Device stability detection method and electronic device

Abstract

The application is suitable for the technical field of equipment performance detection, and provides a device stability detection method and an electronic device. The method comprises the following steps: controlling a moving component of a device to be detected to move according to at least one preset track, wherein the device to be detected comprises a plurality of moving components, and each preset track corresponds to one stability detection type; for any moving component, determining an actual position of the moving component under the corresponding stability detection type based on an image of the moving component collected by a target image collection component in the process in which the moving component moves according to any preset track; and generating detection data based on the actual positions of the plurality of moving components under the corresponding stability detection types, wherein the detection data is used for detecting the stability state of the device to be detected. Through the application, the detection efficiency and detection accuracy of the device stability can be improved.

Description

Technical Field

[0001] This application belongs to the field of equipment performance testing technology, and in particular relates to a method for testing equipment stability and an electronic device. Background Technology

[0002] In the field of intelligent manufacturing, equipment stability testing is a crucial step in ensuring the normal operation of equipment.

[0003] Traditional equipment stability testing relies on manual operation. Testers need to use tools such as dial indicators and micrometers to measure each item, which is inefficient and prone to errors and makes it difficult to guarantee the authenticity of the data. Summary of the Invention

[0004] This application provides a device stability testing method and electronic device, which can improve the testing efficiency and accuracy of device stability.

[0005] In a first aspect, embodiments of this application provide a method for detecting device stability, including: The device under test is controlled to move its moving parts according to at least one preset trajectory. The device under test includes multiple moving parts, and each preset trajectory corresponds to a type of stability detection. For any of the moving parts, based on the image of the moving part acquired by the target image acquisition component during the movement of the moving part according to any preset trajectory, the actual position of the moving part under the corresponding stability detection type is determined. The target image acquisition component is an image acquisition component set on the device under test and corresponding to the moving part. Based on the actual positions of the multiple moving parts under the corresponding stability detection type, detection data is generated, and the detection data is used to detect the stability status of the device under test.

[0006] In this embodiment, by controlling the moving parts of the device under test (DUT) to move along at least one preset trajectory, the DUT includes multiple moving parts. Each preset trajectory corresponds to a stability detection type. For any moving part, based on the image of the moving part acquired by the target image acquisition component (i.e., the image acquisition component set on the DUT and corresponding to the moving part) during the moving part's movement along any preset trajectory, the actual position of the moving part under the corresponding stability detection type is determined. This achieves automated acquisition of the actual position of the moving part under the corresponding stability detection type, reduces the error rate of manual data recording, and ensures the accuracy of the acquired actual position. Based on this, detection data is generated based on the actual positions of the multiple moving parts under the corresponding stability detection types. This detection data is used to detect the stability state of the DUT, enabling automated detection of device stability and improving the detection efficiency and accuracy.

[0007] In some embodiments of the first aspect, the control of the moving parts of the device under test to move according to at least one preset trajectory includes: When the stability detection type is dynamic stability, the moving part is controlled to perform multiple back-and-forth movements between a preset starting position and a preset target position, and the target image acquisition part is controlled to acquire an image of the moving part when it moves to the preset target position. When the stability detection type is static stability, the moving part is controlled to move to a preset target position and remain stationary, and the target image acquisition part is controlled to acquire multiple frames of images when the moving part moves to the preset target position.

[0008] In some embodiments of the first aspect, the generation of detection data further includes: Based on a preset standardized format, the detection data is stored in a preset database.

[0009] Secondly, embodiments of this application provide a method for detecting device stability, including: Acquire detection data of the device under test, wherein the detection data is generated based on the method described in any one of the first aspects above, and the detection data includes the actual positions of multiple moving parts of the device under test under the corresponding stability detection type; Based on the detection data, the stability status of the device under test is detected.

[0010] In this embodiment of the application, by acquiring the detection data of the device under test, and the detection data including the actual positions of multiple moving parts of the device under test under the corresponding stability detection type, the stability state of the device under test can be detected based on the detection data, thereby realizing automated detection of device stability and improving the detection efficiency and accuracy of device stability.

[0011] In some embodiments of the second aspect, the stability detection type includes at least one of dynamic stability and static stability; the step of detecting the stability state of the device under test based on the detection data includes: For any moving part of the device under test, based on the actual position of the moving part under the corresponding stability detection type, the device capability index of the target part corresponding to the stability detection type is determined; when the stability detection type is dynamic stability, the target part includes the moving part and a target image acquisition part corresponding to the moving part; when the stability detection type is static stability, the target part is the target image acquisition part corresponding to the moving part, and the device capability index of the target part is the device capability index of the target image acquisition part in the direction of movement of the moving part; Based on the equipment capability index of the target component, the stability state of the target component is determined; The stability state of the device under test is determined based on the stability state of each target component.

[0012] In some embodiments of the second aspect, determining the device capability index of the target component corresponding to the stability detection type based on the actual position of the moving component under the corresponding stability detection type includes: Determine whether the actual position of the moving part follows a normal distribution under the corresponding stability detection type; If the actual position of the moving part under the corresponding stability detection type follows a normal distribution, then the first average value of the moving part under the corresponding stability detection type is calculated, and based on the first average value of the moving part under the corresponding stability detection type and the actual position, the equipment capability index of the target part is calculated; the first average value of the moving part under the corresponding stability detection type is the average value of the actual position of the moving part under the corresponding stability detection type. If the actual position of the moving part under the corresponding stability detection type does not conform to a normal distribution, it is determined whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the preset transformation, and the corresponding judgment result is obtained. Based on the judgment result and the actual position of the moving part under the corresponding stability detection type, the device capability index of the target part is calculated. The preset transformation includes a fitting distribution transformation or a normal distribution transformation.

[0013] In some embodiments of the second aspect, calculating the device capability index of the target component based on the judgment result and the actual position of the moving component under the corresponding stability detection type includes: If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type meets the applicable conditions, then the actual position of the moving part under the corresponding stability detection type is subjected to a preset transformation to obtain the transformed position of the moving part under the corresponding stability detection type, and the equipment capability index of the target part is calculated based on the transformed position of the moving part under the corresponding stability detection type. If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type does not meet the applicable conditions, then the target quantile of the actual position of the moving part under the corresponding stability detection type is obtained, and the device capability index of the target part is calculated based on the target quantile; the target quantile is the quantile at the cumulative probability position corresponding to a specific statistical feature point of a normal distribution.

