Automatic testing method and device for inductance proximity sensor and related equipment

Abstract

The invention relates to the technical field of sensors, and discloses an automatic testing method and device for an inductance proximity sensor, electronic equipment and a storage medium. The method comprises the following steps: acquiring test configuration data of a tested inductance proximity sensor, and analyzing the test configuration data to obtain a first test distance; moving the tested inductance proximity sensor to a first test position through the test clamp, wherein the distance between the tested inductance proximity sensor and the target at the first test position is a first test distance; acquiring inductance change data and sensor output data of the detected inductance proximity sensor in the moving process; sending the first test distance, the inductance change data and the sensor output data as first test result data to a target machine learning model; and performing correction and optimization processing on the first test result data through the target machine learning model to obtain target test result data corresponding to the tested inductance proximity sensor. Through the method, the test efficiency is improved, and the test error is reduced.

Description

Technical Field

[0001] This application relates to the technical field of sensors, and in particular, to an automated test method, device, electronic device, and storage medium for inductive proximity sensors. Background Art

[0002] An inductive proximity sensor is a sensor widely used in the field of industrial automation. It detects the proximity of an object by measuring the change in inductance value. With the improvement of industrial automation, the requirements for the test efficiency and accuracy of inductive proximity sensors have also increased. Traditional test methods for inductive proximity sensors mostly rely on manual operations and still remain in the semi-automated or manual test stage. Usually, it includes using special test equipment such as micrometers and manual operations to set test parameters, execute the test process, and record test results. For the aforementioned traditional test methods, on the one hand, due to relying on manual operations, the test efficiency is low and the human error is large. On the other hand, the test cycle is long, the operation is complex, and the error rate of test results is high. Summary of the Invention

[0003] The main technical problem to be solved by the embodiments of this application is the complex operation, low efficiency, and many human errors in the test of traditional inductive proximity sensors.

[0004] To solve the above technical problem, the first technical solution adopted by the embodiments of this application is: to provide an automated test method for an inductive proximity sensor, including: obtaining test configuration data corresponding to the inductive proximity sensor to be tested, and parsing the test configuration data to obtain a first test distance; moving the inductive proximity sensor to be tested to a first test position according to the first test distance through a test fixture, where the distance between the inductive proximity sensor to be tested and the target at the first test position is the first test distance; collecting inductance change data during the movement of the inductive proximity sensor to be tested, and the sensor output data of the inductive proximity sensor to be tested; sending the first test distance, the inductance change data, and the sensor output data as first test result data to a trained target machine learning model; and performing correction and optimization processing on the first test result data through the target machine learning model to obtain target test result data corresponding to the inductive proximity sensor to be tested.

[0005] Optionally, the training steps of the target machine learning model include: obtaining the specification data, annotation information data, measurement index data, and historical test data of inductive proximity sensors of various models; successively performing data processing operations of data cleaning, data standardization, and feature engineering on the specification data, the annotation information data, the measurement index data, and the historical test data to obtain corresponding sample data sets; setting an initial machine learning model according to the data characteristics of the historical test data, and training the initial machine learning model with the data in the sample data set; optimizing the initial machine learning model according to preset evaluation indicators and training results to obtain the trained target machine learning model.

[0006] Optionally, after the step of correcting and optimizing the first test result data through the target machine learning model to obtain the target test result data corresponding to the measured inductive proximity sensor, the method further includes: sending the corrected and optimized first test result to a preset artificial intelligence analysis model; performing anomaly analysis and trend analysis on the first test result through the artificial intelligence analysis model to obtain corresponding anomaly analysis result data and trend analysis result data; adding the anomaly analysis result data and the trend analysis result data to the target test result data.

[0007] Optionally, the step of performing anomaly analysis on the first test result through the artificial intelligence analysis model includes: obtaining inductive proximity sensor baseline data, where the inductive proximity sensor baseline data is established after the artificial intelligence analysis model learns the test data of the inductive proximity sensor in the normal state; calculating whether there are abnormal values exceeding a preset threshold range in the first test result according to the inductive proximity sensor baseline data and a preset anomaly analysis algorithm; if there are abnormal values, marking the first test result as an abnormal test result.

