Building whole life cycle database construction method and system based on BIM (Building Information Modeling)

Abstract

The invention discloses a BIM-based building full-life-cycle database construction method and system, relates to the technical field of building cycle databases, and aims to solve the problem of poor effect of building data in collection and management processes. Through data support of the BIM model, the project team can make more intelligent decisions in different stages, the overall performance and sustainability of the project are improved, by starting an encryption transmission mechanism, when data are transmitted to the BIM collaboration platform and transmitted in the platform, the data are encrypted, and the data transmission efficiency is improved. The data are effectively prevented from being leaked and tampered in the transmission process, the safety of the data is enhanced, rapid query of multiple columns of conditions can be achieved through the joint index and coverage index technology, diversified data retrieval requirements in a service scene are met, and the data retrieval efficiency is improved by regularly monitoring the use condition and performance of the index. Invalid indexes can be found and optimized in time, the storage pressure is reduced, and the writing speed is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of building life cycle databases, and in particular to a method and system for constructing a building life cycle database based on BIM. Background Art

[0002] The construction cycle database refers to a system that collects, organizes and stores data related to cyclical changes in the construction industry.

[0003] Chinese patent publication number CN115964793B discloses a BIM model energy consumption simulation method and system coupled to a building performance database. This method primarily utilizes a digital modeling platform and technology to automatically translate multi-level information from the BIM model. Taking into account the inadequate support for externally relevant information provided by existing building information modeling technologies, a dynamic transmission mechanism between the BIM model and external building performance big data was established. Taking into account actual design scenarios, an automated and refined building energy consumption simulation and visualization system was established. While this patent addresses the issue of building database construction, the following issues remain in actual operation:

[0004] 1. The acquired original building data is not effectively processed and stored, resulting in poor data quality.

[0005] 2. Failure to conduct targeted index analysis based on the data retrieval situation, and failure to conduct further analysis and mining of the data, resulting in poor data accuracy.

[0006] 3. The processed building data is not managed more reasonably, resulting in reduced rationality and security of the data. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for constructing a BIM-based building life cycle database. With the data support of the BIM model, the project team can make more informed decisions at different stages and improve the overall performance and sustainability of the project. By enabling the encrypted transmission mechanism, the data is encrypted when it is transmitted to the BIM collaboration platform and when it is transmitted within the platform, which effectively prevents the leakage and tampering of the data during the transmission process and enhances the security of the data. Through the joint index and covering index technology, fast query of multi-column conditions can be achieved to meet the diverse data retrieval needs in business scenarios. By regularly monitoring the usage and performance of the index, invalid indexes can be discovered and optimized in a timely manner, reducing storage pressure and improving writing speed, which can solve the problems in the existing technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The method for constructing a BIM-based building life cycle database includes:

[0010] First, collect and process multi-source data, perform data standardization management on the processed multi-source data, and store the managed multi-source data according to the storage architecture;

[0011] An indexing mechanism is established for the stored data. After the indexing mechanism is established, the stored data is analyzed and mined.

[0012] Preferably, data collection and processing of multi-source data includes:

[0013] Acquire multi-source data from the database, including design phase data, construction phase data, operation and maintenance phase data and related data;

[0014] Design phase data includes architectural design drawings and design documentation; construction phase data includes construction progress data, quality acceptance data, and material and equipment data; operation and maintenance phase data includes equipment operation data, maintenance record data, and energy consumption data; related data includes geographic information data and regulatory standards data;

[0015] The acquired multi-source data is preprocessed. Data preprocessing involves sequentially performing data cleaning, data conversion, data integration, data enhancement, and data verification on the multi-source data.

[0016] Preferably, the processed multi-source data is subjected to data standardization governance, including:

[0017] Before conducting data standard governance, standardization should be formulated first;

[0018] Among them, the specification standards are formulated to standardize and unify professional terminology, data format and data quality;

[0019] After the specification standards are formulated, data fields of multi-source data are mapped;

[0020] Data field mapping is to analyze the meaning and purpose of data fields in multi-source data and map the meaning and purpose of data fields with fields in the standard data model. The standard data model is retrieved from the model library. At the same time, mapping relationships are established for fields with the same meaning but different names.

[0021] After data field mapping is completed, data entity matching is performed;

[0022] Data entity matching is to identify data records representing the same entity in multi-source data, matching is performed based on device code, name, and model. At the same time, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching;

[0023] Integrate the multi-source data after data entity matching, establish a unified data view after the integration is completed, and define the association rules between the integrated data;

[0024] Finally complete the data standard governance of multi-source data.

[0025] Preferably, controlling the view update of the data view includes:

[0026] Real-time extraction of design phase data, construction phase data, operation and maintenance phase data, and the corresponding data retrieval time intervals;

[0027] Determine in real time whether the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data has changed;

[0028] When the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data have not changed, the view update of the data view is controlled according to the view update time interval benchmark value, wherein the value of the view update time interval benchmark value is the maximum value of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data;

[0029] When any of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data changes, the maximum value and minimum value of the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data are retrieved;

[0030] Obtaining, according to the maximum time interval and the minimum data retrieval time interval, an average value of time intervals corresponding to the maximum time interval and the minimum data retrieval time interval as a first time interval average value;

[0031] Obtaining, according to the data retrieval time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data, an average value of the time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data as a second time interval average value;

[0032] Setting a view update time interval of a data view by using the first time interval average and the second time interval average;

[0033] The view update of the data view is controlled according to the view update time interval of the data view.

