Intelligent Management Method and System for Prefabricated Buildings Based on BIM Model

Abstract

The present invention provides an intelligent management method and system for prefabricated buildings based on BIM models. The method includes installing sensors at collection points to collect sensing data such as position, angle, and size in real time; preprocessing the collected sensing data and performing cross-validation on the preprocessed sensing data; calculating the actual error of each collection point of the preprocessed sensing data according to the cross-validation results and the target error range, and generating an error analysis report; comparing the results of the error analysis report with the BIM model to display the differences between the actual components and the design model; and formulating correction or adjustment measures for each collection point where the error in the error analysis report exceeds the acceptable range. The system includes a device layout module, an error analysis module, and an adjustment and correction module. The present invention plays an important role in improving the efficiency, quality, and safety of construction; a closed loop is formed through data collection, analysis, and feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to an intelligent management method and system for prefabricated buildings based on a BIM model. Background Art

[0002] A prefabricated building refers to a building method in which building components are prefabricated in a factory and then transported to the construction site for assembly. Compared with traditional buildings, prefabricated buildings emphasize modularization and standardization, and usually include prefabricated wall panels, floor slabs, roofs, columns, etc. The core lies in dividing the building into multiple components, constructing and processing them in the factory, thereby reducing construction time and labor intensity on site, and improving construction efficiency and quality. During the implementation of prefabricated buildings, they are often used in conjunction with a BIM model (Building Information Modeling) to digitally represent their physical and functional characteristics. However, it often involves the collection and analysis of a large amount of real-time data, including construction, progress, and resource usage. Data sensors may malfunction or the network may be unstable, resulting in information delay or loss; relying too much on real-time data for decision-making may affect the management effect due to inaccurate information.

[0003] Prior Art One, Application No.: CN202310981532.7 discloses a quality information supervision system for prefabricated buildings based on big data, which sets up a data analysis and mining module to calculate the initial energy efficiency of household components, applies data mining and machine learning algorithms to deeply analyze quality information, and through establishing models and algorithms, identifies quality problems and trends, and provides prediction and decision support. It can predict possible quality problems in the future by analyzing quality historical data and take corresponding measures to prevent them. Although it sets up an intelligent decision support module: combining artificial intelligence technology, designing an intelligent decision support system, based on data analysis and model calculation, the system can provide guidance and decision-making suggestions to help users optimize quality management decisions and reduce quality risks. However, there is no emphasis on improving the data source, resulting in the analysis result of quality information being likely to have reduced accuracy due to inaccurate data collection.

[0004] Prior Art Two, Application Number: CN 202410337951.1 discloses an intelligent management method and system for prefabricated buildings based on a BIM model. The method includes: obtaining collection points, and performing clustering processing to obtain point clusters at the same location; determining the degree of significant distribution according to the spatial distribution characteristics of all collection points within the point cluster at the same location where the point to be measured is located and the spatial distance between the point to be measured and other collection points in the corresponding point cluster at the same location; determining the structural error index according to the degree of significant distribution of the collection points and the distance value between the point to be measured and the central position coordinates of the corresponding point cluster at the same location; determining the target error range of the collection point positions according to the structural error indexes of all collection points; and managing the assembly of the component to be measured according to the target error range. Although it can obtain the accurate and objective error accuracy of different collection point positions, thus facilitating the assembly management according to the target error range and improving the intelligent management effect during BIM model simulation. However, it focuses on the optimization of the collection node settings and does not optimize the reliability and stability of the sensing data, resulting in the need to further improve the error of the collected data.

[0005] Prior Art Three, Application Number: CN202310685508.9 discloses a BIM-based prefabricated building management system, including a data unit, a design unit, a production unit and an operation and maintenance unit. The data unit is used to manage the required data of the prefabricated building. The design unit includes a drawing management module and a component management module. The drawing management module designs the prefabricated building structure according to the data of the data unit, and the component management module disassembles and manages the structure of the prefabricated building according to the building structure designed by the drawing management module; the production unit produces and assembles according to the prefabricated building structure disassembled by the design unit; the operation and maintenance unit includes a process management module, and the process management module supervises and promotes the work and processes of the data unit, the design unit and the production unit according to the pre-designed plan. Although it realizes the rapid response between various data and between various links. However, it does not process redundant sensing data, resulting in limited improvement in data reliability.

[0006] Currently, in Prior Art One, Prior Art Two and Prior Art Three, there are problems that the data sensor may malfunction or the network may be unstable, resulting in information delay or loss; and the management effect may be affected due to inaccurate information. Therefore, the present invention provides an intelligent management method and system for prefabricated buildings based on a BIM model. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides an intelligent management method for prefabricated buildings based on a BIM model, which includes the following steps:

[0008] According to the component layout and construction process of the BIM model, select key collection points, and the collection points cover component nodes and connection parts; install sensors at the collection points to collect sensing data of position, angle and size in real time;

[0009] Preprocess the collected sensing data, and perform cross-validation on the preprocessed sensing data; according to the cross-validation results and the target error range, calculate the actual error of each acquisition point of the preprocessed sensing data, and generate an error analysis report; compare the results of the error analysis report with the BIM model to display the differences between the actual components and the design model;

[0010] According to the comparison results, formulate correction or adjustment measures for each acquisition point where the error of the error analysis report exceeds the acceptable range.

[0011] Optionally, the process of selecting key acquisition points includes the following steps:

[0012] Identify the functions and roles of each component in the building to obtain the construction sequence and the installation logic of each component; select the nodes connecting different components and the parts where the components are stressed concentrated as acquisition points; confirm the type of sensor according to the data type of the acquisition points;

[0013] Conduct a feasibility assessment on the acquisition points and the types of sensors; evaluate the types and quantities of sensors;

[0014] Form a preliminary list of key acquisition points, compare the final acquisition points with the component layout of the BIM model; mark the positions of all acquisition points in the BIM model and record the monitoring parameters corresponding to each point.

[0015] Optionally, the process of comparing the results of the error analysis report with the BIM model includes the following steps:

[0016] Perform noise filtering, missing data processing and standardization preprocessing on the collected sensing data to obtain the standardized sensing data; adopt redundant sensor configuration to compare the sensing data between different sensors; at the same time, scan the data timestamps of each sensor, and calculate the mean value of the standardized sensing data of different acquisition points according to the data timestamps to obtain the cross-validated sensing data;

[0017] Extract the design parameters in the BIM model, compare them with the cross-validated sensor data, calculate the absolute error and relative error, classify them according to the preset target error range, identify the acquisition points exceeding the threshold, and organize the error analysis results of each acquisition point into a report to centrally display the actual value, theoretical value and their errors;

[0018] Display the error conditions of key acquisition points classified by importance, and use error bar charts to display the important data points where the safety components exceed the acceptable range.

[0019] Optionally, the process of obtaining the cross-validated sensing data includes the following steps:

[0020] Deploy at least three redundant sensors in the structurally critical areas and at least two redundant sensors in the non-structurally critical areas for basic data collection; connect the redundant sensors to each other using a mesh structure;

[0021] Collect data from redundant sensors at different data points, automatically integrate the outputs of the redundant sensors to form a unified output format; include timestamp, sensor ID, and measurement value information; process each data point through a hash function to generate a unique hash value; use a private key to digitally sign the hash value, and package the data collected by the redundant sensors together with the corresponding hash value and digital signature to form a complete data packet;

[0022] Transmit the data packet through the blockchain network to the blockchain nodes. After receiving the sensor data, the nodes in the blockchain network unpack the data, extract the data content, hash value, and digital signature; use a hash algorithm to perform a hash calculation on the data content, and verify the digital signature using the sender's public key to confirm that the generated hash value is consistent with the received hash value.

