Traction state monitoring method and device, equipment, storage medium and product

Abstract

The invention discloses a traction state monitoring method and device, equipment, a storage medium and a product, and relates to the technical field of computers.The traction state monitoring method comprises the steps that state data, collected by at least one state monitoring device at different positions, of a pulled profile are obtained, and the state data comprise time data and distance data; and performing speed and curvature calculation on the state data based on a preset algorithm to obtain a state monitoring result. According to the method, the problem that the accuracy of monitoring the traction state is reduced due to the fact that the traction speed cannot be accurately monitored by an existing method when the traction mechanism and the traction profile slide due to traction blockage in the actual traction process can be avoided.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a traction status monitoring method, device, equipment, storage medium and product. Background Art

[0002] Pultrusion technology is a manufacturing process for the continuous production of composite profiles. It is usually used to manufacture composite parts with constant cross-sections, such as rods, tubes, plates, etc. This technology can efficiently produce long and strong parts, and can maintain good surface finish and dimensional stability. In the process of applying pultrusion technology to produce composite profiles, it is indeed necessary to strictly monitor and detect the traction state of the product during the traction process, including speed and curvature, to ensure product quality and production efficiency.

[0003] In the related technology, the monitoring of traction speed is mainly aimed at the input motor speed, and closed-loop control is performed through the feedback speed of the motor encoder. However, if the traction is blocked during the actual traction process, causing the traction mechanism and the traction profile to slip, the current method cannot accurately monitor the traction speed, thereby reducing the accuracy of traction status monitoring. Summary of the invention

[0004] The main purpose of this application is to provide a traction status monitoring method, device, equipment, storage medium and product, aiming to solve the technical problem that if traction is blocked during the actual traction process, resulting in sliding between the traction mechanism and the traction profile, the current method cannot accurately monitor the traction speed, thereby reducing the accuracy of traction status monitoring.

[0005] To achieve the above objectives, the present application proposes a traction state monitoring method, the traction state monitoring method comprising:

[0006] Acquire status data of the towed profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data;

[0007] Based on a preset algorithm, the speed and curvature of the state data are calculated to obtain a state monitoring result.

[0008] In one embodiment, the state data includes time data and distance data, and the step of calculating the speed and curvature of the state data based on a preset algorithm to obtain the state monitoring result includes:

[0009] Based on the time data, determining first time data collected by a first monitoring device in the condition monitoring device and second time data collected by a second monitoring device in the condition monitoring device;

[0010] Determining a target speed of the pulled profile based on the first time data and the second time data;

[0011] Based on the distance data, determining a first distance collected by the first monitoring device, a second distance collected by the second monitoring device, and a third distance collected by a third monitoring device in the condition monitoring device;

[0012] Determining a target distance of the pulled profile based on the first distance, the second distance and the third distance;

[0013] Based on the target speed and the target distance of the pulled profile, speed and curvature calculations are performed to obtain a state monitoring result.

[0014] In one embodiment, the step of calculating the speed and curvature based on the target speed and the target distance of the pulled profile to obtain the state monitoring result includes:

[0015] The target speed of the pulled profile is judged to obtain a first monitoring result. If the first monitoring result indicates that the target speed exceeds a preset speed threshold, the first monitoring result is abnormal.

[0016] The target distance of the pulled profile is judged to obtain a second monitoring result. If the second monitoring result is that the target distance exceeds a preset curvature threshold, the first monitoring result is abnormal.

[0017] A status monitoring result is determined based on the first monitoring result and the second monitoring result.

[0018] In one embodiment, after the step of determining the status monitoring result based on the first monitoring result and the second monitoring result, the following steps are included:

[0019] The state monitoring result is judged. If the judgment result is that the state monitoring result is abnormal, the traction equipment is controlled to stop running and an alarm is triggered. If the judgment result is that the state monitoring result is not abnormal, the cutting module is controlled to cut the pulled profile to obtain the target product after cutting.

[0020] In one embodiment, if the judgment result is that the state monitoring result is not abnormal, then the cutting module is controlled to cut the pulled profile to obtain the cut target product, after the step includes:

[0021] Acquire a product image of the target product after cutting;

[0022] Performing curvature detection on the product image using a preset curvature detection model to obtain a curvature detection result;

[0023] Based on the bending detection result, the target products corresponding to the product image are screened to obtain the expected target products.

