Test system, method and apparatus for determining test drawer distortion

Abstract

The present disclosure relates to a test system, a method and a device for determining test drawer deformation, and relates to the technical field of production line testing, and is used for predicting whether the test drawer will be deformed. The system comprises: a cabinet comprising a plurality of test drawers; a mechanical hand comprising a gripper for clamping a test object and for placing the test object into a test drawer, wherein a pressure sensor is arranged on the gripper, and the pressure sensor is used to detect the pressure generated when the gripper contacts the test drawer when placing the test object into the test drawer.

Description

Technical Field

[0001] This disclosure relates to the field of production line testing technology, and in particular to a testing system, a method and apparatus for determining the deformation of a test drawer. Background Technology

[0002] Smartphones and other mobile devices must pass testing on the production line before they can be put into use. Related technologies include... Figure 1 As shown, the system for testing terminals on the terminal production line consists of two parts: a cabinet containing multiple test drawers and a robotic arm with a camera mounted at the end. The test drawers are responsible for testing various functions of the device under test, while the robotic arm is responsible for moving the device under test to the corresponding test drawer.

[0003] To enable the robotic arm to handle (place and retrieve) the object under test, a calibration process is performed during the test environment setup. This process calculates the specific world coordinate system position of each drawer's placement and retrieval location, and the robotic arm executes actions based on this position information. However, due to errors in the current vision algorithm along the Z-axis (perpendicular to the ground), the placement and retrieval coordinates of this point in the Z-axis direction may be slightly higher or lower than the actual physical position of the drawer. The case where the coordinates are slightly lower than the physical position of the drawer will cause slight deformation of the drawer over long-term placement and retrieval operations.

[0004] In related technologies, the device alarm can only be triggered to notify human operators when the drawer deformation reaches a certain level and the test object can no longer be placed in the test drawer. Only after the deformed drawer is repaired and recalibrated can normal testing be resumed. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this disclosure provides a testing system, a method and apparatus for determining the deformation of a test drawer.

[0006] According to a first aspect of the present disclosure, a testing system is provided, including a cabinet with multiple test drawers; a robotic arm including grippers for holding test objects and placing the test objects into the test drawers, wherein the grippers are provided with pressure sensors for detecting the pressure generated when the grippers contact the test drawers when placing the test objects into the test drawers.

[0007] In some embodiments, the pressure sensor is disposed on at least one of a plurality of grippers that are in contact with the test drawer.

[0008] According to a second aspect of the present disclosure, a method for determining the deformation of a test drawer is provided, applied to a test system including a cabinet and a robotic arm, wherein a pressure sensor is provided on the gripper of the robotic arm, and the method includes: detecting a pressure value collected by the pressure sensor on the gripper when the gripper of the robotic arm places a test object into the test drawer of the cabinet; inputting the pressure value into a pre-trained model, wherein the input of the model is the pressure value and the output is the deformation value of the test drawer; and determining the real-time deformation value of the test drawer based on the output of the model.

[0009] In some embodiments, the method further includes: if the real-time deformation value of the test drawer is greater than a preset threshold, adjusting the height of the gripper when the robot places the test object, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0010] In other embodiments, the cabinet includes multiple test drawers; when the gripper of the robotic arm places the test object into each test drawer of the multiple test drawers, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0011] In other embodiments, different positions in the same test drawer correspond to different preset thresholds; the preset thresholds are determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different positions corresponding to different preset thresholds.

[0012] In other embodiments, the model is pre-trained as follows: a training set and a test set are determined, the training set and the test set including multiple data pairs, each of the multiple data pairs including the deformation value of the test drawer and the pressure value collected by the pressure sensor; the pressure values ​​in the training set are input into the prediction model to obtain deformation prediction values, the input layer and output layer of the prediction model including a single neuron, and the network layer including multiple neurons composed of fully connected patterns; the prediction model is trained based on the deformation prediction values ​​and the deformation values ​​in the training set, and the trained model is tested based on the pressure values ​​and deformation values ​​in the test set to obtain the final model.