[0034] Preferably, setting the view update time interval of the data view by using the first time interval average value and the second time interval average value includes:

[0035] Retrieve the average time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data and related data as the current view update time interval benchmark value;

[0036] Retrieving the average value of the first time interval and the average value of the second time interval;

[0037] Comparing the first time interval average value with the second time interval average value, and obtaining a difference between the first time interval average value and the second time interval average value as a first difference parameter;

[0038] Retrieving the difference between the average value of the first time interval and the average value of the second time interval before any time interval of the data retrieval time interval corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data, as the second difference parameter;

[0039] Retrieve the median value of the time interval in the data retrieval time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data;

[0040] The view update time interval of the data view is set by using the first difference parameter and the second difference parameter in combination with the middle value of the time interval in the data retrieval time interval corresponding to the current design stage data, construction stage data, operation and maintenance stage data and related data.

[0041] Preferably, the managed multi-source data is stored according to the storage architecture, including:

[0042] First, the storage architecture is designed. The design of the storage architecture includes: confirming the tiered storage module. The tiered storage model includes the original data layer, the standardized data layer, and the analytical data layer;

[0043] The original data layer is used to store the original data before governance, and is stored in a non-relational database. The standardized data layer is used to store the standardized data after governance, which is classified according to the BIM standard model and uses a relational database to store entity relationships. The analytical data layer is used to store the derived data after mining, and is stored in a time series database.

[0044] Among them, non-relational databases, relational databases, and time series databases are retrieved from the database;

[0045] Partition the managed multi-source data into data partitions, including stage partitions and type partitions;

[0046] Phase partitioning is to partition the multi-source data that has been managed into design phase data, construction phase data, and operation and maintenance phase data; type partitioning is to partition the multi-source data that has been managed into structured data, unstructured data, and semi-structured data;

[0047] Store the managed multi-source data into the designed storage architecture based on the data partitioning situation.

[0048] Preferably, an indexing mechanism is established for the stored data, including:

[0049] Confirm high-frequency query scenarios based on historical query records retrieved from the database. High-frequency query scenarios include the design phase, construction phase, and operation and maintenance phase.

[0050] After the high-frequency query scenarios are determined, key fields are identified. Key fields include primary keys, time fields, and category fields.

[0051] After key fields are identified, select the index type, which includes structured data, unstructured data, and time series data.

[0052] Confirm the high-frequency query scenarios and key fields of the stored data, and then confirm the index type based on the confirmed high-frequency query scenarios and key fields;

[0053] After the index type is confirmed, the complete index mechanism is obtained.

[0054] Preferably, the stored data is analyzed and mined, including:

[0055] The stored data is analyzed and targeted. The analysis targets are defined as follows: the analysis target of the design phase data is to optimize the design scheme, including energy consumption simulation and structural strength analysis; the analysis target of the construction phase data is to predict the progress and early warning of quality risks; the analysis target of the operation and maintenance phase data is to predict equipment failures and optimize energy efficiency;

[0056] After the analysis objectives are defined, the stored data is initially explored using EDA methods. This includes exploring the overall characteristics of the data, its distribution, and the relationship between variables. The basic statistics of the data are then calculated, including the mean, median, standard deviation, and correlation coefficient.

[0057] Based on the defined analysis objectives and preliminary exploration of data, a mining algorithm is selected for the stored data, including support vector machines, hierarchical clustering or Apriori algorithm;

[0058] After the mining algorithm is selected, data mining is performed on the stored data;

[0059] Finally, the analysis and mining of stored data are completed.

[0060] The BIM-based building life cycle database construction system includes:

[0061] Data collaborative management unit, used for:

[0062] Select the BIM collaboration platform and confirm the data interface based on the BIM collaboration platform;

[0063] Integrate the analyzed and mined data with the BIM model, and use the visualization function of the BIM collaboration platform to customize the data visualization interface;

[0064] For equipment failure prediction data in the operation and maintenance phase, an equipment status visualization panel is designed to indicate the health status of the equipment with different colors or icons in the BIM model;

[0065] At the same time, the detailed operating parameters and maintenance record analysis data of the equipment are displayed in the form of pop-up windows or sidebars;

[0066] For quality risk warning data during the construction phase, construction sites with quality risks are highlighted with eye-catching markers in the BIM model, and are linked to detailed risk analysis reports and rectification suggestion documents;

[0067] Then, according to the responsibilities and needs of the construction project, the data access rights rules are formulated. At the same time, the collaborative workflow is formulated based on the analysis and mining of data;

[0068] Finally, collaborative management of analysis and mining data is completed.

[0069] Preferably, it also includes:

[0070] Data encryption backup unit, used for:

[0071] Encrypt the analyzed and mined data. Data encryption means enabling an encrypted transmission mechanism when the data is transmitted to the BIM collaboration platform and within the platform.

[0072] The encrypted transmission mechanism is as follows: data is transmitted via the HTTPS protocol. At the sending end, the data is encrypted using the selected encryption algorithm and transmitted to the receiving end through an encrypted channel. At the receiving end, the data is decrypted using the corresponding decryption key to restore the original data.

[0073] The backup frequency of the analyzed and mined data will be determined based on the data update frequency and the impact of data loss;

[0074] After the backup frequency is determined, the data backup method is confirmed. The data backup method is a combination of full backup and incremental backup.

