measures was not considered, resulting in distorted cost calculations;

[0006] (3) It is difficult to balance multiple optimization objectives such as cost, energy consumption, and carbon emissions;

[0007] (4) The optimization process lacks standardized procedures, relies on the experience of designers, and has poor replicability. Summary of the Invention

[0008] In view of the above, it is necessary to provide a method, device, equipment and medium for optimizing the cost of three-star green buildings, which aims to solve the problems of inaccurate cost calculation and difficulty in balancing multiple optimization objectives in the current cost optimization process of three-star green buildings.

[0009] A method for optimizing the cost of a three-star green building, the method comprising:

[0010] Obtain a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme;

[0011] A green scheme for a three-star green building is constructed according to the screening rules of the measures, and a full life cycle incremental cost prediction model of the green scheme is constructed based on the interaction between the technical measures.

[0012] Multiple technical measures to be optimized are selected from the various technical measures included in the green scheme through cost sensitivity analysis;

[0013] An objective function is constructed based on the annual total energy consumption, the annual carbon emissions, and the full life cycle incremental cost prediction model. Constraints are then constructed based on the three-star green building rating rules to obtain an optimized model.

[0014] Based on the aforementioned multiple technical measures to be optimized, the Pareto front solution set of the optimization model is obtained by solving the orthogonal array, resulting in multiple candidate solutions;

[0015] Calculate the value coefficient and dynamic benefit balance point of each candidate solution, and select the target solution from the multiple candidate solutions based on the value coefficient and the dynamic benefit balance point.

[0016] A three-star green building cost optimization device, the three-star green building cost optimization device comprising:

[0017] The acquisition unit is used to acquire a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme;

[0018] The building unit is used to construct a green scheme for a three-star green building according to the screening rules of the measures, and to construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between the technical measures.

[0019] The selection unit is used to select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis;

[0020] The construction unit is also used to construct an objective function based on the annual total energy consumption, the annual carbon emissions and the full life cycle incremental cost prediction model, and to construct constraints based on the three-star green building rating rules to obtain an optimized model;

[0021] The solution unit is used to solve the Pareto front solution set of the optimization model based on an orthogonal array, based on the multiple technical measures to be optimized, to obtain multiple candidate solutions;

[0022] The selection unit is also used to calculate the value coefficient and dynamic benefit balance point of each candidate scheme, and select the target scheme from the multiple candidate schemes based on the value coefficient and the dynamic benefit balance point.

[0023] A computer device, the computer device comprising:

[0024] A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the three-star green building cost optimization method.

[0025] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the three-star green building cost optimization method.

[0026] As can be seen from the above technical solutions, this invention can construct a green scheme for a three-star green building according to the screening rules of the measures, and construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between the technical measures, so as to accurately predict the building cost; select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis, so as to lock the technical measures with high conversion rate for optimization; construct an objective function based on the annual total energy consumption, annual carbon emissions and the full life cycle incremental cost prediction model, and construct constraints according to the three-star green building scoring rules, so as to take into account multiple optimization objectives; on the basis of multiple technical measures to be optimized, the Pareto front solution set of the optimization model is solved based on orthogonal array, which can improve the solution efficiency; select the target scheme from multiple candidate schemes according to the value coefficient and dynamic benefit balance point, so as to rationally select the optimal cost optimization scheme for a three-star green building. Attached Figure Description

[0027] Figure 1 This is a flowchart of a preferred embodiment of the three-star green building cost optimization method of the present invention;

[0028] Figure 2 This is a functional block diagram of a preferred embodiment of the three-star green building cost optimization device of the present invention;

[0029] Figure 3 This is a schematic diagram of the structure of a computer device that is a preferred embodiment of the method for optimizing the cost of three-star green buildings according to the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the three-star green building cost optimization method of the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0032] The three-star green building cost optimization method is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0033] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), interactive network television (IPTV), smart wearable device, etc.

[0034] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0035] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0036] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0037] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0038] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0039] S10, obtain a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme.

[0040] In this embodiment, basic data such as the project's location, building type (e.g., office, residential, commercial), building area, number of floors, and structural form can be obtained. At the same time, meteorological parameters of the project location, including temperature, humidity, solar radiation intensity, wind direction and speed, can be collected. Resource endowment data such as water resources and renewable energy can also be collected. Furthermore, green building incentive conditions of the project location, including subsidy standards, floor area ratio bonuses, and tax incentives, can be obtained. In order to combine local conventional construction standards and public building energy-saving design standards, a cost database and energy consumption database for benchmark building schemes can be established.