[0014] In some embodiments of the second aspect, the stability detection type includes at least one of dynamic stability and static stability; the acquisition of detection data of the device under test further includes: For any moving part of the device under test, based on the actual position of the moving part under the corresponding stability detection type, the repeatability of the target part corresponding to the stability detection type is determined; When the stability detection type is dynamic stability, the target component includes the moving component and a target image acquisition component corresponding to the moving component; when the stability detection type is static stability, the target component is a target image acquisition component corresponding to the moving component, and the repeatability of the target component is the repeatability of the target image acquisition component in the direction of motion of the moving component.

[0015] Thirdly, embodiments of this application provide a device for detecting device stability, comprising: A motion control module is used to control the motion components of the device under test to move according to at least one preset trajectory. The device under test includes multiple motion components, and each preset trajectory corresponds to a stability detection type. The position determination module is used to determine the actual position of any of the moving parts under the corresponding stability detection type based on the image of the moving part acquired by the target image acquisition component during the moving part's movement along any preset trajectory. The target image acquisition component is an image acquisition component set on the device under test and corresponding to the moving part. The data generation module is used to generate detection data based on the actual positions of the multiple moving parts under the corresponding stability detection type. The detection data is used to detect the stability state of the device under test.

[0016] Fourthly, embodiments of this application provide a device for testing device stability, comprising: The data acquisition module is used to acquire the detection data of the device under test, wherein the detection data is generated based on the method described in any one of the first aspects above, and the detection data includes the actual positions of multiple moving parts of the device under test under the corresponding stability detection type. The status detection module is used to detect the stability status of the device under test based on the detection data.

[0017] Fifthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it causes the electronic device to perform the method as described in any one of the first aspects above, or causes the electronic device to perform the method as described in any one of the second aspects above.

[0018] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a computer, implements the method as described in any one of the first aspects above, or implements the method as described in any one of the second aspects above.

[0019] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when run, causes the method as described in any one of the first aspects above to be performed, or causes the method as described in any one of the second aspects above to be performed.

[0020] It is understood that the beneficial effects of the third to seventh aspects mentioned above can be found in the relevant descriptions in the first and second aspects mentioned above, and will not be repeated here. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of the equipment stability testing system provided in the embodiments of this application; Figure 2 This is a schematic flowchart of the device stability testing method provided in the embodiments of this application; Figure 3 This is another schematic flowchart of the device stability testing method provided in the embodiments of this application; Figure 4 This is another schematic flowchart of the device stability testing method provided in the embodiments of this application; Figure 5 This is another schematic flowchart of the device stability testing method provided in the embodiments of this application; Figure 6 This is a schematic diagram of the device stability testing device provided in an embodiment of this application; Figure 7 This is another structural schematic diagram of the device stability testing device provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0027] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0028] Please see Figure 1 , Figure 1 A schematic diagram of the device stability testing system provided in an embodiment of this application is shown. See also... Figure 1 As shown, the system may include a control device 101, a device under test 102, and an Internet of Things (IoT) device 103.

[0029] The control device 101 is used to control the precise positioning of the moving parts of the device under test (DUT) and trigger image acquisition. For example, the control device 101 can send a motion control command to the DUT 102, and the DUT 102, in response to the motion control command, controls the moving parts to move along at least one preset trajectory. The control device 101 can trigger image acquisition of the moving parts when the moving parts move to a preset target position.

[0030] The control device 101 can be hardware or a combination of hardware and software for motion control and image acquisition triggering. This application does not limit the specific type of the control device 101. For example, the control device 101 can be a programmable logic controller (PLC), or at least one of a motion controller, industrial computer, or embedded controller.

[0031] It should be understood that the control device 101 can also be set on the device under test 102 to achieve precise positioning and image acquisition triggering of the moving parts that are also set on the device under test.

[0032] The device under test 102 can be any device that needs to perform stability testing. It can determine the actual position of the moving part under the corresponding stability testing type based on the acquired image of the moving part, and then generate test data based on the actual position.

[0033] This application does not limit the specific type of the device under test 102. For example, the device under test 102 may be a vision-guided assembly device that requires image acquisition components to guide moving components to complete the assembly operation. This application does not limit the specific type of vision-guided assembly device. For example, vision-guided assembly device may be a heart rate lens assembly unit, a vision-guided feeding and assembly unit, etc.

[0034] The moving parts of the device under test 102 can refer to the motion-related components in the device under test 102. For example, the moving parts can be the axes related to assembly in a vision-guided assembly device.

[0035] The image acquisition component of the device under test 102 can refer to a component used to acquire images of moving parts. This application does not limit the specific type of the image acquisition component. For example, the image acquisition component can be a complementary metal-oxide-semiconductor (CMOS) camera.

[0036] The number of image acquisition components in the device under test 102 can be determined by the number of moving components. For example, if a vision-guided assembly device includes three moving components (X-axis, Y-axis, and R-axis), then the device may include one image acquisition component for acquiring images along the X-axis and Y-axis, and another for acquiring images along the R-axis. If the device includes two moving components (X-axis and Y-axis), then it may include one image acquisition component for acquiring images along the X-axis and Y-axis. The X-axis can refer to the axis used to drive the device to move linearly in the horizontal direction (e.g., left-right direction). The Y-axis can refer to the axis used to drive the device to move linearly in another horizontal direction (e.g., front-back direction). The R-axis can refer to the axis used to drive the device to rotate.

[0037] The IoT device 103 is used to acquire detection data, perform statistical analysis on it, and generate a device stability test report.

[0038] This application does not limit the specific type of the IoT device 103. For example, the IoT device 103 can be at least one of an industrial computer, an edge computing gateway, and a cloud server.

[0039] In this embodiment, the control device 101 controls the moving parts of the device under test 102 to move along at least one preset trajectory. For any moving part, based on the image of the moving part acquired by the target image acquisition device during the movement along any preset trajectory, the actual position of the moving part under the corresponding stability detection type is determined. This achieves automated acquisition of the actual position of the moving part under the corresponding stability detection type, reduces the error rate of manual data recording, and ensures the accuracy of the acquired actual position. Based on this, detection data is generated based on the actual positions of multiple moving parts under the corresponding stability detection type. The IoT device 103 acquires and detects the stability status of the device under test based on the detection data, enabling automated detection of device stability and improving the detection efficiency and accuracy.

