A tunnel construction management system based on construction safety supervision

Abstract

The application discloses a tunnel construction management system based on construction safety supervision, and relates to the field of construction safety management.The tunnel construction management system comprises a UAV module, a modeling module and the like.The UAV module is used for flying in a tunnel and collecting internal structure parameters of the tunnel.The modeling module is used for receiving the internal structure parameters of the tunnel collected by the UAV module in a single operation, and constructing a three-dimensional model of the tunnel based on the internal structure parameters of the tunnel.The three-dimensional model of the tunnel is constructed, the surface similarity of the latest two models is compared to determine the safety condition, the similarity result is synchronously fed back to the corresponding model, and the feedback record is stored in real time.This process realizes accurate acquisition, dynamic monitoring and reliable evaluation of tunnel structure data, makes the safety risk judgment more timely and accurate, and provides effective support for construction safety supervision.

Description

Technical Field

[0001] This invention relates to the field of construction safety management technology, specifically a tunnel construction management system based on construction safety supervision. Background Technology

[0002] Tunnel construction safety management focuses on surrounding rock stability and support quality, strictly controlling blasting, ventilation, and other aspects. It requires real-time monitoring of geology and the environment, enhanced personnel training and emergency drills, thorough equipment inspections and hazard identification, the establishment of a responsibility system, and prevention of risks such as collapses and water inrushes to ensure construction safety.

[0003] Patent application number 202310740015.0 discloses a BIM-based construction safety supervision method, including: S100, pre-setting construction risk areas, pre-setting heat source areas, and pre-setting operational risk areas for construction personnel within the scope of construction safety supervision, and marking them in the BIM model of the construction safety supervision area to obtain the marking parameters of the BIM model of the construction safety supervision area; S200, monitoring the pre-setting heat source areas and pre-setting operational risk areas of construction risk areas within the scope of construction safety supervision through infrared thermal imaging temperature measurement, and obtaining infrared thermal imaging temperature measurement monitoring parameters; S300, analyzing the BIM marking parameters and infrared thermal imaging temperature measurement monitoring parameters of the construction safety supervision area. The application aims to address the following issues: how to predict and label construction risks, how to preset and monitor key construction risk areas, how to analyze safety supervision parameters, how to conduct safety supervision parameter analysis, how to intelligently determine the existence of construction safety risks, and how to provide real-time and accurate location indications for construction safety supervision areas in the BIM model. This is achieved by analyzing the differences between temperature monitoring data and BIM model annotation parameters, obtaining a set of construction safety supervision parameter analysis data; and by using the S400 system to intelligently determine the existence of construction safety risks, issue abnormal alarms, and provide precise location indications for real-time and accurate supervision.

[0004] To address the risk of tunnel collapse, most existing technologies monitor the probability of tunnel collapse by setting up monitoring points, thereby achieving tunnel collapse risk control. However, in order to ensure that the monitoring accuracy meets safety requirements, this monitoring method requires the establishment of a large number of monitoring points, which results in high monitoring and equipment maintenance costs and is not suitable for large-scale tunnel construction scenarios. To address this, a tunnel construction management system based on construction safety supervision is proposed. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a tunnel construction management system based on construction safety supervision, which can effectively solve the problems of the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a tunnel construction management system based on construction safety supervision, comprising: The system comprises a drone module for flying within the tunnel and collecting internal structural parameters; a modeling module for receiving these parameters during a single drone flight and constructing a 3D tunnel model; a storage module for receiving and storing the 3D tunnel models constructed by the modeling module each time it follows the drone flight; an analysis module for retrieving the 3D tunnel models from the storage module and analyzing tunnel safety risks based on these models; and a feedback module for receiving the tunnel safety risk analysis results from the analysis module and providing feedback to the system user regarding tunnel safety.

