A database construction method and system for building construction carbon emission calculation

Abstract

The application discloses a database construction method and system for building construction carbon emission calculation, and relates to the technical field of building construction carbon emission calculation, and comprises the following steps: acquiring a three-level specification data set and constructing a three-level initial knowledge graph; identifying and extracting a construction major category and corresponding major category construction process, acquiring a major category process standard carbon emission value, and updating the three-level initial knowledge graph; dividing the major category construction process into a plurality of middle category construction processes, acquiring a middle category process standard carbon emission value, and further updating and perfecting the three-level initial knowledge graph; establishing an eight-dimensional associated knowledge graph; acquiring a detailed construction project and a corresponding fine-grained standard carbon emission estimation value; after reverse calculation and qualification, acquiring a fine-grained standard carbon emission value, and solidifying the initial eight-dimensional associated knowledge graph; and the method solves the technical problems that three-level data are independent of each other, a plurality of standard data sets cannot work together, and the database adaptability of building construction carbon emission calculation is poor.

Description

Technical Field

[0001] This invention relates to the field of carbon emission calculation technology in building construction, and in particular to a database construction method and system for calculating carbon emissions in building construction. Background Technology

[0002] Calculating carbon emissions from construction is a fundamental task for the construction industry to achieve its carbon reduction targets. Accurate carbon emission data can not only empower construction companies to compete in the low-carbon arena, but also provide data support for setting differentiated emission reduction targets for the construction industry, and provide a basis for the deep decarbonization and sustainable high-quality development of my country's construction industry chain.

[0003] Currently, carbon emission calculations in the construction industry often rely on a three-tiered standard dataset: a coarse-grained national standard dataset (national-level macro-level baseline), a medium-grained industry dataset (process-level structural reference data), and a local scenario-based floating standard dataset (regional floating correction coefficient). However, these three datasets are independent and lack standardized processing, resulting in inconsistencies in units, data dimensions, outlier removal, and ambiguous terminology. Furthermore, the lack of effective data association and fusion methods prevents the linkage of data information across the three datasets, hindering their synergistic effect. Existing carbon emission calculation methods struggle to refine the calculation from coarse-grained national standards to fine-grained construction projects. Process division relies heavily on manual experience, lacking scientific support, and carbon emission calculations lack robust deviation verification and coefficient correction mechanisms, leading to low accuracy. Moreover, the absence of a knowledge graph deeply integrated with carbon emission calculations hinders data traceability and scenario-based adaptation, ultimately resulting in a non-standardized carbon emission database that is ill-suited to actual construction scenarios, failing to meet the technical challenges of meeting the construction industry's routine and precise carbon emission accounting and management needs.

[0004] Therefore, there is an urgent need for a method and system for constructing a carbon emission calculation database for building construction that can achieve standardized collaboration of three-level initial datasets, precise refinement of carbon emission values ​​layer by layer, and closed-loop verification and dynamic updating capabilities, in order to solve the technical problem of poor database adaptability in existing technologies for carbon emission calculation of building construction. Summary of the Invention

[0005] The main objective of this invention is to provide a database construction method and system for calculating carbon emissions from building construction, aiming to solve the technical problem that multiple standard datasets cannot work together in the prior art, resulting in poor database adaptability for calculating carbon emissions from building construction.

[0006] To achieve the above objectives, this invention provides a database construction method for calculating carbon emissions from building construction, comprising the following steps:

[0007] Includes the following steps:

[0008] S10. Obtain a three-level standard dataset, which includes a national standard coarse-grained standard dataset, an industry medium-grained standard dataset, and a local scenario-based floating standard dataset. Based on the three-level standard dataset, extract entities and construct a three-level initial knowledge graph containing standard nodes, construction link nodes, process parameter nodes, business format nodes, regional nodes, and initial association edges.

[0009] S20. Analyze the national standard coarse-grained specification dataset, identify and extract the major construction categories and corresponding major construction procedures, obtain the standard carbon emission value of each major construction procedure, and update the third-level initial knowledge graph.

[0010] S30. Divide the major construction process into multiple intermediate construction processes, map the industry particle reference value in the industry particle specification dataset to each intermediate construction process, calculate the standard carbon emission value of the intermediate process by combining the particle-related correction coefficient, and update and improve the three-level initial knowledge graph.

[0011] S40, extract construction condition data from the local scenario-based floating standard dataset. The construction condition data includes climate condition data, geological condition data, and time period condition data. Add condition nodes to the improved three-level initial knowledge graph, and refine the process parameter nodes into material nodes and equipment nodes to form an eight-dimensional relational knowledge graph containing level standard nodes, construction link nodes, material nodes, equipment nodes, condition nodes, business format nodes, regional nodes, and process parameter nodes. Establish relational edges between each node.

[0012] S50, based on the eight-dimensional association knowledge graph, the medium-level construction process is divided into multiple detailed construction projects; based on the comparison of the same project between the local scenario-based floating standard dataset and the industry granular standard dataset, and combined with the fine granular subdivision dimension, the fine granular adaptation correction coefficient is determined, and the standard carbon emission value of the medium-level process is multiplied by the corresponding fine granular adaptation correction coefficient to calculate the fine granular standard carbon emission estimate value corresponding to each detailed construction project;

[0013] S60, perform reverse aggregation based on the fine particulate standard carbon emission estimate to obtain the aggregated carbon emission estimate for the construction category; if the difference between the aggregated carbon emission estimate for the construction category and the standard carbon emission value for the category process is within a preset threshold range, then update the fine particulate standard carbon emission estimate to the fine particulate standard carbon emission value and solidify the initial eight-dimensional association knowledge graph.

[0014] Furthermore, based on the carbon emission contribution weight, industry adaptation coefficient, and construction fluctuation correction coefficient of each of the major construction procedures, the standard carbon emission value of each major construction procedure is obtained.

[0015] Step S60 further includes: if the difference exceeds a preset threshold, triggering a coefficient correction mechanism to correct the industry adaptation coefficient, construction fluctuation correction coefficient, medium particle correlation correction coefficient and fine particle adaptation correction coefficient, repeating steps S20 to S50 until the difference is within the preset threshold range.

[0016] Further, in step S30, the improved K-means clustering algorithm is used to divide the major construction process into multiple intermediate construction processes. The industry intermediate reference values ​​in the industry intermediate dataset are mapped to each of the intermediate construction processes based on the semantic similarity of the processes. The standard carbon emission values ​​of the intermediate processes are calculated by combining the intermediate particle correlation correction coefficients. The intermediate particle correlation correction coefficients include process parameter correction coefficients and equipment and material correction coefficients.

[0017] Furthermore, before step S10, the method includes the following steps: obtaining a three-level initial dataset related to carbon emissions from building construction, wherein the three-level initial dataset includes a national standard coarse-grained dataset, an industry medium-grained dataset, and a local scenario-based floating dataset; and performing normalization processing on the three-level initial datasets respectively to obtain a three-level normalized dataset, wherein the normalization processing includes data cleaning processing, semantic normalization processing, unit normalization processing, and dimension alignment processing.

[0018] The data cleaning process includes outlier removal and missing value completion. The outlier removal uses a 3D model. The algorithm for missing value completion uses the mean value of similar data for completion; the semantic normalization process uses natural language processing technology to unify the terminology of the three-level initial dataset.

[0019] The unit normalization process is used to uniformly transform carbon emission-related data of different units in the three-level initial dataset; the dimension alignment process is used to classify process parameters, business types, and geographical ranges in the three-level initial dataset.

[0020] Further, in step S10, standard-level entities, construction process entities, process parameter entities, building industry entities, and regional attribute entities are extracted based on the three-level specification dataset. A three-level initial knowledge graph is constructed, containing standard nodes, construction process nodes, process parameter nodes, industry nodes, regional nodes, and initial association edges. The construction of the three-level initial knowledge graph includes the following steps:

[0021] S101, the five core node types are: definition-level standard node, construction link node, process parameter node, business type node, and regional node. Among them, the definition-level standard node corresponds to the standard-level entity in the three-level specification dataset; the construction link node corresponds to the construction link entity, which includes construction major category and construction intermediate category; the process parameter node corresponds to the process parameter entity, which includes major category process parameter node and intermediate category process parameter node; the business type node corresponds to the building business type entity; and the regional node corresponds to the regional attribute entity.

