ATE equipment state detection method and device

Abstract

The invention provides an ATE equipment state detection method and device, and the method comprises the steps: determining a node in ATE equipment in response to a selection instruction, collecting a first analog signal of the ATE equipment in operation through a monitoring system, processing the first analog signal, detecting the processed first analog signal through an abnormality detection system, and determining the state of the ATE equipment according to the detected first analog signal. Therefore, the current state of the ATE equipment is displayed to a user, and when the ATE equipment currently has an abnormal state, the user is prompted. According to the scheme, the analog signals of the designated nodes are set and collected for real-time monitoring processing, so that a user can know the running state of the ATE equipment in time, the abnormal state of the equipment can be recognized, potential problems can be quickly found, preventive maintenance is carried out, accidental faults and maintenance cost are reduced, and the fault downtime of the equipment is shortened.

Description

Technical Field

[0001] The present invention belongs to the technical field of ATE equipment status detection, and in particular relates to a method and device for detecting the status of an ATE equipment. Background Art

[0002] ATE equipment is developing towards higher integration, higher performance, greater intelligence and lower cost. The analog part of ATE equipment is the core part connecting the instrument with the outside world and determines the stability and reliability of the entire equipment.

[0003] As test efficiency and test scenarios become increasingly complex, analog circuits frequently fail, and their diagnostic costs account for the vast majority of the entire system's testing costs. In the future, with the further development and improvement of integrated technology, analog fault diagnosis will face greater challenges, and many traditional fault diagnosis methods will become ineffective.

[0004] Currently, fault diagnosis of ATE equipment primarily relies on temperature and voltage monitoring, but this can only detect problems. Once a problem occurs, manual analysis, location, and resolution are still required. Although this method offers good observation flexibility and versatility, it relies heavily on the experience and capabilities of technicians, which can lead to human errors or omissions during the fault diagnosis process and limit the efficiency and accuracy of fault diagnosis. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention proposes a method for detecting the status of an ATE device, the method comprising: 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; The current status of the ATE device is displayed to the user based on the detection result, and when the detection result indicates that the ATE device is currently in an abnormal state, the early warning system prompts the user that the ATE device is abnormal.

[0006] Specifically, 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 a normally operating ATE device, and collecting a third analog signal from each of the designated nodes of a plurality of types of ATE devices in a preset abnormal state; 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.

[0007] Optionally, the analog signal includes noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data and / or timestamp data; The processing performed on the analog signal includes enhancement, channel correlation, normalization, filtering, frequency analysis, format conversion, feature extraction and / or wavelet transformation.

[0008] Furthermore, 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 shows 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; Accordingly, 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; 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.

[0009] Furthermore, the method further comprises: 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.

[0010] Preferably, 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.

[0011] The present invention also provides an ATE device status detection device, the device comprising: 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; The notification module is used to display the current status of the ATE device to the user based on the detection results, and when the detection results indicate that the ATE device is currently in an abnormal state, the early warning system prompts the user that the ATE device is abnormal.

[0012] The present invention further provides a computer-readable storage medium storing executable instructions for implementing the ATE device status detection method as described above when executed by a processor.

[0013] The present invention has at least the following beneficial effects: The solution proposed by the present invention allows users to flexibly select ATE device nodes that need to be monitored according to actual needs. The monitoring system collects the first analog signal of each designated node of the ATE device in real time, thereby achieving real-time monitoring of the ATE device status, intelligently identifying abnormal status of the ATE device and displaying it to the user. When an abnormality occurs in the ATE device, the early warning system promptly issues a prompt to the user, helping the user to quickly locate the problem and take appropriate measures. This helps to achieve preventive maintenance, avoid serious failures of the ATE device, extend its service life, and reduce maintenance costs. At the same time, this solution is applicable to various ATE devices and has broad application prospects. Furthermore, the solution proposed by the present invention collects analog signals from designated nodes of ATE equipment in normal operation and various preset abnormal states, processes these data, and then adds them to the equipment status database to train the anomaly detection model. This ensures that the model can learn the characteristics of ATE equipment in normal and abnormal states, thereby improving the accuracy and generalization of anomaly detection. The various processing methods for analog signals make the signals easier to analyze and detect, improving the accuracy and efficiency of anomaly detection. Regardless of whether the test results indicate that the ATE equipment is in an abnormal state, verification is performed and the equipment status database is updated based on the verification results. This enriches the database while ensuring the integrity, accuracy, and diversity of the data in the database, thereby continuously optimizing the performance of the anomaly detection model. In addition, the solution proposed in the present invention can also determine the detection accuracy of various types of abnormal conditions based on the verification results, identify which abnormal conditions are easy to be accurately detected and which are easy to be misjudged or missed, so that when an abnormal condition that is easy to be misjudged is detected, by actively setting up an abnormal verification monitoring system with high detection accuracy, whether the abnormal detection system or the early warning system itself has a fault, it is possible to verify whether each system has sufficient reliability, thereby improving the degree of automation of the overall method.

