A steel cage process identification and edge deployment method, device and storage medium

Abstract

The present invention discloses a method, device and storage medium for identifying and edge deploying steel cage processes, comprising the following steps: Step 1: installing a high-definition surveillance camera, collecting steel cage construction process data and creating a data set; Step 2: designing a yolov7-tiny-iRMB model; Step 3: using the data set to train the yolov7-tiny-iRMB model; Step 4: detecting and outputting the steel cage detection frame coordinates to obtain the trajectory coordinates of the steel cage during construction; Step 5: analyzing the steel cage trajectory coordinates to identify the steel cage hoisting start, sleeve connection start, sleeve connection end and hoisting end processes and record the time of the corresponding processes; Step 6: using NVIDIA's embedded development board for model deployment. The present invention realizes the automatic identification of the edge side steel cage lowering process, thereby helping technicians record the lowering time of each steel cage section, effectively controlling the construction progress, preventing the occurrence of collapse accidents, and also improving the digitalization and intelligence level of bored pile construction.

Description

Technical Field

[0001] The present invention relates to the technical field of bored pile construction, and more particularly to a method, device, and storage medium for identifying and deploying a reinforcement cage process. Background Art

[0002] The current construction technology of bored piles is relatively mature and has a high degree of standardization. However, due to inadequate on-site management and the lack of scientific and effective process control methods, hole collapse accidents occur occasionally. Among them, failure to pour concrete in time after cleaning the hole and placing it for too long are one of the causes of hole collapse accidents. How to automatically record the placement time of each section of the steel cage on site and allow on-site technicians to effectively control the steel cages that have been placed for too long has become an important research direction. The main technical problems to be solved in this application include: 1. How to fill the technical gap in the identification of steel cage construction processes based on machine vision in bored pile construction; 2. The quality of wireless signals at the construction site is uneven, and remote transmission of video stream data is prone to data packet loss. The use of edge computing technology to realize local calculation of the video stream solves the problem of data packet loss; 3. Implement a vehicle-mounted steel cage construction process identification device, use a 12V vehicle power supply, and solve the power supply problem of the device.

[0003] To solve the above problems, this application uses technologies such as machine vision, data processing, and edge computing to propose a machine vision-based steel cage process identification and edge deployment method, equipment, and storage medium. This is not only a technological innovation, but also a transformation and upgrade of the bored pile construction method. Summary of the Invention

[0004] One purpose of the present invention is to provide a method, device and storage medium for identifying the process and deploying the edge of a steel cage, which realizes the automatic identification of the lowering process of the edge side steel cage, thereby helping technicians record the lowering time of each section of the steel cage, effectively control the construction progress, prevent the occurrence of collapse accidents, and improve the digitalization and intelligence level of bored pile construction.

[0005] In order to solve the above technical problems, the present invention provides a method for identifying and deploying steel cage processes, comprising the following steps:

[0006] Step 1: Install a high-definition surveillance camera to collect data on the steel cage construction process and create a dataset. Then, divide the dataset into a training set, a validation set, and a test set in proportion.

[0007] Step 2: Design the yolov7-tiny-iRMB model for steel cage target detection;

[0008] Step 3: Use the dataset prepared in step 1 to train the yolov7-tiny-iRMB model designed in step 2. That is, through image enhancement and model training, a model suitable for steel cage detection is obtained, and the model is converted to ONNX format for subsequent edge deployment.

[0009] Step 4: Detect and output the detection frame coordinates of the steel cage to obtain the trajectory coordinates of the steel cage during construction;

[0010] Step 5: By analyzing the obtained steel cage trajectory coordinates, identify the steel cage hoisting start, sleeve connection start, sleeve connection end and hoisting end processes and record the time of the corresponding processes;

[0011] Step 6: Use NVIDIA's embedded development board to deploy the model.