[0013] According to a third aspect of the present disclosure, an apparatus for determining the deformation of a test drawer is provided, applied to a testing system for a cabinet and a robotic arm. The gripper of the robotic arm is equipped with a pressure sensor. The apparatus includes: a detection module for detecting the pressure value collected by the pressure sensor on the gripper when the gripper of the robotic arm places a test object into the test drawer of the cabinet; a processing module for inputting the pressure value into a pre-trained model, wherein the input of the model is the pressure value and the output is the deformation value of the test drawer; and a determination module for determining the real-time deformation value of the test drawer based on the output of the model.

[0014] In some embodiments, the device further includes an adjustment module; the adjustment module is used to adjust the height of the gripper when the robot places the test object if the real-time deformation value of the test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0015] In other embodiments, the cabinet includes multiple test drawers; when the gripper of the robotic arm places the test object into each test drawer of the multiple test drawers, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0016] In other embodiments, different positions in the same test drawer correspond to different preset thresholds; the preset thresholds are determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different positions corresponding to different preset thresholds.

[0017] In other embodiments, the determining module is further configured to determine a training set and a test set, wherein the training set and the test set include multiple data pairs, each of the multiple data pairs including the deformation value of the test drawer and the pressure value collected by the pressure sensor; the processing module is further configured to input the pressure value in the training set into the prediction model to obtain the deformation prediction value, wherein the input layer and output layer of the prediction model include a single neuron, and the network layer includes multiple neurons composed of fully connected patterns; the processing module is further configured to train the prediction model based on the deformation prediction value and the deformation value in the training set, and test the trained model based on the pressure value and the deformation value in the test set to obtain the final model.

[0018] According to a fourth aspect of the present disclosure, an apparatus for determining the deformation of a test drawer is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method for determining the deformation of a test drawer as described in the second aspect and its embodiments above.

[0019] According to a fifth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, which, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the method for determining test drawer deformation as described in the second aspect and its embodiments above.

[0020] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: by setting a pressure sensor on the gripper of the robotic arm, when the gripper puts the test object into the test drawer, the pressure sensor obtains the pressure generated when the test object comes into contact with the test drawer, thereby predicting whether the test drawer will deform and avoiding the risk of excessive pressure causing the production line to stop.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0023] Figure 1 This is a schematic diagram illustrating a system architecture according to an exemplary embodiment;

[0024] Figure 2 This is a structural diagram of a test system according to an exemplary embodiment;

[0025] Figure 3 This is a flowchart illustrating a method for determining the deformation of a test drawer according to an exemplary embodiment;

[0026] Figure 4 This is a flowchart illustrating yet another method for determining the deformation of a test drawer according to an exemplary embodiment;

[0027] Figure 5 This is a system architecture diagram of a model according to an exemplary embodiment;

[0028] Figure 6 This is a flowchart illustrating the training process of a model according to an exemplary embodiment;

[0029] Figure 7 This is a block diagram illustrating a device for determining the deformation of a test drawer according to an exemplary embodiment.

[0030] Figure 8 This is a block diagram illustrating a device for determining the deformation of a test drawer according to an exemplary embodiment;

[0031] Figure 9This is a block diagram illustrating a server apparatus according to an exemplary embodiment. Detailed Implementation

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure.

[0033] As described in the background section, due to errors in the visual algorithm along the Z-axis (perpendicular to the ground), the coordinates of the pick-up / placement point in the Z-axis direction can easily be slightly higher or lower than the actual physical position of the drawer. In the latter case, being slightly lower than the physical position of the drawer will cause minor deformation of the drawer over long-term pick-up / placement operations. In related technologies, an alarm is only triggered to notify manual intervention when the drawer deformation reaches a certain level, preventing the test item from being properly placed in the test drawer. Manual repair and recalibration of the deformed drawer are then required to resume normal testing, severely hindering production line efficiency.

[0034] Based on this, this disclosure proposes a testing system that uses pressure sensors on the grippers of a robotic arm to measure the pressure generated when the grippers place the test object into the test drawer. This allows the system to predict whether the test drawer will deform, thus avoiding the risk of excessive pressure causing the production line to stop.

