Intelligent compression member reinforcing method, device and system based on big data

Abstract

The invention discloses an intelligent compression member reinforcing method, device and system based on big data, according to the reinforcing method, the big data technology is utilized, needed prestress reserve is accurately calculated based on data such as geographic position temperature, sunlight, environment humidity and overload information of a reinforced pier, a segmented tensioning strategy is automatically formulated according to a prestress reserve target, and the intelligent compression member reinforcing effect is achieved. The friction loss is reduced, and the reinforcing effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a method, device and system for intelligent reinforcement of compression components based on big data. Background Art

[0002] Prestressed steel wire ropes can be used to reinforce bridge piers to prevent cracks from reoccurring after the cracks have been repaired and sealed. However, the causes of the cracks are complex and are closely related to the ambient temperature, sunshine, humidity, and overload frequency of the concrete pier. The prestress reserve target is unclear, and the friction between the steel wire rope and the concrete pier wall causes a large loss of prestress during the tensioning process, all of which have a significant impact on the reinforcement effect.

[0003] Patent CN115852858A discloses a structural model of using annular steel wire ropes to reinforce bridge piers, and proposes a basic reinforcement method framework. CN114808767A discloses a steel wire rope annular prestressing construction method, which improves the tension anchoring device.

[0004] The above patents all involve prestressed steel wire ropes to reinforce concrete bridge pier structures, but none of them clearly defines the prestress reserve target or proposes methods to reduce friction losses. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a method, device and system for intelligent reinforcement of compression members based on big data, clarifies the prestress reserve when prestressing concrete bridge piers, and automatically formulates a reasonable strategy for reducing friction loss according to the reinforcement target. The technical solution is as follows: In a first aspect, a method for intelligent reinforcement of a compressive component based on big data is provided, which is applied to a cloud server and includes the following steps: Receiving a high-definition image of a concrete bridge pier surface uploaded by a smart handheld terminal, wherein the compression member is a bridge pier to be reinforced; Using a preset image recognition module, the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking points is identified to generate a friction coefficient matrix, wherein the preset marking points are arranged at intervals on the steel wire rope to form a matrix position layout with m rows and n columns; The cracking stress of the bridge pier to be reinforced is obtained by inputting the trained LSTM neural network model based on the sunshine, temperature, humidity, overload level and overload frequency information of the bridge pier to be reinforced. ; A reinforcement scheme is obtained based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced. The reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model. The reinforcement scheme is transmitted to the smart handheld terminal.

[0006] In some embodiments, a preset image recognition module is used to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at a preset mark point, including: Collect high-definition images of concrete pier surfaces with different degrees of roughness as training samples, and annotate the corresponding friction coefficients for the training samples; Training a target recognition model based on the training samples and the labeled data of the training samples, and using the trained target recognition model as the image recognition module; For a high-definition image of the concrete pier surface to be identified, the friction coefficient between the surface of the pier to be reinforced and the steel wire rope at a preset marking point in the image is obtained based on the image recognition module.

[0007] In some embodiments, the information of sunshine, temperature, humidity, overload level and overload frequency at the location of the bridge pier to be reinforced is input into the trained LSTM neural network model to obtain the cracking stress of the bridge pier to be reinforced. ,include: Receiving positioning information uploaded by a smart handheld terminal, wherein the positioning information represents the position of the bridge pier to be reinforced, and the smart handheld terminal is equipped with a high-precision GPS positioning module; Based on the location of the bridge pier to be reinforced, obtain the annual sunshine, temperature, humidity, overload level and overload frequency information of the location; The sunshine, temperature, humidity, overload level and overload frequency information of the location are input into the trained LSTM neural network model to obtain the cracking stress of the bridge pier to be reinforced. .

[0008] In some embodiments, the training process of the LSTM neural network model includes: The finite element method is used to calculate the cracking stress of the bridge pier according to the temperature, sunshine conditions, humidity, overload level and overload frequency parameters at the bridge pier location. The temperature, sunshine conditions, humidity, overload magnitude, overload frequency parameters and the corresponding cracking stress are used as a training sample; Based on the temperature, sunshine conditions, humidity, and overload probability parameters of the training samples as input features, the pier cracking stress of the training samples is used as the target value to train the LSTM neural network.