[0022] S102. Initial association edge construction: Construct initial association edges between each node. Among them, the national standard initial association edge is the connection edge between the national standard coarse-grained node, the construction category node in the construction link node, and the business format node. The industry initial association edge is the connection edge between the industry granular node, the construction category initial node in the construction link node, and the business format node. The local initial association edge is the connection edge between the local scenario node and the regional node.

[0023] S103. Node attribute supplementation: Supplement the standard reference values ​​and construction fluctuation correction intervals in the three-level standard dataset to the attributes of the corresponding nodes to achieve multi-dimensional linkage of the three-level initial dataset and obtain the three-level initial knowledge graph.

[0024] Further, in step S20, obtaining the standard carbon emission value for each major construction procedure specifically includes:

[0025] S21, Obtain Construction Category carbon emission contribution weight ;

[0026] S22, using formula Calculations are performed, in which, Construction category Estimated carbon emissions for major process categories The reference value for coarse-grained specifications in the national standard coarse-grained specification dataset is... Construction categories determined by combining construction industry types and regional characteristics Industry compatibility coefficient Construction categories determined based on construction efficiency Construction fluctuation correction coefficient;

[0027] S23, Obtain Construction Category For similar construction types, refer to the measured carbon emission values ​​from relevant industry benchmarks. ;

[0028] S24, Estimated carbon emissions for the aforementioned major process categories If a double deviation rate check is performed and passes the check, the estimated carbon emission value for the major process category is determined to be the standard carbon emission value for that major process category; if the double deviation rate check fails, the industry adaptation coefficient is updated. and the construction fluctuation correction coefficient Repeat the above steps until the standard carbon emission values ​​for the major process categories are obtained;

[0029] The dual deviation rate verification includes single-category deviation rate verification and total deviation rate verification; based on the construction category Estimated carbon emissions for major process categories and corresponding measured carbon emissions from the aforementioned industry references. Calculations are performed to obtain the business format deviation value. Based on the calculated business format deviation value and the business format deviation rate threshold, a single major category deviation rate verification is performed. If the calculated business format deviation value is not greater than the business format deviation rate threshold, the single major category deviation rate verification is confirmed to be qualified. The deviation rates for each construction major category are then obtained. The total value of the business is calculated based on the estimated carbon emissions of the major process categories and the reference values ​​of the national standard coarse particle size distribution. The total deviation rate is calculated and obtained. Based on the total deviation rate and the total deviation rate threshold, the total deviation rate is verified. If the total deviation rate is not greater than the total deviation rate threshold, the total deviation rate verification is qualified.

[0030] Furthermore, in step S21,

[0031] Construction categories can be obtained using either the industry percentage method or the process energy consumption percentage method. carbon emission contribution weight The industry proportion method directly adopts the carbon emission proportion of the same type of construction category published in the carbon emission statistics standard of the construction industry as the carbon emission contribution weight; the process energy consumption proportion method uses the proportion of the total core process energy consumption of each construction category as the carbon emission contribution weight. The core process energy consumption includes direct energy consumption of electricity, oil, and gas, as well as the equivalent energy consumption converted from materials.

[0032] Furthermore, in step S50,

[0033] Based on differences in material specifications, equipment models, and working conditions, the aforementioned construction procedures are divided into several detailed construction items.

[0034] Using formula Calculations were performed to obtain detailed construction items. Fine particulate standard carbon emission estimates ,in, To refine the construction project The corresponding national standard-industry adaptation correction factor, To refine the construction project Corresponding local standard adaptation correction coefficients, To refine the construction project Corresponding working condition-process adaptation correction coefficient, , , The value range is [0.95, 1.05].

[0035] Furthermore, it also includes step S70, which involves obtaining the new construction technology and the new local carbon emission standards, supplementing the corresponding nodes in the eight-dimensional association knowledge graph and updating the node attributes and associated edge weights, recalculating the carbon emission value of the new construction procedure corresponding to the new construction technology, and synchronously updating it to the building construction carbon emission calculation database.

[0036] This invention also provides a database construction system for calculating carbon emissions from building construction.

[0037] Includes a data acquisition module for acquiring a three-level standardized dataset;

[0038] The data normalization module is used to perform data cleaning, semantic normalization, unit normalization, and dimension alignment on the initial three-level dataset to obtain a normalized three-level dataset.

[0039] The knowledge graph construction module is used to build a three-level initial knowledge graph based on a three-level standardized dataset, and to gradually update and expand it into an eight-dimensional associated knowledge graph according to the carbon emission values ​​of each process. It also supports dynamic maintenance and node supplementation of the graph.

[0040] The carbon emission value calculation module is used to calculate the carbon emission values ​​of construction categories, construction subcategories, and detailed construction projects in sequence. It is also used to update weights and correct coefficients, so as to refine the carbon emission values ​​from coarse to fine.

[0041] The reverse verification and solidification module is used to reverse aggregate the estimated fine particulate carbon emissions for deviation verification. After the verification is qualified, the eight-dimensional association knowledge graph is solidified and the standard carbon emission values ​​of each level are determined.

[0042] The database update module is used to archive and build a database from the three-level standard dataset, carbon emission values ​​of each level of standards, and solidified knowledge graph, and to update the database content synchronously according to the new construction technology and local carbon emission standards.

[0043] The data acquisition module, the three-level standardized dataset, the data normalization processing module, the knowledge graph construction module, the carbon emission value calculation module, the reverse verification and solidification module, and the database update module are electrically connected in sequence to realize data transmission and interactive feedback.

[0044] Compared with existing technologies, the database construction method for calculating carbon emissions from building construction provided by this invention has the following beneficial effects:

[0045] This invention provides a database construction method for calculating carbon emissions in building construction. Using a three-tier standard dataset as its core foundation, it establishes a complete building carbon emission database construction system, from the three-tier standard dataset to a knowledge graph and finally to database solidification. First, an initial three-tier knowledge graph is constructed to complete the standardized linkage and graph-based construction of the three-tier standard dataset. Then, based on the national standard coarse-grained standard dataset and the initial three-tier knowledge graph, construction categories are split and the standard carbon emission values ​​for each category's procedures are calculated, achieving the procedure-based decomposition of the national standard coarse-grained standard dataset and simultaneously updating the graph node attributes. Next, a division from construction categories to intermediate construction categories is adopted, and the standard carbon emission values ​​for intermediate construction procedures are calculated using intermediate-grained correction coefficients, supplementing the graph's intermediate-class nodes and related edges to achieve deep integration of industry data and procedure division. Finally, a local scenario-based floating standard dataset is integrated, expanding the initial three-tier knowledge graph into an eight-dimensional relational knowledge graph containing national standards, construction stages, materials, equipment, working conditions, business formats, regions, and process parameters, thus constructing a relational knowledge graph of the national standard coarse-grained standard dataset, the industry intermediate-grained standard dataset, and the local scenario-based floating standard dataset. The method establishes a graph-based association channel for the data set; then, based on the eight-dimensional association knowledge graph, it completes the division from medium-level construction to detailed construction projects, and calculates the fine-grained standard carbon emission estimates by combining fine-grained adaptation correction coefficients, achieving precise refinement of carbon emission values ​​from coarse to fine granular; finally, it reverse-aggregates the fine-grained estimates and verifies the deviation with the standard carbon emission values ​​of major process categories. If the deviation is not met, a coefficient correction mechanism is triggered for re-iterative calculation. If the deviation is met, the eight-dimensional association knowledge graph and the corresponding standard carbon emission values ​​at each level are solidified, completing the solidification of the core content of the database; the method of this invention adopts a coarse-grained approach. The technical concept of particle splitting, medium particle refinement, fine particle precise calculation, reverse aggregation verification, coefficient correction iteration, and graph solidification and updating ultimately solidifies an eight-dimensional relational knowledge graph and obtains the standard carbon emission values ​​corresponding to each level. It realizes a complete self-consistent system from coarse to fine and then from fine back to coarse. It achieves precise decomposition and reverse closed-loop verification of building carbon emission data from macro total amount to micro process. It solves the technical problems of existing technologies, such as the independence of three levels of data, lack of scientific support for calculation, inability of multiple standard datasets to work together, and poor database adaptability for building construction carbon emission calculation. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a database construction method for calculating carbon emissions from building construction, according to one embodiment of the present invention.

[0048] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0052] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0053] Please refer to the appendix. Figure 1 This invention provides a database construction method for calculating carbon emissions from building construction, comprising the following steps:

[0054] S10. Obtain a three-level standard dataset, which includes a national standard coarse-grained standard dataset, an industry medium-grained standard dataset, and a local scenario-based floating standard dataset. Based on the three-level standard dataset, extract entities and construct a three-level initial knowledge graph containing standard nodes, construction link nodes, process parameter nodes, business format nodes, regional nodes, and initial association edges.

