An ATE device state detection method and apparatus

Abstract

The application provides an ATE device state detection method and device, the method comprises the following steps: determining a node in the ATE device in response to a selection instruction, collecting a first analog signal of the running ATE device through a monitoring system, processing the first analog signal, detecting the processed first analog signal through an abnormality detection system, and showing the current state of the ATE device to the user, and prompting the user when the ATE device currently has an abnormal state. The scheme can make the user know the running state of the ATE device in time, can identify the abnormal state of the device, can quickly find potential problems, can perform preventive maintenance, can reduce unexpected failures and maintenance costs, and can reduce the downtime of the device.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of ATE device state detection, and particularly relates to an ATE device state detection method and device. BACKGROUND

[0002] ATE devices develop towards higher integration, higher performance, more intelligence and lower cost, and the analog part of the ATE device is the core part of the connection between the instrument and the outside world, which determines the stability and reliability of the whole device.

[0003] Due to the increasing complexity of test efficiency and test scene, analog circuits frequently fail, and the diagnosis cost accounts for the vast majority of the test cost of the whole system. In the future, with the further development and improvement of integration technology, analog fault diagnosis will face greater challenges, and many traditional fault diagnosis methods will fail.

[0004] Currently, the fault diagnosis of the ATE device mainly relies on the monitoring of temperature and voltage, but only problems can be found, and once the problem occurs, a manual operation-dependent way is still needed to further analyze, locate and solve the fault. Although this way performs well in observation flexibility and universality, it seriously depends on the experience and ability of technical personnel, which may lead to human errors or omissions in the fault diagnosis process and limits the efficiency and accuracy of fault diagnosis. SUMMARY

[0005] In order to overcome the defects of the prior art, the application provides an ATE device state detection method, which comprises the following steps:

[0006] In response to a selection instruction, a plurality of specified nodes in the ATE device are determined;

[0007] In response to a detection instruction, a first analog signal of each of the specified nodes of the ATE device in operation is collected by a monitoring system;

[0008] The first analog signal is processed, and the processed first analog signal is detected by an abnormality detection system;

[0009] Based on the detection result, the current state of the ATE device is shown to the user, and when the detection result indicates that the ATE device currently has an abnormal state, the user is prompted by a warning system that the ATE device has an abnormality.

[0010] Specifically, the abnormality detection system comprises an abnormality detection model, and the method for training the abnormality detection model comprises the following steps:

[0011] collecting second analog signals of the specified nodes of the ATE device in normal operation, and collecting third analog signals of the specified nodes of the ATE device in multiple types of preset abnormal states;

[0012] processing the second analog signals and the third analog signals to add data of the processed second analog signals corresponding to the normal state, data of the processed third analog signals, and data of the abnormal state types of the ATE device corresponding to the data into a device state database;

[0013] training the abnormality detection model by taking the device state database as a training sample.

[0014] Optionally, the analog signals include noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data, and / or timestamp data.

[0015] The processing of the analog signals includes enhancement, channel correlation operation, normalization processing, filtering, frequency analysis, format conversion, feature extraction, and / or wavelet change.

[0016] Further, the method further includes:

[0017] verifying the state of the ATE device if the detection result does not indicate that the ATE device currently has an abnormal state;

[0018] If the verification result indicates that the ATE device currently does not have an abnormal state, it is determined that the detection result is accurate, and data of the processed first analog signals is added as analog signal data corresponding to the normal state into the device state database.

[0019] If the verification result indicates that the ATE device currently has an abnormal state, it is determined that the detection result is inaccurate, and data of the processed first analog signals and data of the abnormal state types of the ATE device corresponding to the data are added into the device state database.

[0020] Correspondingly, the method further includes:

[0021] verifying the state of the ATE device if the detection result indicates that the ATE device currently has an abnormal state;

[0022] If the verification result indicates that the ATE device currently does not have an abnormal state, it is determined that the detection result is inaccurate, and data of the processed first analog signals is added as analog signal data corresponding to the normal state into the device state database.