[0012] Preferably, in the step one, a total of two high-definition surveillance cameras are installed above the crawler crane cab and next to the bored piles to record the video of the steel cage construction process, and the lifting images of the steel cage at different stages of construction are captured therefrom, and the steel cage lifting images from two perspectives are obtained as a data set; in addition, Labelimg is used to mark the steel cage training set and validation set, and YOLO labels are generated.

[0013] Preferably, in the step 2, an iRMB module is added to the yolov7-tiny lightweight target detection network to form a yolov7-tiny-iRMB model.

[0014] Preferably, in step three, the model training is specifically as follows: first, the steel cage image is flipped symmetrically on its original axis, and the original data set is expanded several times to enhance the original data set; secondly, the data set is trained; finally, after the model is obtained, the model is converted into ONNX format.

[0015] Preferably, in step 5, the specific method for identifying and recording the steel cage construction process is:

[0016] S51. Create a dictionary dict to record the number, process and time of the steel cage;

[0017] S52: When it is determined that the acquired steel cage trajectory coordinate data is no longer zero, a steel cage width threshold is set. When the width of the steel cage detection frame is greater than the set width threshold and exceeds the time t, it is considered that the detection target is correct, the current process is the start of steel cage hoisting, the steel cage sequence number is increased by 1, and the start and time of the steel cage hoisting corresponding to the sequence number are recorded in the dictionary dict;

[0018] S53, after identifying the start process of the steel cage hoisting, the next process, namely, the start of the steel cage sleeve connection, is identified; a steel cage swing amplitude threshold is set. When the steel cage swing amplitude is less than the set swing amplitude threshold and lasts for 3 seconds, the current process is identified as the start of the steel cage sleeve connection, and the corresponding sequence number and time of the steel cage sleeve connection start are recorded in the dictionary dict;

[0019] S54. After identifying the start of the steel cage sleeve connection process, the next process, namely, the steel cage sleeve connection and the end of wire coiling welding, is identified. First, it is determined whether the steel cage is rising or falling in real time. Secondly, a steel cage height threshold is set. When it is determined that the steel cage is falling in real time and the height is lower than the set height threshold for more than 3 seconds, the current process is identified as the steel cage sleeve connection and the end of wire coiling welding. The corresponding sequence number of the steel cage sleeve connection and the end of wire coiling welding and the time are recorded in the dictionary dict.

[0020] S55. After identifying the completion of the steel cage sleeve connection and wire coil welding process, the next process, namely the completion of the steel cage hoisting, will be identified; the steel cage descent speed and the height of the steel cage above the ground are calculated, and then the completion time of the steel cage hoisting is calculated, and the completion and time of the steel cage hoisting of the corresponding sequence number are recorded in the dictionary dict.

[0021] Preferably, in the process of step S53, a sliding window is used to update the width information of the steel cage, and the size of the sliding window is set to k, and the data in the current window are x1, x2, x3, ..., x k , the average value of the sliding window is Calculated by the following formula:

[0022] Where i is the starting position of the current sliding window, j is the index in the sliding window, and the window sliding frequency is 2s / time.

[0023] Preferably, in step S54, the rising and falling states of the steel cage are judged in real time by a sliding window combined with a slope change method, and the size of the sliding window is set to k, and the y coordinates in the current window are y1, y2, y3, ..., y k , then the slope of the sliding window changes:

[0024] Among them, i is the starting position of the current sliding window, k is the size of the sliding window; if Δslope i When it is greater than 0, it indicates real-time rise; if Δslope i If it is less than 0, it means real-time decrease.

[0025] Preferably, in step six, the specific process is: first, the system is burned and the environment is set up; secondly, the designed file is run in the development board to verify whether the model is inferred normally; again, the inference process is executed on the development board GPU, that is, the input data is forward calculated through the TensorRT engine to obtain the output of the model, thereby realizing model optimization and acceleration; then the inference results are written into the steel cage construction process identification algorithm in real time using the publish-subscribe mode for identifying the steel cage process; finally, a module is installed in the development board to publish the recognition results of the steel cage construction process discrimination algorithm.

