Fabricated building intelligent management method and system based on BIM model

Abstract

The invention provides a fabricated building intelligent management method and system based on a BIM model, and the method comprises the steps: installing a sensor at a collection point, and collecting the position, angle, size and other sensing data in real time; preprocessing the collected sensing data, and carrying out cross validation on the preprocessed sensing data; calculating an actual error of each acquisition point of the preprocessed sensing data according to a cross validation result and a target error range, and generating an error analysis report; the result of the error analysis report is compared with the BIM model, and the difference between the actual component and the design model is displayed; and according to a comparison result, a correction or adjustment measure is formulated for the acquisition points of which the errors of each error analysis report exceed an acceptable range. The system comprises an equipment layout module, an error analysis module and an adjustment and correction module. The method plays an important role in improving the construction efficiency, quality and safety; and a closed loop is formed through data acquisition, analysis and feedback.

Description

Technical Field

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

[0002] Prefabricated buildings refer to a type of construction method in which building components are prefabricated in factories and then transported to construction sites for assembly. Compared with traditional buildings, prefabricated buildings emphasize modularization and standardization, and usually include prefabricated wall panels, floor slabs, roofs and columns. The core is to divide the building into multiple components, construct and process them in the factory, thereby reducing construction time and labor intensity on site and improving construction efficiency and quality. In the implementation of prefabricated buildings, BIM models (Building Information Modeling) are often used in conjunction to digitally express 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 fail or the network may be unstable, resulting in information delays or missing; relying too much on real-time data for decision-making may affect management effectiveness due to inaccurate information.

[0003] Prior art 1, application number: CN202310981532.7 discloses a prefabricated building quality information supervision system based on big data, which sets up a data analysis and mining module, calculates the initial energy efficiency of household components, applies data mining and machine learning algorithms, conducts in-depth analysis of quality information, and identifies quality problems and trends by establishing models and algorithms, and provides prediction and decision support. It can predict quality problems that may occur in the future by analyzing historical quality data, and take corresponding measures to prevent them. Although an intelligent decision support module is set up: combining artificial intelligence technology, an intelligent decision support system is designed, 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 source of data, which leads to the analysis results of quality information. It is very likely that the accuracy of the analysis results will be reduced due to inaccurate data collection.

[0004] Prior art 2, application number: CN 202410337951.1 discloses a method and system for intelligent management of prefabricated buildings based on BIM model, the method comprising: obtaining collection points, clustering processing to obtain clusters of points at the same position; determining the distribution significance according to the spatial distribution characteristics of all collection points in the cluster of points at the same position and the spatial distance between the point to be measured and other collection points in the cluster of points at the same position; determining the structural error index according to the distribution significance of the collection points and the distance value between the point to be measured and the corresponding center position coordinates of the cluster of points at the same position; determining the target error range of the collection point position according to the structural error index of all collection points; and managing the assembly of the components to be measured according to the target error range. Although it is possible to obtain accurate and objective error accuracy of different collection point positions, it is convenient to perform assembly management according to the target error range, thereby improving the intelligent management effect during BIM model simulation. However, the focus is on the optimization of the collection node settings, and the reliability and stability of the sensor data are not optimized, so that the error of the collection data needs to be further improved.

[0005] Prior art three, application number: CN202310685508.9 discloses a prefabricated building management system based on BIM, 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 splits 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 the prefabricated building structure split 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 process of the data unit, the design unit and the production unit according to the preset plan. Although rapid response between various data and between various links is achieved. However, the redundant sensor data is not processed, resulting in limited improvement in data reliability.

[0006] At present, the existing technologies 1, 2 and 3 have the problem that data sensors may fail or the network may be unstable, resulting in information delay or loss; and the management effect may be affected by inaccurate information. Therefore, the present invention provides a method and system for intelligent management of prefabricated buildings based on the BIM model. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides an intelligent management method for prefabricated buildings based on a BIM model, comprising the following steps:

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

[0009] Preprocess the collected sensor data and cross-validate the preprocessed sensor data; calculate the actual error of each collection point of the preprocessed sensor data based on the cross-validation results and the target error range, 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;

[0010] Based on the comparison results, formulate correction or adjustment measures for each collection point where the error in the error analysis report exceeds the acceptable range.

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

[0012] Identify the functions and roles of each component in the building, obtain the construction sequence and installation logic of each component; select the nodes connecting different components and the parts where the components are subjected to concentrated forces as collection points; confirm the type of sensor based on the data type of the collection point;

[0013] Conduct feasibility assessment on the types of collection points and sensors; evaluate the types and quantity of sensors;

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

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

[0016] The collected sensor data is subjected to noise filtering, missing data processing and standardization preprocessing to obtain standardized sensor data; redundant sensor configuration is used to compare sensor data between different sensors; the data timestamps of each sensor are scanned at the same time, and the sensor data after standardization processing at different collection points are averaged according to the data timestamps to obtain cross-validated sensor data;

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

[0018] The error conditions of key acquisition points are displayed according to their importance, and an error bar chart is used to display important data points where safety components exceed the acceptable range.

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

[0020] At least three redundant sensors are deployed in key structural areas, and at least two redundant sensors are deployed in non-key structural areas to collect basic data; redundant sensors are interconnected using a mesh structure;

[0021] Collect data from redundant sensors at different data points, automatically integrate redundant sensor outputs, and form a unified output format; including 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] The data packet is transmitted to the blockchain node through the blockchain network. After the node in the blockchain network receives the sensor data, it unpacks the data and extracts the data content, hash value and digital signature; the data content is hashed using a hash algorithm, and the digital signature is verified 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 exceeding the threshold value includes the following steps:

[0024] The data collected in real time from the sensor is used to calculate the expected data through the building information model. The actual value of the collected data is subtracted from the theoretical value of the expected data to obtain the absolute error, and the relative error is calculated by the absolute error and the theoretical value.

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

[0026] Create a data record table to list the actual values, theoretical values, absolute errors, relative errors and classification results 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 calculated relative errors of each acquisition point includes the following steps:

[0028] Get the relative error value of the current acquisition point. If the current acquisition point has a historical relative error record, calculate the percentage change between the current error and the last error. If there is no historical record, the change rate is set 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 an acceptable stability threshold;

[0030] A comprehensive score is made based on the current relative error, rate of change and average relative error. Each factor is summed up according to the weight to form a score value to judge the comprehensive status of the collection point; each collection point is classified according to the set classification criteria.