[0023] If the verification result shows that the abnormal state currently existing in the ATE device is exactly the same as the abnormal state indicated by the detection result, it is determined that the detection result is accurate, and the data of the processed first analog signal and the data of the abnormal state type of the ATE device corresponding thereto are added to the device state database;

[0024] If the verification result shows that the abnormal state existing in the ATE device is not exactly the same as the abnormal state indicated by the detection result, it is determined that the detection result is not exactly accurate, and the data of the processed first analog signal and the data of the abnormal state type of the ATE device corresponding thereto are added to the device state database.

[0025] Further, the method further comprises:

[0026] Based on all verification results in a preset time period, the detection accuracy of each type of abnormal state is determined respectively, and the abnormal state with a detection accuracy exceeding a first preset ratio value is determined as a first state, and the abnormal state with a detection accuracy not exceeding a second preset ratio value is determined as a second state; the first preset ratio value is not lower than the second preset ratio value;

[0027] If the detection result shows that the abnormal state currently existing in the first ATE device includes the second state, the state of the second ATE device is set to an abnormal state containing at least one first state, and the state of the second ATE device is detected;

[0028] If the result of detecting the second ATE device does not show that the second ATE device currently exists an abnormal state, or does not show that the abnormal state currently existing in the second ATE device contains all the set first states, a prompt is made to the user that the monitoring system, the abnormality detection system or the early warning system fails.

[0029] Preferably, the method further comprises:

[0030] If the detection result shows that the ATE device currently exists an abnormal state and the detection result meets a preset condition, an emergency measure corresponding to the detection result is executed through the early warning system.

[0031] The application further provides an ATE device state detection device, which comprises:

[0032] A selection module is configured to determine a plurality of specified nodes in an ATE device in response to a selection instruction;

[0033] A first acquisition module is configured to acquire, in response to a detection instruction, a first analog signal of each specified node of the ATE device in operation through a monitoring system;

[0034] a detection module, configured to process the first analog signal and detect the processed first analog signal through an anomaly detection system;

[0035] a notification module, configured to show the current state of the ATE device to a user based on the detection result, and prompt the user of the anomaly of the ATE device through a pre-warning system when the detection result indicates that the ATE device currently has an abnormal state.

[0036] The application further provides a computer readable storage medium storing executable instructions for implementing the ATE device state detection method when executed by a processor.

[0037] The application has at least the following beneficial effects:

[0038] The application can enable the user to flexibly select the ATE device node to be monitored according to actual needs, and realize real-time monitoring of the ATE device state by collecting the first analog signal of each specified node of the ATE device through the monitoring system, intelligently identifying the abnormal state of the ATE device and showing it to the user, and timely prompting the user through the pre-warning system when the ATE device is abnormal, helping the user to quickly locate the problem and take corresponding measures, which is helpful for preventive maintenance, avoids serious failure of the ATE device, prolongs the service life, and reduces maintenance costs, and the application is suitable for various ATE devices and has broad application prospects;

[0039] Further, the application can ensure that the model can learn the characteristics of the ATE device in the normal and abnormal states by collecting the analog signals of the specified nodes of the ATE device in the normal operation and various preset abnormal states, adding the processed data to the device state database to train the anomaly detection model, thereby improving the accuracy and generalization ability of the anomaly detection, and the various processing modes of the analog signals can make the signals easier to analyze and detect, improve the accuracy and efficiency of the anomaly detection, verify whether the detection result indicates that the ATE device has an abnormal state, and update the device state database according to the verification result, thereby constantly optimizing the performance of the anomaly detection model while enriching the database and ensuring the integrity, accuracy and diversity of the data in the database;

[0040] In addition, the application can determine the detection accuracy of each type of abnormal state based on the verification result, identify which abnormal state is easy to accurately detect and which is easy to misjudge or miss, and thereby verify whether the anomaly verification monitoring system, the anomaly detection system or the pre-warning system itself has a fault when an abnormal state that is easy to misjudge is detected, so as to verify whether each system has sufficient reliability and improve the automation degree of the overall method.

