Flying probe testing method, flying probe testing device, computer device, storage medium
By automatically setting flying probe test parameters using a learning model, the problem of low testing efficiency caused by manual debugging is solved, and a fast and efficient testing process is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN ORANGE AUTOMATIVE CO LTD
- Filing Date
- 2022-04-29
- Publication Date
- 2026-06-12
AI Technical Summary
In existing technologies, PCB board testing on flying probe testers requires manual adjustment of test parameters, resulting in low testing efficiency.
By learning the model to obtain the first test parameter of the test item to be tested, and using it as the test benchmark value when the conditions are met, manual debugging time is reduced and testing efficiency is improved.
It enables quick setting of test parameters without the need for manual debugging of the test program, thus improving the efficiency of flying probe testing.
Smart Images
Figure CN114910778B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of flying probe testing technology, and in particular to a flying probe testing method, a flying probe testing device, a computer device, and a storage medium. Background Technology
[0002] In related technologies, when testing PCBs on the carrier board of a flying probe tester, it is necessary to manually debug the test program and set the test parameters of the PCB. Each time a PCB is tested, the test parameters of the PCB need to be reset, which increases the time for debugging the test program and seriously affects the efficiency of the flying probe tester. Summary of the Invention
[0003] The main objective of this application is to provide a flying probe testing method, flying probe testing device, computer equipment, and storage medium that can quickly set test parameters through a learning function, eliminating the need for manual debugging of the test program and improving the efficiency of flying probe testing.
[0004] To achieve the above objectives, a first aspect of this application provides a flying probe testing method, the method comprising:
[0005] Obtain the test items to be tested;
[0006] Obtain the policy file;
[0007] The test item to be tested is tested according to the preset learning model to obtain the first test parameter;
[0008] Extract the theoretical test parameters of the test items to be tested from the strategy file;
[0009] If the first test parameter is less than the theoretical test parameter, the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results.
[0010] If multiple test results are equal to the first test parameter, the first test parameter is used as the test baseline value for the test item to be tested.
[0011] In some embodiments, the step of testing the test item to be tested according to a preset learning model to obtain the first test parameter includes:
[0012] The test items to be tested are classified according to the preset classification rules to obtain a test set, wherein the test set consists of multiple test items of the same type.
[0013] The test set is tested according to a preset learning model to obtain the first test parameters.
[0014] In some embodiments, the step of testing the test item to be tested according to a preset learning model to obtain the first test parameter includes:
[0015] Obtain the flying probe position information of the test item to be tested captured by the camera;
[0016] The distribution range of the flying needle test points for the test item to be tested is calculated based on the flying needle position information;
[0017] The test order of the test items to be tested is obtained based on the distribution range of the flying probe test points;
[0018] The test items to be tested are tested according to the preset learning model and the test order to obtain the first test parameters.
[0019] In some embodiments, acquiring the flying probe position information of the test item to be tested captured by the camera includes:
[0020] Obtain the scanned image corresponding to the test item to be tested;
[0021] Obtain the test image corresponding to the test item to be tested;
[0022] If the scanned image and the test image do not match, a substrate image is obtained based on the scanned image.
[0023] The target image is obtained by rotating the substrate image according to the preset panel rotation angle.
[0024] In response to a click operation on a measurement point in the target image, the camera is controlled to capture an image of the measurement point to obtain the flying needle position information of the test item to be tested.
[0025] In some embodiments, obtaining the test item to be tested includes:
[0026] Obtain information about the first target area of the PCB board;
[0027] The PCB board is scanned based on the first target area information to obtain identification information;
[0028] The test item to be tested is obtained based on the identification information.
[0029] In some embodiments, the method further includes:
[0030] The template is obtained based on a visual compensation algorithm;
[0031] The marked coordinates of the test item to be tested are obtained according to the template;
[0032] The origin coordinates of the test item to be tested are corrected based on the marked coordinates.
