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

Abstract

The application discloses a building material heat preservation performance evaluation method and system based on big data, relates to the technical field of building material performance evaluation, and comprises the following steps: acquiring building structure data of a building to be built and setting a preset ratio, constructing an initial heat preservation performance evaluation model, acquiring geographic position and regional historical environment data of the building to be built, and dividing the initial model to obtain an evaluation submodel, extracting building material data of the building to be built based on the structure data, collecting homogeneous building material data in combination with the geographic position and the historical environment data, acquiring a heat preservation performance influence factor according to the homogeneous data and the submodel, acquiring regional predicted environment data, simulating a service scene by fusing historical data, and evaluating the material heat preservation performance based on the submodel. The application has the advantages that through big data integration and fine modeling, the accurate evaluation of the building material heat preservation performance is realized, and reliable support is provided for building material selection and energy-saving design of the building to be built.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building material performance evaluation, in particular to a building material thermal insulation performance evaluation method and system based on big data. BACKGROUND

[0002] The thermal insulation performance of building materials is a core factor affecting building energy efficiency, indoor thermal comfort and carbon emission level, and is directly related to the energy consumption cost and environmental benefits of the whole life cycle of the building. With the popularization of green building concept and the upgrading of energy saving standards, accurate evaluation of the thermal insulation performance of building materials to be built has become a key link for optimizing material selection and improving building energy saving level.

[0003] The existing building material thermal insulation performance evaluation method and system based on big data have the problems of insufficient data integration, isolated use of material parameters or environmental data, disconnection between evaluation and actual service scene, low evaluation model refinement, and difficulty in capturing local area thermal insulation performance differences due to the use of overall building evaluation instead of function zoning, orientation and other subdivided unit modeling. SUMMARY

[0004] To solve the above technical problems, a building material thermal insulation performance evaluation method and system based on big data are provided, which solves the problem of insufficient data integration, isolated use of material parameters or environmental data, disconnection between evaluation and actual service scene, low evaluation model refinement, and difficulty in capturing local area thermal insulation performance differences due to the use of overall building evaluation instead of function zoning, orientation and other subdivided unit modeling.

[0005] To achieve the above purposes, the technical scheme adopted by the present application is as follows:

[0006] A building material thermal insulation performance evaluation method based on big data, comprising:

[0007] Obtaining building structure data to be built, synchronously setting a preset ratio, and constructing an initial thermal insulation performance evaluation model;

[0008] Obtaining building geographic location data to be built, synchronously obtaining historical environmental data of the building area to be built, and then based on the building geographic location data to be built, the historical environmental data of the building area to be built and the building structure data to be built, dividing the initial thermal insulation performance evaluation model to obtain a thermal insulation performance evaluation submodel;

[0009] Based on the to-be-built building structure data, corresponding to-be-built building material data is acquired, and based on to-be-built building geographical position data and to-be-built building region historical environment data, homogenous building material data corresponding to the to-be-built building material data is collected;

[0010] According to the homogenous building material data and the thermal insulation performance evaluation sub-model, a building material thermal insulation performance influence factor is acquired;

[0011] To-be-built building region predicted environment data is acquired, and in combination with to-be-built building region historical environment data, a building material service scene is simulated, and the building material thermal insulation performance is evaluated based on the thermal insulation performance evaluation sub-model.

[0012] In an optional embodiment, the to-be-built building structure data is acquired, and a preset ratio is set to construct an initial thermal insulation performance evaluation model, specifically including:

[0013] The to-be-built building structure data is exported through a building information model platform, including building component type information, building component three-dimensional size information, building component connection mode information and building component distribution position information;

[0014] Based on the building component type information, the building component three-dimensional size information, the building component connection mode information and the building component distribution position information, structure node data, function node data and construction node data are determined from the to-be-built building structure data;

[0015] Based on the building component type information, the structure node data, the function node data and the construction node data, a building component type-basic node type correspondence table is constructed;

[0016] Based on the building component three-dimensional size information and the building component type-basic node type correspondence table, spatial boundary information and stress characteristic information of the basic node type are determined;

[0017] Based on the spatial boundary information of the basic node type and the building component connection mode information, all connection nodes existing within the spatial boundary are determined, and a connection node information matrix is constructed synchronously;

[0018] The connection node information matrix is associated with corresponding building component types and basic node types, and the connection node information matrix is imported into the building component type-basic node type correspondence table synchronously, to obtain a building component type-basic node type-connection node information matrix correspondence table;

[0019] Based on the building component distribution position information and the building component type-basic node type-connection node information matrix correspondence table, a first building component and a first connection node appearing within the spatial boundary corresponding to the basic node type are determined;

[0020] Extract the first building component belonging to the thermal insulation material and the first connecting node connected with the thermal insulation material to obtain thermal insulation performance influencing node information;

[0021] Based on the thermal insulation performance influencing node information, combining with the building design specification and evaluation requirements, a preset ratio is set to scale the three-dimensional size information of the building component corresponding to the thermal insulation performance influencing node, convert the actual size into a virtual size recognizable by the model, and construct a thermal insulation performance initial evaluation model.

[0022] In an optional embodiment, the geographic location data of the to-be-built building is obtained, the historical environmental data of the to-be-built building region is synchronously obtained, and then based on the geographic location data of the to-be-built building, the historical environmental data of the to-be-built building region and the structure data of the to-be-built building, the initial evaluation model of the thermal insulation performance is divided into a model to obtain a thermal insulation performance evaluation submodel, specifically including:

[0023] The geographic location data of the to-be-built building is obtained through a geographic information system, including latitude, longitude, altitude and terrain characteristics;

[0024] Based on the geographic location data of the to-be-built building, a meteorological data platform interface is called to obtain the historical environmental data of the to-be-built building region corresponding to the region;

[0025] The historical environmental data of the to-be-built building region is analyzed to extract key climate factors that have an impact on building thermal insulation performance;

[0026] Combining the structure data of the to-be-built building, the building is preliminarily divided according to building function zoning and orientation, and each partition is taken as a potential submodel evaluation unit;

[0027] Based on the potential submodel evaluation unit, a refined division is performed to construct an independent thermal insulation performance evaluation submodel for each functional partition and orientation region, and the thermal insulation performance evaluation submodel includes the structure parameters, climate influencing factors and submodel evaluation formula of the region.