[0009] In some embodiments, the reinforcement scheme is based on Determine, among which, ; ; ,in, ; ; The constraints are: ; ; ; ; ; ; in: ——Main cost of reinforcement plan, in ten thousand yuan; ——The cost of reducing the friction coefficient of concrete in the reinforcement scheme, in ten thousand yuan, The cost required to reduce the friction coefficient of the concrete surface by 1 in an area of ​​0.2m×0.1m; ——Material and operating costs of the segmented tension anchorage devices in the reinforcement scheme, in ten thousand yuan, The material and operating costs of the segmented tensioning anchorage device generated each time the 360° wire rope is split; ——Material and labor costs of prestressed steel wire ropes in the reinforcement scheme, in ten thousand yuan, The material and labor cost for each 1m of prestressed steel wire rope used in the reinforcement scheme; ——The number of points in the friction coefficient matrix where the friction coefficient needs to be reduced. Assume that the number of rows of points in the friction coefficient matrix where the friction coefficient needs to be reduced is , ; - The friction coefficient of the marking point where the friction coefficient needs to be reduced; ——The friction coefficient of the concrete surface after reinforcement scheme treatment, that is, the target friction coefficient; ——The cross-sectional diameter of the concrete pier to be reinforced, in meters; ——Radius of the cross section of the concrete pier to be reinforced, in meters; ——Vertical height of the area to be reinforced of the concrete pier, in meters; ——Vertical spacing of steel wire ropes in the reinforcement scheme, in m; ——The number of vertically distributed steel wire ropes in the reinforcement scheme; ——The segmented steel wire rope in the reinforcement scheme corresponds to 1 / 2 of the central angle of the circle, in radians. It is the minimum limit value of the arc corresponding to the wire rope segmentation; ——The length of the steel wire rope segments in the reinforcement scheme, in meters; ——The minimum compressive stress that the reinforcement scheme can form on the surface of the concrete pier, in MPa; ——Prestressed steel wire rope tensioning control stress, unit is MPa; ——The maximum allowable stress of prestressed steel wire rope, in MPa.

[0010] In some implementations, the analysis of the reinforcement scheme is obtained based on iterative analysis of an intelligent optimization algorithm, and the intelligent optimization algorithm includes: a simulated annealing algorithm, a particle swarm algorithm, and a grey wolf algorithm.

[0011] In a second aspect, a big data-based intelligent reinforcement device for a compression member is provided, comprising: An image acquisition unit, used to receive a high-definition image of the surface of a concrete pier uploaded by a smart handheld terminal, wherein the compression member is a pier to be reinforced; A friction coefficient analysis unit, which is used to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking points by using an image recognition module based on a convolutional neural network, and generate a friction coefficient matrix, wherein the preset marking points are arranged at intervals on the steel wire rope to form a matrix position layout with m rows and n columns; The pier cracking stress analysis unit is used to obtain the cracking stress of the pier to be reinforced based on the sunshine, temperature, humidity, overload level and overload frequency information of the pier to be reinforced, which is input into the trained LSTM neural network model. ; The reinforcement scheme analysis unit is used to obtain a reinforcement scheme based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced. The reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model, and transmits the reinforcement scheme to the smart handheld terminal.

[0012] According to a third aspect, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0013] In a fourth aspect, a big data-based intelligent reinforcement system for compression components is provided, comprising: Segmented prestressed steel wire ropes are used to reinforce compression members, which are concrete bridge piers; An intelligent segmented tensioning and anchoring module, comprising a tensioning and anchoring mechanical device and an intelligent control system, wherein the tensioning and anchoring mechanical device is connected to the segmented prestressed steel wire rope, and the intelligent control system is connected to the tensioning and anchoring mechanical device, and is used to perform bidirectional tensioning and anchoring on the segmented prestressed steel wire rope by controlling the tensioning and anchoring mechanical device; The smart handheld terminal is connected to the intelligent control system for interactively transmitting tensioning and anchoring data to obtain tensioning force and anchoring force, and monitor the entire tensioning process; it is also used to take ultra-high-pixel photos of the surface of the concrete pier; the smart handheld terminal is also connected to the cloud server for uploading data collected by the smart handheld terminal and receiving data sent by the cloud server; The cloud server is communicatively connected to the smart handheld terminal, and is used to execute the method described in the first aspect above based on the data uploaded by the smart handheld terminal to determine the reinforcement plan and send it to the smart handheld terminal, and is also used to monitor the entire tensioning process based on the data uploaded by the smart handheld terminal.