[0041] Therefore, the application provides an ATE device state detection method and device. The application sets and collects analog signals of specified nodes to perform real-time monitoring and processing, so that users can learn the running state of the ATE device in time, the abnormal state of the device can be effectively identified, potential problems can be quickly found, preventive maintenance can be provided for users, unexpected failures and maintenance costs can be reduced, and downtime caused by device failures can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 The overall method flowchart of the ATE device state detection method provided for embodiment 1 is shown in the figure.

[0044] Figure 2 The method flowchart for training the abnormality detection model is shown in the figure.

[0045] Figure 3 The flowchart of the ATE device state detection method is shown in the figure.

[0046] Figure 4 The method flowchart for verifying the state of each system is shown in the figure.

[0047] Figure 5 The module structure diagram of the ATE device state detection device provided for embodiment 2 is shown in the figure.

[0048] REFERENCE NUMERALS

[0049] 10 - selection module; 20 - first collection module; 30 - detection module; 40 - notification module; 50 - emergency module; 61 - second collection module; 62 - recording module; 63 - training module; 64 - verification module; 71 - classification module; 72 - setting module; 73 - prompt module. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] Hereinafter, various embodiments of the present application will be described more fully. The present application may, however, be embodied in many different forms and should not be construed as limited to the specific embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the application to those skilled in the art.

[0052] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the presence of the disclosed functions, operations, or elements and does not limit one or more functions, operations, or elements from being added. Also, as used in various embodiments of the present application, the terms "include", "have", and their conjugates merely indicate the presence of the features, numbers, steps, operations, elements, components, or combinations thereof, and do not exclude the possibilities of the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0053] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any combination of the listed terms or all combinations thereof. For example, the expression "A or B" or "at least one of A or / and B" can include A, can include B, or can include both A and B.

[0054] The expressions such as "first", "second", etc. used in various embodiments of the present application can modify various constituent elements in various embodiments, but can not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are used only for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element can be referred to as a second element, and likewise, a second element can be referred to as a first element, without departing from the scope of various embodiments of the present application.

[0055] It should be noted that in the present application, unless explicitly specified and defined otherwise, the terms "mount", "connect", "fixed", etc. should be understood broadly, for example, can be fixed connection, can be detachable connection, or integral connection; can be mechanical connection, or electrical connection; can be direct connection, or indirect connection through intermediate medium; can be internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0056] In the present application, those skilled in the art need to understand that the terms indicating the orientation or positional relationship herein are based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0057] The terms used in various embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit various embodiments of the present application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which various embodiments of the present application belong. The terms (such as those defined in a commonly used dictionary) will be interpreted to have the same meaning as the contextual meaning in the relevant technical field and will not be interpreted to have an idealized or overly formal meaning, unless clearly defined in various embodiments of the present application.

[0058] Embodiment 1

[0059] This embodiment proposes an ATE device state detection method, please refer to Figure 1 , the method comprises:

[0060] S100: In response to a selection instruction, determining a plurality of specified nodes in the ATE device.

[0061] Through step S100, the key nodes that need to be monitored in the ATE device can be determined, and the key nodes are monitored in the subsequent steps.

[0062] S200: In response to a detection instruction, acquiring a first analog signal of each specified node of the running ATE device through a monitoring system.

[0063] In this embodiment, the analog signal acquired at the specified node contains data related to the running state of the ATE device, which can include but is not limited to noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data, and timestamp data.

[0064] S300: Process the first analog signal, and detect the processed first analog signal through an anomaly detection system.