[0033] In some embodiments, the method further includes:
[0034] Obtain the second target region information of the test item to be tested;
[0035] Based on the second target area information, the PCB board corresponding to the test item to be tested is raised by a preset distance.
[0036] A second aspect of this application provides a flying probe testing device, the device comprising:
[0037] The first acquisition module is used to acquire the test items to be tested.
[0038] The second acquisition module is used to acquire the policy file;
[0039] The first testing module is used to test the test item to be tested according to a preset learning model to obtain the first test parameters;
[0040] The extraction module is used to extract the theoretical test parameters of the test items to be tested in the strategy file;
[0041] The second testing module is used to perform repeated testing on the test item to be tested a preset number of times according to a preset learning model if the first test parameter is less than the theoretical test parameter, so as to obtain multiple test results.
[0042] The third testing module is used to take the first testing parameter as the test benchmark value of the test item to be tested if multiple test results are equal to the first testing parameter.
[0043] A third aspect of this application provides a computer device including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor is used to perform the method described in any one of the embodiments of the first aspect of this application.
[0044] A fourth aspect of this application provides a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method described in any one of the embodiments of the first aspect of this application.
[0045] The flying probe testing method, flying probe testing device, computer equipment, and storage medium proposed in this application embodiment acquire the test item to be tested, acquire a strategy file, test the test item to be tested according to a preset learning model to obtain a first test parameter, extract the theoretical test parameter of the test item to be tested from the strategy file, if the first test parameter is less than the theoretical test parameter, then the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results, if multiple test results are equal to the first test parameter, the first test parameter is used as the test benchmark value of the test item to be tested, and the test item to be tested can obtain a test benchmark value according to the learning function of the learning model, which can quickly set test parameters without the need for manual debugging of test programs to set test parameters, thus improving the efficiency of flying probe testing. Attached Figure Description
[0046] Figure 1 This is a first flowchart of the flying probe testing method provided in the embodiments of this application;
[0047] Figure 2 yes Figure 1 The first flowchart of the specific method for step S130;
[0048] Figure 3 yes Figure 1 The second flowchart of the specific method for step S130;
[0049] Figure 4 yes Figure 3 A flowchart illustrating the specific method of step S310;
[0050] Figure 5 yes Figure 1 A flowchart illustrating the specific method of step S110;
[0051] Figure 6 This is a second flowchart of the flying probe testing method provided in the embodiments of this application;
[0052] Figure 7 This is the third flowchart of the flying probe testing method provided in the embodiments of this application;
[0053] Figure 8 This is a modular structure diagram of the flying probe testing device provided in the embodiments of this application. Detailed Implementation
[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.
[0055] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.
[0057] In related technologies, when testing PCBs on the carrier board of a flying probe tester, it is necessary to manually debug the test program to set the test parameters of the PCB. Each time a PCB is tested, the test parameters of the PCB need to be reset, which increases the time required to debug the test program and seriously affects the efficiency of the flying probe tester.
[0058] Based on this, the main objective of this application is to propose a flying probe testing method. This method uses a learning model to test the test item to obtain a first test parameter. The theoretical test parameter of the test item is extracted from the strategy file. If the first test parameter is less than the theoretical test parameter, the test item is repeatedly tested a preset number of times according to the preset learning model to obtain multiple test results. If all multiple test results are equal to the first test parameter, the first test parameter is used as the test benchmark value for the test item. This application, by introducing a learning model to obtain the first test parameter and using it as the test benchmark value for the test item, enables rapid setting of test parameters, reduces the time spent debugging test programs, and improves the efficiency of flying probe testing.
[0059] The flying probe testing method, flying probe testing device, computer equipment, and storage medium provided in this application are specifically described through the following embodiments. First, the flying probe testing method in this application embodiment is described.
[0060] Reference Figure 1 The flying probe testing method according to the first aspect of the present application is applied to a flying probe testing device. The flying probe testing method includes, but is not limited to, steps S110 to S160.
