Hidden engineering information tracing method based on BIM (Building Information Modeling)

Abstract

The invention discloses a BIM (Building Information Modeling)-based hidden project information tracing method, which realizes bidirectional synchronization of a physical entity and a digital model by distributing a unique identifier for a hidden project and binding the unique identifier with a BIM preset path attribute and collecting construction data in real time in combination with the Internet of Things. When the pre-embedded path deviation is detected, the system automatically triggers early warning and generates a correction record, the correction record is updated to the BIM database in real time, and it is ensured that the model data are consistent with the real scene pre-embedded position all the time. Meanwhile, through deep fusion of the live-action three-dimensional model and the BIM, the spatial relation of hidden engineering can be visually presented, construction decision optimization is assisted, the reworking risk is remarkably reduced, the engineering quality and the construction efficiency are improved, reliable data support is provided for building intelligent management, and then the problems that dynamic association is not formed between an existing BIM model and a live-action pre-embedded position, and the construction efficiency is poor are solved. The technical problems that BIM model data records are deviated from actual live-action pre-embedded positions, and pre-embedded full-life-cycle tracing is affected are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of information tracing, and in particular to a concealed engineering information tracing method based on BIM. Background Art

[0002] Concealed works are covered after construction is completed, and their quality directly affects the safety and service life of the building. The traditional management method that relies on paper records or two-dimensional drawings has defects such as non-intuitive data, easy loss, and difficulty in dynamic updating, and it is impossible to achieve seamless connection between the construction process and subsequent operation and maintenance. With the development of technology in the construction industry, BIM models have been widely used in the management of the entire life cycle of buildings due to their three-dimensional visualization and multi-dimensional data integration capabilities, effectively solving the limitations of traditional methods. However, for the tracing of buried cable positions in concealed projects, existing technologies still have key deficiencies: although data records of multiple pre-buried cable positions can be screened through real-life surveys and imported into the BIM model, there is no dynamic association mechanism between the BIM model and the actual pre-buried positions, resulting in deviations between the data stored in the model and the real-life pre-buried positions, which cannot accurately reflect the actual construction status, thereby affecting the traceability efficiency and accuracy of buried cable information throughout the entire life cycle. Summary of the Invention

[0003] The purpose of the present invention is to provide a BIM-based hidden engineering information tracing method to solve the technical problem that the existing BIM model and the real-life embedded position have not formed a dynamic association, resulting in a deviation between the BIM model data record and the actual real-life embedded position, which affects the tracing of the entire life cycle of the embedded project.

[0004] The technical solution of the present invention is achieved as follows:

[0005] A BIM-based hidden engineering information tracing method includes the following steps:

[0006] Step S1: Based on the architectural drawings of the real scene survey, a real scene 3D model is constructed using an auxiliary mapping application;

[0007] Step S2: assigning unique identifiers to multiple hidden projects in the real-scene three-dimensional model according to the real-scene three-dimensional model;

[0008] Step S3: Bind the unique identifier to the preset path attribute in the BIM model to establish a dynamic update relationship;

[0009] Step S4: deploying IoT devices to collect pre-buried path data corresponding to the unique identifier during the construction phase in real time, and comparing the data with the preset path attributes;

[0010] Step S5: If it is detected that the embedded path data deviates from the preset path attributes, an early warning is triggered and a correction record is generated, which is updated to the BIM model database in real time.

[0011] A further technical solution is that the specific steps of step S1 include:

[0012] Step S11: Scanning the architectural drawings using the auxiliary mapping application to collect survey data of space dimensions and structural positions, and generating a survey sketch;

[0013] Step S12: superimposing the survey sketch with the original architectural drawing and adjusting the deviation;

[0014] Step S13: converting the calibrated data into the real-scene three-dimensional model through a modeling function;

[0015] Step S14: Use a manual or semi-automatic plug-in tool to supplement the missing pre-embedded paths and check whether the real-scene 3D model is consistent with the real scene.

