Internet of Things secondary lining thickness clearance inspection and management method

Abstract

The invention discloses an Internet of Things secondary lining thickness clearance inspection and management method, and relates to the technical field of tunnel engineering detection.The method comprises the steps that tunnel related data are input in a cloud platform, and after parameter analysis and verification are conducted, tasks are issued to an Internet of Things total station; the total station logs in the cloud end to download data, primary support and secondary lining contours are scanned after positioning and station building are completed, and point cloud data are preprocessed and uploaded. And the cloud calculates the thickness deviation of the data three-dimensional superposition, and generates a thermodynamic diagram. When the thickness deviation exceeds a preset threshold value, the platform pushes early warning and gives optimization suggestions, such as adjustment of template offset, pouring parameters and the like; and the total station receives the optimized parameters, updates a scanning task, forms a measurement, analysis and optimization closed loop, and iteratively optimizes the construction parameters by using a machine learning model. Compared with a traditional method, real-time monitoring and dynamic management are achieved, operation is easy and convenient, construction quality and efficiency are improved, cost is reduced, and technical progress of the tunnel construction industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering detection, and specifically to an Internet of Things inspection and management method for the clearance of the secondary lining thickness. Background Art

[0002] In the field of tunnel engineering construction, the accurate inspection and effective management of the clearance of the secondary lining thickness play a key role in the project quality and safety. With the continuous expansion of the scale of tunnel construction, the traditional inspection methods for the clearance of the secondary lining thickness have gradually revealed many limitations. The early inspection means mainly relied on manual measurement, which was not only inefficient but also greatly affected by human factors, and it was difficult to guarantee the measurement accuracy. For example, when manually measuring with simple tools such as steel tapes, reading errors were likely to occur, and in a complex tunnel environment, the measurement range was limited, and it was impossible to comprehensively obtain the data of the clearance of the secondary lining thickness. This made it difficult to accurately judge whether the actual situation of the secondary lining met the design requirements during the construction process, leaving potential hazards to the project quality.

[0003] Moreover, the traditional tunnel secondary lining thickness detection methods have other obvious defects. On the one hand, real-time monitoring cannot be achieved. Relying on ultrasonic radars or manual measurement, quality inspection can only be carried out after the construction is completed, and it is impossible to obtain real-time data during the construction process, resulting in lagging deviation correction and making it difficult to timely adjust the problems during the construction process, affecting the construction quality and efficiency.

[0004] The traditional methods are complex and inefficient in operation. Point-by-point measurement is required, with a high degree of manual participation. Data analysis requires manual fitting of sectional drawings, which is time-consuming and error-prone. There is a lack of dynamic management, and the measurement results are disjointed from the construction progress, making it difficult to form a closed-loop feedback. Quality control relies on empirical judgment and cannot accurately and systematically control the construction quality.

[0005] In order to solve the above defects, a technical solution is provided now. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems that the traditional tunnel secondary lining thickness detection methods cannot achieve real-time monitoring, are complex and inefficient in operation, and lack dynamic management, and to propose an Internet of Things inspection and management method for the clearance of the secondary lining thickness.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An Internet of Things inspection and management method for the clearance of the secondary lining thickness includes the following steps:

[0009] B1: Cloud configuration of construction site data. Enter the tunnel horizontal and vertical curves, lining types, and CAD design drawings into the cloud platform, perform parameter analysis and verification, bind the lining types to mileage sections, and generate and send digital construction tasks to the Internet of Things total station instrument.

[0010] B2: Total station data synchronization and positioning for setting up a station. The IoT total station logs in to the cloud to download construction site data, calibrates and positions using the resection method or the known point station setup method, and scans the control points to correct the coordinates of the measuring station.

[0011] B3: Automatic scanning and real-time analysis. Set the scanning parameters, the total station scans the primary support and secondary lining contours, preprocesses the point cloud data and uploads it to the cloud, and the cloud performs three-dimensional overlay calculation of the thickness deviation and generates a heat map.

[0012] B4: Dynamic optimization and closed-loop management. When the thickness deviation exceeds the preset threshold, the platform pushes an alarm and gives optimization suggestions. The total station receives the optimization parameters to adjust the scanning task, realizes the closed-loop of measurement, analysis, and optimization, and at the same time performs machine learning model iteration.

[0013] Furthermore, the specific process of B1 is as follows:

[0014] After uploading the horizontal and vertical curve file, automatically parse the file content and extract key parameters, including curve radius, slope, and mileage stake number; The verification rules include: checking the curve continuity and verifying whether the slope value conforms to the design specification.

[0015] If data anomalies are detected, prompt data logic errors and highlight the problem paragraphs.

[0016] After uploading the lining type parameters and CAD design drawings, automatically perform the following operations:

[0017] Geometric parameter comparison: Match the lining contour dimensions in the CAD drawing with the input thickness range. If the deviation exceeds ±5mm, mark it as a design conflict.

[0018] Material consistency verification: Verify whether the steel bar reinforcement ratio defined in the lining type is consistent with the reinforcement layout in the CAD drawing.