[0040] Please see Figure 2 , Figure 2 The diagram shown is a schematic flowchart of a device stability testing method provided in an embodiment of this application. It is intended as an example and not a limitation. This device stability testing method can be applied to electronic devices, which can be... Figure 1 The device under test 102 in the system shown is, for example Figure 1 The control device 101 in the system shown can be installed on the device under test 102. The device stability testing method includes the following steps: Step 201: Control the moving parts of the device under test to move according to at least one preset trajectory. The device under test includes multiple moving parts, and each preset trajectory corresponds to a stability detection type.

[0041] The device under test (DUT) pre-stores preset trajectory parameters (e.g., starting position, target position, velocity, acceleration, etc.) for each moving part under each stability test type. When a test of any moving part under any stability test type is initiated (e.g., the DUT can display a test control interface where the user can select the moving part to be tested and the stability test type, enabling one-click automatic testing), the DUT can drive the moving part to move along the corresponding preset trajectory based on the preset trajectory parameters for that stability test type. The target position can be understood as the ending position of the preset trajectory. Since the DUT typically triggers image acquisition at the target position, the target position can also be understood as the image acquisition position.

[0042] The stability detection types mentioned above can include at least one of dynamic stability and static stability. The preset trajectories corresponding to different stability detection types are usually different. The preset trajectories corresponding to different moving parts under the same stability detection type are also usually different. The target positions corresponding to the same moving part under different stability detection types can be the same or different.

[0043] Step 202: For any moving part, based on the image of the moving part acquired by the target image acquisition component during the moving part's movement along any preset trajectory, determine the actual position of the moving part under the corresponding stability detection type. The target image acquisition component is the image acquisition component set on the device under test and corresponding to the moving part.

[0044] Among them, the image acquisition component corresponding to the moving part can refer to the image acquisition component used to acquire images of the moving part.

[0045] Optionally, the number of image frames to be acquired can be preset. For example, the number of image frames to be acquired can be preset to 32. Then, during the movement of any moving part along its preset trajectory under any stability detection type, after acquiring 32 frames of images of the moving part at the target position corresponding to the preset trajectory, the acquisition of images of the moving part under that stability detection type is stopped.

[0046] It should be understood that the actual position of a moving part under the corresponding stability detection type can be multiple, and it is determined based on multiple frames of images of the moving part. Multiple frames correspond to multiple acquisitions, with each acquisition generating one frame of image, and one actual position of the corresponding moving part is extracted from each frame of image.

[0047] For each frame of image, the device under test can identify preset feature points (such as material edges, positioning marks, etc.) in the image through a template matching algorithm, calculate the coordinate information of the feature point in the camera coordinate system, and determine the actual position of the moving part extracted from the frame of image.

[0048] It should be understood that when any moving part moves along any preset trajectory, other moving parts in the device under test should remain stationary to reduce the risk of cross-coupling interference between different moving parts, and to ensure that the actual position collected only reflects the positioning error of the moving part under test, thereby ensuring the accuracy and reliability of stability detection.

[0049] Step 203: Based on the actual positions of multiple moving parts under the corresponding stability detection type, generate detection data. The detection data is used to detect the stability status of the device under test.

[0050] The detection data includes at least the actual positions of multiple moving parts under the corresponding stability detection type. Optionally, the content of the detection data can be set according to actual needs. For example, the detection data may also include the target positions of multiple moving parts under the corresponding stability detection type.

[0051] In this embodiment, by controlling the moving parts of the device under test (DUT) to move along at least one preset trajectory, the DUT includes multiple moving parts. Each preset trajectory corresponds to a stability detection type. For any moving part, based on the image of the moving part acquired by the target image acquisition component (i.e., the image acquisition component set on the DUT and corresponding to the moving part) during the moving part's movement along any preset trajectory, the actual position of the moving part under the corresponding stability detection type is determined. This achieves automated acquisition of the actual position of the moving part under the corresponding stability detection type, reduces the error rate of manual data recording, and ensures the accuracy of the acquired actual position. Based on this, detection data is generated based on the actual positions of the multiple moving parts under the corresponding stability detection types. This detection data is used to detect the stability state of the DUT, enabling automated detection of device stability, reducing manpower and equipment investment, and improving the detection efficiency and accuracy of device stability.

[0052] In some embodiments of this application, controlling the moving parts of the device under test to move according to at least one preset trajectory may include: When the stability detection type is dynamic stability, the moving part is controlled to move back and forth multiple times between the preset starting position and the preset target position, and the target image acquisition part is controlled to acquire the image when the moving part moves to the preset target position; When the stability detection type is static stability, the moving part is controlled to move to the preset target position and remain stationary, and the target image acquisition part is controlled to acquire multiple frames of images when the moving part moves to the preset target position.

[0053] Optionally, the starting and target positions of each moving part under dynamic stability can be set according to actual needs or empirical values. The target position of each moving part under static stability can also be set according to actual needs or empirical values.

[0054] When the stability detection type is dynamic stability, the device under test can send a first image acquisition command to the target image acquisition component every time the moving part moves to the preset target position. The first image acquisition command is used to instruct the target image acquisition component to acquire a frame of image of the moving part.

[0055] When the stability detection type is static stability, the device under test can send a second image acquisition command to the target image acquisition component after the moving part moves to the preset target position and remains stationary. The second image acquisition command is used to instruct the target image acquisition component to acquire multiple frames of images of the moving part.

[0056] The second image acquisition command is used to instruct the target image acquisition component to acquire multiple frames of images of the moving component. This can mean instructing the target image acquisition component to continuously acquire multiple frames of images of the moving component at a preset time interval or frame rate.

[0057] For example, in a test scenario for the X-axis under dynamic stability, the preset trajectory of the X-axis is to move back and forth 32 times between the starting position A and the target position B. The X-axis is controlled to start from the starting position A, move to the target position B, and then return to the starting position A, repeating this cycle 32 times. Each time the X-axis reaches the target position B, the corresponding target image acquisition unit captures one frame of the X-axis image. In a test scenario for the X-axis under static stability, the preset trajectory is for the X-axis to move from the current position to the target position C and remain stationary. The corresponding target image acquisition unit continuously captures 32 frames of the X-axis image.

[0058] In this embodiment, during the dynamic stability test, only one frame of image is acquired each time the moving part reaches its position, reducing the risk of blurry images being mixed into the data during the movement and ensuring the accuracy and effectiveness of the acquired images. During the static stability test, multiple frames of images are acquired continuously after the moving part reaches its position, which meets the data volume requirements for evaluating the repeatability of the image acquisition component and reduces the risk of misjudgment due to insufficient data.