[0028] In an optional embodiment, based on the structure data of the to-be-built building, corresponding to-be-built building material data is obtained, and based on the geographic location data of the to-be-built building and the historical environmental data of the to-be-built building region, same material data corresponding to the to-be-built building material data is collected, specifically including:

[0029] The material information of each component is extracted from the structure data of the to-be-built building to form a to-be-built building material data list as to-be-built building material data, including material type, manufacturer, model specification, physical performance parameter and thermal performance parameter;

[0030] Define the same building material data determination standard: the material type is the same, the production process and the specification parameter are consistent;

[0031] Based on the geographic location data of the to-be-built building and the regional historical environment data of the to-be-built building, a data collection space range is demarcated;

[0032] In the demarcated space range, through the building industry database, the engineering case library and the building material supplier information platform, the built building project basic information meeting the same quality building material data determination standard is screened;

[0033] Based on the built building project basic information, the used insulation material data is recorded as the same quality insulation material data, the corresponding same quality regional historical environment data is synchronously collected, and the actual use performance data corresponding to the same quality insulation material data in the project is collected;

[0034] The same quality insulation material data, the same quality regional historical environment data and the actual use performance data are integrated to obtain the same quality building material data.

[0035] In an optional embodiment, the building material insulation performance influencing factor is obtained according to the same quality building material data and the insulation performance evaluation sub-model, and specifically includes:

[0036] The same quality insulation material data, the same quality regional historical environment data and the actual use performance data corresponding to the insulation performance evaluation sub-model are obtained from the same quality building material data;

[0037] The same quality insulation material data, the same quality regional historical environment data and the actual use performance data are preprocessed to remove abnormal values and fill in missing values, and the data format is unified to obtain the material attribute set and the environmental influence attribute set corresponding to the insulation performance evaluation sub-model ; , wherein, the density, the specific heat capacity, the thermal conductivity, the maximum temperature difference in the same quality regional historical environment data, the maximum humidity difference, the maximum wind speed difference, the service life in the actual use performance data;

[0038] The material attribute set and the environmental influence attribute set corresponding to the insulation performance evaluation sub-model are integrated to obtain the influencing factor model input variable ;

[0039] The random forest algorithm is adopted to construct the influencing factor model, and the feature importance of the building material insulation performance reference coefficient is synchronously determined , satisfying ;

[0040] Based on the building material insulation performance reference coefficient, the key influencing factor is defined: wherein, is the contribution intensity value of the th variable, is the output variable of the impact factor model, represents the marginal influence rate of ;

[0041] the variable of is taken as a key impact factor, denoted as ;

[0042] a regression equation of the impact factor and the thermal insulation performance is established: wherein, is the predicted value of the thermal insulation performance, is a regression coefficient, is an error term, is the total number of impact factors;

[0043] a determination coefficient of the regression equation of the impact factor and the thermal insulation performance is obtained, and if the determination coefficient is greater than 0.8, the extracted key impact factor is taken as the building material thermal insulation performance impact factor.

[0044] In an optional embodiment, the method further comprises:

[0045] calling a meteorological prediction API to obtain the predicted environmental data of the to-be-built building area wherein, is the monthly average temperature, is the monthly average humidity, is the monthly average wind speed;

[0046] combining the historical environmental data of the to-be-built building area, and adopting a time series fusion formula to define the service scenario parameters, wherein the time fusion formula is: and the defined service scenario parameters are: , wherein, is the fused environmental feature data, is a prediction data weight coefficient, is the predicted environmental data of the to-be-built building area, is the historical environmental data of the to-be-built building area;

[0047] obtaining the building material thermal insulation performance impact factor, and correcting the sub-model evaluation formula:

[0048] wherein, is the factor fluctuation amount, is the contribution intensity value of the The heat transfer coefficient of the sub-region is corrected, The first The sub-model evaluation formula corresponding to the sub-region;

[0049] Obtain the annual heat transfer of each sub-model corresponding region: , wherein The annual heat transfer of the zth sub-region, The sub-region index value, The month index, The building envelope area of the sub-region, The indoor and outdoor temperature difference of the mth month;

[0050] Obtain the total heat transfer of the building: , the thermal insulation performance score is obtained synchronously, and the score threshold is set;

[0051] Based on the thermal insulation performance score and the score threshold, the thermal insulation performance of the building materials in the region is evaluated, wherein The number of sub-regions.

[0052] Further, a building material thermal insulation performance evaluation system based on big data is proposed, which is used to realize the evaluation method of any one of the above, comprising:

[0053] The acquisition module is used to acquire the building structure data, the building geographic location data, the building region historical environment data and the building material data, and is also used to collect the homogeneous building material data corresponding to the building material data;

[0054] The model management module is used to construct the initial thermal insulation performance evaluation model, and is used to divide the initial thermal insulation performance evaluation model based on the building geographic location data, the building region historical environment data and the building structure data, to obtain the thermal insulation performance evaluation sub-model;

[0055] The data processing module is used to pre-process the data collected by the acquisition module, and is used to obtain the building material thermal insulation performance influencing factor according to the homogeneous building material data and the thermal insulation performance evaluation sub-model;

[0056] The evaluation module is used to simulate the service scene of the building material based on the building region predicted environment data and the building region historical environment data, and is used to evaluate the thermal insulation performance of the building material based on the thermal insulation performance evaluation sub-model;

[0057] The display module is used to present the process and result of the building material thermal insulation performance evaluation to the user.