[0007] Furthermore, the drone module is equipped with an upload unit and a ranging module. The upload unit is used to upload three-dimensional coordinates inside the tunnel. The three-dimensional coordinates inside the tunnel are used to create a three-dimensional coordinate sequence according to the upload time sequence. The drone module continuously provides coordinates according to the three-dimensional coordinate sequence and flies back and forth inside the tunnel. The ranging module is used to measure the distance from itself to the inner wall of the tunnel. The three-dimensional coordinates inside the tunnel uploaded by the upload unit are manually edited and uploaded by the system user. The distance between each two adjacent uploaded three-dimensional coordinates inside the tunnel is equal, and the straight-line distance between each two adjacent uploaded three-dimensional coordinates inside the tunnel does not exceed ten centimeters. The center position of the tunnel cross section is preferred for each uploaded three-dimensional coordinate. The ranging module is provided in several groups, and the several groups of ranging modules are deployed in a ring shape at equal intervals on the surface of the UAV module. The deployment path of the several groups of ranging modules and the tunnel cross-sectional profile are on the same plane in real time during the operation of the ranging module.

[0008] Furthermore, the distance data measured by the ranging module is the tunnel internal structure parameter collected by the UAV module. The range measuring modules of the aforementioned groups operate synchronously, and the operating frequency of the range measuring modules of the aforementioned groups follows the rule that: for every centimeter that the UAV module carrying the range measuring module moves forward, the range measuring modules of the aforementioned groups operate synchronously at least once. The number of ranging modules deployed equidistantly in a ring around the surface of the drone module follows the following order: , For reference only; The target perimeter of the tunnel section; Configure a reference unit length for the preset ranging module; Indicates rounding up; in, The value is user-defined on the system side and follows the principle that the higher the accuracy requirement of the tunnel model construction, the smaller the value, and vice versa. The initial value is set to 10 centimeters, ensuring that the number of ranging modules deployed equidistantly around the surface of the drone module is no less than the value obtained from the above formula. .

[0009] Furthermore, the drone module operates within the tunnel, and the frequency at which it collects structural parameters of the tunnel's interior follows the following pattern: Calculate the risk index of the tunnel soil layer: ; In the formula: This represents the weights, set to 0.25, 0.2, 0.15, 0.1, 0.1, 0.05, and 0.15. This indicates the soil moisture content, dry density, void ratio, porosity, liquid limit, plastic limit, plasticity index, and risk index of the tunnel soil layer. When the soil type of the tunnel is not unique and the range is 0 to 1, calibration is performed. for ; ; In the formula: This represents the total number of soil layer types. The risk index for the q-th soil layer; Let q be the configuration weight of the q-th soil layer; Wherein, the configuration weight of the q-th soil layer The ratio of the measured width of the q-th soil layer to the internal height of the tunnel is used to determine the tunnel soil layer risk index. Or after calibration The larger the value, the higher the frequency of collecting the tunnel's internal structural parameters. , To indicate the adjusted application sampling frequency, This indicates the preset basic sampling frequency.

[0010] Furthermore, during the modeling module's operation phase, line segments are drawn based on the distance values ​​and ranging directions measured by the ranging module deployed on the surface of the UAV module, resulting in a circular line segment array. The circular array obtained from each ranging module operation is arranged according to the UAV's analysis path to obtain a circular line segment array queue. The adjacent ends of the circular line segment array queues that are far apart are connected to each other to obtain a three-dimensional model of a cylindrical structure with open ends. Finally, the openings at both ends of the open ends of the three-dimensional model of the cylindrical structure are closed to complete the construction of the tunnel's three-dimensional model.

[0011] Furthermore, the storage module is equipped with a labeling unit, which is used to provide manual operation permissions for system users to draw and label areas on the surface of the tunnel 3D model stored in the storage module that are not involved in the analysis module's processing. The application of annotation units is determined by the system user. When annotation units are not applied, the analysis module retrieves the original tunnel 3D model from the storage module and performs analysis operations. When annotation units are applied, the analysis module draws annotations on the surface of the original tunnel 3D model in areas other than those not processed by the analysis module and performs analysis operations.