[0059] In some embodiments of this application, detection data is generated, and then the process further includes: Based on a preset standardized format, the detection data is stored in a preset database.

[0060] The device under test (DUT) can upload its generated test data to a preset database according to a preset standardized format, and store it in the database as a data table. The data table can be named in the format "software serial number_year / month / day_Data". A new data table is automatically generated every day, and the data is retained for 7 days by default. Here, the software serial number can refer to the serial number of the vision software (such as software used to extract the actual position from the image) in the DUT.

[0061] Optionally, this application does not limit the specific type of the preset standardized format or the specific type of the preset database. For example, the preset standardized format can be a standardized JSON format. Based on this, standardized schemes such as data format standardization, database connection standardization, data table naming rule standardization, data field standardization, and command number system standardization can be used to transmit the actual location, supporting cross-platform data transmission and sharing.

[0062] Data format standardization can include storing all data processed by vision software (e.g., extracted actual locations) in a pre-defined database in a standardized JSON format, using "key":"value" key-value pairs, where each key corresponds to a standardized field name defined in the vision software's thesaurus. This data format standardization scheme is applicable to various vision software such as Vision, Align, Artificial Intelligence (AI), Code, Glue, Laser, and Optical Character Recognition (OCR).

[0063] Database connection standardization can include: visual software uniformly connecting to a preset database, with fixed connection parameters (such as database address, port, username and password), and the encoding method uniformly set to UTF-8.

[0064] Standardizing data table naming rules can include: generating a new data table every day, with the naming format "software serial number_year / month / day_Data", and using different software serial numbers for different vision software.

[0065] Data field standardization can include: data tables containing fields such as ID (auto-incrementing primary key), SN (product unique identifier), Data (JSON format data), Date (timestamp), and DetectType (detection type, command number identifier). Detection type can refer to the type of indicator to be detected, such as the stability of the R-axis (i.e., rotation center stability) or the repeatability accuracy of the axis.

[0066] Standardization of the command number system can include defining unified command numbers corresponding to different detection types. For example, it can include: calibration rotation center command (1-3), calibration command (4-6), associated calibration command (7-9), automatic alignment operation command (20), back-shot detection command (40), guided material picking command (50), shaft repeatability positioning accuracy command (100-102 / 200-202), shaft reset detection command (110-113), shaft positioning accuracy command (120-122), fly-shot test command (130-135), simulated bonding command (140-142), shaft origin stability command (160-162), rotation center stability command (170-172), etc.

[0067] In this embodiment, by setting a preset standardized format to store the detection data in a preset database, unified management and standardized storage of detection data of different moving parts collected by different image acquisition components under different stability detection types are realized, reducing compatibility issues caused by inconsistent data formats and supporting historical data traceability and long-term trend analysis.

[0068] Please see Figure 3 , Figure 3 The diagram shown is a schematic flowchart of a device stability testing method provided in an embodiment of this application. It is intended as an example and not a limitation. This device stability testing method can be applied to electronic devices, which can be... Figure 1 The IoT device 103 in the system shown. The stability testing method for this device includes the following steps: Step 301: Obtain the test data of the device under test.

[0069] Among them, the detection data is based on Figure 2 The method generates the detection data, which includes the actual positions of multiple moving parts of the device under test under the corresponding stability detection type.

[0070] When the device under test stores the test data in a preset database based on a preset standardized format, the IoT device can obtain the test data of the device under test from the preset database.

[0071] Step 302: Based on the detection data, detect the stability status of the device under test.

[0072] In this embodiment of the application, by acquiring the detection data of the device under test, and the detection data including the actual positions of multiple moving parts of the device under test under the corresponding stability detection type, the stability state of the device under test can be detected based on the detection data, thereby realizing automated detection of device stability and improving the detection efficiency and accuracy of device stability.

[0073] like Figure 4 As shown, in some embodiments of this application, the stability detection type includes at least one of dynamic stability and static stability; based on the detection data, detecting the stability state of the device under test may include steps 401 to 403.

[0074] Step 401: For any moving part of the device under test, based on the actual position of the moving part under the corresponding stability test type, determine the device capability index of the target part corresponding to the stability test type.

[0075] When the stability detection type is dynamic stability, the target component includes a moving component and a target image acquisition component corresponding to the moving component. That is, the target component is a combination of a moving component and its corresponding target image acquisition component. For example, based on the actual position of the X-axis under dynamic stability, an IoT device can calculate the device capability index of the X-axis and its corresponding target image acquisition component; based on the actual position of the Y-axis under dynamic stability, it can calculate the device capability index of the Y-axis and its corresponding target image acquisition component; and based on the actual position of the R-axis under dynamic stability, it can calculate the device capability index of the R-axis and its corresponding target image acquisition component.

[0076] When the stability detection type is static stability, the target component is the target image acquisition component corresponding to the moving component, and the device capability index of the target component is the device capability index of the target image acquisition component in the motion direction corresponding to the moving component. For example, based on the actual position of the target image acquisition component on the X-axis under static stability, the IoT device can calculate the device capability index of the target image acquisition component on the X-axis in the motion direction of the X-axis; based on the actual position of the target image acquisition component on the Y-axis under static stability, the device capability index of the target image acquisition component on the R-axis in the motion direction of the R-axis can be calculated; based on the actual position of the target image acquisition component on the R-axis under static stability, the device capability index of the target image acquisition component on the R-axis in the motion direction of the R-axis can be calculated.

[0077] In this embodiment, during static stability assessment, the device capability index of the target image acquisition component is refined into its components in the motion direction corresponding to each moving component. This enables precise location of image acquisition components and moving components with stability defects in the device under test, providing clear guidance for device debugging and maintenance.

[0078] Step 402: Determine the stability state of the target component based on the equipment capability index of the target component.

[0079] The equipment capability index is a quantitative indicator that measures the stable output capability of a component within a specified tolerance range. A higher equipment capability index indicates better consistency of the component in repetitive operations, meaning less fluctuation and higher stability. Therefore, based on the equipment capability index of a target component, its stability state can be accurately determined.

[0080] For example, an IoT device can determine the stability state of the combined component—the X-axis and the corresponding target image acquisition component—based on its device capability index. Furthermore, an IoT device can determine the stability state of the target image acquisition component along the X-axis's direction of motion based on its device capability index in that direction.

