Roadway deformation comparison detection method used after U-shaped shed leg reinforcement

Abstract

The invention relates to the technical field of roadway support, in particular to a roadway deformation comparison detection method used after U-shaped shed leg reinforcement. According to the method, the monitoring point layout optimization instruction is obtained through monitoring optimization analysis on the historical structure data, corresponding layout optimization execution is carried out according to the monitoring point optimization instruction, the optimized monitoring points are obtained after layout optimization execution, the accuracy of the collected data can be improved conveniently through optimization of the detection position, and the accuracy of the collected data is improved. Environment compensation analysis is carried out according to the environment data to obtain monitoring data compensation information, compensated collection data is obtained, interference term elimination is carried out on the collection data, and the correctness of the data is improved; in addition, monitoring frequency adjustment information is obtained by analyzing the deformation state of the shed legs after reinforcement and the reinforcement feedback information, monitoring frequency adjustment is conducted according to the monitoring frequency adjustment information, monitoring adjustment can be automatically conducted according to the actual monitoring state, and limitation of detection resources and human resources is reduced.

Description

Technical Field

[0001] This invention relates to the field of tunnel support technology, specifically a method for comparative detection of tunnel deformation after U-shaped canopy leg reinforcement. Background Technology

[0002] U-shaped supports are a commonly used tunnel support structure. They can withstand the pressure exerted by the surrounding rock or soil. After excavation, the original rock stress balance is disrupted, and the surrounding rock and soil tend to move into the tunnel. The U-shaped support resists this pressure through its structural strength, preventing tunnel collapse. The support legs are a key part of the U-shaped support that resists lateral pressure. Over time, changes in the surrounding geological conditions, or external factors can cause deformation or damage to the support legs, leading to a decrease in their load-bearing capacity. Support leg reinforcement can strengthen the weak points of the support legs, enhancing their ability to resist lateral pressure, effectively preventing excessive deformation and collapse of the rock and soil on both sides, thereby improving the local stability of the tunnel.

[0003] When detecting and comparing the deformation of roadways after traditional U-shaped canopy leg reinforcement, in order to obtain comprehensive and complete data on roadway deformation, it is necessary to set up a large number of high-precision monitoring points in the roadway. Each monitoring point conducts roadway detection according to a preset monitoring frequency. However, roadway deformation is a relatively long process, and detection and human resources are limited. Furthermore, the monitoring sensors are affected by environmental conditions when they are working, which can easily affect the detection equipment and lead to data errors. Summary of the Invention

[0004] This invention provides a method for comparative detection of tunnel deformation after reinforcement of U-shaped canopy legs, in order to solve the above-mentioned technical problems.

[0005] The first aspect of this invention provides a method for comparative detection of tunnel deformation after reinforcement of U-shaped canopy legs, comprising the following steps:

[0006] Step 1: Obtain the historical structural data corresponding to the U-shaped canopy legs before reinforcement, perform monitoring and optimization analysis based on the historical structural data to obtain monitoring point layout optimization instructions, and execute the corresponding layout optimization according to the monitoring point optimization instructions.

[0007] As a further improvement to the present invention, monitoring and optimization analysis are performed based on historical structural data, as follows:

[0008] Historical deformation data and the structural state before reinforcement are obtained from historical structural data. Historical deformation nodes corresponding to the historical roadway deformation before reinforcement are obtained from historical deformation data, as well as the node location information corresponding to each historical roadway deformation node. The deformation value corresponding to each historical roadway deformation node is obtained. The deformation value is divided into multiple data sets according to the values. Each data set is assigned a deformation value. Each historical roadway deformation node is matched with each data set to obtain the corresponding deformation value. The node location information corresponding to each historical roadway deformation node is matched. The deformation values ​​corresponding to the historical roadway deformation nodes with overlapping location information are summed to obtain the total deformation value.

[0009] Based on the structural state before reinforcement, obtain the ground stress data and construction disturbance data; based on the ground stress data and construction disturbance data, obtain the ground stress value and disturbance value corresponding to each part of the U-shaped shed. When the ground stress value is greater than the preset upper limit of ground stress, calculate the difference between the ground stress value corresponding to the part of the U-shaped shed and the upper limit of ground stress, and mark the difference as the stress excess value; similarly, obtain the disturbance excess value corresponding to the disturbance value of each part of the U-shaped shed.

