Construction method and system of building lifecycle database based on BIM

Abstract

The application discloses a building full life cycle database construction method and system based on BIM, relates to the technical field of building cycle databases, and aims at solving the problem of poor effect of building data in the collection and management process. Through data support of the BIM model, the project team can make more wise decisions at different stages, improve the overall performance and sustainability of the project, and through the encryption transmission mechanism, the data is encrypted when being transmitted to the BIM collaborative platform and being transmitted in the platform, so that the leakage and tampering of the data in the transmission process are effectively prevented, and the security of the data is enhanced. Through the joint index and the covering index technology, quick query of multiple column conditions can be realized, diversified data retrieval requirements in business scenarios are met, invalid indexes can be found and optimized in time through regular monitoring of the use and performance of the indexes, and the storage pressure is reduced and the writing speed is improved.

Description

Technical Field

[0001] This invention relates to the field of building lifecycle database technology, specifically to a method and system for constructing a BIM-based building lifecycle database. Background Technology

[0002] A building cycle database is a system for collecting, organizing, and storing data related to the cyclical changes in the construction industry.

[0003] Chinese patent CN115964793B discloses a method and system for simulating energy consumption in a BIM model coupled with a building performance database. It primarily utilizes a digital modeling platform and technology to automatically translate multi-level information from the BIM model. It fully considers the insufficient support of existing building information modeling technologies for external related information and establishes a dynamic transmission mechanism between the BIM model and external building performance big data. Taking into account actual design scenarios, it establishes an automated, refined building energy consumption simulation and visualization system. While this patent solves the problem of building database construction, the following issues still exist in practical operation:

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

[0005] 2. The lack of targeted indexing analysis based on data retrieval and the absence of further data analysis and mining resulted in poor data accuracy.

[0006] 3. The processed building data was not managed properly, resulting in reduced data reliability and security. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for constructing a BIM-based building lifecycle database. 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. By enabling encrypted transmission, data is encrypted both when transmitted to the BIM collaboration platform and within the platform, effectively preventing data leakage and tampering during transmission and enhancing data security. Through composite indexes and covering indexes, rapid queries based on multiple columns can be achieved, meeting diverse data retrieval needs in business scenarios. By regularly monitoring index usage and performance, invalid indexes can be identified and optimized in a timely manner, reducing storage pressure and improving write speed, thus solving problems in existing technologies.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

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

[0010] First, collect and process multi-source data; then, perform data standardization and governance on the processed multi-source data; finally, store the governed multi-source data according to the storage architecture.

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

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

[0013] Data from multiple sources is obtained from the database, including data from the design phase, construction phase, operation and maintenance phase, and related data.

[0014] Design phase data includes architectural drawings and design specifications; 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 undergoes data preprocessing, which involves sequentially performing data cleaning, data transformation, data integration, data enhancement, and data verification.

[0016] Preferably, the processed multi-source data undergoes data standardization and governance, including:

[0017] Before implementing data governance and standardization, standards and norms should be established first.

[0018] Among them, the formulation of standards and specifications involves standardizing and unifying professional terminology, data formats, and data quality;

[0019] After the standards and specifications are established, data fields are mapped to multi-source data.

[0020] Data field mapping involves analyzing the meaning and purpose of data fields in multi-source data and mapping these meanings and purposes to fields in a standard data model. The standard data model is retrieved from a model library. Simultaneously, mapping relationships are established between fields with the same meaning but different names.

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

[0022] Data entity matching identifies data records representing the same entity in multi-source data by matching device codes, names, and models. Meanwhile, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching.

[0023] The multi-source data that has been matched with data entities is integrated, and a unified data view is established after integration. At the same time, the association rules between the integrated data are defined.

[0024] Ultimately, the data standardization and governance of multi-source data was completed.

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

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

[0027] Real-time determination of whether the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data have 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 do not change, the view update of the data view is controlled according to the view update time interval benchmark value, wherein 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 one 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 and minimum data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data will be retrieved.

[0030] The average time interval between the maximum time interval and the minimum data retrieval time interval is obtained based on the maximum time interval and the minimum data retrieval time interval, and is used as the first average time interval.

[0031] The average time interval corresponding to the data retrieval time intervals of the design phase data, construction phase data, operation and maintenance phase data, and related data is obtained as the second average time interval.

[0032] The view update interval of the data view is set using the average of the first time interval and the average of the second time interval;

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

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

[0035] The average time interval of the current design phase data, construction phase data, operation and maintenance phase data, and related data is retrieved as the baseline value for the current view update time interval.