[0035] Figure 1 An exemplary system architecture to which embodiments of this disclosure can be applied is shown.

[0036] like Figure 1 As shown, the system architecture may include a test system 11, a network 12, and a server 13. The network 12 serves as the medium for providing a communication link between the test system 11 and the server 13. The network 12 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0037] The testing system 11 may be equipped with a display screen, allowing users to interact with the server via network 12 to receive or send messages. The server 13 may provide various services. The server can store, analyze, and process the received data, and then feed the processing results back to the terminal device.

[0038] It should be noted that the method for determining the deformation of the test drawer provided in this application embodiment can be executed by the processor built into the test system 11 or by the server 13. The test system 11 obtains the deformation result by communicating and interacting with the server 13. This disclosure does not strictly limit the specific hardware scenario for the application of this method for determining the deformation of the test drawer.

[0039] The model mentioned below can be set up in the test system 11 or in the server 13. In some embodiments, the model can be trained in the server 13, and the trained model can be stored in the server 13 for use in determining the deformation of the test drawer.

[0040] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0041] Figure 2 This is a structural diagram of a test system according to an exemplary embodiment, such as... Figure 2 As shown, it includes the following structure:

[0042] Cabinet 1, including multi-layer test drawers 2;

[0043] The robotic arm 3 includes grippers 4 for holding test objects and placing them into a test drawer. A pressure sensor (not shown in the figure) is provided on the grippers. The pressure sensor is used to detect the pressure generated when the grippers contact the test drawer while placing the test object into the test drawer.

[0044] This embodiment of the invention uses a pressure sensor on the gripper of a robotic arm. When the gripper places a test object into a test drawer, the pressure sensor obtains the pressure generated when the test object contacts the test drawer. This allows the system to predict whether the test drawer will deform, thus avoiding the risk of excessive pressure causing the production line to stop.

[0045] In some embodiments, since large-scale, multi-category testing is typically required during actual testing, the number of test drawers 2 is generally set to multiple, preferably at least two layers.

[0046] In other embodiments, the pressure sensor is disposed on at least one of the plurality of grippers that are in contact with the test drawer.

[0047] Of course, in order to avoid multiple grippers not being on the same horizontal line, which would cause the pressure sensor on at least one gripper to fail to detect pressure when pressure is generated, pressure sensors can be set on multiple grippers separately to more accurately obtain the pressure generated when the test object is placed in the test drawer and comes into contact with the test drawer.

[0048] Furthermore, when pressure sensors on multiple grippers detect pressure, and the detected pressure values ​​are different, the maximum pressure value can be selected as the final detection value.

[0049] Optionally, the robotic arm 3 also includes a positioning device for accurately grasping and placing the test object.

[0050] Some products have high requirements for positional accuracy, and the positioning accuracy of the robotic arm 3 alone may not meet the requirements. Therefore, the robotic arm 3 can also be equipped with a camera device for machine vision positioning. The camera device is used to assist the robotic arm in positioning correction. Based on the image features captured by the camera device, it is compared with the pre-processed image to analyze the deviation and obtain the correct correction information. The testing system controls the robotic arm 3 to perform position compensation based on the correction information to ensure the grasping accuracy of the robotic arm 3, meet the positioning requirements of some test objects with high positional requirements, and ensure the accurate grasping of the test objects.

[0051] Specifically, the camera device is mounted on the stand of the robotic arm 3, so that the lens of the camera device can be aimed at the object to be grasped by the gripper 4. It is mainly used to correct the gripping position accuracy of the gripper 4 on the test object when gripping and placing it.

[0052] Furthermore, the testing system also includes a display screen mounted on cabinet 1, which displays the test results of the test object and the pressure generated when it contacts the test drawer. Optionally, when the pressure value detected by the sensor exceeds a preset pressure value, the testing system can also issue an alarm or display a reminder message on the screen to prompt the user to adjust the grippers in time to prevent the testing system from becoming unusable.