[0008] Optionally, the step of performing trend analysis on the first test result through the artificial intelligence analysis model includes: predicting the trends of the first test distance, the inductance change data, and the sensor output data in the first test result through a preset time series analysis model to obtain test trend prediction data; sending the test trend prediction data to the target machine learning model, and optimizing the correction and optimization functions of the target machine learning model through the test trend prediction data.

[0009] Optionally, after the step of moving the inductive proximity sensor under test close to the first test position according to the first test distance by the test fixture, the method further includes: measuring, by a preset measuring device, a second test distance between the inductive proximity sensor under test and the target; calculating whether the deviation between the first test distance and the second test distance is within a preset distance deviation range; if not within the distance deviation range, adjusting, by the test fixture, the first test distance between the inductive proximity sensor under test and the target, and measuring, by the preset measuring device, the second test distance between the inductive proximity sensor under test and the target, until the deviation between the first test distance and the second test distance is within the distance deviation range.

[0010] Optionally, after the step of obtaining the target test result data corresponding to the inductive proximity sensor under test, the method further includes: sending the target test result data to a preset display module, and displaying the target test result data in a preset graphical manner by the display module; generating a corresponding target test report according to the target test result data, and sending the target test report to a preset recipient.

[0011] To solve the above technical problems, the second technical solution adopted in the embodiments of the present application is: providing an automated test device for an inductive proximity sensor, including: a test data parsing module, configured to obtain test configuration data corresponding to the inductive proximity sensor under test, and parse the test configuration data to obtain a first test distance; a test distance control module, configured to move the inductive proximity sensor under test to a first test position according to the first test distance by a test fixture, wherein the distance between the inductive proximity sensor under test and the target at the first test position is the first test distance; a test data acquisition module, configured to collect inductance change data during the movement of the inductive proximity sensor under test, and sensor output data of the inductive proximity sensor under test; a test data sending module, configured to send the first test distance, the inductance change data, and the sensor output data as first test result data to a trained target machine learning model; a test data optimization module, configured to perform calibration and optimization processing on the first test result data by the target machine learning model to obtain target test result data corresponding to the inductive proximity sensor under test.

[0012] To solve the above technical problems, the third technical solution adopted in the embodiments of the present application is: providing an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method for automatically testing an inductive proximity sensor as described above.

[0013] To solve the above technical problems, the fourth technical solution adopted in the embodiments of the present application is: to provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by an electronic device, cause the electronic device to execute the inductive proximity sensor automated test method as described above.

[0014] Different from the related art, the present application applies automated and intelligent testing technologies. In terms of efficiency and cost, automated testing greatly shortens the testing cycle, reduces the dependence on professionals, effectively improves the testing efficiency and reduces labor costs. In terms of accuracy and reliability, it avoids human operation errors, and with the help of machine learning and artificial intelligence models for real-time calibration and analysis, it improves the testing accuracy and adapts to complex environments. In terms of the testing process, the test program integrates multiple functions, automatically processes data, and simplifies the testing process and data processing complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily illustrated by corresponding drawings. These exemplary illustrations do not limit the embodiments. Elements with the same reference numerals in the drawings represent similar elements. Unless otherwise stated, the drawings in the figures do not constitute a scale limitation.

[0016] Figure 1 It is a schematic diagram of the operating environment of the inductive proximity sensor automated test method provided by the embodiments of the present application.

[0017] Figure 2 It is a schematic diagram of the execution process of the inductive proximity sensor automated test method provided by the embodiments of the present application.

[0018] Figure 3 It is a schematic diagram of the execution process of adjusting the test distance in the inductive proximity sensor automated test method provided by the embodiments of the present application.

[0019] Figure 4 It is a schematic diagram of the execution process of training a machine learning model in the inductive proximity sensor automated test method provided by the embodiments of the present application.