[0058] In an optional embodiment, the model management module comprises:

[0059] a model construction unit configured to construct an initial thermal insulation performance evaluation model;

[0060] a model division unit configured to divide the initial thermal insulation performance evaluation model based on geographic location data of the building to be constructed, historical environment data of the region where the building to be constructed is located, and structure data of the building to be constructed, to obtain a thermal insulation performance evaluation submodel.

[0061] In an optional embodiment, the data processing module comprises:

[0062] a data preprocessing unit configured to perform data preprocessing on the data collected by the acquisition module;

[0063] an influence factor acquisition unit configured to acquire a thermal insulation performance influence factor of the building material based on the homogeneous building material data and the thermal insulation performance evaluation submodel.

[0064] In an optional embodiment, the evaluation module comprises:

[0065] a service simulation unit configured to simulate a service scenario of the building material based on predicted environment data of the region where the building to be constructed is located, in combination with historical environment data of the region where the building to be constructed is located;

[0066] a thermal insulation performance evaluation unit configured to evaluate the thermal insulation performance of the building material based on the thermal insulation performance evaluation submodel.

[0067] Compared with the prior art, the present application has the following advantages:

[0068] The building material thermal insulation performance evaluation method based on big data provided by the present application realizes a fine evaluation framework from the whole to the part by acquiring structure data of the building to be constructed and constructing an initial thermal insulation performance evaluation model, and simultaneously dividing the evaluation submodel in combination with geographic location and environment data, so that the evaluation can reflect the overall thermal insulation performance of the building and accurately capture the performance differences of different functional partitions and orientation regions, thereby providing a basis for targeted optimization.

[0069] The building material thermal insulation performance evaluation method based on big data provided by the present application realizes a strong association between material performance evaluation and actual application scenarios by extracting building material information based on building structure data and collecting homogeneous material data in combination with geographic location and historical environment data, thereby ensuring the comparability and reference value of the evaluation data and laying a reliable data foundation for subsequent influence factor extraction.

[0070] The building material thermal insulation performance evaluation method based on big data provided in the scheme extracts key influence factors from homogeneous data by adopting a random forest algorithm, combines historical and predicted environment data to simulate a service scene, realizes dynamic and full life cycle evaluation of material thermal insulation performance, makes the evaluation result not only reflect current performance, but also predict long-term service performance, and improves the forward-looking and scientific nature of the evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 A flowchart of the building material thermal insulation performance evaluation method based on big data provided in the scheme is shown in the figure.

[0072] Figure 2 A flowchart of the construction of the initial thermal insulation performance evaluation model in the scheme is shown in the figure.

[0073] Figure 3 A flowchart of the acquisition of the thermal insulation performance evaluation sub-model in the scheme is shown in the figure.

[0074] Figure 4 A system framework diagram of the building material thermal insulation performance evaluation system based on big data provided in the scheme is shown in the figure. DETAILED DESCRIPTION

[0075] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be thought of by those skilled in the art.

[0076] REFERENCE Figure 1 - Figure 4 As shown in the figure, a building material thermal insulation performance evaluation method based on big data comprises:

[0077] Obtain building structure data, set a predetermined proportion, and construct an initial thermal insulation performance evaluation model;

[0078] Obtain building geographic location data, obtain historical environment data of the building area, and then divide the initial thermal insulation performance evaluation model based on the building geographic location data, the building area historical environment data and the building structure data to obtain a thermal insulation performance evaluation sub-model;

[0079] Based on the building structure data, obtain corresponding building material data, and based on the building geographic location data and the building area historical environment data, collect homogeneous building material data corresponding to the building material data;

[0080] According to the homogeneous building material data and the thermal insulation performance evaluation sub-model, obtain building material thermal insulation performance influence factors;

[0081] The predicted environment data of the to-be-built building area is acquired, historical environment data of the to-be-built building area is combined, a building material service scene is simulated, and the building material thermal insulation performance is evaluated based on a thermal insulation performance evaluation submodel.

[0082] Further, the to-be-built building structure data is acquired, a preset ratio is set, and a thermal insulation performance initial evaluation model is constructed, specifically including:

[0083] The to-be-built building structure data is exported through a building information model platform, including building component type information, building component three-dimensional size information, building component connection mode information and building component distribution position information;

[0084] Based on the building component type information, the building component three-dimensional size information, the building component connection mode information and the building component distribution position information, the structure node data, the function node data and the construction node data are determined from the to-be-built building structure data;

[0085] Based on the building component type information, the structure node data, the function node data and the construction node data, a building component type-basic node type correspondence table is constructed;

[0086] Based on the building component three-dimensional size information and the building component type-basic node type correspondence table, the spatial boundary information and the stress characteristic information of the basic node type are determined;

[0087] 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 a connection node information matrix is constructed synchronously;

[0088] The connection node information matrix is associated with the corresponding building component type and the basic node type, the connection node information matrix is imported into the building component type-basic node type correspondence table synchronously, and a building component type-basic node type-connection node information matrix correspondence table is obtained;

[0089] Based on the building component distribution position information and the building component type-basic node type-connection node information matrix correspondence table, the first building component and the first connection node appearing in the spatial boundary corresponding to the basic node type are determined;

[0090] The first building component belonging to the thermal insulation material and the first connection node connected with the thermal insulation material are extracted, and thermal insulation performance influence node information is obtained;

[0091] Based on the thermal insulation performance influence node information, the building design specification and the evaluation requirement are combined, a preset ratio is set, the building component three-dimensional size information corresponding to the thermal insulation performance influence node is scaled, the actual size is converted into a virtual size recognizable by the model, and a thermal insulation performance initial evaluation model is constructed.