[0023] Optionally, the process of identifying the collection points with values exceeding the threshold includes the following steps:

[0024] For the data collected in real time from the sensors and the expected data calculated through the building information model, calculate the absolute error by taking the difference between the actual value of the collected data and the theoretical value of the expected data, and calculate the relative error through the absolute error and the theoretical value;

[0025] Set an allowable error range, classify the relative errors of each collection point calculated, and obtain the normal range, critical range, and range exceeding the threshold;

[0026] Create a data record table listing the actual value, theoretical value, absolute error, relative error, and classification result of all collection points; for each collection point, check whether the relative error exceeds the preset threshold range; mark the points exceeding the threshold and record them in the data table.

[0027] Optionally, the process of classifying the relative errors of each collection point calculated includes the following steps:

[0028] Obtain the relative error value of the current collection point. If there is a historical relative error record for the current collection point, calculate the percentage change between the current error and the last error; if there is no historical record, set the change rate to zero;

[0029] Calculate the historical average relative error and determine whether the difference between the current relative error and the average relative error is within the acceptable stability threshold;

[0030] A comprehensive score is calculated by combining the current relative error, the rate of change, and the average relative error. Each factor is summed according to its weight to form a score value to judge the comprehensive status of the acquisition point. Each acquisition point is classified according to the set classification criteria.

[0031] Optionally, add a column for each acquisition point in the data table to record its classification result, and at the same time record the score value and relative error value of this point.

[0032] Optionally, the process of forming a score value to judge the comprehensive status of the acquisition point includes the following steps:

[0033] Define the definition and importance of each factor, and set the dynamic weight of each factor based on data-driven in each evaluation period;

[0034] Convert each factor from an absolute value to a fuzzy category, set the current relative error as a low error, medium error, or high error, and assign a fuzzy value to each category using a membership function;

[0035] By combining the fuzzy scores with the dynamic weights, a weighted comprehensive score is formed.

[0036] Optionally, the process of forming a weighted comprehensive score includes the following steps:

[0037] Assign the dynamic weights of each factor set based on data-driven in the evaluation period to each fuzzy score;

[0038] In the model of the weighted comprehensive score, use a power operation and introduce an exponent greater than 1 to form a non-linear relationship, so that the scoring factors show a non-linear growth characteristic for the total score;

[0039] Multiply the fuzzy scores of all scoring factors by the corresponding dynamic weights and combine them through a non-linear relationship to calculate the overall weighted score.

[0040] An intelligent management system for prefabricated buildings based on a BIM model provided by the present invention includes:

[0041] A device layout module, which is responsible for selecting key acquisition points according to the component layout and construction process of the BIM model, and the acquisition points cover component nodes and connection parts; install sensors at the acquisition points to collect sensing data of position, angle, and size in real time;

[0042] An error analysis module, which is responsible for preprocessing the collected sensing data, and cross-verifying the preprocessed sensing data; calculating the actual error of each acquisition point of the preprocessed sensing data according to the cross-verification result and the target error range, and generating an error analysis report; comparing the result of the error analysis report with the BIM model to display the difference between the actual component and the design model;

[0043] An adjustment and correction module is responsible for formulating correction or adjustment measures for each acquisition point where the error in the error analysis report exceeds the acceptable range according to the comparison result.

[0044] For data acquisition and sensor installation of the present invention, through sensors installed at key acquisition points (such as data on position, angle, and dimension, etc.), real-time monitoring of the component state is achieved to ensure the timeliness and accuracy of data; key positions such as component nodes and connection parts are selected for data acquisition to ensure that key data points are effectively monitored. Significance achieved: Obtaining real-time monitoring data helps to ensure the accuracy during the construction process, which is crucial for the installation of components in prefabricated buildings; by monitoring potential deviations at an early stage, problems can be discovered and solved in a timely manner, thereby reducing the probability of risks and errors occurring during construction; providing true and reliable basic data for data analysis and problem correction, improving the scientificity and effectiveness of subsequent work. For data preprocessing and error analysis, by preprocessing the data (denoising, standardization, etc.), the reliability of the data is improved to ensure the accuracy of the data when used for analysis; combined with the actual data and the preset target error range, the error of each acquisition point is calculated and an error analysis report is generated. Significance achieved: Complete data analysis and error reports provide support for management decisions, helping managers understand the actual state and potential problems, and thus making scientific and reasonable adjustments; by comparing the error report with the BIM model, the differences between the components and the design can be visually displayed, effectively promoting the improvement of construction quality; enhancing the awareness of the construction state, forming transparent and effective communication, and improving efficiency. For formulating correction or adjustment measures, specific correction or adjustment measures are formulated for the acquisition points where the error exceeds the acceptable range to correct the deviation; by adjusting the construction process or method, the accuracy of subsequent component installation is ensured. Significance achieved: Formulating targeted measures can effectively improve the accuracy of subsequent construction, reducing the additional costs and time losses caused by initial errors; by correcting and adjusting in a timely manner, ensuring that the project can proceed smoothly according to the plan and standards, improving the controllability of management; through the analysis and adjustment process, the project team can continuously accumulate experience, providing reference and improvement basis for the management and implementation of future projects.

[0045] Other features and advantages of the present invention will be described in the subsequent description, and partly will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structure specifically pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0047] The accompanying drawings are used to provide a further understanding of the present invention and form a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0048] Figure 1 It is a flowchart of the intelligent management method for prefabricated buildings based on the BIM model in Embodiment 1 of the present invention;

[0049] Figure 2 It is a process diagram for selecting key collection points in Embodiment 2 of the present invention;

[0050] Figure 3 It is a process diagram for comparing the results of the error analysis report with the BIM model in Embodiment 3 of the present invention;

[0051] Figure 4 It is a process diagram for obtaining the cross-validated sensing data in Embodiment 4 of the present invention;

[0052] Figure 5 It is a process diagram for identifying collection points exceeding the threshold in Embodiment 5 of the present invention;

[0053] Figure 6 It is a process diagram for classifying the relative errors of each calculated collection point in Embodiment 6 of the present invention;

[0054] Figure 7 It is a process diagram for forming a scoring value to judge the comprehensive status of the collection points in Embodiment 7 of the present invention;

[0055] Figure 8 It is a process diagram for forming a weighted comprehensive score in Embodiment 8 of the present invention;

[0056] Figure 9 It is a process diagram for formulating corrective or adjustment measures in Embodiment 9 of the present invention;

[0057] Figure 10 It is a block diagram of the intelligent management system for prefabricated buildings based on the BIM model in Embodiment 10 of the present invention. Detailed implementation manners

[0058] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0059] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present application. The singular forms "a", "the", and "said" used in the embodiments of the present application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0060] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0061] Embodiment 1: As Figure 1 shown, an intelligent management method for prefabricated buildings based on a BIM model is provided in an embodiment of the present invention, including the following steps:

[0062] S100: Select key acquisition points according to the component layout and construction process of the BIM model. The acquisition points cover areas such as component nodes and connection parts; install sensors at the acquisition points to collect sensing data such as position, angle, and size in real time;

[0063] S200: Preprocess the collected sensing data, and perform cross-verification on the preprocessed sensing data; calculate the actual error of each acquisition point of the preprocessed sensing data according to the cross-verification result and the target error range, and generate an error analysis report; compare the result of the error analysis report with the BIM model to display the differences between the actual components and the design model;

[0064] S300: According to the comparison result, formulate corrective or adjustment measures for each acquisition point where the error in the error analysis report exceeds the acceptable range.

