Building material thermal insulation performance evaluation method and system based on big data

Abstract

The invention discloses a building material thermal insulation performance evaluation method and system based on big data, and relates to the technical field of building material performance evaluation, and the method comprises the steps: obtaining to-be-built building structure data, setting a preset proportion, and constructing a thermal insulation performance initial evaluation model; obtaining the geographic position of a to-be-built building and regional historical environment data, and dividing the initial model according to the data to obtain evaluation sub-models; extracting to-be-built building material data based on the structural data, and collecting homogeneous building material data in combination with the geographic position and historical environment data; according to the homogeneous data and the sub-model, obtaining a thermal insulation performance influence factor; and acquiring regional prediction environment data, fusing historical data to simulate a service scene, and evaluating the thermal insulation performance of the material based on the sub-model. The method has the advantages that through big data integration and refined modeling, the thermal insulation performance of the building material is accurately evaluated, and reliable support is provided for model selection and energy-saving design of the building material to be built.

Description

Technical Field

[0001] The present invention relates to the technical field of building material performance evaluation, and in particular to a method and system for evaluating the thermal insulation performance of building materials based on big data. Background Art

[0002] The thermal insulation performance of building materials is a key factor influencing a building's energy efficiency, indoor thermal comfort, and carbon emissions, directly impacting its energy costs and environmental benefits throughout its lifecycle. With the increasing adoption of green building concepts and the upgrading of energy-saving standards, accurately assessing the thermal insulation performance of future building materials has become crucial for optimizing material selection and improving building energy efficiency. In regions with significant climate variations, personalized assessments combining building structural characteristics with environmental conditions are particularly important for achieving "dual carbon" goals and promoting sustainable development in the construction industry.

[0003] Existing big data-based building material thermal insulation performance evaluation methods and systems lack data integration and mostly use material parameters or environmental data in isolation, resulting in a disconnect between the evaluation and actual service scenarios. At the same time, their evaluation models are not sophisticated enough and mostly use overall building evaluation rather than modeling by functional zoning, orientation, and other subdivided units, making it difficult to capture the thermal insulation performance differences in local areas. Therefore, it is necessary to provide a big data-based building insulation performance evaluation method and system to solve the above-mentioned problems. Summary of the Invention

[0004] In order to solve the above technical problems, a method and system for evaluating the thermal insulation performance of building materials based on big data are provided. This technical solution solves the problem that the existing method and system for evaluating the thermal insulation performance of building materials based on big data proposed in the above background technology lack data integration and mostly use material parameters or environmental data in isolation, resulting in a disconnection between the evaluation and the actual service scenario. At the same time, the evaluation model has a low degree of refinement and mostly adopts overall building evaluation rather than modeling by subdivided units such as functional zoning and orientation, making it difficult to capture the differences in thermal insulation performance in local areas.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A method for evaluating thermal insulation performance of building materials based on big data, comprising: Obtain the structural data of the building to be built, set the preset proportions simultaneously, and construct an initial evaluation model for thermal insulation performance; Obtaining geographic location data of the building to be built and simultaneously obtaining historical environmental data of the area where the building is to be built; then, based on the geographic location data of the building to be built, the historical environmental data of the area where the building is to be built, and the structural data of the building to be built, dividing the initial thermal insulation performance evaluation model to obtain a thermal insulation performance evaluation sub-model; Based on the structural data of the building to be built, corresponding building material data to be built is obtained, and based on the geographical location data of the building to be built and the historical environmental data of the area where the building to be built is built, homogeneous building material data corresponding to the building material data to be built is collected; Obtain the influencing factors of building material thermal insulation performance based on homogeneous building material data and thermal insulation performance evaluation sub-model; Obtain the predicted environmental data of the area where the building is to be built, combine it with the historical environmental data of the area where the building is to be built, simulate the service scenario of building materials, and simultaneously evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

[0006] In an optional embodiment, the steps of obtaining the structural data of the building to be constructed, setting a preset ratio, and constructing an initial thermal insulation performance evaluation model specifically include: Export the structural data of the building to be built through the building information model platform, including building component type information, building component three-dimensional size information, building component connection method information and building component distribution location information; Determine structural node data, functional node data, and construction node data from the structure data of the building to be constructed based on building component type information, building component three-dimensional size information, building component connection method information, and building component distribution position information; Based on the building component type information, structural node data, functional node data and construction node data, a building component type-foundation node type correspondence table is constructed; Based on the three-dimensional size information of the building components and the corresponding table of building component type and foundation node type, the spatial boundary information and force characteristic information of the foundation node type are determined; Based on the spatial boundary information of the basic node type and the building component connection mode information, all connection nodes existing in the spatial boundary are determined, and the connection node information matrix is ​​simultaneously constructed; Associating the connection node information matrix with the corresponding building component type and foundation node type, and simultaneously importing the connection node information matrix into the building component type-foundation node type correspondence table to obtain the building component type-foundation node type-connection node information matrix correspondence table; Determine the first building component and the first connection node that appear within the spatial boundary corresponding to the basic node type based on the building component distribution position information and the building component type-basic node type-connection node information matrix correspondence table; Extracting a first building component belonging to the thermal insulation material and a first connection node connected to the thermal insulation material to obtain node information affecting thermal insulation performance; Based on the information of nodes affecting thermal insulation performance, combined with building design specifications and assessment requirements, a preset ratio is set, and the three-dimensional size information of building components corresponding to the nodes affecting thermal insulation performance is scaled. The actual size is converted into a virtual size that can be recognized by the model, and an initial thermal insulation performance assessment model is constructed.