[0031] Optionally, add a column to the data table for each collection point to record its classification result, and 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 collection 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 cycle;

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

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

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

[0037] Give each fuzzy score a dynamic weight for each factor in the evaluation cycle based on data-driven settings;

[0038] In the weighted comprehensive score model, power operation is used to introduce an exponent greater than 1 to form a nonlinear relationship, so that the score factors show nonlinear growth characteristics for the total score;

[0039] The fuzzy scores of all scoring factors are multiplied with the corresponding dynamic weights and combined through a non-linear relationship to calculate the overall weighted score.

[0040] The present invention provides an intelligent management system for assembled buildings based on a BIM model, comprising:

[0041] The equipment layout module is responsible for selecting key collection points based on the component layout and construction process of the BIM model. The collection points cover the component nodes and connection parts. Sensors are installed at the collection points to collect sensor data of position, angle and size in real time.

[0042] The error analysis module is responsible for preprocessing the collected sensor data and cross-validating the preprocessed sensor data; based on the cross-validation results and the target error range, the actual error of each acquisition point of the preprocessed sensor data is calculated and an error analysis report is generated; the results of the error analysis report are compared with the BIM model to show the differences between the actual components and the design model;

[0043] The adjustment and correction module is responsible for formulating correction or adjustment measures for each collection point whose error in the error analysis report exceeds the acceptable range based on the comparison results.

[0044] The data collection and sensor installation of the present invention realizes real-time monitoring of component status through sensors installed at key collection points (such as position, angle, size and other data), ensuring the timeliness and accuracy of the data; key locations such as component nodes and connection parts are selected for data collection to ensure that key data points are effectively monitored. Significance achieved: The acquisition of real-time monitoring data helps to ensure accuracy during the construction process, which is crucial for the installation of components in prefabricated buildings; through early monitoring of potential deviations, problems can be discovered and solved in a timely manner, thereby reducing risks and the probability of errors in construction; providing real and reliable basic data for data analysis and problem correction, and improving the scientificity and effectiveness of subsequent work. Data preprocessing and error analysis, by preprocessing the data (denoising, standardization, etc.), improve the reliability of the data and ensure the accuracy of the data when used for analysis; combining the actual data with the preset target error range, calculate the error of each collection point and generate an error analysis report. Significance achieved: Complete data analysis and error reporting provide support for management decisions, helping managers understand the actual status and potential problems, so as to make scientific and reasonable adjustments; by comparing the error report with the BIM model, the difference between the components and the design can be intuitively displayed, effectively promoting the improvement of construction quality; enhancing awareness of the construction status, forming transparent and effective communication, and improving efficiency. Formulate correction or adjustment measures, and formulate specific correction or adjustment measures for the collection points where the error exceeds the acceptable range to correct the deviation; by adjusting the construction process or method, ensure the accuracy of subsequent component installation. Significance achieved: Formulating targeted measures can effectively improve the accuracy of subsequent construction and reduce the additional costs and time losses caused by initial errors; through timely corrections and adjustments, ensure that the project can proceed smoothly according to the plan and standards, and improve the controllability of management; through the analysis and adjustment process, the project team can continuously accumulate experience and provide 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 following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Figure 1 This is a flow chart of the intelligent management method for prefabricated buildings based on the BIM model in Example 1 of the present invention;

[0049] Figure 2 A process diagram of selecting key collection points in Example 2 of the present invention;

[0050] Figure 3 A process diagram of comparing the results of the error analysis report with the BIM model in Example 3 of the present invention;

[0051] Figure 4 A process diagram of obtaining cross-validated sensor data in Example 4 of the present invention;

[0052] Figure 5 This is a process diagram of identifying collection points exceeding the threshold value in Example 5 of the present invention;

[0053] Figure 6 This is a process diagram for classifying the calculated relative errors of each acquisition point in Example 6 of the present invention;

[0054] Figure 7 A process diagram of forming a scoring value to judge the comprehensive status of a collection point in Embodiment 7 of the present invention;

[0055] Figure 8 A process diagram for forming a weighted comprehensive score in Example 8 of the present invention;

[0056] Fig. 9 A process diagram for formulating correction or adjustment measures in Example 9 of the present invention;

[0057] Fig.10 This is a block diagram of the intelligent management system for prefabricated buildings based on the BIM model in Example 10 of the present invention. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with 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 only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0060] When the following description relates 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 only 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 the specific circumstances.

[0061] Example 1: Figure 1 As shown, an embodiment of the present invention provides an intelligent management method for assembled buildings based on a BIM model, comprising the following steps:

[0062] S100: According to the component layout and construction process of the BIM model, key collection points are selected, covering areas such as component nodes and connection parts; sensors are installed at the collection points to collect sensor data such as position, angle and size in real time;

[0063] S200: preprocessing the collected sensor data and cross-validating the preprocessed sensor data; calculating the actual error of each acquisition point of the preprocessed sensor data according to the cross-validation result and the target error range, and generating an error analysis report; comparing the results of the error analysis report with the BIM model to show the difference between the actual component and the design model;

