Data asset intelligent prediction and analysis platform based on BIM technology and method thereof

Abstract

The invention relates to the technical field of deep fusion of a building information model technology and a big data and artificial intelligence technology, in particular to a data asset intelligent prediction and analysis platform and method based on a BIM technology, and the method comprises the steps: S1, standardization and integrated management of BIM data assets, including S11, data standardization interface design; s12, multi-source data fusion is carried out; s2, intelligent data preprocessing and cleaning: S21, automatic data cleaning; s22, performing feature engineering; s3, constructing an intelligent prediction model: S31, constructing a prediction algorithm library; s32, selecting a self-adaptive model; s4, time sequence analysis and trend prediction; s5, multi-dimensional data analysis and visualization: S51, a multi-dimensional analysis framework; s52, an interactive visual interface is established; s53, generating and sharing a report; s6, intelligent decision support and optimization suggestion: S61, risk early warning; s62, cost optimization is carried out; s63, the efficiency is improved; s7, security and privacy protection: S71, data encryption; s72, access control is carried out; and S73, privacy protection is carried out. The method not only inherits the advantages of the BIM technology in the aspects of three-dimensional visualization and information integration, but also realizes deep mining, intelligent prediction and accurate analysis of BIM data assets by introducing a big data processing and machine learning algorithm, and provides unprecedented technical support for full-life-cycle management of building projects.

Description

Technical Field

[0001] The present invention relates to the field of deep integration of building information modeling technology with big data and artificial intelligence technology, and in particular to a data asset intelligent prediction and analysis platform and method based on BIM technology. Background Art

[0002] With the digital transformation of the construction industry, BIM technology has become an important means to improve project management efficiency, optimize design solutions, and reduce construction costs. However, traditional BIM applications are mainly focused on model creation and collision detection in the design phase, and the in-depth mining and utilization of the massive data assets contained in the BIM model are still insufficient. At the same time, with the rapid development of big data and artificial intelligence technologies, how to use these advanced technologies to intelligently process and analyze BIM data to support the refined management of the entire life cycle of the project has become an urgent problem to be solved. The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form of implication that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0003] The purpose of the present invention is to provide a data asset intelligent prediction and analysis platform and method based on BIM technology to solve the technical problems existing in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a data asset intelligent prediction and analysis method based on BIM technology, which comprises: S1. Standardization and integrated management of BIM data assets, including: S11. Data standardization interface design; S12. Multi-source data fusion; S2, intelligent data preprocessing and cleaning, including: S21, automated data cleaning; S22, feature engineering; S3, intelligent prediction model construction, including: S31, prediction algorithm library; S32, adaptive model selection; S4. Time series analysis and trend forecasting; S5. Multi-dimensional data analysis and visualization, including: S51. Multi-dimensional analysis framework; S52. Interactive visualization interface; S53. Report generation and sharing; S6. Intelligent decision support and optimization suggestions, including: S61. Risk warning; S62. Cost optimization; S63. Efficiency improvement; S7. Security and privacy protection, including: S71. Data encryption; S72. Access control; S73. Privacy protection.

[0005] Preferably, step S11 of data standardization interface design includes: Interface specification formulation: Based on international and domestic BIM standards, formulate unified BIM data exchange formats and interface specifications to ensure seamless connection of BIM data on different software platforms and at different design stages; Compatibility testing: Establish a BIM data compatibility testing platform to conduct compatibility testing on various BIM software to ensure the accuracy and integrity of data exchange; Preferably, step S12 of multi-source data fusion includes: Data source identification and access: Identify and access multi-source data to provide geographic location information, real-time operating status, and environmental monitoring data of construction projects, providing rich supplementary information for BIM models; Data fusion algorithm: Develop efficient data fusion algorithm to deeply integrate multi-source data with BIM model and build a comprehensive, dynamic and multi-dimensional building information database; Preferably, step S21 of automated data cleaning includes: Error detection: Use machine learning algorithms to automatically identify error types in BIM data and mark them for subsequent processing; Data correction: Design corresponding correction strategies according to the error type to automatically correct errors in BIM data; Redundant data elimination: Identify and eliminate duplicate records and redundant information in BIM data; Preferably, step S22 feature engineering includes: Feature selection: Extract key features from BIM data based on the analysis objectives; Feature transformation: Perform necessary transformation on the extracted features.