[0065] It should be noted that the processing of the analog signal in step S300 can include but is not limited to enhancement, channel correlation operation, normalization processing, filtering, frequency analysis, format conversion, feature extraction, wavelet transformation, through the processing of the analog signal, the signal can be adjusted to a unified standard range, improve the detectability of each feature in the signal, in order to facilitate comparison and analysis, so that the analog signal better reflects the state of the device;

[0066] Exemplarily, when the analog signal contains noise data, the anomaly detection system can detect the processed first analog signal through wavelet transformation and / or voiceprint recognition technology.

[0067] S400: based on the detection result, the current state of the ATE device is shown to the user, and when the detection result indicates that the ATE device currently has an abnormal state, the user is prompted by the early warning system that the ATE device has an abnormality.

[0068] S500: if the detection result indicates that the ATE device currently has an abnormal state and the detection result meets the preset condition, the emergency measures corresponding to the detection result are executed through the early warning system.

[0069] Optionally, the emergency measures corresponding to the detection result can include but are not limited to cutting off the power supply of the ATE device, enabling the standby circuit and switching the abnormal circuit, component part to the standby circuit.

[0070] It should be noted that the ATE device state detection method proposed in this embodiment can determine whether the ATE device has an abnormal state through the preset detection standard in step S400, and the preset detection standard can be the same as or different from the preset condition used in step S500; exemplarily, for the voltage data of a certain key node, the preset detection standard includes that the voltage is lower than 3V, and the preset condition includes that the voltage is lower than 2.5V, then the method proposed in this embodiment will prompt the user that the ATE device has an abnormality through the early warning system in step S400, but will not execute the step of "executing the emergency measures corresponding to the detection result through the early warning system" in step S500.

[0071] Preferably, the method proposed in this embodiment can preset a detection period, so that after the specified node is determined in step S100, the functions of steps S100 and S200 are periodically executed on the selected specified node.

[0072] Specifically, the anomaly detection system includes an anomaly detection model, please refer to Figures 2-3 The method for training the anomaly detection model includes:

[0073] S610: Collecting second analog signals of each specified node of the ATE device in normal operation and collecting third analog signals of each specified node of the ATE device in multiple types of preset abnormal states.

[0074] Specifically, the method proposed in the embodiment can collect analog signals in normal state and analog signals in various abnormal states under normal operation and different fault states of the ATE device. The user can configure the ATE device into various specified abnormal states such as power failure state, communication failure state, and hardware failure state according to actual needs, so as to ensure that the data collected in step S610 contains sufficient information for effective analysis and feature extraction.

[0075] S620: Processing each second analog signal and each third analog signal to add data of the processed second analog signal corresponding to the normal state, data of the processed third analog signal, and data of the abnormal state type of the ATE device corresponding thereto into the device state database.

[0076] S630: Training the abnormality detection model using the device state database as a training sample.

[0077] Specifically, the fault diagnosis model can train the extracted features by using machine learning algorithms such as support vector machine (SVM), decision tree, or neural network, and compare them with the data in the device state database, so as to establish and train the abnormality detection model. Through training, the abnormality detection model can learn the features of different faults and classify them.

[0078] Preferably, the method further comprises:

[0079] If the detection result does not indicate that the ATE device currently has an abnormal state, verifying the state of the ATE device;

[0080] If the verification result indicates that the ATE device currently does not have an abnormal state, judging that the detection result is accurate, and adding data of the processed first analog signal as analog signal data corresponding to the normal state into the device state database;

[0081] If the verification result indicates that the ATE device currently has an abnormal state, judging that the detection result is inaccurate, and adding data of the processed first analog signal and data of the abnormal state type of the ATE device corresponding thereto into the device state database.

[0082] Correspondingly, the method also comprises:

[0083] If the detection result indicates that the ATE device currently has an abnormal state, verifying the state of the ATE device;

[0084] If the verification result shows that the ATE device currently does not have an abnormal state, it is determined that the detection result is inaccurate, and the data of the processed first analog signal is added to the device state database as analog signal data corresponding to a normal state.

[0085] If the verification result shows that the ATE device currently has an abnormal state that is exactly the same as the abnormal state indicated by the detection result, it is determined that the detection result is accurate, and the data of the processed first analog signal and the data of the abnormal state type of the ATE device corresponding thereto are added to the device state database.