[0007] In an optional embodiment, the geographic location data of the building to be built is obtained, and the historical environmental data of the area of ​​the building to be built is simultaneously obtained. Then, based on the geographic location data of the building to be built, the historical environmental data of the area of ​​the building to be built, and the structural data of the building to be built, the initial thermal insulation performance evaluation model is divided into models to obtain a thermal insulation performance evaluation sub-model, which specifically includes: Obtain geographic location data of the building to be constructed through the geographic information system, including latitude and longitude, altitude and terrain features; Based on the geographic location data of the building to be built, the meteorological data platform interface is called to obtain the historical environmental data of the area corresponding to the building to be built; Analyze historical environmental data of the area where the building is to be built and extract key climate factors that affect the thermal insulation performance of the building; Combined with the structural data of the building to be built, the building is preliminarily divided according to its functional area and orientation, and each area is used as a potential sub-model evaluation unit; Based on the potential sub-model evaluation unit, a detailed division is performed, and an independent thermal insulation performance evaluation sub-model is constructed for each functional zone and orientation area. The thermal insulation performance evaluation sub-model includes the structural parameters, climate influencing factors and sub-model evaluation formula of the area.

[0008] In an optional embodiment, the method of acquiring corresponding material data of the building to be constructed based on the structural data of the building to be constructed, and collecting homogeneous building material data corresponding to the material data of the building to be constructed based on the geographical location data of the building to be constructed and the historical environmental data of the area where the building to be constructed is located, specifically includes: Extracting material information of each component from the structural data of the building to be built to form a data list of materials for the building to be built, which includes material type, manufacturer, model specifications, physical performance parameters, and thermal performance parameters; Define the criteria for determining homogeneous building material data: the material type is the same, and the production process and specification parameters are consistent; Determine the spatial scope of data collection based on the geographic location data of the building to be built and the historical environmental data of the area where the building is to be built; Within the designated space, basic information on completed construction projects that meet the homogeneous building materials data determination standards will be screened through the construction industry database, project case library, and building materials supplier information platform; Based on the basic information of completed building projects, record the insulation material data used as homogeneous insulation material data, simultaneously collect the corresponding homogeneous regional historical environmental data, and collect the actual performance data corresponding to the homogeneous insulation material data in the project; The homogeneous insulation material data, homogeneous regional historical environmental data and actual performance data are integrated to obtain homogeneous building material data.

[0009] In an optional embodiment, obtaining the building material thermal insulation performance influencing factor based on the homogeneous building material data and the thermal insulation performance evaluation sub-model specifically includes: Obtain homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data corresponding to the insulation performance evaluation sub-model from homogeneous building material data; Preprocess homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data to remove outliers, fill missing values, and unify the data format to obtain the material property set corresponding to the insulation performance evaluation sub-model and environmental impact attribute sets ,in For density, is the specific heat capacity, is the thermal conductivity, is the maximum temperature difference in the historical environmental data of the homogeneous region, The maximum humidity difference, is the maximum wind speed difference, The service life is the actual performance data; Integrate the material attribute set and environmental impact attribute set corresponding to the thermal insulation performance evaluation sub-model to obtain the input variables of the impact factor model ; The random forest algorithm is used to construct an influencing factor model and simultaneously determine the characteristic importance of the reference coefficient of thermal insulation performance of building materials ,satisfy ; Definition of key influencing factors based on the reference coefficient of thermal insulation performance of building materials: ,in For the The contribution intensity value of each variable, is the output variable of the impact factor model, express right The marginal impact rate; Will The variables are taken as key influencing factors and recorded as ; Establish the regression equation of influencing factors and thermal insulation performance: in is the predicted value of thermal insulation performance, is the regression coefficient, is the error term, is the total number of impact factors; Obtain the coefficient of determination of the regression equation between the influencing factors and thermal insulation performance. If the coefficient of determination is greater than 0.8, the extracted key influencing factors will be used as the influencing factors of the thermal insulation performance of building materials.

[0010] In an optional embodiment, the step of obtaining predicted environmental data of the area where the building is to be constructed, combining it with historical environmental data of the area where the building is to be constructed, simulating the service scenario of building materials, and simultaneously evaluating the thermal insulation performance of the building materials based on the thermal insulation performance evaluation sub-model may specifically include: Call the weather forecast API to obtain the predicted environmental data for the area where the building is to be built ,in The monthly average temperature, The monthly average humidity, is the monthly average wind speed; Combined with the historical environmental data of the area where the building is to be built, the time series fusion formula is used to define the service scenario parameters. The time fusion formula is: , the service scenario parameters defined are: , where To integrate environmental feature data, is the predicted data weight coefficient, Predict environmental data for the area where the building is to be built, Historical environmental data for the area where the building is to be built; Obtain the influencing factors of building material thermal insulation performance and modify the sub-model evaluation formula: ,in is the factor volatility, For the The corrected heat transfer coefficient of each sub-region; Get the regional annual heat transfer corresponding to each sub-model ,in is the annual heat transfer of the sub-region, is the sub-region index value, is the month index, is the enclosure area of ​​the sub-region, is the indoor and outdoor temperature difference in the mth month; Get the total heat transfer of the building , synchronously obtain the thermal insulation performance score and set the score threshold; The thermal insulation performance of building materials in the area is evaluated based on the thermal insulation performance score and score threshold.

[0011] Furthermore, a building material thermal insulation performance evaluation system based on big data is proposed, which is used to implement any of the above evaluation methods, including: an acquisition module, the acquisition module being used to acquire structural data of the building to be built, geographical location data of the building to be built, historical environmental data of the area where the building to be built is to be built, and material data of the building to be built, and further being used to collect homogeneous building material data corresponding to the material data of the building to be built; A model management module, wherein the model management module is used to construct an initial thermal insulation performance evaluation model, and is used to divide the initial thermal insulation performance evaluation model into models based on the geographical location data of the building to be built, the historical environmental data of the area where the building to be built is to be built, and the structural data of the building to be built, to obtain thermal insulation performance evaluation sub-models; A data processing module, the data processing module is used to perform data preprocessing on the data collected by the acquisition module, and is used to obtain the influencing factors of the thermal insulation performance of the building materials based on the homogeneous building material data and the thermal insulation performance evaluation sub-model; An evaluation module, which is used to simulate the service scenario of building materials based on the predicted environmental data of the area where the building is to be built, combined with the historical environmental data of the area where the building is to be built, and is used to evaluate the thermal insulation performance of the building materials based on the thermal insulation performance evaluation sub-model; The display module is used to present the process and results of the building material thermal insulation performance evaluation to the user.