[0055] S20. Analyze the national standard coarse-grained specification dataset, identify and extract the major construction categories and corresponding major construction procedures, obtain the standard carbon emission value of each major construction procedure, and update the third-level initial knowledge graph.

[0056] S30. Divide the major construction process into multiple intermediate construction processes, map the industry particle reference value in the industry particle specification dataset to each intermediate construction process, calculate the standard carbon emission value of the intermediate process by combining the particle-related correction coefficient, and update and improve the three-level initial knowledge graph.

[0057] S40, extract construction condition data from the local scenario-based floating standard dataset. The construction condition data includes climate condition data, geological condition data, and time period condition data. Add condition nodes to the improved three-level initial knowledge graph, and refine the process parameter nodes into material nodes and equipment nodes to form an eight-dimensional relational knowledge graph containing level standard nodes, construction link nodes, material nodes, equipment nodes, condition nodes, business format nodes, regional nodes, and process parameter nodes. Establish relational edges between each node.

[0058] S50, based on the eight-dimensional association knowledge graph, the medium-level construction process is divided into multiple detailed construction projects; based on the comparison of the same project between the local scenario-based floating standard dataset and the industry granular standard dataset, and combined with the fine granular subdivision dimension, the fine granular adaptation correction coefficient is determined, and the standard carbon emission value of the medium-level process is multiplied by the corresponding fine granular adaptation correction coefficient to calculate the fine granular standard carbon emission estimate value corresponding to each detailed construction project;

[0059] S60, perform reverse aggregation based on the fine particulate standard carbon emission estimate to obtain the aggregated carbon emission estimate for the construction category; if the difference between the aggregated carbon emission estimate for the construction category and the standard carbon emission value for the category process is within a preset threshold range, then update the fine particulate standard carbon emission estimate to the fine particulate standard carbon emission value and solidify the initial eight-dimensional association knowledge graph.

[0060] This invention provides a database construction method for calculating carbon emissions in building construction. Using a three-tier standard dataset as its core foundation, it establishes a complete building carbon emission database construction system, from the three-tier standard dataset to a knowledge graph and finally to database solidification. First, an initial three-tier knowledge graph is constructed to complete the standardized linkage and graph-based construction of the three-tier standard dataset. Then, based on the national standard coarse-grained standard dataset and the initial three-tier knowledge graph, construction categories are split and the standard carbon emission values ​​for each category's procedures are calculated, achieving the procedure-based decomposition of the national standard coarse-grained standard dataset and simultaneously updating the graph node attributes. Next, a division from construction categories to intermediate construction categories is adopted, and the standard carbon emission values ​​for intermediate construction procedures are calculated using intermediate-grained correction coefficients, supplementing the graph's intermediate-class nodes and related edges to achieve deep integration of industry data and procedure division. Finally, a local scenario-based floating standard dataset is integrated, expanding the initial three-tier knowledge graph into an eight-dimensional relational knowledge graph containing national standards, construction stages, materials, equipment, working conditions, business formats, regions, and process parameters, thus constructing a relational knowledge graph of the national standard coarse-grained standard dataset, the industry intermediate-grained standard dataset, and the local scenario-based floating standard dataset. The method establishes a graph-based association channel for the data set; then, based on the eight-dimensional association knowledge graph, it completes the division from medium-level construction to detailed construction projects, and calculates the fine-grained standard carbon emission estimates by combining fine-grained adaptation correction coefficients, achieving precise refinement of carbon emission values ​​from coarse to fine granular; finally, it reverse-aggregates the fine-grained estimates and verifies the deviation with the standard carbon emission values ​​of major process categories. If the deviation is not met, a coefficient correction mechanism is triggered for re-iterative calculation. If the deviation is met, the eight-dimensional association knowledge graph and the corresponding standard carbon emission values ​​at each level are solidified, completing the solidification of the core content of the database; the method of this invention adopts a coarse-grained approach. The technical concept of particle splitting, medium particle refinement, fine particle precise calculation, reverse aggregation verification, coefficient correction iteration, and graph solidification and updating ultimately solidifies an eight-dimensional relational knowledge graph and obtains the standard carbon emission values ​​corresponding to each level. It realizes a complete self-consistent system from coarse to fine and then from fine back to coarse. It achieves precise decomposition and reverse closed-loop verification of building carbon emission data from macro total amount to micro process. It solves the technical problems of existing technologies, such as the independence of three levels of data, lack of scientific support for calculation, inability of multiple standard datasets to work together, and poor database adaptability for building construction carbon emission calculation.

[0061] In a specific embodiment of the present invention, the database construction method for calculating carbon emissions from building construction includes the following steps: S10, obtaining a three-level standard dataset, wherein the three-level standard dataset includes a national standard coarse-grained standard dataset, an industry medium-grained standard dataset, and a local scenario-based floating standard dataset; extracting standard-level entities, construction link entities, process parameter entities, building industry entities, and regional attribute entities based on the three-level standard dataset; and constructing a three-level initial knowledge graph containing standard nodes, construction link nodes, process parameter nodes, industry nodes, regional nodes, and initial associated edges.

[0062] S20. Analyze the national standard coarse-grained specification dataset, identify and extract the major construction categories and corresponding major construction procedures, calculate the standard carbon emission value of each major construction procedure based on the carbon emission contribution weight, industry adaptation coefficient and construction fluctuation correction coefficient of each major construction procedure, and update the standard carbon emission value of each major construction procedure as a node attribute to the third-level initial knowledge graph.

[0063] S30. Divide the major construction process into multiple intermediate construction processes, map the industry particle reference values ​​in the industry particle specification dataset to each intermediate construction process based on the semantic similarity of the process, calculate the standard carbon emission value of the intermediate process by combining the particle-related correction coefficient, and update and improve the third-level initial knowledge graph by supplementing the relevant nodes and associated edges of the intermediate construction processes.

[0064] S40, extract construction condition data from the local scenario-based floating standard dataset. The construction condition data includes climate condition data, geological condition data, and time period condition data. Add condition nodes to the improved three-level initial knowledge graph, and refine the process parameter nodes into material nodes and equipment nodes to form an eight-dimensional relational knowledge graph containing level standard nodes, construction link nodes, material nodes, equipment nodes, condition nodes, business format nodes, regional nodes, and process parameter nodes. Establish relational edges between each node.

[0065] S50, based on the eight-dimensional association knowledge graph, the medium-level construction process is divided into multiple detailed construction projects according to the differences in material specifications, equipment models, and working conditions; based on the comparison of the same project between the local scenario-based floating standard dataset and the industry granular standard dataset, and combined with the fine granular subdivision dimension to determine the fine granular adaptation correction coefficient, the standard carbon emission value of the medium-level process is multiplied by the corresponding fine granular adaptation correction coefficient to obtain the fine granular standard carbon emission estimate value corresponding to each detailed construction project;

[0066] S60, perform reverse aggregation based on the fine particulate standard carbon emission estimate to obtain the aggregated carbon emission estimate for the construction category; if the difference between the aggregated carbon emission estimate for the construction category and the standard carbon emission value for the category process is within a preset threshold range, then update the fine particulate standard carbon emission estimate to the fine particulate standard carbon emission value and solidify the initial eight-dimensional association knowledge graph.

[0067] Understandably, in an optional implementation, in step S10, the three-level specification dataset includes a national standard coarse-grained specification dataset, an industry-specific granular specification dataset, and a local scenario-based floating specification dataset. Based on the three-level specification dataset, named entity recognition and relation extraction technology is used to extract standard-level entities, construction link entities, process parameter entities, building industry entities, and regional attribute entities to construct a three-level initial knowledge graph. Construction link entities include construction categories and construction subcategories. The three-level initial knowledge graph contains standard nodes, construction link nodes, process parameter nodes, industry nodes, and regional nodes, and initial association edges are established between each node. The national standard coarse-grained dataset is a macro-level total carbon emission benchmark dataset for building construction, the industry-specific granular dataset is a process-level structural reference dataset, and the local scenario-based floating dataset is a regional floating correction coefficient dataset.