[0081] The IoT device stores capability index thresholds for each target component. Based on this, the IoT device can compare the capability index of any target component with its corresponding capability index threshold. If the capability index of the target component is greater than or equal to the corresponding capability index threshold, the stability state of the target component can be determined to be stable; otherwise, the stability state of the target component is determined to be unstable. Optionally, the capability index thresholds for each target component can be set according to actual needs or empirical values.

[0082] In this embodiment, by calculating the equipment capability index of each target component and comparing it with the corresponding capability index threshold, real-time monitoring and automatic early warning of equipment stability can be achieved, supporting rapid troubleshooting of equipment anomalies.

[0083] Step 403: Determine the stability state of the device under test based on the stability state of each target component.

[0084] If an IoT device detects that the stability state of any target component is unstable, it can determine that the stability state of the device under test is unstable; if it detects that the stability states of all target components are stable, it can determine that the stability state of the device under test is stable.

[0085] For example, each target component includes a combination of an X-axis and a target image acquisition component corresponding to the X-axis, a combination of a Y-axis and a target image acquisition component corresponding to the Y-axis, a combination of an R-axis and a target image acquisition component corresponding to the R-axis, a target image acquisition component corresponding to the Y-axis, a target image acquisition component corresponding to the R-axis, and a target image acquisition component corresponding to the X-axis, totaling six target components. If the stability state of any one of these six target components is unstable, then the stability state of the device under test can be determined to be unstable. If the stability states of all six target components are stable, then the stability state of the device under test can be determined to be stable.

[0086] In this embodiment, by independently determining the equipment capability index of each target component, a refined assessment of the stability of each component of the equipment under test is achieved. This allows for accurate identification of the specific source of stability defects, providing clear guidance for equipment debugging and maintenance. A comprehensive assessment of the stability status of the equipment under test is also conducted; if the stability of any single target component fails to meet the standard, the entire equipment is deemed unstable. This ensures the accuracy of equipment stability testing and reduces the risk of overlooking overall potential problems due to satisfactory local indicators.

[0087] like Figure 5 As shown, in some embodiments of this application, determining the device capability index of the target component corresponding to the stability detection type based on the actual position of the moving component under the corresponding stability detection type may include steps 501 to 503.

[0088] Step 501: Determine whether the actual position of the moving part follows a normal distribution under the corresponding stability detection type.

[0089] By analyzing the distribution pattern of the actual positions of moving parts under the corresponding stability detection type, IoT devices can determine whether the actual positions of moving parts under the corresponding stability detection type follow a normal distribution. This allows for the selection of appropriate methods and paths for subsequent calculation of device capability indices, ensuring the accuracy and reliability of the device capability indices.

[0090] Step 502: If the actual position of the moving part under the corresponding stability detection type follows a normal distribution, calculate the first average value of the moving part under the corresponding stability detection type, and calculate the equipment capability index of the target part based on the first average value of the moving part under the corresponding stability detection type and the actual position; the first average value of the moving part under the corresponding stability detection type is the average value of the actual position of the moving part under the corresponding stability detection type.

[0091] When the actual position of a moving part under a corresponding stability test type follows a normal distribution, the equipment capability index can be represented by the process capability index Cp or Cpk, which is simple to calculate and statistically efficient. When using Cp to represent the equipment capability index, the first standard deviation of the moving part under the corresponding stability test type can be calculated based on the first average value and the actual position of the moving part, and then the Cp of the target part can be calculated based on this first standard deviation. When using Cpk to represent the equipment capability index, the first standard deviation and within-group standard deviation of the moving part under the corresponding stability test type can be calculated based on the first average value and the actual position of the moving part, and then the Cpk of the target part can be calculated based on this first standard deviation, the within-group standard deviation, and the first average value.

[0092] The formula for calculating Cp is as follows:

[0093] in, This indicates the upper limit of the position of the moving part under the corresponding stability detection type; This indicates the lower limit of the position of the moving part under the corresponding stability detection type; This represents the first standard deviation of the moving part under the corresponding stability test type.

[0094] The formula for calculating Cpk is as follows:

[0095]

[0096]

[0097] in, Indicates the upper limit of process capability index; This represents the within-group standard deviation of the moving parts under the corresponding stability test type; Indicates the lower limit of process capability index; This represents the first average value of the moving part under the corresponding stability test type.

[0098] Step 503: If the actual position of the moving part under the corresponding stability detection type does not conform to a normal distribution, then determine whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the preset transformation, obtain the corresponding judgment result, and calculate the equipment capability index of the target part based on the judgment result and the actual position of the moving part under the corresponding stability detection type; the preset transformation includes fitting distribution transformation or normal distribution transformation.

[0099] The applicable conditions for preset transformations include those for fitted distribution transformations or normal distribution transformations. Applicable conditions for fitted distribution transformations may include that the actual position of the moving part under the corresponding stability detection type matches a known theoretical distribution (e.g., Weibull distribution, log-normal distribution, gamma distribution, exponential distribution, etc.). Applicable conditions for normal distribution transformations may include that the actual position of the moving part under the corresponding stability detection type must be entirely positive (e.g., the Box-Cox transformation requires all data to be positive), or that the actual position of the moving part under the corresponding stability detection type, after a certain monotonic transformation, theoretically approaches a normal distribution (e.g., the Johnson transformation has no strict restrictions on data types, but requires the transformed data to pass the normality test).

[0100] The applicable conditions for the actual position of the moving part under the corresponding stability detection type to satisfy the preset transformation can be: the actual position of the moving part under the corresponding stability detection type to satisfy the applicable conditions of the fitted distribution transformation or the applicable conditions of the normal distribution transformation.

[0101] The fact that the actual position of the moving part does not meet the applicable conditions of the preset transformation under the corresponding stability detection type can mean that the actual position of the moving part does not meet the applicable conditions of the fitted distribution transformation and does not meet the applicable conditions of the normal distribution transformation.

[0102] In this embodiment, by performing a normality test on the actual location and selecting different calculation paths based on the test results, an adaptive matching of the equipment capability index calculation method is achieved, reducing the calculation bias caused by the assumption that all data follow a normal distribution in the traditional method.

[0103] In some embodiments of this application, based on the judgment result and the actual position of the moving part under the corresponding stability detection type, the device capability index of the target part is calculated, including: If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type meets the applicable conditions, then the actual position of the moving part under the corresponding stability detection type is subjected to a preset transformation to obtain the transformed position of the moving part under the corresponding stability detection type, and the equipment capability index of the target part is calculated based on the transformed position of the moving part under the corresponding stability detection type. If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type does not meet the applicable conditions, then the target quantile of the actual position of the moving part under the corresponding stability detection type is obtained, and the equipment capability index of the target part is calculated based on the target quantile; the target quantile is the quantile at the cumulative probability position corresponding to a specific statistical characteristic point of the normal distribution.

