Method and device for analyzing energy consumption based on building elements

Abstract

The invention discloses a method and a device for analyzing energy consumption based on building elements. The method comprises the following steps: collecting data related to the building elements and energy information; building elements are extracted from the collected data, including image extraction and text extraction, extracted image features and text features are fused, and association between the image features and the text features is established; integrating the collected energy data and building element data, and establishing an association relationship between building elements and energy consumption; creating an energy analysis model based on the integrated data, wherein the energy analysis model is used for analyzing and predicting the consumption of building energy and providing energy optimization suggestions; and presenting an analysis result to a user in a visual manner, and providing an interactive interface. According to the method, the high-precision energy analysis model is established by using the extraction result of the building elements and combining the energy consumption data of the building, so that the energy consumption condition of the building can be better predicted, and corresponding energy-saving suggestions and optimization schemes are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent construction engineering information, and in particular to a method and device for analyzing energy consumption based on building elements. Background Art

[0002] With the intensification of the energy crisis and the severity of environmental problems, building energy consumption has become a global focus. Building energy efficiency design aims to reduce building energy consumption and alleviate environmental pressure. To achieve this goal, architects and engineers need to extract building elements and conduct energy analysis.

[0003] Existing building energy analysis methods usually require manual input of building elements, such as building materials, building shape, building orientation, etc. This method is time-consuming and error-prone, and obtaining and processing this information may require professional knowledge. In addition, existing computer-aided design (CAD) software has limited functions in energy analysis and is difficult to meet the needs of building energy efficiency design. Summary of the invention

[0004] In view of the above technical problems in the related art, the present invention provides a method and device for analyzing energy consumption based on building elements, which can solve the above problems.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows: A method for analyzing energy consumption based on building elements comprises the following steps: S100, Data Collection: Collect data related to building elements and energy information; S200, architectural element extraction: extracting architectural elements from the collected data, including image extraction and text extraction, and fusing the extracted image features and text features to establish associations between the image features and the text features; S300, data integration: Integrate the collected energy data with building element data to establish the correlation between building elements and energy consumption; S400, create energy analysis models based on integrated data: used to analyze and predict building energy consumption and provide energy optimization suggestions; S500, Result Display and Interaction: The analysis results are presented to the user in a visual manner and an interactive interface is provided so that the user can further explore the data and optimization solutions.

[0006] Furthermore, the data collected in step S100 related to building elements and energy information include: building drawings, energy usage data, environmental data, and building construction specification content.

[0007] Furthermore, the image extraction in step S200 includes: preprocessing of image data, feature extraction, target detection and segmentation, and creation of an image extraction model; the text extraction in step S200 includes: text preprocessing; using TF-IDF to calculate word frequency and inverse document frequency, extracting words with high TF-IDF values ​​as keywords for further analysis; applying TextRank to construct a word co-occurrence graph, calculating the PageRank value of each word, and extracting important vocabulary; training or loading a pre-trained word vector model, calculating the vector of the word, and performing semantic analysis; and combining the results of TF-IDF, TextRank, and the word vector model to extract key elements in the architectural text.

[0008] Furthermore, step S300 specifically includes the following steps: S310, data cleaning: clean the collected energy usage data to remove duplicate, erroneous or inconsistent data; S320, data matching: matching the cleaned energy usage data with the building element data to determine the corresponding relationship between each building element and the energy usage data; S330, Data Fusion: Fusion of building element data and energy usage data, by creating a unified database, integrating data from different sources into a unified platform; S340, data verification: check whether the fused data is complete and accurate, and whether it complies with the predetermined data structure and specifications; S350, data storage and management: After completing data integration and verification, the integrated data is stored in the database.

[0009] Furthermore, step S400 specifically includes the following steps: S410, model structure determination: choose to use decision tree and neural network algorithms to build the model framework; S420, data preparation: preprocessing the data integrated in step S300; S430, model training: using the prepared data to train the decision tree and neural network algorithms, and optimizing the performance of the model by adjusting the model parameters; S440, model evaluation and optimization: Use the test data set to evaluate the trained energy analysis model, calculate the model's accuracy, recall rate, and F1 value indicators, and adjust the model parameters for iterative optimization based on the evaluation results.