[0092] Specifically, the step is specifically:

[0093] S1.1 Obtain the three-dimensional structure 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 , wherein is the length, is the width, is the height, is the thickness, and i represents the building component number;

[0094] S1.2 Set a preset scale coefficient (such as =1:100), scale the three-dimensional structure data to obtain a scaled parameter set ;

[0095] S1.3 Define the input variable of the initial thermal insulation performance evaluation model as the thermal resistance of the building component , and the output variable as the theoretical heat transfer coefficient ;

[0096] S1.4 Based on Fourier's law, construct the heat transfer basic formula: , wherein Q is the heat transfer amount, is the component area, and ΔT is the indoor and outdoor temperature difference;

[0097] S1.5 Collect historical heat transfer data of the same type of building , determine the coefficients α and β by linear regression fitting ;

[0098] S1.6 Substitute the scaled parameter S' into the fitting formula to generate the initial thermal insulation performance evaluation model ;

[0099] S1.7 Set the model accuracy verification index , wherein is the model predicted value, is the historical actual value;

[0100] S1.8 If e≤0.05, the initial model is retained; otherwise, the coefficients α and β are refitted until the accuracy requirement is met.

[0101] Advantages: Through the building information model (BIM) platform, comprehensive and accurate building structure data is obtained, ensuring the completeness and accuracy of the evaluation of the basic data; by layering the building component type, node data and building a multi-dimensional correspondence table, a clear "component - node - connection relationship" structured system is established, providing ordered data support for subsequent evaluation; key components and connecting nodes related to thermal insulation materials are extracted, focusing on the key factors affecting thermal insulation performance, avoiding irrelevant data interference and improving the evaluation pertinence; combined with the building design specification, the preset proportion is converted into a virtual size to ensure that the model and the actual building have consistent spatial proportions, providing a practical basis for subsequent thermal insulation performance simulation calculation, and finally building an initial evaluation model with comprehensiveness, accuracy and pertinence, laying a reliable foundation for subsequent detailed evaluation.

[0102] Further, the geographic location data of the to-be-built building is obtained, the historical environment data of the to-be-built building region is synchronously obtained, and then based on the geographic location data of the to-be-built building, the historical environment data of the to-be-built building region and the structure data of the to-be-built building, the initial evaluation model of the thermal insulation performance is divided into a sub-model, and the specific steps are as follows:

[0103] The geographic information system is used to obtain the geographic location data of the to-be-built building, including latitude, longitude, altitude and terrain characteristics;

[0104] Based on the geographic location data of the to-be-built building, the meteorological data platform interface is called to obtain the corresponding historical environment data of the to-be-built building region in the region;

[0105] The historical environment data of the to-be-built building region is analyzed, and the key climate factors that affect the building thermal insulation performance are extracted;

[0106] Combined with the structure data of the to-be-built building, the building is preliminarily divided according to the building function partition and orientation, and each partition is taken as a potential sub-model evaluation unit;

[0107] Based on the potential sub-model evaluation unit, the building is further divided, an independent thermal insulation performance evaluation sub-model is constructed for each functional partition and orientation region, and the thermal insulation performance evaluation sub-model includes the structure parameters, climate influence factors and sub-model evaluation formula of the region.

[0108] Specifically, the step is specifically:

[0109] S2.1 Obtain the latitude and longitude (x, y) of the to-be-built building through the GIS interface, and determine its climate partition C (such as cold region, temperate region);

[0110] S2.2 Call the meteorological database API to obtain the historical environment data of the region in the past 10 years, and construct an environment feature matrix , Historical monthly average temperature Historical monthly average humidity, The historical monthly average wind speed, m=1,2,…,12;

[0111] S2.3 Calculate the weighting coefficients of each climate factor , , ,satisfy The weights are determined using the analytic hierarchy process (AHP).

[0112] S2.4 Define the regional influence coefficient: Quantify the impact of climate on thermal insulation performance;

[0113] S2.5 Based on the orientation parameters (such as south and north) in the building structure data, divide the building into n functional areas (such as bedrooms and living rooms), denoted as . ;

[0114] S2.6 Initial Model Introducing the regional influence coefficient, a sub-model evaluation formula is constructed:

[0115] ,in For the first Orientation correction factor for each region (e.g., θ=1.2 for south and θ=0.8 for north);

[0116] S2.7 Calculate the eigenvalues ​​of each sub-model Cluster analysis was used to verify the differences between the sub-models. If the variance is greater than 0.1, the partition is valid;

[0117] S2.8 Outputs n thermal insulation performance evaluation sub-models: Each corresponds to a different functional area.

[0118] Advantages: Through the geographic information system, the geographic location data of the building to be built is accurately obtained, including latitude and longitude, altitude and terrain characteristics, providing accurate spatial reference for subsequent associated regional environmental data; Based on the geographic location, the meteorological data platform interface is called to obtain historical environmental data, ensuring the strong correlation of environmental data and building space location, making the evaluation more in line with the actual regional climate characteristics; By analyzing historical environmental data, key climate factors are extracted, focusing on environmental factors that have a significant impact on thermal performance, avoiding redundant data interference and improving evaluation efficiency; Combined with building structure data, potential sub-model evaluation units are divided according to function and orientation, fully considering the differences in thermal performance caused by functional requirements and orientation differences in different areas, realizing the refinement of evaluation; Based on potential units, independent sub-models containing regional structure parameters, climate impact factors and evaluation formulas are constructed, so that each sub-model can reflect the thermal performance characteristics of specific areas, ensuring the professionalism and accuracy of the evaluation, and laying a structured foundation for subsequent simulation of service scenarios combined with environmental data and accurate evaluation of material thermal performance, effectively improving the scientificity and practicality of the overall evaluation method.