[0014] In some implementations, the smart handheld terminal includes: High-precision GPS positioning module, used to locate the concrete piers to be reinforced; The automated modeling module is used to generate the pier structure to be reinforced and the regional scene model based on the pier height, diameter, sunlight obstruction, and material property information collected on site.

[0015] The present invention provides a method, device and system for intelligent reinforcement of compression components based on big data, which has the following beneficial effects: 1. Based on big data information, the present invention locks the temperature, sunshine, humidity, overload level and frequency of the bridge pier to be reinforced, accurately calculates the cracking load of each concrete pier, and controls the main reinforcement prestress reserve level. It avoids excessive or insufficient prestress reserve caused by different cracking loads of bridge piers in different regions, and effectively improves the reinforcement effect.

[0016] 2. Based on the actual reinforcement conditions and minimizing the reinforcement cost, the present invention adopts an optimization algorithm to determine parameters such as the wire rope model, wire rope segment length, wire rope vertical arrangement spacing, and concrete surface target friction coefficient, to form a targeted reinforcement plan, so as to achieve one plan for each pier column, carry out reinforcement work in a targeted manner, and minimize construction costs.

[0017] 3. The present invention adopts the method of segmented tensioning and anchoring of prestressed steel wire ropes combined with concrete surface treatment to reduce prestressed friction loss. Under the condition of taking into account the construction cost, it solves the stubborn problem of traditional steel wire rope reinforcement of piers and greatly improves the reinforcement efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a device layout diagram of a big data-based intelligent reinforcement system for a compressive member provided in an embodiment of the present application; Figure 2 It is an overall structural diagram of the system provided in the embodiment of the present application; Figure 3It is a schematic diagram of calculating the cracking stress of bridge pier concrete using the LSTM neural network model provided in the embodiment of the present application; Figure 4 It is a flow chart of the intelligent reinforcement method for a compressive member based on big data according to an embodiment of the present application; In the figure, 1. Segmented prestressed steel wire rope; 2. Smart handheld terminal; 3. Smart segmented tensioning and anchoring module; 4. Cloud server. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0020] The embodiment of the present application provides a big data-based intelligent reinforcement system for compression components, including: The segmented prestressed steel wire rope 1 is used to reinforce the compression member, which is a concrete bridge pier. The specific length of the segmented prestressed steel wire rope 1 is determined according to the reinforcement plan calculated by the cloud server, providing a prestress reserve for the concrete to prevent the closed cracks from cracking again. The intelligent handheld terminal 2 is connected to the intelligent control system for interactive transmission of tensioning and anchoring data to obtain tensioning force and anchoring force, and monitor the entire tensioning process; it is also used to take ultra-high pixel photos of the surface of the concrete pier; the intelligent handheld terminal is also connected to the cloud server for uploading data collected by the intelligent handheld terminal and receiving data sent by the cloud server. Specifically, the intelligent handheld terminal 2 is equipped with a high-definition panoramic camera to take ultra-high pixel photos of the surface of the concrete pier column, and is equipped with a high-precision GPS positioning module to locate the concrete pier to be reinforced. The automatic modeling module inputs the information such as the height, diameter, sunlight shielding, and material properties of the pier collected and observed on site, generates the structure of the pier to be reinforced and the regional scene model, interacts with the cloud server in real time with data and images, uploads various information to the cloud server for calculation, and receives the reinforcement plan and calculation cloud map sent by the cloud server to guide the on-site construction. Receive the tensioning force data transmitted by the intelligent segmented tensioning and anchoring module to monitor the entire tensioning process. In one embodiment, after the target concrete pier is determined on site, a smart handheld terminal 2 is used to take high-definition photos of the area to be reinforced, a high-precision GPS positioning module is used to obtain the location information of the concrete pier, and on-site observation data is input to generate a pier structure to be reinforced and a regional scene model, and all information is uploaded to a cloud server.

