A rapid detection method and detection system for motor production line based on visual detection

Abstract

The present application discloses a rapid detection method and detection system for a motor production line based on visual inspection, which belongs to the field of motor production technology, including: a server receives first detection data collected by an acquisition device and generates a control signal; the server feeds back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens unqualified products according to the control signal; establishes a control signal feedback mechanism, dynamically optimizes the control signal, and regularly evaluates the performance of the detection model; performs regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and the control signal feedback mechanism. In the implementation of the technical solution of the present application, data is collected on the motor production line, and a control signal is generated based on the analysis results of the collected data and sent to the production line equipment for adjustment. At the same time, the control signal is detected and optimized to improve the detection results and production quality of the motor production line.

Description

Technical Field

[0001] The present application relates to the technical field of motor production, and specifically to a rapid detection method and detection system for a motor production line based on visual detection. Background Art

[0002] A motor is usually composed of components such as a stator, a rotor, bearings, and wires. It is an electronic device that can convert electrical energy into mechanical energy and is widely used in various industries.

[0003] In the existing motor manufacturing process, motor automated production lines have been widely used. Through intelligent equipment and systems, automated management and control of the motor production process are realized. By integrating mechanical, electrical, and automated control technologies, the automated assembly, testing, debugging, and packaging of motors can be realized, thereby improving motor production efficiency and product quality.

[0004] However, although the current automated production lines are able to achieve efficient motor production, after production is completed, due to reasons such as component tolerance, damage, and installation, the motors produced may have defects and fail to meet factory standards, and thus need to be returned to the factory. The traditional method is to add an additional factory inspection process and perform power-on testing manually, but this method is highly dependent on manual labor, is prone to testing errors, and requires additional processes, which is time-consuming and labor-intensive.

[0005] Therefore, it is necessary to provide a motor production line rapid detection method and detection system based on visual detection to solve the above problems.

[0006] It should be noted that the above information disclosed in this background technology section is only for understanding the background technology of the present application concept, and therefore, it may contain information that does not constitute the prior art. Summary of the invention

[0007] Based on the above-mentioned problems existing in the prior art, the problem to be solved by the present application is: to provide a rapid detection method and detection system for a motor production line based on visual detection, by collecting data from each workstation of the motor production line, and sending corresponding control signals to the production line equipment based on the collected data, and detecting the control signals at the same time, to ensure the accuracy of the production line detection results.

[0008] The technical solution adopted by the present application to solve the technical problem is: a rapid detection method for a motor production line based on visual detection, the rapid detection method comprising:

[0009] The server receives first detection data collected by a collection device, the collection device is set at different positions of the production line, integrates and analyzes the first detection data through a detection model, and generates a control signal according to the analysis result;

[0010] The server feeds back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens out unqualified products according to the control signal. When the number of automatic adjustments is greater than the set threshold, it is determined to be an unqualified product;

[0011] Establish a control signal feedback mechanism to dynamically optimize the control signal and regularly evaluate the performance of the detection model;

[0012] Perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

[0013] During the implementation of the technical solution of the present application, data is collected on the motor production line, and a control signal is generated based on the analysis results of the collected data and sent to the production line equipment for adjustment. At the same time, the control signal is detected and optimized to improve the detection results and production quality of the motor production line.

[0014] Furthermore, the integrated analysis process includes the following steps:

[0015] Performing standardization processing on the first detection data to generate first standardized data;

[0016] Extracting feature values ​​from the first standardized data and establishing a feature database;

[0017] Using a machine learning algorithm to train the data in the feature database, a detection model is established, and the detection model has an input end and an output end;

[0018] The trained detection model is deployed to the server, the first detection data uploaded by the acquisition device is analyzed in real time, and a control signal is generated and fed back to the production line.

[0019] Furthermore, the control signal includes the following operations:

[0020] Automatic adjustment: control signals instruct the equipment to adjust its parameters to optimize the production process;

[0021] Screening out unqualified products: The control signal directly screens out unqualified motor products from the production line;

[0022] Among them, when the number of automatic adjustments is greater than the set threshold, it is judged as an unqualified product.