[0019] Scan the following conflicts based on the preset rule library:

[0020] The lining type does not match the geological conditions;

[0021] There is a spatial conflict between the horizontal and vertical curves and the lining design;

[0022] If conflicts are detected, automatically generate repair solutions, including:

[0023] If the lining thickness in a mileage section is less than the design specification requirements, it is recommended to replace it with a strengthened lining;

[0024] If the slope of the horizontal and vertical curve exceeds the limit, it is recommended to adjust the slope of the section from pile number DK10+500 to DK10+600 to 2.8%;

[0025] Before binding the lining type to the mileage section and generating the digital construction tasks, a secondary verification is automatically performed: verifying the task parameters, including whether the section spacing and the scanning point density meet the equipment performance limits; checking whether the design drawings and parameters in the task package are complete;

[0026] After the task package is encrypted, it is sent to the total station through the Internet of Things protocol; when the total station receives it, a hash verification is performed to ensure data integrity, and if the verification fails, a retransmission is automatically requested.

[0027] Furthermore, the specific operation steps of B2 are as follows:

[0028] The Internet of Things total station logs in to the cloud platform through the construction account and automatically downloads the construction site data of the current construction section, including the design profile and the mileage binding information;

[0029] The instrument positioning and calibration are completed by using the method of resection or the method of setting up the station with known points;

[0030] After the total station completes the initial positioning, it automatically scans the known control points and calculates the residual vector Δ = [ΔX, ΔY, ΔZ], where ΔX, ΔY, and ΔZ respectively represent the residual vectors in the X-axis, Y-axis, and Z-axis directions;

[0031] The coordinates of the measuring station are iteratively corrected through the adjustment model: Until the residual meets ||Δ|| ≤ 2mm; where ||Δ|| represents the norm of the residual vector Δ; i is the index variable in the summation formula, ranging from 1 - n; n is the number of known control points, and n ≥ 3.

[0032] Furthermore, the specific operation steps of B3 are as follows:

[0033] Set the scanning parameters: the section spacing S scan ≤ 1m, and set a scanning section at every interval of S along the longitudinal direction of the tunnel scan to ensure that the deformation characteristics of the entire section are covered; the scanning point spacing d default ≤ 10cm, and the scanning points are arranged in a polar coordinate grid on a single section, and the density meets the following conditions: A 断面 is the section area, and N 点 is the total number of points;

[0034] The total station performs three-dimensional laser scanning on the primary support and the secondary lining profiles according to the preset parameters, generates point cloud data, and uploads it to the cloud in real time after preprocessing;

[0035] The cloud platform performs three-dimensional superposition on the primary support and secondary lining point cloud data through the Boolean operation algorithm, calculates the volume of the difference area, formula: thickness deviation = difference volume / section area, and generates a thickness deviation heat map.

[0036] Further, the specific operation steps for preprocessing the point cloud data at B3 are as follows:

[0037] The preprocessing includes noise filtering and coordinate unification;

[0038] Noise filtering applies the statistical outlier removal algorithm to eliminate abnormal points: If then eliminate p i ; where k is the number of neighborhood points, with a value of 50; p i is the point currently to be judged whether it is an abnormal point; p j is p i the j-th point within the neighborhood of p i -p j || is the distance between point p i and the j-th point p j within the neighborhood; is the sum of the distances between point p i and its k neighborhood points; μ is the average distance of the neighborhood points; σ is the standard deviation; i is the index of the point p i currently to be judged whether it is an abnormal point; j is the index of the points within the neighborhood of p i , ranging from 1 - k;

[0039] The coordinate system is through the iterative closest point algorithm. The iterative process: First, initialize the rotation matrix R and the translation vector t, then calculate the distances between the corresponding points of the initial support point cloud and the secondary lining point cloud. By continuously adjusting R and t, make the sum of the squared distances minimum. When the change amount of the sum of the squared distances between two adjacent iterations is less than the preset threshold, stop the iteration;

[0040] Register the initial support and secondary lining point clouds to the same coordinate system: where represents taking the minimum value with the rotation matrix R and the translation vector t as the optimization variables; i is the index of the points in the point cloud, ranging from 1 to n, and n is the number of points in the point cloud; R is the rotation matrix, used to describe the rotation of the initial support point cloud in space, and the rotation angle and direction are determined through iterative calculation; p i is the i-th point in the initial support point cloud; t is the translation vector, used to describe the translation of the initial support point cloud in space; q i is the i-th point in the lining point cloud.

[0041] Further, the specific operation steps for calculating the volume of the difference region and generating the thickness deviation heat map in B3 are as follows:

[0042] Three-dimensional superposition and difference extraction: Construct the three-dimensional voxel grids of the initial support and the secondary lining, and extract the difference region V 差值 : V 差值 =V 二衬 -V 初支 ; where V差值 is the volume of the difference region after the three-dimensional superposition of the primary support and the secondary lining point cloud data; V 二衬 is the volume of the three-dimensional space constructed by the secondary lining point cloud data; V 初支 is the volume of the three-dimensional space constructed by the primary support point cloud data;

[0043] Integrate the difference voxels to calculate the total volume: V is the total volume calculated after integrating the voxels of the difference region; v ∈ V 差值 is to perform operations on each voxel v belonging to the difference region volume V 差值 ; δ is the resolution of the three-dimensional voxel grid, with a value of 2 mm;