[0041] Specifically, the total cost of the benchmark building scheme can be obtained by calculating the sum of the structural system cost (including foundations, beams, slabs, columns, etc.), the enclosure structure cost (exterior walls, roof, doors and windows, etc.), the HVAC system cost, the lighting system cost, and the water supply and drainage system cost.

[0042] Furthermore, building energy simulation software (such as EnergyPlus) can be used to calculate the total annual energy consumption and annual carbon emissions of the benchmark building scheme.

[0043] S11. Construct a green scheme for a three-star green building according to the screening rules, and construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between technical measures.

[0044] In this embodiment, the green scheme for constructing a three-star green building according to the screening rules includes:

[0045] Obtain the fixed technical measures configured according to the control requirements of the green building evaluation criteria, and obtain all optional technical measures;

[0046] Obtain building type and building environment parameters, and screen the applicability of the optional technical measures based on the building type and building environment parameters to obtain candidate technical measures;

[0047] The candidate technical measures are sorted based on a multidimensional sorting mechanism to obtain a sequence of technical measures;

[0048] Calculate the green building score for each technical measure in the sequence of technical measures;

[0049] The green building score of each technical measure in the sequence of technical measures is accumulated sequentially from front to back until the accumulated value is greater than or equal to the green building three-star target score.

[0050] The green solution is constructed based on the various technical measures involved in the accumulation and the fixed technical measures.

[0051] The optional technical measures may include, but are not limited to:

[0052] (1) Land-saving and outdoor environment technologies: permeable paving, roof greening, underground space development, etc.;

[0053] (2) Energy saving and energy utilization technologies: high-performance building envelope, high-efficiency equipment, renewable energy systems, etc.;

[0054] (3) Water conservation and water resource utilization technologies: water-saving appliances, rainwater reuse, greywater treatment, etc.;

[0055] (4) Material conservation and material resource utilization technologies: green building materials, prefabricated construction, recyclable materials, etc.;

[0056] (5) Indoor environmental quality technologies: fresh air system, air monitoring, sound insulation maintenance, etc.;

[0057] (6) Operation and management technologies: intelligent control systems, energy consumption monitoring platforms, etc.

[0058] The building environment parameters may include the climate zone, resource conditions, and site limitations of the project location.

[0059] Specifically, the green building score for each technical measure in the technical measure sequence can be calculated according to the general green building three-star evaluation standard.

[0060] The target score for green building three-star rating can be a score threshold configured according to actual needs and the green building three-star evaluation criteria.

[0061] In this embodiment, the construction of the full life-cycle incremental cost prediction model for the green solution based on the interaction between technical measures includes:

[0062] Calculate the basic cost of each technical measure and the cost of the interaction between the technical measures;

[0063] The incremental cost of the construction phase of the green solution is calculated based on the basic cost of each technical measure and the cost of interaction between the technical measures.

[0064] Calculate the operational costs of the green solution;

[0065] The full life cycle incremental cost prediction model is constructed based on the incremental costs of the construction phase and the costs of the operation phase.

[0066] The basic cost can be calculated by summing the basic costs such as material costs, equipment costs, and installation costs.

[0067] The interaction cost between various technical measures can be calculated using the following formula:

[0068] ;

[0069] in, This represents the cost of the interaction between various technical measures; m represents the total number of parameters contained in each technical measure; p represents the number of cost items affected (such as material costs, equipment costs, installation costs, etc.). Let represent the partial derivative of the impact of the j-th technical measure on the k-th cost, and let represent the rate of cost change caused by a unit change. These can be determined by fitting historical data or by expert experience. This represents the change in parameters of the j-th technical measure relative to the baseline building scheme.

[0070] The formula for calculating the incremental cost during the construction phase is as follows:

[0071] ;

[0072] in, This represents the incremental cost of the construction phase; This represents the cost of the i-th technical measure in the green solution; represents the cost of the corresponding item in the benchmark building scheme; n represents the number of technical measures.

[0073] To distinguish between m and n, the following example illustrates the concept: If three technical measures are selected, then n=3. The first technical measure is external wall insulation, which includes one parameter: insulation thickness; the second technical measure is the external window system, which includes two parameters: window-to-wall ratio and glass type; and the third technical measure is the photovoltaic system, which includes one parameter: photovoltaic area. Therefore, m=1+2+1=4.

[0074] The operating phase cost can be calculated using the following formula:

[0075] ;

[0076] in, The cost represents the operating phase cost; T represents the building lifespan (usually 50 years); t represents the year number, t=1,2,…,T; This represents the cost savings (in yuan / year) resulting from energy and water conservation in year t. represents the additional operating and maintenance cost of green equipment in year t; r represents the discount rate, which can be configured to 8% according to general regulations.