[0065] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, according to the component layout and construction process of the BIM model, key acquisition points are selected, and the acquisition points cover areas such as component nodes and connection parts; sensors are installed at the acquisition points to collect sensing data such as position, angle, and dimension in real time; secondly, the collected sensing data is preprocessed, and the preprocessed sensing data is cross-validated; according to the cross-validation results and the target error range, the actual error of each acquisition point of the preprocessed sensing data is calculated, and an error analysis report is generated; the results of the error analysis report are compared with the BIM model to display the differences between the actual components and the design model; finally, according to the comparison results, for each acquisition point where the error in the error analysis report exceeds the acceptable range, corrective or adjustment measures are formulated. In step S100 of the above solution, data acquisition and sensor installation, through sensors installed at key acquisition points (such as data on position, angle, and dimension), real-time monitoring of the component state is achieved, ensuring the timeliness and accuracy of the data; key positions such as component nodes and connection parts are selected for data acquisition to ensure that key data points are effectively monitored. The significance achieved is: The acquisition of real-time monitoring data helps to ensure the accuracy during the construction process, which is crucial for the component installation of prefabricated buildings; by monitoring potential deviations early, problems can be discovered and solved in a timely manner, thereby reducing the probability of risks and errors occurring during construction; providing true and reliable basic data for data analysis and problem correction, improving the scientificity and effectiveness of subsequent work. In step S200, data preprocessing and error analysis, by preprocessing the data (denoising, standardization, etc.), the reliability of the data is improved, ensuring the accuracy of the data when used for analysis; combined with the actual data and the preset target error range, the error of each acquisition point is calculated and an error analysis report is generated. The significance achieved is: Perfect data analysis and error reports provide support for management decisions, helping managers understand the actual state and potential problems, and thus making scientific and reasonable adjustments; by comparing the error report with the BIM model, the differences between the components and the design can be intuitively displayed, effectively promoting the improvement of construction quality; enhancing the awareness of the construction state, forming transparent and effective communication, and improving efficiency. In step S300, formulating corrective or adjustment measures, for acquisition points where the error exceeds the acceptable range, specific corrective or adjustment measures are formulated to correct the deviation; by adjusting the construction process or method, the accuracy of subsequent component installation is ensured. The significance achieved is: Formulating targeted measures can effectively improve the accuracy of subsequent construction, reducing the additional costs and time losses caused by initial errors; by timely correcting and adjusting, ensuring that the project can proceed smoothly according to the plan and standards, improving the controllability of management; through the analysis and adjustment process, the project team can continuously accumulate experience, providing reference and improvement basis for the management and implementation of future projects.

[0066] In summary, this embodiment plays an important role in improving the efficiency, quality, and safety of construction; by forming a closed loop through data collection, analysis, and feedback, it not only improves the management considerations of the current construction project but also provides practical references and guidance for future projects.

[0067] Embodiment 2: As Figure 2 shown, on the basis of Embodiment 1, the process of selecting key collection points provided by the embodiment of the present invention includes the following steps:

[0068] S101: Identify the functions and roles of each component in the building to obtain the construction sequence and the installation logic of each component; select the nodes connecting different components and the parts where the components are stressed concentratedly as the collection points; confirm the type of sensor according to the data type of the collection points;

[0069] S102: Conduct a feasibility assessment on the collection points and the types of sensors; evaluate the types (such as displacement sensors, angle sensors) and quantities of sensors;

[0070] S103: Form a preliminary list of key collection points, compare the final collection points with the component layout of the BIM model; mark the positions of all collection points in the BIM model and record the monitoring parameters corresponding to each point.

[0071] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the functions and roles of each component in the building are first identified to obtain the construction sequence and the installation logic of each component; the nodes connecting different components and the parts where the components are stressed are selected as the acquisition points; according to the data types of the acquisition points, the types of sensors are confirmed; secondly, the feasibility of the acquisition points and the types of sensors is evaluated; the types (such as displacement sensors, angle sensors) and quantities of sensors are evaluated; finally, a preliminary list of key acquisition points is formed, and the final acquisition points are compared with the component layout of the BIM model; the positions of all acquisition points are marked in the BIM model, and the monitoring parameters corresponding to each point are recorded. In step S101 of the above solution, identifying the functions of components and selecting acquisition points, by analyzing the functions and roles of each component in the building, clarifying the relationships between different components, as well as the construction sequence and installation logic, provides a scientific basis for subsequent data acquisition; selecting the nodes connecting different components and the parts where the components are stressed as the acquisition points to ensure obtaining effective data at key positions; according to different data types of the acquisition points (such as position, angle, size, etc.), selecting appropriate sensor types. Advantages compared with the existing solution: Compared with the traditional selection method that only relies on experience, the selection based on function and stress analysis is more systematic and scientific, reducing the influence of subjective factors; focusing on the selection of key nodes and stressed parts ensures that the data acquisition points have higher effectiveness and pertinence. The significance achieved: Ensuring the monitoring of important components and improving the quality control ability during the actual construction process; by analyzing the function and stress conditions, effectively reducing potential safety hazards and quality problems caused by improper installation. In step S102 of feasibility evaluation, the technical feasibility of the selected acquisition points and sensors is evaluated to confirm the suitability of the sensors and ensure the feasibility of all data acquisition plans; according to the evaluation results, the quantity and type of the required sensors are optimized to ensure the comprehensiveness and economy of data acquisition. Advantages compared with the existing solution: Compared with the traditional static evaluation method, introducing dynamic technical evaluation ensures the scientificity and flexibility of the configuration, and can be adjusted according to changes in site conditions; through effective evaluation, it avoids budget overruns and subsequent modification costs caused by incorrect sensor configuration. The significance achieved: Ensuring the sustainable operation of the project through reasonable evaluation and improving the efficiency of project commissioning and deployment; effectively configuring sensors can greatly improve the efficiency of the entire construction monitoring and management. In step S103 of forming a preliminary list and comparing with the model, a preliminary list of key acquisition points is compiled, listing the monitoring parameters and positions of each acquisition point to enhance the organization and clarity of information; the final acquisition points are compared with the BIM model, and the positions of all acquisition points are marked in the model to ensure smooth information transfer. Advantages compared with the existing solution: By marking the acquisition points in the BIM model, the visualization degree of information is improved, reducing the complexity of understanding compared with the traditional method; listing and model marking help to track and manage data, improving the efficiency of interaction and collaboration.Significance achieved: To form a consistent understanding among all parties involved for better communication and collaboration; clear catalogs of collection points and model annotations make each link in the project implementation process clearer, reducing misunderstandings and errors in information transmission.

[0072] In summary, this embodiment embodies higher systematicness, scientificity, and rationality; it not only effectively improves the quality and efficiency of data collection but also provides a solid foundation for subsequent analysis and decision-making, ultimately promoting the intelligent and refined development of prefabricated building construction management.

[0073] Embodiment 3: As Figure 3 shown, based on Embodiment 1, the process of comparing the results of the error analysis report with the BIM model provided by the embodiment of the present invention includes the following steps:

[0074] S201: Perform noise filtering, missing data processing, and standardization preprocessing on the collected sensing data to obtain the standardized sensing data; adopt redundant sensor configuration and compare the sensing data between different sensors; at the same time, scan the data timestamps of each sensor, and calculate the mean value of the standardized sensing data at different collection points according to the data timestamps to obtain the cross-validated sensing data;

[0075] Among them, the state estimation update for noise filtering of the collected sensing data:

[0076]

[0077] In the formula, represents the state estimation at time k, and μ k represents the correction error, reflecting the reshaping compensation in the state update (for improving robustness);

[0078] Covariance update:

[0079] P k|k =(I - K k H k )P k|k-1 +η k ·Q k

[0080] In the formula, P k|k represents the updated covariance matrix at time k, and Q k represents the dynamic adjustment coefficient, which adjusts the balance between process noise and observation error through the estimated response effect;

[0081] Nonlinear state equation:

[0082]

[0083] Wherein, represents the state prediction at time k (e.g., position, velocity, etc.), represents the non - linear state transition function, which generates the next state by describing the relationship between the state and the control input, W N,k represents the process noise of each sensor, reflecting the error caused by the inconsistency of each sensor, and N represents the Nth sensor;