[0020] Figure 5 It is a schematic diagram of the system structure of the inductive proximity sensor automated test device provided by the embodiments of the present application.

[0021] Figure 6 It is a schematic diagram of the hardware structure of the electronic device for executing the inductive proximity sensor automated test method provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the objectives, technical solutions, and advantages of the present application more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] It should be noted that if there is no conflict, the various features in the embodiments of the present application can be combined with each other, and all are within the protection scope of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different module division from that in the device schematic diagram or a different order from that in the flowchart.

[0024] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0025] For ease of understanding of this embodiment, first, a detailed introduction to an automated testing method for an inductive proximity sensor disclosed in the embodiments of the present application will be given. Please refer to Figure 1 , Figure 1 which is a schematic diagram of the operating environment of the automated testing method for an inductive proximity sensor provided in the embodiments of the present application. As shown in Figure 1 , the execution subject of the automated testing method for an inductive proximity sensor provided in the embodiments of the present application is generally an electronic device with a certain computing ability, such as a computer device. In some possible implementation manners, the automated testing method for an inductive proximity sensor can be implemented by a processor invoking computer-readable instructions stored in a memory. Among them, Figure 1 the computer device in Figure 1 can be a server. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms. It can be understood that

[0026] Please continue to refer to Figure 2 , Figure 2 which is a schematic diagram of the execution process of the automated testing method for an inductive proximity sensor provided in the embodiments of the present application. As shown in Figure 2 , it includes the following steps S1 to S5.

[0027] S1. Obtain the test configuration data corresponding to the inductive proximity sensor to be measured, and parse the test configuration data to obtain the first test distance.

[0028] Among them, the inductive proximity sensor is a sensor that uses the principle of electromagnetic induction to detect the approach of an object. When a metal object approaches the induction surface of the sensor, it will cause a change in the magnetic field around the sensor, and the sensor judges whether the object is approaching based on this. The test configuration data is a set of pre-set data, which contains various parameters and condition information required for testing the inductive proximity sensor to be measured. It is usually stored in a specific file, database or configuration device and is used to guide the entire test process. The test configuration data may be set by the sensor manufacturer, test engineer or according to the specific application scenario.

[0029] S2. Move the inductive proximity sensor to be measured to the first test position according to the first test distance through the test fixture. Among them, the distance between the inductive proximity sensor to be measured and the target at the first test position is the first test distance.

[0030] Please continue to refer to Figure 3 , Figure 3 which is a schematic diagram of the execution process of adjusting the test distance in the automatic test method of the inductive proximity sensor provided by the embodiment of the present application. As Figure 3 shown, it includes the following steps S21 to S23.

[0031] S21. Measure the second test distance between the inductive proximity sensor to be measured and the target through a preset measuring device. Among them, the preset measuring device is a device specifically used for accurately measuring distances. Common ones include laser rangefinders, ultrasonic rangefinders, etc. The measuring device has high accuracy and reliability and can accurately measure the distance between the inductive proximity sensor to be measured and the target..

[0032] S22. Calculate whether the deviation between the first test distance and the second test distance is within the preset distance deviation range. Compare the calculated distance deviation with the preset distance deviation range. The preset distance deviation range is an allowable deviation interval set according to the performance of the sensor and application requirements. If the distance deviation is within this interval, it is considered that the distance meets the requirements; otherwise, it does not meet the requirements.

[0033] S23. If it is not within the distance deviation range, adjust the first test distance between the inductive proximity sensor to be measured and the target through the test fixture, and measure the second test distance between the inductive proximity sensor to be measured and the target through the preset measuring device until the deviation between the first test distance and the second test distance is within the distance deviation range.

[0034] Among them, the steps between S21 and S23 above can ensure that the sensor operates at the optimal working distance by accurately adjusting the distance between the inductive proximity sensor to be tested and the target so that it is within the preset distance deviation range, thereby improving the measurement accuracy and reliability of the sensor. For example, on an industrial automation production line, precise distance control can enable the inductive proximity sensor to more accurately detect the position and state of an object, reducing misjudgments and missed judgments. In addition, in the process of mass production of inductive proximity sensors, by adjusting and calibrating the distance of each sensor, the performance and quality of the product can be guaranteed to have high consistency. Each sensor can achieve a better working state under the same distance conditions, thereby improving the qualification rate and stability of the entire batch of products.