[0021] The intelligent segmented tensioning and anchoring module 3 includes a tensioning and anchoring mechanical device and an intelligent control system. The tensioning and anchoring mechanical device is connected to the segmented prestressed steel wire rope, and the intelligent control system is connected to the tensioning and anchoring mechanical device, and is used to control the tensioning and anchoring mechanical device to perform bidirectional tensioning on the segmented prestressed steel wire rope and anchor it through bolts, perform wireless data interaction with the intelligent handheld terminal, receive commands from the handheld terminal, and send tensioning and anchoring forces to the handheld terminal, etc.; The cloud server 4 is connected to the smart handheld terminal for determining the reinforcement scheme based on the data uploaded by the smart handheld terminal and sending it to the smart handheld terminal, and is also used to monitor the entire tensioning process based on the data uploaded by the smart handheld terminal. The cloud server has the functions of collecting data from the network, calculating and processing, drawing cloud maps, generating reinforcement schemes, storing data, and exchanging data with the smart handheld terminal.

[0022] The cloud server 4 internally executes a big data-based intelligent reinforcement method for compressive components to determine a reinforcement plan and sends it to the smart handheld terminal to guide on-site construction.

[0023] Specifically, an embodiment of the present application provides a method for intelligently reinforcing a compressive component based on big data and applied to a cloud server. The compressive component is a concrete pier to be reinforced. The reinforcement method includes the following steps: Step 1, receiving a high-definition image of the surface of a concrete pier uploaded by a smart handheld terminal; Step 2: using a preset image recognition module to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking point, and generating a friction coefficient matrix; It can be understood that multiple prestressed steel wire ropes are arranged at different vertical heights of the pier, and a marking point is set on each steel wire rope at a preset length (for example, 10 cm). Assuming that the number of vertical identification points on the surface of the concrete pier to be reinforced area (that is, the number of steel wire ropes at different vertical heights) is m, and the number of horizontal identification points on the surface of the concrete pier to be reinforced area (that is, the number of identification points on each steel wire rope) is n, a marking point position distribution with m rows and n columns is formed, corresponding to a friction coefficient matrix with m rows and n columns.

[0024] The cloud server uses the high-definition pictures of the concrete surface uploaded by the smart handheld device and the embedded image recognition module based on the convolutional neural network. This image recognition module is a fine-grained image recognition module. It identifies the distribution of the friction coefficient between the concrete surface to be reinforced and the wire rope. The recognition point spacing is 10 cm, generates a friction coefficient matrix and a three-dimensional cloud map.

[0025] Specifically, step 2 uses a preset image recognition module to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking point, including: Step 21, collecting high-definition images of concrete pier surfaces with different degrees of roughness as training samples, and marking the corresponding friction coefficients for the training samples; Step 22, training a target recognition model based on the training samples and the labeled data of the training samples, using the trained target recognition model as the image recognition module, wherein the target recognition model is constructed based on a convolutional neural network, and in one embodiment, the target recognition model adopts a YOLO v5 model; Step 23, for the high-definition image of the concrete pier surface to be identified, the friction coefficient between the surface of the pier to be reinforced and the steel wire rope at the preset marking point in the image is obtained based on the image recognition module.

[0026] It can be understood that the friction coefficient represents the degree of roughness of the concrete surface. The concrete surface images with different degrees of roughness have obvious differences and can be identified by the target recognition model. In steps 21 to 23, a large number of high-definition pictures of the pier surface are taken in the early stage, and the corresponding friction coefficient μ is obtained by testing by traditional methods. The high-definition pictures of the pier surface and the corresponding friction coefficient μ are used as training sets and test sets to train the YOLO v5 model. The high-definition pictures of the concrete surface are used as input and the friction coefficient μ is used as output. A target recognition model suitable for the embodiment of the present application can be obtained. The trained model is embedded in the cloud server to obtain the friction coefficient through the collected images.

[0027] Step 3: Based on the information of sunshine, temperature, humidity, overload level and overload frequency at the location of the bridge pier to be reinforced, the trained LSTM neural network model is input to obtain the cracking stress of the bridge pier to be reinforced. ; The embodiment of the present application, based on big data information, locks in parameters such as temperature, sunshine, humidity, overload level and frequency at the location of the bridge pier to be reinforced, accurately calculates the cracking load of each concrete bridge pier, and controls the main reinforcement prestress reserve level. This avoids excessive or insufficient prestress reserve caused by different cracking loads of bridge piers in different regions, and effectively improves the reinforcement effect.

[0028] Specifically, step 3 includes the following steps: Step 31, receiving positioning information uploaded by the smart handheld terminal, wherein the positioning information represents the position of the bridge pier to be reinforced; Step 32, based on the location of the bridge pier to be reinforced, obtain the annual sunshine, temperature, humidity, overload level, and overload frequency information of the location; Step 33: input the sunshine, temperature, humidity, overload level and overload frequency information of the location into the trained LSTM neural network model to obtain the cracking stress of the bridge pier to be reinforced. .