[0010] A right triangle is constructed using the stress excess value and disturbance excess value as the two legs of a right triangle. A circle is then constructed with the right-angle vertex of the right triangle as the center and the total deformation value as the radius. The area of ​​the closed figure constructed by the circle and the right triangle is calculated, and the area value is marked as the monitoring impact value. When the monitoring impact value is greater than a preset threshold, the component of the U-shaped shed corresponding to the monitoring impact value is marked as a key monitoring component. Based on the location information of the key monitoring component, the monitoring point layout is optimized to obtain the corresponding monitoring point layout optimization instruction.

[0011] Step 2: Based on the optimized monitoring points obtained after the layout optimization, acquire the data collected from each optimized monitoring point. The acquired data includes deformation data and environmental data. The deformation data includes displacement data, strain data, and three-dimensional coordinate data of the canopy legs. The environmental data includes temperature data, air pressure data, and vibration data.

[0012] Step 3: Obtain environmental data corresponding to each monitoring point, perform environmental compensation analysis based on the environmental data to obtain monitoring data compensation information, and perform compensation on the collected data corresponding to each monitoring point based on the monitoring data compensation information to obtain compensated collected data.

[0013] As a further improvement to the present invention, environmental compensation analysis is performed based on environmental data, as detailed below:

[0014] Environmental data includes temperature data, air pressure data, and vibration data. Based on the temperature data, the temperature values ​​of each component of the U-shaped canopy at each time are obtained. The temperature change is calculated by the difference between the temperature values ​​at adjacent times and is marked as ΔT. The linear expansion coefficient α of the steel corresponding to the U-shaped canopy is obtained, and the dimensional data of each component of the U-shaped canopy in each direction is obtained and recorded as LC. The steel change ΔL in each direction is calculated using the formula ΔL=LC×ΔT×α.

[0015] The barometric pressure sensitivity characteristics of each sensor corresponding to the U-shaped canopy are obtained. The barometric pressure sensitivity characteristics are identified. When the barometric pressure sensitivity characteristic is barometric pressure sensitive, the corresponding sensor is marked as a barometric pressure sensor. Based on the barometric pressure data, the barometric pressure change values ​​corresponding to two detection times are obtained. Based on the barometric pressure change values, the measurement data values ​​of each barometric pressure sensor corresponding to two detection times are obtained. The difference between the two measurement data is calculated to obtain the data change amount. When the data change amount is greater than a preset threshold, the corresponding data change amount is marked as the barometric pressure influence value.

[0016] Based on the vibration data, obtain the vibration spectrum characteristic data corresponding to the vibration, acquire the corresponding data of each sensor, compare the vibration spectrum characteristic data with the acquired data, and mark the acquired data that is the same as the vibration spectrum characteristic data as the interference vibration data.

[0017] The deviation error value of the sensor is obtained based on the change in steel material, the influence value of the measurement error of the sensor is obtained based on the influence value of air pressure, and the data removal information is obtained based on the interference vibration data. The deviation error value, the influence value of the measurement error, and the data removal information are combined to obtain the detection data compensation information.

[0018] Step 4: Obtain the deformation data after compensation corresponding to the collected data after compensation, perform deformation state analysis on the deformation data after compensation to obtain the deformation state after the reinforcement of the canopy legs, obtain the historical structural data before the reinforcement of the canopy legs from the historical data, perform comprehensive analysis on the deformation state after the reinforcement of the canopy legs and the historical structural data to obtain deformation comparison information, and generate corresponding reinforcement feedback information based on the deformation comparison information.

[0019] As a further improvement of the present invention, deformation state analysis is performed on the compensated deformation data, and the specific analysis is as follows:

[0020] Z1: Identify the deformation data after compensation to obtain the displacement data, strain data and three-dimensional coordinate data of the canopy leg;

[0021] Z2: Based on the displacement data corresponding to the canopy legs, the horizontal displacement value and vertical displacement value corresponding to the unit detection time period are obtained. The horizontal displacement value and the duration corresponding to the unit detection time period are used to calculate the horizontal displacement rate. When the horizontal displacement rate is greater than the preset threshold, the horizontal displacement rate is marked as the horizontal influence value.