[0086] If the verification result shows that the ATE device has an abnormal state that is not exactly the same as the abnormal state indicated by the detection result, it is determined that the detection result is not exactly accurate, and the data of the processed first analog signal and the data of the abnormal state type of the ATE device corresponding thereto are added to the device state database.

[0087] It should be noted that the way of verifying the state of the ATE device can include manual verification and machine verification. By verifying the state of the ATE device and enriching the data of the device state database based on the verification result, the continuous upgrading of the fault diagnosis model is realized. In addition, the method proposed in this embodiment can also continuously iterate and correct the monitoring system and the early warning system based on the verification result.

[0088] Further, referring to Figure 4 , the method further comprises:

[0089] S710: Determine the detection accuracy of each type of abnormal state based on all verification results in the previous preset time period, and determine the abnormal state with a detection accuracy exceeding a first preset ratio value as a first state, and determine the abnormal state with a detection accuracy not exceeding a second preset ratio value as a second state.

[0090] It should be noted that the first preset ratio value is not less than the second preset ratio value. For example, the first state with a detection accuracy exceeding the first preset ratio value is an abnormal state that has been mastered, i.e., the first state, and the second state with a detection accuracy not exceeding the second preset ratio value is an abnormal state that has not been mastered. When the first preset ratio value is not equal to the second preset ratio value, the abnormal state with a detection accuracy between the first preset ratio value and the second preset ratio value is an abnormal state that has not been completely mastered.

[0091] S720: If the detection result shows that the abnormal state currently existing in the first ATE device includes the second state, the state of the second ATE device is set to an abnormal state containing at least one first state, and the state of the second ATE device is detected.

[0092] S730: If the result of the detection on the second ATE device does not indicate that the second ATE device currently has an abnormal state, or does not indicate that the abnormal state currently existing in the second ATE device contains all the set first states, the user is prompted that the monitoring system, the abnormality detection system or the early warning system is malfunctioning.

[0093] Therefore, the method proposed in the embodiment can verify the possibility of malfunction of the monitoring system, the abnormality detection system and the early warning system by setting the mastered abnormal state in another ATE device when it is detected that the abnormal state of the ATE device is an unmastered abnormal state, thereby improving the accuracy and reliability of fault detection.

[0094] Specifically, if the second ATE device can successfully identify and process the mastered abnormal state, it can be preliminarily judged that the monitoring system is operating normally. On the contrary, if it cannot be identified, it may indicate that the monitoring system has potential problems.

[0095] Further, the method proposed in the embodiment dynamically adjusts the first preset ratio value and the second preset ratio value by continuously accumulating and updating the detection accuracy of the abnormal state, thereby adapting to the changes of the device operating environment and the evolving technical requirements. This flexibility ensures the adaptability and foresight of each system, so that the detection and early warning mechanism always remains in an efficient state.

[0096] Embodiment 2

[0097] The embodiment proposes an ATE device state detection device for implementing the ATE device state detection method proposed in Embodiment 1, please refer to Figure 5 , which comprises:

[0098] The selection module 10 is configured to determine a plurality of specified nodes in the ATE device in response to a selection instruction.

[0099] The first acquisition module 20 is configured to acquire the first analog signals of the specified nodes of the ATE device in operation through the monitoring system in response to a detection instruction.

[0100] The detection module 30 is configured to process the first analog signals and detect the processed first analog signals through the abnormality detection system.

[0101] The notification module 40 is configured to show the current state of the ATE device to the user based on the detection result, and when the detection result indicates that the ATE device currently has an abnormal state, the user is prompted by the early warning system that the ATE device has an abnormality.

[0102] The emergency module 50 is configured to execute an emergency measure corresponding to the detection result through a warning system when the detection result indicates that the ATE device currently has an abnormal state and the detection result meets the preset condition.

