Building engineering interaction method and system based on BIM model, and medium

Abstract

The invention relates to the technical field of building data interaction, in particular to a building engineering interaction method and system based on a BIM model and a medium. The method comprises the following steps: collecting construction site environment parameters and structure response data by using a distributed sensor network; preprocessing the data by an edge computing node; generating standardized environment data and key structure indexes; comparing environment threshold values to identify abnormal events; the method comprises the following steps of: displaying spatial positioning and risk levels through a visual interface, receiving a regulation and control instruction input by a user, dynamically adjusting construction parameters of a BIM model, generating an optimized construction progress scheme, comparing the optimized scheme with an original model to identify a construction conflict area, and generating a conflict resolution report. Through the advanced monitoring technology and construction management technology, the response speed and decision-making efficiency of the building engineering project are improved, and the safety guarantee and resource utilization efficiency in the construction process are improved.

Description

Technical Field

[0001] This invention relates to the field of building data interaction technology, and in particular to a building engineering interaction method, system and medium based on BIM model. Background Technology

[0002] In current construction processes, environmental monitoring and structural health assessments often rely on traditional manual inspections and limited sensor data. This method lacks real-time performance and accuracy, failing to promptly identify potential construction risks and making it difficult to effectively guarantee project quality. The limitations of traditional methods are even more pronounced in complex or harsh environments, and they cannot quickly and effectively adjust construction plans during emergency responses. This creates a significant technological gap in anomaly management within construction projects. Furthermore, while existing Building Information Modeling (BIM) technology can provide digital support for the construction process, it is often not fully integrated with real-time data from the construction site, failing to reflect dynamic changes during construction. Particularly in real-time monitoring and adjustment of construction progress, BIM models are mostly used for static design and planning, lacking close linkage with real-time environmental and structural status data. This prevents dynamic optimization based on actual site conditions, resulting in low construction efficiency and insufficient risk control. Summary of the Invention

[0003] Therefore, it is necessary to provide a BIM model-based interactive method, system, and medium for building engineering to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, the BIM model-based interactive method for building engineering includes the following steps:

[0005] Step S1: Collect environmental parameter data and structural response data of the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; extract key structural index data from the structural response data;

[0006] Step S2: Compare standardized environmental data with preset environmental thresholds, and determine the sequence of abnormal environmental events based on the comparison results; analyze the stress deviation ratio of key structures based on key structural index data;

[0007] Step S3: Screen abnormal structural locations based on the environmental anomaly event sequence and the stress deviation ratio of key structures; trigger an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations;

[0008] Step S4: Receive control commands input by the user through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction schedule plan;

[0009] Step S5: Compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

[0010] This invention utilizes a distributed sensor network to collect environmental parameter data and structural response data from the construction site in real time, ensuring comprehensive monitoring and assessment of the construction environment. The standardized processing of environmental parameter data by edge computing nodes improves data consistency and efficiency, providing strong support for subsequent analysis. The extraction of key structural indicator data strengthens the quantitative monitoring of structural health, ensuring timely detection of potential risks. Comparison of standardized environmental data with preset thresholds enables rapid identification of abnormal environmental events, ensuring construction safety and timely environmental monitoring. Analysis of key structural stress deviation ratios further reveals potential structural problems, promoting proactive prevention during construction. Based on the screening of abnormal environmental event sequences and stress deviations, the invention accurately locates abnormal structural points, providing clear work priorities for on-site construction personnel. The interactive visualization interface of the intelligent monitoring terminal enhances the intuitiveness of data presentation, enabling users to quickly understand and address potential risks. Three-dimensional spatial positioning information and risk level assessment provide a scientific basis for decision-making. User-input control commands through the interactive interface ensure... This method enables rapid and effective responses to on-site issues, dynamically adjusting construction parameter configurations in the BIM model. This promotes flexible adaptation and real-time optimization of construction plans, generating optimized construction schedules that significantly improve construction efficiency and reduce resource waste. By comparing construction differences between the optimized plan and the original BIM model, it efficiently identifies key construction conflict areas, reducing unnecessary rework and delays. The generated conflict resolution reports provide detailed evidence for subsequent construction plan adjustments, enhancing the accuracy of project management. The entire approach integrates advanced monitoring technology and construction management concepts, improving the responsiveness and decision-making efficiency of building engineering projects, enhancing safety and resource utilization efficiency during construction, thereby optimizing the overall execution quality and delivery speed of building engineering projects. It achieves a higher level of intelligent management, promotes the digital and information-based transformation of the construction industry, enhances the scientific and refined level of future project management, promotes sustainable development and innovation in building engineering, and comprehensively improves the transparency and controllability of project management. It also provides practical case support for the formulation and implementation of industry standards.

[0011] The present invention also provides a BIM model-based building engineering interaction system for executing the BIM model-based building engineering interaction method described above. The BIM model-based building engineering interaction system includes:

[0012] The perception fusion module is used to collect environmental parameter data and structural response data at the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; and extract key structural index data from the structural response data.

[0013] The anomaly detection module is used to compare standardized environmental data with preset environmental thresholds and determine the sequence of environmental anomalies based on the comparison results; it also analyzes the stress deviation ratio of key structures based on key structural index data.

[0014] The anomaly mapping module is used to screen abnormal structural locations based on the sequence of environmental anomaly events and the stress deviation ratio of key structures; it triggers an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations;

[0015] The progress control module is used to receive control commands input by users through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction progress plan.

[0016] The conflict resolution module is used to compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

[0017] This invention achieves comprehensive collection of construction site environmental and structural response data through the introduction of a perception fusion module, improving the accuracy and real-time performance of data collection. The application of edge computing nodes accelerates the standardization of environmental parameter data, enhancing the efficiency of subsequent analysis. The extraction of key structural indicator data provides crucial quantitative evidence for structural health monitoring. The anomaly identification module rapidly identifies abnormal environmental events by comparing standardized environmental data with preset thresholds. Analysis of the stress deviation ratio of key structures further enhances the monitoring and assessment of structural safety. The anomaly mapping module effectively screens out abnormal structural points, providing important points of focus for construction personnel. An interactive visualization interface... The application of the BIM model enables intuitive information expression. The display of three-dimensional spatial positioning information and risk levels provides a basis for decision-making. The progress control module enhances the flexibility of construction management. The real-time response of user control commands can adjust the configuration of construction parameters in a timely manner to adapt to changes on site. The generation of optimized construction progress plans promotes the improvement of construction efficiency. The conflict resolution module realizes intelligent analysis and optimization of construction plans. By comparing the construction differences between the optimized plan and the original BIM model, potential conflict areas can be quickly identified. The generated conflict resolution report provides direction for subsequent construction improvements. Overall, it improves the accuracy and adaptability of building engineering management and enhances the safety and efficiency of project execution.

[0018] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the BIM model-based building engineering interaction method as described in any of the above claims.

[0019] This invention achieves efficient storage and retrieval of BIM model-based interactive methods for building engineering through the design of a computer-readable storage medium. This facilitates deployment and execution on different devices and in different environments. The stored computer program ensures the consistency and reliability of the method execution. The programmed logic and steps improve the intelligence level of construction management, provide real-time monitoring and analysis capabilities for environmental parameters and structural response data, enhance the dynamic adjustment capability of project construction, and improve construction safety and reliability through the identification and response to abnormal environmental conditions during execution. The integrated interactive visual interface provides users with an intuitive operating experience, effectively promoting interaction and communication between users and the system. It supports flexible adjustment of construction parameter configuration, improving resource allocation efficiency. The generated optimized construction scheme promotes construction progress, and the function of automatically identifying construction conflict areas and generating resolution reports provides practical support for construction management, thus comprehensively improving the management efficiency and quality control capabilities of building engineering projects. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the steps of an interactive method for building engineering based on a BIM model.

[0021] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S4.

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0025] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] To achieve the above objectives, please refer to Figures 1 to 2 A BIM model-based interactive method for building engineering includes the following steps:

[0027] Step S1: Collect environmental parameter data and structural response data of the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; extract key structural index data from the structural response data;

[0028] Step S2: Compare standardized environmental data with preset environmental thresholds, and determine the sequence of abnormal environmental events based on the comparison results; analyze the stress deviation ratio of key structures based on key structural index data;

[0029] Step S3: Screen abnormal structural locations based on the environmental anomaly event sequence and the stress deviation ratio of key structures; trigger an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations;

[0030] Step S4: Receive control commands input by the user through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction schedule plan;

[0031] Step S5: Compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

[0032] This invention utilizes a distributed sensor network to collect environmental parameter data and structural response data from the construction site in real time, ensuring comprehensive monitoring and assessment of the construction environment. The standardized processing of environmental parameter data by edge computing nodes improves data consistency and efficiency, providing strong support for subsequent analysis. The extraction of key structural indicator data strengthens the quantitative monitoring of structural health, ensuring timely detection of potential risks. Comparison of standardized environmental data with preset thresholds enables rapid identification of abnormal environmental events, ensuring construction safety and timely environmental monitoring. Analysis of key structural stress deviation ratios further reveals potential structural problems, promoting proactive prevention during construction. Based on the screening of abnormal environmental event sequences and stress deviations, the invention accurately locates abnormal structural points, providing clear work priorities for on-site construction personnel. The interactive visualization interface of the intelligent monitoring terminal enhances the intuitiveness of data presentation, enabling users to quickly understand and address potential risks. Three-dimensional spatial positioning information and risk level assessment provide a scientific basis for decision-making. User-input control commands through the interactive interface ensure... This method enables rapid and effective responses to on-site issues, dynamically adjusting construction parameter configurations in the BIM model. This promotes flexible adaptation and real-time optimization of construction plans, generating optimized construction schedules that significantly improve construction efficiency and reduce resource waste. By comparing construction differences between the optimized plan and the original BIM model, it efficiently identifies key construction conflict areas, reducing unnecessary rework and delays. The generated conflict resolution reports provide detailed evidence for subsequent construction plan adjustments, enhancing the accuracy of project management. The entire approach integrates advanced monitoring technology and construction management concepts, improving the responsiveness and decision-making efficiency of building engineering projects, enhancing safety and resource utilization efficiency during construction, thereby optimizing the overall execution quality and delivery speed of building engineering projects. It achieves a higher level of intelligent management, promotes the digital and information-based transformation of the construction industry, enhances the scientific and refined level of future project management, promotes sustainable development and innovation in building engineering, and comprehensively improves the transparency and controllability of project management. It also provides practical case support for the formulation and implementation of industry standards.