[0075] After the backup method is confirmed, the analyzed and mined data will be backed up to the cloud storage.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] 1. The method and system for constructing a BIM-based building life cycle database provided by the present invention fully consider the actual situation and needs of the organization in the data governance process, while retaining sufficient flexibility and scalability. Phase partitioning helps to manage data according to different stages of the project life cycle, while type partitioning classifies and stores data according to the degree of structuring.

[0078] 2. The method and system for constructing a BIM-based building life cycle database provided by the present invention can realize fast query of multi-column conditions through joint indexing and covering indexing technology, meet the diverse data retrieval needs in business scenarios, and through regular monitoring of index usage and performance, can timely discover and optimize invalid indexes, reduce storage pressure and improve writing speed. Through data analysis and mining, it can provide data-driven decision support for project management at different stages, help reduce the subjectivity and blindness of decision-making, and improve the scientific nature and accuracy of decision-making.

[0079] 3. The method and system for constructing a BIM-based building life cycle database provided by the present invention, through the data support of the BIM model, enable the project team to make more informed decisions at different stages and improve the overall performance and sustainability of the project. By enabling the encrypted transmission mechanism, the data is encrypted when it is transmitted to the BIM collaboration platform and when it is transmitted within the platform, effectively preventing data leakage and tampering during transmission and enhancing data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 Schematic diagram of the steps for constructing a building life cycle database according to the present invention;

[0081] Figure 2 The figure is a schematic diagram of the construction process of the building life cycle database of the present invention. DETAILED DESCRIPTION

[0082] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 making creative efforts are within the scope of protection of the present invention.

[0083] In order to solve the problem of poor data quality caused by the lack of effective data processing and data storage in the existing technology, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0084] The method for constructing a BIM-based building life cycle database includes:

[0085] First, collect and process multi-source data, perform data standardization management on the processed multi-source data, and store the managed multi-source data according to the storage architecture;

[0086] An indexing mechanism is established for the stored data. After the indexing mechanism is established, the stored data is analyzed and mined.

[0087] Specifically, through data collection and processing, data from different sources (such as design, construction, operation and maintenance stages) can be integrated to avoid data silos. Data standardization governance ensures the consistency of data format, unit and accuracy, making the data more reliable in subsequent analysis and mining. The establishment of a data storage architecture provides a foundation for unified data management, facilitating data query, update and maintenance. The establishment of an indexing mechanism can significantly improve data retrieval efficiency, making it possible to quickly find the required information in a huge data set. Through in-depth analysis and mining of stored data, potential patterns, problems and trends in the entire life cycle of a building can be discovered. A BIM-based building life cycle database can provide data support for each stage of a construction project, helping decision makers make more informed decisions.

[0088] Collect and process multi-source data, including:

[0089] Acquire multi-source data from the database, including design phase data, construction phase data, operation and maintenance phase data and related data;

[0090] Design phase data includes architectural design drawings and design documentation; construction phase data includes construction progress data, quality acceptance data, and material and equipment data; operation and maintenance phase data includes equipment operation data, maintenance record data, and energy consumption data; related data includes geographic information data and regulatory standards data;

[0091] The acquired multi-source data is preprocessed. Data preprocessing involves sequentially performing data cleaning, data conversion, data integration, data enhancement, and data verification on the multi-source data.

[0092] Specifically, through data preprocessing steps, including data cleaning, data conversion, and data integration, redundancy and contradictions in the data can be eliminated, uncertainty can be reduced, and the reliability and accuracy of the data can be improved. Multi-source data can be centrally processed to avoid repeated data collection and processing, thereby improving data processing efficiency. At the same time, the data enhancement step can further improve the quality and availability of the data. By centrally collecting and processing multi-source data, the cost of repeated data collection and processing between different stages and data sources can be reduced. In addition, the use of fusion algorithms to obtain higher-precision data is much more cost-effective than the expensive cost of directly using high-precision equipment.

[0093] Conduct data standardization governance on the processed multi-source data, including:

[0094] Before conducting data standard governance, standardization should be formulated first;

[0095] Among them, the specification standards are formulated to standardize and unify professional terminology, data format and data quality;

[0096] After the specification standards are formulated, data fields of multi-source data are mapped;

[0097] Data field mapping is to analyze the meaning and purpose of data fields in multi-source data and map the meaning and purpose of data fields with fields in the standard data model. The standard data model is retrieved from the model library. At the same time, mapping relationships are established for fields with the same meaning but different names.

[0098] After data field mapping is completed, data entity matching is performed;

[0099] Data entity matching is to identify data records representing the same entity in multi-source data, matching is performed based on device code, name, and model. At the same time, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching;

[0100] Integrate the multi-source data after data entity matching, establish a unified data view after the integration is completed, and define the association rules between the integrated data;

[0101] Finally complete the data standard governance of multi-source data.