[0103] In this embodiment, the analog signals collected at the specified nodes contain data related to the operating state of the ATE device, which can include but is not limited to noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data, and timestamp data.

[0104] The processing of the analog signals by the detection module 30 can include but is not limited to enhancement, channel correlation operation, normalization processing, filtering, frequency analysis, format conversion, feature extraction, and wavelet change.

[0105] Optionally, the emergency measure taken by the emergency module 50 can include but is not limited to cutting off the power supply of the ATE device, enabling a backup circuit, and switching an abnormal circuit or component part to the backup circuit.

[0106] Preferably, the method proposed in this embodiment can preset a detection period, so that after the selection module 10 determines the specified nodes, the functions of the selection module 10 and the first collection module 20 are periodically executed on the selected specified nodes.

[0107] Since the detection module 30 in the device proposed in this embodiment requires a certain cost, the cost of integrating the selection module 10, the first collection module 20, and the detection module 30 in each independent device is relatively high. Further, the detection module 30 can be arranged in a different device from the selection module 10 and the first collection module 20. In this way, the user can collect data through the selection module 10 and the first collection module 20 and send the collected data to the device provided with the detection module 30, so that the device provided with the detection module 30 detects the data, thereby achieving the effect of optimizing the use cost.

[0108] Specifically, the abnormality detection system includes an abnormality detection model, and the device further includes:

[0109] The second collection module 61 is configured to collect second analog signals of the specified nodes of the ATE device in normal operation and collect third analog signals of the specified nodes of the ATE device in multiple types of preset abnormal states.

[0110] The recording module 62 is configured to process each second analog signal and each third analog signal, so as to add the data of the processed second analog signal corresponding to the normal state, the data of the processed third analog signal, and the data of the type of abnormal state of the ATE device corresponding thereto into the device state database.

[0111] The training module 63 is configured to train the abnormality detection model by taking the device state database as a training sample.

[0112] Specifically, the second acquisition module 61 can collect analog signals of normal states and analog signals of various abnormal states during the normal operation and different fault states of the ATE device. The user can configure the ATE device into various specified abnormal states such as power failure state, communication failure state, and hardware failure state according to actual needs, thereby ensuring that the data collected by the second acquisition module 61 contains sufficient information for effective analysis and feature extraction; the fault diagnosis model can use machine learning algorithms such as support vector machines (SVM), decision trees or neural networks to train the extracted features and compare them with the data in the equipment status database to establish and train an anomaly detection model. Through training, the anomaly detection model can learn the characteristics of different faults and classify them.

[0113] The device further comprises:

[0114] The verification module 64 is configured to verify the state of the ATE device when the detection result does not indicate that the ATE device is currently in an abnormal state, so that when the verification result indicates that the ATE device is currently in no abnormal state, the detection result is determined to be accurate, and the processed first analog signal data is added to the device state database as analog signal data corresponding to a normal state; and when the verification result indicates that the ATE device is currently in an abnormal state, the detection result is determined to be inaccurate, and the processed first analog signal data and data corresponding to the abnormal state type of the ATE device are added to the device state database;

[0115] The verification module 64 can also verify the status of the ATE device when the detection result shows that the ATE device is currently in an abnormal state, so that when the verification result shows that the ATE device is not currently in an abnormal state, the detection result is judged to be inaccurate, and the data of the processed first analog signal is added to the device status database as the analog signal data corresponding to the normal state; and when the verification result shows that the abnormal state of the ATE device is exactly the same as the abnormal state shown by the detection result, the detection result is judged to be accurate, and the data of the processed first analog signal and the data of the abnormal state type of the corresponding ATE device are added to the device status database; and when the verification result shows that the abnormal state of the ATE device is not exactly the same as the abnormal state shown by the detection result, the detection result is judged to be inaccurate, and the data of the processed first analog signal and the data of the abnormal state type of the corresponding ATE device are added to the device status database.