[0061] S110, Obtain the test item to be tested;
[0062] S120, retrieve the policy file;
[0063] S130, Test the test item to be tested according to the preset learning model to obtain the first test parameters;
[0064] S140, Extract the theoretical test parameters of the test items to be tested from the strategy file;
[0065] S150, if the first test parameter is less than the theoretical test parameter, then the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results;
[0066] S160, if multiple test results are equal to the first test parameter, the first test parameter shall be used as the test baseline value of the test item to be tested.
[0067] In step S110, the test item to be tested is obtained, where the test item is the information data corresponding to the PCB board on the carrier. The information data corresponding to the PCB board on the carrier is imported into the flying probe testing device to obtain the test item to be tested. According to the set number of panels, multiple PCB boards can be placed on the carrier as many as the number of panels, and multiple PCB boards can be quickly set on the carrier simultaneously. It should be noted that the PCB board is the support of electronic components and also the carrier for the electrical connection of electronic components.
[0068] In step S120, a strategy file is obtained. This strategy file stores the test strategies for the test items to be tested. The strategy file is stored in CSV format in a preset save path within the storage space of the flying probe testing device, and its name is determined according to a preset naming rule. The save path and name can distinguish the test strategies for different test items. The strategy file corresponding to the test item is obtained based on the input save path and name.
[0069] In step S130, the learning model has multiple test parameter options. The test item to be tested is input into the learning model, and the learning model obtains the probability that the test parameter of the test item belongs to multiple test parameter options based on the input. The test parameter option with the highest probability is taken as the first test parameter.
[0070] In step S140, the strategy file includes the theoretical test parameters of the test item to be tested, and the theoretical test parameters are extracted from the strategy file.
[0071] In steps S150 to S160, to ensure the stability of the test results, the test item to be tested needs to be repeatedly tested a preset number of times. If the first test parameter is less than the theoretical test parameter, it indicates that the first test parameter meets the test requirements. To avoid significant fluctuations in the first test parameter, the test item to be tested is repeatedly tested a preset number of times according to the learning model. If the test results of multiple repeated tests are all the same as the first test parameter, then the first test parameter is used as the test benchmark value for the test item to be tested. The test benchmark value is the test parameter set for the test item to be tested. Compared with the manual debugging of the test program, the flying probe testing method of this application obtains the first test parameter through a learning model and sets the first test parameter as the test benchmark value for the test item to be tested. This can quickly and accurately set the test parameters for the test item to be tested, shorten the program debugging time, and improve the testing efficiency. It should be noted that the flying probe testing device can interface with different MES systems and upload the test benchmark value to the MES system through a plugin. For MES systems with different modes, a plugin matching the MES system is loaded, and the test benchmark value is uploaded to the corresponding MES system.
[0072] In some embodiments, such as Figure 2 As shown, step S130 specifically includes, but is not limited to, steps S210 to S220.
[0073] S210, classify the test items to be tested according to the preset classification rules to obtain the test set;
[0074] S220, Test the test set according to the preset learning model to obtain the first test parameters.
[0075] In steps S210 to S220, multiple test items to be tested are classified according to device type to obtain a test set. The device types include resistors, capacitors, inductors, semiconductors, etc., and the test set consists of multiple test items of the same type. The test items to be tested are then tested according to device type to obtain multiple test sets such as resistance test set, capacitance test set, inductance test set, and semiconductor test set. Multiple test sets are then classified and tested according to a preset learning model, that is, each test item to be tested in each type of test set is tested to obtain the first test parameter corresponding to the test item to be tested.
[0076] Multiple test items are classified according to their size to obtain a test set. The size of each test item is obtained from the origin coordinates of its corresponding PCB board on the carrier board. The area of each test item is calculated based on the origin coordinates. The test items are then classified according to the threshold range of their area to obtain multiple test sets, such as the first area test set and the second area test set. The multiple test sets are then classified and tested using a learning model to obtain the first test parameter.