[0014] Thus, the present invention provides a method and apparatus for detecting the status of ATE equipment. The solution proposed in the present invention sets and collects analog signals of designated nodes for targeted real-time monitoring and processing, allowing users to promptly understand the operating status of the ATE equipment, effectively identifying abnormal status of the equipment, facilitating the rapid discovery of potential problems, helping users perform preventive maintenance, reducing unexpected failures and repair costs, and reducing downtime caused by equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 A schematic diagram of the overall method flow of the ATE device status detection method provided in Example 1; Figure 2 A flowchart of the method for training anomaly detection models; Figure 3 It is a flowchart of the ATE equipment status detection method; Figure 4 A flow chart of the method for verifying the status of each system; Figure 5 This is a schematic diagram of the module structure of the ATE equipment status detection device provided in Example 2.

[0017] Reference numerals 10-selection module; 20-first acquisition module; 30-detection module; 40-notification module; 50-emergency module; 61-second acquisition module; 62-recording module; 63-training module; 64-verification module; 71-classification module; 72-setting module; 73-prompt module. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Hereinafter, various embodiments of the present invention will be described more fully. The present invention can have various embodiments, and modifications and variations can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present invention to the specific embodiments disclosed herein, but rather that the present invention should be construed to encompass all modifications, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of the present invention.

[0020] Hereinafter, the terms "include" or "may include" used in various embodiments of the present invention indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present invention, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be understood as excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing.

[0021] In various embodiments of the present invention, the expression "or" or "at least one of A or / and B" includes any or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0022] The expressions (such as "first", "second", etc.) used in the various embodiments of the present invention may modify the various constituent elements in the various embodiments, but may 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 only used to distinguish one element from other elements. For example, a first user device and a second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present invention, a first element may be referred to as a second element, and similarly, a second element may also be referred to as a first element.

[0023] It should be noted that, in the present invention, unless otherwise expressly specified or defined, terms such as "mounted," "connected," and "fixed" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0024] In the present invention, those skilled in the art need to understand that the terms indicating orientation or positional relationships herein are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0025] The terms used in various embodiments of the present invention are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the present invention. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as those of ordinary skill in the art generally understood by the various embodiments of the present invention. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having idealized meaning or too formal meaning, unless clearly defined in various embodiments of the present invention.

[0026] Example 1 This embodiment proposes a method for detecting the status of an ATE device. Figure 1 , the method comprising: S100: In response to a selection instruction, a number of designated nodes are determined in the ATE device.

[0027] Through step S100 , key nodes that need to be monitored can be clearly identified in the ATE device, and the key nodes can be monitored in subsequent steps.

[0028] S200: In response to a detection instruction, a monitoring system collects a first analog signal from each designated node of a running ATE device.

[0029] In this embodiment, the analog signal collected at the specified node contains data related to the operating status of the ATE device, which may include but is not limited to noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data, and timestamp data.

[0030] S300: Process the first analog signal, and detect the processed first analog signal through an abnormality detection system.

[0031] It should be noted that the processing of the analog signal in step S300 may include, but is not limited to, enhancement, channel correlation calculation, normalization, filtering, frequency analysis, format conversion, feature extraction, and wavelet transformation. By processing the analog signal, the signal can be adjusted to a unified standard range, thereby improving the detectability of various features in the signal to facilitate comparison and analysis, thereby enabling the analog signal to better reflect the status of the device. Exemplarily, when the analog signal contains noise data, the anomaly detection system may detect the processed first analog signal through wavelet transform and / or voiceprint recognition technology.

[0032] S400: 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, prompting the user that the ATE device is abnormal through the early warning system.

[0033] S500: If the detection result indicates that the ATE device is currently in an abnormal state and the detection result meets the preset conditions, an emergency measure corresponding to the detection result is executed through the early warning system.

[0034] Optionally, emergency measures corresponding to the detection results may include, but are not limited to, cutting off the power supply of the ATE equipment, enabling a backup circuit, and switching abnormal circuits and components to the backup circuit.