[0033] In this embodiment of the invention, the BIM model-based building engineering interaction method includes the following steps:

[0034] Step S1: Collect environmental parameter data and structural response data of the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; extract key structural index data from the structural response data;

[0035] In this embodiment, during the implementation of the BIM model-based interactive method for building engineering, at least 12 environmental monitoring nodes and 8 structural response acquisition nodes are deployed at the construction site through a distributed sensor network. The environmental monitoring nodes include temperature and humidity sensors, noise decibel sensors, PM2.5 particle monitoring sensors, and wind speed and direction sensors, with a sensing frequency set to once every 10 seconds. The structural response acquisition nodes include structural accelerometers and strain gauges, with a measurement interval of 5 seconds. Data is transmitted to edge computing nodes via ZigBee (a self-organizing wireless transmission protocol), and each edge node embeds a high-performance ARMC. The Ortex-A72 architecture processor employs a multi-channel Kalman filter module to smooth environmental parameters. The filter sliding window is set to 12 time steps, generating standardized environmental data format including timestamps, sensor point numbers, and physical quantity values ​​after unit conversion. The units are unified into the International System of Units (SI). Structural response data is extracted for frequency domain features through Fast Fourier Transform (FFT). Based on this, three-dimensional rigid body mechanics is used to generate key structural index data, including the maximum tensile strain of the main beam, nodal torque, and vertical deformation of the floor slab. All indexes are archived and stored with structural numbers.

[0036] Step S2: Compare standardized environmental data with preset environmental thresholds, and determine the sequence of abnormal environmental events based on the comparison results; analyze the stress deviation ratio of key structures based on key structural index data;

[0037] In this embodiment, various physical quantities in the standardized environmental data are compared with preset environmental thresholds. The threshold range is set as follows: temperature 25-45 degrees Celsius, humidity not less than 40%, noise not exceeding 85 decibels, PM2.5 concentration not exceeding 150 micrograms per cubic meter, and wind speed not exceeding 15 meters per second. The comparison method is to set alarm trigger conditions. Once the data exceeds the upper limit, it is marked as abnormal, and an environmental abnormal event sequence is generated. Each event records the abnormality type, trigger time, duration, and sensor point location number. The key structural index data is processed using the structural stress deviation ratio. The theoretical limit value of the principal tensile strain is set to 2000 με. If the field data shows a deviation of more than 5% of the set value, it is considered as a structural stress abnormality. The deviation ratio is calculated as the percentage of the difference between the actual value and the theoretical design value divided by the theoretical design value. The structural number and the deviation ratio are mapped to generate a structural abnormal point data table.

[0038] Step S3: Screen abnormal structural locations based on the environmental anomaly event sequence and the stress deviation ratio of key structures; trigger an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations;

[0039] In this embodiment, the environmental anomaly event sequence and the structural anomaly point data table are input into the structural anomaly screening engine. The screening logic adopts the structural number cross-retrieval method to mark all components that simultaneously exhibit environmental anomalies and structural deviation ratio anomalies. The selected component points are numbered and archived as abnormal structural points. The BIM intelligent monitoring terminal, developed using the Unity engine, automatically updates the interface at a refresh rate of every two seconds, calls the three-dimensional spatial data of the components in the BIM model, and displays the spatial positioning coordinates (X, Y, Z) through the unique identifier of the component. The interface is overlaid with a color heatmap to mark different risk levels. The risk levels are divided into three categories: low, medium, and high, based on the duration of the anomaly and the magnitude of the deviation ratio. The three-dimensional visualization module integrates the WebGL interface, allowing the browser to directly render the model information. The right side of the interface synchronously displays the structural number, risk level, current sensor reading, and standard threshold difference table.

[0040] Step S4: Receive control commands input by the user through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction schedule plan;

[0041] In this embodiment, the component parameter interface is accessed through the BIM visualization interface. The interface has preset control command options including three types of operations: construction time window adjustment, construction path adjustment, and construction rhythm adjustment. Each type of operation has a parameter setting area. The construction time window adjustment range is set to be selectable within ±72 hours. The construction path adjustment allows path changes within a 15-meter range. The construction rhythm adjustment corresponds to a construction beat frequency that fluctuates between 0.5 and 2.0 times. After all commands are input, they are parsed into JSON format data packets by a local script and uploaded to the scheduling system server. After receiving the commands, the server calls the construction plan scheduling processing module. The hybrid and improved optimizer performs a combined search on the construction parameters and finally generates a construction progress simulation scheme. This simulation scheme outputs the start and end times, spatial paths, participating component numbers, required equipment and manpower ratios for each construction task with an hourly time step.

[0042] Step S5: Compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

[0043] In this embodiment, the optimized construction progress simulation scheme is compared with the standard construction progress data in the original BIM model. A spatial structure mapping comparison method is used to match components one-to-one. The matching criteria are identical component numbers, time window intervals less than 72 hours, and construction path start-end coordinate deviations less than 0.5 meters. A progress comparison data framework is generated, with the component information structure consisting of six fields: component number, original construction time, adjusted construction time, original spatial path, adjusted path, and offset value. Component unit pairs that spatially overlap within the same time window are filtered, and the three-dimensional overlap volume is calculated. Volumes exceeding 20 cubic centimeters are marked as potential conflicts. All potential conflict component pairs generate a conflict unit set, arranged in chronological order. A construction dependency path table is constructed based on the component construction sequence and cross-path conditions. Each path entry records the preceding and following construction sequence, duration, and path length. If a path is affected by a conflict for more than 24 hours or if the path intersection affects more than two paths, it is marked as a critical conflict segment. The critical conflict segment is mapped back to the spatial location of BIM components to form critical conflict area map data, which is highlighted in the model. Based on three preset intervention rules, including construction sequence replacement, spatial avoidance offset, and construction node decomposition and merging, automatic strategy recommendations are made for critical conflict segments. The strategy recommendations are indexed by component number and output the intervention method and adjustment period. All recommendations are summarized into a conflict resolution strategy list data. Finally, the list is embedded in a preset intervention report template, which includes a 3D model of the component, an adjustment timetable, and an impact path diagram. The report data is exported in PDF format as conflict resolution report data for the construction management system to call and display.

[0044] Preferably, step S1 includes the following steps:

[0045] Step S11: Perform multi-channel data streaming on distributed sensing terminals located at different spatial levels within the construction site. The data acquisition cycle should not exceed 5 seconds. The temperature range should be -20℃ to 60℃, the humidity range should be 10%RH to 95%RH, the wind speed range should be 0.1m / s to 15m / s, and the particulate matter concentration range should be 0μg / m³. 3 Up to 500 μg / m 3 And aggregate and generate environmental parameter data;

[0046] Step S12: Simultaneously acquire response data for each structural node, including acceleration range of ±16g, strain range of ±10000με, and displacement range of ±50mm, and integrate them to generate structural response data;

[0047] Step S13: Transmit the environmental parameter data to the edge computing node and use a smoothing filter to remove abnormal fluctuations. The filtering operation uses a window of 11 data points to process the signal edge transitions and generate environmental filter data.

[0048] Step S14: Based on preset monitoring standards, adjust various parameters in the environmental filter data to a uniform range to generate standardized environmental data;

[0049] Step S15: Divide the original structural data into blocks of 512 points each, with an interval of 128 points between each block, to generate structural response fragment data;

[0050] Step S16: Perform feature filtering on the structural response segment data, retain the part of the signal that reflects the main force situation, compress the secondary information, and obtain the structural feature data;

[0051] Step S17: Compare the structural feature data with the standardized environmental data, remove the parts affected by environmental changes, extract the key structural change signals, and finally generate key structural indicator data.

