A data acquisition and analysis system for construction project management

Abstract

This invention discloses a data acquisition and analysis system for construction project management. The system includes: labeling building components using QR code technology to obtain basic data of the building components, uploading this data to a building component management platform, and controlling the building components by recognizing the QR codes; using RFID technology for factory and site management of building components, and constructing engineering models of the building components using RFID and BIM technologies; building an analysis model of the building components; constructing an optimization iterative algorithm to optimize the building component analysis model, analyzing newly added factory and site management data, identifying abnormal data, and achieving data acquisition and analysis for construction project management. This invention can effectively improve the automation level and accuracy of construction project management, enable in-depth analysis, achieve project management prediction, and meet the requirements of construction project management and production.

Description

Technical Field

[0001] This invention relates to the field of construction project management technology, and in particular to a data acquisition and analysis system for construction project management. Background Technology

[0002] Construction project management is a crucial aspect of ensuring the smooth progress of construction projects from design to completion. It involves multiple aspects, including quality, cost, schedule, and safety management, and is of great significance for improving the overall level of the construction industry.

[0003] The history of construction project management can be traced back to ancient civilizations, where simple planning and supervision methods were used to construct temples and palaces. With the advent of the Industrial Revolution, construction technology developed rapidly, and construction project management became more complex and systematic. In the early 20th century, with the emergence of project management theory, scientific methods and tools, such as Gantt charts and the Critical Path Method (CPM), were introduced into construction project management. By the late 20th century, with the development of computer technology, construction project management began to utilize information technology for more precise planning and monitoring. Entering the 21st century, the concepts of globalization and sustainable development have placed higher demands on construction project management, prompting it to develop towards greater efficiency, environmental friendliness, and intelligence.

[0004] The mainstream technologies for current construction project management include a variety of advanced information technologies and automation tools. Among them, Building Information Modeling (BIM) and Geographic Information Systems (GIS) are two particularly critical technologies. BIM is a revolutionary technology that integrates all relevant project information by creating a three-dimensional digital model of the building. BIM not only improves the accuracy and efficiency of the design phase but also provides powerful data support during the construction and operation phases, enabling project teams to better coordinate work, optimize resource allocation, and achieve higher construction quality and operational efficiency. The Jiang'an District Chenjiaji Resettlement Housing and Supporting Facilities Project, jointly submitted by MCC Southern Engineering Technology Co., Ltd. and Glodon Technology Co., Ltd., won the second prize in the "2021 Smart City Pioneer List Excellent Application Case" for its BIM+Smart Construction Site application practice. BIM technology has enabled a fully informatized and intelligent collaborative model in smart city construction, supporting all stages of the construction process. Geographic Information Systems (GIS) are technologies used to capture, store, analyze, and display geospatial data. In construction project management, GIS technology can help project teams better understand the geographical features of the project site, assess environmental impact, plan construction layout, and monitor construction progress. The combined use of GIS and BIM provides a comprehensive geographic and building information perspective for construction projects, greatly improving the quality and speed of decision-making.

[0005] Despite significant advancements in building project management technology, several issues and challenges remain. While BIM technology offers powerful integrated design, construction, and operation and maintenance capabilities in building project management, BIM data collection still requires substantial manual intervention, significantly increasing subjectivity and error. Furthermore, even mature BIM systems often fail to further enhance efficiency during practical application.

[0006] Chinese invention patent CN116258481A discloses a management and control method and system for intelligent construction of building projects. The method includes the following steps: S1, acquiring real-time building image data and constructing a three-dimensional building model based on the real-world scene; S2, constructing an adaptive digital twin model based on the three-dimensional building model; S3, utilizing the interactivity of the digital twin model to monitor and predict the construction site; S4, managing the construction site according to a preset construction schedule and tasks. This invention also discloses a management and control system for intelligent construction of building projects. This invention employs high-precision three-dimensional data and scene fusion technology combining ground and air to construct a high-precision digital twin model that integrates building project data, achieving full connectivity between the building project and internet databases. It comprehensively associates real-time construction data with the three-dimensional scene model, forming an intelligent construction system capable of remote intelligent monitoring, multi-dimensional production data monitoring, and project task allocation and scheduling. However, this invention patent simply performs traditional three-dimensional modeling of the building project and synchronizes it to a remote server for operation, offering no substantial help in viewing the project progress or actual components.