[0053] Figure 3 This is a flowchart illustrating a method for determining the deformation of a test drawer according to an exemplary embodiment, such as... Figure 3 As shown, a testing system applied to a cabinet and a robotic arm, wherein the gripper of the robotic arm is equipped with a pressure sensor, includes the following steps.

[0054] In step S101, when the gripper of the robotic arm places the test object into the test drawer of the cabinet, the pressure value collected by the pressure sensor installed on the gripper is detected.

[0055] It is worth noting that when the robotic arm is in the normal position or slightly higher, the pressure sensor will not collect pressure values ​​when the gripper of the robotic arm puts the test object into the test drawer of the cabinet. Only when the robotic arm is in a slightly lower position will the pressure sensor detect pressure values ​​due to the squeezing when the gripper places the test object into the test drawer.

[0056] In step S102, the pressure value is input into the pre-trained model. The input of the model is the pressure value, and the output is the deformation value of the test drawer.

[0057] Optionally, the output deformation value of the test drawer can be the change in deformation value of the current test drawer compared to the deformation value of the previous test drawer, or it can be the real-time deformation value of the test drawer, which can be determined by a pre-trained model according to actual needs.

[0058] In step S103, the real-time deformation value of the test drawer is determined based on the output of the model.

[0059] In this embodiment of the disclosure, by inputting the pressure value collected by the pressure sensor into the pre-trained model, the model can output the deformation value of the test drawer. Thus, the real-time deformation value of the test drawer can be predicted by the model, so that the user can adjust the test system in a timely manner according to the real-time deformation value of the test drawer, and avoid the production line from being shut down due to the test system being unusable.

[0060] In some embodiments, if the real-time deformation value of the test drawer is greater than a preset threshold, the height of the gripper when the robot places the test object is adjusted so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0061] Optionally, since the cabinet includes multiple test drawers, when the gripper of the robotic arm places the test object into each test drawer of the multiple test drawers, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0062] Since the real-time deformation value is greater than the preset threshold, it means that the robot cannot put the test object into the test drawer. By increasing the height of the gripper, the deformation value of the test drawer is made less than the preset threshold, so that the gripper can put the test object into the test drawer and avoid the test drawer becoming unusable.

[0063] In some embodiments, because the construction of different locations within the test drawer may differ, different locations within the same layer of the test drawer may correspond to different preset thresholds. In one embodiment, the preset threshold for the middle portion of the test drawer is larger than that for the edge portions.

[0064] Based on this, the preset threshold can be determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different preset thresholds corresponding to different positions.

[0065] In this embodiment of the disclosure, by setting different deformation thresholds for different positions of the test drawer, the deformation state of the test system can be obtained more accurately.

[0066] In some embodiments, the model in step S102 is pre-trained as follows: Figure 4 As shown, it includes the following steps:

[0067] In step S201, the training set and the test set are determined.

[0068] The training set and the test set contain multiple data pairs. Each data pair includes the deformation value of the test drawer and the pressure value collected by the pressure sensor.

[0069] In one embodiment, the pressure values ​​collected by the pressure sensor set on the gripper and the deformation values ​​of the test drawer corresponding to the pressure values ​​are obtained in advance, and divided into training set and test set in a ratio of 9:1.

[0070] In step S202, the pressure values ​​in the training set are input into the prediction model to obtain the deformation prediction values.

[0071] Among them, such as Figure 5 As shown, the input and output layers of the prediction model consist of a single neuron, while the network layers consist of multiple neurons composed of fully connected patterns.

[0072] In a fully connected model, for both layers n-1 and n, any neuron in layer n-1 is connected to all neurons in layer n.

[0073] Optionally, the number of network layers in the prediction model is less than or equal to 18, and each layer may include 100 neurons. Of course, the embodiments of this disclosure do not limit the specific number of network layers and the number of neurons.

[0074] Optionally, to enhance the expressive power of the model, the activation function of the prediction model is ReLU, and the loss function of the network is set to the root mean square error (MSE).

[0075] In step S203, a prediction model is trained based on the predicted deformation value and the deformation value in the training set, and the trained model is tested based on the pressure value and deformation value in the test set to obtain the final model.