[0006] Preferably, the prediction algorithm library in step S31 includes: Algorithm integration: Integrate multiple advanced machine learning algorithms and deep learning models to form a rich prediction algorithm library; Algorithm evaluation: Through cross-validation and leave-one-out evaluation methods, the performance of algorithms in the algorithm library is evaluated and compared, and appropriate algorithms are selected for different prediction tasks; Preferably, step S32 of adaptive model selection includes: Model selection strategy: Design an adaptive model selection strategy to automatically select the optimal prediction model or model combination based on data characteristics and prediction task requirements; Model optimization: Use hyperparameter tuning, ensemble learning and other techniques to optimize the selected model to further improve prediction accuracy and robustness.

[0007] Preferably, step S4 of time series analysis and trend prediction includes: Time series modeling: Based on the time series data in the construction project, the prediction model is established using time series analysis technology; Trend forecasting: Predict future trends based on time series models, and provide forecast intervals and confidence levels to provide forward-looking guidance for project decisions.

[0008] Preferably, the multi-dimensional analysis framework of step S51 includes: Dimension definition: Define a multi-dimensional analysis framework based on the characteristics and management requirements of the construction project; Dimension association: Establish associations and hierarchical structures between dimensions, and support users to conduct cross-analysis and combined queries between different dimensions; Preferably, the interactive visual interface in step S52 includes: 3D visualization: Use the 3D graphics library to present the BIM model in 3D on a web page or mobile device, allowing users to observe from all angles; Chart display: The analysis results are displayed on the interface in the form of bar charts, line charts, pie charts, scatter charts, etc., and users are supported to customize the chart style and layout; Interactive operation: Provides rich interactive operation functions, allowing users to easily explore and compare data.

[0009] Preferably, step S53 report generation and sharing includes: Report template design: design a variety of report templates to meet users' different reporting needs; Automatically fill in data: automatically extract data from the database and fill in the report according to the report template and data range selected by the user; One-click sharing: Supports exporting generated reports in PDF and Excel formats and sharing them with project teams, management and stakeholders with one click to promote information sharing and collaborative decision-making.

[0010] Preferably, step S61 risk warning; Risk identification: Automatically identify potential risk points in the project based on predictive analysis results and preset risk thresholds; Risk assessment: Quantitatively evaluate the identified risk points and provide the probability and impact of the risk; Early warning signal: Early warning signals are sent to project managers through pop-up prompts and email notifications, with detailed risk descriptions and response measures; Preferably, step S62 of cost optimization includes: Cost forecasting: forecasting the total cost of a project based on historical costs and future forecast data; Cost comparison analysis: Compare and analyze the cost-effectiveness of different design and construction plans to find out the potential points for cost savings; Optimization suggestions: Provide cost optimization suggestions to project managers based on the results of cost comparison analysis; Preferably, S63 efficiency improvements include: Construction progress forecast: forecast the construction progress of the project based on historical construction progress data and future forecast data; Resource demand forecast: forecast resource demand at each stage in the future based on the construction progress forecast results; Resource allocation optimization: Optimize resource allocation plans based on resource demand forecast results.

[0011] Preferably, step S71 data encryption includes: Storage encryption: Encrypt sensitive data stored in the database to ensure the security of the data during storage; Transmission encryption: HTTPS security protocol is used to encrypt the data transmission process to prevent data from being intercepted or tampered with during transmission; Preferably, step S72 access control includes: User authentication: multiple authentication methods such as username + password, fingerprint recognition, and facial recognition are used to verify user identities to ensure that only legitimate users can access the platform; Permission allocation: Different data access rights and operation rights are allocated according to user roles and responsibilities to prevent unauthorized access and operation; Permission audit: record user data access and operation logs, support post-audit and tracking, and ensure operational compliance; Preferably, step S73 privacy protection includes: Anonymization: Anonymize data involving user privacy to ensure that user privacy is not leaked; Privacy Policy: Establish a clear privacy policy and inform users, clearly informing users of how their data is collected, used, stored and shared, as well as the rights and obligations of users.