[0010] Further, step S420 specifically includes the following steps: S421. Standardize the integrated data, including converting different formats and units of data; S422. Analyze the relationship between building elements and energy usage data through correlation analysis and cluster analysis methods; S423. Evaluate the energy efficiency of the building based on the correlation analysis results and energy indicators.

[0011] An analysis device, comprising: A data input module, used for inputting building element data; Building element extraction module, used to extract building elements and fuse the extracted image features with text features; An energy analysis module, in which the energy analysis model is deployed, analyzes and predicts the building energy consumption based on the extracted building element data, and provides energy optimization suggestions; The result display and interaction module is used to present the analysis results to users in a visual way and provide an interactive interface so that users can further explore data and optimization solutions.

[0012] Furthermore, the data collection module includes a scanning unit and a file importing unit for acquiring architectural drawings, environmental data, and building construction specification contents.

[0013] Furthermore, the building element extraction module includes an image extraction unit and a text extraction unit, which are used to extract images and texts respectively.

[0014] Furthermore, the result display and interaction module includes a display interaction instrument for information display interaction.

[0015] Beneficial effects of the present invention: This application utilizes the extraction results of architectural elements, combined with the energy consumption data of the building, to establish a high-precision energy analysis model, which can better predict the energy consumption of the building and provide corresponding energy-saving suggestions and optimization solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] The present invention is further described in detail below with reference to the accompanying drawings.

[0018] Figure 1 It is a flow chart of a method for analyzing energy consumption based on building elements according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0020] like Figure 1 As shown, according to the present invention, a method for analyzing energy consumption based on building elements is disclosed, comprising the following steps: S100, data collection: collecting data related to building elements and energy information; S200, building element extraction: extracting building elements from the collected data, including image extraction and text extraction, and fusing the extracted image features and text features to establish an association between the image features and the text features; S300, data integration: integrating the collected energy data with the building element data to establish an association between the building elements and energy consumption; S400, creating an energy analysis model based on the integrated data: used to analyze and predict the energy consumption of the building, and provide energy optimization suggestions; S500, result display and interaction: presenting the analysis results to the user in a visual manner, and providing an interactive interface so that the user can further explore the data and optimization solutions. An analysis device is also provided, comprising: a data input module, a building element extraction module, an energy analysis module, and a result display and interaction module, wherein the data input module may be a scanning instrument, which scans and imports building drawings, environmental data, and building construction specification contents into the device, or directly imports them into the device in various file formats (CAD, word), the building element extraction module comprises an image extraction unit and a text extraction unit, which can extract required images and text contents, and associate and fuse them, an energy analysis model is deployed in the energy analysis module, and based on the extracted building element data, the building energy consumption is analyzed and predicted, and energy optimization suggestions are provided, and finally information display and interaction are performed in the result display and interaction module, wherein the display and interaction module may be a touch screen.

[0021] Embodiment 1: This application collects data related to building elements and energy information, including building drawings, energy usage data, environmental data, building construction specifications, etc., among which environmental data, building construction specifications, etc. are used to analyze auxiliary restriction conditions of energy consumption. For example, environmental data and building construction specifications stipulate building construction conditions. The collected data is used to create an energy analysis model. If the collected data is paper material, it can be scanned and imported by a scanning instrument. If it is in computer file format, it can be directly imported through the file import unit.

[0022] Embodiment 2: The architectural element extraction in this application includes image extraction and text extraction.

[0023] Image extraction mainly includes the following steps: Preprocessing of image data: Enhance and resize the image; Feature extraction: Use CNN to automatically extract image features. Common network architectures such as VGG, ResNet, Inception, etc. can identify complex building features; Target detection and segmentation: Apply YOLO (You Only Look Once, used to detect targets in buildings in real time, such as windows, doors, etc.) and SSD (Single Shot MultiBox Detector, quickly detect different objects in buildings) for target detection, and Mask R-CNN for instance segmentation: Generate pixel-level segmentation masks for each detected object, which is suitable for fine-grained building feature extraction; Create an image extraction model: Select a model architecture suitable for the task, such as a target detection model (YOLO, SSD) or a segmentation model (MaskR-CNN) to build an image extraction model for image extraction.