[0016] A further technical solution is that the specific execution steps of step S2 include:

[0017] Step S21: extracting the invisible or hidden hidden component data from the real-scene three-dimensional model, and locking the target range through hierarchical classification;

[0018] Step S22: assigning a unique tag to each hidden component data according to the hidden component data type, location coordinates, and hierarchical relationship;

[0019] Step S23: adding a corresponding identifier field to the unique mark of each hidden component data in the real-scene three-dimensional model database and generating a unique identifier, and displaying it through a visual label;

[0020] Step S24: Verify whether the unique identifiers of all the hidden component data are unique and complete, and synchronously update the mapping relationship between the real-scene 3D model and the unique identifier when it is changed.

[0021] A further technical solution is that the specific execution steps of step S22 include:

[0022] Step S221: Split the hidden component data type, location coordinates, and hierarchical relationship into independent fields and combine them into a fixed-format coding structure;

[0023] Step S222: Check whether the coding structure contains duplication or conflict. If duplication is found, adjust the field order or increase the subdivision level.

[0024] Step S223: Generate the unique tag according to the detection result of step S222.

[0025] A further technical solution is that the specific execution steps of step S3 include:

[0026] Step S31: Filter the path attributes related to the unique identifier from the BIM model, extract the field names and data structures thereof as the basis for binding;

[0027] Step S32: Align the encoding segments of the unique identifier with the preset path attribute fields one by one;

[0028] Step S33: In the BIM model, when the preset path attribute field changes, the code segment corresponding to the unique identifier is automatically updated;

[0029] Step S34: Verify whether the binding relationship triggers the expected update by simulating the modification of the preset path attribute or the unique identifier.

[0030] A further technical solution is that the specific execution steps of step S4 include:

[0031] Step S41: deploying IoT sensors on the pre-buried path in the construction area and at breakpoints along the pre-buried path to collect position coordinates, depth, and direction simulation data in real time;

[0032] Step S42: uploading the simulation data to the cloud in real time via a wireless communication module, and uniformly converting the data into a coordinate format compatible with the preset path attributes;

[0033] Step S43: establishing the preset path attribute database in the cloud and corresponding to the simulation data field;

[0034] Step S44: comparing the simulated data with the preset path attributes point by point, calculating the coordinate deviation value of each node, and marking abnormal points that exceed the allowable range;

[0035] Step S45: When the deviation is detected to exceed the preset threshold, an alarm is automatically triggered, and the specific deviation location and correction suggestions are displayed simultaneously;

[0036] Step S46: Synchronously update the preset path attribute database on the cloud according to the actual construction adjustment situation.

[0037] A further technical solution is that the specific execution steps of step S42 include:

[0038] Step S421: Pass the server address, port number and authentication key of the wireless communication module in the sensor device;

[0039] Step S422: Encapsulate the original path data collected by the sensor into a unified format, wherein the unified format includes a timestamp, coordinate value, and sensor ID field, and send the data to the cloud interface in real time through the wireless communication module;

[0040] Step S423: using a coordinate conversion algorithm to convert the unified format into a local coordinate system that matches the preset path attributes, and retaining an error correction coefficient;

[0041] Step S424: Compare the converted unified format with the field requirements of the preset path attributes. If they meet the requirements, update the cloud database. If the deviation exceeds a threshold, trigger a correction process.

[0042] A further technical solution is that the specific execution steps of step S44 include:

[0043] Step S441: extracting the design coordinates of each node from the preset path attributes, and matching them with the corresponding node coordinates in the simulation data in order to form a point-to-point comparison benchmark;

[0044] Step S442: Calculate the coordinate deviation using the sub-dimension and synthesize the total deviation;

[0045] Step S443: Set the layer deviation threshold, automatically mark the abnormal points according to the calculation results, and mark their deviation directions.