[0116] Furthermore, the device further comprises:

[0117] The classification module 71 is configured to determine the detection accuracy of each type of abnormal state based on all verification results in the preset time period, and determine an abnormal state with a detection accuracy exceeding a first preset ratio value as a first state, and determine an abnormal state with a detection accuracy not exceeding a second preset ratio value as a second state.

[0118] The setting module 72 is configured to set the state of the second ATE device as an abnormal state containing at least one first state when the detection result indicates that the abnormal state currently existing in the first ATE device includes the second state, and detect the state of the second ATE device.

[0119] The prompting module 73 is configured to prompt the user that the monitoring system, the abnormality detection system or the early warning system is malfunctioning when the detection result of the second ATE device does not indicate that the second ATE device currently exists in an abnormal state, or does not indicate that the abnormal state currently existing in the second ATE device contains all the set first states.

[0120] It should be noted that the first preset ratio value is not lower than the second preset ratio value.

[0121] Therefore, the device provided in the embodiment can verify whether the monitoring system, the abnormality detection system and the early warning system are malfunctioning by setting the mastered abnormal state in another ATE device when the abnormal state of the ATE device is an unmastered abnormal state, thereby improving the accuracy and reliability of fault detection.

[0122] Specifically, if the second ATE device can successfully identify and handle the mastered abnormal state, it can be preliminarily judged that the monitoring system is normally running, otherwise, if it cannot be identified, it may indicate that the monitoring system has potential problems.

[0123] Further, the device provided in the embodiment can dynamically adjust the first preset ratio value and the second preset ratio value by continuously accumulating and updating the detection accuracy of the abnormal state.

[0124] Embodiment 3

[0125] The embodiment also provides a computer readable storage medium having computer instructions stored thereon, and the instructions are executed by a processor to implement the steps of the ATE device state detection method provided in the above embodiment 1.

[0126] Note that the computer-readable medium includes a non-transitory and transitory, movable and non-movable medium which can realize information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, disk storage, quantum memory, graphene-based storage medium or other magnetic storage device, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, the computer-readable medium does not include transitory media such as modulated data signals and carriers.

[0127] In summary, the present application provides an ATE device state detection method and device, the scheme provided by the present application sets and collects the analog signal of the specified node to carry out real-time monitoring processing in a targeted manner, so that the user can know the running state of the ATE device in time, the abnormal state of the device can be effectively identified, potential problems can be quickly found, preventive maintenance can be helped, unexpected failures and maintenance costs can be reduced, and downtime caused by device failure can be reduced.

[0128] The above is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting the status of an ATE device, characterized in that: The method comprises: In response to the selection instruction, determining a plurality of designated nodes in the ATE device; In response to a detection instruction, collecting, by a monitoring system, a first analog signal of each of the designated nodes of the running ATE device; processing the first analog signal, and detecting the processed first analog signal through an anomaly detection system; Displaying the current status of the ATE device to the user based on the detection result, and when the detection result indicates that the ATE device is currently in an abnormal state, notifying the user of the abnormality of the ATE device through an early warning system; The method further comprises: If the detection result indicates that the ATE device is currently in an abnormal state, verifying the state of the ATE device; Determine the detection accuracy rate of each type of abnormal state based on all verification results within a previously preset time period, and determine the abnormal state whose detection accuracy rate exceeds a first preset ratio value as a first state, and determine the abnormal state whose detection accuracy rate does not exceed a second preset ratio value as a second state; the first preset ratio value is not less than the second preset ratio value; If the detection result indicates that the abnormal state currently existing in the first ATE device includes the second state, setting the state of the second ATE device to an abnormal state including at least one of the first states, and detecting the state of the second ATE device; If the result of the detection of the second ATE device does not indicate that the second ATE device is currently in an abnormal state, or does not indicate that the abnormal state of the second ATE device currently includes all the set first states, the user is prompted that the monitoring system, the abnormality detection system or the early warning system has a fault.