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

[0037] The average value of the first time interval and the average value of the second time interval are compared, and the difference between the average value of the first time interval and the average value of the second time interval is obtained as the first difference parameter;

[0038] The difference between the average value of the first time interval and the average value of the second time interval before any time interval changes is retrieved from the design phase data, construction phase data, operation and maintenance phase data and related data, and is used as the second difference parameter.

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

[0040] The view update interval of the data view is set by using the first difference parameter and the second difference parameter in combination with the median 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 processed multi-source data is stored according to the storage architecture, including:

[0042] First, design the storage architecture. The storage architecture design is as follows: confirm the tiered storage module. The tiered storage model includes the raw data layer, the normalized data layer, and the analytical data layer.

[0043] The raw data layer preserves the original data before the remediation and uses a non-relational database for storage; the standard data layer stores the standard data after the remediation, classified according to the BIM standard model, and uses a relational database to store entity relationships; the analytical data layer stores the derived data after the excavation and uses a time-series database for storage.

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

[0045] The multi-source data that has been processed is partitioned into data partitions, including stage partitions and type partitions.

[0046] Phase partitioning divides the completed multi-source data into design phase data, construction phase data, and operation and maintenance phase data; type partitioning divides the completed multi-source data into structured data, unstructured data, and semi-structured data.

[0047] Based on the data partitioning, the managed multi-source data will be stored in the designed storage architecture.

[0048] Preferably, the stored data is indexed using an indexing mechanism, including:

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

[0050] Once the high-frequency query scenario is determined, key fields are identified, including the primary key, time field, and category field.

[0051] After the key fields are identified, the index type is selected. The index types include structured data, unstructured data, and time-series data.

[0052] Identify the high-frequency query scenarios and key fields of the stored data, and then determine the index type based on the identified high-frequency query scenarios and key fields.

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

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

[0055] The stored data is analyzed with specific objectives: for design phase data, the objective is to optimize design schemes, including energy consumption simulation and structural strength analysis; for construction phase data, the objective is to predict schedule and provide early warning of quality risks; and for operation and maintenance phase data, the objective is to predict equipment failures and optimize energy efficiency.

[0056] After the analysis target is defined, the stored data will be used to conduct a preliminary exploration using EDA methods. The preliminary exploration includes exploring the overall characteristics of the data, its distribution, and the relationships between variables. Then, the basic statistics of the data will be calculated, including the mean, median, standard deviation, and correlation coefficient.

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

[0058] After selecting a mining algorithm, data mining is performed on the stored data.

[0059] Finally, the stored data is analyzed and mined.

[0060] The system for constructing a BIM-based building lifecycle database includes:

[0061] The data collaboration management unit is used for:

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

[0063] The analyzed and mined data is integrated with the BIM model, and the visualization function of the BIM collaboration platform is used to customize the data visualization interface.

[0064] Specifically, for equipment failure prediction data during the operation and maintenance phase, an equipment status visualization panel is designed to identify the health status of the equipment in the BIM model using different colors or icons.

[0065] At the same time, detailed operating parameters and maintenance records of the equipment will be displayed in the form of pop-ups or sidebars;

[0066] For quality risk warning data during the construction phase, the construction parts with quality risks are highlighted with prominent marks in the BIM model and linked to detailed risk analysis reports and rectification suggestion documents.

[0067] Then, based on the responsibilities and needs of the construction project, data access permission rules are formulated, and collaborative workflows are developed based on the analysis and mining of data.

[0068] Ultimately, this enables collaborative management of analyzed and mined data.

[0069] Preferred options also include:

[0070] The data encryption backup unit is used for:

[0071] The analyzed and mined data is encrypted. Data encryption is enabled when the data is transmitted to the BIM collaboration platform and when it is transmitted within the platform.

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

[0073] The backup frequency for the analyzed and mined data will be determined based on the data update frequency and the impact of lost data.

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

[0076] Compared with the prior art, the beneficial effects of the present invention are as follows:

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

[0078] 2. The method and system for constructing a BIM-based building lifecycle database provided by this invention can achieve rapid querying of multiple columns based on composite indexes and covering indexes, meeting diverse data retrieval needs in business scenarios. By regularly monitoring the usage and performance of indexes, invalid indexes can be identified and optimized in a timely manner, reducing storage pressure and improving write speed. Through data analysis and mining, it can provide data-driven decision support for project management at different stages, helping to reduce the subjectivity and blindness of decision-making and improve the scientificity and accuracy of decisions.