[0068] Optionally, based on the entity dictionary for carbon emissions in the construction field, named entity recognition algorithms are used for entity extraction. For non-standard entities not included in the dictionary, semantic completion and entity annotation are performed in combination with industry rules. Relationship extraction is performed by using dependency parsing and an industry relation rule base to extract the relationships between entity nodes. The industry relation rule base includes four core types of relationships: subordinate relationships (e.g., a class in construction belongs to a major construction category), adaptation relationships (e.g., regional attributes adapt to construction stages), association relationships (e.g., process parameters are associated with construction stages), and reference relationships. Knowledge cleaning is performed to remove duplicates, disambiguate, and complete the extracted entities and relationships, eliminate invalid entities and eliminate entity ambiguities, and complete missing relationships.

[0069] Understandably, in an optional implementation, in step S20, based on the national standard coarse-grained specification dataset in the third-level specification dataset and the third-level initial knowledge graph, the national standard coarse-grained specification dataset is parsed and semantically decomposed to identify and extract construction categories and obtain corresponding construction procedures. Based on the carbon emission contribution weight, industry adaptation coefficient, and construction fluctuation correction coefficient of each construction procedure category, the standard carbon emission value of each construction procedure category is calculated. The standard carbon emission value of the construction procedure category is the decomposition result of the macro total benchmark value in the national standard coarse-grained specification data. The sum of the standard carbon emission values ​​of all construction procedures categories is consistent with the macro total benchmark value. Simultaneously, the standard carbon emission value of the construction procedure category is used as a node attribute to update the third-level initial knowledge graph. In the scheme of this invention, if the error value is within a preset difference range, it is considered consistent.

[0070] Understandably, in an optional implementation, in step S30, based on the standard carbon emission values ​​of the major category of processes, the industry particulate specification dataset, and the updated third-level initial knowledge graph, an improved K-means clustering algorithm is used to divide the major category of construction processes into multiple intermediate category construction processes. The industry particulate reference values ​​in the industry particulate specification dataset are mapped to each of the intermediate category construction processes based on process semantic similarity. The standard carbon emission values ​​of the intermediate category processes are calculated by combining the particulate correlation correction coefficients, which include process parameter correction coefficients and equipment and material correction coefficients. The sum of the standard carbon emission values ​​of all intermediate category processes is consistent with the corresponding standard carbon emission values ​​of the major category processes. The third-level initial knowledge graph is then updated and improved, and relevant nodes and associated edges of the intermediate category construction processes are added.

[0071] Understandably, in an optional implementation, in step S40, based on the standard carbon emission value of the intermediate process, the local scenario-based floating specification dataset, and the improved three-level initial knowledge graph, construction condition data is extracted from the local scenario-based floating specification dataset. The construction condition data includes climate condition data, geological condition data, and time period condition data. New condition nodes are added to the three-level initial knowledge graph, and the original process parameter nodes are refined into material nodes and equipment nodes, forming an initial eight-dimensional relational knowledge graph containing standard nodes, construction link nodes, material nodes, equipment nodes, condition nodes, business format nodes, regional nodes, and process parameter nodes. Relationship edges are established between each node.

[0072] Understandably, in an optional implementation, in step S50, based on the initial eight-dimensional relational knowledge graph, the construction process is divided into multiple detailed construction projects according to differences in material specifications, equipment models, and working conditions; an adaptive correction coefficient matrix is ​​constructed, which is determined by comparing the same project in the local scenario-based floating standard dataset and the industry-wide particulate standard dataset; the standard carbon emission value of the process is multiplied by the corresponding fine-particle adaptive correction coefficient in the adaptive correction coefficient matrix to obtain the estimated fine-particle standard carbon emission value corresponding to each detailed construction project.

[0073] Optionally, in steps S40 and S50, when constructing the eight-dimensional relational knowledge graph, a graph neural network can be used to mine and optimize the relational relationships between entity nodes. Feature extraction and weight calculation can be performed on the implicit relations between working condition nodes, regional nodes, and construction process nodes to optimize the weight configuration of relational edges and improve the adaptability of the knowledge graph to the construction scenario. When refining the fine-grained standard carbon emission estimates corresponding to the construction project, a graph neural network can be used to fuse the features of multi-dimensional nodes such as material specifications, equipment models, and working conditions to accurately mine the influence weight of each dimension node on the refined carbon emission of the construction project, optimize the determination of the fine-grained adaptation correction coefficient, and improve the accuracy of the fine-grained standard carbon emission estimates.

[0074] Understandably, in one scenario of this invention, standard-level entities correspond to standard nodes. Standard-level entities can be national standard coarse-grained specification datasets, industry-specific fine-grained specification datasets, or local scenario-based floating specification datasets. Construction process entities correspond to construction process nodes. Within construction process entities, major construction categories can include rebar construction, concrete construction, formwork construction, scaffolding construction, etc. Minor construction categories can include rebar tying and processing, concrete pouring, concrete transportation, etc. Detailed construction projects can include HRB400E rebar mechanical tying construction, HRB400E rebar manual tying for conventional floor slabs in residential buildings in East China during hot and rainy summer conditions, etc. Process parameter entities correspond to process parameter nodes. Process parameter entities can be refined material-related parameters or equipment-related parameters, such as entities corresponding to construction equipment models, pouring thickness, and material types. Building industry entities correspond to industry nodes. Building industry entities can be specific building types, such as residential buildings, industrial buildings, public buildings, etc. The regional attribute entity corresponds to the regional node. The regional attribute entity can be a regional entity such as South China or Central China. The regional association attribute can be the hot and rainy summer climate of East China, the plain geology, or the local construction specifications of East China.

[0075] Understandably, based on the node set of the initial three-level knowledge graph, the process iterates to improve the node hierarchy of construction links, supplement the major construction categories such as rebar tying and concrete pouring, update the weights of related edges and node attributes, and add details of process parameters; add major construction process nodes, improve their subordinate and verification related edges with construction categories and level standard nodes, update their weights and attributes, and complete the update of the initial three-level knowledge graph; then, the updated initial three-level knowledge graph is iteratively improved, adding equipment nodes and material nodes, optimizing clustering-related related edges; adding working condition nodes, improving all node hierarchy and related edges, supplementing all node attributes, forming an eight-dimensional association system, and calculating the estimated value of fine particulate standard carbon emissions; the eight dimensions fully present eight major dimensions of nodes: level standard, construction link, process parameter, business format, region, equipment, material, and working condition, with related edges covering four categories: subordinate, adaptation, verification, and traceability; all node attributes are improved, and the weights and logic of related edges are fixed.

[0076] The solution of this invention adopts a progressively refined approach, starting with a three-level independent initial node set, then updating the node set after the national standard coarse-grained breakdown, then updating the node set after the construction process class refinement, and finally reaching an eight-dimensional relational knowledge graph. Based on the technical concept of initialization, breakdown, refinement, and solidification, it accurately corresponds to the progressive subdivision process of coarse-grained, major construction process categories, construction process categories, and fine-grained elements, realizing the national standard coarse-grained breakdown and the carbon emission estimation of major process categories. Derivation and estimation of carbon emissions for intermediate-level processes Derivation and estimation of carbon emissions based on fine particulate matter standards The progressive logic of the derivation.

[0077] Furthermore, step S60 also includes: if the difference exceeds a preset threshold, triggering a coefficient correction mechanism to correct the industry adaptation coefficient, construction fluctuation correction coefficient, medium particle correlation correction coefficient, correction coefficient matrix and fine particle adaptation correction coefficient, repeating steps S20 to S50 until the difference is within the preset threshold range.

[0078] Further, before step S10, the method includes the following steps: obtaining a three-level initial dataset related to carbon emissions from building construction. This three-level initial dataset includes a national standard coarse-grained dataset, an industry-specific medium-grained dataset, and a local scenario-based floating dataset, resulting in a three-level standardized dataset. The standardization process includes data cleaning, semantic normalization, unit normalization, and dimension alignment. Specifically, the data cleaning process includes outlier removal and missing value completion. Outlier removal uses the 3σ criterion algorithm, and missing value completion uses the mean-based completion method for similar data types. The semantic normalization process uses natural language processing technology to unify the terminology of the three-level initial dataset. The unit normalization process is used to uniformly transform carbon emission-related data of different units in the three-level initial dataset. The dimension alignment process is used to classify process parameters, business types, and geographical areas in the three-level initial dataset. Understandably, the unit normalization process converts carbon emission-related data of different units in the three-level initial dataset into kgCO2 / unit engineering quantity; the dimension alignment process classifies process parameters, business types, and geographical ranges in the three-level initial dataset according to the target dimension to ensure data dimension consistency; semantic normalization uses natural language processing (NLP) algorithms to process the text description of the three-level initial dataset, unifying terminology, for example, unifying "rebar tying construction" and "rebar tying procedure" into "rebar tying," and "concrete pouring" into "concrete pouring," avoiding terminological ambiguity that could lead to subsequent splitting and mapping deviations; numerical normalization is used to standardize the values ​​of each level of dataset, converting interval values ​​in national standards and discrete values ​​in industry / local standards into standardized numerical formats, extracting the interval mean as the standard reference value for the corresponding standard terminology, while retaining the original interval range and discrete values ​​as auxiliary basis for subsequent correction and verification, and adding a construction fluctuation correction interval to adapt to the industry characteristics of construction process fluctuations; optionally, when removing outliers, a 3 The outlier removal algorithm, tailored to the characteristics of the construction industry, incorporates an exception mechanism for industry-specific scenarios and calculates the mean of the localized, scenario-based floating standard dataset. with standard deviation Automatically identify and remove items exceeding the limit. Abnormal values ​​within the range.