[0024] In one embodiment, before the step of performing curvature detection on the product image using a preset curvature detection model to obtain a curvature detection result, the step includes:

[0025] Obtaining a sample image and an initial curvature detection model;

[0026] Performing a qualified judgment on the sample image, and using the judgment result as a sample label;

[0027] Inputting the sample image into the initial curvature detection model to perform curvature judgment to obtain an initial judgment result;

[0028] Based on the initial judgment result and the sample label, iteratively training the initial curvature detection model to obtain a preset curvature detection model that meets the accuracy requirement;

[0029] The step of acquiring a sample image comprises:

[0030] Photograph the cut historical product to obtain a sample image;

[0031] Based on a preset distortion elimination model, distortion elimination is performed on the sample image after being photographed to obtain a sample image after distortion elimination.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a traction state monitoring device, the traction state monitoring device comprising:

[0033] An acquisition module, used to acquire status data of the towed profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data;

[0034] The calculation module is used to calculate the speed and curvature of the state data based on a preset algorithm to obtain a state monitoring result.

[0035] In addition, to achieve the above objectives, the present application also proposes a traction status monitoring device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the traction status monitoring method described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the traction state monitoring method described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the traction state monitoring method described above are implemented.

[0038] One or more technical solutions proposed in this application have at least the following technical effects:

[0039] Compared with the related art, the monitoring of the traction speed is mainly for the input motor speed, and the closed-loop control is performed by the feedback speed of the motor encoder. However, if the traction is blocked during the actual traction process, resulting in the sliding of the traction mechanism and the traction profile, the current method cannot accurately monitor the traction speed, which reduces the accuracy of the traction state monitoring. In comparison, the present application obtains the state data of the traction profile collected by at least one state monitoring device at different positions, wherein the state data includes time data and distance data; based on a preset algorithm, the speed and curvature of the state data are calculated to obtain the state monitoring result. It can be understood that the present application does not need to perform closed-loop control on the feedback speed of the motor encoder for the input motor speed, and then calculates the speed and curvature of the time data and distance data at different positions by a preset algorithm. Then, after the algorithm calculation, the state monitoring result is obtained, which can avoid the problem that if the traction is blocked during the actual traction process, resulting in the sliding of the traction mechanism and the traction profile, the current method cannot accurately monitor the traction speed, which reduces the accuracy of the traction state monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flow chart of the first embodiment of the traction state monitoring method of the present application;

[0043] Figure 2 A specific flow chart of the traction state monitoring method of this application;

[0044] Figure 3 A schematic diagram of the installation of a condition monitoring device for the traction condition monitoring method of this application;

[0045] Figure 4 This is a schematic diagram of the module structure of the traction state monitoring device according to an embodiment of the present application;

[0046] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the traction state monitoring method in the embodiment of the present application.

[0047] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] The main solution of the embodiment of the present application is: to obtain the status data of the pulled profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data; based on a preset algorithm, the speed and curvature of the status data are calculated to obtain the status monitoring result.

[0051] In the related technology, the monitoring of traction speed is mainly aimed at the input motor speed, and closed-loop control is performed through the feedback speed of the motor encoder. However, if the traction is blocked during the actual traction process, causing the traction mechanism and the traction profile to slip, the current method cannot accurately monitor the traction speed, thereby reducing the accuracy of traction status monitoring.

[0052] The present application collects time data and distance data of the towed profile at different positions by at least one state monitoring device. There is no need to input the motor speed, but to perform closed-loop control through the feedback speed of the motor encoder. Then, the speed and curvature of the time data and distance data at different positions are calculated through a preset algorithm. Then, after the algorithm calculation, the state monitoring result is obtained. This can avoid the problem that if the traction is blocked during the actual traction process, causing the traction mechanism and the traction profile to slip, the current method cannot accurately monitor the traction speed, thereby reducing the accuracy of the traction state monitoring.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the traction state monitoring device as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the embodiment of the present application provides a traction state monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the traction state monitoring method of the present application.