[0007] Chinese invention patent CN111311203B discloses a BIM-based engineering progress control system, including: a BIM model building module, a construction process generation module, a database generation module, a project plan management module, a relationship building module, a construction progress simulation module, and a construction plan vs. actual progress comparison module. This invention configures a corresponding building visualization model for each construction process based on script recording and playback, and each construction process's corresponding BIM model carries corresponding parameter data. This invention is essentially an extension of BIM, merely using and explaining its traditional functions without any substantial innovation, and it does not further analyze or solve problems related to construction progress.

[0008] Therefore, we need a comprehensive building project management technology that, while using the traditional and mature BIM technology, addresses the problem of excessive human intervention in the data collection process, and further analyzes the collected data to conduct in-depth predictions for project management. Summary of the Invention

[0009] This invention provides a data acquisition and analysis system for construction project management to solve the technical problems of existing construction project management systems, such as low automation of data acquisition, insufficient accuracy, lack of in-depth analysis, and inability to achieve project management and prediction.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0011] This invention provides a data acquisition and analysis system for construction project management, comprising:

[0012] QR code technology is used to label building components to obtain basic data of the building components. The basic data of the building components is then uploaded to the building component management platform, which can control the building components by recognizing the QR codes.

[0013] RFID technology is used for factory management of building components to obtain factory management data, RFID technology is used for on-site management of building components to obtain on-site management data, and RFID technology and BIM technology are used to build engineering models of building components.

[0014] Preprocess the basic data, factory management data and site entry management data of building components on the building component management platform, set initial weights according to the different degrees of impact of components on project completion, and build a building component analysis model.

[0015] An optimization iterative algorithm is constructed, and the building component analysis model is optimized using the optimization iterative algorithm to obtain an optimized building component analysis model. The optimized building component analysis model is then used to analyze newly added factory management data and site entry management data to identify abnormal data, thereby realizing data collection and analysis for building engineering management.

[0016] Furthermore, in the aforementioned data collection and analysis system for construction project management, the use of QR code technology to label building components and obtain basic data of the building components, wherein...

[0017] During production, the building components should have a QR code that can be scanned by a mobile device affixed to the main building components;

[0018] The QR code should also contain an identifiable chip that can be identified via RFID technology;

[0019] The basic data of the building components should at least include the category, model, manufacturer, price, project number, and component number of the building components.

[0020] Furthermore, in the aforementioned data acquisition and analysis system for building engineering management, the basic data of the building components is uploaded to a building component management platform. This building component management platform can control the building components by recognizing QR codes.

[0021] The building component management platform should include a data center, a mobile input terminal, a factory scanning terminal, and an entry scanning terminal.

[0022] The data center should be deployed on a high-performance server. The storage capacity of the high-performance server should be selected according to the amount of building component data in the building project, and the concurrent task processing performance of the server should be selected according to the analysis time limit requirements and the number of users online.

[0023] The data center is used to collect data uploaded by mobile input terminals, factory scanning terminals, and entry scanning terminals, analyze the data, and construct building component analysis models.

[0024] The mobile data entry terminal is used to collect basic data of building components;

[0025] The factory scanning terminal should be installed at the outbound location of the building component warehouse to identify outbound information of building components;

[0026] The entry scanning terminal should be installed at the entrance of the building project to identify the entry information of building components.

[0027] Furthermore, in the aforementioned data acquisition and analysis system for construction project management, RFID technology is used for both factory management of building components (obtaining factory management data) and site management of building components (obtaining site management data).

[0028] The RFID technology can identify building components without contact through radio wave signals, enabling the control and management of building components;

[0029] The factory management data includes the information on the departure of the building components and the time information;

[0030] The entry management data includes the entry information and time information of the building components.

[0031] Furthermore, in the aforementioned data acquisition and analysis system for construction project management, the construction of building component engineering models using RFID and BIM technologies is described, wherein...

[0032] The BIM technology utilizes basic building component data collected via RFID technology for data processing, and then processes and inputs this data into the BIM system. , can be represented as:

[0033]

[0034] in, These are actual measured values. To complete, Sampling time, Represents the original data. Indicates data completion;

[0035] The data entered into the BIM system is used to import the building component engineering model into the BIM system.