[0012] Furthermore, during the operation phase of the analysis module, the two most recently stored 3D tunnel models are retrieved from the storage module, and the surface analysis of the retrieved 3D tunnel models is applied to analyze the tunnel safety risks. The surface similarity between two 3D tunnel models is calculated, and the similarity result is used as the tunnel safety risk analysis result. The higher the similarity, the safer the tunnel and the lower the safety risk; conversely, the lower the similarity, the less safe the tunnel and the higher the safety risk. ; In the formula: The surface similarity between two 3D tunnel models; Let V be the bounding box volume of the two tunnel 3D models; For calibration coefficients; This represents the total number of facets on the surface of the tunnel's 3D model. Let v be the area of ​​the v-th facet on the two tunnel 3D models; Let be the angle formed by the infinite expansion of the v-th facet on the two tunnel 3D models. When they do not intersect, the angle is 0. It is a constant; In this context, tunnel 3D model a is the first tunnel 3D model constructed compared to tunnel 3D model b. When the volume of tunnel 3D model a is less than or equal to the volume of tunnel 3D model b, Take 1, otherwise, Take -1, constant Take the number of similarity terms on the right side of the equation above. These are denoted as bounding box similarity, facet area similarity, and facet pose similarity, respectively, i.e., constants. =3.

[0013] Furthermore, the surface of the tunnel 3D model is an approximate cylindrical surface composed of several small faces pieced together.

[0014] Furthermore, the feedback module is equipped with a security risk assessment threshold, and the feedback module is based on the received data. Compare with the safety risk assessment threshold. If the value is not less than the safety risk assessment threshold, the tunnel is considered safe; otherwise, it is considered unsafe. When the feedback module reports the unsafe assessment result to the user on the system side, it simultaneously sends... and The two 3D tunnel models used in the calculation are synchronously fed back to the system user. When the feedback module performs a feedback operation to the system user, it packages the feedback content and forwards it to the storage module, where the system user reads the feedback content.