[0076] Optionally, after training the prediction model based on the training set, the prediction model is tested using the test set to obtain the deformation prediction value output by the prediction model. It is then determined whether the error between the deformation prediction value and the deformation value in the test set is within the allowable range. If it is within the allowable range, the training ends. If it is not within the allowable range, the model is retrained using the training set until the error is within the allowable range.

[0077] The following is combined with Figure 6The training process of the model is explained in detail. Data from the training set is input into the model for forward computation. The difference between the predicted deformation value and the actual deformation value is calculated based on the model's output. The error is then checked to see if it meets the requirements. If the error does not meet the preset requirements, the derivative of the error is calculated and backpropagated through the model to update the network parameters. Forward computation is performed again until the error meets the preset requirements. The number of iterations is then checked to see if it exceeds the preset number. If the error meets the preset requirements and the number of iterations exceeds the preset number, the network parameters are saved, and the initial model training is complete. Next, data from the test set is used to verify whether the model's output deformation value is within the allowable error range. If it is within the allowable range, the model training is complete. If it is not within the allowable range, the model is retrained until the error is within the allowable range.

[0078] In practical use, the robotic arm's gripper acquires the position of the test object through a positioning device and / or a camera device, grasps it, and moves the gripper to the cabinet using a pre-set program. The test object is then placed in the cabinet's test drawer. When the robotic arm is positioned low, the gripper presses against the test drawer, and a pressure sensor on the gripper detects this pressure. This pressure is input into a predictive model to obtain the real-time deformation value of the test drawer. Based on this real-time deformation value, it is determined whether the robotic arm's position needs adjustment and / or the test drawer needs maintenance. When the cabinet detects a test object in the test drawer, it closes the drawer and performs the test according to the program. After the test is completed, the data is transmitted to the control terminal, the test drawer is opened, and the robotic arm removes the test object, completing the test.

[0079] In this embodiment of the disclosure, by pre-training the prediction model, when the pressure sensor detects a pressure value during actual use, the pressure value is input into the prediction model, and the predicted deformation value can be output. The predicted deformation value is used to determine whether the testing system needs to be adjusted and to provide preventive suggestions, which can avoid some safety and efficiency problems in production.

[0080] Based on the same concept, embodiments of this disclosure also provide an apparatus for determining the deformation of a test drawer.

[0081] It is understood that the apparatus for determining the deformation of a test drawer provided in this disclosure includes hardware structures and / or software modules corresponding to each function in order to achieve the above-mentioned functions. In conjunction with the units and algorithm steps of the various examples disclosed in this disclosure, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware 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 the technical solutions of this disclosure.

[0082] Figure 7 This is a block diagram illustrating an apparatus for determining the deformation of a test drawer according to an exemplary embodiment. (Refer to...) Figure 7 The device includes a detection module 701, a processing module 702, a determination module 703, and an adjustment module 704.

[0083] The detection module 701 is configured to detect the pressure value collected by the pressure sensor installed on the gripper when the gripper of the robotic arm places the test object into the test drawer of the cabinet.

[0084] The processing module 702 is configured to input pressure values ​​into a pre-trained model, where the input to the model is the pressure value and the output is the deformation value of the test drawer.

[0085] The determination module 703 is configured to determine the real-time deformation value of the test drawer based on the model's output.

[0086] In some embodiments, the adjustment module 704 is configured to adjust the height of the gripper when the robot places the test object if the real-time deformation value of the test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0087] In other embodiments, the cabinet includes multiple test drawers; when the gripper of the robotic arm places the test object into each test drawer of the multiple test drawers, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

[0088] In other embodiments, different positions in the same test drawer correspond to different preset thresholds; the preset thresholds are determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different positions corresponding to different preset thresholds.

[0089] In other embodiments, the determination module 703 is configured to determine a training set and a test set, the training set and the test set including multiple data pairs, each of the multiple data pairs including the deformation value of the test drawer and the pressure value collected by the pressure sensor.