[0036] Furthermore, the data acquisition and analysis system for construction project management preprocesses the basic data, factory management data, and site entry management data of building components on the building component management platform, and sets initial weights based on the different degrees of impact of components on project completion.

[0037] The basic data, factory management data, and site entry management data of the building components were normalized and scaled to the range (-1, 1). The influencing factors were obtained using the Z-score method. :

[0038]

[0039] in, Represents the standard value of the original data. This represents the mean of the original data. This refers to the basic data, factory management data, and site entry management data of the building components;

[0040] An initial weight was assigned to the aforementioned influencing factors to obtain the construction project score:

[0041]

[0042] in, To score the construction project, As influencing factors, As the initial weights for the corresponding influencing factors, This indicates the number of influencing factors.

[0043] Furthermore, in the aforementioned data acquisition and analysis system for construction project management, the construction of the building component analysis model, wherein...

[0044] The construction project score is decomposed into M sub-models based on a normal distribution, as follows:

[0045]

[0046] in, These are vectors of basic building component data, factory management data, and site entry management data. Indicates the weights after processing. Let denot be the mean of the original data, N represent the probability density function of the multivariate normal distribution, Σi be the covariance matrix of the i-th sub-model, and Σi represent the distribution shape and correlation of this type of data.

[0047] Furthermore, in the aforementioned data acquisition and analysis system for construction project management, the construction of an optimization iterative algorithm, and the use of the optimization iterative algorithm to optimize the building component analysis model to obtain an optimized building component analysis model, wherein...

[0048] The optimization iterative algorithm is an optimization maximum likelihood theory estimation algorithm;

[0049] The optimized maximum likelihood estimation algorithm is as follows: calculate the selection probability density P:

[0050]

[0051] Where k represents the current Gaussian distribution. This represents the posterior probability that the current Gaussian distribution is selected given sample data x. This represents the probability of the current Gaussian distribution being selected. Represents the mixing coefficient. The mean is The covariance is The probability density of the Gaussian distribution at x. Let x represent the conditional probability density function of the j-th Gaussian distribution. This represents the prior probability that the j-th Gaussian distribution is selected;

[0052] The mixing coefficients and mean of the parameters are calculated based on the selection probability density P.

[0053] Furthermore, the mixing coefficients and mean values ​​of the parameters are calculated based on the selection probability density P;

[0054] The mixing coefficient is calculated using the selection probability density. :

[0055]

[0056] Calculate the mean using the selection probability density. :

[0057]

[0058] Mixing coefficients of parameters and mean Continue calculating the probability density until P converges, thus obtaining the optimized building component analysis model.

[0059] Furthermore, the data acquisition and analysis system for construction project management uses the optimized building component analysis model to analyze newly added factory management data and site entry management data, identify abnormal data, and realize data acquisition and analysis for construction project management.

[0060] The newly added factory management data and on-site management data are input into the optimized building component analysis model. A threshold is set, and when the difference between the model output value and the actual value exceeds the threshold, an anomaly is considered to have occurred, thereby realizing the data collection and analysis of building engineering management.

[0061] The beneficial effects of the technical solution provided by this invention include at least the following:

[0062] This invention uses QR code technology to label building components, enabling management and control of these components via QR code recognition on a management platform. It also uses RFID technology for factory and site management data, constructing an engineering model for the building components. Furthermore, it builds and optimizes this analysis model, analyzing newly added factory and site management data to identify anomalies and achieve data collection and analysis for building project management. This effectively solves the technical problems of existing building project management systems, such as low automation, insufficient accuracy, lack of in-depth analysis, and inability to predict project management. It is particularly suitable for precise management and in-depth analysis of building project management, outperforming existing technologies and meeting the requirements of building project management and production. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of a data acquisition and analysis system method for construction project management provided by an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the front of an electronic tag provided in an embodiment of the present invention;

[0066] Figure 3 A schematic diagram of an electronic tag provided in an embodiment of the present invention;

[0067] Figure 4 A schematic diagram of a handheld RFID mobile scanning device provided in an embodiment of the present invention;

[0068] Figure 5This is a schematic diagram of a fixed RFID scanning device provided in an embodiment of the present invention;

[0069] Figure 6 The flowchart of the optimization iterative algorithm provided in the embodiments of the present invention is shown. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0071] Example