[0015] Furthermore, the drone module is interconnected with an upload unit and a ranging module via a wireless network. The drone module is interconnected with a modeling module via a wireless network. The modeling module is interconnected with a storage module via a wireless network. The storage module is interconnected with an annotation unit via a wireless network. The storage module is interconnected with an analysis module via a wireless network. The analysis module is interconnected with a feedback module via a wireless network. The feedback module is interconnected with the storage module via a wireless network.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a tunnel construction management system based on construction safety supervision. During operation, the system uses drones to fly back and forth within the tunnel along a precise coordinate path at equal intervals. Distance measuring devices deployed around the drones collect distance data from the inner wall at high frequency during flight. The data density is dynamically configured according to the tunnel cross-sectional perimeter and modeling accuracy to ensure that the collected structural parameters are comprehensive and detailed. Based on the collected data, the 3D model can exclude areas that do not need to be analyzed by marking, thereby improving the targeting of the model analysis. The system calculates the risk index by combining parameters such as soil moisture content and void ratio, and dynamically adjusts the collection frequency to achieve more intensive collection when the risk is higher. The safety status is judged by comparing the surface similarity of the two latest models. The similarity results are fed back synchronously with the corresponding models, and the feedback records are stored in real time. This process realizes the accurate acquisition, dynamic monitoring and reliable assessment of tunnel structure data, making the judgment of safety risks more timely and accurate, and providing effective support for construction safety supervision. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of a tunnel construction management system based on construction safety supervision. Figure 2 This is a schematic diagram illustrating an example of the tunnel 3D model construction process in this invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example: This embodiment presents a tunnel construction management system based on construction safety supervision, such as... Figure 1 As shown, it includes: The drone module is used to fly inside the tunnel and collect structural parameters of the tunnel's interior. The drone module is equipped with an upload unit and a ranging module. The upload unit is used to upload the three-dimensional coordinates inside the tunnel. The three-dimensional coordinates inside the tunnel are used to create a three-dimensional coordinate sequence according to the upload time sequence. The drone module continuously provides coordinates according to the three-dimensional coordinate sequence and flies back and forth inside the tunnel. The ranging module is used to measure the distance from itself to the inner wall of the tunnel. The tunnel three-dimensional coordinates uploaded by the upload unit are manually edited and uploaded by the system user. The distance between each two adjacent uploaded tunnel three-dimensional coordinates is equal, and the straight-line distance between each two adjacent uploaded tunnel three-dimensional coordinates does not exceed ten centimeters. The tunnel cross-section center position is preferred for each uploaded tunnel three-dimensional coordinate. The ranging module consists of several groups, which are deployed equidistantly around the surface of the UAV module. The deployment path of the ranging modules and the tunnel cross-sectional profile are on the same plane in real time during the operation of the ranging module. The distance data measured by the ranging module is the tunnel internal structural parameters collected by the UAV module. Several sets of ranging modules operate synchronously, and the operating frequency of several sets of ranging modules follows the rule that: for every centimeter the UAV module carrying the ranging module moves forward, several sets of ranging modules operate synchronously at least once. The number of ranging modules deployed equidistantly around the surface of the drone module follows the following order: , For reference only; The target perimeter of the tunnel section; Configure a reference unit length for the preset ranging module; Indicates rounding up; in, The value is user-defined on the system side and follows the principle that the higher the accuracy requirement of the tunnel model construction, the smaller the value, and vice versa. The initial value is set to 10 centimeters, ensuring that the number of ranging modules deployed equidistantly around the surface of the drone module is no less than the value obtained from the above formula. ; The drone module operates inside the tunnel, collecting tunnel internal structural parameters at a frequency that follows the following rules: Calculate the risk index of the tunnel soil layer: ; In the formula: This represents the weights, set to 0.25, 0.2, 0.15, 0.1, 0.1, 0.05, and 0.15. This indicates the soil moisture content, dry density, void ratio, porosity, liquid limit, plastic limit, plasticity index, and risk index of the tunnel soil layer. When the soil type of the tunnel is not unique and the range is 0 to 1, calibration is performed. for ; ; In the formula: This represents the total number of soil layer types. The risk index for the q-th soil layer; Let q be the configuration weight of the q-th soil layer; Wherein, the configuration weight of the q-th soil layer The ratio of the measured width of the q-th soil layer to the internal height of the tunnel is used to determine the tunnel soil layer risk index. Or after calibration The larger the value, the higher the frequency of collecting the tunnel's internal structural parameters. , To indicate the adjusted application sampling frequency, Indicates the preset basic sampling frequency; The above formula comprehensively considers various physical parameters of the tunnel soil layer, such as water content, dry density, and void ratio, and assigns different weights to each parameter, so that the risk index is within the range of 0 to 1, intuitively reflecting the risk level of the soil layer itself. When the tunnel involves multiple soil layers, the risk index of each soil layer is multiplied by the ratio of its measured width to the tunnel's internal height and then summed to achieve risk calibration for complex soil layer conditions, making the risk assessment more consistent with the actual geological conditions. The modeling module is used to receive tunnel internal structure parameters collected by the UAV module in a single run and to build a 3D model of the tunnel based on the tunnel internal structure parameters. During the modeling module operation phase, the distance values ​​and ranging directions measured by the ranging module deployed on the surface of the UAV module are used to draw line segments, resulting in a circular line segment array. The circular array obtained from each ranging module operation is arranged according to the UAV analysis path to obtain a circular line segment array queue. The two ends of the circular line segment array queues that are far apart are connected to each other to obtain a three-dimensional model of a cylindrical structure with open ends. Then, the two ends of the open ends of the three-dimensional model of the cylindrical structure are closed to complete the construction of the tunnel three-dimensional model. The storage module is used to receive and store the tunnel 3D model built by the modeling module each time it runs with the drone module. The storage module has an annotation unit inside. The annotation unit is used to provide manual operation permissions for system users to draw annotations on the surface of the tunnel 3D model stored in the storage module for areas that are not involved in the analysis module's processing. The application of annotation units is decided by the system user. When the annotation units are not applied, the analysis module retrieves the original tunnel 3D model from the storage module and performs analysis operations. When the annotation units are applied, the analysis module draws annotations on the surface of the original tunnel 3D model in areas other than those not processed by the analysis module and performs analysis operations. The analysis module is used to retrieve the tunnel's 3D model from the storage module and analyze the tunnel's safety risks based on the retrieved 3D model surface. During the analysis module's runtime phase, the two most recently stored 3D tunnel models are retrieved from the storage module, and the surface analysis of these retrieved models is applied to assess tunnel safety risks. The surface similarity between two 3D tunnel models is calculated, and the similarity result is used as the tunnel safety risk analysis result. The higher the similarity, the safer the tunnel and the lower the safety risk; conversely, the lower the similarity, the less safe the tunnel and the higher the safety risk. ; In the formula: The surface similarity between two 3D tunnel models; Let V be the bounding box volume of the two tunnel 3D models; For calibration coefficients; This represents the total number of facets on the surface of the tunnel's 3D model. Let v be the area of ​​the v-th facet on the two tunnel 3D models; Let be the angle formed by the infinite expansion of the v-th facet on the two tunnel 3D models. When they do not intersect, the angle is 0. It is a constant; In this context, tunnel 3D model a is the first tunnel 3D model constructed compared to tunnel 3D model b. When the volume of tunnel 3D model a is less than or equal to the volume of tunnel 3D model b, Take 1, otherwise, Take -1, constant Take the number of similarity terms on the right side of the equation above. These are denoted as bounding box similarity, facet area similarity, and facet pose similarity, respectively, i.e., constants. =3; The above formula calculates from three dimensions: bounding box volume, facet area, and facet attitude. By introducing a calibration coefficient to consider the influence of model size, and combining the total number of facets, the area of ​​each corresponding facet, and the included angle, it comprehensively reflects the structural consistency of the two models. The higher the similarity, the smaller the tunnel structure changes and the more stable the safety status. Based on this, the safety risk status of the tunnel can be intuitively judged. The surface of the tunnel's 3D model is an approximate cylindrical surface composed of several small faces pieced together. The feedback module is used to receive the tunnel safety risk analysis results from the analysis module and, based on the analysis results, provide feedback to the system user on whether the tunnel is safe. The feedback module has a security risk assessment threshold set, and the feedback module is based on the received data. Compare with the safety risk assessment threshold. If the value is not less than the safety risk assessment threshold, the tunnel is considered safe; otherwise, it is considered unsafe. When the feedback module reports the unsafe assessment result to the user on the system side, it simultaneously sends... and The two 3D tunnel models used in the calculation are synchronously fed back to the system user. When the feedback module performs a feedback operation to the system user, it packages the feedback content and forwards it to the storage module, where the system user reads the feedback content from the storage module. The drone module has an upload unit and a ranging module that are interconnected via a wireless network. The drone module is also interconnected with a modeling module via a wireless network. The modeling module is interconnected with a storage module via a wireless network. The storage module has an annotation unit interconnected via a wireless network. The storage module is also interconnected with an analysis module via a wireless network. The analysis module is interconnected with a feedback module via a wireless network. The feedback module is interconnected with the storage module via a wireless network.