[0064] S300: Based on the comparison results, formulate correction or adjustment measures for each collection point whose 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: First, according to the component layout and construction process of the BIM model, the key collection points are selected, and the collection points cover areas such as component nodes and connection parts; sensors are installed at the collection points to collect sensor data such as position, angle and size in real time; secondly, the collected sensor data is preprocessed and the preprocessed sensor data is cross-validated; according to the cross-validation results and the target error range, the actual error of each collection point of the preprocessed sensor data is calculated, and an error analysis report is generated; the results of the error analysis report are compared with the BIM model to show the difference between the actual component and the design model; finally, according to the comparison results, for each collection point whose error in the error analysis report exceeds the acceptable range, correction or adjustment measures are formulated. Step S100 of the above scheme is data collection and sensor installation. Through the sensors (such as position, angle and size data) installed at the key collection points, the real-time monitoring of the component status is realized to ensure the timeliness and accuracy of the data; key locations such as component nodes and connection parts are selected for data collection to ensure that key data points are effectively monitored. Significance achieved: The acquisition of real-time monitoring data helps to ensure the accuracy during the construction process, which is crucial for the installation of components of prefabricated buildings; by early monitoring of potential deviations, problems can be discovered and solved in a timely manner, thereby reducing the risk and probability of errors in construction; it provides real and reliable basic data for data analysis and problem correction, and improves the scientificity and effectiveness of subsequent work. Step S200 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 collection point is calculated and an error analysis report is generated. Significance achieved: Perfect data analysis and error reporting provide support for management decisions, helping managers understand the actual status and potential problems, so as to make scientific and reasonable adjustments; by comparing the error report with the BIM model, the difference between the component and the design can be intuitively displayed, effectively promoting the improvement of construction quality; enhancing the awareness of the construction status, forming transparent and effective communication, and improving efficiency. Step S300 Formulate correction or adjustment measures: for the collection points whose errors exceed the acceptable range, formulate specific correction or adjustment measures to correct the deviation; by adjusting the construction process or method, ensure the accuracy of subsequent component installation. Significance achieved: Developing targeted measures can effectively improve the accuracy of subsequent construction and reduce the additional costs and time losses caused by initial errors; through timely corrections and adjustments, ensure that the project can proceed smoothly according to plan and standards, and improve the controllability of management; through the analysis and adjustment process, the project team can continuously accumulate experience and provide reference and improvement basis for future project management and implementation.

[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 current construction projects, but also provides practical reference and guidance for future projects.

[0067] Example 2: Figure 2 As shown, based on Example 1, the process of selecting key collection points provided in this embodiment of the present invention includes the following steps:

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

[0069] S102: Conduct feasibility assessment on the types of collection points and sensors; assess 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 locations 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: the present embodiment first identifies the functions and roles of each component in the building, obtains the construction sequence and the installation logic of each component; selects the nodes connecting different components and the parts where the components are subjected to concentrated forces as the collection points; confirms the type of sensor according to the data type of the collection point; secondly, conducts a feasibility assessment on the types of collection points and sensors; assesses the types of sensors (such as displacement sensors, angle sensors) and quantity; finally forms a preliminary list of key collection points, compares the final collection points with the component layout of the BIM model; marks the locations of all collection points in the BIM model, and records the monitoring parameters corresponding to each point. Step S101 of the above solution identifies the functions of components and selects collection points, and by analyzing the functions and roles of each component of the building, clarifies the relationship between different components and the construction sequence and installation logic, and provides a scientific basis for subsequent data collection; selects the nodes connecting different components and the parts where the components are subjected to concentrated forces as the collection points, and ensures that valid data is obtained at key locations; selects the appropriate sensor type according to the different data types of the collection points (such as position, angle, size, etc.). Advantages compared to existing solutions: Compared with the traditional selection method that relies only on experience, the selection based on function and force analysis is more systematic and scientific, reducing the influence of subjective factors; focusing on the selection of key nodes and stress-bearing parts ensures that the data collection points have higher effectiveness and pertinence. Significance achieved: Ensure the monitoring of important components and improve the quality control ability in the actual construction process; by analyzing the function and stress conditions, effectively reduce the safety hazards and quality problems caused by improper installation. Step S102 Feasibility assessment, conduct technical feasibility assessment on the selected collection points and sensors, confirm the suitability of the sensors, and ensure that all data collection plans are feasible; according to the evaluation results, optimize the number and type of required sensors to ensure the comprehensiveness and economy of data collection. Advantages compared to existing solutions: Compared with the traditional static evaluation method, the introduction of dynamic technical evaluation ensures the scientificity and flexibility of the configuration, and can be adjusted according to changes in site conditions; through effective evaluation, avoid budget overruns and subsequent modification costs caused by incorrect sensor configuration. Significance achieved: Ensure the sustainable operation of the project through reasonable evaluation, improve the efficiency of project commissioning and deployment; effectively configure sensors, which can greatly improve the efficiency of the entire construction monitoring and management. Step S103 forms a preliminary list and compares it with the model, compiles a preliminary list of key collection points, lists the monitoring parameters and locations of each collection point, and enhances the organization and orderliness of information; compares the final collection point with the BIM model, and marks the locations of all collection points in the model to ensure smooth information transmission. Advantages compared to existing solutions: By marking the collection points in the BIM model, the visualization of information is improved, and the complexity of understanding is reduced compared to traditional methods; the list and model annotation help track and manage data, and improve the efficiency of interaction and collaboration.Significance achieved: A consistent understanding is formed among all parties involved to facilitate better communication and collaboration; the clear collection point directory and model annotations make each link in the project implementation process clearer and reduce misunderstandings and errors in information transmission.

[0072] To sum up, this embodiment embodies higher systematicity, 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, and ultimately promotes the intelligent and refined development of prefabricated building construction management.

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

[0074] S201: performing noise filtering, missing data processing and standardization preprocessing on the collected sensor data to obtain standardized sensor data; using redundant sensor configuration to compare sensor data between different sensors; scanning the data timestamp of each sensor at the same time, calculating the mean of the standardized sensor data at different collection points according to the data timestamp to obtain cross-validated sensor data;

[0075] Among them, the state estimation update of the noise filtered collected sensor data is:

[0076]

[0077] In the formula, represents the state estimate at time k, μ k represents the correction error, which is reflected in the remodeling compensation in the state update (to improve robustness);

[0078] Covariance update:

[0079] P k|k =(IK k H k ) k|k-1 +η k Q k

[0080] Where P k|k represents the updated covariance matrix at time k, 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] In the formula, represents the state prediction (e.g., position, velocity, etc.) at time k, represents the nonlinear state transfer function, which generates the next state by describing the relationship between the state and the control input, W N,k It 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] In the formula, z k represents the observation vector at time k, which contains data from various sensors (such as temperature sensors, acceleration sensors, etc.), represents a nonlinear 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 uncertainty inherent in the sensor measurement process;

[0087] Adaptive Kalman gain:

[0088]

