An integrated optimization method for concrete solutions based on cost, performance, and carbon emissions

Abstract

The present invention discloses a comprehensive optimization method for concrete solutions based on cost, performance, and carbon emissions, belonging to the field of concrete solution optimization, including: S1: constructing a large database of concrete solutions, where the theoretical raw material ratio, actual raw material ratio, theoretical performance index, and actual performance index of each historical concrete solution are stored; calculating the significant influencing factors of different raw material ratio errors on performance indexes; S2: calculating the carbon emissions and costs of each raw material in the concrete preparation process to obtain decision target coefficients; S3: based on the decision targets of carbon emission standards and cost budgets, taking the performance indexes as constraint conditions, optimizing the raw material ratio of the target concrete solution. The present invention optimizes the concrete solution from multiple perspectives and directions based on the performance requirements of concrete, comprehensively considering carbon emissions and cost accounting in the concrete preparation process and raw material selection process, so as to meet the environmental protection and carbon tracking requirements.

Description

Technical Field

[0001] The present invention relates to the field of concrete scheme optimization, and specifically relates to a comprehensive optimization method for concrete schemes based on cost, performance and carbon emissions. Background Art

[0002] Concrete is one of the most important civil engineering materials in modern times. With the diversification of the current construction environment, the performance requirements for concrete are getting higher and higher. For concrete in different application environments, it is necessary to change the raw material ratio and types of concrete to meet the performance requirements in harsh construction environments.

[0003] Concrete is the most widely used building material in the world, accounting for 6 - 10% of global anthropogenic carbon dioxide (CO2) emissions. Portland cement (also known as ordinary portland cement) is the main component of concrete and also the main source of concrete carbon emissions. During the cement production process, carbon dioxide emissions mainly come from two time points: approximately 40% of carbon dioxide emissions come from the combustion of fossil fuels during the production process, and the remaining 60% come from the chemical reactions that occur naturally during the processing. The proportion of each component in the concrete mixture has a great impact on carbon emissions.

[0004] With the improvement of environmental protection requirements, more and more scholars have paid attention to the impact of concrete carbon emissions on the environment. During the process of formulating concrete schemes, it is necessary to comprehensively consider carbon emissions, and at the same time, consider costs, so that the concrete schemes can meet the carbon emission requirements, cost and performance requirements at the same time. Therefore, there is an urgent need to propose a comprehensive optimization method for concrete schemes based on cost, performance and carbon emissions. Summary of the Invention

[0005] In view of the above deficiencies of the prior art, the present invention provides a comprehensive optimization method for concrete schemes based on cost, performance and carbon emissions.

[0006] To achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0007] Provide a comprehensive optimization method for concrete schemes based on cost, performance and carbon emissions, which includes the following steps:

[0008] S1: Construct a large database of concrete schemes, and store the theoretical raw material ratio, actual raw material ratio, theoretical performance index and actual performance index of each historical concrete scheme in the large database; calculate the significant influencing factors of different raw material ratio errors on performance indicators;

[0009] S2: Calculate the carbon emissions and costs of each raw material during the concrete preparation process to obtain the decision target coefficients;

[0010] S3: Based on the decision-making objective of carbon emission standards and cost budgets, taking the performance indicators as constraint conditions, optimize the raw material ratio of the target concrete plan.

[0011] Further, step S1 includes:

[0012] S11: Construct a large database of concrete plans, and store the theoretical raw material ratio, actual raw material ratio, theoretical performance indicators, and actual performance indicators of each historical concrete plan in the large database;

[0013] S12: Establish a theoretical raw material ratio data group (a1, a2, …, a n ), an actual raw material ratio data group (A1, A2, …, A n ), a theoretical performance indicator data group (b1, b2, …, b m ), and an actual performance indicator data group (B1, B2, …, B m ); n is the number of types of raw materials in the concrete plan, a n is the theoretical ratio of the nth raw material, A n is the actual ratio of the nth raw material, m is the number of performance indicators of the concrete, b m is the mth theoretical performance indicator, and B m is the mth actual performance indicator;