[0022] In this embodiment, the UAV module flies inside the tunnel, collecting internal structural parameters. The upload unit synchronously uploads the three-dimensional coordinates inside the tunnel and creates a three-dimensional coordinate sequence based on the upload sequence. The UAV module continuously provides coordinates according to the three-dimensional coordinate sequence, flying back and forth inside the tunnel. The ranging module measures the distance from itself to the tunnel wall in real time. The modeling module receives the internal structural parameters of the tunnel collected by the UAV module in a single run and constructs a three-dimensional model of the tunnel based on the internal structural parameters. The storage module receives the three-dimensional model of the tunnel constructed by the modeling module each time it follows the UAV module and stores the three-dimensional model of the tunnel. The annotation unit synchronously provides the system user with manual operation permissions to draw and annotate areas on the surface of the tunnel three-dimensional model stored in the storage module that are not involved in the analysis module's processing. The analysis module then retrieves the tunnel three-dimensional model from the storage module, analyzes the tunnel safety risks based on the retrieved surface of the tunnel three-dimensional model, and finally receives the tunnel safety risk analysis results from the analysis module through the feedback module and provides feedback to the system user on whether the tunnel is safe.

[0023] In the above embodiments, the system uses a drone carrying a ranging module to accurately collect parameters of the tunnel's inner wall, dynamically adjusts the collection frequency according to the soil layer risk index, constructs a three-dimensional model, and stores it. Safety risks are analyzed by comparing the model's surface similarity, and the analysis area is optimized using user annotation functions. This allows for real-time feedback of risk results, achieving precise monitoring and timely early warning of tunnel structural changes, effectively reducing construction safety hazards and ensuring tunnel construction safety.

[0024] See Figure 2 As shown in the image, refer to the arrow. The graphic to the left of the arrow represents a circular array of line segments. By connecting the distant ends of the circular array of line segments to each other, a three-dimensional model of a cylindrical structure with open ends is obtained. Then, the openings at both ends of the open three-dimensional model of the cylindrical structure are closed to complete the construction of the tunnel three-dimensional model, as shown in the image to the right of the arrow. In addition, it should be noted that in the actual construction scenario of the tunnel 3D model, the surface of the tunnel 3D model (right side of the arrow) in the figure should be composed of multiple small faces, and there is a situation where the tunnel 3D model is curved (not shown in the figure).