[0012] In a second aspect, the present invention provides a data asset intelligent prediction and analysis platform based on BIM technology, which adopts the data asset intelligent prediction and analysis method based on BIM technology.

[0013] By adopting the above technical solution, the present invention has the following beneficial effects: 1. Technical Effect: The technical effect of the present invention is remarkable, which is mainly reflected in the following aspects: First, through the standardization and integrated management of BIM data assets, seamless data connection across platforms and stages is achieved, which significantly improves the consistency and availability of data; secondly, the application of intelligent data preprocessing and cleaning technology effectively solves the problem of data quality and lays a solid foundation for subsequent analysis; thirdly, the construction of intelligent prediction model realizes accurate prediction of the future status of the project and provides a scientific basis for project decision-making; finally, multi-dimensional data analysis and visualization functions make complex data intuitive and easy to understand, which enhances the transparency and efficiency of project management.

[0014] 2. Economic Benefits: From the perspective of economic benefits, the application of the present invention can significantly reduce project costs and improve resource utilization efficiency. Through cost optimization suggestions and resource allocation optimization, project managers can more effectively control costs and avoid unnecessary waste. At the same time, the intelligent prediction function helps to discover and solve potential problems in advance, reducing additional costs caused by delays. In addition, the efficient operation of the platform can also shorten the project cycle and improve the overall profitability of the project.

[0015] 3. Social benefits: In terms of social benefits, the application of the present invention has promoted the digital transformation of the construction industry and promoted the deep integration of information technology and the construction industry. This not only improves the level of intelligence in construction projects, but also drives the development of related industrial chains and creates more employment opportunities. At the same time, through accurate prediction and scientific decision-making, the present invention helps to reduce the impact of construction projects on the environment and promote the implementation of green buildings and sustainable development concepts. In addition, the platform's data sharing and collaborative decision-making functions also promote communication and collaboration between project teams, and enhance team cohesion and execution. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 A flow chart of a data asset intelligent prediction and analysis method based on BIM technology provided by an embodiment of the present invention; Figure 2 A flowchart of intelligent decision support and optimization suggestions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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 without creative work are within the scope of protection of the present invention.

[0019] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0020] Combination Figure 1 to Figure 2 As shown, this embodiment provides a data asset intelligent prediction and analysis method based on BIM technology, which includes: S1. Standardization and integrated management of BIM data assets, including: Standardization and integrated management of BIM data assets is the basis for building an intelligent prediction and analysis platform. Since BIM data comes from multiple design stages and multiple participants, its formats are diverse and its standards are inconsistent, which brings huge challenges to the unified management and analysis of data. Therefore, the present invention is first committed to solving the problem of standardization and integration of BIM data.

[0021] S11. Data standardization interface design; Interface specification formulation: Based on international and domestic BIM standards (such as IFC, GB / T, etc.), formulate unified BIM data exchange formats and interface specifications to ensure that BIM data on different software platforms and different design stages can be seamlessly connected.

[0022] Compatibility testing: Establish a BIM data compatibility testing platform to conduct compatibility testing on various BIM software to ensure the accuracy and completeness of the data exchange process.

[0023] S12, multi-source data fusion; Data source identification and access: Identify and access multi-source data such as GIS, IoT, sensor networks, etc. These data sources can provide geographic location information, real-time operating status, environmental monitoring data, etc. of the construction project, providing rich supplementary information for the BIM model.