[0029] In the embodiment of the present application, the cloud server automatically collects the annual sunshine, temperature, humidity, overload level, and overload frequency information of the location based on the actual location information of the bridge pier to be reinforced uploaded by the smart handheld device, and combines the structure of the bridge pier to be reinforced and the regional scene model uploaded by the smart handheld device to calculate the cracking stress of the concrete pier based on the LSTM neural network model completed by embedded training. Perform calculations.

[0030] In the above step 33, the training process of the LSTM neural network model includes: Step 331, using the finite element method, the pier cracking stress is calculated according to the temperature, sunshine conditions, humidity, overload level, and overload frequency parameters; Step 332, taking the temperature, sunshine condition, humidity, overload level, overload frequency parameters and corresponding cracking stress as a training sample; Step 333, based on the temperature, sunshine conditions, humidity, and overload probability parameters of the training samples as input features, the pier cracking stress of the training samples is used as the target value to train the LSTM neural network.

[0031] In the embodiment of the present application, the finite element method is first used to calculate the corresponding pier cracking stress results based on a large number of different parameters such as temperature, sunshine conditions, humidity, overload level frequency, etc., and then the temperature, sunshine conditions, humidity, and overload level frequency are used as characteristic values, and the pier cracking stress is used as the target value. The training set and the test set data are sorted out, and the LSTM neural network model is trained using the training set and the test set data. When the loss function value drops to the minimum value and stabilizes, the training of the LSTM neural network model is completed.

[0032] Step 4, based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced, a reinforcement scheme is obtained, wherein the reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model, and the reinforcement scheme is transmitted to the smart handheld terminal.

[0033] In the embodiment of the present application, the cloud server can design a reinforcement scheme based on the friction coefficient matrix and the cracking stress of the concrete pier to be reinforced, and adopt the most economical reinforcement scheme based on meeting the reinforcement requirements by combining the segmentation of the wire rope, the treatment of the friction coefficient of the concrete surface, and the setting of the wire rope spacing.

[0034] The reinforcement scheme is based on Determine, among which, ; ; ,in, ; ; The constraints are: ; ; ; ; ; ; in: ——Main cost of reinforcement plan, in ten thousand yuan; ——The cost of reducing the friction coefficient of concrete in the reinforcement scheme, in ten thousand yuan, The cost required to reduce the friction coefficient of the concrete surface by 1 in an area of ​​0.2m×0.1m (0.2m is the vertical range required for tensioning the wire rope), The specific value can be determined by referring to the average cost in the current market, including labor, material and other costs. For example, in one estimation method, It can be 1.24; ——The material and operation cost of the segmented tensioning and anchoring device in the reinforcement scheme (i.e., the intelligent segmented tensioning and anchoring module in the embodiment of the present application), in ten thousand yuan, The material and operating costs of the segmented tensioning and anchoring device (i.e., the intelligent segmented tensioning and anchoring module in the embodiment of the present application) generated each time the 360° wire rope is split are: The specific value can refer to the average cost of the current market, including labor, materials and other costs; The calculated portion represents the total number of segments of the 360° rope; for example, in one estimation, It can be 2.4; ——Material and labor costs of prestressed steel wire ropes in the reinforcement scheme, in ten thousand yuan, The material and labor cost for each 1m of prestressed steel wire rope used in the reinforcement scheme, The specific value can refer to the average cost of the current market, including labor, materials and other costs. Part is the total length of wire rope used in the entire reinforcement; for example, in one estimation method, It can be 1.3; ——The number of points in the friction coefficient matrix where the friction coefficient needs to be reduced. Assume that the number of rows of points in the friction coefficient matrix where the friction coefficient needs to be reduced is , ; - The friction coefficient of the marking point where the friction coefficient needs to be reduced; ——The friction coefficient of the concrete surface after reinforcement scheme treatment, that is, the target friction coefficient; ——The cross-sectional diameter of the concrete pier to be reinforced, in meters; ——Radius of the cross section of the concrete pier to be reinforced, in meters; ——Vertical height of the area to be reinforced of the concrete pier, in meters; ——Vertical spacing of steel wire ropes in the reinforcement scheme, in m; ——Vertical distribution quantity of steel wire ropes in the reinforcement scheme; ——1 / 2 of the central angle of the steel wire rope corresponding to the segmented reinforcement scheme, in radians, is the minimum limit value of the arc corresponding to the wire rope segmentation. Although the reinforcement effect can be improved by segmenting the wire rope, there is a minimum limit on the length of the wire rope segmentation. According to research, the minimum corresponding arc of the wire rope segmentation should not be less than 0.056, that is, in one embodiment, =0.056; ——The segment length of the steel wire rope in the reinforcement scheme, in meters; ——The minimum compressive stress that the reinforcement scheme can form on the surface of the concrete pier, in MPa; ——Prestressed steel wire rope tensioning control stress, unit is MPa; ——The maximum allowable stress of prestressed steel wire rope, in MPa.