[0052] In this embodiment, in the BIM model-based interactive method for building engineering, distributed sensing terminals located at different spatial levels on the construction site employ a multi-channel concurrent acquisition method. The terminal sensor nodes are uniformly connected to a local edge acquisition gateway via an RS485 bus structure. Each terminal integrates a temperature and humidity sensor, a laser particle concentration meter, and an ultrasonic anemometer. The sampling period is 4 seconds. The sensor acquisition temperature values ​​are limited to -20℃ to 60℃, humidity to 10%RH to 95%RH, wind speed to 0.1m / s to 15m / s, and PM2.5 and PM10 particulate matter concentrations to 0μg / m³. 3 Up to 500 μg / m 3During each data collection cycle, each terminal node automatically aggregates the sampled values ​​and uploads the data to the edge processing node via the MODBUS protocol. The node then integrates all environmental data by aligning them with timestamps, forming environmental parameter data. This integrated environmental data is encapsulated in JSON format, with fields including timestamp, sensor_id, temperature, humidity, wind_speed, PM2.5 concentration, and PM10 concentration. Each monitoring node on the construction structure synchronously collects structural response data using independent MEMS triaxial accelerometers, resistance strain gauges, and laser displacement gauges. The accelerometer range is set to ±16g, the strain data sampling range to ±10000με, and the displacement range to ±50mm. All three types of sensor data are acquired at a 2kHz sampling frequency. The acquisition and control unit packages and combines the response data from different dimensions into a structural response... Each frame of structural response data includes structural node number, sampling timestamp, three-dimensional acceleration array, axial strain array, and instantaneous displacement value, ultimately forming a structural response data set. All environmental parameter data transmitted to the edge computing nodes first enters the data preprocessing unit through the data caching module. The processing unit uses a weighted moving average method to filter each channel. An independent data caching window is established for each environmental parameter, with a window length set to 11 consecutive time points. The calculation process assigns the highest weight to the middle time point and the median weight to the edge time point. The sliding window is used to extract 11 data segments from the data stream one by one for processing. The filtering result forms a continuous and smooth environmental filtered data stream, which is used for subsequent standardization processing. The filtered environmental data is normalized separately. A fixed ratio mapping rule is used to normalize temperature from [-20, 60] to [0, 1], humidity from [10, 95] to [0, 1], wind speed from [0.1, 15] to [0, 1], and PM2.5.PM5 and PM10 are normalized from [0,500] to [0,1]. A linear mapping function is set for each parameter. After processing, standardized environmental data with uniform dimensions are output. The normalized data fields remain consistent with the original data fields, and only the numerical interval is transformed before being passed to the subsequent structural analysis module. The structural response data processing part divides the response data of each structural node into blocks of 512 sampling points, with an overlap interval of 128 sampling points between each block. After the blocks are divided, structural response fragment data are formed. Each fragment data is encapsulated in NumPy array format. The structure is 3D acceleration × 512 points, strain × 512 points, and displacement × 512 points. The fragment data is used as the input source for feature extraction. PCA (Principal Component Analysis) is used on the structural response fragment data. The (Analysis) method is used for feature dimensionality reduction analysis. First, each segment is zero-mean processed. Then, the covariance matrix is ​​calculated, and principal component vectors with a cumulative contribution rate of over 95% are extracted. Dimensionality reduction is performed independently for the three-dimensional acceleration and strain channels, while the displacement channel retains its original dimension. This process generates structural feature data, represented by compressed principal component value vectors, effectively reducing redundant information interference. The dimensionality-reduced structural feature data is compared with standardized environmental data using timestamps. Data samples within the synchronous time window are selected for analysis. The correlation coefficient between each set of structural feature values ​​and its corresponding environmental parameters is calculated. If the absolute value of the correlation coefficient is greater than 0.7, the structural feature is determined to be an environmentally induced term and is removed. Only structural change features weakly correlated or uncorrelated with environmental disturbances are retained. The filtered feature vectors are then recombined into key structural indicator data, which are ultimately used to determine structural anomaly locations and as input for risk location operations in subsequent interactive modeling modules.

[0053] Preferably, step S2, which involves comparing standardized environmental data with a preset environmental threshold and determining the sequence of abnormal environmental events based on the comparison results, includes:

[0054] A multidimensional comparison matrix is ​​constructed based on standardized environmental data and preset environmental thresholds;

[0055] Determining the environmental deviation vector based on a multidimensional comparison matrix;

[0056] Extract the temporal features of the deviation from the environmental deviation vector;

[0057] Identify the duration and amplitude of exceeding limits in the environmental deviation vector based on deviation time-series features;

[0058] An environmental anomaly level index is assigned based on the preset anomaly level judgment data, corresponding to the duration of the out-of-limit period and the amplitude of the out-of-limit fluctuation.

[0059] Based on the environmental anomaly level index, standardized environmental data is matched with abnormal environments and integrated according to the time axis to construct a sequence of environmental anomaly events.

[0060] In this embodiment, during the construction of a multidimensional comparison matrix between standardized environmental data and preset environmental thresholds, the standardized environmental data is axially aligned with the corresponding upper and lower limits of environmental thresholds according to four dimensions: temperature, humidity, wind speed, and particulate matter concentration. The preset environmental thresholds are generated from monitoring statistics of the past 10 years of construction in the historical construction area. The upper and lower limits for temperature are 10℃ and 40℃, for humidity are 30%RH and 90%RH, for wind speed are 0.5m / s and 12m / s, and for particulate matter concentration are 35μg / m³. 3 With 300 μg / m 3Each parameter is recorded sequentially every 5 seconds. The matrix is ​​structured with timestamps horizontally and environmental monitoring dimensions vertically. A 4x4 N-column comparison matrix is ​​constructed using the NumPy library (Numerical Python), where N represents the number of all time points within the current monitoring period. Environmental data is filled into the matrix according to the dimensions, and whether it falls within a threshold range is recorded. If it falls within the threshold range, it is marked as 0; otherwise, the deviation is calculated based on the proportion of deviation and marked as a positive or negative number to indicate the direction and degree of deviation. In the operation of determining the environmental deviation vector based on the multidimensional comparison matrix, each column of the matrix is ​​used as a set of environmental states at a given time point. The deviation values ​​corresponding to each dimension in each column are calculated, and the deviation values ​​are rearranged into a time series form, constructing four deviation vectors of length N, representing temperature deviation, humidity deviation, wind speed deviation, and particulate matter deviation, respectively. In the operation, the deviation value is calculated by dividing the difference between the actual monitored value and the center value of the corresponding threshold interval by the half-width of the interval. The result ranges from [-1, 1]. An absolute value close to 1 indicates a serious deviation from the center value. This forms a deviation vector matrix with a 4-row, N-column structure, facilitating subsequent time-series analysis. In the operation of extracting the time-series characteristics of the deviation from the environmental deviation vector, a sliding window method is used to process the deviation vector. Each window has 12 data points and a corresponding time length of 1 minute. The sliding window slides one point at a time. Within each window, the duration of positive and negative deviations, the maximum deviation amplitude, the mean deviation degree, and the standard deviation change amplitude are statistically analyzed using the SciPy library (Scientific... The signal processing module in Python (a scientific computing Python library) performs filtering and feature extraction operations on the deviation vector. All extracted features are recorded in matrix form. In the operation of identifying the duration and amplitude of exceeding limits in the environmental deviation vector based on the deviation time series features, conditional judgments are made on the statistical results within each time window. If the consecutive positive or negative deviations exceed 8 points, and the average deviation exceeds 0.6 and the standard deviation exceeds 0.2, it is marked as a duration exceeding limits. At the same time, its start and end times are recorded. All time periods that meet the conditions are merged. If the interval between them is less than 6 points, they are merged into the same time period. The fluctuation amplitude is defined as the maximum deviation within the time period. Subtracting the minimum deviation value from the initial value, all identified out-of-limit periods and corresponding fluctuation amplitudes are compiled into a structured record array. In the process of assigning environmental anomaly level indices corresponding to the out-of-limit duration and fluctuation amplitude based on preset anomaly level judgment data, the preset anomaly level table includes three levels of classification standards: Level 1 anomaly standard is a duration greater than 60 seconds, fluctuation amplitude exceeding 1.2, and average deviation exceeding 0.8; Level 2 anomaly is a duration between 30 and 60 seconds, fluctuation amplitude between 0.8 and 1.2, and average deviation between 0.5 and 0.8; Level 3 anomaly is a duration between 10 and 30 seconds, fluctuation amplitude between 0.4 and 0.8, and average deviation between 0.3 and 0.Between 5, all identified out-of-limit time periods are assigned a level value according to the above standard, with level values ​​set to 3, 2, and 1 respectively, forming a time series anomaly level vector and synchronously recording the corresponding parameter dimensions. Based on the environmental anomaly level index, standardized environmental data is matched for anomaly environment matching and integrated according to the time axis to construct an environmental anomaly event sequence. First, the environmental anomaly level vector is scanned to identify time periods with consecutive levels not equal to 0, determining their start and end positions, and extracting standardized environmental data within the corresponding time periods to construct a complete anomaly event record. Each record includes the anomaly start and end time, influencing parameter dimension, anomaly level, original parameter value, and deviation value. All records are arranged in ascending order of timestamps to construct the final environmental anomaly event sequence.

[0061] Preferably, step S2, analyzing the stress deviation ratio of the key structure based on the key structural index data, includes:

[0062] Reconstruct the key structural framework based on key structural indicator data;

[0063] Identify the center of gravity of the key structural framework;

[0064] Based on the center of gravity of the structural frame, the local center of gravity coordinates of each component in the key structural frame are inferred;

[0065] Analyze the centroid deviation of key structures based on the coordinates of the centroid of the structural frame and local centroids.

[0066] The structural eccentric force is derived based on the pre-set structural gravity and the deviation of the center of gravity of the key structure.

[0067] The system integrates the structural gravity and eccentric force of each structure, and analyzes the additional load data caused by the structural gravity and eccentric force.

[0068] Identify the structural connection topology data in the key structural framework, and connect the additional load data corresponding to adjacent structures based on the structural connection topology data to construct a stress transfer network;

[0069] The stress transmission deviation between adjacent network nodes is analyzed based on the stress transmission network, and mapped to the stress deviation between adjacent structural points to generate the structural stress deviation ratio.