[0024] Data fusion algorithm: Develop an efficient data fusion algorithm to deeply integrate multi-source data with BIM models and build a comprehensive, dynamic, and multi-dimensional building information database. The algorithm needs to consider the temporal synchronization, spatial consistency, and semantic interoperability of the data.

[0025] S2. Intelligent data preprocessing and cleaning, including: In the process of collecting, transmitting and storing BIM data, it is inevitable that errors, redundancy and inconsistency will occur, which will seriously affect the accuracy and reliability of data analysis. Therefore, intelligent data preprocessing and cleaning are key steps to ensure data quality.

[0026] S21, automated data cleaning; Error detection: Use machine learning algorithms to automatically identify error types in BIM data (such as format errors, logical errors, missing values, etc.) and mark them for subsequent processing.

[0027] Data correction: According to the error type, design corresponding correction strategies (such as interpolation, replacement, deletion, etc.) to automatically correct errors in BIM data.

[0028] Redundant data elimination: Identify and eliminate duplicate records and redundant information in BIM data, reduce data storage burden, and improve data analysis efficiency.

[0029] S22, feature engineering; Feature selection: Extract key features from BIM data based on the analysis objectives (such as cost prediction, schedule prediction, risk assessment, etc.). These features should be representative, interpretable, and easy to calculate.

[0030] Feature conversion: Perform necessary conversion processing (such as normalization, standardization, encoding, etc.) on the extracted features to improve the training effect and prediction accuracy of the machine learning model.

[0031] S3, intelligent prediction model construction, including: The intelligent prediction model is the core component of the platform. It uses machine learning algorithms to analyze and model BIM data to predict the future status of the project. The intelligent prediction model of the present invention has the characteristics of self-adaptation, high precision, and scalability.

[0032] S31, prediction algorithm library; Algorithm integration: Integrate multiple advanced machine learning algorithms (such as linear regression, logistic regression, decision tree, random forest, gradient boosting tree, etc.) with deep learning models (such as convolutional neural network, recurrent neural network, long short-term memory network, etc.) to form a rich prediction algorithm library.

[0033] Algorithm evaluation: Through cross-validation, leave-one-out method and other evaluation methods, the performance of algorithms in the algorithm library is evaluated and compared, and appropriate algorithms are selected for different prediction tasks.

[0034] S32, adaptive model selection; Model selection strategy: Design an adaptive model selection strategy to automatically select the optimal prediction model or model combination based on data characteristics and prediction task requirements. This strategy needs to consider factors such as the model's prediction accuracy, training time, and generalization ability.

[0035] Model optimization: Use hyperparameter tuning, ensemble learning and other techniques to optimize the selected model to further improve prediction accuracy and robustness.

[0036] S4. Time series analysis and trend forecasting, including: Time series modeling: For time series data in construction projects (such as construction progress, cost consumption, material usage, etc.), time series analysis technologies such as ARIMA and LSTM are used to establish prediction models.

[0037] Trend forecasting: Predict future trends based on time series models, and provide forecast intervals and confidence levels to provide forward-looking guidance for project decisions.

[0038] S5. Multi-dimensional data analysis and visualization, including: Multi-dimensional data analysis and visualization is one of the important functions of the platform. It can transform complex BIM data into intuitive and easy-to-understand graphical representations, helping users quickly understand the information and rules behind the data.

[0039] S51, multidimensional analysis framework; Dimension definition: Based on the characteristics and management requirements of the construction project, define a multi-dimensional analysis framework including time, space, cost, quality, and safety.

[0040] Dimension association: Establish associations and hierarchies between dimensions, and support users to perform cross-analysis and combined queries between different dimensions.

[0041] S52, interactive visual interface; 3D visualization: Use 3D graphics libraries such as WebGL and Three.js to present BIM models in 3D on web pages or mobile devices, allowing users to observe them in all directions through operations such as rotation, zooming, and translation.

[0042] Chart display: The analysis results are displayed on the interface in the form of bar charts, line charts, pie charts, scatter charts, etc., supporting users to customize chart styles and layouts.