[0022] Z21: Same as above, obtain the vertical influence value corresponding to the vertical displacement value.

[0023] Z22: Extract the horizontal and vertical influence values ​​according to a predetermined ratio to obtain the horizontal influence parameters and vertical influence parameters respectively. Add the horizontal and vertical influence parameters together to obtain the displacement state value.

[0024] Z3: Based on strain data, obtain the directional stress value of the canopy leg in each detection direction. The corresponding detection directions include, but are not limited to, the reverse length direction, the width direction, and the circumferential direction. When the directional stress value corresponding to each detection direction is greater than the preset threshold, the part of the directional stress value exceeding the threshold is marked as the directional strain value. The directional strain value corresponding to each detection direction is calculated according to the predetermined proportional coefficient to obtain the proportional directional strain value corresponding to each detection direction. The proportional directional strain values ​​corresponding to the canopy leg are calculated and summed to obtain the total strain value.

[0025] Z4: Obtain the three-dimensional stereoscopic image of the shed leg based on the three-dimensional coordinate data of the shed leg, obtain the three-dimensional stereoscopic images of the shed leg corresponding to two preset adjacent detection time points, and compare the three-dimensional stereoscopic images corresponding to the two adjacent detection time points to obtain the deformation volume of the shed leg.

[0026] Z5: Substitute the displacement state value, total strain value, and deformation volume of the canopy leg into the formula. The deformation state values ​​are calculated; where BX is the deformation state value; wi is the displacement state value; wi' is the upper limit of displacement; yi is the total strain value; Δyi is the allowable displacement value; pt is the deformation volume of the canopy leg; K1, K2, and K3 are preset weighting factors with values ​​of 2.012, 1.821, and 2.712, respectively.

[0027] Z6: Obtain the pre-set standard value of deformation state. When the deformation state value corresponding to the shed leg is greater than the standard value of deformation state, generate the corresponding shed leg reinforcement deformation state as shed leg deformation abnormal.

[0028] As a further improvement to the present invention, the deformation state after the reinforcement of the canopy legs is comprehensively analyzed with historical structural data. The specific analysis method is as follows:

[0029] Based on historical structural data, the historical deformation states and locations of the canopy legs are obtained. The deformation state after reinforcement is then acquired for each historical deformation location. When the deformation state after reinforcement corresponds to normal deformation, the corresponding reinforcement feedback information indicates successful reinforcement. When the deformation state after reinforcement corresponds to abnormal deformation, the corresponding deformation state value is acquired, along with a pre-set upper limit value for deformation. If the deformation state value exceeds the pre-set upper limit value, the corresponding reinforcement feedback information indicates reinforcement failure. Conversely, if the deformation state value is less than the pre-set upper limit value, the corresponding reinforcement feedback information indicates poor reinforcement effect.

[0030] Step 5: Obtain the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, analyze the deformation state and reinforcement feedback information to obtain the monitoring frequency adjustment information, and adjust the monitoring frequency according to the monitoring frequency adjustment information.

[0031] As a further improvement to the present invention, an analysis is conducted based on the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, and the specific analysis is as follows:

[0032] The deformation status of the canopy legs after reinforcement is obtained for multiple monitoring time periods. When the deformation status of the canopy legs after reinforcement is normal for multiple monitoring time periods, the monitoring sensor of the corresponding canopy leg is marked as a stable stage monitoring sensor. According to the reinforcement feedback information, when the reinforcement feedback information corresponds to poor reinforcement effect, the monitoring sensor of the corresponding canopy leg is marked as a transition stage sensor. When the reinforcement feedback information corresponds to reinforcement failure, the monitoring sensor of the corresponding canopy leg is marked as an abnormal response stage sensor. The stable stage monitoring sensor corresponds to reducing the monitoring frequency, the transition stage sensor corresponds to maintaining the monitoring frequency, and the abnormal response stage sensor corresponds to increasing the detection frequency.