[0089] In the formula, K k represents the adaptive Kalman gain, which is adjusted based on the dynamic changes of observations, H k It 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 capability to significant measurement anomalies and adjust the effect of gain on each observation; Represents the absolute difference of the calculated residuals, which increases the awareness of abnormal observations;

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

[0091] S203: Display the error status of key acquisition points according to importance classification, and use error bar chart to display important data points where 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 sensor data is first subjected to noise filtering, missing data processing and standardization preprocessing to obtain standardized sensor data; redundant sensor configuration is adopted to compare sensor data between different sensors; the data timestamp of each sensor is scanned at the same time, and the sensor data after standardization processing at different collection points are averaged according to the data timestamp to obtain cross-validated sensor data; the design parameters in the BIM model are extracted and compared with the cross-validated sensor data, the absolute error and the relative error are calculated, and the data are classified according to the preset target error range, the collection points exceeding the threshold are identified, and the error analysis results of each collection point are organized into a report to centrally display the actual value, theoretical value and its error; finally, the error conditions of key collection points are displayed according to the importance classification, and the error bar chart is used to display the important data points of the safety components exceeding the acceptable range. The above scheme includes step S201, preprocessing and cross-validation of sensor data. Through advanced noise elimination, environmental interference in sensor data is eliminated, and data quality and reliability are improved. Missing data is filled to ensure the integrity of the data set, avoiding the impact of data vacancies on subsequent analysis results. The scale of sensor data is unified to improve the comparability of different sensor data, laying the foundation for cross-validation. Redundancy and comparison are achieved through data obtained by redundant sensors to improve data reliability. At the same time, the scanning of timestamps further ensures data consistency. The mean of data within the time window is used to provide more robust sensor measurement results and reduce the impact of accidental errors of individual sensors. Step S202, error calculation and report collation, combines the standard design parameters of the BIM model with the actual sensor data to form a basis for comparison between real and theoretical data. Through the calculation of absolute error and relative error, the design deviation is accurately quantified to provide a quantitative basis for subsequent correction measures. According to the preset target error range classification, key problems can be discovered and identified in a timely manner, which is of great significance for construction quality control. The error analysis results are systematically collated and displayed to make the analysis information clear and easy to understand, providing detailed basis and visual support for decision-making. Step S203: Error importance analysis and visual display: Errors are classified according to their importance, allowing construction managers to quickly identify key collection points and optimize subsequent inspection and correction processes. Error bar charts are used to display key data, presenting complex information in an intuitive form, improving the report's comprehensibility and decision-making support capabilities. Safety components that exceed the acceptable range are specially marked to ensure timely identification and handling of potential risks.Compared with the advantages of existing solutions: the multi-level preprocessing and cross-validation mechanism of this embodiment greatly improves the reliability of data compared with the traditional single data collection and analysis method; through detailed error calculation and over-threshold analysis, construction managers can take timely measures to reduce construction risks, and traditional methods often cannot provide such clear quantitative basis; compared with traditional static analysis, it provides real-time data monitoring and dynamic feedback, so that managers can grasp the progress of construction and its quality status at any time and adjust the construction process in time; the use of visualization means makes complex data more operational, reduces decision-making errors caused by poor information transmission, and enhances the efficiency of multi-party communication. The significance achieved is that through efficient error analysis and real-time monitoring, the overall quality of prefabricated building construction is significantly improved, and the safety and reliability of the project are guaranteed; timely identification and correction of errors help to reduce subsequent repair costs, save project time, and improve construction efficiency; through intelligent data analysis, the BIM model truly realizes intelligent management, thereby promoting the digital transformation of the construction industry; through feedback and reporting, a closed-loop management is formed to promote the continuous optimization of project management, which helps to form systematic construction management specifications and standards.

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

[0094] Example 4: Figure 4 As shown, based on Example 3, the process of obtaining cross-validated sensor 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; use a mesh structure to interconnect the redundant sensors;

[0096] S2012: Collect data from redundant sensors at different data points, automatically integrate redundant sensor outputs, and form a unified output format; include information such as timestamp, sensor ID, and measurement 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: The data packet is transmitted to the blockchain node through the blockchain network. After receiving the sensor data, the node in the blockchain network unpacks the data and extracts the data content, hash value and digital signature. The data content is hashed using a hash algorithm, and the digital signature is verified using the sender's public key 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 deployed in the key structural area, and at least two redundant sensors are deployed in the non-key structural area to collect basic data; a mesh structure is used to connect the redundant sensors to each other; secondly, data from redundant sensors at different data points are collected, and the redundant sensor outputs are automatically integrated to form a unified output format; information such as timestamp, sensor ID and measurement value are included; each data point is processed by 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 and the corresponding hash value and digital signature are packaged together to form a complete data packet; finally, the data packet is transmitted to the blockchain node through the blockchain network. After the node in the blockchain network receives the sensor data, it unpacks the data and extracts the data content, hash value and digital signature; the data content is hashed using a hash algorithm, and the digital signature is verified using the sender's public key to confirm that the generated hash value is consistent with the received hash value. Step S2011 of the above scheme is to deploy redundant sensors. At least three redundant sensors are arranged in the key areas of the structure to enhance the redundancy and reliability of the data, effectively reducing the risk of data loss caused by single point failures; at least two sensors are deployed in non-critical areas to provide basic data collection and ensure the overall data quality; the mesh structure connects the redundant sensors to ensure the flexible transmission of data in the network and reduce the data loss caused by the failure of a certain node. The significance achieved: the anti-interference ability in extreme situations is improved, and the continuity and accuracy of data collection are guaranteed; in infrastructure monitoring, the real-time data feedback of the core area is ensured to provide a basis for decision-making and effectively support structural health monitoring. Step S2012 is to collect and integrate data. It automatically integrates the outputs of different sensors and generates a unified output format, including timestamps, sensor IDs and measurements, thereby simplifying the data processing process; a unique hash value is generated for each data point to ensure the integrity and consistency of the data in subsequent transmission; a private key is used for hash signature protection to enhance the security of the data and prevent the data from being tampered with during transmission. Significance achieved: The unified format and unique identifier of data make the data storage and analysis process more efficient, reduce data analysis errors and information asymmetry; eliminate abnormal data, improve the reliability and credibility of data, and form high-quality decision support. Step S2013 Blockchain network transmission and verification: The data packet is transmitted through the blockchain network to ensure the immutability and security of the data in the network; the receiving node unpacks the data, extracts the original data and its hash value and digital signature, and ensures the integrity of the data through hash verification; the digital signature is verified using the sender's public key to ensure the accuracy of the data source.Significance achieved: Data transmission achieved through blockchain technology not only improves data security, but also eliminates dependence on centralized servers and enhances anti-attack capabilities; real-time verification ensures the authenticity of data, increases users' trust in data, and provides a credible data foundation for subsequent analysis and decision-making.