[0025] The following is an application example of the system in a specific implementation scenario based on the above embodiments: A highway tunnel project is currently under construction. In order to monitor tunnel construction safety in real time and prevent risks such as inner wall collapse and structural deformation, the project team introduced a tunnel construction management system based on construction safety supervision to conduct routine safety monitoring of the tunnel construction section.

[0026] II. System Deployment and Parameter Configuration (a) Configuration of UAV Module 3D coordinate setting: System engineers manually edit the 3D coordinates inside the tunnel through the upload unit, with the center of the tunnel section as the preferred coordinate point, and the straight-line distance between two adjacent coordinates is set to 8 centimeters (not exceeding 10 centimeters), forming a 3D coordinate sequence. The drone flies back and forth inside the tunnel according to this sequence.

[0027] Deployment of ranging modules: The target perimeter of the tunnel construction section was measured to be 6 meters, and the system's preset reference unit length for ranging module configuration was 10 centimeters. Based on the formula for calculating the number of ranging modules, dividing the perimeter by the reference unit length and rounding up, it was determined that 60 ranging modules were required. These 60 ranging modules were installed equidistantly in a ring on the surface of the drone, and their deployment path remained on the same plane as the tunnel cross-section contour during operation. Simultaneously, the operating frequency of the ranging modules was set to: for every 1 centimeter the drone moves forward, the 60 ranging modules would run synchronously once, ensuring real-time acquisition of distance data from the tunnel's inner wall.

[0028] (ii) Calculation of sampling frequency Soil Risk Index Analysis: This tunnel section traverses two types of soil layers. The risk index for the first soil layer, calculated based on parameters such as moisture content, dry density, void ratio, porosity, liquid limit, plastic limit, and plasticity index, is 0.6. The risk index for the second soil layer is 0.4. The ratios (i.e., weighting) of the measured width to the tunnel's internal height for both soil layers are 0.6 and 0.4, respectively. After calibration and calculation, the comprehensive soil risk index is 0.52 (within the range of 0 to 1).

[0029] Data acquisition frequency determination: The system presets the basic acquisition frequency to be once every 5 minutes. According to the acquisition frequency formula, the adjusted acquisition frequency is the basic frequency multiplied by (1 + calibrated risk index), which is 7.6 minutes. In actual operation, it is set to acquire tunnel internal structural parameters once every 8 minutes.

[0030] III. Data Acquisition and Modeling Process The drone flew inside the tunnel according to a preset 3D coordinate sequence, with 60 ranging modules operating synchronously. Each centimeter of flight distance was measured against the tunnel wall, acquiring real-time structural parameters. The modeling module received distance data from a single flight, drew a circular array of line segments based on the measured distance and direction, and then arranged and connected these arrays along the flight path to form a 3D model of a cylindrical structure with open ends. Finally, the two ends were closed to complete the 3D tunnel model. The model initially constructed for this monitoring was labeled "Model a," and the model constructed after an 8-minute interval was labeled "Model b." Both models were stored in the storage module. Since there were no temporary construction interference areas on the tunnel wall, the annotation unit was not enabled, and no areas on the model surface were marked and not analyzed.

[0031] IV. Safety Risk Analysis and Feedback (a) Model similarity calculation The analysis module retrieves models a and b from the storage module and calculates their surface similarity. The bounding box volumes of the two models are similar, so a calibration coefficient of 1 is used. The model surface has a total of 500 facets, with an average area similarity of 0.9 for each facet and an average facet pose angle of 10 degrees (0 degrees when they do not intersect). A constant value of 3 is used (corresponding to bounding box similarity, facet area similarity, and facet pose similarity). The final calculated surface similarity between the two models is 0.85.

[0032] (II) Safety Assessment and Feedback The system feedback module presets a safety risk assessment threshold of 0.8. Since the calculated similarity of 0.85 is not less than the threshold of 0.8, the tunnel is determined to be safe in its current state. The feedback module packages and forwards the "tunnel safety" assessment result, the similarity value of 0.85, and models a and b to the storage module. The project engineer reads the feedback content through the storage module and confirms that there is currently no significant structural risk in the tunnel construction section.