[0077] In some embodiments, such as Figure 3 As shown, step S130 specifically includes, but is not limited to, steps S310 to S340.
[0078] S310, acquire the flying probe position information of the test item to be tested captured by the camera;
[0079] S320, calculates the distribution range of the flying needle test points for the test item based on the flying needle position information;
[0080] S330, the test sequence of the test items to be tested is obtained according to the distribution range of the flying probe test points;
[0081] S340 tests the test item to be tested according to the preset learning model and test order to obtain the first test parameters.
[0082] In step S310, after importing the strategy file, a pop-up prompt message is displayed, prompting the user to select whether to enter camera capture mode. The user's selection is received, and the result is evaluated. If the selection is to enter camera capture mode, the location of the flying probe test point corresponding to the test item is photographed, obtaining the flying probe test point location information and flying probe test point type information captured by the camera. If the selection is not to enter camera capture mode, the flying probe test process ends. Since test points have various types, such as traces, vias, and component pins, different types of test points require corresponding designated probes for testing. Based on the flying probe test point type information, a probe for testing that type of test point is selected, and the probe is moved to that test point for flying probe testing.
[0083] In step S320, the distribution range of the flying probe test points for the test item is calculated based on the flying probe test point location information, where the distribution range of the flying probe test points is the test path. For example, if the flying probe test point location information is A(x,y), B(x,y), and C(x,y), then 6 test paths are obtained based on the flying probe test point location information, namely ABC, ACB, BAC, BCA, CAB, and CBA.
[0084] In steps S330 to S340, probe position information is acquired. Based on the probe position information and the distribution range of the flying probe test points, an optimal test path is determined. Multiple test points of the test item are tested according to the test order of the optimal test path to obtain the first test parameter. For example, if the probe position information is A1(x, y), and it is closest to test point A, then the optimal path determined based on the probe position information and the distribution range of the flying probe test points is ABC. The test order is to test test points A, B, and C sequentially to obtain the first test parameter.
[0085] The system acquires probe position information, calculates the distance between probes based on the probe position information, calculates the distance between probes and test points based on the probe position information and the flying probe test point position information, selects the optimal test path from multiple test paths based on the distance between probes, the distance between probes and test points, and the distribution range of flying probe test points, and tests multiple test points of the test item to be tested according to the test order of the optimal test path, avoiding frequent probe movement and speeding up the flying probe test rate.
[0086] In some embodiments, such as Figure 4 As shown, step S310 specifically includes, but is not limited to, steps S410 to S450.
[0087] S410, acquire the scanned image corresponding to the test item to be tested;
[0088] S420, acquire the test image corresponding to the test item to be tested;
[0089] S430: If the scanned image and the test image do not match, obtain the substrate image based on the scanned image;
[0090] S440: Rotate the substrate image according to the preset panel rotation angle to obtain the target image;
[0091] S450, in response to a click operation on a measurement point in the target image, controls the camera to capture an image of the measurement point in order to obtain the flying probe position information of the test item to be tested.
[0092] In step S410, a scanned image corresponding to the test item to be tested is obtained by scanning the PCB board, wherein the scanned image is a physical image of the PCB board.
[0093] In step S420, the test image corresponding to the test item to be tested is obtained, wherein the test image is a component diagram of the PCB board.
[0094] In step S430, if the scanned image matches the test image, it means that the PCB board corresponding to the test item is a single board and no substrate image needs to be set; if the scanned image does not match the test image, it means that the PCB board corresponding to the test item is a multi-panel board. The input position information is received, and the image in the scanned image that matches the position information is used as the substrate image.
[0095] In step S440, the substrate image is copied according to the number of panels to obtain an intermediate image. If the intermediate image does not match the scanned image, the substrate image is rotated according to the panel rotation angle to obtain a target image, which is the scanned image updated from the substrate image.