[0024] The text extraction mainly includes the following steps: Text preprocessing: including word segmentation (splitting text into words or phrases) and removal of stop words (eliminating common words that are not helpful for analysis); using TF-IDF to calculate term frequency (TF) and inverse document frequency (IDF), extracting words with high TF-IDF values ​​as keywords for further analysis; applying TextRank to construct a word co-occurrence graph, calculating the PageRank value of each word, and extracting important vocabulary; using word vector models: training or loading pre-trained word vector models (such as Word2Vec, GloVe), calculating word vectors, and performing semantic analysis; integrating results: combining the results of TF-IDF, TextRank, and word vector models to extract key elements in architectural texts, such as building materials, structural types, etc.

[0025] Finally, the features obtained from image recognition and text analysis are fused through splicing, weighting, etc. to establish the association between image features and text features, determine which image features correspond to which text keywords, and form a comprehensive description of architectural elements.

[0026] Embodiment three: The data integration of this application mainly includes the following steps: Data cleaning: Clean the collected energy usage data to remove duplicate, erroneous or inconsistent data. Check the data for completeness, accuracy and compliance with the predetermined data format and standards. The cleaning process involves data verification, outlier processing, missing value filling and other operations.

[0027] Data matching: Match the cleaned energy usage data with the building element data. Determine the correspondence between each building element (such as walls, windows, doors, etc.) and the energy usage data based on the information in the data source such as building drawings and construction specifications. The matching process involves operations such as data encoding, labeling, and association rule mining.

[0028] Data fusion: Building element data is integrated with energy usage data. By creating a unified database, data from different sources are integrated into a unified platform. During the data fusion process, the integrity and consistency of the data are ensured to avoid data redundancy and conflict.

[0029] Data verification: Check whether the fused data is complete and accurate, and whether it conforms to the predetermined data structure and specifications. The verification process involves operations such as data comparison, logic verification, and visual inspection.

[0030] Data storage and management: After data integration and verification, the integrated data is stored in the database and effectively managed. This includes data backup, recovery, access control and other operations to ensure data security and availability.

[0031] Embodiment 4: The creation of the energy analysis model in this application mainly includes the following steps: Model structure determination: For building design parameters (such as wall materials, window area, orientation, etc.), environmental parameters (such as temperature, humidity, wind speed, etc.), equipment efficiency parameters, historical energy usage data, etc., feature selection is performed through expert knowledge, correlation analysis, feature importance evaluation and other methods. Decision trees and neural network algorithms are selected to build the model framework, and the input and output formats of the data are defined.

[0032] Data preparation: Preprocess the integrated data. Standardize the integrated data, including converting different formats and units of the data; analyze the correlation between building elements and energy use data through correlation analysis and cluster analysis methods; evaluate the energy efficiency of the building based on the correlation analysis results and energy indicators (energy consumption, energy efficiency index, etc.) to obtain the corresponding evaluation structure. Divide the prepared data into test set, training set, and validation set for creating the model.

[0033] Model training: Use training set data to train decision trees and neural network algorithms, and optimize model performance by adjusting model parameters.

[0034] Model evaluation and optimization: Use the test set data to evaluate the trained model and calculate the model's accuracy, recall rate, F1 value and other indicators; adjust the model parameters according to the evaluation results for iterative optimization.

[0035] After verifying the accuracy of the energy analysis model, the analysis and prediction of energy consumption can be completed by inputting new building elements. Based on the results of the analysis and prediction, energy optimization suggestions can be provided, including changing the design of the optimized building, improving the efficiency of equipment, adjusting energy management strategies, etc.

[0036] In summary: combining building element extraction technology with energy analysis technology to achieve seamless integration and mutual complementation of the two, this integrated method can save time and resources and improve efficiency and accuracy in the process of building design and energy assessment; through deep learning, computer vision and other technologies, various elements in the building model, such as walls, windows, doors, etc., can be automatically identified, and building information can be quickly and accurately extracted to provide a reliable data basis for subsequent energy analysis; using the extraction results of building elements, combined with the energy consumption data of the building, a high-precision energy analysis model is established, which can better predict the energy consumption of the building and provide corresponding energy-saving suggestions and optimization solutions; obtaining building elements and energy data from different data sources and integrating and analyzing them can provide a more comprehensive understanding of the structural characteristics and energy consumption of the building, and provide more accurate energy analysis and evaluation; based on the method of energy consumption monitoring based on building element analysis, an intelligent decision support system can be developed. Through the comprehensive analysis of building elements and energy data, the system can give targeted energy optimization suggestions to help building designers, energy managers, etc. make more scientific and feasible decisions.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing energy consumption based on building elements, characterized in that: The following steps are involved: S100, Data Collection: Collect data related to building elements and energy information; S200, architectural element extraction: extracting architectural elements from the collected data, including image extraction and text extraction, and fusing the extracted image features and text features to establish associations between the image features and the text features; S300, data integration: Integrate the collected energy data with building element data to establish the correlation between building elements and energy consumption; S400, create energy analysis models based on integrated data: used to analyze and predict building energy consumption and provide energy optimization suggestions; S500, Result Display and Interaction: The analysis results are presented to the user in a visual manner and an interactive interface is provided so that the user can further explore the data and optimization solutions.