[0026] The present invention provides a computer device, which includes a memory, a processor, and a computer program for steel cage process identification and edge deployment stored in the memory and executable by the processor. When the processor executes the computer program, the steps of the method described above are implemented.

[0027] The present invention provides a storage medium storing a computer program, wherein the computer program implements the steps of the method described above when executed by a processor.

[0028] The present invention has at least the following beneficial effects:

[0029] 1. This application, by utilizing computer vision algorithms and image recognition technology, can achieve real-time monitoring and intelligent identification of the construction site, improve the digitalization and intelligence level of engineering construction, and fill the gap in the automatic identification technology of steel cage construction process in bored pile construction.

[0030] 2. This application realizes the automatic identification of the lowering process of the edge side steel cage, thereby helping technicians record the lowering time of each section of the steel cage, effectively controlling the construction progress, preventing the occurrence of collapse accidents, and also improving the digitalization and intelligence level of bored pile construction.

[0031] 3. The vehicle-mounted edge computing device developed in this application can calculate the video stream locally, reduce the transmission pressure and data packet loss when uploading data to the cloud, and improve the real-time and applicability of the method.

[0032] 4. This application realizes a vehicle-mounted steel cage construction process identification device, which uses a 12V vehicle-mounted power supply to solve the power supply problem of the device.

[0033] Other advantages, objectives and features of the present invention will be reflected in part from the following description and will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a structural diagram of the reinforcement cage data set of the present invention;

[0035] Figure 2 This is a diagram of the iRMB framework of the present invention;

[0036] Figure 3 This is the framework diagram of the yolov7-tiny-iRMB of the present invention;

[0037] Figure 4 This is a flow chart of the steel cage process identification of the present invention;

[0038] Figure 5 This is an implementation diagram of the machine vision-based steel cage process identification device of the present invention. DETAILED DESCRIPTION

[0039] In order to better understand the purpose, structure and function of the present invention, the present invention is further described in detail below with reference to the accompanying drawings so that those skilled in the art can implement it according to the description.

[0040] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified; in the description of the present invention, the terms "horizontal", "longitudinal", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0041] This application uses technologies such as machine vision, data processing, and edge computing to implement a vehicle-mounted device that records the process of steel cage placement on the edge side. It automatically records the current process and placement time of each section of steel cage and transmits it to the visualization cloud platform via the http protocol. From the cloud platform visualization interface, technicians can understand whether there are steel cages on site that have been placed for too long, thereby effectively controlling the placement time of the steel cage, reducing the probability of collapse accidents, and improving the digitalization and intelligence level of bored pile construction. Therefore, a method, equipment, and storage medium for identifying and deploying steel cage processes at the edge based on machine vision is not only a technological innovation, but also a transformation and upgrade of bored pile construction methods.

[0042] This application uses a target detection algorithm to track and identify the steel cage in the camera image to obtain its motion trajectory and coordinates during the hoisting process. The process recognition algorithm is used to analyze the changing trend of the steel cage trajectory coordinates in real time to determine the four process nodes, namely the start of hoisting of each section of the steel cage, the start of sleeve connection, the end of sleeve connection and the end of hoisting, and automatically record the time of each node. This algorithm is deployed on an embedded development board with artificial intelligence computing power and connected to a 12V vehicle power supply to realize an on-board device for edge computing. TensorRT is used to improve the model inference speed, thereby improving the computing performance of the embedded development board. The above method realizes the automatic identification of the lowering process of the edge side steel cage, thereby helping technicians record the lowering time of each section of the steel cage, effectively control the construction progress, and prevent collapse accidents.

[0043] The present invention provides a method for identifying the construction process and edge deployment of a steel cage based on machine vision. The specific implementation steps are as follows:

[0044] S1. Data collection and dataset creation.

[0045] A total of two high-definition surveillance cameras were installed above the crawler crane cab and next to the bored piles to record the video of the steel cage construction process and capture the lifting images of the steel cage at different stages of construction. At the same time, images of the steel cage hoisting were obtained from two perspectives to increase sample diversity.