[0084] Multi - sensor observation model:

[0085]

[0086] Wherein, z k represents the observation vector at time k, containing data from multiple sensors (such as temperature sensors, acceleration sensors, etc.), represents the non - linear observation function (possibly defined by a combination of a set of weights and state variables), describing how the state affects the observation, V M,k represents the observation noise of each sensor, reflecting the inherent uncertainty in the sensor measurement process;

[0087] Adaptive Kalman gain:

[0088]

[0089] Wherein, K k represents the adaptive Kalman gain, which is adjusted based on the dynamic changes of the observations, H k represents the observation matrix composed of the Jacobian matrix of the observation function, reflecting the influence of the state on the observation, R k represents the observation noise covariance matrix, which is dynamically adjusted to reflect the nature of the monitoring error, λ k represents the adaptive coefficient, which is used to enhance the response ability to significant measurement anomalies and adjust the influence of the gain on each observation; represents the absolute difference of the calculated residuals, improving the awareness of abnormal observed values;

[0090] S202: Extract the design parameters in the BIM model, compare them with the cross - validated sensor data, calculate the absolute error and relative error, classify them according to the pre - set target error range, identify the acquisition points exceeding the threshold, and organize the error analysis results of each acquisition point into a report, centrally displaying the actual value, theoretical value and their errors;

[0091] S203: Display the error conditions of the key acquisition points classified by importance, and display them using an error bar chart for the important data points where the safety components exceed the acceptable range.

[0092] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the collected sensing data is first preprocessed by noise filtering, missing data processing, and standardization to obtain the sensing data after standardization processing; a redundant sensor configuration is adopted to compare the sensing data between different sensors; at the same time, the data timestamps of each sensor are scanned, and the mean value of the sensing data after standardization processing at different acquisition points is calculated according to the data timestamps to obtain the cross-validated sensing data; secondly, the design parameters in the BIM model are extracted and compared with the cross-validated sensor data, the absolute error and relative error are calculated, and classification is performed according to the preset target error range to identify the acquisition points exceeding the threshold. The error analysis results of each acquisition point are sorted into a report, which centrally displays the actual value, theoretical value, and their errors; finally, the error conditions of the key acquisition points are displayed according to importance classification, and an error bar chart is used for display, and important data points where the safety components exceed the acceptable range. For step S201 of the above solution, the preprocessing and cross-validation of the sensing data eliminate environmental interference in the sensor data through advanced noise rejection, improving the quality and reliability of the data; filling in missing data ensures the integrity of the data set and avoids affecting subsequent analysis results due to data gaps; unifying the scale of the sensor data improves the comparability between different sensor data and lays a foundation for cross-validation; redundancy and comparison are achieved through the data obtained by redundant sensors, improving the reliability of the data; at the same time, the scanning of timestamps further ensures the consistency of the data; using the mean value of the data within the time window provides a more robust sensor measurement result and reduces the influence of accidental errors of individual sensors. In step S202, the error calculation and report collation combine the standard design parameters of the BIM model with the actual sensor data to form a comparison basis for real and theoretical data; through the calculation of the absolute error and relative error, the design deviation is accurately quantified, providing a quantitative basis for subsequent corrective measures; classification according to the preset target error range can timely discover and identify key problems, which is of great significance for the quality control of construction; systematically organizing and displaying the error analysis results makes the analysis information clear and easy to understand, providing detailed basis and visual support for decision-making. In step S203, the importance analysis and visual display of the error conditions are classified according to the importance of the errors, enabling construction managers to quickly identify the acquisition points that need to be focused on, optimizing subsequent detection and correction processes; using an error bar chart to display key data presents complex information in an intuitive form, improving the comprehensibility of the report and the decision-making support ability; special markings are made for safety components that exceed the acceptable range to ensure the timely identification and handling of potential risks.Advantages compared with existing solutions: The multi-level preprocessing and cross-validation mechanism in this embodiment greatly improves the reliability of data compared with traditional single data collection and analysis methods; through detailed error calculation and over-threshold analysis, construction managers can take timely measures to reduce construction risks, and traditional methods often lack such clear quantitative basis; compared with traditional static analysis, it provides real-time data monitoring and dynamic feedback, enabling managers to keep track of construction progress and its quality status at any time and adjust the construction process in a timely manner; using visualization means makes complex data more operable, reduces decision-making errors caused by poor information transmission, and enhances the efficiency of multi-party communication. The achieved significance: Through efficient error analysis and real-time monitoring, the overall quality of prefabricated building construction is significantly improved, ensuring the safety and reliability of the project; timely identification and correction of errors contribute to reducing subsequent repair costs, saving project time, and improving construction efficiency; through intelligent data analysis, the BIM model truly realizes intelligent management, thus promoting the digital transformation of the construction industry; through feedback and reporting, a closed-loop management is formed, promoting the continuous optimization of project management and helping to form systematic construction management specifications and standards.

[0093] In summary, through multi-level analysis and processing, this embodiment not only improves the quality and reliability of data, but also provides an innovative and systematic solution for error control, greatly promoting the intelligent management and effective supervision of prefabricated buildings.

[0094] Example 4: As Figure 4 shown, based on Example 3, the process of obtaining cross-validated sensing data provided by the embodiment of the present invention includes the following steps:

[0095] S2011: Deploy at least three redundant sensors in key structural areas and at least two redundant sensors in non-key structural areas for basic data collection; connect the redundant sensors to each other using a mesh structure;

[0096] S2012: Collect data from redundant sensors at different data points, automatically integrate the outputs of the redundant sensors to form a unified output format; include information such as timestamp, sensor ID, and measured value; process each data point through a hash function to generate a unique hash value; use a private key to digitally sign the hash value, and package the data collected by the redundant sensors together with the corresponding hash value and digital signature to form a complete data packet;

[0097] S2013: Transmit the data packet through the blockchain network to the blockchain nodes. After the nodes in the blockchain network receive the sensor data, unpack the data and extract the data content, hash value, and digital signature; perform a hash calculation on the data content using the hash algorithm, and verify the digital signature with the public key of the sender to confirm that the generated hash value is consistent with the received hash value.

[0098] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, at least three redundant sensors are first arranged in the key structural areas, and at least two redundant sensors are deployed in the non-key structural areas for basic data collection; the redundant sensors are interconnected using a mesh structure; secondly, the data from the redundant sensors at different data points is collected, and the outputs of the redundant sensors are automatically integrated to form a unified output format, which includes information such as timestamps, sensor IDs, and measured values; each data point is processed through a hash function to generate a unique hash value; the hash value is digitally signed using a private key, and the data collected by the redundant sensors, the corresponding hash value, and the digital signature are packaged together to form a complete data packet; finally, the data packet is transmitted to the blockchain nodes through the blockchain network. After receiving the sensor data, the nodes in the blockchain network unpack the data, extract the data content, hash value, and digital signature; use the hash algorithm to calculate the hash of the data content, and verify the digital signature using the public key of the sender to confirm that the generated hash value is consistent with the received hash value. For the step S2011 of redundant sensor deployment in the above solution, arranging at least three redundant sensors in the key structural areas enhances the redundancy and reliability of the data, effectively reducing the risk of data loss caused by single-point failures; deploying at least two sensors in the non-critical areas provides basic data collection to ensure the overall data quality; the mesh structure connects the redundant sensors to ensure the flexible transmission of data in the network and reduce data loss caused by the failure of a certain node. The achieved significance is: improving the anti-interference ability in extreme situations, ensuring the continuity and accuracy of data collection; in infrastructure monitoring, ensuring real-time data feedback in the core area to provide a basis for decision-making and effectively supporting structural health monitoring. For the step S2012 of data collection and integration, automatically integrating the outputs of different sensors to generate a unified output format, including timestamps, sensor IDs, and measured values, thus simplifying the data processing flow; generating a unique hash value for each data point to ensure the integrity and consistency of the data during subsequent transmission; using a private key for hash signature protection to enhance data security and prevent data from being tampered with during transmission. The achieved significance is: the unified format and unique identifier of the data make the data storage and analysis process more efficient, reducing data parsing errors and information asymmetry; eliminating abnormal data can improve the reliability and credibility of the data and form high-quality decision support. For the step S2013 of blockchain network transmission and verification, the transmission of the data packet through the blockchain network ensures the immutability and security of the data in the network; the receiving node unpacks the data, extracts the original data, its hash value, and digital signature, and ensures the integrity of the data through hash verification; uses the public key of the sender to verify the digital signature to ensure the accuracy of the data source.Significance achieved: The data transmission realized through blockchain technology not only enhances the security of data, but also eliminates the dependence on centralized servers and enhances the anti-attack ability; real-time verification ensures the authenticity of data, increases users' trust in the data, and provides a reliable data basis for subsequent analysis and decision-making.