[0014] S13: Calculate the error of the raw material ratio and screen out the obvious errors to obtain the obvious error coefficient data group of the raw material ratio ΔA = (k1, k2, …, k i );

[0015]

[0016] Among them, ΔA n阈值 is the maximum allowable error of the nth raw material ratio, k i is the obvious error coefficient of the ith raw material ratio, and i is the number of the obvious error coefficient of the raw material ratio;

[0017] S14: Calculate the error of the performance indicators and screen out the obvious errors to obtain the obvious error coefficient data group of the performance indicators ΔB = (K1, K2, …, K j );

[0018]

[0019] Among them, ΔB m阈值 is the maximum allowable error of the mth performance indicator, K j is the obvious error coefficient of the jth performance indicator, and j is the number of the obvious error coefficient of the performance indicator;

[0020] S15: Calculate the explicit error coefficient data groups ΔA and ΔB corresponding to all historical concrete solutions in the large database, and establish an explicit error coefficient matrix A' for the raw material ratio of each historical concrete solution:

[0021]

[0022] Among them, is the explicit error coefficient of the i-th raw material ratio of the I-th concrete solution, and I is the total number of concrete solutions;

[0023] S16: Establish an explicit error coefficient matrix B' for the performance indicators of each historical concrete solution:

[0024]

[0025] Among them, is the explicit error coefficient of the j-th performance indicator of the I-th concrete solution;

[0026] S17: For each concrete solution, the fluctuation of each performance indicator is correlated with the fluctuation of the raw material ratio. According to the change situation between the explicit error coefficients of every two historical concrete solutions, calculate the

[0027] influence weight of the raw material

[0028] ratio on the performance indicator:

[0029]

[0030] Among them, η i-1 is the influence weight of the fluctuation of the i-th raw material ratio on the change of the j-th performance indicator, is the influence weight matrix of the change of the same raw material ratio between two concrete solutions on the change of the same performance indicator;

[0031] S18: Screen out the maximum value η in the influence weight matrix max , and the raw material ratio corresponding to the maximum value η max is used as the significant influencing factor for the performance indicator K j ;

[0032] S19: Repeat steps S17 - S18 to calculate the significant influencing factors corresponding to each performance indicator.

[0033] Furthermore, step S2 includes:

[0034] S21: Calculate the total carbon emissions C 总 of each historical concrete solution according to the preparation process of each historical concrete solution and the preparation process of the raw materials:

[0035] S22: Calculate the cost R of the concrete plan by using the demand T of each historical concrete plan and the raw material demand:

[0036]

[0037] where r' is the operating cost of the enterprise for preparing a unit quantity of concrete, and a e is the theoretical raw material ratio of raw material e, and r'' is the unit price of the raw material;

[0038] S23: Calculate the decision objective coefficient f of each historical concrete plan:

[0039]

[0040] where μ1 is the influence weight of the cost of the concrete plan on the decision objective coefficient, μ2 is the influence weight of the carbon emission of the concrete plan on the decision objective coefficient, and μ1 + μ2 = 1.

[0041] Furthermore, the total carbon emission C 总 is calculated as follows:

[0042]

[0043] where e is the number of the raw material, c e is the carbon emission of the e-th raw material, y is the number of the power-consuming equipment required for preparing the e-th raw material, d y is the power consumption of the power-consuming equipment y, λ1 is the carbon emission factor related to the power consumption, Y is the total amount of power-consuming equipment required for preparing the e-th raw material, z is the number of the oil-consuming equipment required for preparing the e-th raw material, q z is the oil consumption of the oil-consuming equipment z, λ2 is the carbon emission factor related to the oil consumption, Z is the total amount of oil-consuming equipment required for preparing the e-th raw material, v is the number of the process for preparing concrete from the raw material, V is the total amount of processes for preparing concrete from the raw material, c v is the carbon emission of the process v, g is the number of the power-consuming equipment required for the process v, d g is the power consumption of the power-consuming equipment g, G is the total amount of power-consuming equipment required for the process v, x is the number of the oil-consuming equipment required for the process v, q x is the oil consumption of the oil-consuming equipment x, X is the total amount of oil-consuming equipment required for the process v, c h is the carbon savings during the process of preparing concrete, h is the number of the heat recovery process during the process of preparing concrete, r h is the heat recovery amount of the heat recovery process h, λ3 is the carbon emission factor related to the heat recovery, p0 is the error factor during the process of preparing concrete, and λ0 is the carbon emission factor related to the error factor.