[0102] Specifically, through the development of standardized standards, professional terminology, data formats, and data quality are standardized and unified, effectively resolving inconsistencies and compatibility issues across multi-source data and improving data readability and comprehensibility. The data field mapping process not only analyzes the meaning and purpose of data fields in multi-source data but also accurately maps them to fields in the standard data model, ensuring the accuracy of data conversion and integration. Furthermore, mapping relationships are established between fields with the same meaning but different names, further improving data integration efficiency. Data entity matching uses key information such as device code, name, and model to effectively identify data records representing the same entity across multiple sources. For ambiguous data, accurate matching is achieved through manual review or machine learning algorithms, further improving the accuracy of data integration. After data entity matching is completed, multi-source data is integrated and a unified data view is created, making it easier for data users to access and use the data. This step not only improves data usability but also provides strong support for subsequent data analysis and decision-making. After data integration is completed, association rules are defined between data to help reveal the inherent connections and patterns within the data. This is crucial for unlocking the value of data, optimizing business processes, and making more informed decisions. The data governance process fully considers the organization's actual circumstances and needs while retaining sufficient flexibility and scalability. As the organization grows and changes, the solution can be adjusted and optimized based on actual needs, ensuring that data governance remains aligned with the organization's growth.

[0103] Specifically, control the view update of the data view, including:

[0104] Real-time extraction of design phase data, construction phase data, operation and maintenance phase data, and the corresponding data retrieval time intervals;

[0105] Determine in real time whether the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data has changed;

[0106] When the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data have not changed, the view update of the data view is controlled according to the view update time interval benchmark value, wherein the value of the view update time interval benchmark value is the maximum value of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data;

[0107] When any of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data changes, the maximum value and minimum value of the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data are retrieved;

[0108] Obtaining, according to the maximum time interval and the minimum data retrieval time interval, an average value of time intervals corresponding to the maximum time interval and the minimum data retrieval time interval as a first time interval average value;

[0109] Obtaining, according to the data retrieval time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data, an average value of the time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data as a second time interval average value;

[0110] Setting a view update time interval of a data view by using the first time interval average and the second time interval average;

[0111] The view update of the data view is controlled according to the view update time interval of the data view.

[0112] The technical effects of the above-mentioned technical solution are as follows: real-time monitoring of the retrieval intervals for data and related data at each stage. When the interval remains unchanged, the maximum interval is used as the benchmark for view updates, ensuring that the view update rhythm matches the slowest data retrieval rhythm and avoiding data omissions due to overly rapid updates. When the interval changes, the view update interval is set by comprehensively considering the maximum and minimum intervals and the average interval of each stage. This allows for precise adaptation to the rhythm of data changes, ensuring that the data view accurately reflects the latest data status and improving the accuracy of data display. By properly setting the view update interval, duplicate or untimely data display caused by unreasonable update times is avoided, data bias is reduced, and the accuracy and integrity of the data in the data view are guaranteed. This solution provides clear rules for setting the view update interval, with corresponding handling methods for both constant and changing intervals. This stable mechanism ensures that the data view can be updated in a predictable manner under different data retrieval rhythms, maintaining the stability of the data view update process and reducing the risk of system failures caused by chaotic view updates. When the data retrieval time interval changes, the view update time interval is comprehensively determined by calculating multiple average values. This can effectively resist the impact of fluctuations in the data retrieval rhythm on view updates, so that the update of data views is not disturbed by short-term data fluctuations and maintains stable operation. The data retrieval time intervals in different stages (design, construction, operation and maintenance) may be different. This solution can control view updates based on the actual retrieval time intervals of data in each stage. Regardless of whether the time intervals in each stage are stable or the time intervals in a certain stage change, the view update time interval can be adjusted through corresponding rules to adapt to the characteristics and changes of data in different stages, thereby improving the system's adaptability to diverse data scenarios. When the data retrieval time interval changes, the view update time interval is dynamically set by calculating the average values ​​of multiple time intervals. This enables the system to flexibly respond to changes in the data retrieval rhythm, adjust the view update strategy in a timely manner, ensure that the data view can always effectively display data, and enhance the system's adaptability to dynamic changes in data.

[0113] Specifically, setting the view update time interval of the data view by using the first time interval average value and the second time interval average value includes:

[0114] Retrieve the average time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data and related data as the current view update time interval benchmark value;

[0115] Retrieving the average value of the first time interval and the average value of the second time interval;

[0116] Comparing the first time interval average value with the second time interval average value, and obtaining a difference between the first time interval average value and the second time interval average value as a first difference parameter;

[0117] Retrieving the difference between the first time interval average value and the second time interval average value before any time interval of the data retrieval time interval corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data changes as the second difference parameter; wherein the first time interval average value and the second time interval average value before any time interval of the data retrieval time interval corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data changes are obtained in the same manner as the first time interval average value and the second time interval average value after any time interval of the data retrieval time interval corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data changes;

[0118] Retrieve the median value of the time interval in the data retrieval time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data;

[0119] Setting a view update time interval of the data view by using the first difference parameter and the second difference parameter in combination with a median value of a time interval in a data retrieval time interval corresponding to current design phase data, construction phase data, operation and maintenance phase data, and related data;

[0120] The view update time interval of the data view is obtained by the following formula:

[0121]