[0079] Further, in step S30, the improved K-means clustering algorithm is used to divide the major construction process into multiple intermediate construction processes. The industry intermediate reference values ​​in the industry intermediate dataset are mapped to each of the intermediate construction processes based on the semantic similarity of the processes. The standard carbon emission values ​​of the intermediate processes are calculated by combining the intermediate particle correlation correction coefficients. The intermediate particle correlation correction coefficients include process parameter correction coefficients and equipment and material correction coefficients.

[0080] In practice, the steps include:

[0081] S301. Data preprocessing: Extracting major process features from the major construction process categories, including major operation features, process parameter range features, and carbon emission contribution weight features; extracting intermediate process features from the industry particle specification dataset, including intermediate operation features and corresponding industry particle reference values; performing semantic standardization on the major process features and intermediate process features to eliminate terminological ambiguity.

[0082] S302. Clustering the intermediate construction processes: An improved K-means clustering algorithm is used. The major construction process features are used as clustering samples, and the intermediate process features in the industry's granular specification dataset are used as clustering benchmarks. The number of clusters and the clustering threshold are set. Work content that meets the preset conditions is divided into the same intermediate construction process, thus realizing the splitting of the major construction process into intermediate construction processes. The number of clusters is set according to the process complexity of the major construction process. When the clustering threshold is set to 0.8, it means that processes with semantic similarity higher than 0.8 are divided into the same intermediate process. Work content with similar functions and processes is divided into the same intermediate construction process, thus realizing the splitting of the major construction process into intermediate construction processes.

[0083] S303. Map and match the industry particle reference values, use the cosine similarity algorithm to calculate the semantic similarity between each of the medium-class construction processes and the features of each medium-class process in the industry particle specification dataset, and take the industry medium-class reference value with the highest semantic similarity as the corresponding industry particle reference value of the medium-class construction process.

[0084] S304. The particulate reference values ​​in the industry are calibrated, and the sum of all particulate reference values ​​in the industry is calculated. If the sum deviates from the standard carbon emission value of the corresponding major category process, the value of each particulate reference value in the industry is adjusted by weight allocation method until the sum of all adjusted particulate reference values ​​in the industry is consistent with the standard carbon emission value of the corresponding major category process. The standard carbon emission value of each major category process is calculated by combining the particulate-related correction coefficient.

[0085] In the solution of this invention, the core clauses, value basis, and industry-specific requirements of the national standard coarse-grained specification dataset are semantically analyzed sentence by sentence. All construction categories corresponding to the national standard coarse-grained specification dataset are automatically identified and extracted. For example, the categories of steel reinforcement procurement and transportation, steel reinforcement processing, steel reinforcement binding, and steel reinforcement curing are extracted from the steel reinforcement construction category. At the same time, the sub-category construction procedures corresponding to each sub-category are obtained through parsing.

[0086] Further, in step S20, obtaining the standard carbon emission value for each major construction procedure specifically includes:

[0087] S21, Obtain Construction Category carbon emission contribution weight Among them, carbon emission contribution weight The value range is [0.05, 0.8];

[0088] S22, using formula Calculations are performed, in which, Construction category Estimated carbon emissions for major process categories The reference value for coarse-grained specifications in the national standard coarse-grained specification dataset is... Construction categories determined by combining construction industry types and regional characteristics Industry compatibility coefficient Construction categories determined based on construction efficiency Construction fluctuation correction coefficient; among which, industry adaptability coefficient The value range is [0.85, 1.15]; Construction fluctuation correction coefficient The value range is [0.9, 1.2];

[0089] S23, Obtain Construction Category For similar construction types, refer to the measured carbon emission values ​​from relevant industry benchmarks. ;

[0090] S24, Estimated carbon emissions for the aforementioned major process categories If a double deviation rate check is performed and passes the check, the estimated carbon emission value for the major process category is determined to be the standard carbon emission value for that major process category; if the double deviation rate check fails, the industry adaptation coefficient is updated. and the construction fluctuation correction coefficient Repeat the above steps until the standard carbon emission values ​​for the major process categories are obtained;

[0091] The dual deviation rate verification includes single-category deviation rate verification and total deviation rate verification; based on the construction category Estimated carbon emissions for major process categories and corresponding measured carbon emissions from the aforementioned industry references. Calculations are performed to obtain the business format deviation value. Based on the calculated business format deviation value and the business format deviation rate threshold, a single major category deviation rate verification is performed. If the calculated business format deviation value is not greater than the business format deviation rate threshold, the single major category deviation rate verification is confirmed to be qualified. The deviation rates for each construction major category are then obtained. The total value of the business is calculated based on the estimated carbon emissions of the major process categories and the reference values ​​of the national standard coarse particle size distribution. The total deviation rate is calculated, and a total deviation rate verification is performed based on the calculated total deviation rate and a total deviation rate threshold. If the calculated total deviation rate is not greater than the total deviation rate threshold, the total deviation rate verification is qualified. In an optional embodiment of the present invention, the industry adaptability coefficient is determined in conjunction with the construction industry type and regional characteristics, and the construction fluctuation correction coefficient is determined in conjunction with the on-site construction personnel's operational proficiency and the aging degree of construction equipment; the average measured carbon emissions of construction projects in the same region, industry type, and process are selected. The threshold for deviation rate is 2% for conventional construction projects and 2.5% for industrial buildings; the total deviation rate threshold is 2%.

[0092] Furthermore, the construction category can be obtained using either the industry percentage method or the process energy consumption percentage method. carbon emission contribution weight The industry proportion method directly adopts the carbon emission proportion of the same type of construction category published in the carbon emission statistics standard of the construction industry as the carbon emission contribution weight; the process energy consumption proportion method uses the proportion of the total core process energy consumption of each construction category as the carbon emission contribution weight. The core process energy consumption includes direct energy consumption of electricity, oil, and gas, as well as the equivalent energy consumption converted from materials.

[0093] Furthermore, in step S30, the formula is used. Perform calculations to obtain the major construction categories. Construction in progress Estimated carbon emissions of medium-sized processes , For construction The mapped industry standard value for particles. For the corresponding construction categories The average value of particle standard values ​​across all construction-related industries. For construction Process parameter correction coefficients, For construction The equipment and material correction coefficients, and the process parameter correction coefficients are determined based on construction process details and material specifications, while the equipment and material correction coefficients are determined based on the construction equipment model and material type. Among these, the process parameter correction coefficients... The value range is [0.92, 1.08], and the equipment and material correction factor is... The value range is [0.88, 1.12].

[0094] Furthermore, using the formula Calculations were performed to obtain detailed construction items. Fine particulate standard carbon emission estimates ,in, To refine the construction project The corresponding national standard-industry adaptation correction factor, To refine the construction project Corresponding local standard adaptation correction coefficients, To refine the construction project Corresponding working condition-process adaptation correction coefficient, , , The value range is [0.95, 1.05].

[0095] Furthermore, it also includes step S70, which involves obtaining the new construction technology and the new local carbon emission standards, supplementing the corresponding nodes in the eight-dimensional association knowledge graph and updating the node attributes and associated edge weights, recalculating the carbon emission value of the new construction procedure corresponding to the new construction technology, and synchronously updating it to the building construction carbon emission calculation database.