[0079] 3. The method and system for constructing a BIM-based building lifecycle database provided by this invention enables project teams to make more informed decisions at different stages through the data support of the BIM model, thereby improving the overall performance and sustainability of the project. By enabling an encrypted transmission mechanism, the data is encrypted when transmitted to the BIM collaboration platform and during transmission within the platform, effectively preventing data leakage and tampering during transmission and enhancing data security. Attached Figure Description

[0080] Figure 1 This is a schematic diagram illustrating the construction steps of the building life cycle database of the present invention;

[0081] Figure 2 This is a schematic diagram illustrating the construction process of the building lifecycle database of the present invention. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] To address the issue of poor data quality caused by the lack of effective data processing and storage of acquired building source data in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

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

[0085] First, collect and process multi-source data; then, perform data standardization and governance on the processed multi-source data; finally, store the governed multi-source data according to the storage architecture.

[0086] An indexing mechanism is established for the stored data. Once 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, and operation and maintenance stages) can be integrated, avoiding data silos. Data standardization and governance ensure the consistency of data format, units, 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 querying, updating, 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 large dataset. Through in-depth analysis and mining of the stored data, potential patterns, problems, and trends throughout the building's entire life cycle can be discovered. A BIM-based building life cycle database can provide data support for all stages of a building project, helping decision-makers make more informed decisions.

[0088] Data collection and processing of multi-source data, including:

[0089] Data from multiple sources is obtained from the database, including data from the design phase, construction phase, operation and maintenance phase, and related data.

[0090] Design phase data includes architectural drawings and design specifications; 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 undergoes data preprocessing, which involves sequentially performing data cleaning, data transformation, data integration, data enhancement, and data verification.

[0092] Specifically, data preprocessing steps, including data cleaning, data transformation, and data integration, can eliminate redundancy and inconsistencies in the data, reduce uncertainty, and thus improve data reliability and accuracy. Centralized processing of multi-source data avoids redundant data collection and processing, thereby improving data processing efficiency. Simultaneously, data augmentation steps can further enhance data quality and usability. Centralized collection and processing of multi-source data reduces the cost of redundant data collection and processing across different stages and data sources. Furthermore, using fusion algorithms to obtain higher-precision data offers significantly better cost-effectiveness compared to the expensive cost of directly using high-precision equipment.

[0093] The processed multi-source data will undergo data standardization and governance, including:

[0094] Before implementing data governance and standardization, standards and norms should be established first.

[0095] Among them, the formulation of standards and specifications involves standardizing and unifying professional terminology, data formats, and data quality;

[0096] After the standards and specifications are established, data fields are mapped to multi-source data.

[0097] Data field mapping involves analyzing the meaning and purpose of data fields in multi-source data and mapping these meanings and purposes to fields in a standard data model. The standard data model is retrieved from a model library. Simultaneously, mapping relationships are established between fields with the same meaning but different names.

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

[0099] Data entity matching identifies data records representing the same entity in multi-source data by matching device codes, names, and models. Meanwhile, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching.

[0100] The multi-source data that has been matched with data entities is integrated, and a unified data view is established after integration. At the same time, the association rules between the integrated data are defined.

[0101] Ultimately, the data standardization and governance of multi-source data was completed.

[0102] Specifically, through the formulation of norms and standards, the professional terms, data formats, and data quality are standardized and unified, effectively solving the problems of inconsistency and compatibility between multi-source data, improving the readability and comprehensibility of data. In the data field mapping process, not only the meanings and uses of data fields in multi-source data are analyzed, but they are also accurately mapped to the fields in the standard data model, ensuring the accuracy of data conversion and integration. At the same time, the mapping relationships are established for fields with the same meaning but different names, further improving the data integration efficiency. The data entity matching is performed by matching key information such as device codes, names, and models, effectively identifying the data records representing the same entity in multi-source data. For ambiguous data, accurate matching is carried out by means of manual review or assisted by machine learning algorithms, further improving the accuracy of data integration. The multi-source data with completed data entity matching is integrated, and a unified data view is established, enabling data users to obtain and use data more conveniently. This step not only improves the usability of data but also provides strong support for subsequent data analysis and decision-making. After the data integration is completed, the definition of association rules between data helps to reveal the internal connections and laws between data. This is of great significance for deeply mining data value, optimizing business processes, and making more scientific decisions. In the data governance process, the actual situation and needs of the organization are fully considered, while sufficient flexibility and scalability are retained. As the organization develops and changes, this solution can be adjusted and optimized according to actual needs to ensure that data governance is always consistent with the organization's development.