[0077] The full lifecycle incremental cost prediction model can be expressed as follows:

[0078] ;

[0079] in, Indicates the incremental cost over the entire lifecycle; This refers to incremental costs during the design phase (such as green consulting fees, certification fees, etc.). This represents the incremental cost during the t-th year of operation, usually a negative value, indicating savings (assuming an office building with a floor area of ​​10,000 square meters, a baseline building plan with an annual electricity consumption of 1.2 million kWh, an electricity price of 0.65 yuan / kWh, and an annual water consumption of 10,000 cubic meters). 3 Water price 4 yuan / m 3 The green solution utilizes a high-performance building envelope, LED lighting, a photovoltaic system, and water-saving appliances, reducing annual electricity consumption to 900,000 kWh and annual water consumption to 7,000 m³. 3 However, an additional 0.5 million yuan is required annually for photovoltaic operation and maintenance, and 0.3 million yuan for intelligent control system maintenance. This results in an annual saving of 19.9 million yuan, meaning the incremental cost in year t during the operation phase is -19.9 million yuan. This represents the incremental cost during the decommissioning and demolition phase (it can be assumed here that the demolition cost of the green solution is roughly equivalent to that of the baseline building solution, and can be taken as 0. To calculate the incremental cost during the decommissioning and demolition phase, the direct demolition cost of the baseline building solution can be estimated first, including mechanical demolition, removal, landfill costs, and material recycling value. Then, considering the simplification brought by prefabricated construction, steel structures, etc., or the complexity brought by photovoltaic systems, the direct demolition cost of the green solution can be estimated. Finally, the difference between the direct demolition cost of the green solution and the direct demolition cost of the baseline building solution is calculated to obtain the incremental cost during the decommissioning and demolition phase).

[0080] Through the above embodiments, the incremental cost of green technologies can be accurately quantified based on the interaction between technical measures, avoiding distortion in cost calculation.

[0081] S12, Select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis.

[0082] In this embodiment, the selection of multiple technical measures to be optimized from the various technical measures included in the green solution through cost sensitivity analysis includes:

[0083] Calculate the cost elasticity coefficient of each technical measure;

[0084] Based on the discrimination threshold, sensitive technical measures are selected from each technical measure according to the cost elasticity coefficient of each technical measure;

[0085] Construct a cost-benefit matrix for the sensitive technical measures, and select from the sensitive technical measures that have the attributes of low cost and high efficiency and high cost and high efficiency based on the cost-benefit matrix.

[0086] The cost elasticity coefficient of each technical measure can be calculated using the following formula:

[0087] ;

[0088] in, The elasticity coefficient (dimensionless) of the j-th technical measure with respect to the i-th cost; Let represent the partial derivative of the j-th technical measure with respect to the i-th cost; This represents the current parameter value of the j-th technical measure; This represents the value of the i-th cost item.

[0089] In selecting sensitive technical measures from various technical measures based on the cost elasticity coefficient of each technical measure according to the discrimination threshold, the cost elasticity coefficients can be sorted by absolute value first, and then key sensitive factors can be identified based on the discrimination threshold.

[0090] For example: when When ≤0.1, it is determined to be an insensitive technical measure; when 0.1 < When <0.3, it is judged as a medium-sensitivity factor technical measure; when If the value is ≥0.3, it is determined to be a highly sensitive technical measure. Further, moderately sensitive and highly sensitive technical measures are selected as the sensitive technical measures.

[0091] Among them, the incremental cost of each technical measure can be used as a basis ( A two-dimensional cost-benefit matrix is ​​plotted based on the contribution of green building scores (the green building scores that can be obtained from the technical measures taken).

[0092] For example: Collect incremental cost data for each technical measure and estimate its contribution to green building scores. Take the median of the incremental cost and the median contribution to green building scores for all technical measures, and use these as the cost boundary between low and high costs, and the score boundary between low and high scores. Further, if the incremental cost data is less than the cost boundary and the contribution to green building scores is greater than or equal to the score boundary, it is considered to have low-cost, high-efficiency attributes and can be prioritized; if the incremental cost data is greater than or equal to the cost boundary and the contribution to green building scores is greater than or equal to the score boundary, it is considered to have high-cost, high-efficiency attributes and can be prioritized for optimization; if the incremental cost data is less than the cost boundary and the contribution to green building scores is less than the score boundary, it is considered to have low-cost, low-efficiency attributes and can be selected as appropriate; if the incremental cost data is greater than or equal to the cost boundary and the contribution to green building scores is less than the score boundary, it is considered to have high-cost, low-efficiency attributes and can be prioritized for elimination.

[0093] Through the above embodiments, multiple technical measures to be optimized can be reasonably selected, avoiding waste caused by ineffective optimization.