[0096] In step S450, in response to the click operation on the measurement point in the target image, the camera is automatically located at the position of the measurement point, and the camera is controlled to move to the clicked measurement point to take a picture, thereby obtaining the flying needle position information of the test item to be tested.
[0097] By executing steps S410 to S450, the physical device diagram and the measurement points are matched one-to-one. The measurement point parameters can be set on the physical diagram. The location of the measurement point or device can be intuitively identified by taking pictures with the camera, and the measurement point or device in the physical device diagram can be quickly selected, reducing the probability of missing devices or measurement points.
[0098] In some embodiments, such as Figure 5 As shown, step S110 specifically includes, but is not limited to, steps S510 to S530.
[0099] S510, obtain the first target area information of the PCB board;
[0100] S520 scans the PCB board based on the first target area information to obtain the identification information;
[0101] S530 retrieves the test items to be tested based on the identification information.
[0102] In step S510, the first target area information of the PCB board is obtained, wherein the first target area information of the PCB board is information such as the QR code or barcode of the PCB board.
[0103] In steps S520 to S530, the first target area information corresponds to the first target area of the PCB board. The camera is controlled to move to the first target area to scan the PCB board and obtain the corresponding identification information of the PCB board. Based on the identification information, the test item to be tested corresponding to the PCB board is obtained.
[0104] Reference Figure 6 Another embodiment of this application also provides a flying probe testing method, which includes, but is not limited to, steps S610 to S630.
[0105] S610, obtains the template based on the visual compensation algorithm;
[0106] S620, obtain the marker coordinates of the test item to be tested based on the template;
[0107] S630, calibrates the origin coordinates of the test item to be tested based on the marked coordinates.
[0108] In steps S610 to S630, a template matching the test item to be tested is obtained according to the visual compensation algorithm. The marked coordinates of the scanned image corresponding to the test item to be tested are obtained according to the template. The scanned image is corrected from the origin coordinates to the marked coordinates, thereby reducing the error of image scanning.
[0109] Reference Figure 7 Another embodiment of this application also provides a flying probe testing method, which includes, but is not limited to, steps S710 to S720.
[0110] S710, Obtain the second target area information of the test item to be tested;
[0111] S720 raises the PCB board corresponding to the test item to be tested by a preset distance based on the second target area information.
[0112] In step S710, the second target area information of the test item to be tested is obtained, wherein the second target area information is high device, high flying area, which corresponds to the area with a higher position in the PCB board.
[0113] In step S720, the second target area information corresponds to the second target area of the PCB board. Based on the second target area information, the second target area of the PCB board is raised by a preset distance to prevent the probe from hitting the second target area during the test.
[0114] The flying probe testing method proposed in this application obtains the test item to be tested, acquires a strategy file, tests the test item to be tested according to a preset learning model to obtain a first test parameter, extracts the theoretical test parameter of the test item to be tested from the strategy file, and if the first test parameter is less than the theoretical test parameter, the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results. If multiple test results are equal to the first test parameter, the first test parameter is used as the test benchmark value of the test item to be tested. The learning function of the learning model enables the test item to obtain a test benchmark value, which can quickly set test parameters without the need for manual debugging of the test program to set test parameters, thus improving the efficiency of flying probe testing.
[0115] This application also provides a flying probe testing device, such as... Figure 8As shown, the above-mentioned flying probe testing method can be implemented. The flying probe testing device includes a first acquisition module 810, a second acquisition module 820, a first testing module 830, an extraction module 840, a second testing module 850, and a third testing module 860. The first acquisition module 810 is used to acquire the test item to be tested; the second acquisition module 820 is used to acquire the strategy file; the first testing module 830 is used to test the test item to be tested according to a preset learning model to obtain the first test parameter; the extraction module 840 is used to extract the theoretical test parameter of the test item to be tested from the strategy file; the second testing module 850 is used to perform a preset number of repeated tests on the test item to be tested according to the preset learning model if the first test parameter is less than the theoretical test parameter to obtain multiple test results; the third testing module 860 is used to use the first test parameter as the test benchmark value of the test item to be tested if multiple test results are equal to the first test parameter.