[0023] Furthermore, the specific implementation steps for establishing a feedback mechanism are as follows:

[0024] Detect the control signal itself to determine whether there is any abnormality in the process from generation to transmission completion. If there is any abnormality, adjust it or resend the signal;

[0025] Detect the response of production line equipment to determine whether the response time of production line equipment from receiving the correct control signal to making adjustments or screening is normal;

[0026] Collect feedback data from the production line, including the number of production line equipment adjustments during the inspection process and the number of defective products screened out, and calculate the production line stability factor based on the feedback data;

[0027] A dynamic optimization algorithm is established to adjust the parameters of the detection model according to the production line stability factor to adapt to changes in the production process. At the same time, a production line stability factor threshold is set. When the calculation result exceeds the production line stability threshold, an early warning signal is sent.

[0028] Furthermore, the control signal itself is detected by using a verification method based on real-time data analysis. By comparing the control signal with the preset standard, a real-time data analysis is immediately performed after the server generates the control signal to compare the control signal with the actual situation of the production line.

[0029] Furthermore, the response of the production line equipment is detected by using a timestamp-based recording method to record the response time of each device after receiving the control signal and compare it with the standard response time.

[0030] Furthermore, the production line stability factor is calculated through the workstation time period. The time period without adjustment and screening process is taken as the standard time period, and the total number of workstations is obtained. Then, from each screening action, the number of completed workstations is obtained, and the number of completed workstations is divided by the total number of workstations. The result is multiplied by the standard time period, and finally the mean square error is calculated for the calculation result.

[0031] Furthermore, the method for performing regular assessment and maintenance further includes:

[0032] Regularly collect and analyze the output data of the detection model to evaluate its performance;

[0033] Regularly check all components of the control signal feedback mechanism, including signal generation, transmission, reception and response detection;

[0034] Regularly maintain and calibrate production line equipment to ensure that its indicators meet production requirements;

[0035] Regularly conduct simulation tests on the entire production line to simulate various abnormal situations and check the response and processing capabilities of the control signal feedback mechanism and the detection model.

[0036] A motor production line rapid detection system based on visual detection, the rapid detection system comprising:

[0037] A data receiving module is used for the server to receive first detection data collected by a collection device, the collection device is set at different positions of the production line, and integrates and analyzes the first detection data through a detection model, and generates a control signal according to the analysis result;

[0038] The control signal feedback module is used for the server to feed back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens out unqualified products according to the control signal;

[0039] Dynamic optimization module, which is used to establish a control signal feedback mechanism, dynamically optimize the control signal, and regularly evaluate the performance of the detection model;

[0040] The evaluation and maintenance module is used to perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

[0041] The beneficial effects of the present application are as follows: the present application provides a rapid detection method and detection system for a motor production line based on visual inspection, which collects data from the motor production line, generates a control signal based on the analysis result of the collected data and sends it to the production line equipment for adjustment, and at the same time detects and optimizes the control signal, thereby improving the detection result and production quality of the motor production line, and can also significantly reduce production costs and improve production efficiency, and through real-time data collection and analysis, the production line can quickly respond to changes in the production process and reduce downtime caused by equipment failure or operating errors. In addition, the timely screening of unqualified products avoids waste of resources in subsequent processes, thereby reducing overall production costs.

[0042] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 This is an overall schematic diagram of a motor production line rapid detection method based on visual detection in this application;

[0045] Figure 2 This is a schematic diagram of the module structure of a motor production line rapid detection system based on visual inspection in this application. DETAILED DESCRIPTION

[0046] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0048] Embodiment 1: Figure 1 As shown, the present application provides a motor production line rapid detection method based on visual detection, which is applied to the motor production line to detect the motor assembly process during the production process, without adding additional detection lines, thereby improving the motor production efficiency and saving costs. The detection method includes the following steps:

[0049] Step 01: The server receives first detection data collected by a collection device, which is set at different positions of the production line, and integrates and analyzes the first detection data through a detection model, and generates a control signal according to the analysis result;

[0050] Since the traditional manual inspection method is time-consuming and laborious, and the inspection results rely on manual judgment and have low accuracy, collection equipment is set at various positions of the production line to collect real-time data of the motors in the production process, and the collected first inspection data is uploaded to the server for analysis and processing. After the motor is assembled, the inspection process is completed synchronously, so there is no need to add additional inspection lines;

[0051] Among them, the acquisition equipment includes but is not limited to winding testers, insulation resistance testers, vibration analyzers, visual acquisition cameras, etc. The first detection data collected includes but is not limited to the electrical performance of the winding, the integrity of the insulation layer, the vibration frequency, image data, etc. The acquisition equipment is set at various positions of the production line, specifically at various production and assembly positions of the motor. For example, a visual acquisition device is set at the feed point of the motor production line to collect images of the raw materials to be processed; a winding tester is set at the winding station to detect the electrical performance of the winding, etc., to achieve distributed detection. After the motor production is completed, the detection process is completed synchronously;

[0052] The server refers to a hardware module with functions such as data reception, analysis, and storage. For example, in this embodiment, the server may be an industrial computer or a PLC controller. The industrial computer has powerful data processing capabilities and stability, while the PLC controller can achieve fast real-time control, which is suitable for the motor production line rapid detection scenario in this embodiment.