[0094] S13, construct an objective function based on the annual total energy consumption, the annual carbon emissions, and the full life cycle incremental cost prediction model, and construct constraints based on the three-star green building rating rules to obtain an optimized model.

[0095] In this embodiment, the process of constructing an objective function based on the annual total energy consumption, the annual carbon emissions, and the full life-cycle incremental cost prediction model, and constructing constraints based on the three-star green building rating rules, to obtain the optimized model includes:

[0096] The technical measures in the green solution are converted into quantifiable decision variables;

[0097] The objective function is constructed with the goal of making the annual total energy consumption, the annual carbon emissions, and the whole life cycle incremental cost prediction model approach their theoretical minimum values ​​and achieve dynamic equilibrium.

[0098] The constraints are constructed by constraining the sum of the green building scores of each technical measure to be optimized to be greater than or equal to the minimum total score required for three-star green building certification, constraining the upper and lower limits of the decision variables, and constraining the inequality constraint function to be less than or equal to 0.

[0099] The optimization model is constructed based on the objective function and the constraints.

[0100] For example, the decision variable can be represented as: ,in, Represents a vector of decision variables (column vector); This represents the m-th parameter (such as insulation layer thickness, window-to-wall ratio, equipment energy efficiency rating, etc.).

[0101] The objective function can be expressed as:

[0102] ;

[0103] in, Describe the objective function; This represents the objective function for incremental cost over the entire lifecycle. This represents the objective function for total annual energy consumption. This represents the objective function for total annual carbon emissions.

[0104] The constraint condition can be expressed as:

[0105] ;

[0106] in, This represents the green building score obtained by the y-th technical measure; This indicates the minimum total score required for a three-star green building certification; This represents the lower limit of the m-th decision variable (e.g., minimum insulation layer thickness). This represents the upper limit of the m-th decision variable (e.g., maximum window-to-wall ratio); This represents the j-th inequality constraint function (such as specification requirements, physical limitations, etc.).

[0107] Through the above embodiments, a targeted and dedicated optimization model with interactive impact cost quantification and green building three-star rating constraints was designed for the cost optimization problem of green building three-star rating, which can take into account multiple optimization objectives.

[0108] S14. Based on the multiple technical measures to be optimized, the Pareto front solution set of the optimization model is solved using an orthogonal array to obtain multiple candidate solutions.

[0109] In this embodiment, an orthogonal array can be used to configure the experimental scheme to cover multi-factor and multi-level combinations with fewer experiments.

[0110] In this embodiment, a building information model can be established using BIM (Building Information Modeling) software, and a visual programming tool can be used to implement parameter-driven operation and call an energy consumption simulation engine for batch simulation calculations.

[0111] In this embodiment, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm can be used to solve for the Pareto optimal solution set. The algorithm flow is as follows:

[0112] (1) Initialize the population P0, and set the population size to N;

[0113] (2) Perform a fast non-dominated sort on the population and calculate the non-dominated level of each individual;

[0114] (3) Calculate the crowding distance for each individual;

[0115] (4) Generate offspring populations through tournament selection, simulated binary crossover, and polynomial mutation;

[0116] (5) Merge the parent and offspring populations and perform non-dominated sorting and crowding calculation;

[0117] (6) Select the first N individuals as the new generation population;

[0118] (7) Repeat steps (2) to (6) until the maximum number of generations G is reached;

[0119] (8) Output the Pareto front solution set P.

[0120] Through the above embodiments, candidate solutions can be screened based on multidimensional factors.

[0121] S15, calculate the value coefficient and dynamic benefit balance point of each candidate scheme, and select the target scheme from the multiple candidate schemes based on the value coefficient and the dynamic benefit balance point.

[0122] In this embodiment, calculating the value coefficient and dynamic benefit balance point of each candidate solution, and selecting the target solution from the plurality of candidate solutions based on the value coefficient and the dynamic benefit balance point includes:

[0123] The functional coefficients of each candidate scheme were calculated using the analytic hierarchy process (AHP).

[0124] Calculate the cost coefficient for each candidate solution based on the full lifecycle incremental cost prediction model;

[0125] Calculate the quotient of the functional coefficient and the corresponding cost coefficient for each candidate solution to obtain the value coefficient of each candidate solution;

[0126] From each candidate solution, select the candidate solution whose value coefficient is higher than a preset threshold as the candidate high-value solution;

[0127] Plot the marginal cost curve and marginal benefit curve for each candidate high-value solution, and determine the dynamic benefit balance point for each candidate high-value solution based on the marginal cost curve and marginal benefit curve of each candidate high-value solution.