[0043] Interactive operations: Provides a wealth of interactive operation functions (such as click query, filter, drag and drop sort, etc.), allowing users to easily explore and compare data.

[0044] S53, report generation and sharing; Report template design: Design a variety of report templates (such as project progress report, cost analysis report, risk assessment report, etc.) to meet users' different reporting needs.

[0045] Automatically fill in data: Automatically extract data from the database and fill it into the report based on the report template and data range selected by the user.

[0046] One-click sharing: Supports exporting generated reports in PDF, Excel and other formats and sharing them with project teams, management and stakeholders with one click to promote information sharing and collaborative decision-making.

[0047] S6. Intelligent decision support and optimization suggestions, including: Intelligent decision support and optimization suggestions are one of the advanced functions of the platform. It provides project managers with scientific decision-making basis and optimization suggestions based on the results of intelligent prediction and analysis.

[0048] S61, risk warning; Risk identification: Automatically identify potential risk points in the project (such as cost overruns, schedule delays, substandard quality, etc.) based on predictive analysis results and preset risk thresholds.

[0049] Risk assessment: Quantitatively evaluate the identified risk points and provide the probability and impact of the risk.

[0050] Early warning signals: Early warning signals are sent to project managers through pop-up prompts, email notifications, etc., with detailed risk descriptions and response measures.

[0051] S62, cost optimization; Cost forecasting: Predict the total cost of a project based on historical costs and future forecast data.

[0052] Cost comparison analysis: Compare and analyze the cost-effectiveness of different design and construction plans to find potential points for cost savings.

[0053] Optimization suggestions: Provide project managers with cost optimization suggestions (such as material replacement, process improvement, process optimization, etc.) based on the results of cost comparison analysis.

[0054] S63, efficiency improvement; Construction progress forecast: Forecast the construction progress of the project based on historical construction progress data and future forecast data.

[0055] Resource demand forecast: forecast resource demand (such as manpower, material resources, financial resources, etc.) in each future stage based on the construction progress forecast results.

[0056] Resource allocation optimization: Optimize resource allocation plans (such as personnel deployment, material procurement, capital arrangement, etc.) based on resource demand forecast results to improve construction efficiency and resource utilization.

[0057] S7. Security and privacy protection, including: Security and privacy protection is an integral part of the platform, which is related to the security and privacy of user data. The present invention fully considers the needs of security and privacy protection in the process of platform design and implementation.

[0058] S71, data encryption; Storage encryption: Encrypt sensitive data stored in the database (such as AES encryption) to ensure the security of the data during storage.

[0059] Transmission encryption: Use security protocols such as HTTPS to encrypt the data transmission process to prevent data from being intercepted or tampered with during transmission.

[0060] S72, access control; User authentication: Multiple authentication methods such as username + password, fingerprint recognition, and facial recognition are used to verify user identity to ensure that only legitimate users can access the platform.

[0061] Permission allocation: Assign different data access rights and operation permissions according to user roles and responsibilities to prevent unauthorized access and operation.

[0062] Permission audit: records user data access and operation logs, supports post-audit and tracking, and ensures operational compliance.

[0063] S73, privacy protection; Anonymization processing: Anonymize data involving user privacy (such as desensitization) to ensure that user privacy is not leaked.

[0064] Privacy Policy: Establish a clear privacy policy and inform users, clearly informing users of how their data is collected, used, stored and shared, as well as the rights and obligations of users.

[0065] In summary, the present invention has the following beneficial effects: 1. Technical Effect: The technical effect of the present invention is remarkable, which is mainly reflected in the following aspects: First, through the standardization and integrated management of BIM data assets, seamless data connection across platforms and stages is achieved, which significantly improves the consistency and availability of data; secondly, the application of intelligent data preprocessing and cleaning technology effectively solves the problem of data quality and lays a solid foundation for subsequent analysis; thirdly, the construction of intelligent prediction model realizes accurate prediction of the future status of the project and provides a scientific basis for project decision-making; finally, multi-dimensional data analysis and visualization functions make complex data intuitive and easy to understand, which enhances the transparency and efficiency of project management.