[0053] It should be noted that there is a channel for transmitting data between the server and the acquisition device, which can be transmitted by wireless communication in this embodiment, such as local area network, mobile communication, Internet of Things communication, etc., and existing technologies can be applied to improve data transmission efficiency and real-time performance of data during data transmission, which will not be described in detail in this embodiment;

[0054] After receiving the first detection data collected by the collection device, since the first detection data contains multiple types of data, the server is required to integrate and analyze the first detection data. Specifically, the integration and analysis process includes the following steps:

[0055] Step A: performing standardization processing on the first detection data to generate first standardized data;

[0056] Since the sources and types of various data are different, standardization is required during integration and analysis. The technologies used in standardization include but are not limited to data normalization, data type conversion, and data format unification, so as to ensure that data from different sources and types can be uniformly processed and analyzed. For details, please refer to the existing technology.

[0057] Step B: extracting feature values ​​from the first standardized data and establishing a feature database;

[0058] After the standardization process, it is necessary to extract characteristic values ​​that are helpful for motor quality detection from the first standardized data, such as the resistance value of the winding, the thickness of the insulation layer, the frequency range of vibration, etc. These characteristic values ​​will be stored in the characteristic database for subsequent analysis and judgment. During the extraction process, the characteristic value type that is helpful for motor quality detection can be manually input, or it can be automatically judged by the server;

[0059] Step C: Using a machine learning algorithm to train the data in the feature database to establish a detection model, which has an input end and an output end;

[0060] In order to improve the accuracy and efficiency of detection, a machine learning algorithm can be used to train the data in the feature database, so as to establish a detection model that can automatically identify the quality of the motor. The model can determine whether the motor has defects based on the input feature values, thereby achieving rapid detection. Specifically, the detection model can be a support vector machine (SVM), a neural network (NN) or a random forest (RF), etc., and the appropriate algorithm can be selected according to different application scenarios and requirements;

[0061] Step D: deploy the trained detection model to the server, analyze the first detection data uploaded by the acquisition device in real time, and generate a control signal to feed back to the production line;

[0062] After the detection model is established, it needs to be deployed to the server to receive the first detection data uploaded by the acquisition device in real time, and use the model to analyze the data and finally generate control signals. These control signals can be used to guide the equipment on the production line to make corresponding adjustments, or directly used to screen out unqualified motor products;

[0063] The control signal can be used to guide the equipment on the production line to make corresponding adjustments, such as adjusting the tightness of the winding, adjusting the accuracy of the assembly position, etc., to ensure the production quality of the motor. At the same time, the control signal can also be used to screen out unqualified motor products to prevent them from flowing into the next production link or the hands of end users.

[0064] Step 02: The server feeds back the generated control signal to the production line. The equipment on the production line automatically adjusts or screens out unqualified products according to the control signal. When the number of automatic adjustments is greater than the set threshold, it is determined to be an unqualified product.

[0065] After integrating and analyzing the first detection signal, a corresponding control signal is generated. Specifically, the control signal can guide the following operations:

[0066] a. Automatic adjustment: The control signal instructs the equipment to adjust its parameters to optimize the production process. For example, if the detection model finds that the resistance value of the winding is beyond the normal range, the control signal will instruct the winding equipment to adjust the tightness of the winding to ensure that the resistance value returns to an acceptable range. Similarly, if the vibration analyzer detects that the vibration frequency of the motor is abnormal, the control signal will instruct the assembly equipment to adjust the accuracy of the assembly position to reduce the vibration when the motor is running;

[0067] b. Screening out unqualified products: The control signal can also be used to screen out unqualified motor products directly from the production line. Unqualified motor products will be automatically sorted to a specific area to prevent them from flowing into the next production link or the hands of end users;