[0055] In this embodiment, please refer to Figure 2 , Figure 2 A specific flow chart of a traction state monitoring method is provided, and the traction state monitoring method includes steps S100 to S200:

[0056] Step S100, acquiring state data of the towed profile collected by at least one state monitoring device at different positions, wherein the state data includes time data and distance data;

[0057] It should be noted that the executor of this embodiment is a traction status monitoring device. The traction status monitoring device is provided with a product traction production line, and the product traction production line is provided with a laser rangefinder and a cutting module. The status monitoring device is a laser rangefinder. The number of the laser rangefinders is determined by the cutting length of the product set by the product traction production line. For example, for a product with a cutting length of 6 meters (m), 5 laser rangefinders are provided. A laser rangefinder is installed every 1m after the cutting module. The installation position is placed parallel to the traction direction. When the profile moves to the first laser rangefinder, the first laser rangefinder begins to record the effective distance data and the data recording time. When it comes to the second laser rangefinder, the actual speed can be recorded. In addition, it can be observed whether there is curvature based on the distance data of the laser rangefinder. Please refer to Figure 3 , Figure 3 Provides a schematic diagram of the condition monitoring equipment installation.

[0058] It is understandable that after the towed profile passes through the cutting module, the towed state monitoring device obtains the towed state data of the towed profile when passing through the towed state monitoring device through a laser rangefinder.

[0059] Step S200: Based on a preset algorithm, the speed and curvature of the state data are calculated to obtain a state monitoring result.

[0060] It should be noted that the condition monitoring result is abnormal or not. The traction condition monitoring device calculates the speed of the time data and the curvature of the distance data through a preset algorithm to obtain the condition monitoring result of abnormal or not.

[0061] In a feasible implementation manner, step S200 includes the following steps:

[0062] Based on the time data, determining first time data collected by a first monitoring device in the condition monitoring device and second time data collected by a second monitoring device in the condition monitoring device;

[0063] It is understandable that the first monitoring device and the second monitoring device are two adjacent laser rangefinders, and the speed of the towed profile between the two adjacent laser rangefinders can be monitored through the first time data and the second time data of the first monitoring device and the second monitoring device. The towing state monitoring device determines the first time data and the second time data collected by the adjacent laser rangefinders from the time data.

[0064] Determining a target speed of the pulled profile based on the first time data and the second time data;

[0065] It should be noted that the target speed is the actual speed of the towed profile between two adjacent laser rangefinders. The towing state monitoring device calculates the actual speed between two adjacent laser rangefinders according to the target speed calculation formula. The target speed calculation formula is as follows:

[0066]

[0067] Wherein, L1 is the first monitoring device, L2 is the second monitoring device, v is the speed of the profile, t1 is the time when a certain point of the profile passes through L1, t2 is the time when the same point of the profile passes through L2, and d is the distance between L1 and L2.

[0068] Based on the distance data, determining a first distance collected by the first monitoring device, a second distance collected by the second monitoring device, and a third distance collected by a third monitoring device in the condition monitoring device;

[0069] It is understandable that the first monitoring device, the second monitoring device and the third monitoring device are three adjacent laser rangefinders, and the towed profile first passes through the first monitoring device, then passes through the second monitoring device, and then passes through the third monitoring device after passing through the second monitoring device. The towing state monitoring device determines the first distance, the second distance and the third distance collected by the three adjacent laser rangefinders from the time data.

[0070] Determining a target distance between the towed profile and the second monitoring device based on the first distance, the second distance, and the third distance;

[0071] It should be noted that the target distance is the actual distance between the laser rangefinder and the towed profile. The towing state monitoring device calculates the actual distance between the middle laser rangefinder of three adjacent laser rangefinders and the towed profile according to the target distance calculation formula. The target distance calculation formula is as follows:

[0072]

[0073] Among them, L1 is the first monitoring device, L2 is the second monitoring device, and L3 is the third monitoring device. Taking L1 and L3 as the reference, the distance d2′ that L2 should measure under ideal circumstances is calculated. d13 is the known distance between L1 and L3, and d12 and d23 are the distances between L1 and L2, and L2 and L3 respectively.