[0012] In an optional embodiment, the model management module includes: A model building unit, wherein the model building unit is used to construct an initial evaluation model for thermal insulation performance; The model division unit is used to divide the initial thermal insulation performance evaluation model based on the geographical location data of the building to be built, the historical environmental data of the area of ​​the building to be built, and the structural data of the building to be built to obtain a thermal insulation performance evaluation sub-model.

[0013] In an optional embodiment, the data processing module includes: A data preprocessing unit, configured to preprocess the data collected by the acquisition module; The influencing factor acquisition unit is used to obtain the influencing factor of the thermal insulation performance of the building material based on the homogeneous building material data and the thermal insulation performance evaluation sub-model.

[0014] In an optional embodiment, the evaluation module includes: A service simulation unit, which is used to simulate the service scenario of building materials based on the predicted environmental data of the area of ​​the building to be built and the historical environmental data of the area of ​​the building to be built; The thermal insulation performance evaluation unit is used to evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This proposal proposes a big data-based approach to evaluating the thermal insulation performance of building materials. By acquiring structural data of the proposed building and constructing an initial thermal insulation performance assessment model, this approach simultaneously incorporates geographic location and environmental data to divide the assessment sub-models. This approach implements a refined assessment framework, encompassing both the overall and local aspects of the building. This approach allows the assessment to not only reflect the overall thermal insulation performance of the building, but also accurately capture performance differences across different functional zones and orientations, providing a basis for targeted optimization. This proposal proposes a big data-based building material insulation performance evaluation method. By extracting information about the materials to be built based on building structural data and combining it with geographic location and historical environmental data to collect homogeneous material data, it achieves a strong correlation between material performance evaluation and actual application scenarios, ensuring the comparability and reference value of the evaluation data and laying a reliable data foundation for the subsequent extraction of influencing factors. This proposal proposes a big data-based method for evaluating the thermal insulation performance of building materials. By using a random forest algorithm to extract key influencing factors from homogeneous data, and combining historical and predicted environmental data to simulate service scenarios, it achieves a dynamic, full-life cycle evaluation of the thermal insulation performance of materials. The evaluation results can not only reflect the current performance, but also predict the performance after long-term service, thereby improving the foresight and scientific nature of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a method for evaluating thermal insulation performance of building materials based on big data proposed by the present invention; Figure 2 A flow chart for constructing an initial evaluation model for thermal insulation performance in the present invention; Figure 3 This is a flow chart for obtaining the thermal insulation performance evaluation sub-model in the present invention; Figure 4 This is a system framework diagram of a building material thermal insulation performance evaluation system based on big data proposed in the present invention. DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Reference Figure 1 - Figure 4 As shown, a method for evaluating the thermal insulation performance of building materials based on big data includes: Obtain the structural data of the building to be built, set the preset proportions simultaneously, and construct an initial evaluation model for thermal insulation performance; Obtaining geographic location data of the building to be built and simultaneously obtaining historical environmental data of the area where the building is to be built; then, based on the geographic location data of the building to be built, the historical environmental data of the area where the building is to be built, and the structural data of the building to be built, dividing the initial thermal insulation performance evaluation model to obtain a thermal insulation performance evaluation sub-model; Based on the structural data of the building to be built, corresponding building material data to be built is obtained, and based on the geographical location data of the building to be built and the historical environmental data of the area where the building to be built is built, homogeneous building material data corresponding to the building material data to be built is collected; Obtain the influencing factors of building material thermal insulation performance based on homogeneous building material data and thermal insulation performance evaluation sub-model; Obtain the predicted environmental data of the area where the building is to be built, combine it with the historical environmental data of the area where the building is to be built, simulate the service scenario of building materials, and simultaneously evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

[0019] Furthermore, the structural data of the building to be built is obtained, and the preset ratio is set simultaneously to build an initial evaluation model for thermal insulation performance, which specifically includes: Export the structural data of the building to be built through the building information model platform, including building component type information, building component three-dimensional size information, building component connection method information and building component distribution location information; Determine structural node data, functional node data, and construction node data from the structure data of the building to be constructed based on building component type information, building component three-dimensional size information, building component connection method information, and building component distribution position information; Based on the building component type information, structural node data, functional node data and construction node data, a building component type-foundation node type correspondence table is constructed; Based on the three-dimensional size information of the building components and the corresponding table of building component type and foundation node type, the spatial boundary information and force characteristic information of the foundation node type are determined; Based on the spatial boundary information of the basic node type and the building component connection mode information, all connection nodes existing in the spatial boundary are determined, and the connection node information matrix is ​​simultaneously constructed; Associating the connection node information matrix with the corresponding building component type and foundation node type, and simultaneously importing the connection node information matrix into the building component type-foundation node type correspondence table to obtain the building component type-foundation node type-connection node information matrix correspondence table; Determine the first building component and the first connection node that appear within the spatial boundary corresponding to the basic node type based on the building component distribution position information and the building component type-basic node type-connection node information matrix correspondence table; Extracting a first building component belonging to the thermal insulation material and a first connection node connected to the thermal insulation material to obtain node information affecting thermal insulation performance; Based on the information of nodes affecting thermal insulation performance, combined with building design specifications and assessment requirements, a preset ratio is set, and the three-dimensional size information of building components corresponding to the nodes affecting thermal insulation performance is scaled. The actual size is converted into a virtual size that can be recognized by the model, and an initial thermal insulation performance assessment model is constructed.