[0068] Among them, when the number of automatic adjustments is greater than the set threshold, it is determined to be an unqualified product. In this embodiment, the set threshold is three quarters of the number of workstations. For example, when the total number of workstations is 20 and the number of automatic adjustments is greater than 15 times, it is automatically determined to be an unqualified product. Because under normal circumstances, the motor in the production process does not need to be adjusted many times, when the number of adjustments is too many, the motor is determined to be an unqualified product, which is convenient for the subsequent feedback process, thereby providing feedback to the production line;

[0069] In order to realize the above-mentioned automatic adjustment and screening functions, the equipment on the production line needs to be equipped with corresponding actuators, such as servo motors, cylinders, solenoid valves, etc. These actuators can perform precise physical actions according to the received control signals, thereby realizing real-time control of the production process.

[0070] Step 03: Establish a control signal feedback mechanism, dynamically optimize the control signal, and regularly evaluate the performance of the detection model;

[0071] In order to ensure the continuous improvement of the quality inspection process of the motor production line, it is also necessary to establish a control signal feedback mechanism, which will detect the control signal sent to the production line and dynamically optimize it according to the test results, thereby improving the accuracy of the detection model and the overall efficiency of the production line. Specifically, the implementation steps of establishing the feedback mechanism are as follows:

[0072] Step 301: Detect the control signal itself to determine whether there is any abnormality in the process from generation to transmission completion. If there is any abnormality, adjust it or resend the signal;

[0073] The detection of control signals includes many aspects, the most important of which is the source of control signal generation, because it is very likely that an abnormality has occurred when the control signal is generated, or an abnormality has occurred during the transmission process. In either case, the control signal received on the production line is unusable, and may even lead to incorrect adjustments or screening. Therefore, the control signal itself needs to be detected;

[0074] Among them, the control signal itself is detected by using a verification method based on real-time data analysis. By comparing the control signal with the preset standard, a real-time data analysis is immediately performed after the server generates the control signal, and the control signal is compared with the actual situation of the production line. For example, the control signal generated by the server is to adjust the tightness of the winding and increase the resistance of the winding. In the real-time data analysis process, the real-time data on the production line is retrieved, and the current winding resistance is compared with the target resistance to determine whether the control signal is accurate. If there is a deviation, an early warning is triggered, and a feedback signal is sent to the detection model to regenerate the control signal;

[0075] Step 302: Detect the response of the production line equipment to determine whether the response time of the production line equipment from receiving the correct control signal to performing adjustment or screening is normal;

[0076] After generating the correct control signal, it is also necessary to determine whether the response time of the production line equipment is normal, because the motor production line is a mobile production. If the response of the production line equipment times out, it will cause confusion in the detection process of the entire production line, triggering a chain reaction in the subsequent processes, and will also be fed back to the collection equipment, causing the collected data to not match the corresponding process, affecting the subsequent detection process;

[0077] Among them, the response of the production line equipment is detected by using a timestamp-based recording method to record the response time of each device after receiving the control signal and compare it with the standard response time. Different devices have different standard response times. Through comparative analysis, the device with response delay can be quickly located and troubleshooted. Specifically, the response time setting of the production line equipment can refer to the corresponding device manual, which is not described in detail in this embodiment;

[0078] Step 303: Collecting feedback data of the production line, which includes the number of production line equipment adjustments during the inspection process and the number of workstations of the screened defective products, and calculating the production line stability factor based on the feedback data;

[0079] When the raw materials of the motor are normal, the feedback data of the production line usually comes from the assembly process itself. Under normal circumstances, after multiple adjustments, the workstation where unqualified products are screened out should be at the back. If the workstation where unqualified products are screened out is at the front, it may be because there is a problem with the production process of the production line, resulting in the inability to complete most of the motor assembly. At the same time, if the number of adjustments is too many and the workstation with the largest number of unqualified products screened out is still at the front, it means that there is a problem in the process from data collection to data analysis, and the abnormal situation is not detected in the early stage. Therefore, it is necessary to calculate the production line stability factor to evaluate the stability of the production line, so as to facilitate the subsequent adjustment of the assembly process of the production line;

[0080] The production line stability factor is calculated by the workstation time period. Specifically, the time period without adjustment and screening is taken as the standard time period, and the total number of workstations is obtained. Then, the number of completed workstations is obtained from each screening process, and the number of completed workstations is divided by the total number of workstations. The result is multiplied by the standard time period. For example, the total number of workstations is 10. During a certain inspection process, a screening action is generated after completing 3 workstations. The standard time period is 120 seconds, and the calculation result is 0.3*120=36 seconds, indicating that during this inspection process, three workstations were passed, and the three workstations took 36 seconds to complete the screening action.