[0096] This embodiment provides a specific method for constructing a database for calculating carbon emissions from building construction, including the following steps:

[0097] Step 1: Normalization of the three initial datasets (national standard coarse-grained dataset, industry medium-grained dataset, and local scenario-based floating dataset) and construction of the three initial knowledge graphs;

[0098] Data acquisition involved obtaining three initial datasets related to carbon emissions from building construction. These included: a national standard coarse-grained dataset, which contained the macro-level total baseline value Sgb for structural construction stages as specified in a carbon emission calculation standard (Sgb = kgCO2 / unit project quantity), covering the macro-level baseline values ​​for four stages: rebar construction, concrete construction, formwork construction, and scaffolding construction; an industry-specific granular dataset, which contained process-level structural reference data published by the building construction industry; and a local scenario-based floating dataset, which contained local standards for carbon emissions from building construction in southern and eastern my country, formulated based on the climate characteristics, geological conditions, and local carbon reduction requirements of the region. The dataset assumed a correction factor of 1.03 for hot and rainy summer weather conditions, 1.0 for plain geological conditions, and 0.99 for residential building types. It also assumed a local standard adaptation factor of 1.02 for rebar construction and 1.04 for concrete construction. All these local correction factors were formulated based on the climate characteristics, geological conditions, and local carbon reduction requirements of the East China region.

[0099] Normalization processing involves performing various normalization processes on the initial three-level dataset to obtain a normalized three-level dataset.

[0100] Semantic standardization processing employs natural language processing technology to unify terminology, unifying "rebar tying" and "rebar binding" as "rebar tying," and "concrete pouring" and "concrete casting" as "concrete casting." It also standardizes terminology across local data, national standards, and industry data, eliminating ambiguity and ensuring that locally corrected data can be used in conjunction with national and industry standards, while also adapting to the conventions of describing rebar and concrete construction procedures. Unit normalization processing converts all carbon emission data to kgCO2 / unit of work volume. Specifically, concrete construction-related data originally measured in kgCO2 / cubic meter in the local scenario-based floating dataset retains its unit, while local rebar construction-related data is converted to the standard rebar density for East China. kgCO2 / ton of steel reinforcement is used to standardize the unit and avoid local correction failures due to unit differences, while taking into account the unit characteristics of the two major construction categories. Dimension alignment is handled by uniformly classifying business types into residential, industrial, and public buildings, and geographical scope into East China, North China, and South China, etc. Process parameters are classified by material specifications and equipment models. The focus is on aligning the dimensions of local scenario data, such as climate and geological conditions, with the process parameter dimensions of industry data. At the same time, the material specifications and binding density of steel reinforcement construction, and the concrete strength grade and pouring method of concrete construction are aligned with the process parameter dimensions to ensure that local correction data can be integrated into the subsequent carbon emission calculation process and achieve precise binding between local scenarios and the two core construction procedures.

[0101] Construct a three-level initial knowledge graph;

[0102] In practical implementation, the following nodes are defined: standard nodes, construction (reinforcement construction, concrete construction, formwork construction, scaffolding construction) stage nodes, process (reinforcement specifications, binding density, concrete strength grade, pouring method, etc.) parameter nodes, industry type (residential, building) nodes, and region (East China, South China) nodes. The core function of the region node is to carry local modified attributes. Reinforcement construction and concrete construction nodes are defined as major construction category nodes. Initial association edges are constructed: the national standard initial association edge is the association edge between the national standard coarse-grained node, the reinforcement construction category node, the concrete construction category node, and the residential building node; the industry initial association edge is the association edge between the industry medium-grained node, the initial reinforcement binding node, the initial concrete pouring node, and the corresponding process parameter node; and the local initial association edge is the association edge between the East China local node and the climate engineering node. The association edges between the local condition node, plain geology node, rebar construction node, and concrete construction node are established through local initial association edges. This establishes connections between the locally corrected regional conditions, climate conditions, and geological conditions and the two core nodes of rebar construction and concrete construction, providing graph support for the subsequent substitution of local correction coefficients. Node attribute supplementation involves adding attribute information such as national standard benchmark values, industry reference values, and local correction coefficients to the corresponding nodes. Specifically, local correction coefficients are supplemented according to the process type. Local coefficients related to rebar construction are supplemented to the rebar construction node and its associated condition nodes, and local coefficients related to concrete construction are supplemented to the concrete construction node and its associated condition nodes. This achieves multi-dimensional linkage of the three-level initial dataset, resulting in a three-level initial knowledge graph that includes rebar construction and concrete construction, completing the initial embedding of local correction logic at the graph level.

[0103] Step 2: Calculation and updating of standard carbon emission values ​​for major construction procedures;

[0104] To obtain carbon emission contribution weights, the industry percentage method can be used to separately obtain the carbon emission contribution weights for the two major construction categories: steel reinforcement construction and concrete construction. Assuming that the carbon emission share of steel reinforcement construction in residential building structure construction is 28%, and that the carbon emission share of concrete construction is 52%, if the process energy consumption ratio method is adopted, the core process energy consumption (electricity and oil) of steel reinforcement construction accounts for 27.8% of the total energy consumption of structural construction, and the core process energy consumption of concrete construction accounts for 52.2%. After normalization, the results are basically consistent with the industry ratio method. The weight calculation process does not involve local corrections, ensuring the uniformity of the national standard total breakdown. Local corrections only apply to the subsequent carbon emission estimation stage and are respectively adapted to the two major construction categories.

[0105] The carbon emission estimates for major process categories are calculated using the following formula. Calculations are made assuming the national standard reference value for coarse-grained steel is Sgb = 350 kg CO2 / unit project quantity; the industry compatibility coefficient for the major category of steel reinforcement construction is calculated. =0.99, Industry Compatibility Coefficient The residential property type adaptation coefficient is directly determined from the local scenario-based floating dataset and used to adapt to the carbon emission characteristics of steel reinforcement construction in residential buildings in East China; the industry adaptation coefficient for the concrete construction category is also included. =0.99, consistent with the steel reinforcement construction, both suitable for residential properties in East China; assuming the construction fluctuation correction coefficient for steel reinforcement construction... =1.01, the construction fluctuation correction factor for rebar construction is based on the skill level of the rebar construction personnel. If we assume the construction fluctuation correction factor for concrete construction is... =1.02;

[0106] By referencing industry-referenced carbon emission measurements, benchmark values ​​for carbon emissions in the major categories of steel reinforcement and concrete construction in residential buildings in East China were obtained. These benchmark values ​​include measured data from local construction scenarios in East China, indirectly reflecting the basic requirements for local adjustments. They are used to verify whether the estimated values ​​after incorporating local adaptation coefficients are reasonable, and correspond to the two major construction categories respectively.

[0107] The present invention employs a dual deviation rate verification and coefficient correction scheme, wherein the threshold value for the business type deviation rate is 1.5% to 2.5%, and the threshold value for the total deviation rate is 1.5% to 2.5%.

[0108] After the deviation rate of a single major category passes the verification, the total deviation rate is verified. If both deviation rates pass the verification, the standard carbon emission value of the major process of the steel reinforcement construction category and the standard carbon emission value of the major process of the concrete construction category are determined. These values ​​are then used as node attributes to update the third-level initial knowledge graph. At the same time, the association edge attributes between the regional node and the nodes of the two major construction categories are updated, and the application process of the local adaptation coefficient is recorded to achieve traceability of the local correction logic.

[0109] Step 3: Calculation of standard carbon emission values ​​for intermediate-level processes;

[0110] Data preprocessing involves extracting process features from two major construction categories: rebar construction and concrete construction. The rebar construction category includes rebar forming and fixing, process parameter ranges, binding density, and carbon emission contribution weights. The concrete construction category includes concrete mixing, transportation, pouring, curing, process parameter ranges, C30 strength grade, pouring thickness, and carbon emission contribution weights. Sub-category process features corresponding to granular data in the industry are extracted, including sub-category features and reference values ​​for rebar binding and processing, and sub-category features and reference values ​​for concrete pouring and transportation. Semantic standardization is then performed on both major and sub-category process features to eliminate terminological ambiguity and adapt them to the two major construction categories.

[0111] The construction processes were clustered into subcategories using an improved K-means clustering algorithm. The process characteristics of the two major construction categories were used as clustering samples, and the subcategories' process characteristics were used as the clustering benchmark. The number of clusters was set to 2, and the clustering threshold was set to 0.8. The steel reinforcement construction category was divided into two subcategories: HRB400E steel reinforcement binding and steel reinforcement processing and connection. HRB400E steel reinforcement binding was the core subcategorie, contributing approximately 65% ​​of carbon emissions. The concrete construction category was divided into two subcategories: C30 commercial concrete pouring and concrete transportation and curing. C30 commercial concrete pouring was the core subcategorie, contributing approximately 85% of carbon emissions. No local corrections were added during the clustering process to ensure the uniformity of the subcategories. The local correction logic will be further refined in subsequent fine-grained calculations to complete the subcategories for the two major categories.