[0128] The ideal point method is used to calculate the relative closeness of each candidate high-value solution to the corresponding dynamic benefit balance point;

[0129] The candidate high-value solution with the highest relative similarity is determined as the target solution.

[0130] The functional coefficient of each candidate solution can be calculated using the analytic hierarchy process (AHP) based on the following formula:

[0131] ;

[0132] Where F represents the functional coefficient of each candidate scheme, which is a dimensionless value ranging from 0 to 1; This indicates the number of green functional items (green functional items refer to the evaluation dimensions for measuring the overall performance of green buildings, including four aspects: green space, energy conservation, environmental protection, and economy. The score for each function is calculated by normalizing the scheme's performance in the corresponding technical system (such as land conservation, energy conservation, water conservation, material conservation, indoor environment, and operation) against the scoring clauses of the general green building evaluation standard, with a value range of 0-1). This represents the weight of the q-th green function item (which can be determined by expert scoring using the analytic hierarchy process). ); This represents the score for the qth green function (calculated by normalization against the scoring criteria of the general green building evaluation standard).

[0133] The cost coefficient can be the quotient between the total lifecycle incremental cost of the current candidate solution and the maximum total lifecycle incremental cost of all candidate solutions.

[0134] The marginal cost curve has the horizontal axis representing the level of investment in technical measures (i.e., the value of decision variables, such as insulation thickness, photovoltaic area, energy efficiency level, etc.) and the vertical axis representing the marginal value, i.e., the cost increase brought about by each additional unit of investment, i.e., the marginal cost (representing the cost increment brought about by a unit of technical investment).

[0135] For example, to calculate the incremental cost of a measure at different input levels, we have: Incremental cost of the measure itself = Basic cost of each technical measure - Total cost of the benchmark building scheme. Because the interaction effect is negative, the impact of this measure on the cost of other measures (such as the reduction in air conditioning capacity due to increased insulation thickness) should be deducted from the marginal cost. Therefore, incremental cost = Incremental cost of the measure itself + Interaction cost between technical measures, where marginal cost is the derivative of incremental cost with respect to the input level of the technical measure.

[0136] The horizontal axis of the marginal benefit curve represents the level of investment in technical measures (i.e., the value of decision variables, such as insulation thickness, photovoltaic area, energy efficiency level, etc.), and the vertical axis represents the marginal value, which is the increase in benefits brought about by each additional unit of investment (benefits refer to the net present value of energy or water conservation during the operation period), i.e., marginal benefits (representing the incremental benefits brought about by a unit of technical investment).

[0137] For example, calculate the incremental costs of the operation phase under different levels of technological input, discount these incremental costs to the beginning of the construction period, and obtain the total benefit. Marginal benefit is the derivative of the total benefit with respect to the level of technological input.

[0138] Furthermore, when the marginal cost equals the marginal benefit (i.e., the intersection of the two curves), the current level of investment in the corresponding technical measures, i.e., the current value of the decision variable, is determined as the dynamic benefit equilibrium point.

[0139] The step of using the ideal point method to calculate the relative closeness of each candidate high-value solution to the corresponding dynamic benefit balance point includes:

[0140] Obtain the cost, energy consumption, and carbon emissions corresponding to the dynamic benefit balance point;

[0141] An initial decision matrix is ​​constructed using the number of the multiple candidate high-value solutions as the number of rows and the total life-cycle incremental cost, total energy consumption, and total annual carbon emissions as columns.

[0142] Based on the optimal value of the target value of all candidate high-value solutions (minimum cost, energy consumption, and carbon emissions), the target value with the smaller deviation from the balance point of each solution is selected as a reference to obtain the positive ideal solution;

[0143] Using the worst-case scenario (maximum values ​​for cost, energy consumption, and carbon emissions) of all candidate high-value solutions as a reference, the negative ideal solution is obtained.

[0144] Calculate the first distance between each candidate high-value solution and the positive ideal solution, and the second distance between each candidate high-value solution and the negative ideal solution;

[0145] For each candidate high-value solution, the sum of the first distance and the second distance is calculated as the total distance, and the quotient of the second distance and the total distance is calculated as the relative proximity.

[0146] The candidate high-value solution with the highest relative similarity is selected as the target solution.

[0147] The relative proximity value ranges from [0,1]. A larger value indicates that the solution is closer to the positive ideal solution and further away from the negative ideal solution, and thus has better overall performance.

[0148] In addition to obtaining the relative closeness, the relative closeness can be further verified by combining the dynamic benefit balance point of each candidate high-value solution, so as to give priority to the solution with higher net benefit at the dynamic benefit balance point.

[0149] The target scheme may include key indicators such as the parameter configuration of the optimal combination of technical measures, the incremental cost over the entire life cycle, the investment payback period, the energy saving rate, and the carbon emission reduction.