[0070] In this embodiment, during the reconstruction of the key structural frame based on key structural index data, the key structural index data extracted from the BIM model is aligned according to the unique component codes. Data fields include component type, spatial coordinates, component dimensions, material type, and load-bearing capacity. Each data item corresponds to a component node. Using a 3D modeling engine such as the Model Derivative API in Autodesk Forge, these structural nodes are reconstructed in 3D vector form. The reconstruction method involves combining spatial coordinates and component dimensions into a transformation matrix, and then combining them according to component type rules to construct a continuous beam-column-wall assembly. All components are represented in a Cartesian coordinate system in 3D space. During the combination process, it is ensured that the overlap deviation of beam-column node intersections in the X and Y axes does not exceed 5 mm. A structural topology diagram is generated using line segment connections. In this diagram, each node represents an endpoint of a structural component, and edges represent the actual connection relationships between components. During the identification of the structural frame centroid of the key structural frame, the... The reconstructed 3D critical structural model underwent node centroid extraction. A volume-based mass centroid calculation method was employed, using component dimensions and material density information to calculate the self-weight of each component, expressed in kg. The centroid coordinates of the entire structure were obtained by multiplying the component's center position by its mass and summing the results, then dividing the sum by the total mass. These centroid coordinates were stored as floating-point numbers in meters, represented by the origin of the building reference coordinate system. The floating-point error for each dimension was controlled within 0.0001 meters. Based on the centroid of the structural frame, the local centroid coordinates of each component within the critical structural frame were inferred. During the operation, a component type grouping strategy was adopted. Different types of components corresponded to different center of gravity offset rules. For beam components, the midpoint of the cross section was used as the center of gravity coordinate; for column components, the midpoint of the height direction plus half the cross section height was used as the center of gravity coordinate; for wall components, the midpoint of the overall thickness plus offset coefficients in the length and height directions were used for correction. The offset coefficients were determined by the wall material and density. The offset ratio was 0.5 for reinforced concrete structures and 0.35 for lightweight brick walls. After calculation, the local center of gravity coordinates of all components were converted into absolute spatial coordinates relative to the building reference point, and a mapping between component ID and center of gravity coordinates was established. In the process of analyzing the center-of-gravity deviation of key structures based on the coordinates of the center of gravity of the structural frame and its local center of gravity, the vector difference between the local center-of-gravity coordinates of each component and the overall center-of-gravity coordinates of the structural frame is calculated in the X, Y, and Z directions to obtain the three-dimensional offset. The unit of the offset value is meters. If the offset value exceeds 0.05 meters, it is marked as a component with significant center-of-gravity offset. All offset components are grouped according to the offset direction to generate a set of three-dimensional offset vectors. At the same time, the mass value of the corresponding component is recorded. In the process of deriving the eccentric force of the structure based on the preset structural gravity and the center-of-gravity deviation of key structures, the gravitational acceleration is first assumed to be 9.8 m / s². 2As a constant, the mass value of each component is multiplied to obtain the component's weight value, in Newtons. The weight value is then cross-multiplied with the component's offset vector to obtain the eccentric moment exerted by the component on the overall structural frame. The sum of the eccentric moments of all components yields the total structural eccentric force vector, representing the overall degree of eccentricity caused by uneven mass distribution. This vector result is in Newton-meters (N·m), with the direction indicating the offset direction and the modulus indicating the degree of offset. In the process of integrating the structural weight and eccentric force of each structure and analyzing the additional load data caused by the structural weight and eccentric force, the weight value of each component is first used as the basic load item. The eccentric forces of each component are then superimposed according to structural levels to form hierarchical additional loads. These additional loads are decomposed using the finite element force distribution method and the OpenSees platform (Open System for Earthquake Engineering). Simulation (an open system simulation platform for earthquake engineering) is used to simulate the stress on nodes. All nodes are subjected to foundation gravity and additional loads. The simulation analyzes the load redistribution state of the structure under static center-of-gravity shift, outputting the force variation data of each component node in each direction in Newtons. The time step is set to 0.1 seconds, and the total simulation time is 30 seconds. The process involves identifying the structural connection topology data in the key structural frame and connecting the additional load data of adjacent structures based on this topology data to construct a stress transfer network. First, the connection boundary information of structural components is extracted. The connection relationship is determined based on the spatial adjacency of the component endpoints, with a connection threshold set to 3 cm. If the distance between two component endpoints is less than this value, they are considered connected. The connection matrix composed of component IDs and connection IDs is recorded. In the topology network structure, each node is a component endpoint, and each edge is a force transfer path. A mechanical network model is constructed using NetworkX (a network analysis library), connecting the previous order... The simulated additional load values ​​in the segment are attached to each node, and the direction and value of the additional load transmission are recorded. A structural stress transmission network diagram is constructed. Based on the stress transmission network, the stress transmission deviation of adjacent network nodes is analyzed and mapped to the stress deviation of adjacent structural points. In the process of generating the structural stress deviation ratio, for any two connected nodes in the network, if the difference in additional load between the two nodes exceeds 10%, it is marked as a significant stress transmission deviation point. All node pairs are compared cyclically, and their deviation percentages are recorded. The deviation percentage is calculated by subtracting the smaller additional load from the larger additional load and then dividing by the average additional load value. The deviation percentage is output in floating-point form. The deviation values ​​of all structural points and their structural numbers form a stress deviation ratio table. At the same time, this ratio is mapped back to the corresponding components in the BIM model. The deviation level of each component is represented by red, yellow, and green. The final output is a structural stress deviation ratio dataset, which is synchronously updated to the database module of the building interaction platform for visualization and interactive query.

[0071] Most importantly, the structural eccentric force is derived based on the pre-set structural gravity and the deviation of the center of gravity of key structures, including:

[0072] The preset structural gravity is projected onto the critical structural center of gravity deviation data, and a mass impact analysis is performed to obtain the mass impact of the center of gravity deviation.

[0073] Estimate the offset force vector based on the mass effect of the center of gravity deviation and the local center of gravity coordinates;

[0074] The estimated offset force vectors of each component are uniformly projected onto the center of gravity of the structural frame to obtain the resultant force vector of the center of gravity of the structure.

[0075] Based on the resultant force vector of the structural center of gravity, a differential force field is reconstructed on the preset structural gravity to obtain the eccentric force trend of the structure.

[0076] Force field mapping is applied based on the eccentric force trend of the structure to derive the eccentric force of the structure.

[0077] In this embodiment, when constructing the structural eccentricity force derivation module, the preset structural gravity is spatially projected. This is done based on the gravity parameters of each component in the Building Information Modeling (BIM), calculated from the component's volume and material density. All component gravity is uniformly converted into a vector group pointing downwards along the Z-axis. Each gravity vector is mapped to the critical structural center-of-gravity deviation position in the BIM model. The critical structural center-of-gravity deviation data consists of the center-of-gravity coordinate difference extracted from deformation monitoring during construction. Each component's gravity vector is used to calculate its impact on the overall center-of-gravity deviation based on the offset distance between its center-of-gravity coordinates and the overall structural reference center-of-gravity. The mass impact calculation is based on the dot product of mass multiplied by the length of the center-of-gravity offset vector, ultimately generating a center-of-gravity deviation mass impact data table. This projection operation must be performed on all components, and the mass of a single component must not be less than 25 kg, and the center-of-gravity offset value must not be less than 2. The result is a 3D mass influence vector dataset, measured in millimeters. Based on the influence value of each component and the local center of gravity coordinates of the component in the mass influence dataset, the offset force vector of the component is estimated using the vector offset construction method. The specific operations include: constructing a mass influence vector for each component's center of gravity mass influence value. The direction of this vector is the component's center of gravity offset direction, and its magnitude is the component's mass value multiplied by the offset length. Then, according to the component's local coordinate system, this vector is converted into a local coordinate offset force vector. All offset force vectors are normalized using a uniform scale to ensure comparability between components with different dimensions. The standard Euclidean vector normalization method is used, and the normalization coefficient is taken as the reference value of the maximum offset vector magnitude. All vectors are restricted to 0 within the numerical range.Between 0.5 and 80 Newtons, a complete set of component offset force vectors is obtained. These vectors are then projected onto the overall center of gravity of the structural frame. A spatial coordinate transformation matrix method is used: all component offset force vectors are first transformed from their local coordinate systems to the global coordinate system, and then, with the overall center of gravity of the structure as the origin, vector coordinate translation is performed to obtain the offset force vector data after unified projection of the center of gravity. Each component's projected vector is represented in three-dimensional coordinates, and the total resultant force vector is calculated using vector superposition. The three-dimensional resultant force vector includes components in the X, Y, and Z axes, and the value of the total resultant force vector is limited to between 800 and... Within the 16000 Newton interval, the direction of the resultant force vector can be used to determine the overall force offset trend of the structure. The vector data results are exported in CSV structural force field dataset, containing four items: component number, original offset vector value, transformed coordinates, and resultant force vector projection value. Using the resultant force vector of the structural center of gravity as the reference source data, a differential force field reconstruction operation is performed on the structural gravity data recorded in the BIM model. The operation logic is based on the structural force field simulation engine, constructing a structural gravity vector field in three-dimensional space, and then inserting the resultant force vector of the structural center of gravity as a disturbance source to perform a differential operation on the gravity field. The differential method adopts the central difference method, that is, the force state of each structural unit node is recalculated. The force is calculated by adding the self-gravity vector to the disturbance vector. The magnitude of the disturbance vector is reduced inversely proportional to the square of the spatial distance from the node to the center of gravity of the structure. The minimum disturbance force is set to 1 Newton, and the maximum disturbance force must not exceed 100 Newtons. The force state of all disturbed nodes is redefined in three-dimensional vector form and output as a structural eccentric force trend map in a visual form. Based on the generated structural eccentric force trend map, a force field mapping operation is applied. The force field mapping is based on vector field transformation technology. First, the force concentration area is marked in the structural force trend map. Each concentration area must meet the condition that the local force vector density is higher than the average density value by 1.5 times. Then, a unit disturbance is applied with the area as the center. The force vectors are analyzed, and the changes in the force vector responses of surrounding components are observed. The force field mapping relationship is established based on the tensor transmission relationship between disturbance and response. Finite element method (FEM) tools are used to simulate force transmission. Each force field response mapping must be labeled with the starting component number, force direction vector, responding component number, response displacement direction, and value. After all mappings are completed, the final structural eccentric force vector is derived based on the mapping relationship. The magnitude of the eccentric force vector must not be less than 5 Newtons and not more than 1000 Newtons, and its positional accuracy in the structural force model must be controlled within 5 millimeters. All eccentric force vectors are finally summarized into a structural eccentric force mapping data table, along with a 3D structural node force diagram display file.