[0099] In summary, the cross-validated sensing data obtained in this embodiment will have high quality, high credibility, and high security, and provide solid data support for infrastructure monitoring, fault warning, and decision-making. It optimizes the way of data collection and processing, not only improves the effectiveness and practicality of data, but also plays a positive role in promoting scientific decision-making in risk management and resource allocation.

[0100] Embodiment 5: As Figure 5 shown, on the basis of Embodiment 3, the process of identifying the acquisition points exceeding the threshold provided by the embodiment of the present invention includes the following steps:

[0101] S2021: For the data collected in real time from the sensor and the expected data calculated through the building information model, the absolute error is obtained by subtracting the actual value of the collected data from the theoretical value of the expected data, and the relative error is calculated through the absolute error and the theoretical value;

[0102] S2022: Set an allowable error range, classify the relative errors of each acquisition point calculated, and obtain the normal range, critical range, and out-of-threshold range;

[0103] Normal range: Acquisition points with an error within ±5% indicate that the data is close to the theoretical value, normal and reliable;

[0104] Critical range: The error is between ±5% and ±10%, indicating that the data needs further observation and may require intervention for quality control;

[0105] Out-of-threshold range: Acquisition points with an error exceeding ±10% indicate significant deviation and should be processed preferentially;

[0106] S2023: Create a data record table, list the actual value, theoretical value, absolute error, relative error, and classification results of all acquisition points; for each acquisition point, check whether the relative error exceeds the preset threshold range; mark the out-of-threshold points and record them in the data table.

[0107] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the data collected in real time from the sensors and the expected data calculated through the building information model are used. The absolute error is obtained by taking the difference between the actual value of the collected data and the theoretical value of the expected data, and the relative error is calculated through the absolute error and the theoretical value. Secondly, a permitted error range is set, and the relative errors of each acquisition point calculated are classified to obtain the normal range, the critical range, and the over-threshold range. Normal range: Acquisition points with an error within ±5% indicate that the data is close to the theoretical value, normal and reliable. Critical range: An error between ±5% and ±10% indicates that the data needs further observation and may require intervention for quality control. Over-threshold range: Acquisition points with an error exceeding ±10% indicate a significant deviation and should be processed preferentially. Finally, a data record table is created, listing the actual value, theoretical value, absolute error, relative error, and classification result of all acquisition points. For each acquisition point, check whether the relative error exceeds the preset threshold range. Mark the over-threshold points and record them in the data table. In step S2021 of the above solution, data collection and error calculation ensure the timeliness and adaptability of information through the data collected by the sensors, enabling the monitoring results to reflect the current structural state. The calculation of the absolute error reveals the deviation between the actual data and the theoretical value, providing basic information for subsequent classification. The definition of the relative error enables reasonable comparison of data of different magnitudes and units, facilitating the identification of potential problems. The achieved significance is to provide a reliable basis for data analysis, enabling decision-makers to evaluate whether the structural performance meets the set standards based on the difference between accurate measurement data and expected values. Accurate error calculation lays a solid foundation for risk assessment, structural safety monitoring, and quality control work. In step S2022, error classification and preset range establishment set the permitted error range as the benchmark for subsequent classification processing. The definitions of the normal range, critical range, and over-threshold range help quickly identify the health status of the data and clarify the items that need to be prioritized. The achieved significance is that through the classification of errors, the focus can be concentrated on important and intervention-needed acquisition points, optimizing the allocation and management of resources, avoiding missing major risks due to minor problems, improving the monitoring efficiency, enabling decision-makers to quickly extract key issues from a large amount of data, and increasing the response speed of the entire data monitoring and processing process. In step S2023, data recording and over-threshold point identification create a data record table that systematizes the indicators of all acquisition points, facilitating subsequent analysis and review. The error checking process for each acquisition point provides a standardized method to ensure that each acquisition point is concerned and the over-threshold points can be clearly marked.Achieved Significance: The records improve the traceability of data, facilitating subsequent analysis, rectification, and decision-making processes, ensuring that each abnormal collection point is fully investigated and processed; by marking points beyond the threshold, corresponding quality control and maintenance measures can be promptly formulated to ensure the structural safety and reduce potential risks; it can provide strong data support for structural monitoring, risk assessment, and decision-making, thereby enhancing the safety and reliability of the overall project.

[0108] In summary, through precise data collection and calculation, strict error classification, and effective recording and analysis, this embodiment not only improves the scientificity and feasibility of data processing but also provides a reliable basis for future engineering decisions. It can not only enhance the safety monitoring efficiency of the project but also lay a good foundation for the sustainability and effective management of the project.

[0109] Example 6: As Figure 6 shown, based on Example 5, the process of classifying the relative errors of each calculated collection point provided by the embodiment of the present invention includes the following steps:

[0110] S20221: Obtain the relative error value of the current collection point. If there is a historical relative error record for the current collection point, calculate the percentage change between the current error and the last error; if there is no historical record, set the change rate to zero.

[0111] S20222: Calculate the historical average relative error and determine whether the difference between the current relative error and the average relative error is within the acceptable stability threshold.

[0112] S20223: Combine the current relative error, change rate, and average relative error for a comprehensive score. Sum each factor according to its weight to form a score value to judge the comprehensive status of the collection point; classify each collection point according to the set classification criteria; add a column in the data table for each collection point to record its classification result, and at the same time record the score value and relative error value of this point.

[0113] If the scoring result falls within the specified normal range, mark it as "normal range";

[0114] If the scoring result falls within the critical range, mark it as "critical range";

[0115] If the scoring result falls within the out-of-threshold range, mark it as "out-of-threshold range".