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

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

[0105] Real-time judgment of whether the data retrieval time intervals corresponding to the design-phase data, construction-phase data, operation and maintenance-phase data, and related data have 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 reference value, where the value of the view update time interval reference 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 one 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 and minimum data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data will be retrieved.

[0108] The average time interval between the maximum time interval and the minimum data retrieval time interval is obtained based on the maximum time interval and the minimum data retrieval time interval, and is used as the first average time interval.

[0109] The average time interval corresponding to the data retrieval time intervals of the design phase data, construction phase data, operation and maintenance phase data, and related data is obtained as the second average time interval.

[0110] The view update interval of the data view is set using the average of the first time interval and the average of the second time interval;

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

[0112] The technical effects of the above solution are as follows: Real-time monitoring of data at each stage and the time interval for retrieving related data. When the interval remains constant, the maximum time interval is used as the view update benchmark to ensure that the view update rhythm matches the slowest data retrieval rhythm, avoiding data omissions due to overly rapid updates. When the interval changes, the maximum, minimum, and average time intervals of each stage are comprehensively considered when setting the view update time interval, accurately adapting to the data change rhythm and ensuring the data view accurately reflects the latest data status, thus improving the accuracy of data display. By reasonably setting the view update time interval, the problem of duplicate or untimely data display caused by unreasonable update times is avoided, reducing data deviation and ensuring the accuracy and completeness of data in the data view. This solution provides clear rules for setting the view update time interval, with corresponding handling methods for both constant and changing time 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 due to chaotic view updates. When the data retrieval interval changes, the view update interval is determined by calculating multiple average values. This effectively mitigates the impact of fluctuations in the data retrieval rhythm on view updates, ensuring stable operation of the data view unaffected by short-term data fluctuations. The data retrieval intervals may differ across different stages (design, construction, and operation). This solution controls view updates based on the actual retrieval intervals at each stage. Whether the intervals are stable across stages or change in a particular stage, the view update interval can be adjusted according to appropriate rules to adapt to the characteristics and changes of data at different stages, improving the system's adaptability to diverse data scenarios. When the data retrieval interval changes, the view update interval is dynamically set by calculating the average values ​​of multiple intervals. This allows the system to flexibly respond to changes in the data retrieval rhythm, adjust the view update strategy promptly, and ensure that the data view always effectively displays data, enhancing the system's adaptability to dynamic data changes.

[0113] Specifically, the view update interval of the data view is set using the average of the first time interval and the average of the second time interval, including:

[0114] The average time interval of the current design phase data, construction phase data, operation and maintenance phase data, and related data is retrieved as the baseline value for the current view update time interval.

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

[0116] The average value of the first time interval and the average value of the second time interval are compared, and the difference between the average value of the first time interval and the average value of the second time interval is obtained as the first difference parameter;

[0117] The difference between the average value of the first time interval and the average value of the second time interval before any one of the data retrieval time intervals for the design phase data, construction phase data, operation and maintenance phase data, and related data changes is used as the second difference parameter; wherein, the average value of the first time interval and the average value of the second time interval before any one of the data retrieval time intervals for the design phase data, construction phase data, operation and maintenance phase data, and related data changes are obtained in the same way as the average value of the first time interval and the average value of the second time interval after any one of the data retrieval time intervals for the design phase data, construction phase data, operation and maintenance phase data, and related data changes;

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

[0119] The view update interval of the data view is set by using the first difference parameter and the second difference parameter in combination with the median 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;

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

[0121]

[0122] Among them, T g T represents the view update time interval of the data view; c01 and T c02 These represent the first difference parameter and the second difference parameter, respectively; T z This represents the median value of the time interval among the data retrieval intervals corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data; T d01 and T d02 These represent the average time intervals after any change in the current view update time interval baseline value, design phase data, construction phase data, operation and maintenance phase data, and related data retrieval time intervals. Specifically, |T d01 -T d02 | represents the absolute value of the difference between the current view update interval baseline and the average interval after the change, reflecting the overall difference in the current and changed data retrieval rhythms. exp(-∣T d01 -T d02 |) Adjust the weights exponentially based on this difference, [1+exp(-|T d01 -T d02Based on the previous calculations, the data will be retrieved to determine the degree of rhythmic change |T c01 -T c02 |The median value of the time interval and the weight T after considering the overall change in rhythm z *[1+exp(-|T d01 -T d02 Divide by |) and add 1 to form an adjustment factor, which is used to update the baseline value T at the current view time interval. d01 Based on this, adjustments are made to obtain the final data view update interval T. g The formula reflects the current data retrieval rhythm (the baseline value T for the current view update interval). d01 ), degree of change (|T) c01 -T c02 |), intermediate level (T) z ) and overall change differences (|T) d01 -T d02 The formula organically combines multiple key factors, such as T, and uses specific mathematical operations to reasonably balance the influence weight of each factor on the view update interval, avoiding the dominance of a single factor. This ensures that the set view update interval comprehensively reflects the multifaceted characteristics of the data retrieval rhythm, guaranteeing its rationality. As the data retrieval interval changes, the relevant parameters in the formula (such as T) also change. c01 T c02 T d01 T d02 (etc.) will change accordingly. The formula can calculate and adjust the view update interval in real time based on these changes. It can accurately adjust the update interval size for different data rhythm changes, ensuring that the view update interval closely matches the actual data changes and improving accuracy.