[0099] In summary, the cross-validated sensor 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. The optimization of data collection and processing methods not only improves the effectiveness and practicality of data, but also has a positive role in promoting scientific decision-making in risk management and resource allocation.

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

[0101] S2021: Data collected in real time from sensors, expected data calculated by building information model, absolute error obtained by subtracting actual value of collected data from theoretical value of expected data, relative error obtained by calculating absolute error and theoretical value;

[0102] S2022: setting an allowable error range, classifying the calculated relative errors of each collection point to obtain a normal range, a critical range, and an over-threshold range;

[0103] Normal range: The sampling points with an error within ±5% represent data close to the theoretical value, which is normal and reliable.

[0104] Critical range: The error is between ±5% and ±10%, indicating that the data needs further observation and quality control may need to be intervened;

[0105] Beyond the threshold range: The sampling points with an error exceeding ±10% indicate significant deviations and should be processed as a priority;

[0106] S2023: Create a data record table to list the actual values, theoretical values, absolute errors, relative errors and classification results of all acquisition points; for each acquisition point, check whether the relative error exceeds the preset threshold range; mark the points exceeding the threshold 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, the data collected in real time from the sensor is firstly calculated through the building information model to obtain the expected data, and the actual value of the collected data is subtracted from the theoretical value of the expected data to obtain the absolute error, and the relative error is calculated by the absolute error and the theoretical value; secondly, an allowable error range is set, and the calculated relative errors of each collection point are classified to obtain a normal range, a critical range and an over-threshold range; normal range: the collection points with an error within ±5% represent that the data are close to the theoretical value, normal and reliable; critical range: the error is between ±5% and ±10%, indicating that the data needs further observation and may need to be intervened for quality control; over-threshold range: the collection points with an error exceeding ±10% indicate that there is a significant deviation and should be handled as a priority; finally, a data record table is created to list the actual values, theoretical values, absolute errors, relative errors and classification results of all collection points; for each collection point, check whether the relative error exceeds the preset threshold range; mark the over-threshold points and record them in the data table. In the step S2021 of the above scheme, data collection and error calculation ensures the timeliness and adaptability of information through the data collected by the sensor, so that the monitoring results can reflect the current structural state; the calculation of absolute error reveals the deviation between the actual data and the theoretical value, providing basic information for subsequent classification; the definition of relative error enables data of different magnitudes and units to be reasonably compared, which is convenient for identifying potential problems. The significance achieved: It provides a reliable basis for data analysis, allowing decision makers to evaluate whether the structural performance meets the set standards based on the difference between accurate measured data and expected values; accurate error calculation lays a solid foundation for risk assessment, structural safety monitoring and quality control. Step S2022 Error classification and preset range establishes the allowable error range, making it the benchmark for subsequent classification processing; the definition of normal range, critical range and over-threshold range helps to quickly identify the health status of the data and clarify the projects that need priority attention. The significance achieved: By classifying the errors, the focus can be focused on the important and intervention-required collection points, optimizing the allocation and management of resources, and avoiding missing major risks due to minor problems; it improves the monitoring efficiency, allowing decision makers to quickly extract key issues from a large amount of data, and improve the response speed of the entire data monitoring and processing process. Step S2023: Data recording and identification of over-threshold points. The creation of data recording tables enables the various indicators of all collection points to form a systematic archive, which is convenient for subsequent analysis and review. A standardized method is provided for the error checking process of each collection point to ensure that each collection point is paid attention to and the over-threshold points can be clearly marked.Significance achieved: Records improve the traceability of data, facilitate subsequent analysis, rectification and decision-making processes, and ensure that each abnormal collection point is fully investigated and processed; by marking the over-threshold points, corresponding quality control and maintenance measures can be formulated in a timely manner to ensure the safety of the structure 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, this embodiment not only improves the scientificity and feasibility of data processing through accurate data collection and calculation, strict error classification, and effective recording and analysis, but also provides a reliable basis for future engineering decisions. It can not only improve 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: Figure 6 As shown, based on Example 5, the process of classifying the calculated relative errors of each acquisition point provided in the embodiment of the present invention includes the following steps:

[0110] S20221: Obtain the relative error value of the current acquisition point. If the current acquisition point has a historical relative error record, calculate the percentage change between the current error and the last error. If there is no historical record, the change rate is set 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 an acceptable stability threshold;

[0112] S20223: Perform a comprehensive score based on the current relative error, change rate and average relative error, add up each factor by weight, and form a score value to judge the comprehensive status of the collection point; classify each collection point according to the set classification standard; add a column to each collection point in the data table to record its classification result, and record the score value and relative error value of this point;

[0113] If the score result falls within the short-term normal range, it is marked as “normal range”;

[0114] If the scoring result falls within the critical range, it is marked as “critical range”;

[0115] If the scoring result falls within the threshold range, it is marked as "threshold range".