[0035] S3, collecting inductance change data of the inductive proximity sensor under test during movement, and sensor output data of the inductive proximity sensor under test.

[0036] Among them, it is necessary to pre-set the movement mode of the inductance proximity sensor to be tested, such as moving at a constant speed along a straight line, or moving back and forth according to a certain pattern, etc. The movement range is usually determined according to the design detection range of the sensor and the actual application scenario. According to the characteristics of the sensor and the test requirements, the appropriate data acquisition frequency is determined. The acquisition frequency should be high enough to capture small changes in inductance, but not too high to avoid generating too much data and increasing the burden of storage and processing.

[0037] S4. Send the first test distance, inductance change data and sensor output data as first test result data to the trained target machine learning model.

[0038] Please continue reading Figure 4 , Figure 4 is a schematic diagram of the execution flow of training a machine learning model in the automated testing method for an inductive proximity sensor provided in an embodiment of the present application, such as Figure 4 As shown, the following steps S41 to S44 are included.

[0039] S41. Obtain the specification data, marking information data, measurement index data, and historical test data of inductive proximity sensors of various models. For example, the specification data, marking information data, measurement index data, and historical test data of inductive proximity sensors with models such as M08, M12, M18, and M30. The specification data may include the size, detection distance, operating voltage, output type (such as NPN / PNP), protection level (IP level), etc. of the sensor. The marking information data may include the model, manufacturer, production batch, applicable environment, etc. of the sensor. The measurement index data may include performance indicators such as the response time, repeatability accuracy, temperature drift, and anti-interference ability of the sensor. The historical test data may include the test results of the sensor under different conditions, such as durability tests, environmental tests (temperature, humidity, vibration, etc.), and performance test data.

[0040] S42. Perform data processing operations of data cleaning, data standardization, and feature engineering on the specification data, marking information data, measurement index data, and historical test data in sequence to obtain the corresponding sample data set. For example, handle missing values, remove outliers, and unify the data format during the data cleaning process.

[0041] S43. Set an initial machine learning model according to the data characteristics of the historical test data, and train the initial machine learning model with the data in the sample data set.

[0042] S44. Optimize the initial machine learning model according to the preset evaluation metrics and training results to obtain the trained target machine learning model. For example, use the preset evaluation metrics (such as accuracy, recall rate, F1 score, mean squared error, etc.) to evaluate the performance of the model, and verify the generalization ability of the model through cross-validation or test set validation.

[0043] Among them, the steps between the above S41 to S44 can more accurately predict the performance of inductive proximity sensors through the comprehensive analysis of various data and the training of machine learning models. For example, predict the probability of sensor failure and changes in detection accuracy in advance, which helps to perform maintenance and replacement in a timely manner, reducing downtime and losses during the production process. The trained target machine learning model can provide support for production management and decision-making. For example, reasonably arrange the production plan and adjust the equipment operation parameters according to the performance prediction results of the sensor, improving production efficiency and management level.

[0044] S5. Perform calibration and optimization processing on the first test result data through the target machine learning model to obtain the target test result data corresponding to the measured inductive proximity sensor.