[0066] 2. Economic Benefits: From the perspective of economic benefits, the application of the present invention can significantly reduce project costs and improve resource utilization efficiency. Through cost optimization suggestions and resource allocation optimization, project managers can more effectively control costs and avoid unnecessary waste. At the same time, the intelligent prediction function helps to discover and solve potential problems in advance, reducing additional costs caused by delays. In addition, the efficient operation of the platform can also shorten the project cycle and improve the overall profitability of the project.

[0067] 3. Social benefits: In terms of social benefits, the application of the present invention has promoted the digital transformation of the construction industry and promoted the deep integration of information technology and the construction industry. This not only improves the level of intelligence in construction projects, but also drives the development of related industrial chains and creates more employment opportunities. At the same time, through accurate prediction and scientific decision-making, the present invention helps to reduce the impact of construction projects on the environment and promote the implementation of green buildings and sustainable development concepts. In addition, the platform's data sharing and collaborative decision-making functions also promote communication and collaboration between project teams, and enhance team cohesion and execution.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data asset intelligent prediction and analysis method based on BIM technology, characterized in that: include: S1. Standardization and integrated management of BIM data assets, including: S11. Data standardization interface design; S12. Multi-source data fusion; S2, intelligent data preprocessing and cleaning, including: S21, automated data cleaning; S22, feature engineering; S3, intelligent prediction model construction, including: S31, prediction algorithm library; S32, adaptive model selection; S4. Time series analysis and trend forecasting; S5. Multi-dimensional data analysis and visualization, including: S51. Multi-dimensional analysis framework; S52. Interactive visualization interface; S53. Report generation and sharing; S6. Intelligent decision support and optimization suggestions, including: S61. Risk warning; S62. Cost optimization; S63. Efficiency improvement; S7. Security and privacy protection, including: S71. Data encryption; S72. Access control; S73. Privacy protection.

2. The data asset intelligent prediction and analysis method based on BIM technology according to claim 1 is characterized in that: include: Step S11: Data standardization interface design includes: Interface specification formulation: Based on international and domestic BIM standards, formulate unified BIM data exchange formats and interface specifications to ensure seamless connection of BIM data on different software platforms and at different design stages; Compatibility testing: Establish a BIM data compatibility testing platform to conduct compatibility testing on various BIM software to ensure the accuracy and integrity of data exchange; Step S12 multi-source data fusion includes: Data source identification and access: Identify and access multi-source data to provide geographic location information, real-time operating status, and environmental monitoring data of construction projects, providing rich supplementary information for BIM models; Data fusion algorithm: Develop efficient data fusion algorithm to deeply integrate multi-source data with BIM models and build a comprehensive, dynamic and multi-dimensional building information database.

3. The data asset intelligent prediction and analysis method based on BIM technology according to claim 1 is characterized in that: include: Step S21 of automatic data cleaning includes: Error detection: Use machine learning algorithms to automatically identify error types in BIM data and mark them for subsequent processing; Data correction: Design corresponding correction strategies according to the error type to automatically correct errors in BIM data; Redundant data elimination: Identify and eliminate duplicate records and redundant information in BIM data; Step S22 feature engineering includes: Feature selection: Extract key features from BIM data based on the analysis objectives; Feature transformation: Perform necessary transformation on the extracted features.

4. The data asset intelligent prediction and analysis method based on BIM technology according to claim 1 is characterized in that: Step S31 prediction algorithm library, including: Algorithm integration: Integrate multiple advanced machine learning algorithms and deep learning models to form a rich prediction algorithm library; Algorithm evaluation: Through cross-validation and leave-one-out evaluation methods, the performance of algorithms in the algorithm library is evaluated and compared, and appropriate algorithms are selected for different prediction tasks; Step S32, adaptive model selection, includes: Model selection strategy: Design an adaptive model selection strategy to automatically select the optimal prediction model or model combination based on data characteristics and prediction task requirements; Model optimization: Use hyperparameter tuning, ensemble learning and other techniques to optimize the selected model to further improve prediction accuracy and robustness.