[0074] Based on the target speed and the target distance of the pulled profile, speed and curvature are judged to obtain a state monitoring result.

[0075] It is understandable that the traction status monitoring device makes abnormal judgments on the speed and curvature of the towed profile based on the target speed and target distance to obtain the status monitoring results.

[0076] In a feasible implementation manner, based on the target speed and the target distance of the towed profile, the step of determining the speed and curvature to obtain the state monitoring result includes the following steps:

[0077] The target speed of the pulled profile is judged to obtain a first monitoring result. If the first monitoring result indicates that the target speed exceeds a preset speed threshold, the first monitoring result is abnormal.

[0078] It should be noted that the traction status monitoring device compares the real-time speed data with a preset speed threshold. If the real-time speed exceeds the preset speed threshold range, the first monitoring result is considered abnormal.

[0079] The target distance of the pulled profile is judged to obtain a second monitoring result. If the second monitoring result is that the target distance exceeds a preset distance threshold, then the second monitoring result is abnormal;

[0080] It is understandable that the traction state monitoring device compares the calculated target distance with the preset distance threshold. If the calculated target distance exceeds the preset distance threshold range, the second monitoring result is considered abnormal, that is, the bending of the profile exceeds the allowable range.

[0081] Specifically, the measured actual distance d2 is compared with the ideal distance d2′, and the deviation Δd2 is calculated. The specific formula is as follows:

[0082] Δd2=d2-d'2

[0083] Specifically, after obtaining the distance measured by each rangefinder to the profile, a three-dimensional coordinate system is established according to the position of the rangefinder to determine the position of each measuring point relative to the ideal straight line, and the deviation (Δd) of each measuring point relative to the ideal straight line is calculated. This deviation includes size and direction information. The data of all measuring points are used to fit a curve. This curve represents the actual bending shape of the profile. By analyzing the distance difference between the fitting curve and the ideal straight line, the bending degree and direction of the profile at each position can be obtained.

[0084] A status monitoring result is determined based on the first monitoring result and the second monitoring result.

[0085] It should be noted that the traction state monitoring device determines the final state monitoring result through the first monitoring result and the second monitoring result, wherein if any one of the first monitoring result and the second monitoring result is abnormal, the state monitoring result is abnormal.

[0086] In this embodiment, the traction status monitoring equipment monitors the actual speed of the traction speed in the pultrusion process and the degree of bending of the product on the horizontal plane by recording the time difference and distance data of multiple laser rangefinders, thereby increasing the external closed-loop data of the equipment, reducing the loss rate of the product, increasing the objectivity of the collected data and reducing the risk of product pultrusion failure.

[0087] The embodiment of the present application provides a traction state monitoring method. In a feasible implementation manner, the step of determining the state monitoring result based on the first monitoring result and the second monitoring result includes the following steps:

[0088] The state monitoring result is judged. If the judgment result is that the state monitoring result is abnormal, the traction equipment is controlled to stop running and an alarm is triggered. If the judgment result is that the state monitoring result is not abnormal, the cutting module is controlled to cut the pulled profile to obtain the target product after cutting.

[0089] It is understandable that the product traction production line is also equipped with traction equipment. If the traction state monitoring device determines that the state monitoring result shows that the speed or curvature is abnormal, it will enter the abnormal processing process, control the traction equipment to stop running, and start the sound and light alarm to remind the operator to pay attention, and record the abnormal situation, wait for the operator to check and solve the problem, and manually restart the production process. If the traction state monitoring device determines that the state monitoring result shows that the speed or curvature is not abnormal, it will enter the normal processing process. When the traction profile arrives at the preset position in the product traction production line, the traction state monitoring device sends a cutting instruction to the cutting module to prepare to cut the profile. The cutting module completes the cutting action according to the instruction to obtain the target product after cutting.

[0090] In a feasible implementation manner, if the judgment result is that the state monitoring result is not abnormal, then the cutting module is controlled to cut the towed profile, and the step of obtaining the cut target product includes the following steps:

[0091] Acquire a product image of the target product after cutting;

[0092] It should be noted that the traction state monitoring device is provided with a shooting device, which is a high-resolution industrial camera to ensure that the image of the cut product can be clearly captured. The traction state monitoring device obtains the image of the cut product captured by the high-resolution industrial camera.