[0045] As a preferred implementation, after the above step S5, the corrected and optimized first test result can also be sent to a preset artificial intelligence analysis model. Then, the artificial intelligence analysis model performs anomaly analysis and trend analysis on the first test result to obtain corresponding anomaly analysis result data and trend analysis result data. For example, the artificial intelligence analysis model will compare the received first test result with the normal data pattern learned during the training process. If some data points deviate significantly from the normal pattern, the model will determine it as an anomaly. For example, when the inductance value of a sensor should fluctuate within a relatively stable range during normal operation, if the inductance value suddenly exceeds this range significantly at a certain moment, the model will identify it as an abnormal situation. The model can use statistical analysis methods (such as calculating the mean, standard deviation, and judging whether the data is abnormal through the Z-score), clustering analysis methods (dividing the data into different clusters, and abnormal data may be in isolated small clusters), or deep learning-based anomaly detection algorithms (such as autoencoders, judging whether the data is abnormal through the reconstruction error), etc. for anomaly analysis. Trend analysis aims to discover the change trend of the first test result over time or other variables. The model will perform time series analysis on the data to predict the change of the sensor performance index in the next period of time. For example, by analyzing the change of the sensor output signal over time, it can be judged whether its performance is gradually improving, declining, or remaining stable. Common methods include the moving average method, exponential smoothing method, ARIMA (Autoregressive Integrated Moving Average Model), etc., which can smooth the data and extract the long-term trend and periodic changes of the data. Finally, the anomaly analysis result data and trend analysis result data are added to the target test result data.

[0046] As an alternative implementation, the step of performing anomaly analysis on the first test result by the artificial intelligence analysis model may include: First, obtain the inductance close to the sensor baseline data, where the inductance close to the sensor baseline data is established after the artificial intelligence analysis model learns the test data of the inductance close to the sensor under normal conditions. Then, according to the inductance close to the sensor baseline data and a preset anomaly analysis algorithm, calculate whether there are abnormal values in the first test result that exceed the preset threshold range. Finally, if there are abnormal values, mark the first test result as an abnormal test result. By allowing the artificial intelligence analysis model to learn the test data of the inductance close to the sensor under normal conditions to establish the baseline data, the characteristics and patterns of the sensor during normal operation can be accurately captured. Since different inductance close sensors may have differences due to factors such as production process and use environment, the actual learning method can customize the reference standard of the normal state for each sensor. Compared with the general fixed threshold judgment method, it can more accurately reflect the true operating condition of the sensor, thus greatly improving the accuracy of anomaly detection.

[0047] As another alternative implementation, the step of performing trend analysis on the first test result through the artificial intelligence analysis model may include: First, predict the trends of the first test distance, inductance change data, and sensor output data in the first test result through a preset time series analysis model to obtain test trend prediction data. Then, send the test trend prediction data to the target machine learning model, and optimize the calibration and optimization functions of the target machine learning model through the test trend prediction data. Using the preset time series analysis model to predict the trends of the first test distance, inductance change data, and sensor output data comprehensively considers multiple key factors closely related to the performance of the inductance proximity sensor. Different data dimensions reflect the working state of the sensor from different perspectives. By integrating and analyzing these data, the changing trend of the sensor performance over time can be captured more comprehensively and accurately, avoiding the limitations of single-data-dimension analysis.

[0048] As yet another alternative implementation, after the step of obtaining the target test result data corresponding to the measured inductance proximity sensor, the target test result data may also be sent to a preset display module, and the target test result data is displayed in a preset graphical manner through the display module. Then, generate a corresponding target test report based on the target test result data and send the target test report to a preset recipient. Displaying the target test result data in a preset graphical manner can present complex data in the form of intuitive graphs (such as line charts, bar charts, pie charts, etc.). For example, using a line chart to display the inductance change data of the inductance proximity sensor at different time points can allow users to see the change trend of the data at a glance and quickly grasp the dynamic changes in the sensor performance. Compared with viewing a large number of digital tables, graphical display is clearer and easier to understand. Graphical display helps users discover potential patterns and anomalies in the data. By observing the trend, fluctuations, and relationships between different data series of the graph, it is easier to identify periodic changes, abnormal peaks or valleys in the sensor performance, etc., providing strong support for further analysis and decision-making.