[0123] The technical effects of the above solution are as follows: the current view update interval baseline value is the average of the data intervals at each stage, reflecting the overall situation of the current data retrieval rhythm; the first difference parameter reflects the difference between the average values ​​of two time intervals calculated at two different times, reflecting the changes in the data retrieval rhythm; the second difference parameter is a similar difference from the past, used for comparative analysis of changing trends; the median value of the time interval represents the median level of the data retrieval time interval, balancing the overall time interval characteristics. The formula, by integrating these parameters and using specific mathematical operations, takes into account the current status, changes, and trends of the data retrieval rhythm, thereby dynamically adjusting the view update interval. This allows the view update interval to change reasonably with the 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 time interval to set the view update interval, it can accurately adapt to the actual changes in the data. It avoids the problems of untimely or excessively frequent data updates that may occur with fixed update intervals, enabling the data view to accurately reflect the latest data status and improving the accuracy of data display. The solution provides clear rules and calculation formulas to determine the view update interval, and can adjust it based on established logic in the face of changes in the data retrieval time 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 can dynamically adjust the view update interval based on various changes in the data retrieval interval at different stages through the calculation of relevant parameters. Whether it's a change in the data retrieval rhythm at individual stages or a change in the overall rhythm, it can effectively adapt, improving the system's adaptability to diverse data scenarios and dynamic data changes.

[0124] The processed multi-source data will be stored according to the storage architecture, including:

[0125] First, design the storage architecture. The storage architecture design is as follows: confirm the tiered storage module. The tiered storage model includes the raw data layer, the normalized data layer, and the analytical data layer.

[0126] The raw data layer preserves the original data before the remediation and uses a non-relational database for storage; the standard data layer stores the standard data after the remediation, classified according to the BIM standard model, and uses a relational database to store entity relationships; the analytical data layer stores the derived data after the excavation and uses a time-series database for storage.

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

[0128] The multi-source data that has been processed is partitioned into data partitions, including stage partitions and type partitions.

[0129] Phase partitioning divides the completed multi-source data into design phase data, construction phase data, and operation and maintenance phase data; type partitioning divides the completed multi-source data into structured data, unstructured data, and semi-structured data.

[0130] Based on the data partitioning, the managed multi-source data will be stored in the designed storage architecture.

[0131] Specifically, by establishing a raw data layer to retain the original data before governance, the integrity and historical traceability of the data are ensured. This is crucial for subsequent data auditing, problem investigation, and decision support. The standardized data layer uses the BIM standard model to classify the data and uses a relational database to store entity relationships, which helps to achieve data standardization and normalization. Standardized data is easier to manage and analyze, improving data quality and usability. The analytical data layer uses a time-series database to store the derived data after mining. This type of database is particularly suitable for processing time-series data and can efficiently support data analysis and mining tasks. The layered storage model design makes the storage architecture highly flexible. The raw data layer, standardized data layer, and analytical data layer each undertake different responsibilities, are independent yet interconnected, and can meet the data storage needs of different scenarios. The data partitioning strategy (including stage partitioning and type partitioning) makes the data more orderly and efficient in storage. Stage partitioning helps to manage data according to different stages of the project lifecycle, while type partitioning classifies and stores data according to the degree of data structure, both of which help improve the efficiency of data retrieval and processing.

[0132] To address the issue of poor data accuracy in existing technologies due to the lack of targeted indexing analysis based on data retrieval and the absence of further data analysis and mining, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0133] The stored data is indexed, including:

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

[0135] Once the high-frequency query scenario is determined, key fields are identified, including the primary key, time field, and category field.

[0136] After the key fields are identified, the index type is selected. The index types include structured data, unstructured data, and time-series data.

[0137] Identify the high-frequency query scenarios and key fields of the stored data, and then determine the index type based on the identified high-frequency query scenarios and key fields.