[0033] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows:

[0034] 1. This invention obtains monitoring point layout optimization instructions by monitoring and optimizing historical structural data, and executes corresponding layout optimization according to the monitoring point optimization instructions. After the layout optimization is executed, the optimized monitoring points are obtained. Optimizing the detection position can facilitate the improvement of the accuracy of the collected data. Furthermore, environmental compensation analysis is performed based on environmental data to obtain monitoring data compensation information. Based on the detection data compensation information, the collected data corresponding to each monitoring point is compensated to obtain compensated collected data. Interference items are removed from the collected data to increase the accuracy of the data.

[0035] 2. This invention obtains monitoring frequency adjustment information by analyzing the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, and adjusts the monitoring frequency according to the monitoring frequency adjustment information. It can automatically adjust the monitoring according to the actual monitoring state, reducing the limitations of detection resources and human resources. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not deliberately drawn to scale according to the actual size, but are intended to show the main idea of ​​this application.

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 In one embodiment of the present invention, the method for comparative detection of tunnel deformation after reinforcement of U-shaped canopy legs includes the following steps:

[0040] Step 1: Optimize the layout of monitoring points. Obtain historical structural data corresponding to the U-shaped canopy legs before reinforcement. Based on the historical structural data, perform monitoring optimization analysis to obtain monitoring point layout optimization instructions, and execute the corresponding layout optimization according to the monitoring point optimization instructions.

[0041] Monitoring and optimization analysis are performed based on historical structural data, as detailed below:

[0042] Historical deformation data and the structural state before reinforcement are obtained from historical structural data. Historical deformation nodes corresponding to the historical roadway deformation before reinforcement are obtained from historical deformation data, as well as the node location information corresponding to each historical roadway deformation node. The deformation value corresponding to each historical roadway deformation node is obtained. The deformation value is divided into multiple data sets according to the values. Each data set is assigned a deformation value. Each historical roadway deformation node is matched with each data set to obtain the corresponding deformation value. The node location information corresponding to each historical roadway deformation node is matched. The deformation values ​​corresponding to the historical roadway deformation nodes with overlapping location information are summed to obtain the total deformation value.

[0043] Based on the structural state before reinforcement, obtain the ground stress data and construction disturbance data; based on the ground stress data and construction disturbance data, obtain the ground stress value and disturbance value corresponding to each part of the U-shaped shed. When the ground stress value is greater than the preset upper limit of ground stress, calculate the difference between the ground stress value corresponding to the part of the U-shaped shed and the upper limit of ground stress, and mark the difference as the stress excess value; similarly, obtain the disturbance excess value corresponding to the disturbance value of each part of the U-shaped shed.

[0044] A right triangle is constructed using the stress excess value and disturbance excess value as the two legs of a right triangle. A circle is then constructed with the right-angle vertex of the right triangle as the center and the total deformation value as the radius. The area of ​​the closed figure constructed by the circle and the right triangle is calculated, and the area value is marked as the monitoring impact value. When the monitoring impact value is greater than a preset threshold, the component of the U-shaped shed corresponding to the monitoring impact value is marked as a key monitoring component. Based on the location information of the key monitoring component, the monitoring point layout is optimized to obtain the corresponding monitoring point layout optimization instruction.

[0045] Step 2: Deformation data acquisition. Based on the optimized monitoring points obtained after the layout optimization, acquire the data collected from each optimized monitoring point. The acquired data includes deformation data and environmental data. Deformation data includes displacement data, strain data, and three-dimensional coordinate data corresponding to the canopy legs. Environmental data includes temperature data, air pressure data, and vibration data.

[0046] Step 3: Environmental factor monitoring and compensation. Obtain environmental data corresponding to each monitoring point, perform environmental compensation analysis based on the environmental data to obtain monitoring data compensation information, and perform compensation on the collected data corresponding to each monitoring point based on the monitoring data compensation information to obtain compensated collected data.

[0047] Environmental compensation analysis is conducted based on environmental data, as detailed below:

[0048] Environmental data includes temperature data, air pressure data, and vibration data. Based on the temperature data, the temperature values ​​of each component of the U-shaped canopy at each time are obtained. The temperature change is calculated by the difference between the temperature values ​​at adjacent times and is marked as ΔT. The linear expansion coefficient α of the steel corresponding to the U-shaped canopy is obtained, and the dimensional data of each component of the U-shaped canopy in each direction is obtained and recorded as LC. The steel change ΔL in each direction is calculated using the formula ΔL=LC×ΔT×α.