[0078] Most importantly, the data on the structural gravity and eccentricity of each structure are integrated, and the additional loads caused by the structural gravity and eccentricity are analyzed, including:

[0079] The overall load of the structure is obtained by performing a coupled synthesis process based on the structure's own gravity and structural eccentric force for each structure.

[0080] Mapping the stress state of local components based on the overall structural load;

[0081] Based on the local component stress state, the local centroid coordinates are fitted with the stress direction to generate the component offset load trajectory;

[0082] Based on preset material properties and component offset load trajectories, the data of the structure's own weight and the additional load caused by structural eccentricity are reconstructed.

[0083] In this embodiment, during the stage of coupling and synthesizing the structure's own gravity and structural eccentric force, it is necessary to calculate the gravity vector of each component based on its geometric volume and material density parameters in the BIM model. The gravity vector is uniformly represented as a unit downward vector in the Z-axis direction, and its starting and ending coordinates are recorded according to the component's position in the three-dimensional coordinate system. The eccentric force vector originates from the previous stage of structural eccentric force derivation and adopts a three-dimensional spatial vector representation, containing both direction and magnitude components. All component gravity vectors and eccentric force vectors are coupled and synthesized using a vector superposition method. The superposition method requires that the two vectors be added in the three-dimensional spatial coordinates to retain the complete vector direction and numerical relationship. The synthesized overall structural load vector must satisfy the following requirements: directional angle error less than 3 degrees, modulus error not exceeding 5 Newtons. The final output data is a set of overall structural load vectors, formatted as a TXT data table with four fields: structural number, gravity vector value, eccentric force vector value, and synthesized load vector value. The overall load direction and distribution are visualized as a GLB (3D model binary format) model diagram. When performing local component stress state mapping based on the overall structural load vector data, the component ID must be used to decompose and project each overall structural load vector to the center of gravity of each component. This operation is based on the spatial coordinates of the components in the BIM model. The load vector undergoes coordinate transformation and is projected onto the component's local coordinate system. All component local coordinate systems are established with the centroid as the origin and the component's principal axis as the X-axis. Load mapping employs the spatial rigid body force projection formula for numerical transformation; that is, the overall load vector is decomposed into three sets of local components along the component's principal axis, lateral axis, and vertical axis. The local force component vector data obtained for each component must completely record its direction, magnitude, projection error, and component spatial positioning information. The mapping error must not exceed 2 Newtons, and the projection direction deviation must not exceed 2.5 degrees. All data is saved in JSON structured format, with recorded fields including component number, global load vector, local coordinate system definition, and projection vectors X, Y, and Z. Information such as component values ​​and error ranges is output as a graphical view of the component's stress state. When performing force direction fitting analysis to generate the component's offset load trajectory for the local stress state of each component, a three-dimensional force vector time series analysis method is required. First, in the local coordinate system of each component, the change path of each projected force vector in the time series is recorded. The starting point of the force vector is taken as the local centroid coordinate, and the ending point is taken as the end point of the load action direction, forming a three-dimensional spatial force action trajectory. Each trajectory segment is continuously recorded with a time step of 0.1 seconds, and the total recording time is not less than 30 seconds. Spline curve interpolation is used to perform continuous curve fitting on the discrete trajectory points. The fitting result requires that the trajectory curvature error not exceed 0.02. Spatial position deviation must not exceed 3 mm. Each component ultimately obtains a complete offset load trajectory curve. The curve data is recorded as an OBJ format 3D line segment model, with an additional CSV format numerical point column. Fields include timestamp, X-axis coordinate, Y-axis coordinate, Z-axis coordinate, and corresponding force magnitude. All trajectories can be used for component spatial deformation trend analysis and stress concentration area identification. After completing the component offset load trajectory fitting, it is necessary to reconstruct the additional load data caused by the structure's own gravity and structural eccentricity by combining preset material property characteristics. Material property characteristics include Young's modulus, Poisson's ratio, density, elastic limit, yield stress, and maximum tensile stress. Each component has bound this type of attribute data in the BIM model. The finite element method (FEM) is used for mechanical analysis. The method simulates the stress-strain state of each component under load. The simulation input consists of offset load trajectory points and material property data. The component model is discretized using eight-node solid elements, with element size limited to no more than 30 mm and a maximum total mesh size of no less than 3000. A concentrated force is applied to each element point according to the direction and magnitude of the force on the trajectory. The system calculates the magnitude and direction of the additional load generated by each element. Finally, all element additional loads are combined into a total additional load vector for the component. All component additional load data are summarized to generate a database of the overall additional loads of the structure. The output format is an SQL database file with component number as the primary key, and additional load vector, element number, and stress-strain values ​​as fields. A VTP (VTK Polydata) format 3D vector map is attached to display the direction and intensity distribution of the additional loads. Color coding in the map indicates the magnitude of the additional load, and the arrow direction indicates the direction of force. The arrow length is linearly proportional to the load magnitude.

[0084] Preferably, step S3, which involves screening abnormal structural locations based on environmental anomaly event sequences and key structural stress deviation ratios, includes:

[0085] Simulate the building environment field based on a sequence of abnormal environmental events;

[0086] Utilize simulated building environment fields to perform architectural engineering simulations on the original BIM model;

[0087] By projecting the critical structural stress deviation ratio onto the corresponding structural connection point in the simulated building project, the adjusted simulated building project is obtained.

[0088] Assess the environmental impact of a series of anomalous environmental events, quantify it into environmental impact parameters, and estimate environmental forces.

[0089] Environmental forces are applied to adjust the simulated building project and the stress deviation ratio at structural connection points is updated;

[0090] By comparing the preset structural stress deviation ratio threshold with the updated stress deviation ratio, when the updated stress deviation ratio is greater than the preset structural stress deviation ratio threshold, the structural connection point corresponding to the updated stress deviation ratio is located and marked as an abnormal structural point.

[0091] In this embodiment, during the simulation of the building environment field based on the sequence of abnormal environmental events, it is necessary to extract environmental parameter sequences from multiple historical building operation monitoring data sources, including wind speed, wind direction, rainfall, air pressure, and temperature data. A sequence prediction model based on Long Short-Term Memory (LSTM) networks is used to predict the time series of external environmental parameters for the next 24 hours. The predicted environmental event sequences are discretized hourly, with each hourly event forming a five-dimensional environmental vector. This vector is input into a three-dimensional grid-based building space simulation module. An external meteorological field is constructed by building cubic grid cells with a size of 0.5 meters. The simulation module uses a CFD (Computational Fluid Dynamics) framework for three-dimensional field calculations, setting the calculation step size to 0.1 seconds, iteratively solving the air pressure and velocity fields, and outputting the wind vector field and thermal gradient field in each iteration cycle, thereby generating three-dimensional dynamic data of the building environment field. During the simulation of the building environment field and the original BIM model for building engineering simulation, the BIM model needs to be imported into the multiphysics simulation platform ANSYS. In Fluent, the external flow field boundary of the building was constructed. Based on the simulated airflow velocity vector and thermal gradient vector, boundary conditions were applied to the building's exterior walls, roof, open structural openings, and ventilation vents. The simulation was performed in Fluid-Structure Interaction (FSI) mode, with a simulation time of 3600 seconds and a solution step size of 1 second. The building structure stiffness parameter was set to an elastic modulus of 2.1 × 10⁻⁶. 11 Pa, Poisson's ratio is 0.3, and density is 7850 kg / m³. 3After the simulation is completed, the stress variation data and nodal displacement data of the structural components during the simulation period are exported. In the process of projecting the critical structural stress deviation ratio onto the corresponding structural connection points in the simulated building project, it is necessary to first extract the component numbers and connection point location information of the critical structure from the BIM model. The stress data of the corresponding numbered components in the simulation results are then matched with the critical structural stress deviation ratio. Three-dimensional Euclidean distance is used for connection point matching, with a distance threshold set to 0.02 meters. If the simulated connection point and the stress deviation ratio data point are successfully matched, the deviation ratio value is mapped to the static offset factor of the simulated connection point, constructing a new offset structure. A force field is constructed and an adjusted simulation building engineering model is generated. This model retains the original BIM topology and physical parameters. When assessing the environmental impact of an abnormal environmental event sequence, a weighted scoring model is used to linearly combine and score five parameters: wind speed, wind direction, temperature, rainfall intensity, and air pressure. The weights are 0.3 for wind speed, 0.2 for wind direction, 0.2 for temperature, 0.2 for rainfall intensity, and 0.1 for air pressure. The obtained scores are standardized to the range [0, 1] and defined as environmental impact parameters. The environmental force is then obtained by multiplying the environmental impact parameters by the area of ​​action and the local wind pressure value in the local environmental field at each connection point. The area of ​​action is calculated from the triangular mesh area of ​​the building surface in the BIM model. The wind pressure value is taken from the average nodal pressure value output by the simulation. During the process of applying environmental forces to adjust the simulated building project, the environmental force vector calculated above is applied to the local coordinate system of each structural connection point. The structural mechanics simulation is re-performed using a finite element solver with a simulation step size of 1 second and a total duration of 3600 seconds. The stress values ​​of the structural nodes in the simulation model are updated. The principal stress direction and magnitude of each connection point are updated through the stress solver, and the stress deviation ratio of each connection point is recalculated according to the mapping relationship. The result is compared with the preset structural stress deviation ratio. When setting the threshold and updating the stress deviation ratio, the structural stress deviation ratio threshold is set to 0.15. That is, the stress deviation ratio of structural connection points is considered abnormal if it is above 0.15. Using the correspondence between structural numbers and connection point indices in the BIM model, all structural connection points with an updated stress deviation ratio exceeding 0.15 are screened. The locations of connection points exceeding the threshold are stored in three-dimensional coordinates and marked as abnormal structural points. At the same time, a highlighted red sphere is added to the corresponding structural point in the BIM model visualization interface. The marker radius is set to 20% of the thickness of the component where the connection point is located, and a text number is attached to identify it, forming an interactive abnormal structural visualization model.