[0033] In summary, during operation, the system in the above embodiments utilizes a drone to fly back and forth within the tunnel along a precise, equidistant coordinate path. A surrounding ranging device synchronously collects high-frequency distance data from the inner wall during flight. The data density is dynamically configured based on the tunnel's perimeter and modeling accuracy, ensuring comprehensive and detailed structural parameters. Simultaneously, the 3D model constructed based on the collected data allows for the exclusion of areas requiring analysis through annotation, enhancing the model's analytical focus. The system calculates a risk index by combining parameters such as soil moisture content and porosity, dynamically adjusting the collection frequency to achieve more intensive collection for higher risks. Safety status is assessed by comparing the surface similarity of the two latest models, with similarity results synchronously fed back to the corresponding models and feedback records stored in real time. This process enables precise acquisition, dynamic monitoring, and reliable assessment of tunnel structural data, making safety risk assessment more timely and accurate, and providing effective support for construction safety supervision.

[0034] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tunnel construction management system based on construction safety supervision, characterized in that, include: The drone module is used to fly inside the tunnel and collect structural parameters of the tunnel's interior. The drone module is equipped with an upload unit and a ranging module. The upload unit is used to upload three-dimensional coordinates inside the tunnel. The three-dimensional coordinates inside the tunnel are used to create a three-dimensional coordinate sequence according to the upload time sequence. The drone module continuously provides coordinates according to the three-dimensional coordinate sequence and flies back and forth inside the tunnel. The ranging module is used to measure the distance from itself to the inner wall of the tunnel. The three-dimensional coordinates inside the tunnel uploaded by the upload unit are manually edited and uploaded by the system user. The distance between each two adjacent uploaded three-dimensional coordinates inside the tunnel is equal, and the straight-line distance between each two adjacent uploaded three-dimensional coordinates inside the tunnel does not exceed ten centimeters. The center position of the tunnel cross section is used as the three-dimensional coordinates inside the tunnel uploaded. The ranging module is provided in several groups, and the several groups of ranging modules are deployed in a ring shape at equal intervals on the surface of the UAV module. The deployment path of the several groups of ranging modules and the tunnel cross-sectional profile are on the same plane in real time during the operation of the ranging module. The distance data measured by the ranging module is the tunnel internal structure parameter collected by the UAV module. The range measuring modules of the aforementioned groups operate synchronously, and the operating frequency of the range measuring modules of the aforementioned groups follows the rule that: for every centimeter that the UAV module carrying the range measuring module moves forward, the range measuring modules of the aforementioned groups operate synchronously at least once. The number of ranging modules deployed equidistantly in a ring around the surface of the drone module follows the following order: g is a reference value; C is the target perimeter of the tunnel section; Configure a reference unit length for the preset ranging module; Indicates rounding up; in, The value is defined by the system user and follows the principle that the higher the accuracy requirement of the tunnel model construction, the smaller the value, and vice versa. The initial value is set to 10 centimeters, so that the number of ranging modules deployed at equal intervals around the surface of the UAV module is not less than g obtained from the above formula. The drone module operates inside the tunnel, and the frequency at which it collects structural parameters of the tunnel's interior follows the following pattern: Calculate the risk index of the tunnel soil layer: ； In the formula: Indicates weight, These represent the soil moisture content, dry density, void ratio, porosity, liquid limit, plastic limit, and plasticity index of the tunnel soil layer, respectively. The risk index S of the tunnel soil layer is in the range of 0 to 1. When the soil layer where the tunnel is located is not unique, S is calibrated as S′. ； In the formula: Q represents the total number of soil types; The risk index for the q-th soil layer; Let q be the configuration weight of the q-th soil layer; Wherein, the configuration weight of the q-th soil layer The ratio of the measured width of the q-th soil layer to the internal height of the tunnel is taken. The larger the tunnel soil layer risk index S or the calibrated S′ value, the higher the frequency of collecting the internal structural parameters of the tunnel. P represents the adjusted application sampling frequency. Indicates the preset basic sampling frequency; The modeling module is used to receive tunnel internal structure parameters collected by the UAV module in a single run and to build a 3D model of the tunnel based on the tunnel internal structure parameters. The storage module is used to receive and store the 3D tunnel model built by the modeling module each time it runs with the drone module. The analysis module is used to retrieve the tunnel's 3D model from the storage module and analyze the tunnel's safety risks based on the retrieved 3D model surface. During the operation of the analysis module, the two most recently stored 3D tunnel models are retrieved from the storage module, and the surface analysis of the retrieved 3D tunnel models is applied to analyze the tunnel safety risks. The surface similarity between two 3D tunnel models is calculated, and the similarity result is used as the tunnel safety risk analysis result. The higher the similarity, the safer the tunnel and the lower the safety risk; conversely, the lower the similarity, the less safe the tunnel and the higher the safety risk. ； In the formula: The surface similarity between two 3D tunnel models; Let V be the bounding box volume of the two tunnel 3D models; Here, u represents the calibration coefficient; u is the total number of facets on the surface of the tunnel's 3D model. Let v be the area of ​​the v-th facet on the two tunnel 3D models; Let x be the angle formed by the infinite expansion of the v-th facet on the two tunnel 3D models. When they do not intersect, the angle is 0; x is a constant. In this context, tunnel 3D model a is the first tunnel 3D model constructed compared to tunnel 3D model b. When the volume of tunnel 3D model a is less than or equal to the volume of tunnel 3D model b, Take 1, otherwise, Take -1, constant Take the number of similarity terms on the right side of the equation above. These are denoted as bounding box similarity, facet area similarity, and facet pose similarity, respectively, i.e., constant x=3; The feedback module is used to receive the tunnel safety risk analysis results from the analysis module and, based on the analysis results, provide feedback to the system user on whether the tunnel is safe.