[0122] Among them, T g Indicates the view update time interval of the data view; T c01 and T c02 Represent the first difference parameter and the second difference parameter respectively; T z Indicates the median value of the time interval in the data retrieval time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data; T d01 and T d02 They represent the current view update interval benchmark value and the average value of the data retrieval interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data after any time interval changes. Specifically, |T d01 -T d02 | is the absolute value of the difference between the current view update interval baseline value and the average time interval after the change, reflecting the difference in the overall data retrieval rhythm between the current and the changed. exp(-|T d01 -T d02 |) adjusts the weight exponentially according to the difference, [1+exp(-|T d01 -T d02|)] Based on the previous calculation, the data is retrieved to obtain the degree of rhythm change |T c01 -T c02 |The median value of the time interval and the weight T after considering the overall change of rhythm z *[1+exp(-|T d01 -T d02 |)] and add 1 to form an adjustment multiplier for updating the time interval benchmark value T in the current view d01 Adjust based on the data view to get the final data view update time interval T g The formula will be the current status of the data retrieval rhythm (the current view update time interval benchmark value T d01 ), degree of change (|T c01 -T c02 ∣), intermediate level (T z ) and the overall change difference (|T d01 -T d02 Through specific mathematical operations, the weight of each factor affecting the view update interval is reasonably balanced to avoid a single factor dominating, so that the set view update interval can comprehensively reflect the various characteristics of the data retrieval rhythm and ensure rationality. As the data retrieval interval changes, the relevant parameters in the formula (such as T c01 、T c02 、T d01 、T d02 The formula can adjust the view update interval based on these changes in real time. It can precisely adjust the update interval for different data change rhythms, ensuring that the view update interval closely matches the actual data changes, improving accuracy.

[0123] The technical effects of the above-mentioned technical solution are as follows: the current view update interval baseline value represents the average of the data intervals across the current phase, reflecting the overall status of the current data retrieval rhythm; the first difference parameter represents the difference between the average values ​​of two time intervals calculated at different times, reflecting the changes in the data retrieval rhythm; the second difference parameter represents the difference between similar values, used for comparative analysis of changing trends; and the median interval value represents the middle level of the data retrieval interval, balancing the overall interval characteristics. By integrating these parameters and using specific mathematical relationships, the formula dynamically adjusts the view update interval, taking into account the current status, changes, and trends of the data retrieval rhythm. This allows the view update interval to adapt appropriately to changes in the data retrieval rhythm, ensuring that the data view displays the latest data in a timely and accurate manner. By integrating multiple parameters related to the data retrieval interval to set the view update interval, it accurately adapts to actual data changes. This avoids the problems of untimely or overly frequent data updates that can occur with fixed update intervals, ensuring that the data view accurately reflects the latest data status and improves data display accuracy. The solution provides clear rules and calculation formulas to determine the view update interval, allowing for adjustments based on established logic to accommodate changes in the data retrieval interval. This stable mechanism reduces the randomness and uncertainty of view updates, maintains the stability of the data view update process, and ensures stable system operation. It dynamically adjusts the view update interval based on various changes in the data retrieval interval at different stages by calculating relevant parameters. This effectively adapts to both changes in the data retrieval rhythm at individual stages and changes in the overall rhythm, improving the system's adaptability to diverse data scenarios and dynamic data changes.

[0124] The managed multi-source data is stored according to the storage architecture, including:

[0125] First, the storage architecture is designed. The design of the storage architecture includes: confirming the tiered storage module. The tiered storage model includes the original data layer, the standardized data layer, and the analytical data layer;

[0126] The original data layer is used to store the original data before governance, and is stored in a non-relational database. The standardized data layer is used to store the standardized data after governance, which is classified according to the BIM standard model and uses a relational database to store entity relationships. The analytical data layer is used to store the derived data after mining, and is stored in a time series database.

[0127] Among them, non-relational databases, relational databases, and time series databases are retrieved from the database;

[0128] Partition the managed multi-source data into data partitions, including stage partitions and type partitions;

[0129] Phase partitioning is to partition the multi-source data that has been managed into design phase data, construction phase data, and operation and maintenance phase data; type partitioning is to partition the multi-source data that has been managed into structured data, unstructured data, and semi-structured data;

[0130] Store the managed multi-source data into the designed storage architecture based on the data partitioning situation.

[0131] Specifically, by establishing a raw data layer to retain raw data before governance, data integrity and historical traceability are ensured. This is crucial for subsequent data audits, troubleshooting, and decision support. The normative data layer uses the BIM standard model to classify data and a relational database to store entity relationships, which helps achieve data standardization and normalization. Standardized data is easier to manage and analyze, improving data quality and availability. The analytical data layer uses a time series database to store mined derivative data. This database type is particularly well-suited for processing time series data and can efficiently support data analysis and mining tasks. The hierarchical storage model design provides a high degree of flexibility in the storage architecture. The raw data layer, normative data layer, and analytical data layer each have distinct responsibilities, while being both independent and interrelated, to meet the data storage needs of different scenarios. Data partitioning strategies (including stage partitioning and type partitioning) ensure more organized and efficient data storage. Stage partitioning facilitates data management according to different stages of the project lifecycle, while type partitioning categorizes and stores data based on its level of structure, both of which help improve the efficiency of data retrieval and processing.

[0132] In order to solve the problem in existing technologies that there is no targeted index analysis based on the data retrieval situation, and no further analysis and mining of the data, which leads to poor data accuracy, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0133] Establish an indexing mechanism for stored data, including:

[0134] Confirm high-frequency query scenarios based on historical query records retrieved from the database. High-frequency query scenarios include the design phase, construction phase, and operation and maintenance phase.

[0135] After the high-frequency query scenarios are determined, key fields are identified. Key fields include primary keys, time fields, and category fields.

[0136] After key fields are identified, select the index type, which includes structured data, unstructured data, and time series data.