[0046] A further technical solution is that the specific execution steps of step S5 include:

[0047] Step S51: According to the deviation type and value, match the preset graded warning rules to determine the warning level and response priority;

[0048] Step S52: Multi-dimensional warning synchronization is triggered through sound and light alarms, mobile terminal push, and cloud log recording;

[0049] Step S53: automatically generate a record containing correction suggestions, responsible party identification, and correction status fields, and bind it to the BIM model node;

[0050] Step S54: Push the correction record in the form of an incremental update package to the BIM model database through the API interface, refresh the abnormal node attributes, and retain the historical version for retrospective comparison.

[0051] A further technical solution is that the specific execution steps of step S51 include:

[0052] Step S511: Establish a hierarchical threshold matrix of deviation types and value ranges, and correspond each combination to a preset warning level and response priority to form a dynamic matching rule library;

[0053] Step S512: Input the calculated deviation type and value into the rule library, match the closest threshold interval, output the corresponding warning level identifier and priority label, and synchronize them to the warning system;

[0054] Step S513: Based on the matching results, the preset response strategy is automatically triggered to allocate resources according to priority.

[0055] The beneficial effects of the present invention are:

[0056] The present invention realizes two-way synchronization between physical entities and digital models by assigning unique identifiers to concealed projects and binding them to BIM preset path attributes, combining with the Internet of Things to collect construction data in real time. When a deviation in the embedded path is detected, the system automatically triggers an early warning and generates a correction record, which is updated to the BIM database in real time to ensure that the model data is always consistent with the real-life embedded position. At the same time, the deep integration of the real-life three-dimensional model and BIM can intuitively present the spatial relationship of concealed projects, assist in the optimization of construction decisions, significantly reduce the risk of rework, improve project quality and construction efficiency, and provide reliable data support for intelligent building management, thereby solving the technical problem that the existing BIM model and the real-life embedded position have not formed a dynamic association, resulting in a deviation between the BIM model data record and the actual real-life embedded position, affecting the full life cycle traceability of the embedded position. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of the steps of a BIM-based concealed engineering information tracing method provided by the present invention;

[0058] Figure 2 A flowchart of step S1 in a BIM-based concealed engineering information tracing method provided by the present invention;

[0059] Figure 3 A flowchart of step S2 in a BIM-based concealed engineering information tracing method provided by the present invention;

[0060] Figure 4 A flowchart of step S3 in a BIM-based concealed engineering information tracing method provided by the present invention;

[0061] Figure 5 A flowchart of step S4 in a BIM-based concealed engineering information tracing method provided by the present invention;

[0062] Figure 6 This is a flowchart of step S5 in the BIM-based hidden engineering information tracing method provided by the present invention. DETAILED DESCRIPTION

[0063] In order to better understand the technical content of the present invention, specific embodiments are provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0064] See also Figures 1 to 6The present invention provides a BIM-based hidden engineering information tracing method, comprising the following steps:

[0065] Step S1: Based on the architectural drawings of the real scene survey, a real scene 3D model is constructed using an auxiliary mapping application;

[0066] Step S2: assigning unique identifiers to multiple hidden projects in the real-scene 3D model according to the real-scene 3D model;

[0067] Step S3: Bind the unique identifier to the preset path attribute in the BIM model to establish a dynamic update relationship;

[0068] Step S4: deploy IoT devices to collect pre-buried path data corresponding to the unique identifier during the construction phase in real time, and compare the data with the preset path attributes;

[0069] Step S5: If a deviation is detected between the embedded path data and the preset path attributes, an early warning is triggered and a correction record is generated, which is updated to the BIM model database in real time.

[0070] In an embodiment of the present invention, the real-scene survey architectural drawings may be digital atlases generated through field measurements, covering master plans, building plans, elevations, structural drawings, and equipment pipelines. The auxiliary drawing application may be BIM drawing software. Concealed components include pre-buried wire segments, junction boxes, insulation layers, buried wire paths, and pipeline directions. The unique identifier may be a QR code or a UUID. The preset path attributes include the material, specifications, and construction time path of the pre-buried components of the concealed project. IoT devices include laser scanners, drones, and sensors. The pre-buried path data includes the depth and bending radius of the pre-buried wires.