[0044] Thickness deviation quantification: Divide the difference volume by section and calculate the average thickness deviation of each section V i is the difference volume of the i-th section, A i is the designed area of the section; output the maximum deviation Δt max , the minimum deviation Δt min and the standard deviation σ t ;

[0045] Map the thickness deviation value Δt i to the three-dimensional tunnel model and color it according to the gradient. Specifically: red: over-excavation > +15 mm, blue: under-excavation < -15 mm; the color intensity I has a linear relationship with the absolute value of the deviation:

[0046] Furthermore, the specific operation steps of the B4 are as follows:

[0047] When the thickness deviation exceeds the preset threshold, the platform automatically pushes a warning to the construction terminal and generates an optimization suggestion. The process is as follows:

[0048] Deviation threshold setting: Preset the thickness deviation threshold Δt th = ±15 mm, and adjust it dynamically according to the project requirements; monitor the thickness deviation Δt of each section in real time i , and the trigger condition is: Satisfy |Δt i | > Δt th ;

[0049] Level 1 warning 15 mm < |Δt i | ≤ 25 mm: Send a text message notification to the construction team;

[0050] Level 2 warning |Δt i | > 25 mm: Automatically suspend construction and start the emergency response process;

[0051] Template offset calculation: Based on the direction and magnitude of the thickness deviation, calculate the horizontal template offset Δx: Δx = kl × Δt i ; where kl is a coefficient used to correlate the thickness deviation with the horizontal template offset;

[0052] Adjustment of pouring parameters: Concrete slump correction: Based on the volume V of the deviation area 差值 , dynamically adjust the slump S new : where α is an empirical coefficient with a value range of 0.1 mm -1 -10.1 mm -1 , reduce the slump during overexcavation and increase it during under-excavation; S default is the default concrete slump, i.e., the initial setting value when no deviation is considered; A 断面 is the cross-sectional area;

[0053] Optimization of vibration time: Based on the deviation distribution heat map, shorten the vibration time T for the overexcavation area and extend it for the under-excavation area: where, T new is the vibration time after optimization and adjustment; T default is the default vibration time, i.e., the initial set vibration duration when no deviation is considered; β is a fixed coefficient with a value of 0.3, taking a negative sign for overexcavation and a positive sign for under-excavation; |Δt i | is the absolute value of the thickness deviation of the i-th cross-section; Δt th is the preset thickness deviation threshold, with a value of 15 mm;

[0054] Then, optimize the template offset, concrete slump, and vibration time respectively through the correction mechanism;

[0055] After the total station receives the optimization parameters, it automatically updates the scanning task for the next construction cycle, forming a closed loop of measurement, analysis, and optimization.

[0056] Furthermore, the correction mechanism in B4 includes:

[0057] Collect the construction environment temperature, humidity, and concrete pouring speed, and calculate the differences from the normal construction environment temperature, humidity, and concrete pouring speed respectively to obtain the temperature difference wf, humidity difference sy, and speed difference sl. Compare the temperature difference wf, humidity difference sy, and speed difference sl with a number of preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively. Different scores are set for the preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively, so as to determine the temperature score, humidity score, and speed score;

[0058] After normalizing the obtained temperature score, humidity score, and speed score, use the temperature score as the bottom circle radius and the temperature score as the height to establish a cone model. Select the vertex of the cone model as the center of the sphere and the speed score as the diameter to establish a spherical body. Calculate the volume of the heterogeneous body formed by the cone and the spherical body and record it as the parameter correction value;

[0059] Compare the obtained parameter correction value with a number of preset parameter correction value intervals. Different correction indices are respectively set for the number of parameter correction value intervals, so as to determine the correction index. Multiply the calculated template horizontal offset Δx, slump S new and vibration time T new by the correction index respectively to obtain the corrected template horizontal offset, slump, and vibration time.

[0060] Furthermore, the specific operation steps for forming a closed loop of measurement, analysis, and optimization in B4 are as follows:

[0061] Section spacing compression: If the cyclic deviation exceeds the limit for two consecutive times, the section spacing S scan is reduced to 0.5 m;

[0062] Scan point encryption: The point spacing d in the over-excavated area is adjusted to 5 cm. Formula: where d new represents the adjusted scan point spacing in the over-excavated area; d default represents the default scan point spacing; γ is a fixed coefficient with a value of 0.4, and the minimum limit for d new ≥ 3 cm;

[0063] After the next cycle of scanning, the platform compares the deviation values before and after optimization with and calculates the optimization efficiency η: If η < 50%, trigger algorithm self-check and prompt for manual intervention;

[0064] Machine learning model iteration: Historical data including deviation values, optimization parameters, and environmental conditions are stored in the cloud database, and a random forest model is trained to predict the best k, α, β: Objective function: where i represents the index of the data sample; N is the number of data samples; is the thickness deviation value of the i-th section predicted by the machine learning model; is the actually measured thickness deviation value of the i-th section; The model is updated once a month, gradually reducing the dependence on manual parameter adjustment.