[0049] The barometric pressure sensitivity characteristics of each sensor corresponding to the U-shaped canopy are obtained. The barometric pressure sensitivity characteristics are identified. When the barometric pressure sensitivity characteristic is barometric pressure sensitive, the corresponding sensor is marked as a barometric pressure sensor. Based on the barometric pressure data, the barometric pressure change values ​​corresponding to two detection times are obtained. Based on the barometric pressure change values, the measurement data values ​​of each barometric pressure sensor corresponding to two detection times are obtained. The difference between the two measurement data is calculated to obtain the data change amount. When the data change amount is greater than a preset threshold, the corresponding data change amount is marked as the barometric pressure influence value.

[0050] Based on the vibration data, obtain the vibration spectrum characteristic data corresponding to the vibration, acquire the corresponding data of each sensor, compare the vibration spectrum characteristic data with the acquired data, and mark the acquired data that is the same as the vibration spectrum characteristic data as the interference vibration data.

[0051] The deviation error value of the sensor is obtained based on the change in steel material, the influence value of the measurement error of the sensor is obtained based on the influence value of air pressure, and the data removal information is obtained based on the interference vibration data. The deviation error value, the influence value of the measurement error, and the data removal information are combined to obtain the detection data compensation information.

[0052] Step 4: Deformation comparison analysis. Obtain the deformation data after compensation corresponding to the collected data after compensation. Perform deformation state analysis on the deformation data after compensation to obtain the deformation state after the reinforcement of the canopy legs. Obtain the historical structural data corresponding to the canopy legs before reinforcement from the historical data. Perform comprehensive analysis on the deformation state after the reinforcement of the canopy legs and the historical structural data to obtain deformation comparison information. Generate corresponding reinforcement feedback information based on the deformation comparison information.

[0053] The deformation state of the compensated deformation data is analyzed, and the specific analysis is as follows:

[0054] Z1: Identify the deformation data after compensation to obtain the displacement data, strain data and three-dimensional coordinate data of the canopy leg;

[0055] Z2: Based on the displacement data corresponding to the canopy legs, the horizontal displacement value and vertical displacement value corresponding to the unit detection time period are obtained. The horizontal displacement value and the duration corresponding to the unit detection time period are used to calculate the horizontal displacement rate. When the horizontal displacement rate is greater than the preset threshold, the horizontal displacement rate is marked as the horizontal influence value.

[0056] Z21: Same as above, obtain the vertical influence value corresponding to the vertical displacement value.

[0057] Z22: Extract the horizontal and vertical influence values ​​according to a predetermined ratio to obtain the horizontal influence parameters and vertical influence parameters respectively. Add the horizontal and vertical influence parameters together to obtain the displacement state value.

[0058] Z3: Based on strain data, obtain the directional stress value of the canopy leg in each detection direction. The corresponding detection directions include, but are not limited to, the reverse length direction, the width direction, and the circumferential direction. When the directional stress value corresponding to each detection direction is greater than the preset threshold, the part of the directional stress value exceeding the threshold is marked as the directional strain value. The directional strain value corresponding to each detection direction is calculated according to the predetermined proportional coefficient to obtain the proportional directional strain value corresponding to each detection direction. The proportional directional strain values ​​corresponding to the canopy leg are calculated and summed to obtain the total strain value.

[0059] Z4: Obtain the three-dimensional stereoscopic image of the shed leg based on the three-dimensional coordinate data of the shed leg, obtain the three-dimensional stereoscopic images of the shed leg corresponding to two preset adjacent detection time points, and compare the three-dimensional stereoscopic images corresponding to the two adjacent detection time points to obtain the deformation volume of the shed leg.

[0060] Z5: Substitute the displacement state value, total strain value, and deformation volume of the canopy leg into the formula. The deformation state values ​​are calculated; where BX is the deformation state value; wi is the displacement state value; wi' is the upper limit of displacement; yi is the total strain value; Δyi is the allowable displacement value; pt is the deformation volume of the canopy leg; K1, K2, and K3 are preset weighting factors with values ​​of 2.012, 1.821, and 2.712, respectively.