[0046] A total of 600 images were captured and divided into training set, validation set and test set according to the ratio of 7:2:1. Labelimg was used to label the steel cage training set and validation set, and YOLO labels were generated. The steel cage dataset structure is as follows: Figure 1 shown.

[0047] S2. Design the yolov7-tiny-iRMB model.

[0048] The construction site environment is complex and there are many interference factors. During the shooting process, the camera may experience backlighting, dimming, overexposure, underexposure, occlusion, etc., which will affect the target detection network's detection effect on the steel cage. The steel cage process recognition algorithm mainly uses the coordinate changes of the steel cage detection frame as input. The unstable steel cage detection effect may lead to the possibility of misjudgment of the process recognition algorithm. To avoid this situation, this application adds an iRMB module to the yolov7-tiny lightweight target detection network to enhance the robustness and anti-interference ability of the detection network and improve the model detection accuracy. After adding the iRMB module, the parameters of the entire model only increase slightly, which will not increase the computing burden of the equipment.

[0049] iRMB module framework is as follows Figure 2As shown in Figure 2, where Attn Mat is the attention matrix. iRMB includes standard convolution, multi-head self-attention, and DW-Conv modules. The DW-Conv module performs convolution only on the input channel dimension, not on the output channel dimension. This significantly reduces the number of model parameters and complexity. Furthermore, during the convolution operation, DW-Conv performs convolution only on the channel dimension, not the spatial dimension. This maintains the spatial dimensions of the input feature map and helps preserve the spatial characteristics of the image. The feature map of each input channel is convolved only with the corresponding convolution kernel, maintaining the independence of the input channels, enabling better feature extraction, and reducing the risk of overfitting to a certain extent.

[0050] like Figure 3 As shown, the iRMB module is added after layers 57, 65, and 73 of the yolov7-tiny network. Within each branch, an attention mechanism is used to achieve long-range feature interaction and global context fusion. The attention mechanism adaptively adjusts the feature weights at each position to enhance the representation of important features and suppress unimportant ones. Features from each branch are concatenated channel-wise to form a holistic feature representation. By processing multiple branches in parallel and introducing an attention mechanism, the iRMB operation fully utilizes multi-scale and multi-semantic feature information, enhancing the network's expressive power and feature representation capabilities.

[0051] S3. Train the yolov7-tiny-iRMB model.

[0052] First, we used image flipping to perform axisymmetric flips on the rebar cage images: horizontal flipping, vertical flipping, and horizontal-vertical flipping. This process tripled the original dataset. After performing these enhancements, the dataset expanded from 600 images to 1,800.

[0053] Next, write the path to the yolov7-tiny-iRMB.yaml file and the Rebar Cage dataset to the train.py file and start training the network model on an Ubuntu 20.04 system and a GeForce GTX 2080Ti GPU. The training process uses the SGD stochastic gradient descent algorithm. In this experiment, the number of training epochs is set to 400, the initial learning rate is set to 0.001, and the training batch size is set to 24.

[0054] Finally, obtain the model best.pt and start the onnx model conversion code gen_wts.py to convert the model architecture to obtain the best.onnx model to match the TensorRT accelerated inference model format in the subsequent edge deployment process.

[0055] S4. Obtain the trajectory coordinates of the reinforcement cage.

[0056] Start the detect.py file, select best.pt for weights, select the source path to implement the recorded steel cage construction video or use Opencv to call the camera to obtain the steel cage construction video on site, and detect the detection frame coordinates xyxy of the steel cage and output them, so as to obtain the trajectory coordinates of the steel cage during construction.

[0057] S5, steel cage construction process identification algorithm. Figure 4 shown.

[0058] 1. Create a dictionary dict_cage to record the number, process and time of the steel cage.