[0090] The processing module 702 is configured to input the stress values ​​in the training set into the prediction model to obtain the deformation prediction value. The input layer and output layer of the prediction model include a single neuron, and the network layer includes multiple neurons composed of fully connected patterns.

[0091] The processing module 702 is also configured to train a prediction model based on the deformation prediction value and the deformation value in the training set, and to test the trained model based on the pressure value and deformation value in the test set to obtain the final model.

[0092] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0093] Figure 8 This is a block diagram illustrating an apparatus 100 for determining the deformation of a test drawer according to an exemplary embodiment. (Refer to...) Figure 8 The device 100 may include one or more of the following components: processing component 102, memory 104, power component 106, multimedia component 108, audio component 110, input / output (I / O) interface 112, sensor component 114, and communication component 116.

[0094] Processing component 102 typically controls the overall operation of device 100, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 102 may include one or more processors 120 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 102 may include one or more modules to facilitate interaction between processing component 102 and other components. For example, processing component 102 may include a multimedia module to facilitate interaction between multimedia component 108 and processing component 102.

[0095] Memory 104 is configured to store various types of data to support the operation of device 100. Examples of such data include instructions for any application or method operating on device 100, contact data, phonebook data, messages, pictures, videos, etc. Memory 104 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0096] The power component 106 provides power to the various components of the device 100. The power component 106 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 100.

[0097] The multimedia component 108 includes a screen that provides an output interface between the device 100 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 108 includes a front-facing camera and / or a rear-facing camera. When the device 100 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0098] Audio component 110 is configured to output and / or input audio signals. For example, audio component 110 includes a microphone (MIC) configured to receive external audio signals when device 100 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 104 or transmitted via communication component 116. In some embodiments, audio component 110 also includes a speaker for outputting audio signals.

[0099] I / O interface 112 provides an interface between processing component 102 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0100] Sensor assembly 114 includes one or more sensors for providing status assessments of various aspects of device 100. For example, sensor assembly 114 may detect the on / off state of device 100, the relative positioning of components such as the display and keypad of device 100, changes in the position of device 100 or a component of device 100, the presence or absence of user contact with device 100, the orientation or acceleration / deceleration of device 100, and temperature changes of device 100. Sensor assembly 114 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 114 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 114 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0101] Communication component 116 is configured to facilitate wired or wireless communication between device 100 and other devices. Device 100 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 116 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 116 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0102] In an exemplary embodiment, the apparatus 100 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0103] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 104 including instructions, which can be executed by a processor 120 of the device 100 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0104] Figure 9 This is a block diagram illustrating an apparatus 200 for determining the deformation of a test drawer according to an exemplary embodiment. For example, apparatus 200 may be provided as a server. (Refer to...) Figure 9 The device 200 includes a processing component 222, which further includes one or more processors, and memory resources represented by memory 232 for storing instructions, such as application programs, that can be executed by the processing component 222. The application programs stored in memory 232 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 222 is configured to execute instructions to perform the aforementioned method for determining the deformation of the test drawer.

[0105] Device 200 may also include a power supply component 226 configured to perform power management of device 200, a wired or wireless network interface 250 configured to connect device 200 to a network, and an input / output (I / O) interface 258. Device 200 may operate on an operating system stored in memory 232, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0106] It is understood that in this disclosure, "multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0107] It is further understood that the terms "first," "second," etc., are used to describe various types of information, but this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and do not indicate a specific order or degree of importance. In fact, the expressions "first," "second," etc., are completely interchangeable. For example, without departing from the scope of this disclosure, first information can also be referred to as second information, and similarly, second information can also be referred to as first information.

[0108] It is further understood that the terms “center,” “longitudinal,” “lateral,” “front,” “rear,” “up,” “down,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” and “outer,” etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this embodiment and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation.

[0109] It can be further understood that, unless otherwise specified, "connection" includes both direct connections where no other components exist between the two parties and indirect connections where other components exist between them.