[0093] Performing curvature detection on the product image using a preset curvature detection model to obtain a curvature detection result;

[0094] It is understandable that the preset curvature detection model can detect the curvature of the product image. The traction state monitoring equipment removes image noise and improves image quality through the preset curvature detection model, and uses edge detection algorithms (such as Canny edge detection) to highlight the product outline, separate the product area from the background, and perform feature extraction on the separated product image, identify the key points of the product, such as endpoints and inflection points, etc., and extract the geometric features of the product, such as length, width and angle, etc., and perform curvature detection on the feature image obtained by feature extraction, obtain the curvature value of each product, and compare the obtained curvature value with the preset curvature threshold. If the curvature is less than or equal to the preset threshold, the curvature detection result is that the product is qualified; otherwise, the curvature detection result is that the product is unqualified.

[0095] Based on the curvature detection result, the target products corresponding to the product image are screened to obtain the expected target products.

[0096] It should be noted that the traction status monitoring equipment screens out qualified products that meet expectations based on the bending test results.

[0097] In a feasible implementation manner, the curvature detection of the product image is performed using a preset curvature detection model, and before the step of obtaining the curvature detection result, the following steps are included:

[0098] Obtaining a sample image and an initial curvature detection model;

[0099] It is understandable that the sample image is an accurate image of the product after cutting without lens distortion. The initial curvature detection model is an untrained deep learning model. The traction state monitoring device obtains an accurate image of the product after cutting without lens distortion and an untrained initial curvature detection model.

[0100] Performing a qualified judgment on the sample image, and using the judgment result as a sample label;

[0101] It should be noted that the traction status monitoring device judges the sample images after distortion is eliminated to determine which products are qualified and which are unqualified, and records the judgment results as sample labels and pairs them with the corresponding sample images.

[0102] Inputting the sample image into the initial curvature detection model to perform curvature judgment to obtain an initial judgment result;

[0103] It is understandable that the initial judgment result is the result of whether the sample image predicted by the initial curvature detection model is qualified. The traction state monitoring device inputs the sample image into the initial curvature detection model to perform curvature judgment and obtain the initial judgment result.

[0104] Based on the initial judgment result and the sample label, iteratively training the initial curvature detection model to obtain a preset curvature detection model that meets the accuracy requirement;

[0105] It should be noted that the traction status monitoring equipment continuously adjusts the model parameters according to the initial judgment results and sample labels, performs multiple iterative training until the model reaches satisfactory accuracy, tries different learning rate, batch size, optimizer and other hyperparameter combinations, and finds the optimal training strategy. When the model reaches the expected accuracy, the trained model is saved.

[0106] The step of obtaining the sample image includes the following steps:

[0107] Photographing the cut historical product to obtain a photographed historical product image;

[0108] It is understandable that the historical product is a product that has been produced. The traction state monitoring device uses a shooting device to shoot the cut and produced product to obtain a shot historical product image.

[0109] Based on the calibration algorithm, distortion is eliminated for the captured historical product image, and the historical product image after distortion elimination is used as a sample image.

[0110] It should be noted that the traction status monitoring equipment first needs to calibrate the camera to obtain the camera's internal parameters (such as focal length, principal point position, etc.) and distortion coefficient, use a standard calibration plate with a known geometric pattern, capture images of the calibration plate from different angles and distances, extract the corner point positions of the calibration plate from the captured image, and use a calibration algorithm (such as the calibrateCamera function in OpenCV) to calculate the camera's internal parameters and distortion coefficient. Once the camera's internal parameters and distortion coefficient are obtained, these parameters can be used to correct the distortion in the image, and then, obtain the historical product image after distortion elimination, and use the historical product image after distortion elimination as the sample image.

[0111] In this embodiment, the traction state monitoring device performs curvature detection on the produced products through a preset curvature detection model, thereby avoiding detection omissions during the production process and improving the efficiency of product production.