[0072] This embodiment provides a data acquisition and analysis system for building engineering management. It uses QR code technology to label building components, obtaining basic data for these components. This basic data is then uploaded to a building component management platform, which can control the components by recognizing the QR codes. RFID technology is used for both factory and site management, generating factory and site management data. RFID and BIM technologies are used to construct engineering models of the building components. The system preprocesses the basic, factory, and site management data on the building component management platform, setting initial weights based on the different degrees of impact of each component on project completion, and then builds a building component analysis model. An optimization iterative algorithm is constructed and used to optimize the building component analysis model, resulting in an optimized model. This optimized model is then used to analyze newly added factory and site management data, identifying anomalies and achieving data acquisition and analysis for building engineering management.

[0073] Please refer to Figure 1 The flowchart shown here illustrates a data acquisition and analysis system method for construction project management, which includes the following steps:

[0074] Step 1:

[0075] On the one hand, QR code technology is used to label building components to obtain basic data of the building components. In this embodiment, the electronic tag should at least contain a QR code and a barcode that can be read by devices such as mobile phones and scanners.

[0076] Please refer to Figure 2 The diagram shown is of the front of the electronic tag and Figure 3 The diagram shown is an illustration of an electronic tag.

[0077] It should be noted that the QR code and barcode on the label should correspond to the same building component, and the basic data of the building component should at least include the category, model, manufacturer, price, project number, and component number of the building component.

[0078] It should be noted that the electronic tag should also contain an identifiable chip, capable of being identified via RFID technology. In this embodiment, the electronic tags selected should be rationally chosen based on the design and construction cycle of the engineering components to ensure subsequent identification. The electronic tags should be deployed at key measurement locations in the main building's construction. For example, for main water supply and drainage pipes, they should be deployed in the middle or at the connection points of the pipe body. In actual engineering, they are deployed at pipe connections to facilitate later verification.

[0079] On the other hand, in this embodiment, electronic data needs to be collected, summarized, and centrally processed. In this embodiment, it is called a building component management platform. The building component management platform should include a data center, a mobile input terminal, a factory scanning terminal, and an entry scanning terminal.

[0080] It's important to note that the data center should be deployed on high-performance servers. The storage capacity of these servers should be selected based on the amount of building component data in the construction project, and the concurrent task processing performance should be chosen based on the analysis timeframe and the number of users online. Since the scale and data volume of engineering projects vary greatly, we have chosen online cloud servers for the data center deployment. This offers significant advantages over traditional data center deployment environments. Resources such as bandwidth, storage, memory, and GPUs can be rented on demand. After initial heavy computation, the performance requirements of the cloud servers can be appropriately reduced. Furthermore, most cloud servers implement dual-site, triple-backup systems, ensuring security and reliability, and also saving on personnel maintenance costs.

[0081] In this embodiment, China Telecom Cloud host is selected, which is low in cost and can meet deployment requirements.

[0082] It should be noted that the data center is used to collect data uploaded by mobile input terminals, factory scanning terminals, and entry scanning terminals, analyze the data, and build analysis models of building components.

[0083] The mobile data entry terminal is used to collect basic data of building components. Since it requires mobile operation, this embodiment uses a handheld RFID scanning device. Please refer to [reference needed]. Figure 4 The diagram shows a handheld RFID mobile scanning device.

[0084] The factory scanning terminal should be installed at the outbound location of the building component warehouse to identify outbound information of building components. Please refer to [link / reference]. Figure 5 The diagram shows a fixed RFID scanning device. Since the entry scanning terminal should be installed at the entrance of the building project to identify the entry information of building components, it should be deployed in a saturated manner during installation to comprehensively monitor the entry and exit of building components.

[0085] Step Two:

[0086] On the one hand, RFID technology is used to manage building components at the factory to obtain factory management data, and on the other hand, RFID technology is used to manage building components at the site to obtain site management data.

[0087] RFID technology enables contactless identification of building components through radio wave signals, thereby facilitating the management and control of these components.

[0088] Factory management data includes information on the release and timing of building components.

[0089] Entry management data includes information on the arrival and time of building components.

[0090] It should be noted that the time information associated with building components has a major impact on the management and control of construction projects and is a key factor for later analysis, which requires special attention.