[0035] In the above formula, W1, W2, and W3 represent the cost of reducing the friction coefficient of concrete in the reinforcement scheme, the material and operation cost of the intelligent segmented tensioning anchor module in the reinforcement scheme, and the material and labor cost of the prestressed steel wire rope in the reinforcement scheme. The relationship between these three costs is one of increase and the other of decrease. The sum of the three costs is used as the objective function, and the optimization algorithm is used to calculate the scheme with the lowest total cost, and the friction coefficient of the concrete surface after treatment is obtained. , reinforcement scheme: vertical spacing of steel wire rope h, length of steel wire rope segments , Prestressed steel wire rope tensioning control stress . Control stress based on required prestressing wire rope tensioning Determine the maximum allowable stress of prestressed steel wire rope , thereby determining the wire rope model that meets the tension control stress.

[0036] In the cloud server, the above formula can be solved by using simulated annealing algorithm, particle swarm algorithm, grey wolf algorithm and other intelligent optimization algorithms to determine l, μ s , h, σ max Parameters such as wire rope model, wire rope segment length, wire rope vertical spacing, concrete surface target friction coefficient, etc. are determined to form a reinforcement plan, which is transmitted to the smart handheld terminal to guide on-site construction.

[0037] The embodiment of the present application is based on actual reinforcement conditions and adopts an optimization algorithm to determine parameters such as the wire rope model, wire rope segment length, wire rope vertical arrangement spacing, and concrete surface target friction coefficient based on minimizing reinforcement costs, so as to form a targeted reinforcement plan, with one plan for each pier column, carry out reinforcement work in a targeted manner, and minimize construction costs.

[0038] The embodiment of the present application adopts the method of segmented tensioning and anchoring of prestressed steel wire ropes combined with concrete surface treatment to reduce prestressed friction loss. While taking into account the construction cost, it solves the stubborn problems of traditional steel wire rope reinforcement of piers and greatly improves the reinforcement efficiency.

[0039] Based on the above-mentioned intelligent reinforcement method for compressive components based on big data, the embodiment of the present application provides an intelligent reinforcement device for compressive components based on big data, including: An image acquisition unit, used to receive a high-definition image of the surface of a concrete pier uploaded by a smart handheld terminal, wherein the image is an image of a compression component during reinforcement, and the compression component is a pier to be reinforced; A friction coefficient analysis unit, which is used to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking points by using an image recognition module based on a convolutional neural network, and generate a friction coefficient matrix, wherein the preset marking points are arranged at intervals on the steel wire rope to form a matrix position layout with m rows and n columns; The pier cracking stress analysis unit is used to obtain the cracking stress of the pier to be reinforced based on the sunshine, temperature, humidity, overload level and overload frequency information of the pier to be reinforced, which is input into the trained LSTM neural network model. ; The reinforcement scheme analysis unit is used to obtain a reinforcement scheme based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced. The reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model, and transmits the reinforcement scheme to the smart handheld terminal.

[0040] For the specific definition of the intelligent reinforcement device for pressure-bearing components based on big data, please refer to the definition of the intelligent reinforcement method for pressure-bearing components based on big data in the above text, which will not be repeated here. Each unit in the above-mentioned intelligent reinforcement device for pressure-bearing components based on big data can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the cloud server 4 in the form of hardware, or can be stored in the memory in the cloud server 4 in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units.