[0119] Further, based on the building structure data to be built, corresponding building material data to be built is obtained, and based on the geographic location data of the building to be built and the regional historical environmental data of the building to be built, homogeneous building material data corresponding to the building material data to be built is collected, specifically including:

[0120] Extracting material information of each component from building structure data to be built forms a list of building material data to be built, which is building material data to be built, including material type, manufacturer, model specification, physical performance parameters and thermal performance parameters;

[0121] Define homogeneous building material data determination criteria: same material type, consistent production process and specification parameters;

[0122] Based on the geographic location data of the building to be built and the regional historical environmental data of the building to be built, the spatial range of data collection is determined;

[0123] Within the determined spatial range, through the building industry database, engineering case library and building material supplier information platform, the basic information of the completed building project that meets the homogeneous building material data determination criteria is screened;

[0124] Based on the basic information of the completed building project, record the thermal insulation material data used as homogeneous thermal insulation material data, simultaneously collect the corresponding homogeneous regional historical environmental data, and collect the actual use performance data corresponding to the homogeneous thermal insulation material data in the project;

[0125] Integrate homogeneous thermal insulation material data, homogeneous regional historical environmental data and actual use performance data to obtain homogeneous building material data.

[0126] It can be understood that this step first extracts the material information of each component from the building structure data to be built to form a building material data list to be built containing material type, manufacturer, model specification, physical performance parameters and thermal performance parameters; then define the same material type, production process and specification parameter consistent homogenous building material determination standard; then based on the geographical location and regional historical environment data of the building to be built, the collection range is drawn, and the built project information meeting the homogeneity standard is screened out in the range through the building industry database platform, and the thermal insulation material data, corresponding regional historical environment data and actual use performance data are recorded, and finally these data are integrated to obtain the homogenous building material data. The advantages are: through accurate extraction of building material data to be built, the definiteness of the evaluation object is ensured; the strict homogeneity determination standard ensures the comparability of the collected data, avoiding the evaluation deviation caused by material difference; combined with the geographical location and environmental data to draw the range, the homogenous material data collected has high relevance with the service environment of the building to be built, which improves the data reference value; multi-channel collection and integration of data provide rich and reliable empirical basis for subsequent extraction of thermal insulation performance influence factors and accurate evaluation of material performance, which enhances the scientificity and practicality of the evaluation method.

[0127] Further, according to the homogenous building material data and the thermal insulation performance evaluation sub-model, the building material thermal insulation performance influence factor is obtained, specifically including:

[0128] The homogenous thermal insulation material data, homogenous regional historical environment data and actual use performance data corresponding to the thermal insulation performance evaluation sub-model are obtained from the homogenous building material data;

[0129] The homogenous thermal insulation material data, homogenous regional historical environment data and actual use performance data are preprocessed to remove outliers and fill in missing values, and the data format is unified to obtain the material attribute set and the environmental influence attribute set corresponding to the thermal insulation performance evaluation sub-model and environmental influence attribute set , wherein is the density, is the specific heat capacity, is the thermal conductivity, is the maximum temperature difference in the homogenous regional historical environment data, is the maximum humidity difference, is the maximum wind speed difference, is the service life in the actual use performance data;

[0130] The material attribute set and the environmental influence attribute set corresponding to the thermal insulation performance evaluation sub-model are integrated to obtain the influence factor model input variable:

[0131] ​The random forest algorithm is used to construct the influencing factor model, and the characteristic importance of the building material thermal performance reference coefficient is determined simultaneously , meet ;

[0132] Based on the building material thermal performance reference coefficient, the key influencing factors are defined: , wherein is the contribution intensity value of the first variable, is the output variable of the influencing factor model, represents the marginal influence rate of ;

[0133] The variables of are taken as the key influencing factors, denoted as ;

[0134] The regression equation of the influencing factor and the thermal performance is established: , wherein is the predicted value of the thermal performance, is the regression coefficient, is the error term, is the total number of influencing factors;

[0135] The determination coefficient of the regression equation of the influencing factor and the thermal performance is obtained, and if the determination coefficient is greater than 0.8, the extracted key influencing factors are taken as the building material thermal performance influencing factors.

[0136] Specifically, this step first extracts homogeneous thermal insulation material data, homogeneous regional historical environment data and actual use 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), material attribute set (including density, specific heat capacity, thermal conductivity, etc.) and environmental impact attribute set (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 impact factor model. Then, the random forest algorithm is used to build the model, determine the feature importance of each variable (satisfying the weight sum of 1), define the key impact factors (the contribution intensity value of the jth variable, combined with the feature importance and marginal influence rate), select the variables with a contribution intensity value greater than 0.1 as the key impact factors, and then establish the regression equation of the impact factors and the thermal insulation performance. After verification by the determination coefficient (greater than 0.8), the extracted key impact factors are used as the building material thermal insulation performance impact factors. The advantages are as follows: data preprocessing ensures the accuracy and consistency of the input data, laying a reliable foundation for subsequent analysis; the random forest algorithm can effectively quantify the feature importance of each variable, and the key factors are defined by combining the marginal influence rate, which realizes the scientific quantification and screening of the impact factors, avoiding subjective judgment bias; by setting the threshold (F j >0.1) and the determination coefficient verification (R²>0.8), the significance of the key impact factors and the fitting effect of the model are guaranteed, so that the extracted impact factors can accurately reflect the core driving factors of the material thermal insulation performance, providing scientific and reliable parameter support for subsequent thermal insulation performance evaluation based on the sub-model, and improving the accuracy and persuasiveness of the overall evaluation method.