[0065] Compared with the prior art, the beneficial effects of the present invention are:

[0066] (1) Through the combination of Internet of Things technology and total station, the present invention realizes the real-time monitoring and dynamic management of the thickness of the secondary lining of the tunnel; during the construction process, the total station can collect data in real time and upload it to the cloud platform, enabling construction personnel to promptly detect deviations and make adjustments, avoiding the problem of lagging deviation correction caused by the inability to monitor in real time in traditional methods, and significantly improving the construction quality and efficiency;

[0067] (2) The method of the present invention is simple to operate and greatly reduces the manual participation. Through the automatic scanning of the total station and the data analysis of the cloud platform, there is no need to measure point by point and manually fit the cross-section diagram as in traditional methods, which not only saves time but also reduces the error rate caused by manual operation. At the same time, machine learning models are used to analyze and predict historical data, further optimizing the construction parameters and processes, and reducing the construction cost and resource waste;

[0068] (3) The present invention realizes the closed-loop management of measurement, analysis, and optimization. When the thickness deviation exceeds the preset threshold, the platform will automatically push a warning and give optimization suggestions, and the total station will adjust the scanning task according to the optimization parameters to form a closed loop. Through the iteration of the machine learning model, the prediction and optimization schemes are continuously optimized, improving the intelligent level of the construction process and promoting the technological progress of the tunnel construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings;

[0070] Figure 1 It is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0072] It should be understood that the terms "including" and "comprising" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0073] It should also be understood that the terms used in this disclosure statement are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure statement and the claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in this disclosure statement and the claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0074] As Figure 1 shown, an inspection and management method for the net clearance of the secondary lining thickness in the Internet of Things includes the following steps:

[0075] Step 1: Cloud configuration of construction site data. Enter the horizontal and vertical curves of the tunnel, lining type, and CAD design drawings on the cloud platform, perform parameter analysis and verification, bind the lining type to the mileage section, and generate and send a digital construction task to the Internet of Things total station.

[0076] Enter the horizontal and vertical curves of the tunnel, lining type, and corresponding CAD design drawings on the cloud platform; the specific process is as follows:

[0077] After uploading the horizontal and vertical curve file (DWG / XML), the system automatically analyzes the file content and extracts key parameters (such as curve radius, slope, mileage stake number); the verification rules include: checking the curve continuity (such as whether the distance between adjacent stake numbers changes suddenly); verifying whether the slope value meets the design specifications (such as the maximum longitudinal slope ≤ 3%); if data anomalies are detected (such as stake number jumping or slope exceeding the limit), the system prompts "data logic error" and highlights the problem section; after uploading the lining type parameters (such as thickness, concrete grade) and CAD design drawings, the system automatically performs the following operations: Geometric parameter comparison: Match the lining contour dimensions (inner diameter, outer diameter) in the CAD drawing with the entered thickness range. If the deviation exceeds ±5mm, it is marked as "design conflict"; Material consistency verification: Verify whether the steel bar reinforcement ratio defined in the lining type is consistent with the reinforcement layout in the CAD drawing.

[0078] Scan the following conflicts based on a preset rule library (such as industry specifications, project special requirements): The lining type does not match the geological conditions (such as the soft rock section is not bound to the reinforced lining); There is a spatial conflict between the horizontal and vertical curves and the lining design (such as the curve radius is too small, resulting in difficult lining installation); If conflicts are detected, the system automatically generates a repair plan, including: If the lining thickness in the mileage section is less than the design specification requirements, the system recommends "replace with a reinforced lining (thickness + 20mm)"; If the slope of the horizontal and vertical curve exceeds the limit, the system suggests "adjust the slope of the section from DK10 + 500 to DK10 + 600 to 2.8%".

[0079] Bind each lining type to the designed mileage section, generate digital construction tasks and assign them to the IoT total station; after binding the lining type to the mileage section, the system automatically performs a secondary check before generating the digital construction task: verify whether the task parameters (such as section spacing, scanning point density) meet the equipment performance limitations; check whether the design drawings and parameters in the task package are complete; encrypt the task package and send it to the total station through the IoT protocol (such as MQTT); perform a hash check when receiving the total station to ensure data integrity, and automatically request retransmission if the check fails.

[0080] Step 2: Total station data synchronization and positioning station establishment. The IoT total station logs in to the cloud to download the work point data, uses the rear intersection method or the known point station establishment method to calibrate the positioning, and scans the control points to correct the coordinates of the measuring station.

[0081] The IoT total station logs into the cloud platform through the construction account and automatically downloads the work point data of the current construction section, including the design outline and mileage binding information; the instrument positioning and calibration are completed using the rear intersection method or the known point station building method, and the station building error is controlled within ±2mm;

[0082] After the total station completes the initial positioning, it automatically scans the known control points (≥3) and calculates the residual vector Δ=[ΔX, ΔY, ΔZ], where ΔX, ΔY, and ΔZ represent the residual vectors in the X-axis, Y-axis, and Z-axis directions respectively; the coordinates of the measuring station are iteratively corrected through the adjustment model: Until the residual satisfies ||Δ||≤2mm; where ||Δ|| represents the norm (modulus) of the residual vector Δ, which is used to measure the overall size of the residual; i is the index variable in the summation formula, from 1-n; n is the number of known control points, and n≥3.