[0112] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the traction status monitoring method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0113] This application also provides a traction status monitoring device, please refer to Figure 4 , the traction state monitoring device comprises:

[0114] An acquisition module 10, for acquiring status data of the towed profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data;

[0115] The calculation module 20 is used to calculate the speed and curvature of the state data based on a preset algorithm to obtain a state monitoring result.

[0116] Optionally, the calculation module includes:

[0117] A determination module is used to determine, based on the time data, first time data collected by a first monitoring device in the state monitoring device and second time data collected by a second monitoring device in the state monitoring device; determine a target speed of the pulled profile based on the first time data and the second time data; determine, based on the distance data, a first distance collected by the first monitoring device, a second distance collected by the second monitoring device, and a third distance collected by a third monitoring device in the state monitoring device; determine a target distance of the pulled profile based on the first distance, the second distance, and the third distance; perform speed and curvature calculations based on the target speed and the target distance of the pulled profile to obtain a state monitoring result.

[0118] Optionally, the determining module includes:

[0119] A judgment module is used to judge the target speed of the pulled profile to obtain a first monitoring result. If the first monitoring result is that the target speed exceeds a preset speed threshold, the first monitoring result is abnormal; judge the target distance of the pulled profile to obtain a second monitoring result. If the second monitoring result is that the target distance exceeds a preset curvature threshold, the first monitoring result is abnormal; and determine a status monitoring result based on the first monitoring result and the second monitoring result.

[0120] Optionally, the judging module includes:

[0121] The cutting module is used to judge the status monitoring result. If the status monitoring result is abnormal, the traction equipment is controlled to stop running and trigger an alarm. If the status monitoring result is normal, the cutting module is controlled to cut the pulled profile to obtain the target product after cutting.

[0122] Optionally, the cutting module comprises:

[0123] The screening module is used to obtain a product image of the target product after cutting; perform curvature detection on the product image through a preset curvature detection model to obtain a curvature detection result; based on the curvature detection result, screen the target product corresponding to the product image to obtain a target product that meets expectations.

[0124] Optionally, the screening module includes:

[0125] A training module is used to obtain a sample image and an initial curvature detection model; make a qualified judgment on the sample image and use the judgment result as a sample label; input the sample image into the initial curvature detection model to make a curvature judgment and obtain an initial judgment result; based on the initial judgment result and the sample label, iteratively train the initial curvature detection model to obtain a preset curvature detection model that meets the accuracy requirements; photograph the historical product after cutting to obtain a sample image after photographing; based on a preset distortion elimination model, perform distortion elimination on the sample image after photographing to obtain a sample image after distortion elimination.

[0126] The traction state monitoring device provided by the present application adopts the traction state monitoring method in the above embodiment, which can solve the technical problem of traction state monitoring. Compared with the prior art, the beneficial effects of the traction state monitoring device provided by the present application are the same as the beneficial effects of the traction state monitoring method provided by the above embodiment, and the other technical features in the traction state monitoring device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0127] The present application provides a traction state monitoring device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the traction state monitoring method in the above-mentioned embodiment one.

[0128] Reference below Figure 5 , which shows a schematic diagram of the structure of a traction state monitoring device suitable for implementing the embodiment of the present application. The traction state monitoring device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, tablet computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The traction state monitoring device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0129] like Figure 5As shown, the traction state monitoring device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the traction state monitoring device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the traction state monitoring device to communicate with other devices wirelessly or by wire to exchange data. Although the traction state monitoring device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0130] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0131] The traction state monitoring device provided by the present application adopts the traction state monitoring method in the above embodiment, which can solve the technical problem of traction state monitoring. Compared with the prior art, the beneficial effects of the traction state monitoring device provided by the present application are the same as the beneficial effects of the traction state monitoring method provided by the above embodiment, and the other technical features in the traction state monitoring device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0132] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0133] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0134] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the traction state monitoring method in the above-mentioned embodiment.

[0135] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0136] The computer-readable storage medium may be included in the traction state monitoring device; or may exist independently without being assembled into the traction state monitoring device.

[0137] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the traction state monitoring device, the traction state monitoring device: obtains state data of the pulled profile collected by at least one state monitoring device at different positions, wherein the state data includes time data and distance data; and calculates the speed and curvature of the state data based on a preset algorithm to obtain a state monitoring result.