[0071] Specifically, the present invention realizes two-way synchronization between physical entities and digital models by assigning unique identifiers to concealed projects and binding them to BIM preset path attributes, and combining the Internet of Things to collect construction data in real time. When a deviation in the embedded path is detected, the system automatically triggers an early warning and generates a correction record, which is updated to the BIM database in real time to ensure that the model data is always consistent with the real-life embedded position. At the same time, the deep integration of the real-life three-dimensional model and BIM can intuitively present the spatial relationship of concealed projects, assist in the optimization of construction decisions, significantly reduce the risk of rework, improve project quality and construction efficiency, and provide reliable data support for intelligent building management, thereby solving the technical problem that the existing BIM model and the real-life embedded position have not formed a dynamic association, resulting in a deviation between the BIM model data record and the actual real-life embedded position, affecting the traceability of the entire embedded life cycle.

[0072] Preferably, the specific steps of step S1 include:

[0073] Step S11: Scan the architectural drawings using an auxiliary mapping application to collect survey data of space dimensions and structural positions and generate a survey sketch;

[0074] Step S12: Overlay the survey sketch with the original architectural drawing and adjust the deviation;

[0075] Step S13: converting the calibrated data into a real-scene three-dimensional model through a modeling function;

[0076] Step S14: Use a manual or semi-automatic plug-in tool to supplement the missing pre-buried paths and check whether the real-scene 3D model is consistent with the real scene.

[0077] In an embodiment of the present invention, architectural drawings are scanned with high precision through an auxiliary mapping application, key spatial dimensions and structural position data are collected, and a preliminary survey sketch is generated; the survey sketch is then overlaid and compared with the original design drawing, and precise alignment of the drawing and real-life data is achieved through coordinate calibration and deviation adjustment; the calibrated data is then converted into a high-fidelity real-life three-dimensional model using a modeling function, completely restoring the architectural spatial structure; finally, missing embedded paths (such as pipelines, steel meshes, etc.) in the BIM model are supplemented through manual or semi-automatic plug-in tools, and the consistency of the model with the actual construction scene is verified in combination with on-site verification, thereby ensuring that the three-dimensional model meets the design specifications.

[0078] Preferably, the specific steps of step S2 include:

[0079] Step S21: extracting invisible or hidden component data from the real-scene 3D model, and locking the target range through hierarchical classification;

[0080] Step S22: assign a unique tag to each hidden component data according to the hidden component data type, location coordinates, and hierarchical relationship;

[0081] Step S23: adding a corresponding identifier field to the unique tag of each hidden component data in the real-scene 3D model database and generating a unique identifier, and displaying it through a visual label;

[0082] Step S24: Verify whether the unique identifiers of all hidden component data are unique and complete, and synchronously update the mapping relationship between the real scene 3D model and the unique identifier when it is changed.

[0083] In an embodiment of the present invention, data of invisible or hidden components are extracted from the real-life three-dimensional model, and the target range is accurately locked in combination with floor-level classification to ensure that subsequent operations focus on the area to be detected; then, a unique tag is assigned to each component based on the component's data type, spatial coordinates, and hierarchical relationship to achieve accurate correspondence between data and physical entities; then, a unique identifier is generated for each tag in the three-dimensional model database, and its position is intuitively displayed through visual labels such as color and icons, facilitating rapid positioning and verification by construction personnel; finally, a verification mechanism is used to ensure that all unique identifiers are non-duplicate and cover all hidden components, and the mapping relationship between the unique identifier and the entity is automatically synchronized when the model is updated to avoid management confusion caused by model changes.

[0084] Preferably, the specific steps of step S22 include:

[0085] Step S221: Split the hidden component data type, location coordinates, and hierarchical relationship into independent fields and combine them into a fixed-format coding structure;

[0086] Step S222: Check whether there is duplication or conflict in the coding structure. If duplication is found, adjust the field order or increase the subdivision level.

[0087] Step S223: Generate a unique tag based on the detection result of step S222.