[0083] Step 3: Automated scanning and real-time analysis. Set scanning parameters. The total station scans the primary support and secondary lining contours. Pre-process the point cloud data and upload it to the cloud. The cloud performs 3D superposition to calculate thickness deviation and generate a thermal map.

[0084] Set scanning parameters: Section spacing S scan ≤1m, along the longitudinal direction of the tunnel, every S scan Set a scanning section to ensure that the deformation characteristics of the entire section are covered; the scanning point spacing d default ≤10cm: Scanning points are arranged on a single cross section in a polar coordinate grid, and the density meets the following conditions: A 断面 is the cross-sectional area, N 点 is the total number of points; ensure that the point density adapts to the curvature change (e.g. the dome area is denser to d = 5cm);

[0085] The total station performs 3D laser scanning on the primary support and secondary lining contours according to preset parameters, generates point cloud data, and uploads it to the cloud in real time after preprocessing; preprocessing includes noise filtering and coordinate unification. The noise filtering application is based on the statistical outlier removal (SOR) algorithm to remove abnormal points: if Then remove p i ; k is the number of neighboring points, which is 50; p i is the point to be judged as an outlier; p j For p i The jth point in the neighborhood; ||p i -p j || is point p i and the jth point p in the neighborhood j The distance between For point p i The sum of the distances between it and its k neighboring points; μ is the average distance between neighboring points; σ is the standard deviation; i is the point p that is currently to be judged as an outlier. i The index of p; j is p i The index of the point in the neighborhood, from 1-k;

[0086] The coordinate system uses the I CP (iterative closest point) algorithm. The iterative process is as follows: first, the rotation matrix R and the translation vector t are initialized, and then the distance between the corresponding points of the initial support point cloud and the second lining point cloud is calculated. By continuously adjusting R and t, the sum of the squares of the distances is minimized. When the change in the sum of the squares of the distances between two adjacent iterations is less than the preset threshold, the iteration is stopped.

[0087] Align the primary support and secondary support point clouds to the same coordinate system: in It means that the rotation matrix R and the translation vector t are used as optimization variables to find the minimum value; i is the index of the point in the point cloud from 1 to n, and n is the number of points in the point cloud; R is the rotation matrix used to describe the rotation of the initial point cloud in space, and the appropriate rotation angle and direction are determined by iterative calculation; p i is the i-th point in the initial support point cloud; t is the translation vector, which is used to describe the translation of the initial support point cloud in space; q i is the i-th point in the lining point cloud.

[0088] The cloud platform uses Boolean operation algorithm to perform three-dimensional superposition of the primary support and secondary lining point cloud data, calculate the volume of the difference area (formula: thickness deviation = difference volume / cross-sectional area), and generate a thickness deviation heat map; the specific process is as follows:

[0089] 3D superposition and difference extraction: construct a 3D voxel grid (resolution δ = 2 mm) of the primary support and secondary lining, and extract the difference area V by voxelized Boolean subtraction operation 差值 :V 差值 =V 二衬-V 初支 ; where V 差值 is the volume of the difference region after the 3D superposition of the primary support and the secondary lining point cloud data; V 二衬 is the volume of the 3D space constructed by the secondary lining point cloud data; V 初支 is the volume of the 3D space constructed by the primary support point cloud data; Integrate the difference voxels to calculate the total volume: V is the total volume calculated after integrating the voxels of the difference region; v ∈ V 差值 is to perform operations on each voxel v belonging to the volume V of the difference region 差值 ; δ is the resolution of the 3D voxel grid, with a value of 2mm;

[0090] Thickness deviation quantification: Divide the difference volume by section and calculate the average thickness deviation of each section V i is the difference volume of the i-th section, A i is the designed area of the section; Output the maximum deviation Δt max , the minimum deviation Δt min and the standard deviation σ t ;

[0091] Map the thickness deviation value Δt i to the 3D tunnel model and color it according to the gradient. Specifically, red: over-excavation > +15mm, blue: under-excavation < -15mm; The color intensity I has a linear relationship with the absolute value of the deviation:

[0092] Step 4: Dynamic optimization and closed-loop management. When the thickness deviation exceeds the preset threshold, the platform pushes an alarm and gives optimization suggestions. The total station receives the optimization parameters to adjust the scanning task, realizing the closed-loop of measurement - analysis - optimization, and at the same time performing machine learning model iteration;

[0093] When the thickness deviation exceeds the preset threshold (±15mm), the platform automatically pushes an alarm to the construction terminal and generates optimization suggestions (such as formwork offset, pouring parameter adjustment); Deviation threshold setting: Preset thickness deviation threshold Δt th = ±15mm, supporting dynamic adjustment according to project requirements; Real-time monitor the thickness deviation Δt i of each section, and the triggering condition is: Satisfy |Δt i | > Δt th ; First-level alarm 15mm < |Δt i | ≤ 25mm: Push a text message notification to the construction team; Second-level alarm |Δt i | > 25mm: Automatically suspend construction and start the emergency response process.