[0092] Preferably, step S3, which involves triggering an interactive visualization interface via an intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural points, includes:

[0093] The intelligent monitoring terminal will automatically push early warning notifications by triggering abnormal structural points, and at the same time adjust the projection of the simulated building project into a three-dimensional building coordinate system.

[0094] Based on the three-dimensional building coordinate system, the abnormal structural points are converted into spatial three-dimensional coordinates to obtain the three-dimensional spatial positioning information of the abnormal structural points.

[0095] Determine the impact range of abnormal structural points in the adjusted simulated building project based on three-dimensional spatial positioning information;

[0096] Risk level assessment of abnormal structural points is conducted based on the scope of abnormal impact to generate risk level assessment results;

[0097] The three-dimensional spatial location information and risk level assessment results of abnormal structural points are output to an interactive visualization interface.

[0098] In this embodiment, during the process of triggering the intelligent monitoring terminal to automatically push early warning notifications through abnormal structural points, it is necessary to set the structural connection point anomaly marker as the trigger signal source. A lightweight communication module based on MQTT (Message Queuing Telemetry Transport) is used to subscribe to a structural anomaly channel with the building project number as the subject. This channel receives alarm data when the stress deviation ratio of the connection point exceeds 0.15. The data format is a JSON string containing fields such as "component number," "3D coordinates," "deviation ratio," and "simulation model index." Once the message is received, the MQTT client writes the anomaly data to the middleware Kafka message queue. The early warning control module driven by Kafka reads the anomaly data and calls the integrated control interface to send a push command to the designated intelligent monitoring terminal. The push command uses HTTP. The data is transmitted via POST, including the alarm level, building ID, and structural point index. Simultaneously, the simulation model containing this structural point is re-established in the BIM 3D platform using the adjusted simulation results to create a new 3D building coordinate system. This new system uses the BIM model origin as a reference point, with the X, Y, and Z axes corresponding to the building's actual geographical location and height, respectively, all in meters. During the conversion of the abnormal structural point into spatial coordinates based on the 3D building coordinate system, the component index information of the abnormal structural point needs to be obtained from the BIM model. The local 3D coordinate position of the component and the geometric parameters of its connecting nodes are then obtained by calling the BIM platform API. This geometric position is mapped to the 3D building coordinate system based on the simulation model using a rigid transformation matrix. The transformation matrix is ​​initialized using a preset spatial alignment method for building model components, including the spatial alignment of control points between the simulation model and the BIM model. For registration, the number of control points should be no less than 10, and the registration error should be controlled within 0.005 meters. Obtain the XYZ coordinates of each abnormal structural point in the 3D building coordinate system, in meters, with three decimal places for spatial positioning information. Based on the 3D spatial positioning information, determine the influence range of the abnormal structural point in the simulated building project. First, treat the abnormal structural point as the center of a spherical influence source, setting the influence radius to 2.5 meters. Construct a spherical envelope region in the 3D building coordinate system. Then, retrieve the component numbers of all components within this region that belong to the same BIM system structural unit as the component containing the abnormal structural point. Use the BIM component topology connection table to index the connection relationships. Each component number corresponds to no less than 3 structural connection points. Extract the spatial location and latest stress deviation ratio of these connection points to determine if they are in a dynamic state. The dynamic change judgment condition is that the stress deviation ratio change rate exceeds 0 per unit time.02. If the conditions are met, the point is marked as an anomalous impact point. All marked points are included in the set of impact ranges of anomalous structural points. This set is used as input for subsequent risk level judgment. In the process of assessing the risk level of anomalous structural points through the anomalous impact range, a multi-factor linear risk assessment model is constructed. The model input includes the deviation ratio of the anomalous structural point body, the number of connection points within the impact range, and the ratio of the maximum stress change rate to the average deviation of the connection points. The weights of these four factors are set to 0.4, 0.2, 0.2, and 0.2, respectively. The assessment results are standardized into a risk score within the interval [0,1] after weighted summation. The score is then divided into three risk levels: 0.0 to 0.33 is low risk, 0.34 to 0.66 is medium risk, and 0.67 to 1.00 is high risk. Finally, the risk level assessment result is formed. Each result is combined with the three-dimensional positioning information to form structural data. The data format is {point ID, coordinates, risk value, risk level, number of affected connection points}. In the process of outputting the 3D spatial location information and risk level assessment results of abnormal structural points to an interactive visualization interface, the WebGL framework Three.js is embedded into the 3D view of the BIM model. An abnormal annotation layer is added to the component coordinate level. This layer uses red, orange, and yellow spheres to represent high, medium, and low risk points, respectively. The size of the spheres is proportional to the component thickness, defaulting to 0.2 times the minimum cross-sectional thickness of the component. When the mouse hovers over a sphere, a tooltip component is bound to display the structural point number, XYZ coordinates, risk value, and risk level text. When the user clicks on a risk point sphere, the backend interface is called to load all abnormal influence connection point data of the simulation model containing that point, and these are connected to the abnormal center point in 3D space with a semi-transparent blue line segment, thus constructing a 3D graphic of the abnormal propagation path. The graphic refresh rate is set to 30 frames per second, supporting the concurrent display of no less than 50 risk points and real-time response to interactive operations.

[0099] Preferably, step S4 includes the following steps:

[0100] Step S41: Receive control commands input by the user through the interactive interface. The user can input construction control commands by drop-down selection, interval sliding and area marking. The construction rhythm control interval is set to 2 to 10 hours per day, and the spatial operation radius adjustment range is 3 to 20 meters.

[0101] Step S42: The input control content includes adjusting the construction sequence of components, re-dividing task zones, and setting the density of construction personnel deployment. The input results are then converted into a structured control instruction set.

[0102] Step S43: Perform a one-to-one mapping and matching between the structured control instruction set and the preset original BIM model component database. The matching conditions include consistent component numbers, matching construction process labels, and the control period falling within the model's set time window. Generate a parameter matching list. The set time window is no earlier than 3 days from the original plan and no later than 5 days from the original plan.

[0103] Step S44: Dynamically update the component construction attributes in the preset BIM model according to the parameter matching list;

[0104] Step S45: Reconstruct the timeline in the BIM model and input the updated component construction attributes into the reconstructed timeline to optimize the construction schedule in the BIM model.

[0105] In this embodiment, when receiving control commands input by the user through the interactive interface, the interactive interface is built on the Vue front-end framework, accesses BIM 3D model data through the Three.js plugin, and is configured with a slider component, a drop-down selection component, and a rectangle annotation component to realize the input operation of construction control commands. The construction rhythm control interval is set in the form of a slider to set the input range. The initial value is set to 8 hours per day, and the left and right ends of the slider correspond to the minimum value of 2 hours and the maximum value of 10 hours, with a step unit of 0.For 5 hours, the spatial operation radius adjustment uses a dual-channel input method: a numerical slider and a visual selection. The numerical slider range is set from 3 meters to 20 meters, with a default initial value of 12 meters. Area annotation is performed by clicking on component nodes in the BIM model interface and stretching them to generate a 3D envelope box. The component number is automatically obtained and stored in the front-end cache area. All user input is submitted to the back-end service interface using a unified JSON structure. Each record contains the fields "Control Type," "Input Method," "Input Value," and "Associated Component Number Set." Input adjustments include changes to the component construction sequence, re-division of task zones, and setting the density of construction personnel. The front-end interface... The above inputs are translated into a structured control instruction set. Construction sequence adjustment is achieved by dragging and dropping components to sort them, inputting the construction priority value of each component. Priorities are integers starting from 1 and not allowed to be duplicated. Task partitioning is redefined by selecting components and batch setting the "Task Partition Number" field value. Deployment density is set using a numerical input box to indicate the number of construction workers per 10 square meters, in person / 10㎡, with a range of 1 to 8 workers. All instructions are converted into structured JSON objects with fields including "Component ID," "Priority," "Partition Number," "Personnel Density," "Control Radius," and "Control Period." Information is uniformly uploaded to the control instruction set buffer. The buffer uses the component ID as the primary key index. When performing a one-to-one mapping and matching between the structured control instruction set and the preset original BIM model component database, the backend system calls the component attribute table in the BIM model component index module, reads the component IDs recorded in the control instruction set one by one, and performs precise matching with the component number field in the BIM model. At the same time, it compares and verifies the construction process label field. The construction process label is named with a unified process code, in the format of "process type-process sequence number", such as "RCF-02" indicating the second stage of concrete pouring. If the two labels match, it is the first stage. The matching criteria further determine whether the time period set by the control command falls within the time window set by the model. The model time window is based on the "planned start date" and "planned end date," allowing no more than 3 days in advance and no more than 5 days in delay. The time judgment is compared using ISO format date strings. All successfully matched records will generate a parameter matching list with "component ID" as the index key. The list is stored in CSV format, with fields including "component ID," "matching status," "work process consistency," "time window validity," and "control type." When dynamically updating the component construction attributes in the BIM model based on the parameter matching list, the Parameter in the Revit API interface must be called.The `SET` function locates the target component in the BIM model database using its component ID, and then writes the corresponding construction attribute values ​​from the control instruction set into the component attributes one by one. If the control type is construction sequence adjustment, it writes the "construction priority" parameter field to the component (integer data type). If the control type is task zoning adjustment, it writes the "construction zoning code" parameter field to the component (string data type, no more than 10 characters). If the control type is personnel density setting, it updates the "personnel density" attribute field (floating-point data type, unit: person / 10㎡). After each attribute update operation, the BIM model version management module is automatically called to generate a new version record and update the metadata table. The metadata record includes "component ID", "modified field", "modification time", and "modified value". When reconstructing the timeline in the BIM model and inputting the updated component construction attributes into the reconstructed timeline, the scheduling and sorting module first reads all updated component "construction priority" and "construction zone code" values. The "Work Zone Code" is used to sort components based on both zone number and priority. Components with the same priority are sorted from west to east and from bottom to top according to their spatial location. The start time and duration of construction for each component are re-arranged. The default duration is calculated by dividing the area of ​​each component by the personnel density and multiplying by the base time coefficient T_base (T_base = 1 hour). For example, if the area of ​​a component is 20㎡ and the personnel density is 4 people / 10㎡, the construction duration is 20 / 4 = 5 hours. The start time is set 0.5 hours after the completion time of the previous component. All reconstructed component time sequence information is merged into a new timeline sequence and synchronously written into the BIM model time control module. The final reconstructed timeline file is output in the form of a Gantt chart, with the timeline unit in hours. The coverage period extends for at least 15 days from the project base date. The time relationships between all nodes are redefined as a combination logic based on component dependencies and control command constraints.