[0020] Specifically, the steps are: S1.1 Obtain the 3D structural data of the building to be built through the Building Information Model (BIM) platform interface, including the geometric parameter set of the wall, roof, and ground ,in For length, For width, For height, is the thickness, i represents the building component number; S1.2 Set the preset scale factor k (e.g. k=1:100), scale the 3D structure data, and obtain the scaled parameter set. ; S1.3 Define the input variables of the initial insulation performance evaluation model as the thermal resistance of building components. ( is the thermal conductivity of the material), the output variable is the theoretical heat transfer coefficient ; S1.4 Based on Fourier's law, the basic heat transfer formula is constructed: , where Q is the heat transfer, is the component area, ΔT is the temperature difference between indoor and outdoor; S1.5 Collect historical heat transfer data of similar buildings , fitted by linear regression , determination coefficients α, β; S1.6 Substitute the scaled parameter S′ into the fitting formula to generate the initial evaluation model of thermal insulation performance ; S1.7 Setting model accuracy verification indicators ,in is the model prediction value, is the actual historical value; S1.8 If e≤0.05, retain the initial model; otherwise, refit the coefficients α and β until the accuracy requirements are met.

[0021] The advantages are: obtaining comprehensive and accurate building structure data through the Building Information Model (BIM) platform, ensuring the integrity and accuracy of the basic evaluation data; establishing a clear "component-node-connection relationship" structured system by sorting out the building component types and node data layer by layer and constructing a multi-dimensional correspondence table, providing orderly data support for subsequent evaluations; focusing on extracting components and connection nodes related to insulation materials, achieving precise focus on key factors affecting insulation performance, avoiding interference from irrelevant data, and improving the targeted evaluation; setting preset proportions in combination with building design specifications and converting them into virtual dimensions, ensuring that the spatial proportions of the model are consistent with those of the actual building, providing a realistic basis for subsequent insulation performance simulation calculations, and the final constructed initial evaluation model is comprehensive, accurate, and targeted, laying a solid foundation for subsequent refined evaluations.

[0022] Furthermore, the geographic location data of the building to be built is obtained, and the historical environmental data of the area where the building to be built is obtained simultaneously. Then, based on the geographic location data of the building to be built, the historical environmental data of the area where the building to be built and the structural data of the building to be built, the initial thermal insulation performance evaluation model is divided into models to obtain the thermal insulation performance evaluation sub-model, which specifically includes: Obtain geographic location data of the building to be constructed through the geographic information system, including latitude and longitude, altitude and terrain features; Based on the geographic location data of the building to be built, the meteorological data platform interface is called to obtain the historical environmental data of the area corresponding to the building to be built; Analyze historical environmental data of the area where the building is to be built and extract key climate factors that affect the thermal insulation performance of the building; Combined with the structural data of the building to be built, the building is preliminarily divided according to its functional area and orientation, and each area is used as a potential sub-model evaluation unit; Based on the potential sub-model evaluation units, a detailed division is performed and an independent thermal insulation performance evaluation sub-model is constructed for each functional zone and orientation area. The thermal insulation performance evaluation sub-model includes the structural parameters, climate influencing factors and sub-model evaluation formula of the area.

[0023] Specifically, the steps are: S2.1 Obtain the longitude and latitude (x, y) of the building to be constructed through the GIS interface and determine the climate zone C to which it belongs (e.g., severe cold zone, temperate zone); S2.2 Call the meteorological database API to obtain the historical environmental data of the area for the past 10 years and construct the environmental feature matrix ,in The historical monthly average temperature, The historical monthly average humidity, is the historical monthly average wind speed, m=1,2,…,12; S2.3 Calculate the weight coefficient of each climate factor , , ,satisfy , the weights were determined by analytic hierarchy process; S2.4 Define regional impact coefficient , quantifying the impact of climate on thermal insulation performance; S2.5 Based on the orientation parameters (such as south and north) in the building structure data, the building is divided into n functional areas (such as bedrooms and living rooms), which are recorded as ; S2.6 Initial Model Introducing the regional impact coefficient, constructing the sub-model evaluation formula: ,in is the orientation correction factor for the jth region (e.g., θ = 1.2 for south and θ = 0.8 for north); S2.7 Calculate the eigenvalues ​​of each sub-model , cluster analysis was used to verify the differences of sub-models. If the variance is > 0.1, the division is valid; S2.8 Output n thermal insulation performance evaluation sub-models: , corresponding to each functional area.

[0024] The advantages are: the geographic location data of the building to be built, including longitude and latitude, altitude and terrain features, can be accurately obtained through the geographic information system, providing a precise spatial benchmark for the subsequent association of regional environmental data; historical environmental data can be obtained by calling the meteorological data platform interface based on the geographic location, ensuring a strong correlation between environmental data and the building's spatial location, making the evaluation more in line with the actual regional climate characteristics; key climate factors can be extracted by analyzing historical environmental data, focusing on environmental factors that have a significant impact on thermal insulation performance, avoiding redundant data interference and improving evaluation efficiency; potential sub-model evaluation units are divided according to functional zoning and orientation based on building structure data, fully considering the differences in thermal insulation performance caused by functional requirements and orientation differences in different regions, and realizing refined evaluation; independent sub-models containing regional structural parameters, climate influencing factors and evaluation formulas are constructed based on potential units, so that each sub-model can reflect the thermal insulation performance characteristics of a specific region in a targeted manner, which not only ensures the professionalism and accuracy of the evaluation, but also lays a structured foundation for the subsequent combination of environmental data to simulate service scenarios and accurately evaluate the thermal insulation performance of materials, effectively improving the scientificity and practicality of the overall evaluation method.

[0025] Furthermore, based on the structural data of the building to be built, the corresponding material data of the building to be built is obtained, and based on the geographical location data of the building to be built and the historical environmental data of the area where the building to be built is built, homogeneous building material data corresponding to the material data of the building to be built is collected, specifically including: Extracting material information of each component from the structural data of the building to be built to form a data list of materials for the building to be built, which includes material type, manufacturer, model specifications, physical performance parameters, and thermal performance parameters; Define the criteria for determining homogeneous building material data: the material type is the same, and the production process and specification parameters are consistent; Determine the spatial scope of data collection based on the geographic location data of the building to be built and the historical environmental data of the area where the building is to be built; Within the designated space, basic information on completed construction projects that meet the homogeneous building materials data determination standards will be screened through the construction industry database, project case library, and building materials supplier information platform; Based on the basic information of completed building projects, record the insulation material data used as homogeneous insulation material data, simultaneously collect the corresponding homogeneous regional historical environmental data, and collect the actual performance data corresponding to the homogeneous insulation material data in the project; The homogeneous insulation material data, homogeneous regional historical environmental data and actual performance data are integrated to obtain homogeneous building material data.