[0137] Confirm the high-frequency query scenarios and key fields of the stored data, and then confirm the index type based on the confirmed high-frequency query scenarios and key fields;

[0138] After the index type is confirmed, the complete index mechanism is obtained.

[0139] Specifically, by establishing indexes for high-frequency query scenarios and key fields, query time can be significantly reduced, improving data retrieval speed. This is especially true in critical business scenarios such as the design, construction, and maintenance phases, where fast data access is crucial for business decision-making and operational efficiency. Indexing mechanisms enable databases to process query requests more efficiently, reducing the consumption of system resources such as CPU and memory. This not only improves the performance of individual queries but also contributes to the stable operation of the entire database system and the optimal allocation of resources. Indexing helps maintain data consistency, especially with composite and covering indexes. These indexes ensure that data remains organized and quickly accessible after updates and deletes, reducing query errors caused by data inconsistencies. Designing indexes for high-frequency query scenarios and key fields supports more complex query requirements. For example, using composite and covering indexes enables fast queries across multiple columns, meeting diverse data retrieval needs in business scenarios. Regularly monitoring index usage and performance can promptly identify and optimize ineffective indexes, reducing storage pressure and improving write speeds. Furthermore, as business evolves and data volumes grow, the indexing mechanism can be flexibly adjusted to adapt to new query requirements.

[0140] Analyze and mine stored data, including:

[0141] The stored data is analyzed and targeted. The analysis targets are defined as follows: the analysis target of the design phase data is to optimize the design scheme, including energy consumption simulation and structural strength analysis; the analysis target of the construction phase data is to predict the progress and early warning of quality risks; the analysis target of the operation and maintenance phase data is to predict equipment failures and optimize energy efficiency;

[0142] After the analysis objectives are defined, the stored data is initially explored using EDA methods. This includes exploring the overall characteristics of the data, its distribution, and the relationship between variables. The basic statistics of the data are then calculated, including the mean, median, standard deviation, and correlation coefficient.

[0143] Based on the defined analysis objectives and preliminary exploration of data, a mining algorithm is selected for the stored data, including support vector machines, hierarchical clustering or Apriori algorithm;

[0144] After the mining algorithm is selected, data mining is performed on the stored data;

[0145] Finally, the analysis and mining of stored data are completed.

[0146] Specifically, specific analysis objectives for each phase (design, construction, and operation and maintenance) were clearly defined before the analysis began. This goal-oriented approach ensures targeted analysis, improving efficiency and pertinence. Exploratory Data Analysis (EDA) was employed for preliminary exploration, which helps to fully understand the overall characteristics, distribution, and variable relationships of the data. EDA is a crucial step in data analysis, providing valuable insights and hypotheses for subsequent data mining. By calculating basic statistics (mean, median, standard deviation, correlation coefficient, etc.), the solution provides a solid statistical foundation for data analysis. These statistics help reveal the central tendency, dispersion, and intervariate correlations of the data. Based on different analysis objectives, the solution flexibly selects a variety of data mining algorithms (such as support vector machines, hierarchical clustering, and Apriori algorithms). This algorithmic diversity accommodates diverse data and analysis requirements, improving analysis accuracy and applicability. Through data analysis and mining, the solution provides data-driven decision support for project management at different stages. This helps reduce subjectivity and blindness in decision-making, while enhancing the scientific nature and accuracy of decision-making.

[0147] In order to solve the problem in the existing technology that the processed building data is not managed more reasonably, which leads to the reduction of the rationality and security of the data, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0148] The BIM-based building life cycle database construction system includes:

[0149] Data collaborative management unit, used for:

[0150] Select the BIM collaboration platform and confirm the data interface based on the BIM collaboration platform;

[0151] Integrate the analyzed and mined data with the BIM model, and use the visualization function of the BIM collaboration platform to customize the data visualization interface;

[0152] For equipment failure prediction data in the operation and maintenance phase, an equipment status visualization panel is designed to indicate the health status of the equipment with different colors or icons in the BIM model;

[0153] At the same time, the detailed operating parameters and maintenance record analysis data of the equipment are displayed in the form of pop-up windows or sidebars;

[0154] For quality risk warning data during the construction phase, construction sites with quality risks are highlighted with eye-catching markers in the BIM model, and are linked to detailed risk analysis reports and rectification suggestion documents;

[0155] Then, according to the responsibilities and needs of the construction project, the data access rights rules are formulated. At the same time, the collaborative workflow is formulated based on the analysis and mining of data;

[0156] Finally, collaborative management of analysis and mining data is completed.

[0157] Specifically, by selecting an appropriate BIM collaboration platform and confirming data interfaces based on the platform, data interoperability between different systems was effectively improved, enabling seamless data integration. Centralized data management and collaborative work avoided information silos and improved data consistency and accuracy. Leveraging the visualization capabilities of the BIM collaboration platform, a customized data visualization interface enabled project managers to more intuitively understand project status and improve decision-making efficiency. During the operation and maintenance phase, a visual dashboard for equipment status displayed real-time, enabling operators to quickly respond to equipment failures and reduce repair costs. During the construction phase, quality risk warning data was highlighted within the BIM model with prominent markers, helping the construction team promptly identify and address potential quality issues, avoiding the additional costs of later rectification. Links to risk analysis reports and rectification recommendations provided the construction team with detailed problem-solving solutions, enhancing construction quality and safety. Data access rights were established based on project responsibilities and requirements, ensuring the proper flow and security of information. The implementation of a permissions management system prevented unauthorized access and data leakage, protecting sensitive project information. The development of collaborative workflows based on analyzed and mined data helped optimize project execution and improve work efficiency. With the support of BIM technology, workflows have become more transparent and controllable, helping project teams work more collaboratively and reducing communication costs. It not only focuses on the construction phase, but also covers data management during the operation and maintenance phase, enabling full lifecycle management of construction projects. With the data support of the BIM model, project teams can make more informed decisions at different stages, improving the overall performance and sustainability of the project. The visual interface and interactive design make project information easier to understand and use, enhancing the user experience.