[0112] The industry particle reference value mapping and matching is based on the cosine similarity algorithm. The semantic similarity between each construction process under the two major construction categories and each process in the industry particle dataset is calculated. The semantic similarity between the HRB400E model rebar tying process and the manual tying process is 98%, and the industry particle reference value for manual tying is selected as the industry particle standard value. The semantic similarity between the C30 commercial concrete pouring process and the commercial concrete pouring process is 97%, and the industry particle reference value for commercial concrete pouring is selected as the industry particle standard value.

[0113] Calculate and obtain the standard carbon emission estimate for intermediate processes, update the initial knowledge graph at level 3, supplement intermediate nodes and associated edges for HRB400E model rebar tying and C30 commercial concrete pouring, and associate regional nodes.

[0114] Step 4: Construct an eight-dimensional relational knowledge graph;

[0115] Based on the standard carbon emission values ​​of intermediate processes for HRB400E rebar tying and C30 ready-mixed concrete pouring, the local scenario-based floating specification dataset for East China, and the updated level-three initial knowledge graph, construction condition data is extracted from the local scenario-based floating dataset, including climate conditions (e.g., high temperature and heavy rainfall), geological conditions (e.g., plains, hydrogeological conditions), and time-period conditions (e.g., summer). Condition nodes are added to the level-three initial knowledge graph, and the original process parameter nodes are refined into material nodes (HRB400E rebar, C30 ready-mixed concrete) and equipment nodes (manual tying hooks, electric tying machines, concrete pumps, mixer trucks). This ultimately forms an eight-dimensional relational knowledge graph containing level-standard nodes, construction process nodes, material nodes, equipment nodes, condition nodes, business type nodes, regional nodes, and process parameter nodes.

[0116] Two-way association edges are established between each node and weight values ​​are configured to strengthen the association logic of local related nodes. At the same time, the association relationship between steel reinforcement construction and concrete construction is distinguished. The specific association logic is as follows: regional nodes are strongly associated with climate condition nodes, geological condition nodes, and time period condition nodes. Climate condition nodes are associated with HRB400E steel reinforcement binding equipment nodes and C30 commercial concrete pouring equipment nodes, respectively. Geological condition nodes are associated with C30 commercial concrete pouring process parameter nodes (pouring thickness). Material nodes (HRB400E steel reinforcement) are strongly associated with HRB400E steel reinforcement binding intermediate nodes. Equipment nodes are associated with corresponding intermediate construction nodes, and process parameter nodes (binding density, pouring thickness, etc.) are bound to corresponding intermediate construction nodes. This completes the construction of an eight-dimensional association knowledge graph, providing graph support for subsequent fine-grained process division and calculation.

[0117] Step 5: Calculation of estimated carbon emissions for fine particulate matter;

[0118] The detailed classification principle, based on an eight-dimensional relational knowledge graph, divides the HRB400E rebar tying and C30 commercial concrete pouring subcategories according to three dimensions of difference: material specifications, equipment models, and working conditions (refining construction projects). This ensures that the detailed classification fits the actual construction scenario on site, achieving precise refinement from medium categories to fine granularities;

[0119] The adaptation correction coefficients are determined by comparing the same project in the local scenario-based floating standard dataset and the granular standard dataset in the industry, and by combining the node association relationships of the eight-dimensional association knowledge graph, to determine the national standard-industry adaptation correction coefficients, local standard adaptation correction coefficients, and working condition-process adaptation correction coefficients for each sub-category.

[0120] The knowledge graph is updated by adding each sub-category node, corresponding correction coefficient, and fine-grained estimated value to the eight-dimensional relational knowledge graph. Relationship edges are established between sub-category nodes and intermediate category nodes, working condition nodes, equipment nodes, and material nodes, improving the hierarchical relationship of the graph and enabling graph-based traceability from intermediate category to sub-category. At the same time, the local correction logic of each sub-category is recorded to ensure that the entire process is verifiable.

[0121] Step 6: Reverse verification and spectrum solidification;

[0122] Reverse aggregation calculation is performed based on the estimated carbon emissions of fine particles to obtain the aggregated carbon emissions of the construction category. If the difference between the aggregated carbon emissions of the construction category and the standard carbon emissions of the category process is within a preset threshold range, the estimated carbon emissions of fine particles are updated to the fine particle standard carbon emissions, and the initial eight-dimensional association knowledge graph is solidified.

[0123] The present invention also provides a database construction system for calculating carbon emissions from building construction, including a data acquisition module for acquiring a three-level standard dataset;

[0124] The data normalization module is used to perform data cleaning, semantic normalization, unit normalization, and dimension alignment on the initial three-level dataset to obtain a normalized three-level dataset.

[0125] The knowledge graph construction module is used to build a three-level initial knowledge graph based on a three-level standardized dataset, and to gradually update and expand it into an eight-dimensional associated knowledge graph according to the carbon emission values ​​of each process. It also supports dynamic maintenance and node supplementation of the graph.

[0126] The carbon emission value calculation module is used to calculate the carbon emission values ​​of construction categories, construction subcategories, and detailed construction projects in sequence. It is also used to update weights and correct coefficients, so as to refine the carbon emission values ​​from coarse to fine.

[0127] The reverse verification and solidification module is used to reverse aggregate the estimated fine particulate carbon emissions for deviation verification. After the verification is qualified, the eight-dimensional association knowledge graph is solidified and the standard carbon emission values ​​of each level are determined.

[0128] The database update module is used to archive and build a database from the three-level standard dataset, carbon emission values ​​of each level of standards, and solidified knowledge graph, and to update the database content synchronously according to the new construction technology and local carbon emission standards.

[0129] The data acquisition module, the three-level standardized dataset, the data normalization processing module, the knowledge graph construction module, the carbon emission value calculation module, the reverse verification and solidification module, and the database update module are electrically connected in sequence to realize data transmission and interactive feedback.

[0130] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A database construction method for calculating carbon emissions from building construction, characterized in that, Includes the following steps: S10. Obtain a three-level standard dataset, which includes a national standard coarse-grained standard dataset, an industry medium-grained standard dataset, and a local scenario-based floating standard dataset. Based on the three-level standard dataset, extract entities and construct a three-level initial knowledge graph containing standard nodes, construction link nodes, process parameter nodes, business format nodes, regional nodes, and initial association edges. S20. Analyze the national standard coarse-grained specification dataset, identify and extract the major construction categories and corresponding major construction procedures, obtain the standard carbon emission value of each major construction procedure, and update the third-level initial knowledge graph. S30. Divide the major construction process into multiple intermediate construction processes, map the industry particle reference value in the industry particle specification dataset to each intermediate construction process, calculate the standard carbon emission value of the intermediate process by combining the particle-related correction coefficient, and update and improve the three-level initial knowledge graph. S40, extract construction condition data from the local scenario-based floating standard dataset. The construction condition data includes climate condition data, geological condition data, and time period condition data. Add condition nodes to the improved three-level initial knowledge graph, and refine the process parameter nodes into material nodes and equipment nodes to form an eight-dimensional relational knowledge graph containing level standard nodes, construction link nodes, material nodes, equipment nodes, condition nodes, business format nodes, regional nodes, and process parameter nodes. Establish relational edges between each node. S50, based on the eight-dimensional association knowledge graph, the medium-level construction process is divided into multiple detailed construction projects; based on the comparison of the same project between the local scenario-based floating standard dataset and the industry granular standard dataset, and combined with the fine granular subdivision dimension, the fine granular adaptation correction coefficient is determined, and the standard carbon emission value of the medium-level process is multiplied by the corresponding fine granular adaptation correction coefficient to calculate the fine granular standard carbon emission estimate value corresponding to each detailed construction project; S60, perform reverse aggregation based on the fine particulate standard carbon emission estimate to obtain the aggregated carbon emission estimate for the construction category; if the difference between the aggregated carbon emission estimate for the construction category and the standard carbon emission value for the category process is within a preset threshold range, then update the fine particulate standard carbon emission estimate to the fine particulate standard carbon emission value and solidify the initial eight-dimensional association knowledge graph.