[0026] As can be understood, this step first extracts material information for each component from the structural data of the building to be built, forming a list of building material data for the building to be built, including material type, manufacturer, model specifications, physical performance parameters, and thermal performance parameters. Next, criteria for determining homogeneous building materials with the same material type, production process, and consistent specifications are defined. The collection scope is then defined based on the geographic location and regional historical environmental data of the building to be built. Within this scope, information on completed projects that meet the homogeneity criteria is screened through platforms such as construction industry databases. The insulation material data, corresponding regional historical environmental data, and actual performance data are recorded, and finally, these data are integrated to obtain homogeneous building material data. The advantages of this method are: By accurately extracting the building material data for the building to be built, the clarity of the assessment object is ensured; strict homogeneity criteria ensure the comparability of the collected data, avoiding assessment bias caused by material differences; By combining geographic location and environmental data to define the scope, the collected homogeneous material data is highly correlated with the service environment of the building to be built, enhancing the data's reference value; and the multi-channel data collection and integration provides a rich and reliable empirical basis for the subsequent extraction of factors affecting thermal insulation performance and the precise evaluation of material performance, enhancing the scientific and practical nature of the assessment method.

[0027] Furthermore, based on the homogeneous building material data and the thermal insulation performance evaluation sub-model, the factors affecting the thermal insulation performance of building materials are obtained, including: Obtain homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data corresponding to the insulation performance evaluation sub-model from homogeneous building material data; Preprocess homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data to remove outliers, fill missing values, and unify the data format to obtain the material property set corresponding to the insulation performance evaluation sub-model and a set of environmental impact attributes, where For density, is the specific heat capacity, is the thermal conductivity, is the maximum temperature difference in the historical environmental data of the homogeneous region, The maximum humidity difference, is the maximum wind speed difference, The service life is the actual performance data; Integrate the material attribute set and environmental impact attribute set corresponding to the thermal insulation performance evaluation sub-model to obtain the input variables of the impact factor model ; The random forest algorithm is used to construct an influencing factor model and simultaneously determine the characteristic importance of the reference coefficient of thermal insulation performance of building materials ,satisfy ; Definition of key influencing factors based on the reference coefficient of thermal insulation performance of building materials: ,in For the The contribution intensity value of each variable, is the output variable of the impact factor model, express right The marginal impact rate; Will The variables are taken as key influencing factors and recorded as ; Establish the regression equation of influencing factors and thermal insulation performance: in is the predicted value of thermal insulation performance, is the regression coefficient, is the error term, is the total number of impact factors; Obtain the coefficient of determination of the regression equation between the influencing factors and thermal insulation performance. If the coefficient of determination is greater than 0.8, the extracted key influencing factors will be used as the influencing factors of the thermal insulation performance of building materials.

[0028] Specifically, this step first extracts homogeneous insulation material data, homogeneous regional historical environmental data, and actual usage performance data corresponding to the thermal insulation performance evaluation sub-model from the homogeneous building material data. Through preprocessing (removing outliers, filling missing values, and unifying data formats), a set of material properties (including density, specific heat capacity, thermal conductivity, etc.) and a set of environmental impact properties (including maximum temperature difference, humidity difference, wind speed difference, and service life, etc.) are obtained, and then the two are integrated into the input variables of the influencing factor model; then the random forest algorithm is used to construct the model to determine the characteristic importance of each variable (satisfying the sum of weights to be 1), based on which the key influencing factors are defined (the contribution intensity value of the jth variable, combined with the characteristic importance and marginal influence rate), and variables with a contribution intensity value greater than 0.1 are screened as key influencing factors, and then a regression equation of the influencing factors and thermal insulation performance is established. After verification by the determination coefficient (needs to be greater than 0.8), the extracted key influencing factors are used as the influencing factors of the thermal insulation performance of building materials. Its advantages are: data preprocessing ensures the accuracy and consistency of input data, laying a reliable foundation for subsequent analysis; the use of random forest algorithm can effectively quantify the characteristic importance of each variable, and combine the marginal influence rate to define the key factors, thus achieving scientific quantification and screening of influencing factors and avoiding subjective judgment bias; by setting the threshold (F j >0.1) and determination coefficient verification (R²>0.8) ensure the significance of key influencing factors and the fitting effect of the model, so that the extracted influencing factors can accurately reflect the core driving factors of the thermal insulation performance of the material, providing scientific and reliable parameter support for the subsequent thermal insulation performance evaluation based on the sub-model, and improving the accuracy and persuasiveness of the overall evaluation method.

[0029] Furthermore, the predicted environmental data of the area where the building is to be built is obtained, combined with the historical environmental data of the area where the building is to be built, the service scenario of the building materials is simulated, and the thermal insulation performance of the building materials is evaluated based on the thermal insulation performance evaluation sub-model. Specifically, the following are included: Call the weather forecast API to obtain the predicted environmental data for the area where the building is to be built ,in The monthly average temperature, The monthly average humidity, is the monthly average wind speed; Combined with the historical environmental data of the area where the building is to be built, the time series fusion formula is used to define the service scenario parameters. The time fusion formula is: , the service scenario parameters defined are: , where To integrate environmental feature data, is the predicted data weight coefficient, Predict environmental data for the area where the building is to be built, Historical environmental data for the area where the building is to be built; Obtain the influencing factors of building material thermal insulation performance and modify the sub-model evaluation formula: ,in is the factor volatility, For the The corrected heat transfer coefficient of each sub-region; Get the regional annual heat transfer corresponding to each sub-model ,in is the annual heat transfer of the sub-region, is the sub-region index value, is the month index, is the enclosure area of ​​the sub-region, is the indoor and outdoor temperature difference in the mth month; Get the total heat transfer of the building , synchronously obtain the thermal insulation performance score and set the score threshold; The thermal insulation performance of building materials in the area is evaluated based on the thermal insulation performance score and score threshold.