[0116] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the relative error value of the current acquisition point is first obtained. If the current acquisition point has a historical relative error record, 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 to determine whether the difference between the current relative error and the average relative error is within an acceptable stability threshold; finally, a comprehensive score is performed based on the current relative error, the change rate and the dynamic stability, and each factor is summed up according to the weight to form a score value to determine the comprehensive state of the acquisition point; each acquisition point is classified according to the set classification standard; a column is added to each acquisition point in the data table to record its classification result, and the score value and relative error value of this point are recorded at the same time; if the score result falls within the short-term 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 super-threshold range, it is marked as "super-threshold range". Step S20221 of the above scheme obtains the relative error value of the current collection point and calculates the percentage of change. Obtaining the current relative error of each collection point is the basis for analyzing the data, ensuring that all subsequent steps are based on the latest actual value; calculating the percentage of 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 discovered in time, facilitating rapid response; if there is a lack of historical records, the rate of change is set to zero to prevent misleading due to insufficient comparison data. Step S20222 calculates the historical average relative error and judges 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 and misjudgments caused by accidental anomalies can be reduced; stability judgment can help identify persistent problems, and take corresponding preventive or adjustment measures to ensure data collection quality. Step S20223 integrates the current relative error, rate of change and dynamic stability to form a comprehensive score, which is summed up according to the pre-set weights to evaluate the status of the collection points; according to the classification criteria, the collection points are divided into "normal range", "critical range" or "exceeding threshold range". Significance: The design of comprehensive scoring makes data analysis more flexible and multi-dimensional, not only considering the size of the absolute error, but also reflecting the changing trend and historical stability of the data; clear classification provides a simplified analysis result, making the monitoring and decision-making process more intuitive and helpful in formulating corresponding intervention measures; adding classification results, scoring values ​​and relative error values ​​for each collection point in the data table to achieve systematic management of information, which is convenient for future reference and statistical analysis; improving data transparency so that monitoring personnel can quickly understand the status of each collection point.Significance: It helps to formulate long-term monitoring strategies and rapid response measures; the recording and management of data also provides 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, timely 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] Example 7: Figure 7 As shown, based on Example 6, the process of forming a scoring value to judge the comprehensive status of the collection point provided in 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-driven in each evaluation cycle;

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

[0121] S202233: A weighted composite score is formed by combining the fuzzy score with the dynamic weight.

[0122] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first defines the definition and importance of each factor, and sets the dynamic weight of each factor based on data-driven in each evaluation cycle; secondly, each factor is converted from an absolute value to a fuzzy category, the current relative error is set to low error, medium error or high error, and a membership function is used to assign a fuzzy value to each category; finally, a weighted comprehensive score is formed by combining the fuzzy score with the dynamic weight. The definition and setting of the dynamic weight of step S202231 of the above scheme ensures that the attributes and importance of each factor in the evaluation are clearly defined by clarifying the definition of each factor (such as the current relative error, the amplitude of change and the historical average relative error); the setting of the dynamic weight enables the scoring model to reflect the state changes of the collection point in real time; by analyzing historical data, the existing environment and business needs, the weight is dynamically adjusted to improve the adaptability of the model. The significance achieved: In different situations (for example, 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 factors related to current business needs, the evaluation results will be more practical, helping decision makers to make timely and effective management measures. Step S202232 Fuzzy category conversion and membership assignment, converting absolute values ​​into fuzzy categories, so that the score does not only rely on a clear numerical range, but also takes into account the uncertainty and complexity of the data; applying the membership function to each fuzzy category makes the attribution relationship of different categories smoother; for example, the current relative error score may belong to the "medium error" and "high error" categories at the same time, reflecting the multidimensional characteristics of the data state. Significance achieved: Through fuzzy processing, the model can effectively reduce the adverse effects caused by extreme values ​​and variable data, and improve the stability and reliability of the score; the score is no longer a single precise value, but a judgment within a relative range, providing a multidimensional insight into the data state and helping users understand the actual performance of the collection point. Step S202233 Weighted comprehensive score formation, by combining fuzzy scores with dynamic weights, the transformation from multiple indicators to a single score value is realized, so that multiple interrelated factors can be considered comprehensively; using a specific formula to calculate the comprehensive score, the contribution of each factor to the final score is clarified, which is convenient for tracking and explaining the formation process of the results. Significance achieved: The comprehensive score can intuitively reflect the overall status of the collection point, facilitate classification and management, and support real-time monitoring and evaluation decisions; it combines multiple complex measurement indicators into one score value to help managers quickly identify problem areas and take corresponding measures based on the score to improve management efficiency.

[0123] In summary, this embodiment establishes a comprehensive scoring model based on dynamic weights and fuzzy logic, which reflects technical innovation and logical rigor. 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 constantly changing environment. It enhances the flexibility, reliability and ease of use of data evaluation, and promotes the improvement of enterprises in data-driven decision-making.

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

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

[0126] S2022332: Use power operation in the weighted comprehensive score model and introduce an exponent greater than 1 to form a nonlinear relationship, so that the score factors show nonlinear growth characteristics for the total score;

[0127] S2022333: 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;

[0128] in,

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

[0130] The working principle and beneficial effects of the above technical solution are as follows: First, the present embodiment assigns a dynamic weight of each factor based on data-driven setting in the evaluation cycle to each fuzzy score; secondly, a power operation is used in the weighted comprehensive scoring model to introduce an exponent greater than 1 to form a nonlinear relationship, so that the scoring factor exhibits a nonlinear growth characteristic to the total score; finally, the fuzzy scores of all scoring factors are multiplied by the corresponding dynamic weights, and combined through a nonlinear relationship to calculate the overall weighted score. Step S2022331 of the above scheme sets the dynamic weight to assign a dynamic weight based on data-driven to each fuzzy score; implementation enables the scoring model to flexibly adjust the importance of each scoring factor according to changes in the external environment, historical performance and current situation; the dynamic weight takes into account different scenarios in actual applications, which helps to accurately reflect the actual influence of each factor. The significance achieved: the adaptability and flexibility of the scoring system are improved, so that the model not only relies on fixed weights, but can respond to changes in a timely manner, ensuring the real-time and accuracy of the decision-making basis; it can guide decision makers to pay more attention to factors with significant performance fluctuations in a specific period, thereby optimizing resource allocation and management strategies. Step S2022332 introduces a nonlinear relationship, and uses power operation to introduce an exponent greater than 1, so that the contribution of the fuzzy score to the overall score shows a nonlinear growth characteristic; when the value of some scoring factors increases, the impact on the total score shows an accelerated growth; the nonlinear relationship ensures that when the importance of the scoring factor is high, its impact on the overall result can be greatly amplified, thereby strengthening the identification of outstanding performance. Significance achieved: Decision makers can more clearly identify the key factors that have a significant impact on the results, 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, thereby achieving 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 combines the nonlinear relationship for comprehensive calculation; it can produce an overall weighted score that reflects the result of the combined effect of all important factors; through this combined score, a comprehensive and highly sensitive result is obtained, which not only takes into account the relative importance of each factor, but also shows its corresponding influence amplification effect. Significance achieved: The final weighted score provides a comprehensive perspective for decision-making. Decision-makers can make effective analysis and judgments based on this score, providing guidance for formulating strategies and action plans. The comprehensive score not only simplifies the complexity of the data, but also provides a clear and direct performance evaluation, which helps enterprises to conduct more efficient performance management and innovation-driven.