[0116] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the relative error value of the current acquisition point is obtained. If there is a historical relative error record for the current acquisition point, the percentage change between the current error and the last error is calculated; if there is no historical record, the change rate is set to zero. Secondly, the historical average relative error is calculated, and it is judged whether the difference between the current relative error and the average relative error is within an acceptable stability threshold. Finally, a comprehensive score is calculated by combining the current relative error, the change rate, and the dynamic stability. Each factor is summed according to the weight to form a score value to judge the comprehensive state of the acquisition point; according to the set classification criteria, each acquisition point is classified; a column is added to the data table for each acquisition point to record its classification result, and at the same time, the score value and the relative error value of this point are recorded; if the score result falls within the set normal range, it is marked as "normal range"; if the score result falls within the critical range, it is marked as "critical range"; if the score result falls within the over-threshold range, it is marked as "over-threshold range". Step S20221 of the above solution obtains the relative error value of the current acquisition point and calculates the percentage change. Obtaining the current relative error of each acquisition point is the basis for analyzing data, ensuring that all subsequent steps are based on the latest actual value; calculating the percentage change between the current error and the last error can identify the magnitude of the error change and evaluate its stability. Significance: By comparing the current error with the historical error, abnormal fluctuations can be detected in a timely manner, facilitating quick response; if there is a lack of historical records, the change rate is set to zero to prevent misleading due to insufficient comparison data. Step S20222 calculates the historical average relative error and judges the stability. Calculating the historical average relative error enables the current data to be compared with past performance, providing a standard baseline; by judging the difference between the current relative error and the average relative error, it can be identified whether there are obvious fluctuations or anomalies. Significance: By considering historical data, the current state can be evaluated more comprehensively, reducing misjudgments caused by accidental anomalies; stability judgment can help identify persistent problems and take corresponding preventive or adjustment measures to ensure the quality of data acquisition. Step S20223 is the comprehensive scoring and classification. The current relative error, the change rate, and the dynamic stability are integrated to form a comprehensive score, which is summed according to the pre-set weight to evaluate the state of the acquisition point; according to the classification criteria, the acquisition points are divided into "normal range", "critical range", or "over-threshold range". Significance: The design of the comprehensive score makes data analysis more flexible and multi-dimensional, not only considering the magnitude of the absolute error, but also reflecting the change trend and historical stability of the data; the clear classification provides a simplified analysis result, making the monitoring and decision-making process more intuitive, helping to formulate corresponding intervention measures; adding the classification result, the score value, and the relative error value to the data table for each acquisition point realizes the systematic management of information, facilitating future reference and statistical analysis; improving data transparency enables the monitoring personnel to quickly understand the state of each acquisition point.Significance: It helps to formulate long-term monitoring strategies and rapid response measures; the recording and management of data also provide a basis for subsequent analysis and review, facilitating the identification of changing trends and patterns, and achieving better data governance and quality control.

[0117] In summary, this embodiment improves the systematicness and scientificity of data quality monitoring; it can effectively analyze each collection point through scientific methods, promptly identify and handle abnormal situations, thereby maintaining accuracy and reliability in the entire data collection system, and ensuring the effectiveness and rationality of decision-making.

[0118] Embodiment 7: As Figure 7 shown, based on Embodiment 6, the process of forming a score value to judge the comprehensive status of the collection point provided by the embodiment of the present invention includes the following steps:

[0119] S202231: Define the definition and importance of each factor, and set the dynamic weight of each factor based on data drive in each evaluation period;

[0120] S202232: Convert each factor from an absolute value to a fuzzy category, set the current relative error as low error, medium error, or high error, and assign a fuzzy value to each category using a membership function;

[0121] S202233: Form a weighted comprehensive score by combining the fuzzy scores with the dynamic weights.

[0122] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the definitions and importance of each factor are first defined, and the dynamic weight of each factor is set based on data-driven in each evaluation period. Secondly, each factor is converted from an absolute value to a fuzzy category, and the current relative error is set to a low error, a medium error, or a high error, and a membership function is used to assign fuzzy values to each category. Finally, a weighted comprehensive score is formed by combining the fuzzy scores with the dynamic weights. In step S202231 of the above solution, the definition and setting of the dynamic weight ensure that the attributes and importance of each factor in the evaluation are clearly defined by clarifying the definitions of each factor (such as the current relative error, the change range, and the historical average relative error); the setting of the dynamic weight enables the scoring model to reflect the state changes of the acquisition points in real time; by analyzing historical data, the existing environment, and business requirements, the weights are dynamically adjusted, improving the adaptability of the model. The significance achieved is that in different situations (such as equipment maintenance, seasonal changes, or emergencies), the model can automatically adjust the focus of attention, making the evaluation results more accurate and relevant; by focusing on the factors related to the current business requirements, the evaluation results will be more practical, helping decision-makers to take timely and effective management measures. In step S202232, the fuzzy category conversion and membership assignment convert the absolute values into fuzzy categories, making the scoring not only depend on a clear numerical range, but also take into account the uncertainty and complexity of the data; applying the membership function to each fuzzy category makes the membership relationship of different categories smoother; for example, the current relative error score may belong to both the "medium error" and "high error" categories, reflecting the multi-dimensional characteristics of the data state. The significance achieved is that through fuzzy processing, the model can effectively reduce the adverse effects caused by extreme values and fluctuating data, improving the stability and reliability of the scoring; the score is no longer a single exact value, but a judgment within a relative range, providing multi-dimensional insights into the data state and helping users understand the actual performance of the acquisition points. In step S202233, the formation of the weighted comprehensive score realizes the conversion from multiple indicators to a single score value by combining the fuzzy scores with the dynamic weights, enabling multiple interrelated factors to be comprehensively considered; using a specific formula to calculate the comprehensive score clarifies the contribution of each factor to the final score, facilitating the tracking and interpretation of the result formation process. The significance achieved is that the comprehensive score can intuitively reflect the overall state of the acquisition points, facilitating classification and management, supporting real-time monitoring and evaluation decisions; integrating multiple complex measurement indicators into a single score value helps managers quickly identify problem areas and take corresponding measures based on the score, improving management efficiency.

[0123] In summary, this embodiment establishes a comprehensive scoring model based on dynamic weights and fuzzy logic, reflecting innovation in technology and rigor in logic. The final comprehensive score can not only reflect the health status of each collection point but also provide reliable support for decision-making in a changing environment. It enhances the flexibility, reliability, and usability of data evaluation, promoting the improvement of enterprises in data-driven decision-making.

[0124] Embodiment 8: As Figure 8 shown, based on Embodiment 7, the process of forming a weighted comprehensive score provided by the embodiment of the present invention includes the following steps:

[0125] S2022331: Assign dynamic weights to each factor based on data-driven for each evaluation period for each fuzzy score;

[0126] S2022332: Use a power operation in the weighted comprehensive scoring model, introducing an exponent greater than 1 to form a non-linear relationship, such that the scoring factors exhibit non-linear growth characteristics for the total score;

[0127] S2022333: Multiply the fuzzy scores of all scoring factors by their corresponding dynamic weights and combine them through the non-linear relationship to calculate the overall weighted score;

[0128] where

[0129] and the variables represent the fuzzy scores of the current relative error, the change amplitude, and the historical average relative error respectively; W current 、W variation and W history are the dynamic weights corresponding to the above-mentioned fuzzy scores respectively; p is a constant greater than 1, amplifying the influence of the fuzzy score; for example, when p = 2, if a certain fuzzy score is 3, the contribution of this part to the total score will be 9.

[0130] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, a dynamic weight for each factor is set based on data-driven for each fuzzy score in the evaluation period; second, a power operation is adopted in the weighted comprehensive scoring model, and an exponent greater than 1 is introduced to form a non-linear relationship, so that the scoring factors show non-linear growth characteristics for the total score; finally, the fuzzy scores of all scoring factors are multiplied by the corresponding dynamic weights and combined through the non-linear relationship to calculate the overall weighted score. Step S2022331 of the above solution sets the dynamic weight to assign a data-driven dynamic weight to each fuzzy score; the implementation enables the scoring model to flexibly adjust the importance of each scoring factor according to external environmental changes, historical performance, and current situations; the dynamic weight takes into account different scenarios in practical applications and helps to accurately reflect the actual influence of each factor. The achieved significance is as follows: It improves the adaptability and flexibility of the scoring system, enabling the model to not only rely on fixed weights but also respond to changes in a timely manner, ensuring the timeliness and accuracy of the decision-making basis; it can guide decision-makers to pay more attention to factors with significant fluctuations in performance within a specific period, thereby optimizing resource allocation and management strategies. Step S2022332 introduces a non-linear relationship. By using a power operation and introducing an exponent greater than 1, the contribution of the fuzzy score to the overall score shows non-linear growth characteristics; when the value of some scoring factors increases, the impact on the total score shows accelerating growth; the non-linear relationship ensures that when the importance of the scoring factor is relatively high, its impact on the overall result can be greatly amplified, thereby strengthening the identification of outstanding performance. The achieved significance is as follows: Decision-makers can more clearly identify the key factors that have a significant impact on the result, which is crucial in many scenarios (such as risk management, performance evaluation, etc.); by emphasizing important factors, it can promote enterprises to more effectively concentrate resources on the areas that are most effective in improving performance, and thus achieve the optimal allocation of resources. Step S2022333 combines and calculates the overall weighted score, multiplies the fuzzy scores of all scoring factors by the corresponding dynamic weights, and conducts comprehensive calculations in combination with the non-linear relationship; it can generate an overall weighted score that reflects the result of the combined action of all important factors; through this combined score, a comprehensive and highly sensitive result is obtained, which takes into account both the relative importance of each factor and shows the corresponding influence amplification effect. The achieved significance is as follows: The finally obtained weighted score provides a comprehensive perspective for decision-making. Decision-makers can conduct effective analysis and judgment based on this score, providing a guiding basis for formulating strategies and action plans; the comprehensive score not only simplifies the data complexity but also provides a clear and direct performance evaluation, helping enterprises to conduct more efficient performance management and innovation drive.