[0088] In an embodiment of the present invention, the data type, spatial coordinates and hierarchical relationship of the hidden component are split into independent fields, and a standardized "type-coordinate-level" triplet coding structure is constructed to ensure fine data granularity and clear logic; the coding structure is then checked for repeatability and conflict, and potential conflicts are eliminated by adjusting the field order or adding direction attributes after adding coordinates, thereby ensuring the global uniqueness of the coding; finally, a unique tag is generated based on the verified code, which can not only accurately map the physical properties of the component, such as embedded pipeline-3F-01, indicating three layers of embedded pipeline number 01, and steel bar-4F-02 indicating three layers of steel bar number 02, but also establish a one-to-one correspondence with the entities in the three-dimensional model database, providing a reliable basis for digital tracking and construction management of concealed projects.

[0089] Preferably, the specific steps of step S3 include:

[0090] Step S31: Filter the path attributes related to the unique identifier from the BIM model, extract the field name and data structure as the basis for binding;

[0091] Step S32: Align the encoding segments of the unique identifier with the preset path attribute fields one by one;

[0092] Step S33: In the BIM model, when the preset path attribute field changes, the code segment of the corresponding unique identifier is automatically updated;

[0093] Step S34: Verify whether the binding relationship triggers the expected update by simulating the modification of the preset path attribute or the unique identifier.

[0094] In an embodiment of the present invention, the path attribute field associated with the unique identifier is extracted from the BIM model, and the coding segments (such as "material-location-number") are aligned one by one with the preset BIM attribute fields (such as "component type-floor-serial number") to achieve accurate mapping of the data structure; when the path attribute field (such as floor, component type) in the BIM model changes, the system automatically and synchronously updates the corresponding coding segment of the unique identifier to ensure that the identifier is always consistent with the model data; finally, by simulating the modification of the path attribute or unique identifier, it is verified whether the binding relationship can trigger the expected update logic to prevent data deviations caused by field conflicts or mapping errors.

[0095] Preferably, the specific steps of step S4 include:

[0096] Step S41: deploy IoT sensors on the pre-buried paths in the construction area and at breakpoints along the pre-buried paths to collect position coordinates, depth, and direction simulation data in real time;

[0097] Step S42: uploading the simulation data to the cloud in real time via the wireless communication module and uniformly converting it into a coordinate format compatible with the preset path attributes;

[0098] Step S43: Establish a preset path attribute database in the cloud and correspond it to the simulation data field;

[0099] Step S44: compare the simulated data with the preset path attributes point by point, calculate the coordinate deviation value of each node, and mark abnormal points that exceed the allowable range;

[0100] Step S45: When the deviation is detected to exceed the preset threshold, an alarm is automatically triggered, and the specific deviation location and correction suggestions are displayed simultaneously;

[0101] Step S46: Synchronously update the preset path attribute database in the cloud based on the actual construction adjustment situation.

[0102] In an embodiment of the present invention, IoT sensors are deployed on the pre-buried paths and key breakpoints in the construction area to collect analog data such as position coordinates, depth and direction in real time, and upload the data to the cloud through the NB-IoT wireless communication module to ensure that the data format is compatible with the preset path attributes (such as the coordinate system in the BIM model); then, a preset path attribute database is constructed on the cloud, and the sensor data is aligned with the preset fields one by one to achieve standardized storage of data; by comparing the sensor data with the preset path attributes point by point, the coordinate deviation value is automatically calculated and the offset exceeds the limit or the direction deviation is abnormal, triggering a visual alarm and providing correction suggestions simultaneously to assist construction personnel in accurately adjusting the pre-buried path; finally, the cloud database is dynamically updated according to the on-site correction results to ensure that the preset path attributes are consistent with the actual construction status in real time.