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

[0139] Specifically, by creating indexes for high-frequency query scenarios and key fields, query time can be significantly reduced, improving data retrieval speed. Especially in critical business scenarios such as the design, construction, and maintenance phases, rapid data access is crucial for business decision-making and operational efficiency. Indexing mechanisms enable databases to process query requests more efficiently, thereby 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 rational allocation of resources. Index creation helps maintain data consistency, particularly in the application of composite and covering indexes. These indexes ensure that data remains ordered and quickly accessible after update and deletion operations, reducing query errors caused by data inconsistency. Index design targeting high-frequency query scenarios and key fields can support more complex query requirements. For example, through composite and covering index techniques, fast queries based on multiple column conditions can be achieved, meeting diverse data retrieval needs in business scenarios. By regularly monitoring index usage and performance, invalid indexes can be identified and optimized in a timely manner, reducing storage pressure and improving write speed. Furthermore, as business develops and data volume grows, the indexing mechanism can be flexibly adjusted to adapt to new query requirements.

[0140] Analyzing and mining the stored data, including:

[0141] The stored data is analyzed with specific objectives: for design phase data, the objective is to optimize design schemes, including energy consumption simulation and structural strength analysis; for construction phase data, the objective is to predict schedule and provide early warning of quality risks; and for operation and maintenance phase data, the objective is to predict equipment failures and optimize energy efficiency.

[0142] After the analysis target is defined, the stored data will be used to conduct a preliminary exploration using EDA methods. The preliminary exploration includes exploring the overall characteristics of the data, its distribution, and the relationships between variables. Then, the basic statistics of the data will be calculated, including the mean, median, standard deviation, and correlation coefficient.

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

[0144] After selecting a mining algorithm, data mining is performed on the stored data.

[0145] Finally, the stored data is analyzed and mined.

[0146] Specifically, the specific analytical objectives for different stages (design, construction, and operation and maintenance) were clearly defined before the analysis began. This goal-oriented approach ensures that the analysis is targeted and improves its efficiency and relevance. Exploratory Data Analysis (EDA) was used for initial exploration, which helps to comprehensively understand the overall characteristics, distribution, and relationships between variables in 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 correlations between variables. Depending on the analytical objectives, the solution flexibly selected various data mining algorithms (such as support vector machines, hierarchical clustering, and the Apriori algorithm). This algorithmic diversity can handle different types of data and analytical needs, improving the accuracy and applicability of the analysis. Through data analysis and mining, the solution provides data-driven decision support for project management at different stages. This helps reduce the subjectivity and blind spots in decision-making, improving its scientific rigor and accuracy.

[0147] To address the issue of reduced data integrity and security caused by the lack of proper management of processed building data in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0148] The system for constructing a BIM-based building lifecycle database includes:

[0149] The data collaboration management unit is used for:

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

[0151] The analyzed and mined data is integrated with the BIM model, and the visualization function of the BIM collaboration platform is used to customize the data visualization interface.

[0152] For equipment failure prediction data during the operation and maintenance phase, a device status visualization panel is designed to identify the health status of the equipment in the BIM model using different colors or icons.

[0153] At the same time, detailed operating parameters and maintenance records of the equipment will be displayed in the form of pop-ups or sidebars;

[0154] For quality risk warning data during the construction phase, the construction parts with quality risks are highlighted with prominent marks in the BIM model and linked to detailed risk analysis reports and rectification suggestion documents.

[0155] Then, based on the responsibilities and needs of the construction project, data access permission rules are formulated, and collaborative workflows are developed based on the analysis and mining of data.

[0156] Ultimately, this enables collaborative management of analyzed and mined data.

[0157] Specifically, by selecting a suitable BIM collaboration platform and confirming data interfaces with that platform, the interoperability of data between different systems was effectively improved, achieving seamless data integration. Centralized data management and collaborative work avoided information silos, improved data consistency and accuracy, and utilized the visualization capabilities of the BIM collaboration platform to customize data visualization interfaces, enabling project managers to more intuitively understand the project status and improve decision-making efficiency. Especially during the operation and maintenance phase, the equipment status visualization panel can display the real-time health status of equipment, helping maintenance personnel to quickly respond to equipment failures and reduce maintenance costs. During the construction phase, quality risk warning data is highlighted with prominent markers through the BIM model, helping the construction team to promptly identify and address potential quality issues, avoiding additional costs from later rectification. Links to risk analysis reports and rectification suggestion documents provide the construction team with detailed problem solutions, improving construction quality and safety. Data access permission rules were formulated based on the responsibilities and needs of the building project, ensuring the reasonable flow and security of information. The implementation of the access control system prevented unauthorized access and data leakage, protecting sensitive project information. Developing collaborative workflows based on data analysis and mining helps optimize project execution processes and improve work efficiency. With the support of BIM technology, workflows become more transparent and controllable, facilitating better collaboration among project teams, reducing communication costs, and encompassing data management not only during the construction phase but also during the operation and maintenance phase, thus achieving full lifecycle management of building projects. Data support from the BIM model enables project teams to make more informed decisions at different stages, improving overall project performance and sustainability. Visual interfaces and interactive designs make project information easier to understand and use, enhancing the user experience.