2. The tunnel construction management system based on construction safety supervision according to claim 1, characterized in that, During the modeling module's operation phase, line segments are drawn based on the distance values ​​and ranging directions measured by the ranging module deployed on the surface of the UAV module, resulting in a circular line segment array. The circular array obtained from each ranging module operation is arranged according to the UAV's analysis path to obtain a circular line segment array queue. The adjacent ends of the circular line segment array queues that are far apart are connected to each other to obtain a three-dimensional model of a cylindrical structure with open ends. Finally, the open ends of the three-dimensional model of the cylindrical structure are closed to complete the construction of the tunnel's three-dimensional model.

3. A tunnel construction management system based on construction safety supervision according to claim 1, characterized in that, The storage module is equipped with a labeling unit, which is used to provide manual operation permissions for system users to draw and label areas on the surface of the tunnel 3D model stored in the storage module that are not involved in the analysis module's processing. The application of annotation units is determined by the system user. When annotation units are not applied, the analysis module retrieves the original tunnel 3D model from the storage module and performs analysis operations. When annotation units are applied, the analysis module draws annotations on the surface of the original tunnel 3D model in areas other than those not processed by the analysis module and performs analysis operations.

4. A tunnel construction management system based on construction safety supervision according to claim 1, characterized in that, The surface of the tunnel 3D model is an approximate cylindrical surface composed of several small faces pieced together.

5. A tunnel construction management system based on construction safety supervision according to claim 1, characterized in that, The feedback module is equipped with a security risk assessment threshold, and the feedback module is based on the received data. Compare with the safety risk assessment threshold. If the value is not less than the safety risk assessment threshold, the tunnel is considered safe; otherwise, it is considered unsafe. When the feedback module reports the unsafe assessment result to the user on the system side, it simultaneously sends... The two 3D tunnel models used in the calculation are synchronously fed back to the system user. When the feedback module performs a feedback operation to the system user, it packages the feedback content and forwards it to the storage module, where the system user reads the feedback content.

6. A tunnel construction management system based on construction safety supervision according to claim 1, characterized in that, The drone module is interconnected with an upload unit and a ranging module via a wireless network. The drone module is interconnected with a modeling module via a wireless network. The modeling module is interconnected with a storage module via a wireless network. The storage module is interconnected with an annotation unit via a wireless network. The storage module is interconnected with an analysis module via a wireless network. The analysis module is interconnected with a feedback module via a wireless network. The feedback module is interconnected with the storage module via a wireless network.

Images