[0044] Furthermore, step S3 includes:

[0045] S31: Determine the required target performance indicators according to the construction conditions of the concrete to form a target performance indicator data set wherein, is the ζ-th target performance indicator, and ζ is the number of target performance indicators;

[0046] S32: Retrieve all historical concrete solutions in the large database, and match the true performance indicator data sets (B1, B2, …, B m ) of each concrete solution with the target performance indicator data set ;

[0047] First, perform semantic matching on the performance indicators in the true performance indicator data sets (B1, B2, …, B m ) and the target performance indicator data set . If the semantic matching is successful, calculate the error rate m between the successfully semantically matched true performance indicator data B and the target performance indicator data If the semantic matching fails, the error rate

[0048] S33: Calculate the total error rate between the target performance indicator data set

[0049]

[0050] and the true performance indicator data sets of each concrete solution, where τ is the number of the error rate, and σ is the sum of the number of successfully semantically matched performance indicators and the number of unsuccessfully semantically matched performance indicators during the matching process;

[0051] S34: According to the total error rate obtained during the matching process with each concrete solution and sort the total error rate from small to large. The concrete solution corresponding to the minimum value of the total error rate is the best-matched concrete solution with the optimal performance;

[0052] S35: Starting from the best-matched concrete solution with the optimal performance, traverse each concrete solution in turn according to the decision target coefficient corresponding to the concrete solution until the first concrete solution with the decision target coefficient f ≤ f 阈值 is screened out as the most-matched concrete solution;

[0053] S36: For the target performance index in the target performance index data group that fails to achieve semantic matching, combine the significant influencing factors corresponding to the performance index obtained in step S19, screen the significant influencing factors of the target performance index that fails to achieve semantic matching, and add them to the raw material ratio data group in the most matching concrete plan to complete the optimization of the concrete plan.

[0054] The beneficial effects of the present invention are as follows: Based on the performance requirements of concrete, comprehensively considering the carbon emissions and cost accounting in the concrete preparation process and raw material selection process, the concrete plan is optimized from multiple angles and directions to meet the environmental protection requirements. During the concrete optimization process, fully refer to the performance, carbon emissions and cost data in the historical concrete plan, and continue to optimize in the historical concrete plan, effectively improving the reliability of the concrete plan optimization, reducing the difficulty of data processing. The optimized concrete plan not only meets the requirements of carbon emissions and cost control, but also ensures the economic feasibility and value of the concrete plan. Description of the Drawings

[0055] Figure 1 It is a flowchart of a comprehensive optimization method for a concrete plan based on cost, performance and carbon emissions. Detailed Embodiments

[0056] The following describes the detailed embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the detailed embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0057] As Figure 1 shown, a comprehensive optimization method for a concrete plan based on cost, performance and carbon emissions includes the following steps:

[0058] S1: Build a large database of concrete plans, and save the theoretical raw material ratio, actual raw material ratio, theoretical performance index and actual performance index of each historical concrete plan in the large database; calculate the significant influencing factors of different raw material ratio errors on the performance index.