[0106] Preferably, step S5 includes the following steps:

[0107] Step S51: Compare the optimized construction schedule plan with the preset construction schedule plan to establish a progress comparison data framework according to the rules of consistent component numbers, construction time window interval of less than 3 days, and minimum clear distance between components of less than 15 cm.

[0108] Step S52: In the progress comparison data framework, mark the component units that have overlapping positions or intersecting paths within the same time window, construct a three-dimensional overlapping block dataset, and remove interference items with an overlapping volume of less than 20 cubic centimeters to generate a component conflict unit set.

[0109] Step S53: Based on the component conflict unit set, establish a construction dependency path table between components according to the construction logic sequence, and construct construction interference matrix data by combining the construction duration and spatial movement path length of each component, wherein the construction duration is not less than 3 hours and the spatial movement path length does not exceed 20 meters.

[0110] Step S54: Filter out conflict segments from the construction interference matrix data that affect more than two construction paths or have a delay period of more than 1 day, and map them back to their spatial locations in the BIM model through component numbers, marking them as key conflict area map data.

[0111] Step S55: Based on the key conflict area map data, adjust the strategy according to the construction sequence, spatial avoidance plan and construction node rearrangement rules, automatically generate a three-element recommendation item including component number, processing method and adjustment time, and summarize it into a conflict resolution strategy list data;

[0112] Step S56: Embed the conflict resolution strategy list data into the pre-formatted construction intervention report template, attach spatial location screenshots and expected progress impact assessments, and finally generate conflict resolution report data.

[0113] In this embodiment, the optimized construction schedule model and the BIM component models built in the preset construction schedule are loaded into the BIM (Building Information Modeling) platform. Bidirectional data indexing and matching are performed using the unique component number field. Structured Query Language (SQL) is used to retrieve and compare component information in the model database, including component number, component location information, and construction time parameters. When constructing the schedule comparison data framework, three constraints must be set: first, the component numbers must be completely identical; second, the construction time window interval between the original model and the optimized plan must not exceed three days, i.e., if the planned construction time for the optimized component is T1 and the original planned time is T0, then the absolute value of T1 minus T0 must be less than or equal to 3; third, the net distance between components must not exceed fifteen centimeters. Spatial Boolean operations are used to calculate the boundary distance between adjacent components, using a 3D modeling engine such as Navisworks' Clash. The Detective tool performs spatial analysis. After generating the data framework, it calls the 3D spatial Boolean logic operation module to analyze all component unit sets within the same construction time window. Spatial overlap detection is performed in each set. Using the boundary voxel representation of 3D components, a volume intersection matrix is ​​established to determine if the volume of the intersection area exceeds a set threshold of 20 cubic centimeters. The intersection volume is quickly calculated, and items with a volume less than 20 cubic centimeters are marked as interference and removed from the result set. Finally, components with overlapping positions or paths are selected to form a component conflict unit set. Using the component ID list in the component conflict unit set, the components are arranged according to the construction logic order defined in the original construction plan. The spatial paths between adjacent components are extracted. The construction path definition plugin embedded in the BIM model is used to extract the path from the material storage yard to the final positioning point for each component. The calculated path length must not exceed 20 meters. All path information is then processed. The data is represented in quantitative form, generating a path record for each path. Simultaneously, the construction duration of each component is extracted from the plan and recorded in hours, with a minimum duration of three hours. All component paths, durations, and logical order are integrated into an adjacency matrix to construct a construction interference matrix. Each element in this matrix is ​​either 0 or 1, where 1 indicates conflict or interference, and 0 indicates no intersection. After constructing the construction interference matrix, a depth-first search is performed to analyze and filter all conflicting paths in the matrix, determining which component conflicts simultaneously affect more than two independent construction path branches. Under the conditions of simultaneously affecting at least two paths and accumulating construction delays exceeding twenty-four hours, the conflicting component numbers are extracted. Using the component ID, the BIM model is backtracked to extract its corresponding 3D coordinates and component outline geometry, which are then labeled in the 3D model scene and recorded as key conflict area metadata. After constructing the key conflict area metadata...Based on the construction logic sequence adjustment strategy, the original construction flowchart is rearranged, and the order of nodes with conflicting paths is readjusted. Then, the spatial avoidance calculation engine is invoked to redistribute the construction paths and spatial operation points of conflicting components. Simultaneously, the start and end times under the new sequence are fine-tuned using construction node rearrangement rules to ensure that the construction time of the updated components meets the project's global time control constraints. The generated ternary recommendation data includes fields such as component number, handling method (e.g., changing path, delaying operation, replacing component position), and adjustment time. Finally, all ternary recommendation items are organized into a conflict resolution strategy list and embedded with pre-designed construction procedures. The pre-report template, provided by the ReportGenerator module in the BIM interaction platform, includes a report number, project name, a summary of conflicting components, a summary of recommended strategies, screenshots of component spatial locations, and a summary of the expected schedule impact assessment. The screenshot data is generated using the Capture_Viewport function in the BIM engine, which sets 3D observation points and view parameters at the coordinates of key conflicting elements and exports JPG images. The schedule impact assessment field is generated by comparing the construction sequence diagram and the Gantt chart, highlighting components with an impact time exceeding one day in red.

[0114] The present invention also provides a BIM model-based building engineering interaction system for executing the BIM model-based building engineering interaction method described above. The BIM model-based building engineering interaction system includes:

[0115] The perception fusion module is used to collect environmental parameter data and structural response data at the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; and extract key structural index data from the structural response data.

[0116] The anomaly detection module is used to compare standardized environmental data with preset environmental thresholds and determine the sequence of environmental anomalies based on the comparison results; it also analyzes the stress deviation ratio of key structures based on key structural index data.

[0117] The anomaly mapping module is used to screen abnormal structural locations based on the sequence of environmental anomaly events and the stress deviation ratio of key structures; it triggers an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations;

[0118] The progress control module is used to receive control commands input by users through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction progress plan.

[0119] The conflict resolution module is used to compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

[0120] This invention achieves comprehensive collection of construction site environmental and structural response data through the introduction of a perception fusion module, improving the accuracy and real-time performance of data collection. The application of edge computing nodes accelerates the standardization of environmental parameter data, enhancing the efficiency of subsequent analysis. The extraction of key structural indicator data provides crucial quantitative evidence for structural health monitoring. The anomaly identification module rapidly identifies abnormal environmental events by comparing standardized environmental data with preset thresholds. Analysis of the stress deviation ratio of key structures further enhances the monitoring and assessment of structural safety. The anomaly mapping module effectively screens out abnormal structural points, providing important points of focus for construction personnel. An interactive visualization interface... The application of the BIM model enables intuitive information expression. The display of three-dimensional spatial positioning information and risk levels provides a basis for decision-making. The progress control module enhances the flexibility of construction management. The real-time response of user control commands can adjust the configuration of construction parameters in a timely manner to adapt to changes on site. The generation of optimized construction progress plans promotes the improvement of construction efficiency. The conflict resolution module realizes intelligent analysis and optimization of construction plans. By comparing the construction differences between the optimized plan and the original BIM model, potential conflict areas can be quickly identified. The generated conflict resolution report provides direction for subsequent construction improvements. Overall, it improves the accuracy and adaptability of building engineering management and enhances the safety and efficiency of project execution.

[0121] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the BIM model-based building engineering interaction method as described in any of the above claims.