[0103] Preferably, the specific steps of step S42 include:

[0104] Step S421: The server address, port number and authentication key of the wireless communication module are passed to the sensor device;

[0105] Step S422: Encapsulate the original path data collected by the sensor into a unified format, which includes a timestamp, coordinate value, and sensor ID field, and send it to the cloud interface in real time through the wireless communication module;

[0106] Step S423: Using a coordinate conversion algorithm, convert the unified format into a local coordinate system that matches the preset path attributes, and retain the error correction coefficient;

[0107] Step S424: Compare the converted unified format with the field requirements of the preset path attributes. If they meet the requirements, update the cloud database. If the deviation exceeds the threshold, trigger the correction process.

[0108] In an embodiment of the present invention, the server address, port number and authentication key of the wireless communication module are configured in the sensor device to ensure the stability and security of data transmission; then the coordinate, depth and direction original path data collected by the sensor are encapsulated into a unified format including timestamp, coordinate value and sensor ID, and uploaded to the cloud interface in real time through the wireless communication module to achieve data standardization and efficient transmission; the high-precision Bursa seven-parameter method is used in the cloud to convert the original data into a local coordinate system that matches the preset path attributes, and retains error correction coefficients such as translation and rotation parameters to compensate for errors caused by sensor deviation or coordinate system differences; finally, by comparing the converted data with the field requirements of the preset path attributes, if they meet the requirements, the cloud database is updated; if the deviation exceeds the allowable range, the repositioning or correction of the sensor parameter correction process is automatically triggered to ensure the consistency of the embedded path data with the design specifications.

[0109] Preferably, the specific steps of step S44 include:

[0110] Step S441: extract the design coordinates of each node from the preset path attributes, and match them with the corresponding node coordinates in the simulation data in order to form a point-to-point comparison benchmark;

[0111] Step S442: Calculate the coordinate deviation using the sub-dimension and synthesize the total deviation;

[0112] Step S443: Set the layer deviation threshold, automatically mark the abnormal points according to the calculation results, and mark their deviation directions.

[0113] In an embodiment of the present invention, the design coordinate points of each node are extracted from the preset path attributes, and point-to-point matching is performed with the corresponding node coordinates in the simulation data collected by the sensor to establish an accurate comparison benchmark; then, the coordinate deviation is calculated using dimensions such as the X / Y / Z axes, and the total deviation value is generated through a vector synthesis algorithm to achieve quantitative analysis of the error; on this basis, hierarchical deviation thresholds are set according to the engineering accuracy requirements (such as millimeter-level thresholds for key nodes and centimeter-level thresholds for ordinary nodes), and the system automatically marks abnormal points that exceed the threshold and annotates their deviation direction (such as "east deviation +15mm" or "north deviation -8mm"), providing construction personnel with an intuitive correction basis.

[0114] Preferably, the specific steps of step S5 include:

[0115] Step S51: According to the deviation type and value, match the preset graded warning rules to determine the warning level and response priority;

[0116] Step S52: Multi-dimensional warning synchronization is triggered through sound and light alarms, mobile terminal push, and cloud log recording;

[0117] Step S53: automatically generate a record containing correction suggestions, responsible party identification, and correction status fields, and bind it to the BIM model node;

[0118] Step S54: Push the correction record to the BIM model database in the form of an incremental update package through the API interface, refresh the abnormal node attributes, and retain the historical version for retrospective comparison.

[0119] In an embodiment of the present invention, according to the coordinate offset or direction error deviation type and the millimeter / centimeter level numerical value, the preset hierarchical warning rules are matched, and the warning level (such as level one emergency / level two warning) and response priority (such as immediate suspension of work / limited rectification) are dynamically determined to achieve differentiated management; then, multi-dimensional warnings are triggered synchronously through sound and light alarms, mobile terminal push (received in real time by construction personnel and management personnel) and cloud log records (BIM model archiving) to ensure that abnormal information is quickly transmitted to relevant personnel; the BIM model automatically generates a record containing correction suggestions, for example, adjusting the direction by +5°, the construction team / supervision unit identification and the pending / completed status, and binds it to the corresponding node in the BIM model, such as PVC-1F-Water Supply-05, to form a traceable chain of responsibility; finally, the correction record is pushed to the BIM model database in the form of an incremental update package through the API interface, and the abnormal node attributes are refreshed in real time, that is, the coordinate value or status label is refreshed and updated, while retaining the historical version to support data backtracking and comparative analysis.