[0104] The above-mentioned calculation of the device capability index of the target component based on the transformed position of the moving component under the corresponding stability detection type may include: calculating the second average value of the moving component under the corresponding stability detection type, and calculating the device capability index of the target component based on the second average value of the moving component under the corresponding stability detection type and the transformed position.

[0105] In this embodiment, if the actual position of the moving part under the corresponding stability detection type satisfies the applicable conditions of the normal distribution transformation, then the actual position is subjected to the normal distribution transformation. If the actual position of the moving part under the corresponding stability detection type satisfies the applicable conditions of the fitted distribution transformation, then the actual position is subjected to the fitted distribution transformation. If the actual position of the moving part under the corresponding stability detection type satisfies both the applicable conditions of the fitted distribution transformation and the applicable conditions of the normal distribution transformation, then either the actual position can be subjected to the fitted distribution transformation or the actual position can be subjected to the normal distribution transformation.

[0106] Alternatively, the IoT device can first determine whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the normal distribution transformation. If the applicable conditions of the normal distribution transformation are not met, then it can determine whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the fitted distribution transformation.

[0107] After an IoT device performs a normal distribution transformation on the actual position of a moving part under a corresponding stability detection type, the transformed position of the moving part under the corresponding stability detection type conforms to a normal distribution. In this case, Cp or Cpk can be used to represent the device capability index. Cp or Cpk is calculated based on the second average value of the moving part under the corresponding stability detection type and multiple transformed positions. The calculation formula for Cp or Cpk can be found in the calculation formula when the actual position of the moving part under the corresponding stability detection type conforms to a normal distribution, and will not be repeated here.

[0108] When IoT devices fit a distribution transformation to the actual position of moving parts under a corresponding stability detection type, the process performance index Pp or Ppk can be used to represent the device capability index. This method is statistically efficient, utilizes all information from the distribution, and is more accurate than non-parametric methods. When using Pp to represent the device capability index, the second standard deviation of the moving part under the corresponding stability detection type can be calculated based on the second average value and the transformed position, and then the Pp of the target part can be calculated based on this second standard deviation. Conversely, when using Ppk to represent the device capability index, the second standard deviation of the moving part under the corresponding stability detection type can be calculated based on the second average value and the transformed position, and then the Ppk of the target part can be calculated based on this second standard deviation and the second average value.

[0109] The formula for calculating Pp is as follows:

[0110] in, This represents the second standard deviation of the moving part under the corresponding stability test type.

[0111] The formula for calculating Ppk is as follows:

[0112]

[0113]

[0114] in, Indicates the upper limit of process performance index; Indicates the lower limit process performance index; This represents the second average value of the moving parts under the corresponding stability test type.

[0115] When the actual position of a moving part under a corresponding stability detection type does not meet the applicable conditions of a fitted distribution transformation or a normal distribution transformation, IoT devices can use a nonparametric method based on quantiles to calculate the device capability index of the target part. This nonparametric process performance index, Pnp or Pnpk, is used to represent the device capability index, which requires no distribution assumptions and exhibits strong robustness. Specifically, IoT devices can sort the actual positions of the moving parts under the corresponding stability detection type in ascending order to obtain an ordered sequence. From this ordered sequence, the target quantiles required to calculate the device capability index of the target part can be obtained, and the device capability index of the target part can be calculated based on these target quantiles.

[0116] The target quantiles mentioned above include at least the 99.865th percentile and the 0.135th percentile, and may also include the 50th percentile.

[0117] The 99.865th percentile indicates that 99.865% of the actual locations are less than or equal to this value, corresponding to a normal distribution. The 0.135th percentile indicates that 0.135% of the actual locations are less than or equal to this value, corresponding to a normal distribution. The 50th percentile (also known as the median) indicates the position of 50% of actual locations that are less than or equal to this value.

[0118] In a normal distribution, the mean is... The corresponding cumulative probability is 50%. The corresponding cumulative probability is 0.135%. The corresponding cumulative probability is 99.865%. This embodiment, by selecting quantiles at the same cumulative probability positions as these specific statistical feature points—namely the 50th percentile, 0.135th percentile, and 99.865th percentile—can simulate a normal distribution using nonparametric methods. The range of data corresponding to the expansion, without assuming that the data follows a normal distribution.

[0119] When using Pnp to represent the equipment capability index, the Pnp of the target component can be calculated based on the 99.865th percentile and the 0.135th percentile. When using Pnpk to represent the equipment capability index, the Pnpk of the target component can be calculated based on the 99.865th percentile, the 0.135th percentile, and the 50th percentile.

[0120] The formula for calculating PnP is as follows:

[0121] in, This represents the 99.865th percentile. This represents the 0.135th percentile.

[0122] The formula for calculating Pnpk is as follows:

[0123]

[0124]

[0125] in, This represents the upper limit of the performance index for nonnormal processes; Indicates the 50th percentile; This indicates the lower limit of the performance index for non-normal processes.

[0126] In this embodiment, when the actual position of the moving part under the corresponding stability detection type does not conform to a normal distribution, two optional paths are provided: normal distribution transformation and fitted distribution transformation. If a normal distribution transformation (e.g., Box-Cox transformation or Johnson transformation) is used and the transformation is successful, the equipment capability index is calculated based on the transformed position, which can make full use of mature statistical tools while retaining the original data information. If a fitted distribution transformation is used and the fitting is successful, the actual position is fitted to a known theoretical distribution such as the Weibull distribution or the log-normal distribution, and an equivalent equipment capability index is calculated based on the probability characteristics of the theoretical distribution, which can achieve quantitative evaluation without changing the data form. If neither of the above two transformations is applicable, a nonparametric method based on quantiles is used to calculate the equipment capability index. This method is free from distribution assumptions, has strong robustness, and can effectively deal with complex data forms such as bimodal distribution, discrete distribution, and extreme skewness. This embodiment achieves full coverage evaluation of data with arbitrary distribution patterns through the progressive processing strategy of normality test, preset transformation, and nonparametric method. It not only ensures the accuracy of equipment capability index, but also ensures the engineering applicability of the solution in actual industrial sites, providing a complete, reliable and highly adaptable quantitative analysis method for the stability testing of the equipment under test.