[0131] In summary, the weighted comprehensive score formed in this embodiment achieves the combination of precision, flexibility and dynamics, enabling the effective identification and utilization of changes in various factors during the decision-making process; not only improves the scientificity and practicability of the scoring model, realizes effective decision support driven by data, but also lays a solid foundation for the continuous improvement and strategic development of the enterprise.

[0132] Embodiment 9: As Figure 9 shown, on the basis of Embodiment 1, the process of formulating corrective or adjustment measures provided by the embodiment of the present invention includes the following steps:

[0133] S301: Mark all sensor acquisition points that exceed the acceptable range, measure the specific error value of each point, and record it in a database; analyze the potential factors affecting the error, such as temperature change, humidity, construction technology, and material properties, etc.; re-examine the procedures and processes of the construction link, and analyze whether there are mistakes in the plan or irregularities in the operation of workers.

[0134] S302: If the error is caused by a malfunction of the sensor or equipment, the equipment needs to be calibrated or replaced in time, and data acquisition is carried out again; if the cause of the error is improper construction technology, targeted technical guidance needs to be formulated; adjust the construction drawings according to the results of the error analysis.

[0135] S303: Formulate the corrective measures into specific implementation plans, and continue to use the sensor to monitor the effect of the corrective measures. If the new data still exceeds the acceptable range, adjust the corrective strategy in time.

[0136] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, all sensor acquisition points exceeding the acceptable range are first marked out, and the specific error value of each point is measured and recorded in a database; potential factors affecting the error are analyzed, such as temperature change, humidity, construction technology, and material properties, etc.; the procedures and processes of the construction link are re-reviewed to analyze whether there are mistakes in the plan or non-standard operations of workers; secondly, if the error is caused by the failure of the sensor or equipment, the equipment needs to be calibrated or replaced in a timely manner, and data acquisition is carried out again; if the cause of the error is improper construction technology, targeted technical guidance needs to be formulated; the construction drawings are adjusted according to the results of the error analysis; finally, the corrective measures are formulated into specific implementation plans, and the effects of the corrective measures are continuously monitored using sensors. If the new data still exceeds the acceptable range, the corrective strategy is adjusted in a timely manner. In step S301 of the above solution, the error is identified and analyzed. By marking out all sensor acquisition points exceeding the acceptable range and measuring the specific error values, a list and record of errors can be formed; it helps to quickly identify which components or connection parts have problems; analyzing the potential factors affecting the error (such as temperature change, humidity, construction technology, and material properties) provides data support for subsequent measures; understanding the influencing factors can help solve the problem fundamentally rather than just dealing with it temporarily; re-reviewing the procedures and processes of the construction link is convenient for discovering mistakes in the plan or non-standard operations of workers, which can prevent frequent construction errors to a greater extent and provide a basis for subsequent implementation guidelines. The achieved significance is: accurately identifying and recording the error points exceeding the standard can provide a precise target for subsequent adjustments and avoid waste of resources; by reviewing the construction procedures, it can provide guidance for the construction team to ensure more standardized and efficient execution in future work and reduce the occurrence of future errors. In step S302, technical and design corrections are implemented. Timely calibration or replacement of the equipment can ensure the accuracy of sensor data, avoid data deviation caused by equipment failure, and thus improve the reliability of monitoring; adjusting the construction technology and formulating targeted technical guidance can promote the technical improvement of the construction team, strengthen the compliance with relevant technical standards, and improve the overall construction quality; correcting the construction drawings according to the results of the error analysis can ensure that subsequent construction can be carried out within a reasonable error range, which helps to ensure the consistency between the feasibility of the design and the implementation. The achieved significance is: by implementing this series of practical operations, the error caused by equipment failure can be effectively reduced, the effectiveness of the overall system monitoring and construction can be improved, and thus potential future errors and risks can be reduced; through technical guidance and drawing adjustments, it can be ensured that construction resources and manpower can be used more effectively, thereby reducing the time and economic losses caused by rework.Step S303 formulates an implementation plan and monitors and corrects the effects. The corrective measures are formulated into specific implementation plans, clarifying responsibilities and time nodes, providing an operable framework for execution, and improving the execution efficiency and sense of responsibility of the team. By continuing to use sensors to monitor the effects of the corrective measures, any new data can be quickly fed back to ensure the effectiveness of the corrective measures. If the new data still exceeds the acceptable range, the strategy can be adjusted more promptly. The achieved significance: By establishing a continuous monitoring mechanism, a closed-loop feedback can be formed, which helps to continuously optimize the construction quality and management level; the combination of the implementation plan and effect monitoring makes the decision-making process more scientific and evidence-based, improving the transparency and efficiency of management, and providing an important reference for implementing improvement measures in similar projects in the future.

[0137] In summary, this embodiment constitutes a complete set of comprehensive corrective and adjustment measures. Through the identification of errors, cause analysis, and formulation of implementation strategies, an efficient closed-loop management system is formed. It not only helps to correct current errors but also provides valuable experience and improvement suggestions for future construction, thus promoting the intelligent and standardized development of the overall prefabricated building management.

[0138] Embodiment 10: As Figure 10 shown, based on Embodiments 1 - 9, the intelligent management system for prefabricated buildings based on BIM models provided by the embodiments of the present invention includes:

[0139] The equipment layout module is responsible for selecting key collection points according to the component layout and construction process of the BIM model. The collection points cover areas such as component nodes and connection parts; sensors are installed at the collection points to collect sensing data such as position, angle, and size in real time;

[0140] The error analysis module is responsible for preprocessing the collected sensing data, cross-verifying the preprocessed sensing data; calculating the actual error of each collection point of the preprocessed sensing data according to the cross-verification result and the target error range, and generating an error analysis report; comparing the result of the error analysis report with the BIM model to display the differences between the actual components and the design model;

[0141] The adjustment and correction module is responsible for formulating corrective or adjustment measures for each collection point where the error in the error analysis report exceeds the acceptable range according to the comparison result.