[0035] It should be noted that the ATE device status detection method proposed in this embodiment can determine whether the ATE device is in an abnormal state through a 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; illustratively, for the voltage data of a key node, the preset detection standard includes a voltage lower than 3V, and the preset condition includes a voltage lower than 2.5V. In this case, the method proposed in this embodiment will prompt the user of an abnormality in the ATE device through the early warning system in step S400, but will not execute the step of "executing emergency measures corresponding to the detection results through the early warning system" in step S500.

[0036] Preferably, the method proposed in this embodiment may preset a detection period, so that after the designated node is determined in step S100 , the functions of step S100 and step S200 are periodically executed on the selected designated node.

[0037] Specifically, the anomaly detection system includes an anomaly detection model, see Figure 2-Figure 3 , methods for training anomaly detection models include: S610: collecting second analog signals from designated nodes of ATE equipment in normal operation, and collecting third analog signals from designated nodes of ATE equipment in multiple types of preset abnormal states.

[0038] Specifically, the method proposed in this embodiment can collect analog signals in normal states and analog signals in various abnormal states when the ATE device is operating normally and in different fault states. 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 in step S610 contains sufficient information for effective analysis and feature extraction.

[0039] S620: Process 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 corresponding ATE device to the device status database.

[0040] S630: Train an anomaly detection model using the device status database as a training sample.

[0041] Specifically, 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 the anomaly detection model. Through training, the anomaly detection model can learn the characteristics of different faults and classify them.

[0042] Preferably, the method further comprises: If the test result does not indicate that the ATE device is currently in an abnormal state, verify 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.

[0043] Accordingly, the method also includes: If the detection result shows that the ATE device is currently in an abnormal state, the state of the ATE device is verified; 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 shows 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 data of the processed 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 data of the processed first analog signal and its corresponding data of the abnormal state type of the ATE device are added to the device status database.

[0044] It should be noted that the methods for verifying the status of ATE equipment may include manual verification and machine verification. By verifying the status of ATE equipment and enriching the data of the equipment status database based on the verification results, the continuous upgrading of the fault diagnosis model can be achieved. In addition, the method proposed in this embodiment can also continuously iterate and correct the monitoring system and early warning system based on the verification results.

[0045] Further, see Figure 4 , the method further comprises: S710: Based on all verification results within a previously preset time period, determine the detection accuracy of each type of abnormal state, and determine the abnormal state whose detection accuracy exceeds a first preset ratio value as a first state, and determine the abnormal state whose detection accuracy does not exceed a second preset ratio value as a second state.

[0046] It should be noted that the first preset ratio value is not lower than the second preset ratio value; illustratively, the first state in which the detection accuracy exceeds the first preset ratio value is an abnormal state that has been mastered, that is, the first state is, and the second state in which the detection accuracy does not exceed 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 in which the detection accuracy is between the first preset ratio value and the second preset ratio value is an abnormal state that has not been fully mastered.

[0047] S720: If the detection result indicates that the current abnormal state of the first ATE device includes the second state, set the state of the second ATE device to an abnormal state including at least one first state, and detect the state of the second ATE device.

[0048] S730: 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, a prompt is given to the user that a failure has occurred in the monitoring system, the abnormality detection system, or the early warning system.

[0049] Therefore, the method proposed in this embodiment can verify whether the monitoring system, the abnormality detection system, and the early warning system have a possibility of failure by setting a known abnormal state on another ATE device when the abnormal state of the ATE device is detected as an unknown abnormal state, thereby improving the accuracy and reliability of fault detection. Specifically, if the second ATE device can successfully identify and process the abnormal state that has been grasped, it can be preliminarily determined that the monitoring system is operating normally; conversely, if it fails to identify it, it may indicate that there is a potential problem with the monitoring system.

[0050] Furthermore, the method proposed in this embodiment dynamically adjusts the first preset ratio value and the second preset ratio value by continuously accumulating and updating the detection accuracy of abnormal conditions, thereby adapting to changes in the equipment operating environment and 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.

[0051] Example 2 This embodiment proposes an ATE device status detection device for implementing the ATE device status detection method proposed in Example 1. Figure 5 , the device comprises: A selection module 10 is configured to determine a number of designated nodes in the ATE device in response to a selection instruction; A first acquisition module 20 is configured to acquire, in response to a detection instruction, a first analog signal from each designated node of the running ATE device through a monitoring system; a detection module 30, configured to process the first analog signal and detect the processed first analog signal through an anomaly detection system; Notification module 40, configured to display the current status of the ATE device to the user based on the detection results, and to notify the user of the abnormality of the ATE device through the early warning system when the detection results indicate that the ATE device is currently in an abnormal state; The emergency module 50 is configured to execute emergency measures corresponding to the detection results through the early warning system when the detection results indicate that the ATE equipment is currently in an abnormal state and the detection results meet preset conditions.