[0138] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0141] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned traction state monitoring method, and can solve the technical problem of traction state monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the traction state monitoring method provided by the above-mentioned embodiment, and will not be repeated here.

[0142] The present application also provides a computer program product, including a computer program, which implements the steps of the traction state monitoring method as described above when executed by a processor.

[0143] The computer program product provided by the present application can solve the technical problem of traction state monitoring. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the traction state monitoring method provided by the above embodiment, which will not be repeated here.

[0144] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A traction state monitoring method, characterized in that: The traction state monitoring method comprises: Acquire status data of the towed profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data; Based on a preset algorithm, the speed and curvature of the state data are calculated to obtain a state monitoring result.

2. The traction state monitoring method according to claim 1, characterized in that: The step of calculating the speed and curvature of the state data based on a preset algorithm to obtain the state monitoring result includes: Based on the time data, determining first time data collected by a first monitoring device in the condition monitoring device and second time data collected by a second monitoring device in the condition monitoring device; Determining a target speed of the pulled profile based on the first time data and the second time data; Based on the distance data, determining a first distance collected by the first monitoring device, a second distance collected by the second monitoring device, and a third distance collected by a third monitoring device in the condition monitoring device; Determining a target distance between the towed profile and the second monitoring device based on the first distance, the second distance, and the third distance; Based on the target speed and the target distance of the pulled profile, speed and curvature are judged to obtain a state monitoring result.

3. The traction state monitoring method according to claim 2, characterized in that: The step of determining the speed and curvature based on the target speed and the target distance of the towed profile to obtain the status monitoring result comprises: The target speed of the pulled profile is judged to obtain a first monitoring result. If the first monitoring result indicates that the target speed exceeds a preset speed threshold, the first monitoring result is abnormal. The target distance of the pulled profile is judged to obtain a second monitoring result. If the second monitoring result is that the target distance exceeds a preset distance threshold, then the second monitoring result is abnormal; A status monitoring result is determined based on the first monitoring result and the second monitoring result.

4. The traction state monitoring method according to claim 3, characterized in that: The step of determining a status monitoring result based on the first monitoring result and the second monitoring result comprises: The state monitoring result is judged. If the judgment result is that the state monitoring result is abnormal, the traction equipment is controlled to stop running and an alarm is triggered. If the judgment result is that the state monitoring result is not abnormal, the cutting module is controlled to cut the pulled profile to obtain the target product after cutting.

5. The traction state monitoring method according to claim 4, characterized in that: If the judgment result is that the state monitoring result is not abnormal, then the cutting module is controlled to cut the pulled profile to obtain the target product after cutting, which includes: Acquire a product image of the target product after cutting; Performing curvature detection on the product image using a preset curvature detection model to obtain a curvature detection result; Based on the curvature detection result, the target products corresponding to the product image are screened to obtain the expected target products.

6. The traction state monitoring method according to claim 5, characterized in that: The step of performing curvature detection on the product image by using a preset curvature detection model to obtain a curvature detection result includes: Obtaining a sample image and an initial curvature detection model; Performing a qualified judgment on the sample image, and using the judgment result as a sample label; Inputting the sample image into the initial curvature detection model to perform curvature judgment to obtain an initial judgment result; Based on the initial judgment result and the sample label, iteratively training the initial curvature detection model to obtain a preset curvature detection model that meets the accuracy requirement; The step of obtaining the sample image includes: Photographing the cut historical product to obtain a photographed historical product image; Based on the calibration algorithm, distortion is eliminated for the captured historical product image, and the historical product image after distortion elimination is used as a sample image.

7. A traction state monitoring device, characterized in that: The device comprises: An acquisition module, used to acquire status data of the towed profile collected by at least one status monitoring device at different positions, wherein the status data includes time data and distance data; The calculation module is used to calculate the speed and curvature of the state data based on a preset algorithm to obtain a state monitoring result.

8. A traction status monitoring device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the traction state monitoring method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the traction state monitoring method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the traction state monitoring method according to any one of claims 1 to 6 are implemented.

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