[0142] The working principle and beneficial effects of the above technical solution are as follows: The equipment layout module in this embodiment selects key acquisition points according to the component layout and construction process of the BIM model, and the acquisition points cover areas such as component nodes and connection parts; sensors are installed at the acquisition points to collect sensing data such as position, angle, and dimension in real time; the error analysis module preprocesses the collected sensing data and performs cross-verification on the preprocessed sensing data; according to the cross-verification results and the target error range, calculates the actual error of each acquisition point of the preprocessed sensing data, and generates an error analysis report; compares the results of the error analysis report with the BIM model to display the differences between the actual components and the design model; the adjustment and correction module formulates correction or adjustment measures for each acquisition point whose error in the error analysis report exceeds the acceptable range according to the comparison results. The equipment layout module of the above solution accurately selects key acquisition points according to the component layout and construction process of the BIM model, ensuring the comprehensiveness and effectiveness of data acquisition to the greatest extent; installs sensors at key positions to monitor the position information, angle, and dimension of components in real time, ensuring the immediate acquisition of data. Advantages: Compared with traditional methods, this module can select acquisition points more intelligently and systematically, greatly improving the management level of the construction site; traditional methods often rely on manual observation and recording, while this module can perform real-time monitoring, reducing the occurrence of errors and delays. Significance achieved: Through effective data acquisition, the accuracy of component installation is improved, and installation deviations caused by human factors are reduced; a solid data foundation is laid for data analysis and error correction, ensuring the scientific nature of subsequent processes. The error analysis module denoises and standardizes the collected sensing data, and uses cross-verification to enhance the reliability of the data; calculates the actual error of each acquisition point, generates a detailed error analysis report, and at the same time compares it with the BIM model to analyze the differences between the actual component state and the design model. Advantages: Compared with traditional inspection methods, using a data-driven approach for error analysis can more objectively and scientifically reflect the actual situation; by systematically generating error analysis reports, managers can quickly locate problems, reduce response time, and improve decision-making efficiency. Significance achieved: Strengthen the real-time monitoring of construction quality, be able to take measures quickly after discovering deviations, and control construction risks at an early stage; help construction managers make more reasonable decisions based on data, and contribute to promoting the construction process towards a more efficient and high-quality direction. The adjustment and correction module formulates specific correction or adjustment measures for the acquisition points in the error analysis report that exceed the acceptable range, including fine-tuning of construction processes or improvement of methods; with the help of real-time data feedback, it can update and adjust the construction plan in a timely manner to ensure construction accuracy. Advantages: Compared with traditional methods, this module provides a more flexible response mechanism and can promptly handle problems that occur during the construction process; through continuous data collection and feedback, a closed-loop management is formed, improving the overall construction efficiency and quality.Significance achieved: Real-time error correction ensures that the component state meets the design requirements and prevents subsequent problems caused by potential quality hazards; each correction process can provide rich data accumulation and lessons for future construction, contributing to the construction of an intelligent management knowledge base.

[0143] In summary, through a series of modular functions such as equipment layout, error analysis, and correction, this embodiment realizes comprehensive intelligence from data collection, analysis to decision-making. Compared with traditional management solutions, it has higher accuracy, real-time performance, and flexibility, which helps to improve construction efficiency and quality, reduce the occurrence probability of errors and safety hazards, and has far-reaching significance for the intelligent transformation of the overall construction industry.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of equivalent technologies of the present invention, the present invention is also intended to include these changes and modifications.

Claims

1. An intelligent management method for prefabricated buildings based on BIM models, characterized in that The steps include: According to the component layout and construction process of the BIM model, select key acquisition points that cover component nodes and connection parts; install sensors at the acquisition points to collect sensing data of position, angle, and dimension in real time; Preprocess the collected sensing data and perform cross - validation on the preprocessed sensing data; According to the cross - validation results and the target error range, calculate the actual error of each acquisition point of the preprocessed sensing data and generate an error analysis report; Compare the results of the error analysis report with the BIM model to display the differences between the actual components and the design model; According to the comparison results, formulate correction or adjustment measures for each acquisition point where the error in the error analysis report exceeds the acceptable range; Compare the results of the error analysis report with the BIM model. Perform noise filtering, missing data processing, and standardization preprocessing on the collected sensing data to obtain the standardized sensing data; adopt redundant sensor configuration and compare the sensing data between different sensors; Extract the design parameters from the BIM model, compare them with the sensor data after cross - validation, calculate the absolute error and relative error, classify them according to the preset target error range, identify the acquisition points exceeding the threshold, and organize the error analysis results of each acquisition point into a report to centrally display the actual value, theoretical value, and their errors.

2. The intelligent management method for prefabricated buildings based on BIM models according to claim 1, wherein, The process of selecting key acquisition points includes the following steps: Identify the functions and roles of each component in the building to obtain the construction sequence and the installation logic of each component; select the nodes connecting different components and the parts where the component is stressed as acquisition points; confirm the type of sensor according to the data type of the acquisition point; Conduct a feasibility assessment of the acquisition points and the type of sensors; evaluate the type and quantity of sensors; Form a preliminary list of key acquisition points, compare the final acquisition points with the component layout of the BIM model; mark the positions of all acquisition points in the BIM model and record the monitoring parameters corresponding to each point.

3. The intelligent management method for prefabricated buildings based on BIM models according to claim 1, characterized in that Simultaneously scan the data timestamps of each sensor, and calculate the mean value of the standardized sensing data of different acquisition points according to the data timestamps to obtain the cross - validated sensing data.

4. The intelligent management method for prefabricated buildings based on BIM models according to claim 1, wherein, The process of identifying the acquisition points exceeding the threshold includes the following steps: For the data collected in real time from the sensors and the expected data calculated through the building information model, calculate the absolute error by taking the difference between the actual value of the collected data and the theoretical value of the expected data, and calculate the relative error through the absolute error and the theoretical value; Set an allowable error range, classify the relative errors of each calculated acquisition point to obtain the normal range, critical range, and over - threshold range; Create a data record table listing the actual value, theoretical value, absolute error, relative error, and classification results of all acquisition points; For each acquisition point, check whether the relative error exceeds the preset threshold range; Mark the over - threshold points and record them in the data table.

5. The intelligent management method for prefabricated buildings based on BIM models according to claim 4, characterized in that The process of classifying the relative errors of each calculated acquisition point in the process of identifying the acquisition points exceeding the threshold includes the following steps: Obtain the relative error value of the current acquisition point. If there is a historical relative error record for the current acquisition point, calculate the percentage change between the current error and the last error; if there is no historical record, set the change rate to zero. Calculate the historical average relative error and determine whether the difference between the current relative error and the average relative error is within the acceptable stability threshold. Combine the current relative error, change rate, and average relative error for a comprehensive score. Sum each factor according to its weight to form a score value to judge the comprehensive status of the acquisition point; classify each acquisition point according to the set classification criteria.

6. The intelligent management method for prefabricated buildings based on BIM models according to claim 4, wherein, Add a column to the data table for each acquisition point to record its classification result, and at the same time record the score value and relative error value of this point.

7. The intelligent management method for prefabricated buildings based on BIM models according to claim 1, wherein Display the error conditions of key acquisition points classified by importance, using an error bar chart to show important data points where safety components exceed the acceptable range.

8. An intelligent management system for prefabricated buildings based on BIM models, characterized in that, Include: The device layout module is responsible for selecting key acquisition points according to the component layout and construction process of the BIM model. The acquisition points cover component nodes and connection parts; install sensors at the acquisition points to collect sensing data of position, angle, and size in real time. The error analysis module is responsible for preprocessing the collected sensing data and cross-validating the preprocessed sensing data. According to the cross-validation results and the target error range, calculate the actual error of each acquisition point of the preprocessed sensing data and generate an error analysis report. Compare the results of the error analysis report with the BIM model to show the differences between the actual components and the design model. The adjustment and correction module is responsible for formulating correction or adjustment measures for each acquisition point where the error in the error analysis report exceeds the acceptable range according to the comparison results. Compare the results of the error analysis report with the BIM model. Perform noise filtering, missing data processing, and standardization preprocessing on the collected sensing data to obtain the standardized sensing data; adopt redundant sensor configurations and compare the sensing data between different sensors. Extract the design parameters from the BIM model and compare them with the cross-validated sensor data. Calculate the absolute error and relative error, classify them according to the pre-set target error range, identify the acquisition points exceeding the threshold, and organize the error analysis results of each acquisition point into a report to centrally display the actual value, theoretical value, and their errors.

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