[0049] The inductive proximity sensor automated testing method provided by the embodiments of this application uses technologies such as automated testing, machine learning, and artificial intelligence to test the inductive proximity sensor. In terms of efficiency and cost, the automated testing process significantly shortens the testing cycle, reduces the dependence on professional personnel, greatly improves the testing efficiency, and reduces the labor cost. In terms of accuracy and reliability, the automatic control of the computer program avoids human operation errors, and the machine learning and artificial intelligence models perform real-time calibration and analysis, significantly improving the testing accuracy, being able to adapt to complex and changing testing environments, solving problems in a timely manner, and enhancing the stability and reliability of the testing. In terms of process simplification, the testing program integrates functions such as parameter setting, execution, and result evaluation, automatically completes data preprocessing, calibration, and analysis, reduces manual intervention and data processing complexity. In addition, the in-depth analysis of the artificial intelligence model can provide test results and diagnostic information in real time, providing strong support for user decision-making.

[0050] Please continue to refer to Figure 5 , Figure 5 which is a schematic diagram of the system structure of the inductive proximity sensor automated testing device provided by the embodiments of this application. As Figure 5 shown, the inductive proximity sensor automated testing device 50 includes: a test data parsing module 51, a test distance control module 52, a test data acquisition module 53, a test data sending module 54, and a test data optimization module 55.

[0051] The test data parsing module 51 is used to obtain the test configuration data corresponding to the inductive proximity sensor to be tested, and parse the test configuration data to obtain the first test distance.

[0052] The test distance control module 52 is used to move the inductive proximity sensor to be tested to the first test position according to the first test distance through a test fixture, where the distance between the inductive proximity sensor to be tested and the target at the first test position is the first test distance.

[0053] The test data acquisition module 53 is used to collect the inductance change data of the inductive proximity sensor to be tested during the movement process, as well as the sensor output data of the inductive proximity sensor to be tested.

[0054] The test data sending module 54 is used to send the first test distance, the inductance change data, and the sensor output data as the first test result data to the trained target machine learning model.

[0055] The test data optimization module 55 is used to perform calibration and optimization processing on the first test result data through the target machine learning model to obtain the target test result data corresponding to the inductive proximity sensor to be tested.

[0056] As an alternative implementation, the inductive proximity sensor automated testing device 50 further includes a machine learning model training module, which is specifically configured to obtain the specification data, annotation information data, measurement index data, and historical test data of inductive proximity sensors of various models; perform data processing operations of data cleaning, data standardization, and feature engineering on the specification data, the annotation information data, the measurement index data, and the historical test data in sequence to obtain corresponding sample data sets; set an initial machine learning model according to the data characteristics of the historical test data, and train the initial machine learning model with the data in the sample data set; optimize the initial machine learning model according to preset evaluation metrics and training results to obtain the trained target machine learning model.

[0057] As an alternative implementation, the test data optimization module 55 is further configured to send the corrected and optimized first test result to a preset artificial intelligence analysis model; perform anomaly analysis and trend analysis on the first test result through the artificial intelligence analysis model to obtain corresponding anomaly analysis result data and trend analysis result data; add the anomaly analysis result data and the trend analysis result data to the target test result data.

[0058] As an alternative implementation, the test data optimization module 55 is further configured to obtain inductive proximity sensor baseline data, where the inductive proximity sensor baseline data is established after the artificial intelligence analysis model learns the test data of the inductive proximity sensor in a normal state; calculate whether there are abnormal values exceeding a preset threshold range in the first test result according to the inductive proximity sensor baseline data and a preset anomaly analysis algorithm; if there are abnormal values, mark the first test result as an abnormal test result.

[0059] As an alternative implementation, the test data optimization module 55 is further configured to predict the trends of the first test distance, the inductance change data, and the sensor output data in the first test result through a preset time series analysis model to obtain test trend prediction data; send the test trend prediction data to the target machine learning model, and optimize the correction and optimization functions of the target machine learning model through the test trend prediction data.

[0060] As an alternative implementation, the test distance control module 52 is further configured to measure a second test distance between the inductive proximity sensor under test and the target through a preset measuring device; calculate whether the deviation between the first test distance and the second test distance is within a preset distance deviation range; if not within the distance deviation range, adjust the first test distance between the inductive proximity sensor under test and the target through the test fixture, and measure the second test distance between the inductive proximity sensor under test and the target through the preset measuring device until the deviation between the first test distance and the second test distance is within the distance deviation range.