2. The method according to claim 1, characterized in that The anomaly detection system includes an anomaly detection model, and the method for training the anomaly detection model includes: collecting a second analog signal from each of the designated nodes of the ATE device in normal operation, and collecting a third analog signal from each of the designated nodes of the ATE device in multiple types of preset abnormal states; Processing each of the second analog signals and each of the third analog signals to add data of the processed second analog signals corresponding to normal states, data of the processed third analog signals and data of abnormal states of the corresponding ATE devices to a device status database; The anomaly detection model is trained using the device status database as a training sample.

3. The method according to claim 1 or 2, characterized in that The analog signal includes noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data and / or time stamp data.

4. The method according to claim 1 or 2, characterized in that The processing performed on the analog signal includes enhancement, channel correlation, normalization, filtering, frequency analysis, format conversion, feature extraction and / or wavelet transformation.

5. The method according to claim 2, characterized in that The method further comprises: If the detection result does not indicate that the ATE device is currently in an abnormal state, verifying the state of the ATE device; If the verification result indicates that the ATE device is not in an abnormal state, the detection result is determined to be accurate, and the processed data of the first analog signal is added to the device state database as analog signal data corresponding to a normal state; If the verification result indicates that the ATE device is currently in an abnormal state, the detection result is determined to be inaccurate, and the processed data of the first analog signal and the corresponding data of the abnormal state type of the ATE device are added to the device state database.

6. The method according to claim 2, characterized in that The method further comprises: If the verification result indicates that the ATE device is not in an abnormal state, the detection result is determined to be inaccurate, and the processed data of the first analog signal is added to the device state database as analog signal data corresponding to a normal state; If the verification result indicates that the current abnormal state of the ATE device is exactly the same as the abnormal state indicated by the detection result, the detection result is determined to be accurate, and the processed data of the first analog signal and the corresponding data of the abnormal state type of the ATE device are added to the device status database; If the verification result shows that the abnormal state of the ATE device is not exactly the same as the abnormal state shown by the detection result, it is determined that the detection result is not completely accurate, and the processed data of the first analog signal and the corresponding data of the abnormal state type of the ATE device are added to the device status database.

7. The method according to claim 1, characterized in that The method further comprises: If the detection result indicates that the ATE device is currently in an abnormal state and the detection result meets a preset condition, an emergency measure corresponding to the detection result is executed through the early warning system.

8. An ATE equipment status detection device, characterized in that: The device comprises: A selection module, configured to determine a plurality of designated nodes in the ATE device in response to a selection instruction; a first acquisition module, configured to acquire, in response to a detection instruction, a first analog signal of each designated node of the running ATE device through a monitoring system; a detection module, configured to process the first analog signal and detect the processed first analog signal through an anomaly detection system; a notification module, configured to display the current status of the ATE device to the user based on the detection result, and to notify the user of the abnormality of the ATE device through an early warning system when the detection result indicates that the ATE device is currently in an abnormal state; A verification module, configured to verify the status of the ATE device if the detection result indicates that the ATE device is currently in an abnormal state; a classification module, configured to determine, based on all verification results within a previously preset time period, a detection accuracy rate for each type of abnormal state, and determine an abnormal state whose detection accuracy rate exceeds a first preset ratio value as a first state, and determine an abnormal state whose detection accuracy rate does not exceed a second preset ratio value as a second state; the first preset ratio value is not less than the second preset ratio value; a setting module configured to, when the detection result indicates that the abnormal state currently existing in the first ATE device includes the second state, set the state of the second ATE device to an abnormal state including at least one of the first states, and detect the state of the second ATE device; and a prompting module configured to prompt the user that a failure has occurred in the monitoring system, the abnormality detection system, or the early warning system when a result of detecting the second ATE device does not indicate that the second ATE device is currently in an abnormal state, or does not indicate that the abnormal state currently in the second ATE device includes all of the set first states.

9. A computer-readable storage medium, characterized in that Executable instructions are stored, and when executed by a processor, they are used to implement the method according to any one of claims 1 to 7.

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