[0116] It should be noted that the flying probe testing device in this application embodiment also includes a multi-functional testing module, which can support LED OPENPIN programming, power-on and other testing methods.
[0117] The flying probe testing device of this application embodiment is used to perform the flying probe testing method in the above embodiment. Its specific processing procedure is the same as that of the flying probe testing method in the above embodiment, and will not be described in detail here.
[0118] The flying probe testing device proposed in this application obtains the test item to be tested and a strategy file, tests the test item to be tested according to a preset learning model to obtain a first test parameter, extracts the theoretical test parameter of the test item to be tested from the strategy file, and if the first test parameter is less than the theoretical test parameter, the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results. If multiple test results are equal to the first test parameter, the first test parameter is used as the test benchmark value of the test item to be tested. The learning function of the learning model enables the test item to obtain a test benchmark value, and the test parameters can be set quickly without the need for manual debugging of the test program to set the test parameters, thus improving the efficiency of flying probe testing.
[0119] This application also provides a computer device, including:
[0120] At least one processor, and,
[0121] A memory that is communicatively connected to at least one processor; wherein,
[0122] The memory stores instructions that are executed by at least one processor to cause the at least one processor to perform the method as described in any of the embodiments of the first aspect of this application when executing the instructions.
[0123] The computer device includes: processor, memory, input / output interface, communication interface, and bus.
[0124] The processor can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to achieve the technical solutions provided in the embodiments of this application.
[0125] The memory can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor using the flying probe testing method of the embodiments of this application.
[0126] Input / output interfaces are used to implement information input and output;
[0127] The communication interface is used to enable communication and interaction between this device and other devices. Communication can be achieved via wired means (e.g., USB, Ethernet cable) or wireless means (e.g., mobile network, Wi-Fi, Bluetooth); and
[0128] A bus is used to transfer information between various components of a device, such as processors, memory, input / output interfaces, and communication interfaces.
[0129] The processor, memory, input / output interfaces, and communication interfaces communicate with each other within the device via a bus.
[0130] This application also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the flying probe testing method of this application.
[0131] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0132] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0133] It will be understood by those skilled in the art that Figures 1 to 7 The technical solutions shown do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0135] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0136] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0137] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0138] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 apparatuses or units may be electrical, mechanical, or other forms.
[0139] 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.
[0140] Furthermore, the functional units in the various embodiments of this application 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.
[0141] 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0142] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A flying probe testing method, characterized in that, The method includes: Obtain the test item to be tested; wherein, the test item to be tested is the information data of the PCB board; Obtain the strategy file; wherein the strategy file is used to store the test strategy for the test item to be tested; The test item to be tested is tested according to a preset learning model to obtain a first test parameter; wherein, the learning model includes multiple test parameter options, and the first test parameter is the test parameter option with the highest probability; Extract the theoretical test parameters of the test items to be tested from the strategy file; If the first test parameter is less than the theoretical test parameter, the test item to be tested is repeated a preset number of times according to the preset learning model to obtain multiple test results. If multiple test results are equal to the first test parameter, the first test parameter is used as the test baseline value for the test item to be tested; wherein, the test baseline value is the test parameter set for the test item to be tested; The step of testing the test item to be tested according to a preset learning model to obtain the first test parameters includes: The test items are classified according to the device type or the size of the test item to obtain a test set. The test set consists of multiple test items of the same type. The size of the test item is the area calculated based on the origin coordinates of the corresponding PCB board on the carrier board. The test set is classified and tested according to a preset learning model to obtain the first test parameter corresponding to the test item. The step of testing the test item to be tested according to a preset learning model to obtain the first test parameters includes: The system acquires the flying needle position information of the test item to be tested captured by the camera; calculates the distribution range of flying needle test points for the test item to be tested based on the flying needle position information, where the distribution range of flying needle test points is the test path; calculates the distance between flying needles based on the flying needle position information; calculates the distance between flying needles and test points based on the flying needle position information and the distribution range of flying needle test points; obtains the optimal test path from the test path based on the distance between flying needles, the distance between flying needles and test points, and the distribution range of flying needle test points; and tests multiple test points of the test item to be tested according to the test order of the optimal test path and a preset learning model to obtain the first test parameters.