[0052] In this embodiment, the analog signal collected at the specified node includes data related to the operating status of the ATE device, which may include but is not limited to noise data, voltage data, current data, temperature data, frequency data, pulse data, signal phase data, and timestamp data; The processing performed by the detection module 30 on the analog signal may include but is not limited to enhancement, channel correlation calculation, normalization, filtering, frequency analysis, format conversion, feature extraction, and wavelet transformation.

[0053] Optionally, the emergency measures taken by the emergency module 50 may include but are not limited to cutting off the power supply of the ATE equipment, enabling a backup circuit, and switching abnormal circuits and components to the backup circuit.

[0054] Preferably, the method proposed in this embodiment may preset a detection period so that after the selection module 10 determines the designated node, the functions of the selection module 10 and the first acquisition module 20 are periodically executed on the selected designated node; 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 acquisition module 20, and the detection module 30 in each independent device is relatively high; further, the detection module 30 can be set in a different device from the selection module 10 and the first acquisition module 20. Thus, the user can collect data through the selection module 10 and the first acquisition module 20, and send the collected data to the device provided with the detection module 30, so that the data can be detected by the device provided with the detection module 30, thereby achieving the effect of optimizing the use cost.

[0055] Specifically, the anomaly detection system includes an anomaly detection model, and the device also includes: The second acquisition module 61 is configured to acquire the second analog signal of each designated node of the ATE device in normal operation, and to acquire the third analog signal of each designated node of the ATE device in multiple types of preset abnormal states; a recording module 62 for processing each second analog signal and each third analog signal to add data of the processed second analog signal corresponding to a normal state, data of the processed third analog signal and data of an abnormal state type of the corresponding ATE device to a device status database; The training module 63 is used to train the anomaly detection model using the device status database as a training sample.

[0056] 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.

[0057] The device further comprises: 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; 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.

[0058] Furthermore, the device further comprises: a classification module 71 for determining, based on all verification results within a pre-set time period, a detection accuracy rate for each type of abnormal state, and determining an abnormal state whose detection accuracy rate exceeds a first pre-set ratio value as a first state, and determining an abnormal state whose detection accuracy rate does not exceed a second pre-set ratio value as a second state; a setting module 72 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; The prompt module 73 is used to prompt the user that a failure has occurred in the monitoring system, the abnormality detection system, or the early warning system when 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 currently in the second ATE device includes all the set first states.

[0059] It should be noted that the first preset ratio value is not lower than the second preset ratio value; Therefore, when the apparatus proposed in this embodiment detects that the abnormal state of an ATE device is an unknown abnormal state, the setting module 72 can set a known abnormal state on another ATE device to verify whether the monitoring system, the abnormality detection system, and the early warning system have a possibility of failure, thereby improving the accuracy and reliability of fault detection. Specifically, if the second ATE device can successfully identify and process the abnormal state that has been grasped, it can be preliminarily determined that the monitoring system is operating normally; conversely, if it fails to identify it, it may indicate that there is a potential problem with the monitoring system.

[0060] Furthermore, the device proposed in this embodiment can dynamically adjust the first preset ratio value and the second preset ratio value by continuously accumulating and updating the detection accuracy of abnormal states.

[0061] Example 3 This embodiment further provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed by a processor, the steps of the ATE device status detection method provided in the above embodiment 1 are implemented.

[0062] It should be noted that computer-readable media includes permanent and non-permanent, removable and non-removable media that can be used to store information using any method or technology. 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 cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0063] In summary, the present invention provides a method and apparatus for detecting the status of ATE equipment. The solution proposed in the present invention sets and collects analog signals from specified nodes for targeted real-time monitoring and processing, allowing users to promptly understand the operating status of the ATE equipment. It can effectively identify abnormal status of the equipment, facilitate rapid discovery of potential problems, help users perform preventive maintenance, reduce unexpected failures and repair costs, and reduce downtime caused by equipment failures.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

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; The current status of the ATE device is displayed to the user based on the detection result, and when the detection result indicates that the ATE device is currently in an abnormal state, the early warning system prompts the user that the ATE device is abnormal.

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 a normally operating ATE device, and collecting a third analog signal from each of the designated nodes of a plurality of types of ATE devices in a preset abnormal state; 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 detection result indicates 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 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 6, characterized in that The method further comprises: 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.

8. 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.

9. 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; The notification module is used to display the current status of the ATE device to the user based on the detection results, and when the detection results indicate that the ATE device is currently in an abnormal state, the early warning system prompts the user that the ATE device is abnormal.

10. 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 8.

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