[0131] In summary, the weighted comprehensive score formed in this embodiment achieves a combination of accuracy, flexibility and dynamism, so that changes in various factors can be effectively identified and utilized in the decision-making process; it not only improves the scientificity and practicality of the scoring model, realizes data-driven effective decision-making support, but also lays a solid foundation for the continuous improvement and strategic development of the enterprise.

[0132] Example 9: Fig. 9 As shown, based on Example 1, the process of formulating correction or adjustment measures provided by the embodiment of the present invention includes the following steps:

[0133] S301: Mark all sensor collection points that are beyond the acceptable range, measure the specific error value of each point, and record it in a database; analyze potential factors that affect the error, such as temperature changes, humidity, construction technology, and material properties; review the construction process and procedures to analyze whether there are any planning errors or irregular operations by workers;

[0134] S302: If the error is caused by a sensor or equipment failure, the equipment needs to be calibrated or replaced in time, and data collection needs to be performed again. If the error is caused by improper construction technology, targeted technical guidance needs to be formulated. The construction drawings need to be adjusted based on the results of the error analysis.

[0135] S303: Formulate the corrective measures into specific implementation plans, continue to use sensors to monitor the effectiveness of the corrective measures, and if the new data still exceeds the acceptable range, adjust the corrective strategy in a timely manner.

[0136] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first marks all sensor collection points that exceed the acceptable range, measures the specific error value of each point, and records them in a database; analyzes potential factors affecting the error, such as temperature changes, humidity, construction technology, and material properties; re-examines the procedures and processes of the construction link, and analyzes whether there are planning errors or irregular operations of workers; secondly, if the error is caused by sensor or equipment failure, the equipment needs to be calibrated or replaced in time, and data collection needs to be performed again; if the cause of the error is improper construction technology, targeted technical guidance needs to be formulated; based on the results of the error analysis, the construction drawings are adjusted; finally, the corrective measures are formulated as specific implementation plans, and sensors continue to be used to monitor the effects of the corrective measures. If the new data still exceeds the acceptable range, the correction strategy is adjusted in time. Step S301 of the above scheme identifies and analyzes errors. By marking all sensor collection points that exceed 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 errors (such as temperature changes, 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 temporarily deal with it; re-examining the procedures and processes of the construction link can facilitate the discovery of planning errors or irregularities in workers' operations, which can prevent frequent construction errors to a greater extent and provide a basis for subsequent implementation guidelines. Significance achieved: Accurately identifying and recording the error points that exceed the standard can provide precise targets for subsequent adjustments and avoid waste of resources; by reviewing the construction procedures, it can provide guidance to the construction team to ensure that they can be executed more standardized and efficiently in future work and reduce the occurrence of future errors. Step S302 implements technical and design corrections, and timely calibrates or replaces equipment to ensure the accuracy of sensor data and avoid data deviations caused by equipment failure, thereby improving the reliability of monitoring; adjusts construction technology and formulates targeted technical guidance to promote the technical improvement of the construction team, strengthen compliance with relevant technical standards, and improve the overall construction quality; corrects the construction drawings based on the error analysis results to ensure that subsequent construction can be carried out within a reasonable error range, which helps the feasibility of the design and the consistency of implementation. Significance achieved: By implementing this series of practical operations, the errors caused by equipment failures can be effectively reduced, and the effectiveness of the overall monitoring and construction of the system can be improved, thereby reducing potential errors and risks in the future; by adjusting technical guidance and drawings, it is ensured that construction resources and manpower can be used more effectively, thereby reducing time and economic losses caused by rework.Step S303 formulates an implementation plan and monitors the correction effect. The corrective measures are formulated as specific implementation plans, and responsibilities and time nodes are clearly defined, providing an operational framework for execution, thereby improving the team's execution efficiency and sense of responsibility. 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. Significance achieved: By establishing a continuous monitoring mechanism, a closed-loop feedback loop can be formed, which helps to continuously optimize the construction quality and management level. The combination of implementation plans and effect monitoring makes the decision-making process more scientific and based on evidence, improves the transparency and efficiency of management, and provides an important reference for the implementation of improvement measures in similar projects in the future.

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

[0138] Example 10: Fig.10 As shown, on the basis of Embodiment 1 to Embodiment 9, the BIM model-based prefabricated building intelligent management system provided by the embodiment of the present invention includes:

[0139] The equipment layout module is responsible for selecting key collection points based on 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 sensor data such as position, angle and size in real time.

[0140] The error analysis module is responsible for preprocessing the collected sensor data and cross-validating the preprocessed sensor data; based on the cross-validation results and the target error range, the actual error of each acquisition point of the preprocessed sensor data is calculated and an error analysis report is generated; the results of the error analysis report are compared with the BIM model to show the differences between the actual components and the design model;

[0141] The adjustment and correction module is responsible for formulating correction or adjustment measures for each collection point whose error in the error analysis report exceeds the acceptable range based on the comparison results.