[0059] Step S1 specifically includes:

[0060] S11: Build a large database of concrete plans, and save the theoretical raw material ratio, actual raw material ratio, theoretical performance index and actual performance index of each historical concrete plan in the large database;

[0061] S12: Establish a theoretical raw material ratio data group (a1, a2,..., a n ) and an actual raw material ratio data group (A1, A2,..., An ) Theoretical performance index data set (b1, b2, …, b m ) and actual performance index data set (B1, B2, …, B m ); n is the number of raw material types in the concrete plan, a n is the theoretical proportion of the nth raw material, A n is the actual proportion of the nth raw material, m is the number of concrete performance indexes, b m is the mth theoretical performance index, B m is the mth actual performance index;

[0062] S13: Calculate the error of the raw material proportion and screen out the dominant errors to obtain the dominant error coefficient data set of the raw material proportion ΔA = (k1, k2, …, k i );

[0063]

[0064] Among them, ΔA n阈值 is the maximum allowable error of the nth raw material proportion, k i is the dominant error coefficient of the ith raw material proportion, and i is the number of the dominant error coefficient of the raw material proportion;

[0065] S14: Calculate the error of the performance index and screen out the dominant errors to obtain the dominant error coefficient data set of the performance index ΔB = (K1, K2, …, K j );

[0066]

[0067] Among them, ΔB m阈值 is the maximum allowable error of the mth performance index, K j is the dominant error coefficient of the jth performance index, and j is the number of the dominant error coefficient of the performance index;

[0068] S15: Calculate the dominant error coefficient data sets ΔA and ΔB corresponding to all historical concrete plans in the large database, and establish a dominant error coefficient matrix A′ of the raw material proportion for each historical concrete plan:

[0069]

[0070] Among them, is the dominant error coefficient of the ith raw material proportion of the Ith concrete plan, and I is the total number of concrete plans;

[0071] S16: Establish a dominant error coefficient matrix B′ of the performance index for each historical concrete plan:

[0072]

[0073] Among them, is the explicit error coefficient of the j-th performance index of the I-th concrete plan;

[0074] S17: For each concrete plan, the fluctuation of each performance index is correlated with the fluctuation of the raw material ratio. According to the change situation of the explicit error coefficients between every two historical concrete plans, calculate the influence weight of the raw material ratio on the performance index:

[0075]

[0076] Among them, η i-1 is the influence weight of the fluctuation of the i-th raw material ratio on the change of the j-th performance index, is the influence weight matrix of the change of the same raw material ratio between two concrete plans on the change of the same performance index;

[0077] S18: Screen out the maximum value η in the influence weight matrix max , and the raw material ratio corresponding to the maximum value η max is used as the significant influencing factor for the performance index K j ;

[0078] S19: Repeat steps S17 - S18 to calculate the significant influencing factors corresponding to each performance index. The present invention evaluates the influence depth of the raw material ratio on the performance index through the error difference of the raw material ratio and the corresponding performance index error difference in two historical concrete plans, and is subsequently used to optimize the raw material ratio of concrete plans with different performance requirements, and add raw materials or modify the raw material ratio specifically.

[0079] S2: Calculate the carbon emissions and costs of each raw material in the concrete preparation process to obtain the decision target coefficient.

[0080] Step S2 specifically includes:

[0081] S21: According to the preparation process of each historical concrete plan and the preparation process of the raw materials, calculate the total carbon emissions C 总 of each historical concrete plan:

[0082] S22: Use the demand T of each historical concrete plan and the raw material demand to calculate the cost R of the concrete plan:

[0083]

[0084] Among them, r′ is the operating cost of the enterprise for preparing a unit amount of concrete, and a e is the theoretical raw material ratio of raw material e, and r″ is the unit price of the raw material;

[0085] S23: Calculate the decision target coefficient f for each historical concrete plan:

[0086]

[0087] Among them, μ1 is the influence weight of the cost of the concrete plan on the decision target coefficient, μ2 is the influence weight of the carbon emission of the concrete plan on the decision target coefficient, and μ1 + μ2 = 1. The decision target coefficient uses the natural exponential function. As the cost and carbon emission increase, the decision target coefficient becomes larger, indicating that it deviates more from the requirements of cost and carbon emission control, and this concrete plan needs to be eliminated.