[0127] In some embodiments of this application, the above-mentioned detection of the stability state of the device under test further includes: Based on the stability status of the device under test, a stability test report for the device under test is generated.

[0128] After completing the stability assessment of the device under test, the IoT device can automatically generate a structured stability test report, which can then be viewed remotely.

[0129] This application does not limit the output format and content of the stability test report. For example, the stability test report can be output in PDF or HTML format. The stability test report should at least include a report header, test conditions, device capability indices for each target component, anomaly details, and recommendations. The report header may include the report number, generation date, device model and serial number, etc. Test conditions may include the test date, test time period, test environment parameters, list of moving parts participating in the test, list of image acquisition components participating in the test, stability test type, and number of acquisition frames for each test group, etc. Anomaly details and recommendations may include the abnormal component, the type of abnormal stability, and recommended measures, etc.

[0130] In this embodiment, by automatically generating a stability test report, the abstract stability judgment results can be transformed into a structured and visualized document, allowing users to quickly understand the stability status of the device without needing professional statistical knowledge.

[0131] In some embodiments of this application, the stability detection type includes at least one of dynamic stability and static stability; after acquiring the detection data of the device under test, the method further includes: For any moving part of the device under test, the repeatability of the target part corresponding to the stability test type is determined based on the actual position of the moving part under the corresponding stability test type. When the stability detection type is dynamic stability, the target component includes the moving component and the target image acquisition component corresponding to the moving component; when the stability detection type is static stability, the target component is the target image acquisition component corresponding to the moving component, and the repeatability of the target component is the repeatability of the target image acquisition component in the direction of motion of the moving component.

[0132] IoT devices can calculate the range of the actual position of a moving part under a corresponding stability detection type (i.e., the difference between the maximum and minimum values ​​among multiple actual positions) and use this range as the repeatability accuracy of the target part corresponding to the stability detection type; alternatively, based on the actual position of the moving part under the corresponding stability detection type, they can calculate the first standard deviation of the moving part under the corresponding stability detection type and determine the product of 6 and the first standard deviation as the repeatability accuracy of the target part corresponding to the stability detection type.

[0133] It should be understood that the repeatability of the target component corresponding to the stability detection type can also be determined by other means based on the actual position of the moving component under the corresponding stability detection type, and this application does not limit this.

[0134] In this embodiment, by distinguishing between dynamic stability and static stability test types, the repeatability of each target component is calculated separately. This achieves independent evaluation of the repeatability of the combined component (moving part and corresponding target image acquisition component) and the individual target image acquisition component, reducing the risk of confusion between two different types of errors. This embodiment combines repeatability with the device capability index. Repeatability provides an intuitive measure of fluctuation, while the device capability index provides a comprehensive evaluation of relative specification margins. These two complement each other, providing a multi-level, multi-dimensional quantitative analysis method for the stability testing of the device under test.

[0135] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0136] Corresponding to the device stability testing method in the above embodiments, Figure 6 A schematic diagram of the device stability testing apparatus provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0137] Reference Figure 6 The aforementioned equipment stability testing device may include: The motion control module 601 is used to control the motion components of the device under test to move according to at least one preset trajectory. The device under test includes multiple motion components, and each preset trajectory corresponds to a stability detection type. The position determination module 602 is used to determine the actual position of any moving part under the corresponding stability detection type based on the image of the moving part acquired by the target image acquisition component during the moving part's movement along any preset trajectory. The target image acquisition component is an image acquisition component set on the device under test and corresponding to the moving part. The data generation module 603 is used to generate detection data based on the actual positions of multiple moving parts under the corresponding stability detection type. The detection data is used to detect the stability status of the device under test.

[0138] In some embodiments, the motion control module 601 is specifically used for: When the stability detection type is dynamic stability, the moving part is controlled to move back and forth multiple times between the preset starting position and the preset target position, and the target image acquisition part is controlled to acquire the image when the moving part moves to the preset target position; When the stability detection type is static stability, the moving part is controlled to move to the preset target position and remain stationary, and the target image acquisition part is controlled to acquire multiple frames of images when the moving part moves to the preset target position.

[0139] In some embodiments, the above-mentioned device stability testing apparatus may further include: The data storage module is used to store the test data in a preset database based on a preset standardized format.

[0140] Corresponding to the device stability testing method in the above embodiments, Figure 7 A schematic diagram of the device stability testing apparatus provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0141] Reference Figure 7 The aforementioned equipment stability testing device may include: Data acquisition module 701 is used to acquire the test data of the device under test, the test data being based on the above. Figure 2 The method shown generates detection data including the actual positions of multiple moving parts of the device under test under the corresponding stability detection type. The status detection module 702 is used to detect the stability status of the device under test based on the detection data.

[0142] In some embodiments, the stability detection type includes at least one of dynamic stability and static stability; the state detection module 702 includes: The index determination unit is used to determine the device capability index of any moving part of the device under test based on the actual position of the moving part under the corresponding stability detection type. When the stability detection type is dynamic stability, the target part includes the moving part and the target image acquisition part corresponding to the moving part. When the stability detection type is static stability, the target part is the target image acquisition part corresponding to the moving part, and the device capability index of the target part is the device capability index of the target image acquisition part in the direction of movement of the moving part. The first determining unit is used to determine the stability state of the target component based on the equipment capability index of the target component. The second determining unit is used to determine the stability state of the device under test based on the stability state of each target component.

[0143] In some embodiments, the index determining unit is specifically used for: Determine whether the actual position of the moving part follows a normal distribution under the corresponding stability detection type; If the actual position of the moving part under the corresponding stability detection type follows a normal distribution, then the first average value of the moving part under the corresponding stability detection type is calculated, and based on the first average value of the moving part under the corresponding stability detection type and the actual position, the equipment capability index of the target part is calculated; the first average value of the moving part under the corresponding stability detection type is the average value of the actual position of the moving part under the corresponding stability detection type. If the actual position of the moving part under the corresponding stability detection type does not conform to a normal distribution, it is determined whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the preset transformation, and the corresponding judgment result is obtained. Based on the judgment result and the actual position of the moving part under the corresponding stability detection type, the equipment capability index of the target part is calculated. The preset transformation includes fitting distribution transformation or normal distribution transformation.