2. The flying probe testing method according to claim 1, characterized in that, The step of acquiring the flying probe position information of the test item to be tested captured by the camera includes: Obtain the scanned image corresponding to the test item to be tested; Obtain the test image corresponding to the test item to be tested; If the scanned image and the test image do not match, a substrate image is obtained based on the scanned image. The target image is obtained by rotating the substrate image according to the preset panel rotation angle. In response to a click operation on a measurement point in the target image, the camera is controlled to capture an image of the measurement point to obtain the flying needle position information of the test item to be tested.
3. The flying probe testing method according to claim 1, characterized in that, The acquisition of the test item to be tested includes: Obtain information about the first target area of the PCB board; The PCB board is scanned based on the first target area information to obtain identification information; The test item to be tested is obtained based on the identification information.
4. The flying probe testing method according to any one of claims 1 to 3, characterized in that, The method further includes: The template is obtained based on a visual compensation algorithm; The marked coordinates of the test item to be tested are obtained according to the template; The origin coordinates of the test item to be tested are corrected based on the marked coordinates.
5. The flying probe testing method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain the second target region information of the test item to be tested; Based on the second target area information, the PCB board corresponding to the test item to be tested is raised by a preset distance.
6. A flying probe testing device, characterized in that, The device includes: The first acquisition module is used to acquire the test item to be tested; wherein, the test item to be tested is the information data of the PCB board; The second acquisition module is used to acquire a strategy file; wherein the strategy file is used to store the test strategy of the test item to be tested; The first testing module is used to test the test item to be tested according to a preset learning model to obtain the first test parameter; wherein, the learning model includes multiple test parameter options, and the first test parameter is the test parameter option with the highest probability; The extraction module is used to extract the theoretical test parameters of the test items to be tested in the strategy file; The second testing module is used to perform repeated testing on the test item to be tested a preset number of times according to a preset learning model if the first test parameter is less than the theoretical test parameter, so as to obtain multiple test results. The third testing module is used to take the first testing parameter as the test benchmark value of the test item to be tested if multiple test results are equal to the first testing parameter; wherein the test benchmark value is the test parameter set for the test item to be tested. The step of testing the test item to be tested according to a preset learning model to obtain the first test parameters includes: The test items are classified according to the device type or the size of the test item to obtain a test set. The test set consists of multiple test items of the same type. The size of the test item is the area calculated based on the origin coordinates of the corresponding PCB board on the carrier board. The test set is classified and tested according to a preset learning model to obtain the first test parameter corresponding to the test item. The step of testing the test item to be tested according to a preset learning model to obtain the first test parameters includes: The system acquires the flying needle position information of the test item to be tested captured by the camera; calculates the distribution range of flying needle test points for the test item to be tested based on the flying needle position information, where the distribution range of flying needle test points is the test path; calculates the distance between flying needles based on the flying needle position information; calculates the distance between flying needles and test points based on the flying needle position information and the distribution range of flying needle test points; obtains the optimal test path from the test path based on the distance between flying needles, the distance between flying needles and test points, and the distribution range of flying needle test points; and tests multiple test points of the test item to be tested according to the test order of the optimal test path and a preset learning model to obtain the first test parameters.
7. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a program, which the processor executes when the program is executed: The method as described in any one of claims 1 to 5.
8. A storage medium, wherein the storage medium is a computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a computer, enables the computer to perform: The method as described in any one of claims 1 to 5.