[0137] Further, the predicted environment data of the to-be-built building area is obtained, combined with the historical environment data of the to-be-built building area, the service scene of the building material is simulated, and the thermal insulation performance of the building material is evaluated based on the thermal insulation performance evaluation sub-model, specifically including: calling the weather prediction API to obtain the predicted environment data of the to-be-built building area , wherein is the monthly average temperature, is the monthly average humidity, is the monthly average wind speed;

[0138] Combined with the historical environment data of the to-be-built building area, the time series fusion formula is used to define the service scene parameters, wherein the time fusion formula is: , and the defined service scene parameters are: , wherein is the fused environment feature data, is the prediction data weight coefficient, is the predicted environment data of the to-be-built building area, is the historical environment data of the to-be-built building area;

[0139] Obtain the influencing factors of building material thermal insulation performance and revise the sub-model evaluation formula accordingly:

[0140] ,in, For factor volatility, For the first The corrected heat transfer coefficients for each sub-region For the first The sub-model evaluation formula corresponding to each sub-region;

[0141] Obtain the annual heat transfer of the region corresponding to each sub-model ,in, For the first Annual heat transfer in each sub-region This is the sub-region index value. Indexed by month, For the area of ​​the enclosure structure of the sub-region, The indoor-outdoor temperature difference in month m;

[0142] Obtain the total heat transfer of the building: Simultaneously acquire thermal insulation performance scores and set scoring thresholds;

[0143] The thermal insulation performance of building materials within a region is evaluated based on thermal insulation performance scores and scoring thresholds. This represents the number of sub-regions.

[0144] Specifically, this step first calls the weather forecast API to obtain the predicted environmental data of the to-be-built building area, including the monthly average temperature, monthly average humidity, and monthly average wind speed; then, combined with the historical environmental data of the to-be-built building area, the time series fusion formula (fusing the predicted data and the historical data according to the weight coefficient) is used to generate the fused environmental feature data, which defines the service scenario parameters including the fused environmental data and the 10-year service period; then, the corrected heat transfer coefficient of each sub-region is obtained by using the building material thermal insulation performance influence factor correction sub-model evaluation formula; the annual heat transfer amount of each sub-model corresponding area (the product of the monthly accumulated heat transfer coefficient, the envelope structure area, and the indoor and outdoor temperature difference) is calculated, and the building total heat transfer amount is obtained by summarizing, the thermal insulation performance score is obtained synchronously and the threshold is set, and finally the thermal insulation performance of the building material in the area is evaluated based on the score and the threshold. The advantages are as follows: by obtaining the predicted environmental data and fusing it with the historical data, both the environmental change trend and the long-term climate rule are taken into account, making the service scenario more consistent with the actual service conditions of the material; the influence factor correction sub-model is introduced, making the heat transfer coefficient calculation more accurate to reflect the correlation between the environment and the material performance; the heat transfer amount is calculated according to the sub-region and the month, realizing the refinement and dynamic evaluation; by setting the total heat transfer amount, the score and the threshold, the thermal insulation performance evaluation result is quantized, intuitive and has a clear standard to follow, providing a reliable basis for scientific evaluation and optimization suggestion of the material thermal insulation performance, and improving the practicality and accuracy of the evaluation method.

[0145] Further, the calculation logic of the thermal insulation performance score is defined, and the score rule is determined based on the building total heat transfer amount, for example, the total heat transfer amount is compared with the benchmark building heat transfer amount, and the score is calculated according to the formula (such as ), wherein, is the thermal insulation performance score, is the total heat transfer amount of the building to be evaluated, is the benchmark building heat transfer amount, and the score range is set between [0, 1], and the higher the score, the better the thermal insulation performance.

[0146] According to the energy-saving design standard of the area where the building is located, the climate zoning requirement and the industry specification, combined with the functional type of the to-be-built building (such as residential building, public building), the score threshold is set, for example, divided into “excellent” ( ≥0.8), “good” (0.6≤ <0.8) and “poor” ( <0.6), and the qualified standard of thermal insulation performance corresponding to different levels is defined.

[0147] For the area corresponding to each thermal insulation performance evaluation sub-model (such as each functional partition and different orientation area), the thermal insulation performance score calculated by it is extracted to form a regional score list, ensuring that the score of each region is directly related to the heat transfer amount, material properties and environmental influence factors of the region.

[0148] The heat preservation performance score of each region is compared with the preset threshold to determine the heat preservation performance level of each region. For example, a south-facing room with a score of 0.85 corresponds to "excellent", and a north-facing room with a score of 0.55 corresponds to "poor".

[0149] The evaluation results of all regions are summarized to analyze the differences and causes of the heat preservation performance of different regions, such as whether the "poor" level region is caused by the high thermal conductivity of the material, the large area of the envelope structure, or the significant environmental temperature difference. Identify weak links in heat preservation performance.

[0150] In combination with the building material data of each region (such as material type and thermal parameters), the suitability of the material in the corresponding region is evaluated, such as whether the heat preservation material used in the "poor" level region meets the energy saving requirements of the climate zone, or the material parameters do not match the environmental characteristics of the region.

[0151] Based on the overall and regional evaluation levels, a comprehensive evaluation report is generated to clearly define the overall heat preservation performance level of the building and the specific performance of each region. For "poor" level regions, material optimization suggestions are proposed (such as replacing low thermal conductivity materials or increasing the thickness of the insulation layer), and for "excellent" and "good" level regions, material selection experience that can be promoted is summarized.

[0152] Verify the rationality of the evaluation results by comparing the scores of each region with the actual performance of similar building materials in similar environments. If the deviation is within an acceptable range (such as ±5%), the evaluation is confirmed to be effective; otherwise, go back to the heat transfer calculation, scoring rules or threshold setting steps and re-evaluate after correction.