[0059] 2. When the steel cage target is detected, the acquired trajectory coordinate data is no longer zero. In order to eliminate the possibility of false detection, this application determines whether there is a false detection by the size of the steel cage detection frame, and sets the steel cage width threshold to false_cage_w. When the detection frame width is greater than false_cage_w and exceeds the time t, it is considered that the detection target is correct, the current process is the start of the steel cage hoisting, the steel cage sequence number cagecount is increased by 1, and {the start of the cagecount-th steel cage hoisting: time} is recorded in dict_cage.

[0060] 3. After identifying the start of the steel cage hoisting process, the next process - the steel cage sleeve connection will be identified (if the current steel cage sequence number cagecount is 1, this process does not need to be identified, and the first steel cage does not need sleeve connection); at the same time, we need to filter valid data in real time to eliminate the interference of false detection data on the algorithm and improve the stability of the algorithm. We use a sliding window to update the width information of the steel cage. Let the size of the sliding window be k, and the data in the current window are x1, x2, x3, ..., x k The average value of the sliding window is It can be calculated by the following formula:

[0061] Where i is the starting position of the current sliding window and j is the index in the sliding window. This formula means that each time the window is moved, the data in the current window is summed and divided by the window size k to get the average value.

[0062] Each time we move the window, we only need to add the new element and subtract the oldest element in the window, then calculate the sum of the elements in the window again and divide it by the window size to get the new average value. The window sliding frequency is 2s / time.

[0063] 4. This application sets the steel cage swing amplitude threshold to x_dif. When the steel cage swing amplitude is less than x_dif and lasts for 3 seconds, it is recognized that the current process is the start of steel cage sleeve connection, and records {cagecount-th steel cage sleeve connection start: time} in dict_cage.

[0064] 5. After identifying the start process of the steel cage sleeve connection, the next process - steel cage sleeve connection and wire coiling welding will be identified. During the steel cage sleeve connection and wire coiling welding process, the crane will continuously change the lifting height of the steel cage, which makes it extremely difficult for us to identify this process. This application will use a sliding window combined with a slope change method to judge the rising and falling status of the steel cage in real time. The specific formula is as follows:

[0065] Assume that the size of the sliding window is k, and the y coordinates in the current window are y1, y2, y3, ..., y k . Then the slope of the sliding window changes: Where i is the starting position of the current sliding window and k is the size of the sliding window. This formula calculates the rate of change of the y-coordinate (i.e., the slope change) within the current window each time the window is moved.

[0066] Then, we can determine the trend of slope change by setting a threshold. i When it is greater than 0, it indicates real-time rise; if Δslope i If it is less than 0, it means real-time decrease.

[0067] Set the rebar cage height threshold y_dif. When it is determined that the rebar cage is descending in real time and its height is lower than y_dif for more than 3 seconds, the current process is identified as the rebar cage sleeve connection and the end of wire welding, and {cagecount-th rebar cage sleeve connection and wire welding end: time} is recorded in dict_cage.

[0068] 6. After identifying the completion of the steel cage sleeve connection and wire coil welding process, the next process - the completion of the steel cage hoisting process - will be identified.

[0069] First, calculate the descending speed of the steel cage. The specific formula is as follows:

[0070] Among them, y i is the height of the steel cage process when it is identified as the steel cage sleeve connection and wire welding process. Δt represents the time from the end of the steel cage sleeve connection and wire welding to the disappearance of the steel cage target from the screen. This formula represents the average speed of the steel cage lowering v ↓i .

[0071] Although the steel cage disappears from the camera image, a part of the steel cage is still exposed on the ground. This application uses the height y of the steel cage exposed on the ground. j and the average speed v of the steel cage lowering ↓i Calculate the time when the steel cage hoisting is completed. The formula is as follows:

[0072] Record in dict_cage {the hoisting of the cagecount-th steel cage ends: time t j}.

[0073] S7. Edge deployment.