[0061] Z6: Obtain the pre-set standard value of deformation state. When the deformation state value corresponding to the shed leg is greater than the standard value of deformation state, generate the corresponding shed leg reinforcement deformation state as shed leg deformation abnormal.

[0062] The deformation state after reinforcement of the canopy legs was comprehensively analyzed in conjunction with historical structural data. The specific analysis method was as follows:

[0063] Based on historical structural data, the historical deformation states and locations of the canopy legs are obtained. The deformation state after reinforcement is then acquired for each historical deformation location. When the deformation state after reinforcement corresponds to normal deformation, the corresponding reinforcement feedback information indicates successful reinforcement. When the deformation state after reinforcement corresponds to abnormal deformation, the corresponding deformation state value is acquired, along with a pre-set upper limit value for deformation. If the deformation state value exceeds the pre-set upper limit value, the corresponding reinforcement feedback information indicates reinforcement failure. Conversely, if the deformation state value is less than the pre-set upper limit value, the corresponding reinforcement feedback information indicates poor reinforcement effect.

[0064] Step 5: Dynamically adjust the monitoring frequency, obtain the deformation status and reinforcement feedback information after the reinforcement of the canopy legs, analyze the deformation status and reinforcement feedback information after the reinforcement of the canopy legs to obtain the monitoring frequency adjustment information, and adjust the monitoring frequency according to the monitoring frequency adjustment information.

[0065] Based on the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, the specific analysis is as follows:

[0066] The deformation status of the canopy legs after reinforcement is obtained for multiple monitoring time periods. When the deformation status of the canopy legs after reinforcement is normal for multiple monitoring time periods, the monitoring sensor of the corresponding canopy leg is marked as a stable stage monitoring sensor. According to the reinforcement feedback information, when the reinforcement feedback information corresponds to poor reinforcement effect, the monitoring sensor of the corresponding canopy leg is marked as a transition stage sensor. When the reinforcement feedback information corresponds to reinforcement failure, the monitoring sensor of the corresponding canopy leg is marked as an abnormal response stage sensor. The stable stage monitoring sensor corresponds to reducing the monitoring frequency, the transition stage sensor corresponds to maintaining the monitoring frequency, and the abnormal response stage sensor corresponds to increasing the detection frequency.

[0067] The above-described 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 do 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 method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs, characterized in that, Includes the following steps: Step 1: Obtain the historical structural data corresponding to the U-shaped canopy legs before reinforcement, perform monitoring and optimization analysis based on the historical structural data to obtain monitoring point layout optimization instructions, and execute the corresponding layout optimization according to the monitoring point optimization instructions; Step 2: Based on the optimized monitoring points obtained after the layout optimization, acquire the data collected from the corresponding detection data of each optimized monitoring point; Step 3: Obtain environmental data corresponding to each monitoring point, perform environmental compensation analysis based on the environmental data to obtain monitoring data compensation information, and perform compensation on the collected data corresponding to each monitoring point based on the monitoring data compensation information to obtain compensated collected data; Step 4: Obtain the deformation data after compensation corresponding to the collected data after compensation, perform deformation state analysis on the deformation data after compensation to obtain the deformation state after the reinforcement of the canopy leg, obtain the historical structural data before the reinforcement of the canopy leg in the historical data, perform comprehensive analysis on the deformation state after the reinforcement of the canopy leg and the historical structural data to obtain deformation comparison information, and generate corresponding reinforcement feedback information based on the deformation comparison information. Step 5: Obtain the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, analyze the deformation state and reinforcement feedback information to obtain the monitoring frequency adjustment information, and adjust the monitoring frequency according to the monitoring frequency adjustment information.

2. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 1, characterized in that, The monitoring and optimization analysis based on historical structural data is as follows: Historical deformation data and the structural state before reinforcement are obtained from historical structural data. Historical deformation data are used to obtain the corresponding historical roadway deformation nodes before reinforcement, as well as the node position information corresponding to each historical roadway deformation node. The deformation amount corresponding to each historical roadway deformation node is obtained. The deformation amount is divided into multiple data sets according to the values. Each data set is assigned a deformation value. Each historical roadway deformation node is matched with each data set to obtain the corresponding deformation value. The node position information corresponding to each historical roadway deformation node is matched. The deformation values ​​corresponding to the historical roadway deformation nodes with overlapping position information are summed to obtain the total deformation value. Based on the structural condition before reinforcement, in-situ stress data and construction disturbance data were obtained; Based on ground stress data and construction disturbance data, the ground stress value and disturbance value corresponding to each part of the U-shaped shed are obtained. When the ground stress value is greater than the preset upper limit of ground stress, the ground stress value corresponding to the part of the U-shaped shed is calculated with the upper limit of ground stress to obtain the difference, and the difference is marked as the stress excess value. Similarly, the disturbance excess value corresponding to the disturbance value of each part of the U-shaped shed is obtained; The monitoring impact value is obtained by comprehensively analyzing the stress excess value, disturbance excess value and total deformation value. When the monitoring impact value is greater than the preset threshold, the component of the U-shaped shed corresponding to the monitoring impact value is marked as the key monitoring component. The monitoring point layout is optimized according to the location information of the key monitoring component to obtain the corresponding monitoring point layout optimization instruction.

3. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 2, characterized in that, The comprehensive analysis based on stress excess value, disturbance excess value, and total deformation value is conducted as follows: a right triangle is constructed using the stress excess value and disturbance excess value as the two legs of the right triangle, and a circle is constructed with the right-angle vertex of the right triangle as the center and the total deformation value as the radius. The area of ​​the closed figure constructed by the circle and the right triangle is calculated, and the area value is marked as the monitoring impact value.

4. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 1, characterized in that, The acquisition of data obtained from the detection and collection of each optimized monitoring point includes deformation data and environmental data. The deformation data includes displacement data, strain data, and three-dimensional coordinate data corresponding to the canopy legs. The environmental data includes temperature data, air pressure data, and vibration data.

5. The method for comparative detection of tunnel deformation after reinforcement of U-shaped canopy legs according to claim 1, characterized in that, The environmental compensation analysis based on environmental data is as follows: Environmental data includes temperature data, air pressure data, and vibration data; based on the temperature data, the temperature values ​​of each component of the U-shaped canopy at each time are obtained, the temperature change is calculated by the difference between the temperature values ​​at adjacent times, and the temperature change is marked as ΔT; the linear expansion coefficient α of the steel corresponding to the U-shaped canopy is obtained, and the dimensional data of each component of the U-shaped canopy in each direction is obtained, and the dimensional data is recorded as LC. The change in steel material ΔL in each direction is calculated using the formula ΔL=LC×ΔT×α; The barometric pressure sensitivity characteristics of each sensor corresponding to the U-shaped canopy are obtained. The barometric pressure sensitivity characteristics are identified. When the barometric pressure sensitivity characteristic is barometric pressure sensitive, the corresponding sensor is marked as a barometric pressure sensor. Based on the barometric pressure data, the barometric pressure change value corresponding to two detection times is obtained. Based on the barometric pressure change value, the measurement data value of each barometric pressure sensor corresponding to two detection times is obtained. The difference between the two measurement data is calculated to obtain the data change amount. When the data change amount is greater than the preset threshold, the corresponding data change amount is marked as the barometric pressure influence value. Based on the vibration data, obtain the vibration spectrum characteristic data corresponding to the vibration, acquire the data collected by each sensor, compare the vibration spectrum characteristic data with the acquired data, and mark the acquired data that is the same as the vibration spectrum characteristic data as the interference vibration data. The deviation error value of the sensor is obtained based on the change in steel material, the influence value of the measurement error of the sensor is obtained based on the influence value of air pressure, and the data removal information is obtained based on the interference vibration data. The deviation error value, the influence value of the measurement error, and the data removal information are combined to obtain the detection data compensation information.

6. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 1, characterized in that, The deformation state analysis of the compensated deformation data is as follows: Z1: Identify the deformation data after compensation to obtain the displacement data, strain data and three-dimensional coordinate data corresponding to the canopy legs; Z2: Based on the displacement data corresponding to the canopy legs, the horizontal displacement value and vertical displacement value corresponding to the unit detection time period are obtained. The horizontal displacement value and the duration corresponding to the unit detection time period are used to calculate the horizontal displacement rate. When the horizontal displacement rate is greater than the preset threshold, the horizontal displacement rate is marked as the horizontal influence value. Z21: Same as above, obtain the vertical influence value corresponding to the vertical displacement value; Z22: Extract the horizontal and vertical influence values ​​according to a predetermined ratio to obtain the horizontal influence parameters and vertical influence parameters respectively, and sum the horizontal and vertical influence parameters to obtain the displacement state value; Z3: Based on strain data, obtain the directional stress value of the canopy leg in each detection direction. When the directional stress value of each detection direction is greater than the preset threshold, mark the part of the directional stress value that exceeds the threshold as the directional strain value. Calculate the directional strain value of each detection direction according to the predetermined proportional coefficient to obtain the proportional directional strain value of each detection direction. Calculate and sum the proportional directional strain values ​​of the canopy leg to obtain the total strain value. Z4: Obtain the three-dimensional stereoscopic image of the shed leg based on the three-dimensional coordinate data of the shed leg, obtain the three-dimensional stereoscopic images of the shed leg at two preset adjacent detection time points, and compare the three-dimensional stereoscopic images of the shed leg at the two adjacent detection time points to obtain the deformation volume of the shed leg. Z5: The deformation state value is obtained by comprehensively calculating the displacement state value, total strain value, and deformation volume of the canopy leg; Z6: Obtain the pre-set standard value of deformation state. When the deformation state value corresponding to the shed leg is greater than the standard value of deformation state, generate the corresponding shed leg reinforcement deformation state as shed leg deformation abnormal.

7. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 6, characterized in that, The method for comprehensively calculating the displacement state value, total strain value, and deformation volume of the canopy legs is as follows: Substitute the displacement state value, total strain value, and deformation volume of the canopy legs into the formula. The deformation state values ​​are calculated; where BX is the deformation state value; wi is the displacement state value; wi' is the upper limit of displacement; yi is the total strain value; Δyi is the allowable displacement value; pt is the deformation volume of the canopy leg; K1, K2, and K3 are all preset weighting factors.

8. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 7, characterized in that, The specific analysis method for comprehensively analyzing the deformation state after reinforcement of the canopy legs with historical structural data is as follows: Based on historical structural data, the historical deformation state and location of the canopy legs are obtained. The deformation state of the canopy legs after reinforcement is obtained for each historical deformation location. When the deformation state of the canopy legs after reinforcement is normal, the corresponding reinforcement feedback information is obtained as reinforcement successful. When the deformation state of the canopy legs after reinforcement is abnormal, the corresponding deformation state value is obtained, and a preset deformation upper limit value is obtained. When the deformation state value is greater than the preset deformation upper limit value, the corresponding reinforcement feedback information is generated as reinforcement failed. Conversely, when the deformation state value is less than the preset upper limit of deformation, the corresponding reinforcement feedback information is generated as "poor reinforcement effect".

9. The method for comparative detection of roadway deformation after reinforcement of U-shaped canopy legs according to claim 1, characterized in that, The analysis is based on the deformation state and reinforcement feedback information after the reinforcement of the canopy legs, and the specific analysis is as follows: The deformation status of the canopy legs after reinforcement is obtained for multiple monitoring time periods. When the deformation status of the canopy legs after reinforcement is normal for multiple monitoring time periods, the monitoring sensor of the corresponding canopy leg is marked as a stable stage monitoring sensor. According to the reinforcement feedback information, when the reinforcement feedback information corresponds to poor reinforcement effect, the monitoring sensor of the corresponding canopy leg is marked as a transition stage sensor. When the reinforcement feedback information corresponds to reinforcement failure, the monitoring sensor of the corresponding canopy leg is marked as an abnormal response stage sensor. The stable stage monitoring sensor corresponds to reducing the monitoring frequency, the transition stage sensor corresponds to maintaining the monitoring frequency, and the abnormal response stage sensor corresponds to increasing the detection frequency.

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