[0153] Further, a building material heat preservation performance evaluation system based on big data is proposed to implement any of the above evaluation methods, comprising:

[0154] The acquisition module is used to acquire building structure data, building geographic location data, building region historical environment data, and building material data, and is also used to collect homogeneous building material data corresponding to the building material data;

[0155] The model management module is used to construct an initial heat preservation performance evaluation model, and is used to divide the initial heat preservation performance evaluation model based on the building geographic location data, building region historical environment data, and building structure data to obtain a heat preservation performance evaluation sub-model.

[0156] The data processing module is used for data preprocessing of the data collected by the acquisition module, and is used to obtain building material heat preservation performance influencing factors according to the homogeneous building material data and the heat preservation performance evaluation sub-model.

[0157] An evaluation module is configured to predict environment data based on the building area, combine historical environment data of the building area, simulate a service scenario of the building material, and evaluate the thermal insulation performance of the building material based on the thermal insulation performance evaluation sub-model.

[0158] A display module is configured to present the process and result of the evaluation of the thermal insulation performance of the building material to a user.

[0159] Further, the model management module comprises:

[0160] A model construction unit is configured to construct an initial thermal insulation performance evaluation model.

[0161] A model division unit is configured to divide the initial thermal insulation performance evaluation model based on geographical location data of the building area, historical environment data of the building area, and structural data of the building area, to obtain a thermal insulation performance evaluation sub-model.

[0162] Further, the data processing module comprises:

[0163] A data preprocessing unit is configured to preprocess data collected by the acquisition module.

[0164] An influence factor acquisition unit is configured to acquire an influence factor of the thermal insulation performance of the building material based on homogeneous building material data and the thermal insulation performance evaluation sub-model.

[0165] Further, the evaluation module comprises:

[0166] A service simulation unit is configured to simulate a service scenario of the building material based on predicted environment data of the building area and historical environment data of the building area.

[0167] A thermal insulation performance evaluation unit is configured to evaluate the thermal insulation performance of the building material based on the thermal insulation performance evaluation sub-model.

[0168] The application has the advantages that: the building material heat preservation performance full-process accurate evaluation is realized by relying on big data technology, structured data is obtained through a building information model, a multi-dimensional evaluation system is constructed by combining geographic information and meteorological data, and the data comprehensiveness and correlation are ensured; a sub-model division strategy is adopted, an evaluation unit is refined according to a functional division and an orientation, key influence factors are extracted by combining homogeneous material data, and the evaluation pertinence and scientificity are improved; historical and predicted environment data are fused to simulate a service scene, a heat transfer coefficient is dynamically corrected, heat transfer quantity is quantified, and a score threshold value is set, and the quantitative evaluation from material selection to a full life cycle is realized; the system integrates data acquisition, model management, evaluation analysis and other modules, manual intervention is reduced by automatic process, reliable decision support is provided for building material optimization and energy-saving design of a building to be built, and the evaluation accuracy, timeliness and practicability are considered.

[0169] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection required by the present application is defined by the appended claims and their equivalents.

Claims

1. A big data based building material thermal insulation performance evaluation method, characterized in that, The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device.

2. The building material thermal insulation performance evaluation method based on big data according to claim 1, characterized in that, The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal insulation performance evaluation method and device. The application relates to a building material thermal The connection node information matrix is associated with the corresponding building component type and the basic node type, the connection node information matrix is imported into a building component type-basic node type corresponding table synchronously, and a building component type-basic node type-connection node information matrix corresponding table is obtained; Based on the building component distribution position information and the building component type-basic node type-connection node information matrix corresponding table, a first building component and a first connection node appearing in a space boundary corresponding to the basic node type are determined; The first building component belonging to the thermal insulation material and the first connection node connected with the thermal insulation material are extracted, and thermal insulation performance influence node information is obtained; Based on the thermal insulation performance influence node information, combining the building design specification and the evaluation requirement, a preset proportion is set, the three-dimensional size information of the building component corresponding to the thermal insulation performance influence node is scaled, the actual size is converted into a virtual size recognizable by the model, and a thermal insulation performance initial evaluation model is constructed.

3. The building material thermal insulation performance evaluation method based on big data according to claim 1, characterized in that, The geographic location data of the building to be built is obtained, the historical environment data of the building to be built is obtained synchronously, and then the building to be built is divided into a model based on the geographic location data of the building to be built, the historical environment data of the building to be built and the building structure data, and a thermal insulation performance evaluation submodel is obtained, specifically including: The geographic location data of the building to be built is obtained through a geographic information system, including longitude, latitude, altitude and terrain characteristics; Based on the geographic location data of the building to be built, a meteorological data platform interface is called to obtain the historical environment data of the building to be built in the region; The historical environment data of the building to be built is analyzed, and key climate factors affecting the building thermal insulation performance are extracted; Combined with the building structure data, the building is preliminarily divided according to the building function partition and the orientation, and each partition is taken as a potential submodel evaluation unit; Based on the potential submodel evaluation unit, the building is refined and divided, and an independent thermal insulation performance evaluation submodel is constructed for each functional partition and orientation region, and the thermal insulation performance evaluation submodel includes the structure parameters, climate influence factors and submodel evaluation formula of the region.