[0061] As an alternative implementation, the test data optimization module 55 is further configured to send the target test result data to a preset display module, and display the target test result data in a preset graphical manner through the display module; generate a corresponding target test report according to the target test result data, and send the target test report to a preset recipient.

[0062] It should be noted that the above-mentioned automated test device for inductive proximity sensors can execute the method for automated testing of inductive proximity sensors provided in the embodiments of the present application, and has functional modules and beneficial effects corresponding to the execution of the method. For technical details not described in detail in the embodiments of the automated test device for inductive proximity sensors, reference may be made to the method for automated testing of inductive proximity sensors provided in the embodiments of the present application.

[0063] Please continue to refer to Figure 6 , Figure 6 which is a schematic hardware structure diagram of an electronic device for executing the method for automated testing of inductive proximity sensors provided in the embodiments of the present application. As shown in Figure 6 , the electronic device 600 includes:

[0064] One or more processors 610 and a memory 620. Figure 6 Here, one processor 610 is taken as an example.

[0065] The processor 610 and the memory 620 can be connected through a bus or other means. Figure 6 Here, connection through a bus is taken as an example.

[0066] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules corresponding to the method for automated testing of inductive proximity sensors in the embodiments of the present application. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 620, that is, implements the method for automated testing of inductive proximity sensors in the above method embodiments.

[0067] The memory 620 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the inductive proximity sensor automated test device, etc. In addition, the memory 620 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 620 may optionally include a memory remotely located with respect to the processor 610, and these remote memories may be connected to the inductive proximity sensor automated test device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0068] The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, execute the inductive proximity sensor automated test method in any of the above method embodiments. For example, execute the method steps S1 to S5 described above Figure 2 in the method steps S21 to S23 described above Figure 3 in the method steps S41 to S44 described above Figure 4 to implement the functions of the modules 51 - 55 in Figure 5 the above.

[0069] The above product can execute the method provided by the embodiments of the present application and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, reference may be made to the method provided by the embodiments of the present application.

[0070] The embodiments of the present application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which when executed by one or more processors, such as Figure 6 one of the processors 610 in the above, can enable the above one or more processors to execute the inductive proximity sensor automated test method in any of the above method embodiments. For example, execute the method steps S1 to S5 described above Figure 2 in the method steps S21 to S23 described above Figure 3 in the method steps S41 to S44 described above Figure 4 to implement the functions of the modules 51 - 55 in Figure 5 the above.

[0071] An embodiment of the present application provides a computer program product. The computer program product includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by the electronic device, the electronic device can execute the inductive proximity sensor automated testing method in any of the above method embodiments. For example, execute the method steps S1 to S5 described above Figure 2 in the method steps S1 to S5 Figure 3 in the method steps S21 to S23 Figure 4 in the method steps S41 to S44, and implement Figure 5 the functions of modules 51 - 55 in

[0072] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] Through the description of the above embodiments, those of ordinary skill in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course also by hardware. Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order, and there are many other changes in different aspects of the present application as described above. For the sake of brevity, they are not provided in detail; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An automated test method for an inductive proximity sensor, characterized in that Including: Obtain the test configuration data corresponding to the inductive proximity sensor to be measured, and parse the test configuration data to obtain the first test distance; Move the inductive proximity sensor to be measured to the first test position according to the first test distance through a test fixture, wherein the distance between the inductive proximity sensor to be measured and the target at the first test position is the first test distance; Collect the inductance change data of the inductive proximity sensor to be measured during the movement process, as well as the sensor output data of the inductive proximity sensor to be measured; Send the first test distance, the inductance change data, and the sensor output data as the first test result data to the trained target machine learning model; Perform correction and optimization processing on the first test result data through the target machine learning model to obtain the target test result data corresponding to the inductive proximity sensor to be measured.