[0041] As an example, the intelligent reinforcement device for compressive components based on big data provided in the embodiment of the present application can be directly embodied as a combination of software modules executed by a processor. The software module can be located in a storage medium, and the storage medium is located in a memory. The processor reads the executable instructions included in the software module in the memory, and combines with the necessary hardware (for example, including a processor and other components connected to a bus) to complete the intelligent reinforcement method for compressive components based on big data provided in the embodiment of the present application.

[0042] The embodiment of the present application also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by the processor, the steps of the above-mentioned intelligent reinforcement method for pressure-bearing components based on big data are implemented. The computer-readable storage medium includes: an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, and any suitable combination of the above. It can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage node, etc.

[0043] The present invention is not limited to the above-mentioned specific implementation modes. Various changes made by ordinary technicians in this field based on the above-mentioned concepts without creative work are all within the protection scope of the present invention.

Claims

1. A method for intelligent reinforcement of compression components based on big data, applied to cloud servers, characterized in that: The steps include: Receiving a high-definition image of a concrete bridge pier surface uploaded by a smart handheld terminal, wherein the compression member is a bridge pier to be reinforced; Using a preset image recognition module, the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking points is identified to generate a friction coefficient matrix, wherein the preset marking points are arranged at intervals on the steel wire rope to form a matrix position layout with m rows and n columns; The cracking stress of the bridge pier to be reinforced is obtained by inputting the trained LSTM neural network model based on the sunshine, temperature, humidity, overload level and overload frequency information of the bridge pier to be reinforced. ; A reinforcement scheme is obtained based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced. The reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model. The reinforcement scheme is transmitted to the smart handheld terminal.

2. According to the big data-based intelligent reinforcement method for compressive components of claim 1, it is characterized in that: The preset image recognition module is used to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset mark point, including: Collect high-definition images of concrete pier surfaces with different degrees of roughness as training samples, and annotate the corresponding friction coefficients for the training samples; Training a target recognition model based on the training samples and the labeled data of the training samples, and using the trained target recognition model as the image recognition module; For a high-definition image of the concrete pier surface to be identified, the friction coefficient between the surface of the pier to be reinforced and the steel wire rope at a preset marking point in the image is obtained based on the image recognition module.

3. The intelligent reinforcement method for compressive components based on big data according to claim 1 is characterized in that: The sunshine, temperature, humidity, overload level and overload frequency information of the bridge pier to be reinforced are input into the trained LSTM neural network model to obtain the cracking stress of the bridge pier to be reinforced. ,include: Receiving positioning information uploaded by a smart handheld terminal, wherein the positioning information represents the position of the bridge pier to be reinforced, and the smart handheld terminal is equipped with a high-precision GPS positioning module; Based on the location of the bridge pier to be reinforced, obtain the annual sunshine, temperature, humidity, overload level and overload frequency information of the location; The acquired sunshine, temperature, humidity, overload level, and overload frequency information are input into the trained LSTM neural network model to obtain the cracking stress of the bridge pier to be reinforced. .

4. The intelligent reinforcement method for compressive components based on big data according to claim 1 is characterized in that: The training process of the LSTM neural network model includes: The finite element method is used to calculate the cracking stress of the bridge pier according to the temperature, sunshine conditions, humidity, overload level and overload frequency parameters at the bridge pier location. The temperature, sunshine conditions, humidity, overload magnitude, overload frequency parameters and the corresponding cracking stress are used as a training sample; Based on the temperature, sunshine conditions, humidity, and overload probability parameters of the training samples as input features, the pier cracking stress of the training samples is used as the target value to train the LSTM neural network.