[0030] Specifically, this step first calls the weather forecast API to obtain the predicted environmental data of the area where the building is to be built, including the average monthly temperature, average monthly humidity, and average monthly wind speed; then, combined with the historical environmental data of the area where the building is to be built, the fused environmental feature data is generated through the time series fusion formula (the predicted data and the historical data are fused according to the weight coefficient), and the service scenario parameters including the fused environmental data and a 10-year service period are defined; then, the sub-model evaluation formula is corrected using the influencing factor of the thermal insulation performance of building materials to obtain the corrected heat transfer coefficient of each sub-area; then, the annual regional heat transfer corresponding to each sub-model is calculated (the product of the monthly cumulative heat transfer coefficient, the area of ​​the enclosing structure, and the indoor and outdoor temperature difference) and the total building heat transfer is summarized, the thermal insulation performance score is obtained simultaneously, and the threshold is set. Finally, the thermal insulation performance of the building materials in the area is evaluated based on the score and threshold. Its advantages are: by obtaining predicted environmental data and integrating it with historical data, it takes into account both environmental change trends and long-term climate laws, making the service scenario more in line with the actual service conditions of the material; introducing the influencing factor correction sub-model, the heat transfer coefficient calculation can more accurately reflect the relationship between the environment and material performance; calculating the heat transfer by sub-region and month, realizing the refinement and dynamicization of the evaluation; through the setting of total heat transfer, score and threshold, the thermal insulation performance evaluation results are quantified, intuitive and have clear standards to rely on, providing a reliable basis for the scientific evaluation and optimization suggestions of the thermal insulation performance of the material, and improving the practicality and accuracy of the evaluation method.

[0031] Furthermore, the calculation logic of thermal insulation performance score is clarified, and the scoring rules are determined based on the total heat transfer of the building. For example, the total heat transfer is compared with the heat transfer of the benchmark building, and the formula (such as ) to calculate the score, where To score the thermal insulation performance, is the total heat transfer of the building to be evaluated, The heat transfer of the benchmark building is set in the range of [0,1], and the higher the score, the better the thermal insulation performance.

[0032] According to the energy-saving design standards, climate zoning requirements and industry specifications of the region where the building is located, combined with the functional type of the building to be built (such as residential buildings, public buildings), set the scoring threshold, such as "excellent" ( ≥0.8) "Good" (0.6≤ <0.8) "poor" ( <0.6) and clearly define the thermal insulation performance qualification standards corresponding to different levels.

[0033] For each area corresponding to the thermal insulation performance evaluation sub-model (such as functional zones and areas with different orientations), the calculated thermal insulation performance scores are extracted to form a regional score list to ensure that the score of each area is directly related to the heat transfer, material properties and environmental influencing factors of the area.

[0034] The thermal insulation performance score of each area is compared with the preset threshold to determine the thermal insulation performance level of each area. For example, a score of 0.85 for a south-facing room corresponds to "excellent", and a score of 0.55 for a north-facing room corresponds to "poor".

[0035] Summarize the assessment results of all areas and analyze the differences and causes of thermal insulation performance in different areas. For example, whether the "poor" grade area is caused by excessively high material thermal conductivity, excessively large enclosing structure area, or significant ambient temperature differences, and identify weak links in thermal insulation performance.

[0036] Combined with building material data for each region (such as material type and thermal parameters), a targeted assessment of the material's suitability in the corresponding region can be conducted. For example, whether the insulation materials used in a "poor" grade area do not meet the energy-saving requirements of the climate zone, or whether the material parameters do not match the regional environmental characteristics.

[0037] Based on the overall and regional assessment levels, a comprehensive assessment report is generated to clarify the overall insulation performance level of the building and the specific performance of each area. Material optimization suggestions are made for "poor" grade areas (such as replacing low thermal conductivity materials and increasing the thickness of the insulation layer). For "excellent" and "good" grade areas, applicable material selection experience is summarized.

[0038] To verify the rationality of the evaluation results, compare the scores of each area with the actual performance of homogeneous building materials in similar environments. If the deviation is within an acceptable range (such as ±5%), the evaluation is confirmed to be valid; otherwise, the heat transfer calculation, scoring rules or threshold setting steps are reviewed and revised before re-evaluation.

[0039] Furthermore, a building material thermal insulation performance evaluation system based on big data is proposed, which is used to implement any of the above evaluation methods, including: An acquisition module is used to acquire structural data of the building to be built, geographical location data of the building to be built, historical environmental data of the area where the building to be built is to be built, and material data of the building to be built, and is also used to collect homogeneous building material data corresponding to the material data of the building to be built; The model management module is used to construct an initial thermal insulation performance evaluation model, and is used to divide the initial thermal insulation performance evaluation model into sub-models based on the geographical location data of the building to be built, the historical environmental data of the area where the building to be built is located, and the structural data of the building to be built. The data processing module is used to pre-process the data collected by the acquisition module and obtain the influencing factors of the thermal insulation performance of building materials based on homogeneous building material data and the thermal insulation performance evaluation sub-model; An evaluation module is used to simulate the service scenario of building materials based on the predicted environmental data of the area where the building is to be built, combined with the historical environmental data of the area where the building is to be built, and to evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model; The display module is used to present the process and results of the building material thermal insulation performance evaluation to the user.

[0040] Furthermore, the model management module includes: A model building unit, the model building unit is used to construct an initial evaluation model of thermal insulation performance; The model division unit is used to divide the initial thermal insulation performance evaluation model based on the geographical location data of the building to be built, the historical environmental data of the area of ​​the building to be built, and the structural data of the building to be built, so as to obtain a thermal insulation performance evaluation sub-model.

[0041] Furthermore, the data processing module includes: A data preprocessing unit, which is used to preprocess the data collected by the acquisition module; The influencing factor acquisition unit is used to obtain the influencing factors of the thermal insulation performance of building materials based on homogeneous building material data and the thermal insulation performance evaluation sub-model.