[0158] The data encryption backup unit is used for:

[0159] The analyzed and mined data is encrypted. Data encryption is enabled when the data is transmitted to the BIM collaboration platform and when it is transmitted within the platform.

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

[0161] The backup frequency for the analyzed and mined data will be determined based on the data update frequency and the impact of lost data.

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

[0164] Specifically, by enabling encrypted transmission mechanisms, data is encrypted both when transmitted to the BIM collaboration platform and within the platform itself. This effectively prevents data leakage and tampering during transmission, enhancing data security. Using HTTPS protocol for data transmission further ensures data security, as HTTPS itself has encryption, authentication, and integrity verification functions. Backup frequency is determined based on data update frequency and the impact of lost data, ensuring timely backup of important data while avoiding unnecessary resource waste. The more scientific and reasonable backup frequency setting guarantees both data integrity and improved 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, thus 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" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

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

Claims

1. A method for constructing a BIM-based building lifecycle database, characterized in that, include: First, collect and process multi-source data; then, perform data standardization and governance on the processed multi-source data; finally, store the governed 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. The processed multi-source data will undergo data standardization and governance, including: Before implementing data governance and standardization, standards and norms should be established first. Among them, the formulation of standards and specifications involves standardizing and unifying professional terminology, data formats, and data quality; After the standards and specifications are established, data fields are mapped to multi-source data. Data field mapping involves analyzing the meaning and purpose of data fields in multi-source data and mapping these meanings and purposes to fields in a standard data model. The standard data model is retrieved from a model library. Simultaneously, mapping relationships are established between fields with the same meaning but different names. After the data field mapping is completed, data entity matching is performed. Data entity matching identifies data records representing the same entity in multi-source data by matching device codes, names, and models. Meanwhile, for ambiguous data in the matching process, manual review or machine learning algorithms are used to assist in accurate matching. The multi-source data that has been matched with data entities is integrated, and a unified data view is established after integration. At the same time, the association rules between the integrated data are defined. Ultimately, the data standardization and governance of multi-source data was completed; Controlling view updates of data views includes: Real-time extraction of design phase data, construction phase data, operation and maintenance phase data, and related data retrieval time intervals; Real-time determination of whether the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data have changed; When the data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data do not change, the view update of the data view is controlled according to the view update time interval benchmark value, wherein 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 one 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 and minimum data retrieval time intervals corresponding to the design phase data, construction phase data, operation and maintenance phase data, and related data will be retrieved. The average time interval between the maximum time interval and the minimum data retrieval time interval is obtained based on the maximum time interval and the minimum data retrieval time interval, and is used as the first average time interval. The average time interval corresponding to the data retrieval time intervals of the design phase data, construction phase data, operation and maintenance phase data, and related data is obtained as the second average time interval. The view update interval of the data view is set using the average of the first time interval and the average of the second time interval; The view update of the data view is controlled according to the view update time interval of the data view; Setting the view update interval of the data view using the average of the first time interval and the average of the second time interval includes: The average time interval of the current design phase data, construction phase data, operation and maintenance phase data, and related data is retrieved as the baseline value for the current view update time interval. Retrieve the average value of the first time interval and the average value of the second time interval; The average value of the first time interval and the average value of the second time interval are compared, and the difference between the average value of the first time interval and the average value of the second time interval is obtained as the first difference parameter; The difference between the average value of the first time interval and the average value of the second time interval before any time interval changes is retrieved from the design phase data, construction phase data, operation and maintenance phase data and related data, and is used as the second difference parameter. Retrieve the median value of the time intervals corresponding to the current design phase data, construction phase data, operation and maintenance phase data, and related data; The view update interval of the data view is set by using the first difference parameter and the second difference parameter in combination with the median 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.