[0120] Preferably, the specific steps of step S51 include:

[0121] Step S511: Establish a hierarchical threshold matrix of deviation types and value ranges, and correspond each combination to a preset warning level and response priority to form a dynamic matching rule library;

[0122] Step S512: Input the calculated deviation type and value into the rule library, match the closest threshold interval, output the corresponding warning level identifier and priority label, and synchronize them to the warning system;

[0123] Step S513: Based on the matching results, the preset response strategy is automatically triggered to allocate resources according to priority.

[0124] In the embodiment of the present invention, allocating resources by priority includes prioritizing high-priority tasks to construction teams or supervision units.

[0125] By establishing a hierarchical threshold matrix of coordinate offset or directional error deviation types and millimeter / centimeter numerical ranges, a dynamic matching rule library is constructed, and each deviation combination is mapped to a preset warning level and response priority, thereby realizing the structuring and scalability of the warning rules; subsequently, the BIM model inputs the deviation type and numerical value calculated in real time into the rule library, and through fuzzy matching or interval positioning algorithm, quickly identifies the closest threshold interval, outputs the corresponding warning level identification (such as "red alert") and priority label (such as "high priority"), and synchronizes it to the BIM model to ensure the real-time and consistency of the warning information; finally, the preset response strategy (such as sound and light alarm, personnel dispatch instructions) is automatically triggered according to the matching results, and resources are allocated according to priority to form a closed-loop management.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A BIM-based hidden engineering information tracing method, characterized in that: The following steps are involved: Step S1: Based on the architectural drawings of the real scene survey, a real scene 3D model is constructed using an auxiliary mapping application; Step S2: assigning unique identifiers to multiple hidden components in the real-scene three-dimensional model according to the real-scene three-dimensional model; Step S3: Bind the unique identifier to the preset path attribute in the BIM model to establish a dynamic update relationship; Step S4: deploying IoT devices to collect pre-buried path data corresponding to the unique identifier during the construction phase in real time, and comparing the data with the preset path attributes; Step S5: If it is detected that the embedded path data deviates from the preset path attributes, an early warning is triggered and a correction record is generated, which is updated to the BIM model database in real time.

2. The BIM-based hidden engineering information tracing method according to claim 1 is characterized in that: The specific steps of step S1 include: Step S11: Scanning the architectural drawings using the auxiliary mapping application to collect survey data of space dimensions and structural positions, and generating a survey sketch; Step S12: superimposing the survey sketch with the original architectural drawing and adjusting the deviation; Step S13: converting the calibrated data into the real-scene three-dimensional model through a modeling function; Step S14: Use a manual or semi-automatic plug-in tool to supplement the missing pre-embedded paths and check whether the real-scene 3D model is consistent with the real scene.

3. The BIM-based hidden engineering information tracing method according to claim 1 is characterized in that: The specific steps of step S2 include: Step S21: extracting the invisible or hidden data of the plurality of hidden components from the real-scene three-dimensional model, and locking the target range through hierarchical classification; Step S22: assigning a unique tag to each hidden component data according to the hidden component data type, location coordinates, and hierarchical relationship; Step S23: adding a corresponding identifier field to the unique mark of each hidden component data in the real-scene three-dimensional model database and generating a unique identifier, and displaying it through a visual label; Step S24: Verify whether the unique identifiers of all the hidden component data are unique and complete, and synchronously update the mapping relationship between the real-scene 3D model and the unique identifier when it is changed.

4. The BIM-based hidden engineering information tracing method according to claim 3 is characterized in that: The specific steps of step S22 include: Step S221: Split the hidden component data type, location coordinates, and hierarchical relationship into independent fields and combine them into a fixed-format coding structure; Step S222: Check whether the coding structure contains duplication or conflict. If duplication is found, adjust the field order or increase the subdivision level. Step S223: Generate the unique tag according to the detection result of step S222.