[0150] Through the above embodiments, a reasonable cost optimization scheme for three-star green buildings can be obtained.

[0151] In this embodiment, after selecting the target solution from the plurality of candidate solutions based on the value coefficient and the dynamic benefit balance point, the method further includes:

[0152] Record the actual costs after construction based on the target scheme;

[0153] Calculate the deviation rate of the actual cost relative to the cost predicted by the life-cycle incremental cost prediction model;

[0154] The partial derivative parameters of the whole life cycle incremental cost prediction model are adjusted according to the deviation rate to optimize the whole life cycle incremental cost prediction model.

[0155] Furthermore, after the building is put into operation, actual energy consumption data can be collected through the energy consumption monitoring platform to verify the energy-saving effect.

[0156] The step of correcting the partial derivative parameters of the whole life cycle incremental cost prediction model based on the deviation rate includes:

[0157] Parameter values ​​can be calculated by back-calculating from actual data (for parameters with a clear causal relationship (such as partial derivatives), they can be calculated directly).

[0158] The parameters of the full life cycle incremental cost prediction model are corrected by weighting and fusing the back-calculated parameter values ​​with historical experience using the exponential smoothing method.

[0159] The revised parameters are categorized and stored according to climate zone, building type, and technical measure type, and then applied to subsequent projects.

[0160] Through the above embodiments, the parameter library can be continuously optimized, making cost predictions more realistic.

[0161] As can be seen from the above technical solutions, this invention can construct a green scheme for a three-star green building according to the screening rules of the measures, and construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between the technical measures, so as to accurately predict the building cost; select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis, so as to lock the technical measures with high conversion rate for optimization; construct an objective function based on the annual total energy consumption, annual carbon emissions and the full life cycle incremental cost prediction model, and construct constraints according to the three-star green building scoring rules, so as to take into account multiple optimization objectives; on the basis of multiple technical measures to be optimized, the Pareto front solution set of the optimization model is solved based on orthogonal array, which can improve the solution efficiency; select the target scheme from multiple candidate schemes according to the value coefficient and dynamic benefit balance point, so as to rationally select the optimal cost optimization scheme for a three-star green building.

[0162] like Figure 2 The diagram shown is a functional block diagram of a preferred embodiment of the three-star green building cost optimization device of the present invention. The three-star green building cost optimization device 11 includes an acquisition unit 110, a construction unit 111, a selection unit 112, and a solution unit 113. The module / unit referred to in this invention refers to a series of computer program segments that can be executed by a processor and perform a fixed function, and are stored in memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0163] The acquisition unit 110 is used to acquire a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme.

[0164] The construction unit 111 is used to construct a green scheme for a three-star green building according to the measures screening rules, and to construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between technical measures.

[0165] The selection unit 112 is used to select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis;

[0166] The construction unit 111 is also used to construct an objective function based on the annual total energy consumption, the annual carbon emissions and the full life cycle incremental cost prediction model, and to construct constraints based on the three-star green building rating rules to obtain an optimized model.

[0167] The solution unit 113 is used to solve the Pareto front solution set of the optimization model based on an orthogonal array, based on the multiple technical measures to be optimized, to obtain multiple candidate solutions;

[0168] The selection unit 112 is also used to calculate the value coefficient and dynamic benefit balance point of each candidate scheme, and select the target scheme from the plurality of candidate schemes according to the value coefficient and the dynamic benefit balance point.

[0169] As can be seen from the above technical solutions, this invention can construct a green scheme for a three-star green building according to the screening rules of the measures, and construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between the technical measures, so as to accurately predict the building cost; select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis, so as to lock the technical measures with high conversion rate for optimization; construct an objective function based on the annual total energy consumption, annual carbon emissions and the full life cycle incremental cost prediction model, and construct constraints according to the three-star green building scoring rules, so as to take into account multiple optimization objectives; on the basis of multiple technical measures to be optimized, the Pareto front solution set of the optimization model is solved based on orthogonal array, which can improve the solution efficiency; select the target scheme from multiple candidate schemes according to the value coefficient and dynamic benefit balance point, so as to rationally select the optimal cost optimization scheme for a three-star green building.

[0170] like Figure 3 The diagram shown is a schematic diagram of the computer equipment used in a preferred embodiment of the method for optimizing the cost of three-star green buildings according to the present invention.

[0171] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a three-star green building cost optimization program.

[0172] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0173] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0174] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as the code of a three-star green building cost optimization program, but also to temporarily store data that has been output or will be output.

[0175] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing a three-star green building cost optimization program) and calls data stored in the memory 12 to perform various functions of the computer device 1 and process data.