[0158] Data encryption backup unit, used for:

[0159] Encrypt the analyzed and mined data. Data encryption means enabling an encrypted transmission mechanism when the data is transmitted to the BIM collaboration platform and within the platform.

[0160] The encrypted transmission mechanism is as follows: data is transmitted via the HTTPS protocol. At the sending end, the data is encrypted using the selected encryption algorithm and transmitted to the receiving end through an encrypted channel. At the receiving end, the data is decrypted using the corresponding decryption key to restore the original data.

[0161] The backup frequency of the analyzed and mined data will be determined based on the data update frequency and the impact of data loss;

[0162] After the backup frequency is determined, the data backup method is confirmed. The data backup method is a combination of full backup and incremental backup.

[0163] After the backup method is confirmed, the analyzed and mined data will be backed up to the cloud storage.

[0164] Specifically, by enabling an encrypted transmission mechanism, data is encrypted both when being transferred to the BIM collaboration platform and within the platform. This effectively prevents data leakage and tampering during transmission, enhancing data security. Using the HTTPS protocol for data transmission further ensures data security, as the HTTPS protocol inherently includes encryption, authentication, and integrity verification. Backup frequency is determined based on the frequency of data updates and the impact of data loss, ensuring timely backup of important data while avoiding unnecessary resource waste. This more scientific and rational backup frequency ensures both data integrity and backup efficiency. A combination of full and incremental backups minimizes backup time and storage space usage. Full backups ensure data integrity, while incremental backups only back up data that has changed since the last backup, improving backup efficiency. Storing backup data in cloud storage enables remote backup and disaster recovery. Cloud storage offers advantages such as scalability, high availability, and low cost, providing users with more reliable data storage services. Cloud storage also facilitates data access and management, allowing users to access their data anytime, anywhere via the internet.

[0165] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0166] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for constructing a BIM-based building life cycle database, characterized in that: include: First, collect and process multi-source data, perform data standardization management on the processed multi-source data, and store the managed multi-source data according to the storage architecture; An indexing mechanism is established for the stored data. After the indexing mechanism is established, the stored data is analyzed and mined.

2. The method for constructing a BIM-based building life cycle database according to claim 1, characterized in that: Collect and process multi-source data, including: Acquire multi-source data from the database, including design phase data, construction phase data, operation and maintenance phase data and related data; Design phase data includes architectural design drawings and design documentation; construction phase data includes construction progress data, quality acceptance data, and material and equipment data; operation and maintenance phase data includes equipment operation data, maintenance record data, and energy consumption data; related data includes geographic information data and regulatory standards data; The acquired multi-source data is preprocessed. Data preprocessing involves sequentially performing data cleaning, data conversion, data integration, data enhancement, and data verification on the multi-source data.

3. The method for constructing a BIM-based building life cycle database according to claim 1, characterized in that: Conduct data standardization governance on the processed multi-source data, including: Before conducting data standard governance, standardization should be formulated first; Among them, the specification standards are formulated to standardize and unify professional terminology, data format and data quality; After the specification standards are formulated, data fields of multi-source data are mapped; Data field mapping is to analyze the meaning and purpose of data fields in multi-source data and map the meaning and purpose of data fields with fields in the standard data model. The standard data model is retrieved from the model library. At the same time, mapping relationships are established for fields with the same meaning but different names. After data field mapping is completed, data entity matching is performed; Data entity matching is to identify data records representing the same entity in multi-source data, matching is performed based on device code, name, and model. At the same time, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching; Integrate the multi-source data after data entity matching, establish a unified data view after the integration is completed, and define the association rules between the integrated data; Finally complete the data standard governance of multi-source data.

4. The method for constructing a BIM-based building life cycle database according to claim 3, characterized in that: Controls updates to data views, including: Real-time extraction of design phase data, construction phase data, operation and maintenance phase data, and the corresponding data retrieval time intervals; Determine in real time whether the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data has changed; When the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data have not changed, the view update of the data view is controlled according to the view update time interval benchmark value, wherein the value of the view update time interval benchmark value is the maximum value of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data; When any of the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data changes, the maximum value and minimum value of the data retrieval time interval corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data are retrieved; Obtaining, according to the maximum time interval and the minimum data retrieval time interval, an average value of time intervals corresponding to the maximum time interval and the minimum data retrieval time interval as a first time interval average value; Obtaining, according to the data retrieval time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data, an average value of the time intervals corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data as a second time interval average value; Setting a view update time interval of a data view by using the first time interval average and the second time interval average; The view update of the data view is controlled according to the view update time interval of the data view.