[0144] In some embodiments, the index determining unit is specifically used for: If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type meets the applicable conditions, then the actual position of the moving part under the corresponding stability detection type is subjected to a preset transformation to obtain the transformed position of the moving part under the corresponding stability detection type, and the equipment capability index of the target part is calculated based on the transformed position of the moving part under the corresponding stability detection type. If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type does not meet the applicable conditions, then the target quantile of the actual position of the moving part under the corresponding stability detection type is obtained, and the equipment capability index of the target part is calculated based on the target quantile; the target quantile is the quantile at the cumulative probability position corresponding to a specific statistical characteristic point of the normal distribution.

[0145] In some embodiments, the stability detection type includes at least one of dynamic stability and static stability; the above-mentioned device stability detection apparatus may further include: The accuracy determination module is used to determine the repeatability of the target component corresponding to the stability detection type for any moving component of the device under test, based on the actual position of the moving component under the corresponding stability detection type. When the stability detection type is dynamic stability, the target component includes the moving component and the target image acquisition component corresponding to the moving component; when the stability detection type is static stability, the target component is the target image acquisition component corresponding to the moving component, and the repeatability of the target component is the repeatability of the target image acquisition component in the direction of motion of the moving component.

[0146] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0147] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8 As shown, the electronic device 8 of this embodiment includes: at least one processor 80 ( Figure 8 (Only one is shown in the diagram), memory 81, and computer program 82 stored in said memory 81 and executable on said at least one processor 80, wherein said processor 80 executes said computer program 82 to implement the steps in any of the above method embodiments.

[0148] The electronic device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 8 and does not constitute a limitation on electronic device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0149] The processor 80 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0150] In some embodiments, the memory 81 may be an internal storage unit of the electronic device 8, such as a hard disk or memory of the electronic device 8. In other embodiments, the memory 81 may be an external storage device of the electronic device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 8. Furthermore, the memory 81 may include both internal and external storage units of the electronic device 8. The memory 81 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0153] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device or electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for testing equipment stability, characterized in that, include: The device under test is controlled to move its moving parts according to at least one preset trajectory. The device under test includes multiple moving parts, and each preset trajectory corresponds to a type of stability detection. For any of the moving parts, based on the image of the moving part acquired by the target image acquisition component during the movement of the moving part according to any preset trajectory, the actual position of the moving part under the corresponding stability detection type is determined. The target image acquisition component is an image acquisition component set on the device under test and corresponding to the moving part. Based on the actual positions of the multiple moving parts under the corresponding stability detection type, detection data is generated, and the detection data is used to detect the stability status of the device under test.

2. The method according to claim 1, characterized in that, The control of the moving parts of the device under test to move according to at least one preset trajectory includes: When the stability detection type is dynamic stability, the moving part is controlled to perform multiple back-and-forth movements between a preset starting position and a preset target position, and the target image acquisition part is controlled to acquire an image of the moving part when it moves to the preset target position. When the stability detection type is static stability, the moving part is controlled to move to a preset target position and remain stationary, and the target image acquisition part is controlled to acquire multiple frames of images when the moving part moves to the preset target position.

3. The method according to claim 1 or 2, characterized in that, The generation of detection data then includes: Based on a preset standardized format, the detection data is stored in a preset database.

4. A method for testing equipment stability, characterized in that, include: Acquire detection data of the device under test, wherein the detection data is generated based on the method described in any one of claims 1 to 3, and the detection data includes the actual positions of multiple moving parts of the device under test under the corresponding stability detection type; Based on the detection data, the stability status of the device under test is detected.

5. The method according to claim 4, characterized in that, The stability detection type includes at least one of dynamic stability and static stability; the step of detecting the stability state of the device under test based on the detection data includes: For any moving part of the device under test, based on the actual position of the moving part under the corresponding stability detection type, the device capability index of the target part corresponding to the stability detection type is determined; when the stability detection type is dynamic stability, the target part includes the moving part and a target image acquisition part corresponding to the moving part; when the stability detection type is static stability, the target part is the target image acquisition part corresponding to the moving part, and the device capability index of the target part is the device capability index of the target image acquisition part in the direction of movement of the moving part; Based on the equipment capability index of the target component, the stability state of the target component is determined; The stability state of the device under test is determined based on the stability state of each target component.

6. The method according to claim 5, characterized in that, The step of determining the equipment capability index of the target component corresponding to the stability detection type based on the actual position of the moving component under the corresponding stability detection type includes: Determine whether the actual position of the moving part follows a normal distribution under the corresponding stability detection type; If the actual position of the moving part under the corresponding stability detection type follows a normal distribution, then the first average value of the moving part under the corresponding stability detection type is calculated, and based on the first average value of the moving part under the corresponding stability detection type and the actual position, the equipment capability index of the target part is calculated; the first average value of the moving part under the corresponding stability detection type is the average value of the actual position of the moving part under the corresponding stability detection type. If the actual position of the moving part under the corresponding stability detection type does not conform to a normal distribution, it is determined whether the actual position of the moving part under the corresponding stability detection type meets the applicable conditions of the preset transformation, and the corresponding judgment result is obtained. Based on the judgment result and the actual position of the moving part under the corresponding stability detection type, the device capability index of the target part is calculated. The preset transformation includes a fitting distribution transformation or a normal distribution transformation.

7. The method according to claim 6, characterized in that, The step of calculating the device capability index of the target component based on the judgment result and the actual position of the moving component under the corresponding stability detection type includes: If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type meets the applicable conditions, then the actual position of the moving part under the corresponding stability detection type is subjected to a preset transformation to obtain the transformed position of the moving part under the corresponding stability detection type, and the equipment capability index of the target part is calculated based on the transformed position of the moving part under the corresponding stability detection type. If the judgment result indicates that the actual position of the moving part under the corresponding stability detection type does not meet the applicable conditions, then the target quantile of the actual position of the moving part under the corresponding stability detection type is obtained, and the device capability index of the target part is calculated based on the target quantile; the target quantile is the quantile at the cumulative probability position corresponding to a specific statistical feature point of a normal distribution.

8. The method according to claim 4, characterized in that, The stability detection type includes at least one of dynamic stability and static stability; after acquiring the detection data of the device under test, the process further includes: For any moving part of the device under test, based on the actual position of the moving part under the corresponding stability detection type, the repeatability of the target part corresponding to the stability detection type is determined; When the stability detection type is dynamic stability, the target component includes the moving component and a target image acquisition component corresponding to the moving component; when the stability detection type is static stability, the target component is a target image acquisition component corresponding to the moving component, and the repeatability of the target component is the repeatability of the target image acquisition component in the direction of motion of the moving component.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 1 to 3.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the electronic device to implement the method as described in any one of claims 4 to 8.

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