5. The method for intelligent prediction and analysis of data assets based on BIM technology according to claim 1 is characterized in that: Step S4: time series analysis and trend prediction, including: Time series modeling: Based on the time series data in the construction project, the prediction model is established using time series analysis technology; Trend forecasting: Predict future trends based on time series models, and provide forecast intervals and confidence levels to provide forward-looking guidance for project decisions.

6. The method for intelligent prediction and analysis of data assets based on BIM technology according to claim 1 is characterized in that: Step S51: The multi-dimensional analysis framework includes: Dimension definition: Define a multi-dimensional analysis framework based on the characteristics and management requirements of the construction project; Dimension association: Establish associations and hierarchical structures between dimensions, and support users to conduct cross-analysis and combined queries between different dimensions; Step S52: the interactive visualization interface includes: 3D visualization: Use the 3D graphics library to present the BIM model in 3D on a web page or mobile device, allowing users to observe from all angles; Chart display: The analysis results are displayed on the interface in the form of bar charts, line charts, pie charts, scatter charts, etc., and users are supported to customize the chart style and layout; Interactive operation: Provides rich interactive operation functions, allowing users to easily explore and compare data; Step S53 report generation and sharing includes: Report template design: design a variety of report templates to meet users' different reporting needs; Automatically fill in data: automatically extract data from the database and fill in the report according to the report template and data range selected by the user; One-click sharing: Supports exporting generated reports in PDF and Excel formats and sharing them with project teams, management and stakeholders with one click to promote information sharing and collaborative decision-making.

7. The method for intelligent prediction and analysis of data assets based on BIM technology according to claim 1 is characterized in that: Step S61 risk warning includes: Risk identification: Automatically identify potential risk points in the project based on predictive analysis results and preset risk thresholds; Risk assessment: Quantitatively evaluate the identified risk points and provide the probability and impact of the risk; Early warning signal: Early warning signals are sent to project managers through pop-up prompts and email notifications, with detailed risk descriptions and response measures; Step S62 cost optimization includes: Cost forecasting: forecasting the total cost of a project based on historical costs and future forecast data; Cost comparison analysis: Compare and analyze the cost-effectiveness of different design and construction plans to find out the potential points for cost savings; Optimization suggestions: Provide cost optimization suggestions to project managers based on the results of cost comparison analysis; Step S63 efficiency improvement includes: Construction progress forecast: forecast the construction progress of the project based on historical construction progress data and future forecast data; Resource demand forecast: forecast resource demand at each stage in the future based on the construction progress forecast results; Resource allocation optimization: Optimize resource allocation plans based on resource demand forecast results.

8. The method for intelligent prediction and analysis of data assets based on BIM technology according to claim 1 is characterized in that: Step S71 data encryption includes: Storage encryption: Encrypt sensitive data stored in the database to ensure the security of the data during storage; Transmission encryption: HTTPS security protocol is used to encrypt the data transmission process to prevent data from being intercepted or tampered with during transmission; Step S72 access control includes: User authentication: multiple authentication methods such as username + password, fingerprint recognition, and facial recognition are used to verify user identities to ensure that only legitimate users can access the platform; Permission allocation: Different data access rights and operation rights are allocated according to user roles and responsibilities to prevent unauthorized access and operation; Permission audit: record user data access and operation logs, support post-audit and tracking, and ensure operational compliance; Step S73 privacy protection includes: Anonymization: Anonymize data involving user privacy to ensure that user privacy is not leaked; Privacy Policy: Establish a clear privacy policy and inform users, clearly informing users of how their data is collected, used, stored and shared, as well as the rights and obligations of users.

9. A data asset intelligent prediction and analysis platform based on BIM technology, characterized in that: The data asset intelligent prediction and analysis method based on BIM technology described in claim 1 is adopted.

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