2. The automated test method for an inductive proximity sensor according to claim 1, wherein The training steps of the target machine learning model include: Obtain the specification data, annotation information data, measurement index data, and historical test data of various models of inductive proximity sensors; Perform data processing operations of data cleaning, data standardization, and feature engineering on the specification data, the annotation information data, the measurement index data, and the historical test data in sequence to obtain the corresponding sample data set; Set an initial machine learning model according to the data characteristics of the historical test data, and train the initial machine learning model with the data in the sample data set; Optimize the initial machine learning model according to the preset evaluation index and training result to obtain the trained target machine learning model.

3. The automated test method for an inductive proximity sensor according to claim 1, wherein After the step of performing correction and optimization processing on the first test result data through the target machine learning model to obtain the target test result data corresponding to the inductive proximity sensor to be measured, it further includes: Send the corrected and optimized first test result to a preset artificial intelligence analysis model; Perform anomaly analysis and trend analysis on the first test result through the artificial intelligence analysis model to obtain the corresponding anomaly analysis result data and trend analysis result data; Add the anomaly analysis result data and the trend analysis result data to the target test result data.

4. The automated test method for an inductive proximity sensor according to claim 3, characterized in that, The step of performing anomaly analysis on the first test result through the artificial intelligence analysis model includes: Obtain the inductive proximity sensor baseline data, wherein the inductive proximity sensor baseline data is established after the artificial intelligence analysis model learns the test data of the inductive proximity sensor in the normal state; According to the inductive proximity sensor baseline data and a preset anomaly analysis algorithm, calculate whether there are abnormal values exceeding the preset threshold range in the first test result; If there are abnormal values, mark the first test result as an abnormal test result.

5. The automated test method for an inductive proximity sensor according to claim 3, characterized in that The step of performing trend analysis on the first test result through the artificial intelligence analysis model includes: Predict the trends of the first test distance, the inductance change data, and the sensor output data in the first test result through a preset time series analysis model to obtain test trend prediction data; Send the test trend prediction data to the target machine learning model, and optimize the calibration and optimization functions of the target machine learning model through the test trend prediction data.

6. The automated test method for an inductive proximity sensor according to claim 1, wherein After the step of moving the inductive proximity sensor under test to the first test position according to the first test distance by the test fixture, the method further includes: Measuring a second test distance between the inductive proximity sensor under test and the target by a preset measuring device; Calculating whether the deviation between the first test distance and the second test distance is within a preset distance deviation range; If it is not within the distance deviation range, adjusting the first test distance between the inductive proximity sensor under test and the target by the test fixture, and measuring the second test distance between the inductive proximity sensor under test and the target by the preset measuring device until the deviation between the first test distance and the second test distance is within the distance deviation range.

7. The automated testing method for an inductive proximity sensor according to claim 1, characterized in that, After the step of obtaining the target test result data corresponding to the inductive proximity sensor under test, the method further includes: Sending the target test result data to a preset display module, and displaying the target test result data in a preset graphical manner by the display module; Generating a corresponding target test report according to the target test result data, and sending the target test report to a preset recipient.

8. An automatic testing device for an inductive proximity sensor, characterized in that, Comprising: A test data parsing module, configured to obtain test configuration data corresponding to the inductive proximity sensor under test, and parse the test configuration data to obtain a first test distance; A test distance control module, configured to move the inductive proximity sensor under test to a first test position according to the first test distance by a test fixture, wherein the distance between the inductive proximity sensor under test and the target at the first test position is the first test distance; A test data acquisition module, configured to acquire inductance change data during the movement of the inductive proximity sensor under test, and sensor output data of the inductive proximity sensor under test; A test data sending module, configured to send the first test distance, the inductance change data, and the sensor output data as first test result data to a trained target machine learning model; A test data optimization module, configured to perform calibration and optimization processing on the first test result data through the target machine learning model to obtain target test result data corresponding to the inductive proximity sensor under test.

9. An electronic device, characterized in that, Comprising: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method for automatically testing an inductive proximity sensor according to any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by an electronic device, the electronic device is enabled to execute the method for automatically testing an inductive proximity sensor according to any one of claims 1-7.

Images