[0074] This paper uses NVIDIA's embedded development board for environment construction and algorithm deployment. The deployment process is as follows:

[0075] 1. First, burn Ubuntu 18.04 to the development board's storage medium—the TF card. Configure the required environment and install various support packages, such as CUDA, pip3, jtop, opencv, pytorch, torchvision, and other dependencies and libraries. The aforementioned storage medium can also be a magnetic disk drive, traditional hard disk drive (HDD), rotational hard disk, magnetic recording storage, magnetic storage medium, solid-state drive (SSD), semiconductor storage device, flash memory hard disk, non-volatile storage medium, portable flash drive, USB flash memory, portable storage device, etc.

[0076] 2. After the system is burned and the environment is set up, the TF card is used as the main storage medium in the embedded system to store the operating system, application programs, and other software. Run detect.py on the development board to verify whether the model is inferring normally.

[0077] 3. Use the API provided by TensorRT to add the onnx format model to the TensorRT engine and select the appropriate inference algorithm and calculation precision (FP32, FP16, or INT8, etc.).

[0078] 4. Execute inference on the development board's GPU, using the TensorRT engine to perform forward computations on the input data to generate the model's output. TensorRT optimizes and parallelizes the model based on the GPU's hardware characteristics and the model's structure to improve inference speed and efficiency.

[0079] 5. Use the publish-subscribe model to write the inference results into the steel cage construction process discrimination algorithm in real time to identify the steel cage process.

[0080] 6. The development board is equipped with a SIM7600G-H 4G module to publish the recognition results of the steel cage construction process discrimination algorithm.

[0081] The present invention also provides an embodiment of a steel cage process identification device based on machine vision, such as Figure 5 Shown, including:

[0082] Video acquisition module 11, used for collecting steel cage lowering video stream data in real time;

[0083] The inference module 22 is used for inference of the steel cage detection model to obtain the coordinates of the steel cage motion trajectory; and for calculation of the steel cage process identification algorithm to obtain the current process and time of the steel cage in real time;

[0084] The data transmission module 33 is used to transmit text data of the current process and time of the steel cage;

[0085] The heat dissipation module 44 is used to reduce the temperature of the device so that the device can perform computing performance smoothly;

[0086] The positioning module 55 is used to determine the position of the device.

[0087] The present invention also provides a computer device, which includes a memory, a processor, and a computer program for steel cage process identification and edge deployment stored in the memory and executable by the processor. When the processor executes the computer program, the steps of the method described in this application are implemented.

[0088] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the present application are implemented.

[0089] It will be understood that the present invention is described by way of some embodiments, and those skilled in the art will appreciate that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and embodiments. They can be fully applied to various fields suitable for the present invention, and further modifications can be easily implemented by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for identifying and deploying steel cage processes, characterized in that: The steps include: Step 1: Install a high-definition surveillance camera to collect data on the steel cage construction process and create a dataset. Then, divide the dataset into a training set, a validation set, and a test set in proportion. Step 2: Design the yolov7-tiny-iRMB model for steel cage target detection; Step 3: Use the dataset prepared in step 1 to train the yolov7-tiny-iRMB model designed in step 2. That is, through image enhancement and model training, a model suitable for steel cage detection is obtained, and the model is converted to ONNX format for subsequent edge deployment. Step 4: Detect and output the detection frame coordinates of the steel cage to obtain the trajectory coordinates of the steel cage during construction; Step 5: By analyzing the obtained steel cage trajectory coordinates, identify the steel cage hoisting start, sleeve connection start, sleeve connection end and hoisting end processes and record the time of the corresponding processes; Step 6: Use NVIDIA's embedded development board to deploy the model; Among them, the specific methods for identifying and recording the steel cage construction process are: S51. Create a dictionary dict to record the number, process and time of the steel cage; S52: When it is determined that the acquired steel cage trajectory coordinate data is no longer zero, a steel cage width threshold is set. When the width of the steel cage detection frame is greater than the set width threshold and exceeds the time t, it is considered that the detection target is correct, the current process is the start of steel cage hoisting, the steel cage sequence number is increased by 1, and the start and time of the steel cage hoisting corresponding to the sequence number are recorded in the dictionary dict; S53, after identifying the start process of the steel cage hoisting, the next process, namely, the start of the steel cage sleeve connection, is identified; a steel cage swing amplitude threshold is set. When the steel cage swing amplitude is less than the set swing amplitude threshold and lasts for 3 seconds, the current process is identified as the start of the steel cage sleeve connection, and the corresponding sequence number and time of the steel cage sleeve connection start are recorded in the dictionary dict; S54. After identifying the start of the steel cage sleeve connection process, the next process, namely, the steel cage sleeve connection and the end of wire coiling welding, is identified. First, it is determined whether the steel cage is rising or falling in real time. Secondly, a steel cage height threshold is set. When it is determined that the steel cage is falling in real time and the height is lower than the set height threshold for more than 3 seconds, the current process is identified as the steel cage sleeve connection and the end of wire coiling welding. The corresponding sequence number of the steel cage sleeve connection and the end of wire coiling welding and the time are recorded in the dictionary dict. S55. After identifying the completion of the steel cage sleeve connection and wire coil welding process, the next process, namely the completion of the steel cage hoisting, will be identified; the steel cage descent speed and the height of the steel cage above the ground are calculated, and then the completion time of the steel cage hoisting is calculated, and the completion and time of the steel cage hoisting of the corresponding sequence number are recorded in the dictionary dict.