[0176] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes these applications to implement the steps in the various embodiments of the three-star green building cost optimization method described above, for example... Figure 1 The steps are shown.

[0177] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into an acquisition unit 110, a construction unit 111, a selection unit 112, and a solution unit 113.

[0178] The integrated unit, implemented as a software functional module, can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the three-star green building cost optimization method described in the various embodiments of this invention.

[0179] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0180] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0181] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0182] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0183] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0184] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0185] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the computer device 1 and other computer devices.

[0186] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0187] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0188] It will be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0189] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a three-star green building cost optimization method, and the processor 13 can execute the multiple instructions to achieve:

[0190] Obtain a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme;

[0191] A green scheme for a three-star green building is constructed according to the screening rules of the measures, and a full life cycle incremental cost prediction model of the green scheme is constructed based on the interaction between the technical measures.

[0192] Multiple technical measures to be optimized are selected from the various technical measures included in the green scheme through cost sensitivity analysis;

[0193] An objective function is constructed based on the annual total energy consumption, the annual carbon emissions, and the full life cycle incremental cost prediction model. Constraints are then constructed based on the three-star green building rating rules to obtain an optimized model.

[0194] Based on the aforementioned multiple technical measures to be optimized, the Pareto front solution set of the optimization model is obtained by solving the orthogonal array, resulting in multiple candidate solutions;

[0195] Calculate the value coefficient and dynamic benefit balance point of each candidate solution, and select the target solution from the multiple candidate solutions based on the value coefficient and the dynamic benefit balance point.

[0196] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0197] It should be noted that all the data involved in this case was legally obtained.

[0198] If any AI models, software tools, or components not belonging to this company appear in the embodiments of this invention, they are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this invention has been obtained by an entity authorized (with the knowledge and consent) or fully authorized by all parties through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0199] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0200] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0201] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0203] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0204] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0205] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the cost of a three-star green building, characterized in that, The cost optimization method for three-star green buildings includes: Obtain a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme; A green scheme for a three-star green building is constructed according to the screening rules of the measures, and a full life cycle incremental cost prediction model of the green scheme is constructed based on the interaction between the technical measures. Multiple technical measures to be optimized are selected from the various technical measures included in the green scheme through cost sensitivity analysis; An objective function is constructed based on the annual total energy consumption, the annual carbon emissions, and the full life cycle incremental cost prediction model. Constraints are then constructed based on the three-star green building rating rules to obtain an optimized model. Based on the aforementioned multiple technical measures to be optimized, the Pareto front solution set of the optimization model is obtained by solving the orthogonal array, resulting in multiple candidate solutions; Calculate the value coefficient and dynamic benefit balance point of each candidate solution, and select the target solution from the multiple candidate solutions based on the value coefficient and the dynamic benefit balance point; The life-cycle incremental cost prediction model for the green solution, based on the interaction between technological measures, includes: The calculation involves the basic cost of each technical measure and the cost of the interaction between the technical measures. The incremental cost of the construction phase of the green solution is calculated based on the basic cost of each technical measure and the cost of interaction between the technical measures. Calculate the operational costs of the green solution; The full life cycle incremental cost prediction model is constructed based on the incremental costs of the construction phase and the costs of the operation phase. The interaction cost between various technical measures is calculated using the following formula: ； in, This represents the cost of the interaction between various technical measures; m represents the total number of parameters contained in each technical measure; p represents the number of cost items affected. Let represent the partial derivative of the impact of the j-th technical measure on the k-th cost, and let represent the rate of cost change caused by a unit change. This represents the change in parameters of the j-th technical measure relative to the baseline building scheme.

2. The cost optimization method for three-star green buildings as described in claim 1, characterized in that, The green scheme for constructing a three-star green building according to the screening rules includes: Obtain the fixed technical measures configured according to the control requirements of the green building evaluation criteria, and obtain all optional technical measures; Obtain building type and building environment parameters, and screen the applicability of the optional technical measures based on the building type and building environment parameters to obtain candidate technical measures; The candidate technical measures are sorted based on a multidimensional sorting mechanism to obtain a sequence of technical measures; Calculate the green building score for each technical measure in the sequence of technical measures; The green building score of each technical measure in the sequence of technical measures is accumulated sequentially from front to back until the accumulated value is greater than or equal to the green building three-star target score. The green solution is constructed based on the various technical measures involved in the accumulation and the fixed technical measures.

3. The cost optimization method for three-star green buildings as described in claim 1, characterized in that, The selection of multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis includes: Calculate the cost elasticity coefficient of each technical measure; Based on the discrimination threshold, sensitive technical measures are selected from each technical measure according to the cost elasticity coefficient of each technical measure; Construct a cost-benefit matrix for the sensitive technical measures, and select from the sensitive technical measures that have the attributes of low cost and high efficiency and high cost and high efficiency based on the cost-benefit matrix.