[0091] On the other hand, RFID and BIM technologies are used to construct engineering models of building components. BIM technology is used to process basic data of building components collected via RFID technology during data processing, and this data is then entered into the BIM system. , can be represented as:

[0092]

[0093] in, These are actual measured values. To complete, Sampling time, Represents the original data. Indicates data completion;

[0094] Data entered into the BIM system is used to import the building component engineering model into the BIM system.

[0095] Step 3:

[0096] On the one hand, the basic data, factory management data, and site entry management data of building components on the building component management platform are preprocessed, and initial weights are set according to the different degrees of impact of the components on project completion.

[0097] Data on building component foundations, factory management, and site entry management were normalized and scaled to the range (-1, 1). The Z-score method was then used to obtain the influencing factors. :

[0098]

[0099] in, Represents the standard value of the original data. This represents the mean of the original data. This represents basic data, factory management data, and site entry management data for building components.

[0100] Initial weights were assigned to the influencing factors to obtain the construction project score:

[0101]

[0102] in, To score the construction project, As influencing factors, As the initial weights for the corresponding influencing factors, This indicates the number of influencing factors.

[0103] On the other hand, we build analysis models for building components.

[0104] The scoring of construction projects is decomposed into M sub-models based on a normal distribution, as follows:

[0105]

[0106] in, These are vectors of basic building component data, factory management data, and site entry management data. Indicates the weights after processing. Let denot be the mean of the original data, N represent the probability density function of the multivariate normal distribution, Σi be the covariance matrix of the i-th sub-model, and Σi represent the distribution shape and correlation of this type of data.

[0107] Step Four:

[0108] On the one hand, an optimization iterative algorithm is constructed, and the optimization iterative algorithm is used to optimize the building component analysis model to obtain an optimized building component analysis model.

[0109] Please refer to Figure 6 The flowchart of the optimization iterative algorithm is shown.

[0110] The optimization iterative algorithm is an optimization of the maximum likelihood theory estimation algorithm.

[0111] The optimized maximum likelihood estimation algorithm is to calculate the selection probability density P:

[0112]

[0113] Where k represents the current Gaussian distribution. This represents the posterior probability that the current Gaussian distribution is selected given sample data x. This represents the probability of the current Gaussian distribution being selected. Represents the mixing coefficient. The mean is The covariance is The probability density of the Gaussian distribution at x. Let x represent the conditional probability density function of the j-th Gaussian distribution. This represents the prior probability that the j-th Gaussian distribution is selected;

[0114] The mixing coefficient is calculated using the selection probability density. :

[0115]

[0116] Calculate the mean using the selection probability density. :

[0117]

[0118] Mixing coefficients of parameters and mean Continue calculating the probability density until P converges, thus obtaining the optimized building component analysis model.

[0119] On the other hand, by using an optimized building component analysis model to analyze newly added factory management data and site entry management data, abnormal data can be identified, thereby realizing data collection and analysis for building engineering management.

[0120] Add new factory management data and site entry management data to optimize the building component analysis model, set thresholds, and consider an anomaly when the model output value differs from the actual value by more than the threshold, thus realizing data collection and analysis for building project management.