[0042] Furthermore, the assessment modules include: The service simulation unit is used to simulate the service scenario of building materials based on the predicted environmental data of the area where the building is to be built and the historical environmental data of the area where the building is to be built; The thermal insulation performance evaluation unit is used to evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

[0043] The advantages of the present invention are: relying on big data technology to achieve accurate evaluation of the thermal insulation performance of building materials throughout the entire process, obtaining structured data through building information models, and building a multi-dimensional evaluation system in combination with geographic information and meteorological data to ensure data comprehensiveness and relevance; adopting a sub-model division strategy, refining evaluation units according to functional zoning and orientation, and extracting key influencing factors in combination with homogeneous material data to improve the pertinence and scientific nature of the evaluation; integrating historical and predicted environmental data to simulate service scenarios, and realizing quantitative evaluation from material selection to the entire life cycle by dynamically correcting the heat transfer coefficient, quantifying the heat transfer amount, and setting scoring thresholds; the system integrates modules such as data acquisition, model management, and evaluation analysis, and automates the processing flow to reduce manual intervention, providing reliable decision-making support for the optimization of building materials and energy-saving design to be built, while taking into account evaluation accuracy, timeliness, and practicality.

[0044] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating thermal insulation performance of building materials based on big data, characterized in that: include: Obtain the structural data of the building to be built, set the preset proportions simultaneously, and construct an initial evaluation model for thermal insulation performance; Obtaining geographic location data of the building to be built and simultaneously obtaining historical environmental data of the area where the building is to be built; then, based on the geographic location data of the building to be built, the historical environmental data of the area where the building is to be built, and the structural data of the building to be built, dividing the initial thermal insulation performance evaluation model to obtain a thermal insulation performance evaluation sub-model; Based on the structural data of the building to be built, corresponding building material data to be built is obtained, and based on the geographical location data of the building to be built and the historical environmental data of the area where the building to be built is built, homogeneous building material data corresponding to the building material data to be built is collected; Obtain the influencing factors of building material thermal insulation performance based on homogeneous building material data and thermal insulation performance evaluation sub-model; Obtain the predicted environmental data of the area where the building is to be built, combine it with the historical environmental data of the area where the building is to be built, simulate the service scenario of building materials, and simultaneously evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

2. The method for evaluating thermal insulation performance of building materials based on big data according to claim 1, characterized in that: The method of obtaining the structural data of the building to be built and simultaneously setting a preset ratio to construct an initial evaluation model for thermal insulation performance specifically includes: Export the structural data of the building to be built through the building information model platform, including building component type information, building component three-dimensional size information, building component connection method information and building component distribution location information; Determine structural node data, functional node data, and construction node data from the structure data of the building to be constructed based on building component type information, building component three-dimensional size information, building component connection method information, and building component distribution position information; Based on the building component type information, structural node data, functional node data and construction node data, a building component type-foundation node type correspondence table is constructed; Based on the three-dimensional size information of the building components and the corresponding table of building component type and foundation node type, the spatial boundary information and force characteristic information of the foundation node type are determined; Based on the spatial boundary information of the basic node type and the building component connection information, all connection nodes existing in the spatial boundary are determined, and the connection node information matrix is ​​simultaneously constructed; Associating the connection node information matrix with the corresponding building component type and foundation node type, and simultaneously importing the connection node information matrix into the building component type-foundation node type correspondence table to obtain the building component type-foundation node type-connection node information matrix correspondence table; Determine the first building component and the first connection node that appear within the spatial boundary corresponding to the basic node type based on the building component distribution position information and the building component type-basic node type-connection node information matrix correspondence table; Extracting a first building component belonging to the thermal insulation material and a first connection node connected to the thermal insulation material to obtain node information affecting thermal insulation performance; Based on the information of nodes affecting thermal insulation performance, combined with building design specifications and assessment requirements, a preset ratio is set, and the three-dimensional size information of building components corresponding to the nodes affecting thermal insulation performance is scaled. The actual size is converted into a virtual size that can be recognized by the model, and an initial thermal insulation performance assessment model is constructed.

3. The method for evaluating thermal insulation performance of building materials based on big data according to claim 1, characterized in that: The method of obtaining geographic location data of the building to be built and simultaneously obtaining historical environmental data of the area of ​​the building to be built is then divided into a model based on the geographic location data of the building to be built, the historical environmental data of the area of ​​the building to be built, and the structural data of the building to be built, to obtain a thermal insulation performance evaluation sub-model, specifically including: Obtain geographic location data of the building to be constructed through the geographic information system, including latitude and longitude, altitude and terrain features; Based on the geographic location data of the building to be built, the meteorological data platform interface is called to obtain the historical environmental data of the area corresponding to the building to be built; Analyze historical environmental data of the area where the building is to be built and extract key climate factors that affect the thermal insulation performance of the building; Combined with the structural data of the building to be built, the building is preliminarily divided according to its functional area and orientation, and each area is used as a potential sub-model evaluation unit; Based on the potential sub-model evaluation unit, a detailed division is performed, and an independent thermal insulation performance evaluation sub-model is constructed for each functional zone and orientation area. The thermal insulation performance evaluation sub-model includes the structural parameters, climate influencing factors and sub-model evaluation formula of the area.

4. The method for evaluating thermal insulation performance of building materials based on big data according to claim 1, characterized in that: The method of acquiring corresponding building material data based on the structure data of the building to be built, and collecting homogeneous building material data corresponding to the building material data based on the geographical location data of the building to be built and the historical environmental data of the area where the building to be built is located, specifically includes: Extracting material information of each component from the structural data of the building to be built to form a data list of materials for the building to be built, which includes material type, manufacturer, model specifications, physical performance parameters, and thermal performance parameters; Define the criteria for determining homogeneous building material data: the material type is the same, and the production process and specification parameters are consistent; Determine the spatial scope of data collection based on the geographic location data of the building to be built and the historical environmental data of the area where the building is to be built; Within the designated space, basic information on completed construction projects that meet the homogeneous building materials data determination standards will be screened through the construction industry database, project case library, and building materials supplier information platform; Based on the basic information of completed building projects, record the insulation material data used as homogeneous insulation material data, simultaneously collect the corresponding homogeneous regional historical environmental data, and collect the actual performance data corresponding to the homogeneous insulation material data in the project; The homogeneous insulation material data, homogeneous regional historical environmental data and actual performance data are integrated to obtain homogeneous building material data.