[0094] Template offset calculation: Based on the direction (overexcavation / underexcavation) and magnitude of the thickness deviation, calculate the horizontal template offset Δx: Δx = kl × Δt i ; where kl is a coefficient used to correlate the thickness deviation with the horizontal template offset, and its specific value is determined according to the actual engineering situation;

[0095] Adjustment of pouring parameters: Concrete slump correction: Based on the volume V of the deviation area 差值 , dynamically adjust the slump S new : where α is an empirical coefficient, and its value range is 0.1mm -1 -10.0mm -1 , reduce the slump during overexcavation and increase it during underexcavation; S default is the default concrete slump, that is, the initial setting value when the deviation is not considered; A 断面 is the cross-sectional area;

[0096] Optimization of vibration time: Based on the heat map of deviation distribution, shorten the vibration time T in the overexcavation area and extend it in the underexcavation area: where, T new is the vibration time after optimization and adjustment; T default is the default vibration time, that is, the initial set vibration duration when the deviation is not considered; β is a fixed coefficient with a value of 0.3, taking a negative sign for overexcavation and a positive sign for underexcavation; |Δt i | is the absolute value of the thickness deviation of the i-th cross-section; Δt th is the preset thickness deviation threshold, with a value of 15mm;

[0097] Then, optimize the template offset, concrete slump, and vibration time respectively through the correction mechanism. The specific process is as follows:

[0098] Collect the construction environment temperature, humidity, and concrete pouring speed, calculate the differences from the normal construction environment temperature, humidity, and concrete pouring speed respectively, and obtain the temperature difference wf, humidity difference sy, and speed difference sl. Compare the temperature difference wf, humidity difference sy, and speed difference sl with a number of preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively. Different scores between 0 and 1 are set for the preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively, so as to determine the temperature score, humidity score, and speed score. After normalizing the obtained temperature score, humidity score, and speed score, use the temperature score as the bottom circle radius and the temperature score as the height to establish a cone model. Select the vertex of the cone model as the center of the sphere and the speed score as the diameter to establish a spherical body, calculate the volume of the heterogeneous body formed by the cone and the spherical body, and record it as the correction parameter value. Compare the obtained correction parameter value with a number of preset correction parameter value intervals, and different correction indices are set for the number of correction parameter value intervals respectively, so as to determine the correction index. Multiply the calculated formwork horizontal offset Δx, slump S new and vibration time T new by the correction index respectively to obtain the corrected formwork horizontal offset, slump, and vibration time.

[0099] After the total station receives the optimization parameters, it automatically updates the scanning task for the next construction cycle to form a measurement - analysis - optimization closed loop; the process is as follows:

[0100] After the total station receives the instruction, it automatically adjusts the scanning parameters for the next cycle:

[0101] Section spacing compression: If the deviation between two consecutive cycles exceeds the limit, the section spacing S scan is reduced to 0.5 m; Scanning point densification: The point spacing d in the over - excavated area is adjusted to 5 cm, formula: where d new represents the adjusted scanning point spacing in the over - excavated area; d default represents the default scanning point spacing; γ is a fixed coefficient with a value of 0.4, and the minimum limit of d new ≥ 3 cm; After the next cycle of scanning, the platform compares the deviation values before and after optimization with to calculate the optimization efficiency η: If η < 50%, trigger the algorithm self - check and prompt for manual intervention;

[0102] Machine learning model iteration: Historical data (deviation values, optimization parameters, environmental conditions) are stored in the cloud database, and a random forest model is trained to predict the best k, α, β: Objective function: where i represents the index of the data sample; N is the number of data samples; is the thickness deviation value of the i - th section predicted by the machine learning model; is the thickness deviation value of the i-th cross-section obtained by actual measurement; the model is updated once a month to gradually reduce the dependence on manual parameter adjustment.

[0103] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An inspection and management method for the net clearance of the secondary lining thickness in the Internet of Things, characterized in that, It includes the following steps: B1: Cloud configuration of construction site data. Enter the tunnel horizontal and vertical curves, lining types, and CAD design drawings on the cloud platform, perform parameter parsing and verification, bind the lining types to mileage sections, generate and send digital construction tasks to the IoT total station; B2: Total station data synchronization and positioning station setup. The IoT total station logs in to the cloud to download construction site data, uses the resection method or known point station setup method to calibrate and position, and scans the control points to correct the survey station coordinates; B3: Automatic scanning and real-time analysis. Set the scanning parameters, the total station scans the primary support and secondary lining contours, preprocesses the point cloud data and uploads it to the cloud, and the cloud performs three-dimensional overlay calculations for thickness deviation and generates a heat map; B4: Dynamic optimization and closed-loop management. When the thickness deviation exceeds the preset threshold, the platform pushes an alarm and gives optimization suggestions. The total station receives the optimization parameters to adjust the scanning task, realizes the closed-loop of measurement, analysis, and optimization, and at the same time performs machine learning model iteration.

2. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 1, wherein, The specific process of B1 is as follows: After uploading the horizontal and vertical curve file, automatically parse the file content and extract key parameters, including curve radius, slope, and mileage stake number; The verification rules include: checking the curve continuity and verifying whether the slope value meets the design specifications; If data anomalies are detected, prompt data logic errors and highlight the problem paragraphs; After uploading the lining type parameters and CAD design drawings, automatically perform the following operations: Geometric parameter comparison: Match the lining contour dimensions in the CAD drawing with the entered thickness range. If the deviation exceeds ±5mm, mark it as a design conflict; Material consistency verification: Verify whether the steel bar reinforcement ratio defined in the lining type is consistent with the reinforcement layout in the CAD drawing; Scan the following conflicts based on the preset rule library: The lining type does not match the geological conditions; There are spatial conflicts between the horizontal and vertical curves and the lining design; If conflicts are detected, automatically generate repair solutions, including: If the lining thickness of the mileage section is less than the design specification requirements, it is recommended to replace it with a strengthened lining; If the slope of the horizontal and vertical curve exceeds the limit, it is recommended to adjust the slope of the section from DK10+500 to DK10+600 to 2.8%; Before binding the lining type to the mileage section and generating the digital construction task, automatically perform secondary verification: Verify the task parameters, including whether the section spacing and scanning point density meet the equipment performance limits; Check whether the design drawings and parameters in the task package are complete; After encrypting the task package, send it to the total station through the IoT protocol; When the total station receives it, perform a hash check to ensure data integrity. If the check fails, automatically request retransmission.

3. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 1, characterized in that, The specific operation steps of B2 are as follows: The IoT total station logs in to the cloud platform through the construction account and automatically downloads the construction site data of the current construction section, including the design contour and mileage binding information; Complete the instrument positioning calibration using the resection method or known point station setup method; After the total station completes the initial positioning, automatically scan the known control points and calculate the residual vector Δ = [ΔX, ΔY, ΔZ], where ΔX, ΔY, and ΔZ respectively represent the residual vectors in the X-axis, Y-axis, and Z-axis directions; Iteratively correct the coordinates of the measuring station through the adjustment model: Until the residual satisfies ||Δ|| ≤ 2 mm; where ||Δ|| represents the norm of the residual vector Δ; i is the index variable in the summation formula, ranging from 1 - n; n is the number of known control points, and n ≥ 3.

4. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 1, characterized in that The specific operation steps of B3 are as follows: Set scanning parameters: cross-section spacing S scan ≤1 m, set a scanning cross-section at every interval of S along the longitudinal direction of the tunnel to ensure covering the deformation characteristics of the whole cross-section; scanning point spacing d scan ≤10 cm. Layout scanning points on a single cross-section according to a polar coordinate grid, and the density meets the following conditions: default ≤10 cm, Layout scanning points on a single cross-section according to a polar coordinate grid, and the density meets the following conditions: A 断面 is the cross-sectional area, and N 点 is the total number of points; The total station performs three-dimensional laser scanning on the primary support and the secondary lining contours according to preset parameters, generates point cloud data, and uploads it to the cloud in real time after preprocessing; The cloud platform performs three-dimensional superposition on the primary support and secondary lining point cloud data through the Boolean operation algorithm, calculates the volume of the difference area, formula: thickness deviation = difference volume / cross-sectional area, and generates a thickness deviation heat map.

5. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 4, characterized in that The specific operation steps of the preprocessing of the point cloud data at B3 are as follows: The preprocessing includes noise filtering and coordinate unification; The noise filtering application is based on a statistical outlier removal algorithm to eliminate outliers: If then eliminate p i ; where k is the number of neighborhood points, with a value of 50; p i is the point currently to be judged whether it is an outlier; p j is the j-th point within the neighborhood of p i ; ||p i - p j || is the distance between point p i and the j-th point p j within the neighborhood; is the sum of the distances between point p i and its k neighborhood points; μ is the average distance of neighborhood points; σ is the standard deviation; i is the index of the point p that is currently to be determined whether it is an abnormal point i ; j is the index of the points within the neighborhood of p i , from 1 - k; The coordinate system passes through the iterative closest point algorithm. The iterative process: first initialize the rotation matrix R and the translation vector t, then calculate the distance between the corresponding points of the primary support point cloud and the secondary lining point cloud. By continuously adjusting R and t, the sum of the squared distances is minimized. When the change amount of the sum of the squared distances between two adjacent iterations is less than the preset threshold, the iteration stops; Register the primary support and the secondary lining point clouds to the same coordinate system: where means to find the minimum value with the rotation matrix R and the translation vector t as the optimization variables; i is the index of the points in the point cloud, ranging from 1 to n, where n is the number of points in the point cloud; R is the rotation matrix used to describe the rotation of the initial point cloud in space, and the rotation angle and direction are determined through iterative calculation; p i is the i-th point in the initial point cloud; t is the translation vector used to describe the translation of the initial point cloud in space; q i is the i-th point in the lining point cloud.

6. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 4, wherein, The specific operation steps of calculating the volume of the difference area and generating a thickness deviation heat map at B3 are as follows: 3D superposition and difference extraction: Construct the 3D voxel grids of the primary support and the secondary lining, and extract the difference region V through voxelized Boolean subtraction operation 差值 : V 差值 = V 二衬 - V 初支 ; where V 差值 is the volume of the difference region after the 3D superposition of the point cloud data of the primary support and the secondary lining; V 二衬 is the 3D space volume constructed by the point cloud data of the secondary lining; V 初支 is the 3D space volume constructed by the point cloud data of the primary support; Integrate the differential voxels to calculate the total volume: V is the total volume calculated after integrating the voxels in the differential region; v ∈ V 差值 is to operate on each voxel v belonging to the differential region volume V 差值 ; δ is the resolution of the three-dimensional voxel grid, with a value of 2mm; Thickness deviation quantification: Divide the differential volume by the cross-section, and calculate the average thickness deviation of each cross-section V i is the differential volume of the i-th cross-section, A i is the designed area of the cross-section; Output maximum deviation Δt max , minimum deviation Δt min and standard deviation σ t ; Map the thickness deviation value Δt i to the 3D tunnel model and color it according to the gradient. Specifically, red: overexcavation > +15 mm, blue: under-excavation < -15 mm; the color intensity I has a linear relationship with the absolute value of the deviation:

7. A method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 1, characterized in that, The specific operation steps of B4 are as follows: When the thickness deviation exceeds the preset threshold, the platform automatically pushes a warning to the construction terminal and generates optimization suggestions. The process is as follows: Deviation threshold setting: preset the thickness deviation threshold Δt th = ±15 mm, dynamically adjusted according to engineering requirements; real-time monitor the thickness deviation Δt of each section i , and the triggering condition is: Satisfy |Δt i | > Δt th ; Level 1 warning: 15mm < |Δt i | ≤ 25mm: Send a text message notification to the construction team; Secondary warning | Δt i | > 25 mm: Automatically suspend construction and initiate the emergency response process; Template offset calculation: According to the direction and magnitude of the thickness deviation, calculate the horizontal offset Δx of the template: Δx = kl × Δt i ; where kl is a coefficient used to correlate the thickness deviation with the horizontal offset of the template Adjustment of pouring parameters: Concrete slump correction: According to the volume V of the deviation area 差值 , dynamically adjust the slump S new : where α is an empirical coefficient, and the value range is 0.1 mm -1 -10.1 mm -1 , reduce the slump in case of overexcavation and increase it in case of under-excavation; S default is the default concrete slump, that is, the initial setting value when the deviation is not considered; A 断面 is the cross-sectional area; Optimization of vibration time: Based on the deviation distribution heat map, shorten the vibration time T for the over-excavated area and extend it for the under-excavated area: Among them, T new is the vibration time after optimization and adjustment; T default is the default vibration time, that is, the initial set vibration duration when the deviation is not considered; β is a fixed coefficient, with a value of 0.3, taking a negative sign for over-excavation and a positive sign for under-excavation; |Δt i | is the absolute value of the thickness deviation of the i-th section; Δt th is the preset thickness deviation threshold, with a value of 15 mm; Then, the template offset, concrete slump, and vibration time are optimized through the correction mechanism respectively; After receiving the optimization parameters, the total station automatically updates the scanning task for the next construction cycle, forming a closed loop of measurement, analysis, and optimization.

8. The method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 7, characterized in that, The correction mechanism in B4 includes: Collect the construction environment temperature, humidity, and concrete pouring speed, and calculate the differences with the normal construction environment temperature, humidity, and concrete pouring speed respectively to obtain the temperature difference wf, humidity difference sy, and speed difference sl. Compare the temperature difference wf, humidity difference sy, and speed difference sl with a number of preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively. Different scores are set for a number of preset temperature difference intervals, humidity difference intervals, and speed difference intervals respectively, so as to determine the temperature score, humidity score, and speed score; After normalizing the obtained temperature score, humidity score, and speed score, use the temperature score as the bottom circle radius and the temperature score as the height to establish a cone model. Select the vertex of the cone model as the center of the sphere and the speed score as the diameter to establish a spherical body. Calculate the volume of the heterogeneous body formed by the cone and the spherical body and record it as the correction parameter value; Compare the obtained parameter correction value with several preset parameter correction value ranges, where different correction exponents are respectively set for the several parameter correction value ranges, so as to determine the correction exponent. Multiply the calculated formwork horizontal offset Δx, slump S new and vibration time T new by the correction exponent to obtain the corrected formwork horizontal offset, slump and vibration time respectively.

9. A method for inspecting and managing the clearance of the secondary lining thickness in the Internet of Things according to claim 7, characterized in that, The specific operation steps of forming a closed loop of measurement, analysis, and optimization in B4 are as follows: Cross-section spacing compression: If the deviation exceeds the limit for two consecutive cycles, the cross-section spacing S scan is reduced to 0.5 m; Scanning point encryption: The point spacing d in the over-excavated area is adjusted to 5 cm, formula: where d new represents the adjusted scanning point spacing in the over-excavated area; d default represents the default scanning point spacing; γ is a fixed coefficient with a value of 0.4, and the minimum limit of d new ≥ 3 cm; After the next cycle scan, the platform compares the deviation values before and after optimization and calculate the optimization efficiency η: If η < 50%, trigger the algorithm self-check and prompt for manual intervention; Iteration of Machine Learning Model: Historical data including deviation values, optimization parameters, and environmental conditions are stored in the cloud database. Train a random forest model to predict the optimal k, α, β: Objective function: where i represents the index of the data sample; N is the number of data samples; is the thickness deviation value of the i-th section predicted by the machine learning model; is the thickness deviation value of the i-th section obtained by actual measurement; The model is updated monthly to gradually reduce the dependence on manual parameter tuning.

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