5. The intelligent reinforcement method for compressive components based on big data according to claim 1 is characterized in that: The reinforcement scheme is based on Determine, among which, ; ； ,in, ; ； The constraints are: ; ； ； ； ； ； in: ——Main cost of reinforcement plan, in ten thousand yuan; ——The cost of reducing the friction coefficient of concrete in the reinforcement scheme, in ten thousand yuan, The cost required to reduce the friction coefficient of the concrete surface by 1 in an area of ​​0.2m×0.1m; ——Material and operating costs of the segmented tension anchorage devices in the reinforcement scheme, in ten thousand yuan, The material and operating costs of the segmented tensioning anchorage device generated each time the 360° wire rope is split; ——Material and labor costs of prestressed steel wire ropes in the reinforcement scheme, in ten thousand yuan, The material and labor cost for each 1m of prestressed steel wire rope used in the reinforcement scheme; ——The number of points in the friction coefficient matrix where the friction coefficient needs to be reduced. Assume that the number of rows of points in the friction coefficient matrix where the friction coefficient needs to be reduced is , ; - The friction coefficient of the marking point where the friction coefficient needs to be reduced; ——The friction coefficient of the concrete surface after reinforcement scheme treatment, that is, the target friction coefficient; ——The cross-sectional diameter of the concrete pier to be reinforced, in meters; ——Radius of the cross section of the concrete pier to be reinforced, in meters; ——Vertical height of the area to be reinforced of the concrete pier, in meters; ——Vertical spacing of steel wire ropes in the reinforcement scheme, in m; ——The number of vertically distributed steel wire ropes in the reinforcement scheme; ——The segmented steel wire rope in the reinforcement scheme corresponds to 1 / 2 of the central angle of the circle, in radians. It is the minimum limit value of the arc corresponding to the wire rope segmentation; ——The length of the steel wire rope segments in the reinforcement scheme, in meters; ——The minimum compressive stress that the reinforcement scheme can form on the surface of the concrete pier, in MPa; ——Prestressed steel wire rope tensioning control stress, unit is MPa; ——The maximum allowable stress of prestressed steel wire rope, in MPa.

6. The intelligent reinforcement method for compressive components based on big data according to claim 1 is characterized in that: The analysis of the reinforcement scheme is obtained based on iterative analysis of an intelligent optimization algorithm, and the intelligent optimization algorithm includes: a simulated annealing algorithm, a particle swarm algorithm, and a grey wolf algorithm.

7. An intelligent reinforcement device for compression components based on big data, characterized in that: include: An image acquisition unit, used to receive a high-definition image of the surface of a concrete pier uploaded by a smart handheld terminal, wherein the compression member is a pier to be reinforced; A friction coefficient analysis unit, which is used to identify the friction coefficient between the surface of the bridge pier to be reinforced and the steel wire rope at the preset marking points by using an image recognition module based on a convolutional neural network, and generate a friction coefficient matrix, wherein the preset marking points are arranged at intervals on the steel wire rope to form a matrix position layout with m rows and n columns; The pier cracking stress analysis unit is used to obtain the cracking stress of the pier to be reinforced based on the sunshine, temperature, humidity, overload level and overload frequency information of the pier to be reinforced, which is input into the trained LSTM neural network model. ; The reinforcement scheme analysis unit is used to obtain a reinforcement scheme based on the friction coefficient matrix of the marked points and the cracking stress calculation of the bridge pier to be reinforced. The reinforcement scheme includes the determined segment length of the wire rope, the vertical spacing of the wire rope, the target friction coefficient of the concrete surface, and the wire rope model, and transmits the reinforcement scheme to the smart handheld terminal.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An intelligent reinforcement system for compression components based on big data, characterized in that: include: Segmented prestressed steel wire ropes are used to reinforce compression members, which are concrete bridge piers; An intelligent segmented tensioning and anchoring module, comprising a tensioning and anchoring mechanical device and an intelligent control system, wherein the tensioning and anchoring mechanical device is connected to the segmented prestressed steel wire rope, and the intelligent control system is connected to the tensioning and anchoring mechanical device, and is used to perform bidirectional tensioning and anchoring on the segmented prestressed steel wire rope by controlling the tensioning and anchoring mechanical device; The smart handheld terminal is connected to the intelligent control system for interactively transmitting tensioning and anchoring data to obtain tensioning force and anchoring force, and monitor the entire tensioning process; it is also used to take ultra-high-pixel photos of the surface of the concrete pier; the smart handheld terminal is also connected to the cloud server for uploading data collected by the smart handheld terminal and receiving data sent by the cloud server; The cloud server is communicatively connected with the smart handheld terminal, and is used to execute any of the methods described in claims 1-6 based on the data uploaded by the smart handheld terminal to determine the reinforcement plan and send it to the smart handheld terminal, and is also used to monitor the entire tensioning process based on the data uploaded by the smart handheld terminal.

10. The intelligent reinforcement system for compression members based on big data according to claim 9 is characterized in that: The intelligent handheld terminal comprises: High-precision GPS positioning module, used to locate the concrete piers to be reinforced; The automated modeling module is used to generate the pier structure to be reinforced and the regional scene model based on the pier height, diameter, sunlight obstruction, and material property information collected on site.

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