[0088] Total carbon emission C 总 The calculation method is as follows:

[0089]

[0090] Among them, e is the number of the raw material, c e is the carbon emission of the e-th raw material, y is the number of the power-consuming equipment required to prepare the e-th raw material, d y is the power consumption of the power-consuming equipment y, λ1 is the carbon emission factor related to the power consumption, Y is the total amount of power-consuming equipment required to prepare the e-th raw material, z is the number of the oil-consuming equipment required to prepare the e-th raw material, q z is the oil consumption of the oil-consuming equipment z, λ2 is the carbon emission factor related to the oil consumption, Z is the total amount of oil-consuming equipment required to prepare the e-th raw material, v is the number of the process for preparing concrete from the raw material, V is the total amount of processes for preparing concrete from the raw material, c v is the carbon emission of the process v, g is the number of the power-consuming equipment required for the process v, d g is the power consumption of the power-consuming equipment g, G is the total amount of power-consuming equipment required for the process v, x is the number of the oil-consuming equipment required for the process v, q x is the oil consumption of the oil-consuming equipment x, X is the total amount of oil-consuming equipment required for the process v, c h is the carbon savings during the process of preparing concrete, h is the number of the heat recovery process during the process of preparing concrete, r h is the heat recovery amount of the heat recovery process h, λ3 is the carbon emission factor related to the heat recovery, p0 is the error factor during the process of preparing concrete, and λ0 is the carbon emission factor related to the error factor.

[0091] S3: Based on the decision target of the carbon emission standard and cost budget, with the performance index as the constraint condition, optimize the raw material ratio of the target concrete plan.

[0092] Step S3 specifically includes:

[0093] S31: Determine the required target performance indicators according to the construction conditions of the concrete to form a target performance indicator data set. Among them, is the ζ-th target performance indicator, and ζ is the number of target performance indicators;

[0094] S32: Retrieve all historical concrete plans in the large database, and match the true performance indicator data sets (B1, B2, …, B m ) of each concrete plan with the target performance indicator data set for matching;

[0095] First, semantically match the true performance indicator data sets (B1, B2, …, B m ) with the performance indicators in the target performance indicator data set . If the semantic match is successful, calculate the error rate m of the successfully semantically matched true performance indicator data B and the target performance indicator data If the semantic match is not successful, the error rate of the two

[0096] S33: Calculate the total error rate of the target performance indicator data set

[0097]

[0098] with the true performance indicator data sets of each concrete plan, where τ is the error rate number, and σ is the sum of the number of successfully semantically matched performance indicators and the number of unsuccessfully semantically matched performance indicators during the matching process;

[0099] S34: According to the total error rate obtained during the matching with each concrete plan, and sort the total error rate from small to large. The concrete plan corresponding to the minimum value of the total error rate is the best-matched concrete plan with the optimal performance;

[0100] S35: Starting from the best-matched concrete plan with the optimal performance, traverse each concrete plan in turn according to the decision target coefficient corresponding to the concrete plan until the first concrete plan with the decision target coefficient f ≤ f 阈值 is selected as the most-matched concrete plan. f 阈值 represents the maximum allowable value of the decision target coefficient, representing the maximum range of cost and carbon emission control;

[0101] S36: For the target performance indicators in the target performance indicator data group that have not been successfully semantically matched, combine the significant influencing factors corresponding to the performance indicators obtained in step S19, screen the significant influencing factors of the target performance indicators that have not been successfully semantically matched, and add them to the raw material ratio data group in the most matching concrete plan to complete the optimization of the concrete plan.