[0122] This invention achieves efficient storage and retrieval of BIM model-based interactive methods for building engineering through the design of a computer-readable storage medium. This facilitates deployment and execution on different devices and in different environments. The stored computer program ensures the consistency and reliability of the method execution. The programmed logic and steps improve the intelligence level of construction management, provide real-time monitoring and analysis capabilities for environmental parameters and structural response data, enhance the dynamic adjustment capability of project construction, and improve construction safety and reliability through the identification and response to abnormal environmental conditions during execution. The integrated interactive visual interface provides users with an intuitive operating experience, effectively promoting interaction and communication between users and the system. It supports flexible adjustment of construction parameter configuration, improving resource allocation efficiency. The generated optimized construction scheme promotes construction progress, and the function of automatically identifying construction conflict areas and generating resolution reports provides practical support for construction management, thus comprehensively improving the management efficiency and quality control capabilities of building engineering projects.

[0123] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0124] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A building engineering interaction method based on BIM model, characterized in that, Includes the following steps: Step S1: Collect environmental parameter data and structural response data of the construction site through a distributed sensor network; Use edge computing nodes to convert environmental parameter data into standardized environmental data; extract key structural index data from structural response data. Step S2: Compare standardized environmental data with preset environmental thresholds, and determine the sequence of abnormal environmental events based on the comparison results; analyze the stress deviation ratio of key structures based on key structural index data; Step S3: Screen for abnormal structural locations based on the sequence of abnormal environmental events and the stress deviation ratio of key structures; The interactive visualization interface is triggered by the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural points. Step S4: Receive control commands input by the user through the interactive interface, and dynamically adjust the construction parameter configuration in the preset BIM model according to the control commands to generate an optimized construction schedule plan; Step S5: Compare the construction differences between the optimized construction schedule plan and the preset construction schedule plan, identify key construction conflict areas, and generate a conflict resolution report.

2. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Perform multi-channel data streaming on distributed sensing terminals located at different spatial levels within the construction site. The data acquisition cycle should not exceed 5 seconds. The temperature range should be -20℃ to 60℃, the humidity range should be 10%RH to 95%RH, the wind speed range should be 0.1m / s to 15m / s, and the particulate matter concentration range should be 0μg / m³. 3 Up to 500 μg / m 3 And aggregate and generate environmental parameter data; Step S12: Simultaneously acquire response data for each structural node, including acceleration range of ±16g, strain range of ±10000με, and displacement range of ±50mm, and integrate them to generate structural response data; Step S13: Transmit the environmental parameter data to the edge computing node and use a smoothing filter to remove abnormal fluctuations. The filtering operation uses a window of 11 data points to process the signal edge transitions and generate environmental filter data. Step S14: Based on preset monitoring standards, adjust various parameters in the environmental filter data to a uniform range to generate standardized environmental data; Step S15: Divide the original structural data into blocks of 512 points each, with an interval of 128 points between each block, to generate structural response fragment data; Step S16: Perform feature filtering on the structural response segment data, retain the part of the signal that reflects the main force situation, compress the secondary information, and obtain the structural feature data; Step S17: Compare the structural feature data with the standardized environmental data, remove the parts affected by environmental changes, extract the key structural change signals, and finally generate key structural indicator data.

3. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S2 involves comparing standardized environmental data with preset environmental thresholds and determining the sequence of abnormal environmental events based on the comparison results, including: A multidimensional comparison matrix is ​​constructed based on standardized environmental data and preset environmental thresholds; Determining the environmental deviation vector based on a multidimensional comparison matrix; Extract the temporal features of the deviation from the environmental deviation vector; Identify the duration and amplitude of exceeding limits in the environmental deviation vector based on deviation time-series features; An environmental anomaly level index is assigned based on the preset anomaly level judgment data, corresponding to the duration of the out-of-limit period and the amplitude of the out-of-limit fluctuation. Based on the environmental anomaly level index, standardized environmental data is matched with abnormal environments and integrated according to the time axis to construct a sequence of environmental anomaly events.

4. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S2, which involves analyzing the stress deviation ratio of key structures based on key structural index data, includes: Reconstruct the key structural framework based on key structural indicator data; Identify the center of gravity of the key structural framework; Based on the center of gravity of the structural frame, the local center of gravity coordinates of each component in the key structural frame are inferred; Analyze the centroid deviation of key structures based on the coordinates of the centroid of the structural frame and local centroids. The structural eccentric force is derived based on the pre-set structural gravity and the deviation of the center of gravity of the key structure. The system integrates the structural gravity and eccentric force of each structure, and analyzes the additional load data caused by the structural gravity and eccentric force. Identify the structural connection topology data in the key structural framework, and connect the additional load data corresponding to adjacent structures based on the structural connection topology data to construct a stress transfer network; The stress transmission deviation between adjacent network nodes is analyzed based on the stress transmission network, and mapped to the stress deviation between adjacent structural points to generate the structural stress deviation ratio.

5. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S3, which involves screening for anomalous structural locations based on environmental anomaly event sequences and critical structural stress deviation ratios, includes: Simulate the building environment field based on a sequence of abnormal environmental events; Utilize simulated building environment fields to perform architectural engineering simulations on pre-set BIM models; By projecting the critical structural stress deviation ratio onto the corresponding structural connection point in the simulated building project, the adjusted simulated building project is obtained. Assess the environmental impact of a series of anomalous environmental events, quantify it into environmental impact parameters, and estimate environmental forces. Environmental forces are applied to adjust the simulated building project and the stress deviation ratio at structural connection points is updated; By comparing the preset structural stress deviation ratio threshold with the updated stress deviation ratio, when the updated stress deviation ratio is greater than the preset structural stress deviation ratio threshold, the structural connection point corresponding to the updated stress deviation ratio is located and marked as an abnormal structural point.

6. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S3, which involves triggering an interactive visualization interface via the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural points, includes: The intelligent monitoring terminal will automatically push early warning notifications by triggering abnormal structural points, and at the same time adjust the projection of the simulated building project into a three-dimensional building coordinate system. Based on the three-dimensional building coordinate system, the abnormal structural points are converted into spatial three-dimensional coordinates to obtain the three-dimensional spatial positioning information of the abnormal structural points. Determine the impact range of abnormal structural points in the adjusted simulated building project based on three-dimensional spatial positioning information; Risk level assessment of abnormal structural points is conducted based on the scope of abnormal impact to generate risk level assessment results; The three-dimensional spatial location information and risk level assessment results of abnormal structural points are output to an interactive visualization interface.

7. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Receive control commands input by the user through the interactive interface. The user can input construction control commands by drop-down selection, interval sliding and area marking. The construction rhythm control interval is set to 2 to 10 hours per day, and the spatial operation radius adjustment range is 3 to 20 meters. Step S42: The input control content includes adjusting the construction sequence of components, re-dividing task zones, and setting the density of construction personnel deployment. The input results are then converted into a structured control instruction set. Step S43: Perform a one-to-one mapping and matching between the structured control instruction set and the preset original BIM model component database. The matching conditions include consistent component numbers, matching construction process labels, and the control period falling within the model's set time window. Generate a parameter matching list. The set time window is no earlier than 3 days from the original plan and no later than 5 days from the original plan. Step S44: Dynamically update the component construction attributes in the preset BIM model according to the parameter matching list; Step S45: Reconstruct the timeline in the BIM model and input the updated component construction attributes into the reconstructed timeline to optimize the construction schedule in the BIM model.

8. The interactive method for building engineering based on BIM model according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Compare the optimized construction schedule plan with the preset construction schedule plan to establish a progress comparison data framework according to the rules of consistent component numbers, construction time window interval of less than 3 days, and minimum clear distance between components of less than 15 cm. Step S52: In the progress comparison data framework, mark the component units that have overlapping positions or intersecting paths within the same time window, construct a three-dimensional overlapping block dataset, and remove interference items with an overlapping volume of less than 20 cubic centimeters to generate a component conflict unit set. Step S53: Based on the component conflict unit set, establish a construction dependency path table between components according to the construction logic sequence, and construct construction interference matrix data by combining the construction duration and spatial movement path length of each component, wherein the construction duration is not less than 3 hours and the spatial movement path length does not exceed 20 meters. Step S54: Filter out conflict segments from the construction interference matrix data that affect more than two construction paths or have a delay period of more than 1 day, and map them back to their spatial locations in the BIM model through component numbers, marking them as key conflict area map data. Step S55: Based on the key conflict area map data, adjust the strategy according to the construction sequence, spatial avoidance plan and construction node rearrangement rules, automatically generate a three-element recommendation item including component number, processing method and adjustment time, and summarize it into a conflict resolution strategy list data; Step S56: Embed the conflict resolution strategy list data into the pre-formatted construction intervention report template, attach spatial positioning screenshots and expected progress impact assessments, and finally generate conflict resolution report data.

9. A building engineering interactive system based on BIM model, characterized in that, For executing the BIM model-based building engineering interaction method as described in claim 1, the BIM model-based building engineering interaction system comprises: The perception fusion module is used to collect environmental parameter data and structural response data at the construction site through a distributed sensor network; convert the environmental parameter data into standardized environmental data using edge computing nodes; and extract key structural index data from the structural response data. The anomaly detection module is used to compare standardized environmental data with preset environmental thresholds and determine the sequence of environmental anomalies based on the comparison results; it also analyzes the stress deviation ratio of key structures based on key structural index data. The anomaly mapping module is used to screen abnormal structural locations based on the sequence of environmental anomaly events and the stress deviation ratio of key structures; it triggers an interactive visualization interface through the intelligent monitoring terminal to display the three-dimensional spatial location information and risk level assessment results of the abnormal structural locations; The progress control module is used to receive control commands input by users through the interactive interface, and dynamically adjust the construction parameter configuration in the BIM model according to the control commands to generate an optimized construction progress plan. The conflict resolution module is used to compare the construction differences between the optimized construction schedule plan and the original BIM model, identify key construction conflict areas, and generate a conflict resolution report.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the BIM model-based building engineering interaction method as described in any one of claims 1 to 8.

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