2. The method for constructing a BIM-based building lifecycle database according to claim 1, characterized in that, Data collection and processing of multi-source data, including: Data from multiple sources is obtained from the database, including data from the design phase, construction phase, operation and maintenance phase, and related data. Design phase data includes architectural drawings and design specifications; 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 undergoes data preprocessing, which involves sequentially performing data cleaning, data transformation, data integration, data enhancement, and data verification.

3. The method for constructing a BIM-based building lifecycle database according to claim 1, characterized in that, The processed multi-source data will be stored according to the storage architecture, including: First, design the storage architecture. The storage architecture design is as follows: confirm the tiered storage module. The tiered storage model includes the raw data layer, the normalized data layer, and the analytical data layer. The raw data layer preserves the original data before the remediation and uses a non-relational database for storage; the standard data layer stores the standard data after the remediation, classified according to the BIM standard model, and uses a relational database to store entity relationships; the analytical data layer stores the derived data after the excavation and uses a time-series database for storage. Among them, non-relational databases, relational databases, and time-series databases are retrieved from the database; The multi-source data that has been processed is partitioned into data partitions, including stage partitions and type partitions. Phase partitioning divides the completed multi-source data into design phase data, construction phase data, and operation and maintenance phase data; type partitioning divides the completed multi-source data into structured data, unstructured data, and semi-structured data. Based on the data partitioning, the managed multi-source data will be stored in the designed storage architecture.

4. The method for constructing a BIM-based building lifecycle database according to claim 1, characterized in that, The stored data is indexed, including: High-frequency query scenarios are identified based on historical query records retrieved from the database. These high-frequency query scenarios include the design phase, construction phase, and operation and maintenance phase. Once the high-frequency query scenario is determined, key fields are identified, including the primary key, time field, and category field. After the key fields are identified, the index type is selected. The index types include structured data, unstructured data, and time-series data. Identify the high-frequency query scenarios and key fields of the stored data, and then determine the index type based on the identified high-frequency query scenarios and key fields. Once the index type is confirmed, the complete indexing mechanism is obtained.

5. The method for constructing a BIM-based building lifecycle database according to claim 1, characterized in that, Analyzing and mining the stored data, including: The stored data is analyzed with specific objectives: for design phase data, the objective is to optimize design schemes, including energy consumption simulation and structural strength analysis; for construction phase data, the objective is to predict schedule and provide early warning of quality risks; and for operation and maintenance phase data, the objective is to predict equipment failures and optimize energy efficiency. After the analysis target is defined, the stored data is used to conduct a preliminary exploration using EDA methods. The preliminary exploration includes exploring the overall characteristics of the data, its distribution, and the relationships between variables. Then, the basic statistics of the data are calculated, including the mean, median, standard deviation, and correlation coefficient. Based on the defined analysis objectives and preliminary exploration data, the data mining algorithm is selected, including support vector machine, hierarchical clustering or Apriori algorithm; After selecting a mining algorithm, data mining is performed on the stored data. Finally, the stored data is analyzed and mined.

6. A system for constructing a BIM-based building lifecycle database, applied in the method for constructing a BIM-based building lifecycle database as described in any one of claims 1-5, characterized in that, include: The data collaboration management unit is used for: Select a BIM collaboration platform and confirm the data interface based on the BIM collaboration platform; The analyzed and mined data is integrated with the BIM model, and the visualization function of the BIM collaboration platform is used to customize the data visualization interface. Specifically, for equipment failure prediction data during the operation and maintenance phase, an equipment status visualization panel is designed to identify the health status of the equipment in the BIM model using different colors or icons. At the same time, detailed operating parameters and maintenance records of the equipment will be displayed in the form of pop-ups or sidebars; For quality risk warning data during the construction phase, the construction parts with quality risks are highlighted with prominent marks in the BIM model and linked to detailed risk analysis reports and rectification suggestion documents. Then, based on the responsibilities and needs of the construction project, data access permission rules are formulated, and collaborative workflows are developed based on the analysis and mining of data. Ultimately, this enables collaborative management of analyzed and mined data.

7. The system for constructing a BIM-based building lifecycle database according to claim 6, characterized in that, Also includes: The data encryption backup unit is used for: The analyzed and mined data is encrypted. Data encryption is enabled 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 selected encryption algorithm is used to encrypt the data, and the encrypted data is transmitted to the receiving end through the encrypted channel. At the receiving end, the corresponding decryption key is used to decrypt the data and restore the original data. The backup frequency for the analyzed and mined data will be determined based on the data update frequency and the impact of lost data. 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. Once the backup method is confirmed, the analyzed and mined data will be backed up to cloud storage.

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