[0121] It should also 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. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0122] Finally, it should be noted that the above description is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiments of the present invention have been described, those skilled in the art can make several improvements and modifications without departing from the principles of the present invention once they know the basic inventive concept of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data acquisition and analysis system for construction project management, characterized in that, include: QR code technology is used to label building components to obtain basic data of the building components. The basic data of the building components is then uploaded to the building component management platform, which can control the building components by recognizing the QR codes. RFID technology is used for factory management of building components to obtain factory management data, RFID technology is used for on-site management of building components to obtain on-site management data, and RFID technology and BIM technology are used to build engineering models of building components. Preprocess the basic data, factory management data and site entry management data of building components on the building component management platform, set initial weights according to the different degrees of impact of components on project completion, and build a building component analysis model. An optimization iterative algorithm is constructed, and the building component analysis model is optimized using the optimization iterative algorithm to obtain an optimized building component analysis model. The optimized building component analysis model is then used to analyze newly added factory management data and site entry management data to identify abnormal data and realize data collection and analysis for building engineering management. The process involves using RFID technology for both factory-outgoing and on-site management of building components, resulting in factory management data and on-site management data. The RFID technology can identify building components without contact through radio wave signals, enabling the control and management of building components; The factory management data includes the information on the departure of the building components and the time information; The entry management data includes the entry information and time information of the building components; The construction of building component engineering models using RFID and BIM technologies is described above. The BIM technology utilizes basic building component data collected via RFID technology for data processing, and then processes and inputs this data into the BIM system. , is represented as: ； in, These are actual measured values. To complete, Sampling time, Represents the original data. Indicates data completion; The data entered into the BIM system is used to import the building component engineering model into the BIM system. The preprocessing of basic building component data, factory management data, and site entry management data on the building component management platform involves setting initial weights based on the varying degrees of impact of components on project completion. The basic data, factory management data, and site entry management data of the building components were normalized and scaled to the range (-1, 1). The influencing factors were obtained using the Z-score method. : ； in, Represents the standard value of the original data. This represents the mean of the original data. This refers to the basic data, factory management data, and site entry management data of the building components; An initial weight was assigned to the aforementioned influencing factors to obtain the construction project score: ； in, To score the construction project, As influencing factors, As the initial weights for the corresponding influencing factors, Indicates the number of influencing factors; The aforementioned construction of the building component analysis model, wherein... The construction project score is decomposed into M sub-models based on a normal distribution, as follows: ； in, These are vectors of basic building component data, factory management data, and site entry management data. Indicates the weight after processing. Σi represents the mean of the original data, N represents the probability density function of the multivariate normal distribution, and Σi is the covariance matrix of the i-th sub-model, representing the distribution shape and correlation of this type of data. The aforementioned construction of an optimization iterative algorithm is used to optimize the building component analysis model, resulting in an optimized building component analysis model. The optimization iterative algorithm is an optimization maximum likelihood theory estimation algorithm; The optimized maximum likelihood estimation algorithm is as follows: calculate the selection probability density P: ； Where k represents the current Gaussian distribution. This represents the posterior probability that the current Gaussian distribution is selected given sample data x. This represents the probability of the current Gaussian distribution being selected. Represents the mixing coefficient. The mean is covariance is The probability density of the Gaussian distribution at x. Let represent the conditional probability density of data x under the j-th Gaussian distribution. This represents the prior probability that the j-th Gaussian distribution is selected; Calculate the mixing coefficients and mean of the parameters based on the selection probability density P; The mixing coefficient is calculated using the selection probability density. : ； Calculate the mean using the selection probability density. : ； Mixing coefficients of parameters and mean Continue calculating the probability density until P converges, thus obtaining the optimized building component analysis model.

2. The data acquisition and analysis system for construction project management as described in claim 1, characterized in that, The method involves using QR code technology to label building components, thereby obtaining basic data about the building components. During production, the building components should have a QR code that can be scanned by a mobile device affixed to the main building components; The QR code should also contain an identifiable chip that can be identified via RFID technology; The basic data of the building components should at least include the category, model, manufacturer, price, project number, and component number of the building components.

3. The data acquisition and analysis system for construction project management as described in claim 1, characterized in that, The basic data of the building components is uploaded to the building component management platform, which can control the building components by recognizing QR codes. The building component management platform should include a data center, a mobile data entry terminal, a factory scanning terminal, and an entry scanning terminal. The data center should be deployed on a high-performance server. The storage capacity of the high-performance server should be selected according to the amount of building component data in the building project, and the concurrent task processing performance of the server should be selected according to the analysis time limit requirements and the number of users online. The data center is used to collect data uploaded by mobile input terminals, factory scanning terminals, and entry scanning terminals, analyze the data, and construct building component analysis models. The mobile data entry terminal is used to collect basic data of building components; The factory scanning terminal should be installed at the outbound location of the building component warehouse to identify outbound information of building components; The entry scanning terminal should be installed at the entrance of the building project to identify the entry information of building components.

4. The data acquisition and analysis system for construction project management as described in claim 1, characterized in that, The optimized building component analysis model is used to analyze newly added factory exit management data and site entry management data to identify abnormal data, thereby achieving data collection and analysis for building engineering management. The newly added factory management data and on-site management data are input into the optimized building component analysis model. A threshold is set, and when the difference between the model output value and the actual value exceeds the threshold, an anomaly is considered to have occurred, thereby realizing the data collection and analysis of building engineering management.

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