[0110] It is further understood that although operations are described in a specific order in the accompanying drawings in the embodiments of this disclosure, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining the deformation of a test drawer, characterized in that, A testing system including a cabinet and a robotic arm, the cabinet comprising multiple test drawers, the robotic arm including grippers for holding test objects and placing the test objects into the test drawers, the grippers of the robotic arm being equipped with pressure sensors, the method comprising: When the gripper of the robotic arm holds the test object and places it into the test drawer of the cabinet, the gripper contacts the test drawer and detects the pressure value collected by the pressure sensor installed on the gripper. The pressure value is input into a pre-trained model, which takes the pressure value as input and outputs the deformation value of the test drawer. Based on the output of the model, the real-time deformation value of the test drawer is determined, and the test system is adjusted in a timely manner according to the real-time deformation value of the test drawer. If the real-time deformation value of the test drawer is greater than a preset threshold, the height of the gripper of the robot arm when placing the test object is adjusted so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

2. The method according to claim 1, characterized in that, When the gripper of the robotic arm places the test object into each layer of the multi-layer test drawer, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

3. The method according to claim 1, characterized in that, Different preset thresholds correspond to different positions in the same test drawer; The preset threshold is determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different preset thresholds corresponding to different positions.

4. The method according to claim 2 or 3, characterized in that, The model was pre-trained using the following method: Determine the training set and the test set, which include multiple data pairs. Each of the multiple data pairs includes the deformation value of the test drawer and the pressure value collected by the pressure sensor. The stress values ​​in the training set are input into the prediction model to obtain deformation prediction values. The input and output layers of the prediction model include a single neuron, and the network layer includes multiple neurons composed of fully connected patterns. The prediction model is trained based on the predicted deformation value and the deformation value in the training set, and the trained model is tested based on the pressure value and deformation value in the test set to obtain the final model.

5. An apparatus for determining the deformation of a test drawer, characterized in that, A testing system for use with cabinets and robotic arms, wherein the cabinet includes multiple test drawers, and the robotic arm includes grippers for holding test objects and placing the test objects into the test drawers, and pressure sensors are provided on the grippers of the robotic arm. The device includes: The detection module is used to detect the pressure value collected by the pressure sensor installed on the gripper when the gripper of the robot arm holds the test object and puts the test object into the test drawer of the cabinet. The processing module is used to input the pressure value into a pre-trained model, wherein the input of the model is the pressure value and the output is the deformation value of the test drawer; The determination module is used to determine the real-time deformation value of the test drawer based on the output of the model, and to adjust the test system in a timely manner according to the real-time deformation value of the test drawer. An adjustment module is used to adjust the height of the gripper when the robot places the test object if the real-time deformation value of the test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

6. The apparatus according to claim 5, characterized in that, When the gripper of the robotic arm places the test object into each layer of the multi-layer test drawer, the height of the gripper of the robotic arm is adjusted when the real-time deformation value of each test drawer is greater than a preset threshold, so that the real-time deformation value of the test drawer determined based on the pressure value collected by the pressure sensor is less than the preset threshold.

7. The apparatus according to claim 5, characterized in that, Different positions in the same test drawer correspond to different preset thresholds; the preset thresholds are determined based on the position point where the robotic gripper places the test object and the real-time deformation data value, with different positions corresponding to different preset thresholds.

8. The apparatus according to claim 6 or 7, characterized in that, The determining module is further configured to determine a training set and a test set, wherein the training set and the test set include multiple data pairs, and each of the multiple data pairs includes the deformation value of the test drawer and the pressure value collected by the pressure sensor. The processing module is also used to input the stress values ​​in the training set into the prediction model to obtain deformation prediction values. The input layer and output layer of the prediction model include a single neuron, and the network layer includes multiple neurons composed of fully connected patterns. The processing module is further configured to train the prediction model based on the predicted deformation value and the deformation value in the training set, and to test the trained model based on the pressure value and deformation value in the test set to obtain the final model.

9. An apparatus for determining the deformation of a test drawer, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the method for determining the deformation of the test drawer as described in any one of claims 1-4.

10. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal is able to perform the method for determining the deformation of the test drawer as described in any one of claims 1-4.

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