[0081] Then, other processes are calculated in the same way, and the mean square error is calculated for the calculation results to obtain the production line stability factor. The mean square error calculation can refer to the relevant formula, which will not be explained in detail here.

[0082] It should be noted that, although the above calculation results can also be obtained by directly timing the screening process, the direct timing method does not take into account the number and frequency of adjustment action stations, and cannot fully reflect the stability of the production line. Therefore, it is necessary to adopt the method in this embodiment;

[0083] Step 304: Establish a dynamic optimization algorithm to adjust the parameters of the detection model according to the production line stability factor to adapt to changes in the production process, and set a production line stability factor threshold. When the calculation result exceeds the production line stability threshold, send an early warning signal;

[0084] In addition to considering the assembly process of the production line, it is also necessary to adjust the parameters of the detection model according to the production line stability factor to prevent abnormal operation of the production line due to the output of the detection model, and set the production line stability factor threshold. When the calculation result exceeds the production line stability factor threshold, a warning signal is sent first to promptly inspect and repair the production line.

[0085] Step 04: Perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

[0086] In order to ensure continuous improvement in the quality inspection process of the motor production line, in addition to establishing a control signal feedback mechanism and a dynamic optimization algorithm, the entire system needs to be regularly evaluated and maintained, including regular evaluation of the performance of the detection model to ensure its accuracy and reliability, and checking the control signal feedback mechanism to ensure that it can detect and handle abnormal situations in a timely manner.

[0087] The specific implementation steps are as follows:

[0088] Step 401: Regularly collect and analyze the output data of the detection model to evaluate its performance. By comparing the prediction results of the detection model with the actual results, the model's accuracy, recall rate and other indicators can be calculated; if the model performance is found to be degraded, it is necessary to adjust the model parameters or retrain the model in time to adapt to changes in the production process.

[0089] Step 402: Regularly check the various components of the control signal feedback mechanism, including signal generation, transmission, reception and response detection; ensure that all equipment and software are operating normally without failure or performance degradation. For any problems found, repair or upgrade them in a timely manner.

[0090] Step 403: Regularly maintain and calibrate the production line equipment to ensure that its various indicators meet production requirements; for key equipment, a preventive maintenance plan can be set up to perform regular inspections and maintenance to reduce the occurrence of equipment failures.

[0091] Step 404: Regularly conduct simulation tests on the entire production line, simulate various abnormal situations, and check the response and processing capabilities of the control signal feedback mechanism and the detection model; through simulation tests, potential problems can be discovered and optimized and adjusted in advance.

[0092] Embodiment 2: Figure 2 As shown, the present application also proposes a motor production line rapid detection system based on visual detection, the system runs the rapid detection method in embodiment 1, and the system includes:

[0093] A data receiving module is used for the server to receive first detection data collected by a collection device, the collection device is set at different positions of the production line, and integrates and analyzes the first detection data, and generates a control signal according to the analysis result;

[0094] The control signal feedback module is used for the server to feed back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens out unqualified products according to the control signal;

[0095] Dynamic optimization module, which is used to establish a control signal feedback mechanism, dynamically optimize the control signal, and regularly evaluate the performance of the detection model;

[0096] The evaluation and maintenance module is used to perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

[0097] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A rapid inspection method for a motor production line based on visual inspection, characterized in that: The rapid test method includes: The server receives first detection data collected by a collection device, the collection device is set at different positions of the production line, integrates and analyzes the first detection data through a detection model, and generates a control signal according to the analysis result; The server feeds back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens out unqualified products according to the control signal. When the number of automatic adjustments is greater than the set threshold, it is determined to be an unqualified product; Establish a control signal feedback mechanism, dynamically optimize the control signal, and regularly evaluate the performance of the detection model; the specific implementation steps for establishing the feedback mechanism are as follows: Detect the control signal itself to determine whether there is any abnormality in the process from generation to transmission completion. If there is any abnormality, adjust it or resend the signal; Detect the response of production line equipment to determine whether the response time of production line equipment from receiving the correct control signal to making adjustments or screening is normal; Collect feedback data from the production line, including the number of production line equipment adjustments during the inspection process and the number of defective products screened out, and calculate the production line stability factor based on the feedback data; Establish a dynamic optimization algorithm to adjust the parameters of the detection model according to the production line stability factor to adapt to changes in the production process. At the same time, set the production line stability factor threshold. When the calculation result exceeds the production line stability threshold, send an early warning signal; Perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