2. A method for analyzing energy consumption based on building elements according to claim 1, characterized in that: The data collected in step S100 related to building elements and energy information include: building drawings, energy usage data, environmental data, and building construction specification content.

3. The method for analyzing energy consumption based on building elements according to claim 1, characterized in that: The image extraction in step S200 includes: preprocessing of image data, feature extraction, target detection and segmentation, and creation of an image extraction model; the text extraction in step S200 includes: text preprocessing; using TF-IDF to calculate word frequency and inverse document frequency, extracting words with high TF-IDF values ​​as keywords for further analysis; applying TextRank to construct a word co-occurrence graph, calculating the PageRank value of each word, and extracting important vocabulary; training or loading a pre-trained word vector model, calculating the vector of the word, and performing semantic analysis; and extracting key elements in the architectural text by combining the results of TF-IDF, TextRank, and the word vector model.

4. The method for analyzing energy consumption based on building elements according to claim 1, characterized in that: Step S300 specifically includes the following steps: S310, data cleaning: clean the collected energy usage data to remove duplicate, erroneous or inconsistent data; S320, data matching: matching the cleaned energy usage data with the building element data to determine the corresponding relationship between each building element and the energy usage data; S330, Data Fusion: Fusion of building element data and energy usage data, by creating a unified database, integrating data from different sources into a unified platform; S340, data verification: check whether the fused data is complete and accurate, and whether it complies with the predetermined data structure and specifications; S350, data storage and management: After completing data integration and verification, the integrated data is stored in the database.

5. The method for analyzing energy consumption based on building elements according to claim 1, characterized in that: Step S400 specifically includes the following steps: S410, model structure determination: choose to use decision tree and neural network algorithms to build the model framework; S420, data preparation: preprocessing the data integrated in step S300; S430, model training: using the prepared data to train the decision tree and neural network algorithms, and optimizing the performance of the model by adjusting the model parameters; S440, model evaluation and optimization: Use the test data set to evaluate the trained energy analysis model, calculate the model's accuracy, recall rate, and F1 value indicators, and adjust the model parameters for iterative optimization based on the evaluation results.

6. The method for analyzing energy consumption based on building elements according to claim 5, characterized in that: Step S420 specifically includes the following steps: S421. Standardize the integrated data, including converting different formats and units of data; S422. Analyze the relationship between building elements and energy usage data through correlation analysis and cluster analysis methods; S423. Evaluate the energy efficiency of the building based on the correlation analysis results and energy indicators.

7. An analysis device, characterized in that: include: A data input module, used for inputting building element data; Building element extraction module, used to extract building elements and fuse the extracted image features with text features; An energy analysis module, in which the energy analysis model according to any one of claims 1 to 6 is deployed, which analyzes and predicts the energy consumption of the building based on the extracted building element data, and provides energy optimization suggestions; The result display and interaction module is used to present the analysis results to users in a visual way and provide an interactive interface so that users can further explore data and optimization solutions.

8. The method for analyzing energy consumption based on building elements according to claim 7, characterized in that: The data collection module includes a scanning unit and a file importing unit, which are used to obtain architectural drawings, environmental data, and building construction specification contents.

9. The method for analyzing energy consumption based on building elements according to claim 7, characterized in that: The building element extraction module includes an image extraction unit and a text extraction unit, which are used to extract images and texts respectively.

10. The method for analyzing energy consumption based on building elements according to claim 7, characterized in that: The result display and interaction module includes a display interaction instrument for information display interaction.

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