5. The method for evaluating thermal insulation performance of building materials based on big data according to claim 1, characterized in that: The method of obtaining the influencing factors of the thermal insulation performance of building materials based on homogeneous building material data and the thermal insulation performance evaluation sub-model specifically includes: Obtain homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data corresponding to the insulation performance evaluation sub-model from homogeneous building material data; Preprocess homogeneous insulation material data, homogeneous regional historical environmental data, and actual performance data to remove outliers, fill missing values, and unify the data format to obtain the material property set corresponding to the insulation performance evaluation sub-model and environmental impact attribute sets ,in For density, is the specific heat capacity, is the thermal conductivity, is the maximum temperature difference in the historical environmental data of the homogeneous region, The maximum humidity difference, is the maximum wind speed difference, The service life is the actual performance data; Integrate the material attribute set and environmental impact attribute set corresponding to the thermal insulation performance evaluation sub-model to obtain the input variables of the impact factor model ; The random forest algorithm is used to construct an influencing factor model and simultaneously determine the characteristic importance of the reference coefficient of thermal insulation performance of building materials ,satisfy ; Definition of key influencing factors based on the reference coefficient of thermal insulation performance of building materials: ,in For the The contribution intensity value of each variable, is the output variable of the impact factor model, express right The marginal impact rate; Will The variables are taken as key influencing factors and recorded as ; Establish the regression equation of influencing factors and thermal insulation performance: in is the predicted value of thermal insulation performance, is the regression coefficient, is the error term, is the total number of impact factors; Obtain the coefficient of determination of the regression equation between the influencing factors and thermal insulation performance. If the coefficient of determination is greater than 0.8, the extracted key influencing factors will be used as the influencing factors of the thermal insulation performance of building materials.

6. The method for evaluating thermal insulation performance of building materials based on big data according to claim 1, characterized in that: The method of obtaining predicted environmental data of the area where the building is to be built, combining it with historical environmental data of the area where the building is to be built, simulating the service scenario of building materials, and simultaneously evaluating the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model specifically includes: Call the weather forecast API to obtain the predicted environmental data for the area where the building is to be built ,in The monthly average temperature, The monthly average humidity, is the monthly average wind speed; Combined with the historical environmental data of the area where the building is to be built, the time series fusion formula is used to define the service scenario parameters. The time fusion formula is: , the service scenario parameters defined are: , where To integrate environmental feature data, is the predicted data weight coefficient, Predict environmental data for the area where the building is to be built, Historical environmental data for the area where the building is to be built; Obtain the influencing factors of building material thermal insulation performance and modify the sub-model evaluation formula: ,in is the factor volatility, For the The corrected heat transfer coefficient of each sub-region; Get the regional annual heat transfer corresponding to each sub-model ,in is the annual heat transfer of the th sub-region, is the sub-region index value, is the month index, is the enclosure area of ​​the sub-region, is the indoor and outdoor temperature difference in the mth month; Get the total heat transfer of the building , synchronously obtain the thermal insulation performance score and set the score threshold; The thermal insulation performance of building materials in the area is evaluated based on the thermal insulation performance score and score threshold.

7. A building material thermal insulation performance evaluation system based on big data, used to implement the evaluation method according to any one of claims 1 to 6, characterized in that: include: an acquisition module, the acquisition module being used to acquire structural data of the building to be built, geographical location data of the building to be built, historical environmental data of the area where the building to be built is to be built, and material data of the building to be built, and further being used to collect homogeneous building material data corresponding to the material data of the building to be built; A model management module, wherein the model management module is used to construct an initial thermal insulation performance evaluation model, and is used to divide the initial thermal insulation performance evaluation model into models based on the geographical location data of the building to be built, the historical environmental data of the area where the building to be built is to be built, and the structural data of the building to be built, to obtain thermal insulation performance evaluation sub-models; A data processing module, the data processing module is used to perform data preprocessing on the data collected by the acquisition module, and is used to obtain the influencing factors of the thermal insulation performance of the building materials based on the homogeneous building material data and the thermal insulation performance evaluation sub-model; An evaluation module, which is used to simulate the service scenario of building materials based on the predicted environmental data of the area where the building is to be built, combined with the historical environmental data of the area where the building is to be built, and is used to evaluate the thermal insulation performance of the building materials based on the thermal insulation performance evaluation sub-model; The display module is used to present the process and results of the building material thermal insulation performance evaluation to the user.

8. The building material thermal insulation performance evaluation system based on big data according to claim 7 is characterized in that: The model management module includes: A model building unit, wherein the model building unit is used to construct an initial evaluation model for thermal insulation performance; The model division unit is used to divide the initial thermal insulation performance evaluation model based on the geographical location data of the building to be built, the historical environmental data of the area of ​​the building to be built, and the structural data of the building to be built to obtain a thermal insulation performance evaluation sub-model.

9. The building material thermal insulation performance evaluation system based on big data according to claim 7, characterized in that: The data processing module includes: A data preprocessing unit, configured to preprocess the data collected by the acquisition module; The influencing factor acquisition unit is used to obtain the influencing factor of the thermal insulation performance of the building material based on the homogeneous building material data and the thermal insulation performance evaluation sub-model.

10. The building material thermal insulation performance evaluation system based on big data according to claim 7, characterized in that: The evaluation module includes: A service simulation unit, which is used to simulate the service scenario of building materials based on the predicted environmental data of the area of ​​the building to be built and the historical environmental data of the area of ​​the building to be built; The thermal insulation performance evaluation unit is used to evaluate the thermal insulation performance of building materials based on the thermal insulation performance evaluation sub-model.

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