4. The cost optimization method for three-star green buildings as described in claim 1, characterized in that, The objective function is constructed based on the annual total energy consumption, the annual carbon emissions, and the full life-cycle incremental cost prediction model, and constraints are constructed according to the three-star green building rating rules to obtain the optimized model, which includes: The technical measures in the green solution are converted into quantifiable decision variables; The objective function is constructed with the goal of making the annual total energy consumption, the annual carbon emissions, and the whole life cycle incremental cost prediction model approach their theoretical minimum values ​​and achieve dynamic equilibrium. The constraints are constructed by constraining the sum of the green building scores of each technical measure to be optimized to be greater than or equal to the minimum total score required for three-star green building certification, constraining the upper and lower limits of the decision variables, and constraining the inequality constraint function to be less than or equal to 0. The optimization model is constructed based on the objective function and the constraints.

5. The cost optimization method for three-star green buildings as described in claim 1, characterized in that, The calculation of the value coefficient and dynamic benefit balance point of each candidate solution, and the selection of the target solution from the plurality of candidate solutions based on the value coefficient and the dynamic benefit balance point, includes: The functional coefficients of each candidate scheme were calculated using the analytic hierarchy process (AHP). Calculate the cost coefficient for each candidate solution based on the full lifecycle incremental cost prediction model; Calculate the quotient of the functional coefficient and the corresponding cost coefficient for each candidate solution to obtain the value coefficient of each candidate solution; From each candidate solution, select the candidate solution whose value coefficient is higher than a preset threshold as the candidate high-value solution; Plot the marginal cost curve and marginal benefit curve for each candidate high-value solution, and determine the dynamic benefit balance point for each candidate high-value solution based on the marginal cost curve and marginal benefit curve of each candidate high-value solution. The ideal point method is used to calculate the relative closeness of each candidate high-value solution to the corresponding dynamic benefit balance point; The candidate high-value solution with the highest relative similarity is determined as the target solution.

6. The cost optimization method for three-star green buildings as described in claim 1, characterized in that, After selecting the target solution from the plurality of candidate solutions based on the value coefficient and the dynamic benefit balance point, the method further includes: Record the actual costs after construction based on the target scheme; Calculate the deviation rate of the actual cost relative to the cost predicted by the life-cycle incremental cost prediction model; The partial derivative parameters of the whole life cycle incremental cost prediction model are adjusted according to the deviation rate to optimize the whole life cycle incremental cost prediction model.

7. A three-star green building cost optimization device, characterized in that, The three-star green building cost optimization device includes: The acquisition unit is used to acquire a pre-constructed benchmark building scheme, and the annual total energy consumption and annual carbon emissions of the benchmark building scheme; The building unit is used to construct a green scheme for a three-star green building according to the screening rules of the measures, and to construct a full life cycle incremental cost prediction model for the green scheme based on the interaction between the technical measures. The selection unit is used to select multiple technical measures to be optimized from the various technical measures included in the green scheme through cost sensitivity analysis; The construction unit is also used to construct an objective function based on the annual total energy consumption, the annual carbon emissions and the full life cycle incremental cost prediction model, and to construct constraints based on the three-star green building rating rules to obtain an optimized model; The solution unit is used to solve the Pareto front solution set of the optimization model based on an orthogonal array, based on the multiple technical measures to be optimized, to obtain multiple candidate solutions; The selection unit is also used to calculate the value coefficient and dynamic benefit balance point of each candidate solution, and select the target solution from the plurality of candidate solutions based on the value coefficient and the dynamic benefit balance point. The life-cycle incremental cost prediction model for the green solution, based on the interaction between technological measures, includes: Calculate the basic cost of each technical measure and the cost of the interaction between the technical measures; The incremental cost of the construction phase of the green solution is calculated based on the basic cost of each technical measure and the cost of interaction between the technical measures. Calculate the operational costs of the green solution; The full life cycle incremental cost prediction model is constructed based on the incremental costs of the construction phase and the costs of the operation phase. The interaction cost between various technical measures is calculated using the following formula: ； in, This represents the cost of the interaction between various technical measures; m represents the total number of parameters contained in each technical measure; p represents the number of cost items affected. Let represent the partial derivative of the impact of the j-th technical measure on the k-th cost, and let represent the rate of cost change caused by a unit change. This represents the change in parameters of the j-th technical measure relative to the baseline building scheme.

8. A computer device, characterized in that, The computer device includes: A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the three-star green building cost optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the three-star green building cost optimization method as described in any one of claims 1 to 6.

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