[0142] The working principle and beneficial effects of the above technical solution are as follows: the equipment layout module of this embodiment selects key collection points according to the component layout and construction process of the BIM model, and the collection points cover areas such as component nodes and connection parts; sensors are installed at the collection points to collect sensor data such as position, angle and size in real time; the error analysis module preprocesses the collected sensor data and cross-validates the preprocessed sensor data; according to the cross-validation results and the target error range, the actual error of each collection point of the preprocessed sensor data is calculated, and an error analysis report is generated; the results of the error analysis report are compared with the BIM model to show the difference between the actual component and the design model; the adjustment and correction module formulates correction or adjustment measures for each collection 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 collection points according to the component layout and construction process of the BIM model to ensure the comprehensiveness and effectiveness of data collection to the greatest extent; sensors are installed at key positions to monitor the position information, angle and size of components in real time to ensure the immediate acquisition of data. Advantages: Compared with traditional methods, this module can select collection points more intelligently and systematically, greatly improving the management level of the construction site; traditional methods often rely on manual observation and recording, this module can perform real-time monitoring and reduce the occurrence of errors and delays. Significance achieved: Through effective data collection, 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 sensor data, and uses cross-validation to enhance the reliability of the data; calculates the actual error of each collection point, and generates a detailed error analysis report, and compares it with the BIM model to analyze the difference between the actual component status and the design model. Advantages: Compared with traditional inspection methods, the use of data-driven error analysis can reflect the actual situation more objectively and scientifically; by systematically generating error analysis reports, managers can quickly locate problems, reduce response time, and improve decision-making efficiency. Significance achieved: Strengthen real-time monitoring of construction quality, and take prompt measures after deviations are found to control construction risks at an early stage; help construction managers make more reasonable decisions based on data, and help promote the construction process to a more efficient and high-quality direction. The adjustment and correction module formulates specific correction or adjustment measures for the collection points that exceed the acceptable range in the error analysis report, including fine-tuning of the construction process or improvement of the method; with the help of real-time data feedback, the construction plan can be updated and adjusted in time to ensure construction accuracy. Advantages: Compared with traditional methods, this module provides a more flexible response mechanism that can respond to problems that arise during the construction process in a timely manner; through continuous data collection and feedback, a closed-loop management is formed to improve overall construction efficiency and quality.Significance achieved: Correct errors in real time to ensure that the component status meets the design requirements and prevent subsequent problems caused by quality risks; each correction process can provide rich data accumulation and lessons learned for future construction, which helps to build a knowledge base for intelligent management.

[0143] In summary, this embodiment realizes comprehensive intelligence from data collection, analysis to decision-making through a series of modular functions such as equipment layout, error analysis and correction. Compared with traditional management solutions, it has higher accuracy, real-time and flexibility, helps to improve construction efficiency and quality, and reduce the probability of errors and safety hazards. It has far-reaching significance for the intelligent transformation of the entire 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 belong to the scope of equivalent technologies of the present invention, the present invention is also intended to include these modifications and variations.

Claims

1. A method for intelligent management of prefabricated buildings based on BIM model, characterized in that: The following steps are involved: According to the component layout and construction process of the BIM model, select key collection points, which cover the component nodes and connection parts; install sensors at the collection points to collect sensor data of position, angle and size in real time; Preprocessing the collected sensor data and cross-validating the preprocessed sensor data; According to the cross-validation results and the target error range, the actual error of each acquisition point of the preprocessed sensor data is calculated, and an error analysis report is generated; Compare the results of the error analysis report with the BIM model to show the differences between the actual components and the design model; Based on the comparison results, formulate correction or adjustment measures for each collection point where the error in the error analysis report exceeds the acceptable range.

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

3. The intelligent management method for prefabricated buildings based on the BIM model according to claim 1, characterized in that: The results of the error analysis report are compared with the BIM model. The collected sensor data are subjected to noise filtering, missing data processing and standardization preprocessing to obtain standardized sensor data. Redundant sensor configuration is used to compare sensor data between different sensors.

4. The intelligent management method for prefabricated buildings based on the BIM model according to claim 3, characterized in that: At the same time, the data timestamp of each sensor is scanned, and the sensor data after standardized processing at different collection points are averaged according to the data timestamp to obtain the cross-validated sensor data.

5. The intelligent management method for prefabricated buildings based on the BIM model according to claim 3, characterized in that: 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 classified according to the pre-set target error range. The collection points exceeding the threshold are identified and the error analysis results of each collection point are compiled into a report to centrally display the actual value, theoretical value and its error.

6. The intelligent management method for prefabricated buildings based on the BIM model according to claim 5, characterized in that: The process of identifying the collection points exceeding the threshold includes the following steps: The data collected in real time from the sensor is used to calculate the expected data through the building information model. The actual value of the collected data is subtracted from the theoretical value of the expected data to obtain the absolute error, and the relative error is calculated by the absolute error and the theoretical value. Set an allowable error range, classify the calculated relative errors of each collection point, and obtain the normal range, critical range, and over-threshold range; Create a data record table to list the actual values, theoretical values, absolute errors, relative errors and classification results of all acquisition points; For each collection point, check whether the relative error exceeds the preset threshold range; The threshold points are marked and recorded in the data table.

7. The intelligent management method for prefabricated buildings based on the BIM model according to claim 6, characterized in that: The process of identifying the collection points exceeding the threshold and classifying the calculated relative errors of each collection point includes the following steps: Get the relative error value of the current acquisition point. If the current acquisition point has a historical relative error record, calculate the percentage change between the current error and the last error. If there is no historical record, the change rate is set 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 an acceptable stability threshold; A comprehensive score is made based on the current relative error, rate of change and average relative error. Each factor is summed up according to the weight to form a score value to judge the comprehensive status of the collection point; each collection point is classified according to the set classification criteria.

8. The intelligent management method for prefabricated buildings based on the BIM model according to claim 6, characterized in that: Add a column to each collection point in the data table to record its classification result, and also record the score value and relative error value of this point.

9. The intelligent management method for prefabricated buildings based on the BIM model according to claim 5, characterized in that: The error conditions of key acquisition points are displayed according to their importance, and an error bar chart is used to display important data points where safety components exceed the acceptable range.

10. An intelligent management system for prefabricated buildings based on BIM model, characterized in that: Include: The equipment layout module is responsible for selecting key collection points based on the component layout and construction process of the BIM model. The collection points cover the component nodes and connection parts. Sensors are installed at the collection points to collect sensor data of position, angle and size in real time. The error analysis module is responsible for preprocessing the collected sensor data and cross-validating the preprocessed sensor data; According to the cross-validation results and the target error range, the actual error of each acquisition point of the preprocessed sensor data is calculated, and an error analysis report is generated; Compare the results of the error analysis report with the BIM model to show the differences between the actual components and the designed model; The adjustment and correction module is responsible for formulating correction or adjustment measures for each collection point whose error in the error analysis report exceeds the acceptable range based on the comparison results.

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