[0102] Based on the performance requirements of concrete, this invention comprehensively considers the carbon emissions and cost accounting in the concrete preparation process and raw material selection process, optimizes the concrete plan from multiple perspectives and directions, and meets the environmental protection requirements. During the concrete optimization process, fully refer to the performance, carbon emissions and cost data in the historical concrete plan, continue to optimize in the historical concrete plan, effectively improve the reliability of the concrete plan optimization, reduce the difficulty of data processing, and the optimized concrete plan not only meets the carbon emissions and cost control requirements, but also ensures the economic feasibility and value of the concrete plan.

Claims

1. An integrated optimization method for concrete solutions based on cost, performance, and carbon emissions, characterized in that, It includes the following steps: S1: Construct a large database of concrete solutions, which stores the theoretical raw material ratio, actual raw material ratio, theoretical performance indicators, and actual performance indicators of each historical concrete solution; calculate the significant influencing factors of different raw material ratio errors on performance indicators; S2: Calculate the carbon emissions and costs of each raw material during the concrete preparation process to obtain the decision target coefficients; S3: Based on the decision-making objectives of carbon emission standards and cost budgets, and taking the performance indicators as constraints, optimize the raw material ratio of the target concrete solution; The step S1 includes: S11: Construct a large database of concrete solutions, which stores the theoretical raw material ratio, actual raw material ratio, theoretical performance indicators, and actual performance indicators of each historical concrete solution; S12: Establish a theoretical raw material ratio data set (a1, a2, …, a n ), an actual raw material ratio data set (A1, A2, …, A n ), a theoretical performance index data set (b1, b2, …, b m ) and an actual performance index data set (B1, B2, …, B m ); n is the number of types of raw materials in the concrete plan, a n is the theoretical ratio of the nth raw material, A n is the actual ratio of the nth raw material, m is the number of performance indexes of the concrete, b m is the mth theoretical performance index, B m is the mth actual performance index; S13: Calculate the error of the raw material ratio and screen out the dominant errors to obtain the dominant error coefficient data set ΔA = (k1, k2, …, k i ); Among them, ΔA n阈值 is the maximum allowable error of the nth raw material ratio, k i is the dominant error coefficient of the ith raw material ratio, and i is the number of the dominant error coefficient of the raw material ratio; S14: Calculate the error of the performance index and screen out the dominant errors to obtain the data set of the dominant error coefficients of the performance index ΔB = (K1, K2, …, K j ); where, ΔB m阈值 is the maximum allowable error of the m-th performance index, K j is the dominant error coefficient of the j-th performance index, and j is the number of the dominant error coefficient of the performance index; S15: Calculate the explicit error coefficient data groups ΔA and ΔB corresponding to all historical concrete solutions in the large database, and establish an explicit error coefficient matrix A' of the raw material ratio for each historical concrete solution: Among them, is the explicit error coefficient of the i-th raw material ratio of the I-th concrete plan, and I is the total amount of concrete plans; S16: Establish an explicit error coefficient matrix B' of the performance indicators for each historical concrete solution: Among them, is the explicit error coefficient of the j-th performance index of the I-th concrete scheme; S17: For each concrete solution, the fluctuation of each performance indicator is correlated with the fluctuation of the raw material ratio. According to the change situation between the explicit error coefficients of every two historical concrete solutions, calculate the influence weight of the raw material ratio on the performance indicator: Among them, η i-1 is the influence weight of the fluctuation of the i-th raw material ratio on the change of the j-th performance index, is the influence weight matrix of the same raw material ratio change between two concrete schemes on the change of the same performance index; S18: Screen out the weight matrix that affects the maximum value η in max , and use the raw material ratio corresponding to the maximum value η max as the significant influencing factor for the performance index K j ; S19: Repeat steps S17 - S18 to calculate the significant influencing factors corresponding to each performance indicator.