5. The method for constructing a BIM-based building life cycle database according to claim 4, characterized in that: Setting a view update time interval of a data view by using the first time interval average and the second time interval average includes: Retrieve the average time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data and related data as the current view update time interval benchmark value; Retrieving the average value of the first time interval and the average value of the second time interval; Comparing the first time interval average value with the second time interval average value, and obtaining a difference between the first time interval average value and the second time interval average value as a first difference parameter; Retrieving the difference between the average value of the first time interval and the average value of the second time interval before any time interval of the data retrieval time interval corresponding to the design phase data, the construction phase data, the operation and maintenance phase data, and the related data, as the second difference parameter; Retrieve the median value of the time interval in the data retrieval time interval corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data; The view update time interval of the data view is set by using the first difference parameter and the second difference parameter in combination with the middle value of the time interval in the data retrieval time interval corresponding to the current design stage data, construction stage data, operation and maintenance stage data and related data.

6. The method for constructing a BIM-based building life cycle database according to claim 1, characterized in that: The managed multi-source data is stored according to the storage architecture, including: First, the storage architecture is designed. The design of the storage architecture includes: confirming the tiered storage module. The tiered storage model includes the original data layer, the standardized data layer, and the analytical data layer; The original data layer is used to store the original data before governance, and is stored in a non-relational database. The standardized data layer is used to store the standardized data after governance, which is classified according to the BIM standard model and uses a relational database to store entity relationships. The analytical data layer is used to store the derived data after mining, and is stored in a time series database. Among them, non-relational databases, relational databases, and time series databases are retrieved from the database; Partition the managed multi-source data into data partitions, including stage partitions and type partitions; Phase partitioning is to partition the multi-source data that has been managed into design phase data, construction phase data, and operation and maintenance phase data; type partitioning is to partition the multi-source data that has been managed into structured data, unstructured data, and semi-structured data; Store the managed multi-source data into the designed storage architecture based on the data partitioning situation.

7. The method for constructing a BIM-based building life cycle database according to claim 1, characterized in that: Establish an indexing mechanism for stored data, including: Confirm high-frequency query scenarios based on historical query records retrieved from the database. High-frequency query scenarios include the design phase, construction phase, and operation and maintenance phase. After the high-frequency query scenarios are determined, key fields are identified. Key fields include primary keys, time fields, and category fields. After key fields are identified, select the index type, which includes structured data, unstructured data, and time series data. Confirm the high-frequency query scenarios and key fields of the stored data, and then confirm the index type based on the confirmed high-frequency query scenarios and key fields; After the index type is confirmed, the complete index mechanism is obtained.

8. The method for constructing a BIM-based building life cycle database according to claim 1, characterized in that: Analyze and mine stored data, including: The stored data is analyzed and targeted. The analysis targets are defined as follows: the analysis target of the design phase data is to optimize the design scheme, including energy consumption simulation and structural strength analysis; the analysis target of the construction phase data is to predict the progress and early warning of quality risks; the analysis target of the operation and maintenance phase data is to predict equipment failures and optimize energy efficiency; After the analysis objectives are defined, the stored data is initially explored using EDA methods. This includes exploring the overall characteristics of the data, its distribution, and the relationships between variables. Basic statistics of the data are then calculated, including the mean, median, standard deviation, and correlation coefficient. Based on the defined analysis objectives and preliminary exploration of data, a mining algorithm is selected for the stored data, including support vector machines, hierarchical clustering or Apriori algorithm; After the mining algorithm is selected, data mining is performed on the stored data; Finally, the analysis and mining of stored data are completed.

9. A BIM-based building life cycle database construction system, applied to a BIM-based building life cycle database construction method according to any one of claims 1 to 8, characterized in that: include: Data collaborative management unit, used for: Select the BIM collaboration platform and confirm the data interface based on the BIM collaboration platform; Integrate the analyzed and mined data with the BIM model, and use the visualization function of the BIM collaboration platform to customize the data visualization interface; For equipment failure prediction data in the operation and maintenance phase, an equipment status visualization panel is designed to indicate the health status of the equipment with different colors or icons in the BIM model; At the same time, the detailed operating parameters and maintenance record analysis data of the equipment are displayed in the form of pop-up windows or sidebars; For quality risk warning data during the construction phase, construction sites with quality risks are highlighted with eye-catching markers in the BIM model, and are linked to detailed risk analysis reports and rectification suggestion documents; Then, according to the responsibilities and needs of the construction project, the data access rights rules are formulated. At the same time, the collaborative workflow is formulated based on the analysis and mining of data; Finally, collaborative management of analysis and mining data is completed.

10. The BIM-based building life cycle database construction system according to claim 9, characterized in that: Also includes: Data encryption backup unit, used for: Encrypt the analyzed and mined data. Data encryption means enabling the encryption transmission mechanism when the data is transmitted to the BIM collaboration platform and when it is transmitted within the platform. The encrypted transmission mechanism is as follows: data is transmitted via the HTTPS protocol. At the sending end, the data is encrypted using the selected encryption algorithm and transmitted to the receiving end through an encrypted channel. At the receiving end, the data is decrypted using the corresponding decryption key to restore the original data. The backup frequency of the analyzed and mined data will be determined based on the data update frequency and the impact of data loss; After the backup frequency is determined, the data backup method is confirmed. The data backup method is a combination of full backup and incremental backup. After the backup method is confirmed, the analyzed and mined data will be backed up to the cloud storage.

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