2. The reinforcement cage process identification and edge deployment method according to claim 1, characterized in that: In the step 1, a total of two high-definition surveillance cameras are installed above the crawler crane cab and next to the bored piles to record the video of the steel cage construction process, and the lifting images of the steel cage at different stages of construction are captured from the cameras. At the same time, the steel cage lifting images from two perspectives are obtained as a data set; in addition, Labelimg is used to label the steel cage training set and validation set, and YOLO labels are generated.

3. The reinforcement cage process identification and edge deployment method according to claim 1, characterized in that: In the step 2, the iRMB module is added to the yolov7-tiny lightweight target detection network to design and form a yolov7-tiny-iRMB model.

4. The reinforcement cage process identification and edge deployment method according to claim 2, characterized in that: In step three, the model training is specifically as follows: first, the steel cage image is flipped symmetrically along its original axis, and the original data set is expanded several times to enhance the original data set; secondly, the data set is trained; finally, after the model is obtained, the model is converted into ONNX format.

5. The reinforcement cage process identification and edge deployment method according to claim 1, characterized in that: During the step S53, the width information of the steel cage is updated using a sliding window. Assume that the size of the sliding window is k and the data in the current window is , the average value of the sliding window is , calculated using the following formula: ; Where i is the starting position of the current sliding window, j is the index in the sliding window; the window sliding frequency is 2s / time.

6. The reinforcement cage process identification and edge deployment method according to claim 1, characterized in that: In step S54, the rising and falling states of the steel cage are judged in real time by combining the sliding window with the slope change method. Assume that the size of the sliding window is k and the y coordinate in the current window is , then the slope of the sliding window changes: ; Where i is the starting position of the current sliding window, k is the size of the sliding window; if When it is greater than 0, it means real-time increase; if If it is less than 0, it means real-time decrease.

7. The reinforcement cage process identification and edge deployment method according to claim 1, characterized in that: In step six, the specific process is as follows: first, the system is burned and the environment is set up; second, the designed file is run in the development board to verify whether the model is inferring normally; third, the inference process is executed on the GPU of the development board, that is, the input data is forward calculated through the TensorRT engine to obtain the output of the model, thereby realizing model optimization and acceleration; then, the inference results are written into the steel cage construction process recognition algorithm in real time using the publish-subscribe mode for identifying the steel cage process; finally, a module is installed in the development board to publish the recognition results of the steel cage construction process discrimination algorithm.

8. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program for steel cage process identification and edge deployment stored in the memory and executable by the processor. When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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