4. The building material thermal insulation performance evaluation method based on big data according to claim 1, characterized in that, Based on the building structure data, the corresponding building material data is obtained, and based on the geographic location data of the building to be built and the historical environment data of the building to be built in the region, the same building material data corresponding to the building material data is collected, specifically including: The material information of each component is extracted from the building structure data to form a building material data list, which is taken as the building material data, including material type, manufacturer, model specification, physical performance parameters and thermal performance parameters; The same building material data determination standard is defined: the material type is the same, the production process and the specification parameter are consistent; Based on the geographic location data of the building to be built and the historical environment data of the building to be built in the region, the data collection space range is determined; In the determined space range, the building industry database, the engineering case library and the building material supplier information platform are used to screen the basic information of the completed building project which meets the same building material data determination standard; Based on the basic information of the built building project, the data of the insulation material used is recorded as homogeneous insulation material data, the corresponding homogeneous area historical environment data is synchronously collected, and the actual use performance data corresponding to the homogeneous insulation material data in the project is collected; The homogeneous insulation material data, the homogeneous area historical environment data and the actual use performance data are integrated to obtain homogeneous building material data.

5. The building material thermal insulation performance evaluation method based on big data according to claim 1, characterized in that, The building material insulation performance influencing factor is obtained according to the homogeneous building material data and the insulation performance evaluation sub-model, and specifically includes: The homogeneous insulation material data, the homogeneous area historical environment data and the actual use performance data corresponding to the insulation performance evaluation sub-model are obtained from the homogeneous building material data; The homogeneous thermal insulation material data, the homogeneous region historical environment data and the actual use performance data are preprocessed to remove abnormal values and fill in missing values, and the data formats are unified to obtain a material attribute set corresponding to a thermal insulation performance evaluation sub-model and an environmental impact attribute set , wherein, is a density, is a specific heat capacity, is a thermal conductivity, is a maximum temperature difference in the homogeneous region historical environment data, is a maximum humidity difference, is a maximum wind speed difference, is a service life in the actual use performance data; Integrate the material attribute set and the environmental impact attribute set corresponding to the thermal insulation performance evaluation sub-model to obtain an impact factor model input variable ; The random forest algorithm is used to construct an influencing factor model, and the characteristic importance of the building material thermal performance reference coefficient is determined simultaneously , meet ; Based on the building material thermal performance reference system coefficient, a key influencing factor is defined: wherein, is the contribution intensity value of the th variable, is the output variable of the influencing factor model, represents the marginal influence rate on ; Taking the variable of as the key influencing factor, denoted as ; A regression equation of influencing factors and heat preservation performance is established: wherein, is a predicted value of heat preservation performance, is a regression coefficient, is an error term, is a total number of influencing factors; The determination coefficient of the regression equation of the influencing factor and the insulation performance is obtained, and if the determination coefficient is greater than 0.8, the extracted key influencing factor is taken as the building material insulation performance influencing factor.

6. The building material thermal insulation performance evaluation method based on big data according to claim 5, characterized in that, The building material insulation performance is evaluated based on the insulation performance evaluation sub-model by simulating the service scene of the building material in combination with the historical environment data of the building area to be built, and specifically includes: Call the weather forecast API to obtain the predicted environment data of the region where the building to be built is located: wherein, is the monthly average temperature, is the monthly average humidity, is the monthly average wind speed; In combination with the historical environment data of the to-be-built building area, a time sequence fusion formula is adopted to define the service scene parameters, wherein the time fusion formula is: The defined service scene parameters are: In the formula, is the fusion environment feature data, is a prediction data weight coefficient, is the predicted environment data of the to-be-built building area, is the historical environment data of the to-be-built building area, is a preset service life of the building material; The building material insulation performance influencing factor is obtained, and the sub-model evaluation formula is corrected: wherein, is a factor fluctuation, is a modified heat transfer coefficient of the th sub-region, is a modified heat transfer coefficient of the th sub-region corresponding to the sub-model evaluation formula; Obtain the annual heat transfer of each sub-model corresponding to the region: wherein, is the annual heat transfer of the mth sub-region, is the annual heat transfer of the mth sub-region, is the sub-region index value, is the month index, is the building envelope area of the sub-region, is the indoor-outdoor temperature difference of the mth month; Obtaining the total heat transfer amount of the building: , synchronously obtaining the thermal insulation performance score, and setting a score threshold; The thermal insulation performance of the building materials in the region is evaluated based on the thermal insulation performance score and the score threshold, wherein is the number of sub-regions.

7. A big data based building material thermal insulation performance evaluation system for implementing the evaluation method according to any one of claims 1-6, characterized in that, The obtaining module is used to obtain building structure data, building geographic location data, building area historical environment data and building material data to be built, and is also used to collect homogeneous building material data corresponding to the building material data to be built; The model management module is used to construct an initial insulation performance evaluation model, and is used to divide the initial insulation performance evaluation model based on the building geographic location data, the building area historical environment data and the building structure data to be built to obtain an insulation performance evaluation sub-model; The data processing module is used to pre-process the data collected by the obtaining module, and is used to obtain the building material insulation performance influencing factor according to the homogeneous building material data and the insulation performance evaluation sub-model; The evaluation module is used to simulate the service scene of the building material in combination with the historical environment data of the building area to be built based on the predicted environment data of the building area to be built, and is used to evaluate the insulation performance of the building material based on the insulation performance evaluation sub-model; The display module is used to present the process and result of the building material insulation performance evaluation to the user. The model management module includes:

8. The building material thermal insulation performance evaluation system based on big data according to claim 7, characterized in that, The model construction unit is used to construct an initial insulation performance evaluation model; The model division unit is used to divide the initial insulation performance evaluation model based on the building geographic location data, the building area historical environment data and the building structure data to be built to obtain an insulation performance evaluation sub-model. The data processing module includes:

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

10. The building material thermal insulation performance evaluation system based on big data according to claim 7, characterized in that, ​ The service simulation unit is used for predicting environment data based on the to-be-built building area, simulating a building material service scene in combination with historical environment data of the to-be-built building area. The thermal insulation performance evaluation unit is used for evaluating the thermal insulation performance of the building material based on a thermal insulation performance evaluation sub-model.

Images