2. The database construction method for calculating carbon emissions from building construction according to claim 1, characterized in that, Based on the carbon emission contribution weight, industry adaptability coefficient and construction fluctuation correction coefficient of each of the major construction procedures, the standard carbon emission value of each major construction procedure is obtained. Step S60 further includes: if the difference exceeds a preset threshold, triggering a coefficient correction mechanism to correct the industry adaptation coefficient, construction fluctuation correction coefficient, medium particle correlation correction coefficient and fine particle adaptation correction coefficient, repeating steps S20 to S50 until the difference is within the preset threshold range.

3. The database construction method for calculating carbon emissions from building construction according to claim 1, characterized in that, In step S30, the improved K-means clustering algorithm is used to divide the major construction process into multiple intermediate construction processes. The industry intermediate particle reference values ​​in the industry intermediate particle specification dataset are mapped to each intermediate construction process based on the semantic similarity of the process. The standard carbon emission values ​​of the intermediate processes are calculated by combining the intermediate particle related correction coefficients. The intermediate particle related correction coefficients include process parameter correction coefficients and equipment and material correction coefficients.

4. The database construction method for calculating carbon emissions from building construction according to claim 1, characterized in that, Before step S10, the method further includes the following steps: obtaining a three-level initial dataset related to carbon emissions from building construction, wherein the three-level initial dataset includes a national standard coarse-grained dataset, an industry medium-grained dataset, and a local scenario-based floating dataset; and performing normalization processing on the three-level initial datasets respectively to obtain a three-level normalized dataset, wherein the normalization processing includes data cleaning processing, semantic normalization processing, unit normalization processing, and dimension alignment processing. The data cleaning process includes outlier removal and missing value completion. The outlier removal uses a 3D model. The algorithm for missing value completion uses the mean value of similar data for completion; the semantic normalization process uses natural language processing technology to unify the terminology of the three-level initial dataset. The unit normalization process is used to uniformly transform carbon emission-related data of different units in the three-level initial dataset; the dimension alignment process is used to classify process parameters, business types, and geographical ranges in the three-level initial dataset.

5. The database construction method for calculating carbon emissions from building construction according to any one of claims 1 to 4, characterized in that, In step S10, standard-level entities, construction process entities, process parameter entities, building industry entities, and regional attribute entities are extracted based on the three-level specification dataset. A three-level initial knowledge graph is constructed, containing standard nodes, construction process nodes, process parameter nodes, industry nodes, regional nodes, and initial association edges. The construction of the three-level initial knowledge graph includes the following steps: S101, the five core node types are: definition-level standard node, construction link node, process parameter node, business type node, and regional node. Among them, the definition-level standard node corresponds to the standard-level entity in the three-level specification dataset; the construction link node corresponds to the construction link entity, which includes construction major category and construction intermediate category; the process parameter node corresponds to the process parameter entity, which includes major category process parameter node and intermediate category process parameter node; the business type node corresponds to the building business type entity; and the regional node corresponds to the regional attribute entity. S102. Initial association edge construction: Construct initial association edges between each node. Among them, the national standard initial association edge is the connection edge between the national standard coarse-grained node, the construction category node in the construction link node, and the business format node. The industry initial association edge is the connection edge between the industry granular node, the construction category initial node in the construction link node, and the business format node. The local initial association edge is the connection edge between the local scenario node and the regional node. S103. Node attribute supplementation: Supplement the standard reference values ​​and construction fluctuation correction intervals in the three-level standard dataset to the attributes of the corresponding nodes to achieve multi-dimensional linkage of the three-level initial dataset and obtain the three-level initial knowledge graph.

6. The database construction method for calculating carbon emissions from building construction according to any one of claims 1 to 4, characterized in that, In step S20, obtaining the standard carbon emission value for each major construction procedure specifically includes: S21, Obtain Construction Category carbon emission contribution weight ; S22, using formula Calculations are performed, in which, Construction category Estimated carbon emissions for major process categories The reference value for coarse-grained specifications in the national standard coarse-grained specification dataset is... Construction categories determined by combining construction industry types and regional characteristics Industry compatibility coefficient Construction categories determined based on construction efficiency Construction fluctuation correction coefficient; S23, Obtain Construction Category For similar construction types, refer to the measured carbon emission values ​​from relevant industry benchmarks. ; S24, Estimated carbon emissions for the aforementioned major process categories If a double deviation rate check is performed and passes the check, the estimated carbon emission value for the major process category is determined to be the standard carbon emission value for that major process category; if the double deviation rate check fails, the industry adaptation coefficient is updated. and the construction fluctuation correction coefficient Repeat the above steps until the standard carbon emission values ​​for the major process categories are obtained; The dual deviation rate verification includes single-category deviation rate verification and total deviation rate verification; based on the construction category Estimated carbon emissions for major process categories and corresponding measured carbon emissions from the aforementioned industry references. Calculations are performed to obtain the business format deviation value. Based on the calculated business format deviation value and the business format deviation rate threshold, a single major category deviation rate verification is performed. If the calculated business format deviation value is not greater than the business format deviation rate threshold, the single major category deviation rate verification is confirmed to be qualified. The deviation rates for each construction major category are then obtained. The total value of the business is calculated based on the estimated carbon emissions of the major process categories and the reference values ​​of the national standard coarse particle size distribution. The total deviation rate is calculated and obtained. Based on the total deviation rate and the total deviation rate threshold, the total deviation rate is verified. If the total deviation rate is not greater than the total deviation rate threshold, the total deviation rate verification is qualified.

7. The database construction method for calculating carbon emissions from building construction according to claim 5, characterized in that, In step S21, Construction categories can be obtained using either the industry percentage method or the process energy consumption percentage method. carbon emission contribution weight The industry proportion method directly adopts the carbon emission proportion of the same type of construction category published in the carbon emission statistics standard of the construction industry as the carbon emission contribution weight; the process energy consumption proportion method uses the proportion of the total core process energy consumption of each construction category as the carbon emission contribution weight. The core process energy consumption includes direct energy consumption of electricity, oil, and gas, as well as the equivalent energy consumption converted from materials.

8. The database construction method for calculating carbon emissions from building construction according to claim 1, characterized in that, In step S50, Based on differences in material specifications, equipment models, and working conditions, the aforementioned construction procedures are divided into several detailed construction items. Using formula Calculations were performed to obtain detailed construction items. Fine particulate standard carbon emission estimates ,in, The carbon emission estimate is for the medium-sized process. To refine the construction project The corresponding national standard-industry adaptation correction factor, To refine the construction project Corresponding local standard adaptation correction coefficients, To refine the construction project Corresponding working condition-process adaptation correction coefficient, , , The value range is [0.95, 1.05].

9. The database construction method for calculating carbon emissions from building construction according to any one of claims 1 to 4, characterized in that, It also includes step S70, which involves obtaining the new construction technology and the new local carbon emission standards, supplementing the corresponding nodes in the eight-dimensional association knowledge graph and updating the node attributes and associated edge weights, recalculating the carbon emission value of the new construction procedure corresponding to the new construction technology, and synchronously updating it to the building construction carbon emission calculation database.

10. A database construction system for calculating carbon emissions from building construction, characterized in that, This method is used to implement the database construction method for calculating carbon emissions from building construction as described in any one of claims 1 to 9. Includes a data acquisition module for acquiring a three-level standardized dataset; The data normalization module is used to perform data cleaning, semantic normalization, unit normalization, and dimension alignment on the initial three-level dataset to obtain a normalized three-level dataset. The knowledge graph construction module is used to build a three-level initial knowledge graph based on a three-level standardized dataset, and to gradually update and expand it into an eight-dimensional associated knowledge graph according to the carbon emission values ​​of each process. It also supports dynamic maintenance and node supplementation of the graph. The carbon emission value calculation module is used to calculate the carbon emission values ​​of construction categories, construction subcategories, and detailed construction projects in sequence. It is also used to update weights and correct coefficients, so as to refine the carbon emission values ​​from coarse to fine. The reverse verification and solidification module is used to reverse aggregate the estimated fine particulate carbon emissions for deviation verification. After the verification is qualified, the eight-dimensional association knowledge graph is solidified and the standard carbon emission values ​​of each level are determined. The database update module is used to archive and build a database from the three-level standard dataset, carbon emission values ​​of each level of standards, and solidified knowledge graph, and to update the database content synchronously according to the new construction technology and local carbon emission standards. The data acquisition module, the three-level standardized dataset, the data normalization processing module, the knowledge graph construction module, the carbon emission value calculation module, the reverse verification and solidification module, and the database update module are electrically connected in sequence to realize data transmission and interactive feedback.

Images