5. The BIM-based hidden engineering information tracing method according to claim 1 is characterized in that: The specific steps of step S3 include: Step S31: Filter the path attributes related to the unique identifier from the BIM model, extract the field names and data structures thereof as the basis for binding; Step S32: Align the encoding segments of the unique identifier with the preset path attribute fields one by one; Step S33: In the BIM model, when the preset path attribute field changes, the code segment corresponding to the unique identifier is automatically updated; Step S34: Verify whether the binding relationship triggers the expected update by simulating the modification of the preset path attribute or the unique identifier.

6. The BIM-based hidden engineering information tracing method according to claim 1 is characterized in that: The specific steps of step S4 include: Step S41: deploying IoT sensors on the pre-buried path in the construction area and at breakpoints along the pre-buried path to collect position coordinates, depth, and direction simulation data in real time; Step S42: uploading the simulation data to the cloud in real time via a wireless communication module, and uniformly converting the simulation data into a coordinate format compatible with the preset path attributes; Step S43: establishing the preset path attribute database in the cloud and corresponding to the simulation data field; Step S44: comparing the simulated data with the preset path attributes point by point, calculating the coordinate deviation value of each node, and marking abnormal points that exceed the allowable range; Step S45: When the deviation is detected to exceed the preset threshold, an alarm is automatically triggered, and the specific deviation location and correction suggestions are displayed simultaneously; Step S46: Synchronously update the preset path attribute database on the cloud according to the actual construction adjustment situation.

7. The BIM-based hidden engineering information tracing method according to claim 6 is characterized in that: The specific steps of step S42 include: Step S421: Pass the server address, port number and authentication key of the wireless communication module in the sensor device; Step S422: Encapsulate the original path data collected by the sensor into a unified format, wherein the unified format includes a timestamp, coordinate value, and sensor ID field, and send the data to the cloud interface in real time through the wireless communication module; Step S423: using a coordinate conversion algorithm to convert the unified format into a local coordinate system that matches the preset path attributes, and retaining an error correction coefficient; Step S424: Compare the converted unified format with the field requirements of the preset path attributes. If they meet the requirements, update the cloud database. If the deviation exceeds a threshold, trigger a correction process.

8. The BIM-based hidden engineering information tracing method according to claim 6 is characterized in that: The specific steps of step S44 include: Step S441: extracting the design coordinates of each node from the preset path attributes, and matching them with the corresponding node coordinates in the simulation data in order to form a point-to-point comparison benchmark; Step S442: Calculate the coordinate deviation using the sub-dimension and synthesize the total deviation; Step S443: Set the layer deviation threshold, automatically mark the abnormal points according to the calculation results, and mark their deviation directions.

9. The BIM-based hidden engineering information tracing method according to claim 1 is characterized in that: The specific steps of step S5 include: Step S51: According to the deviation type and value, match the preset graded warning rules to determine the warning level and response priority; Step S52: Multi-dimensional warning synchronization is triggered through sound and light alarms, mobile terminal push, and cloud log recording; Step S53: automatically generate a record containing correction suggestions, responsible party identification, and correction status fields, and bind it to the BIM model node; Step S54: Push the correction record in the form of an incremental update package to the BIM model database through the API interface, refresh the abnormal node attributes, and retain the historical version for retrospective comparison.

10. The BIM-based concealed engineering information tracing method according to claim 9, characterized in that: The specific steps of step S51 include: Step S511: Establish a hierarchical threshold matrix of deviation types and value ranges, and correspond each combination to a preset warning level and response priority to form a dynamic matching rule library; Step S512: Input the calculated deviation type and value into the rule library, match the closest threshold interval, output the corresponding warning level identifier and priority label, and synchronize them to the warning system; Step S513: Based on the matching results, the preset response strategy is automatically triggered to allocate resources according to priority.

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