2. The comprehensive optimization method for concrete solutions based on cost, performance, and carbon emissions according to claim 1, wherein The step S2 includes: S21: Calculate the total carbon emissions C of each historical concrete solution according to the preparation process of each historical concrete solution and the preparation process of raw materials. 总 : S22: Use the demand T of each historical concrete solution and the raw material demand to calculate the cost R of the concrete solution: Among them, r′ is the operating cost of the enterprise preparing a unit quantity of concrete, and a e is the theoretical raw material ratio of raw material e, and r″ is the unit price of the raw material; S23: Calculate the decision target coefficient f of each historical concrete solution: Among them, μ1 is the influence weight of the cost of the concrete solution on the decision target coefficient, μ2 is the influence weight of the carbon emissions of the concrete solution on the decision target coefficient, and μ1 + μ2 = 1.

3. The comprehensive optimization method for concrete solutions based on cost, performance, and carbon emissions according to claim 1, characterized in that The total carbon emissions C 总 is calculated as follows: Among them, e is the serial number of the raw material, and c e is the carbon emission of the e-th raw material, y is the serial number of the power-consuming equipment required for preparing the e-th raw material, and d y is the power consumption of the power-consuming equipment y, λ1 is the carbon emission factor related to the power consumption, Y is the total amount of power-consuming equipment required for preparing the e-th raw material, z is the serial number of the oil-consuming equipment required for preparing the e-th raw material, and q z is the oil consumption of the oil-consuming equipment z, λ2 is the carbon emission factor related to the oil consumption, Z is the total amount of oil-consuming equipment required for preparing the e-th raw material, v is the serial number of the process for preparing concrete from the raw material, V is the total amount of processes for preparing concrete from the raw material, and c v is the carbon emission of the process v, g is the serial number of the power-consuming equipment required for the process v, and d g is the power consumption of the power-consuming equipment g, G is the total amount of power-consuming equipment required for the process v, x is the serial number of the oil-consuming equipment required for the process v, and q x is the oil consumption of the oil-consuming equipment x, X is the total amount of oil-consuming equipment required for the process v, and c h is the carbon saving amount during the process of preparing concrete, h is the serial number of the heat recovery process during the process of preparing concrete, and r h is the heat recovery amount of the heat recovery process h, λ3 is the carbon emission factor related to the heat recovery, p0 is the error factor during the process of preparing concrete, and λ0 is the carbon emission factor related to the error factor.

4. The comprehensive optimization method for concrete solutions based on cost, performance, and carbon emissions according to claim 1, characterized in that, The step S3 includes: S31: Determine the required target performance indicators according to the construction conditions of the concrete to form a target performance indicator data set wherein is the ζ-th target performance indicator, and ζ is the number of target performance indicators; S32: Retrieve all historical concrete solutions in the large database, and match the real performance index data groups (B1, B2, …, B m ) of each concrete solution with the target performance index data group ; First, perform semantic matching on the performance index data groups (B1, B2, …, B m ) and the performance indexes within the target performance index data group . If the matching is successful, calculate the error rate m between the successfully semantically matched real performance index data B and the target performance index data . If the matching is not successful, the error rate S33: Calculate the target performance index data set The total error rate with the true performance index data set of each concrete scheme Among them, τ is the number of the error rate, and σ is the sum of the number of pairs of performance indicators with successful semantic matching and the number of performance indicators with unsuccessful semantic matching during the matching process; S34: According to the total error rate obtained during the matching process with each concrete plan and sort the total error rate in ascending order. The concrete plan corresponding to the minimum value of the total error rate is the concrete plan with the optimal matching performance; S35: Starting from the concrete plan with the optimal performance, traverse each concrete plan in sequence, and based on the decision target coefficient corresponding to the concrete plan, until the concrete plan with the decision target coefficient f ≤ f 阈值 is screened out for the first time as the most matching concrete plan, where f 阈值 is the maximum value allowed for the decision target coefficient; S36: For the target performance indicators in the target performance indicator data group that have not been successfully semantically matched, combine the significant influencing factors corresponding to the performance indicators obtained in step S19, screen the significant influencing factors of the target performance indicators that have not been successfully semantically matched, and add them to the raw material ratio data group in the most matching concrete solution to complete the optimization of the concrete solution.

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