2. The method for rapid inspection of a motor production line based on visual inspection according to claim 1, characterized in that: The integrated analysis process includes the following steps: Performing standardization processing on the first detection data to generate first standardized data; Extracting feature values ​​from the first standardized data and establishing a feature database; Using a machine learning algorithm to train the data in the feature database, a detection model is established, and the detection model has an input end and an output end; The trained detection model is deployed to the server, the first detection data uploaded by the acquisition device is analyzed in real time, and a control signal is generated and fed back to the production line.

3. The method for rapid inspection of a motor production line based on visual inspection according to claim 1 is characterized in that: The control signals include the following operations: Automatic adjustment: control signals instruct the equipment to adjust its parameters to optimize the production process; Screening out unqualified products: The control signal directly screens out unqualified motor products from the production line; Among them, when the number of automatic adjustments is greater than the set threshold, it is judged as an unqualified product.

4. The method for rapid inspection of a motor production line based on visual inspection according to claim 1 is characterized in that: The control signal itself is detected by using a verification method based on real-time data analysis. By comparing the control signal with the preset standard, a real-time data analysis is performed immediately after the server generates the control signal to compare the control signal with the actual situation of the production line.

5. The method for rapid inspection of a motor production line based on visual inspection according to claim 1 is characterized in that: The response of production line equipment is detected by using a timestamp-based recording method to record the response time of each device after receiving the control signal and compare it with the standard response time.

6. The method for rapid inspection of a motor production line based on visual inspection according to claim 1 is characterized in that: The production line stability factor is calculated through the workstation time period. The time period without adjustment and screening process is taken as the standard time period, and the total number of workstations is obtained. Then, from each screening action, the number of completed workstations is obtained, and the number of completed workstations is divided by the total number of workstations. The result is multiplied by the standard time period, and the mean square error is calculated for the calculation result.

7. The method for rapid inspection of a motor production line based on visual inspection according to claim 1 is characterized in that: Methods for conducting regular assessment and maintenance further include: Regularly collect and analyze the output data of the detection model to evaluate its performance; Regularly check all components of the control signal feedback mechanism, including signal generation, transmission, reception and response detection; Regularly maintain and calibrate production line equipment to ensure that its indicators meet production requirements; Regularly conduct simulation tests on the entire production line to simulate various abnormal situations and check the response and processing capabilities of the control signal feedback mechanism and the detection model.

8. A motor production line rapid inspection system based on visual inspection, characterized by: The rapid detection system includes: A data receiving module is used for the server to receive first detection data collected by a collection device, the collection device is set at different positions of the production line, and integrates and analyzes the first detection data through a detection model, and generates a control signal according to the analysis result; The control signal feedback module is used for the server to feed back the generated control signal to the production line, and the equipment on the production line automatically adjusts or screens out unqualified products according to the control signal; The dynamic optimization module is used to establish a control signal feedback mechanism, dynamically optimize the control signal, and regularly evaluate the performance of the detection model; the specific implementation steps for establishing the feedback mechanism are as follows: Detect the control signal itself to determine whether there is any abnormality in the process from generation to transmission completion. If there is any abnormality, adjust it or resend the signal; Detect the response of production line equipment to determine whether the response time of production line equipment from receiving the correct control signal to making adjustments or screening is normal; Collect feedback data from the production line, including the number of production line equipment adjustments during the inspection process and the number of defective products screened out, and calculate the production line stability factor based on the feedback data; Establish a dynamic optimization algorithm to adjust the parameters of the detection model according to the production line stability factor to adapt to changes in the production process. At the same time, set the production line stability factor threshold. When the calculation result exceeds the production line stability threshold, send an early warning signal; The evaluation and maintenance module is used to perform regular evaluation and maintenance to ensure the long-term effectiveness of the detection model and control signal feedback mechanism.

9. The motor production line rapid detection system based on visual detection according to claim 8 is characterized by: Used to implement the motor production line rapid detection method based on visual detection as described in any one of claims 1 to 7.

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