A four-probe flying probe test equipment path optimization method and device and a storage medium

Abstract

The present application relates to four-probe flying probe test equipment path optimization method, device and storage medium, the method includes task input module receives basic constraint condition and test task information, if using fixed grouping mode, according to the basic constraint condition fixedly divide four probes into two groups, the distance of two groups of probes from the current test task to the selectable next test task is calculated in turn and the moving path of each group of probes is planned, if using the variable grouping mode that allows probe to change group in each step test process, the optimization objective function of each step of each probe is calculated in turn and the moving path of each probe is planned, according to the moving path of each planned probe, control execution module controls each probe to move in turn and completes test task. The mathematical model constructed more comprehensively and accurately reflects the actual situation of four-probe flying probe test, provides more reliable theoretical basis for path optimization, and helps to improve the optimization precision of test path.

Description

Technical Field

[0001] The present invention relates to the field of printed circuit board testing, and in particular to a method, a device and a storage medium for optimizing the path of a four-probe flying probe test device. Background Art

[0002] Printed circuit boards (PCBs) are key components of electronic products, making their quality inspection crucial. Flying probe testing, one of the primary methods for PCB testing, offers advantages such as high precision, high coverage, and high flexibility. Four-probe testing equipment is widely used in flying probe testing, but current research on path optimization for four-probe testing is insufficient.

[0003] Existing research on flying probe test path optimization primarily focuses on algorithm development and small-scale experimental verification, but lacks comprehensive modeling and optimization of four-probe systems for practical applications. Furthermore, existing research has not adequately translated the flying probe test path optimization problem into a multidimensional traveling salesman problem (TSP) for in-depth explanation and modeling, and the efficiency and applicability of the solution algorithms used need to be improved. Traditional multi-TSP solution methods are difficult to directly apply in four-probe test scenarios due to the unique constraints of four-probe testing, such as positional constraints between probes, the requirement for simultaneous arrival of two probes for test tasks, and the varying probe grouping methods depending on the test task. These constraints increase the complexity of the problem.

[0004] Existing research on the modeling of four-probe flying probe test path optimization problem is incomplete, and does not fully consider practical factors such as complex position constraints between probes, task synchronization requirements, and dynamic grouping. As a result, the model is out of touch with the actual test scenario and cannot effectively guide test path optimization.

[0005] Traditional algorithms are inefficient when optimizing four-probe test paths, making it difficult to find high-quality solutions within a reasonable timeframe. Due to the complexity of the four-probe test problem, traditional algorithms are prone to falling into local optima and are unable to meet the efficiency and quality requirements of actual production.

[0006] Existing optimization methods lack flexibility and adaptability when dealing with different test task distributions and test point layouts. For example, when test tasks are unevenly distributed or test point layouts are complex, existing methods cannot effectively adjust probe paths, resulting in extended testing time and wasted resources. Summary of the Invention

[0007] The present invention provides a path optimization method, device and storage medium for a four-probe flying probe test device, aiming to solve at least one of the technical problems existing in the prior art.

[0008] The technical solution of the present invention is a four-probe flying probe test equipment path optimization method, which is applied to a four-probe flying probe test equipment path optimization system. The four-probe flying probe test equipment path optimization system includes a task input module, a path planning module, and a control execution module connected in sequence. The four-probe flying probe test equipment path optimization method includes the following steps:

[0009] S100, the task input module receives basic constraint conditions and test task information;

[0010] S200: If a fixed grouping method is used, the four probes are fixedly divided into two groups according to the basic constraints, each group including two probes, and the distances of the two groups of probes from the current test task to the optional next test task are calculated respectively, and the movement paths of each group of probes are planned;

[0011] S300, if a variable grouping method is used that allows probes to change groups during each step of the test, the optimization objective function of each probe is calculated in turn for each step, and the moving path of each probe is planned;

[0012] S400 , according to the planned moving path of each probe, the control execution module controls each probe to move in sequence to complete the test task.

[0013] Furthermore, in step S100,

[0014] The basic constraints include that the probe must not exceed the boundaries of the printed circuit board PCBA and the boundaries of the product model when working and avoiding;

[0015] The basic constraint condition also includes that at any working moment or the next moment, the coordinate relationship constraints between the probes are satisfied, specifically: suppose the probe At some point in The position coordinates in the plane rectangular coordinate system are ( , ), then the coordinate constraints of each probe in the 𝑥 direction are:

[0016] ≤ ≤ ≤ ,

[0017] The equal sign is only Adjacent probes are allowed to In the same projection position;

[0018] Also includes each probe in The direction constraints are The distance between the directionally adjacent probes is greater than a preset minimum distance value.

[0019] Further, the step S200 comprises:

[0020] S210, fix four probes in a group, the first group of probes including two probes with smaller X coordinate values, the second group of probes including two probes with larger X coordinate values;

[0021] S220, a strategy of making the moving path shortest based on a greedy task selection, respectively calculate the first group of probes and the second group of probes from the current test task to the selectable next test task, select the test task closest to the current test task as the next test task;

[0022] S250, repeat step S220, update the moving path of the first group of probes and the second group of probes until all test tasks are completed.

[0023] Further, in the step S220, in a test task sequence , the total time distance path length of the first group of probes or the second group of probes is:

[0024] ,

[0025] wherein, is the path length of the two probes of the group of probes moving from the test task to the test task , is the path length of the two probes of the group of probes moving from the test task to the test task , is the path length of the two probes of the group of probes moving from the test task to the test task , is the path length of the two probes of the group of probes returning from the test task to the test task ;

[0026] wherein, in a single test task, the path length is the larger value of the distance generated in the process of the two probes of the group of probes moving from the test task to the test task .​

[0027] ,

[0028] The first probe in the group From the test task Move to Test Task The moving distance, The second probe in the probe group From the test task Move to Test Task The moving distance,

[0029] Set test tasks The coordinates of the two test points are:

[0030] and ,and < ,

[0031] When the equipment test completes the test task Then move to the test task Continue testing, test tasks The coordinates of the two test points are:

[0032] and ,and < ,

[0033] probe from Move to , corresponding to the probe The moving distance is:

[0034] ,

[0035] probe from Move to , corresponding to the probe The moving distance is:

[0036] .

[0037] Furthermore, in step S200, the optimization objective function of the fixed group is:

[0038] ,

[0039] when < When , it indicates the first group of probes the second group of probes the first group of probes the second group of probes the first group of probes the first group of probes The next test task selected needs to satisfy the non-collision condition of the probes, so the test task selected needs to be the second group of probes the first group of probes the first group of probes

[0040] When > the second group of probes the first group of probes the second group of probes the first group of probes the second group of probes the first group of probes the second group of probes The next test task selected needs to satisfy the non-collision condition of the probes, so the test task selected needs to be the first group of probes the first group of probes the first group of probes

[0041] Further, the step S300 comprises:

[0042] S310, initially grouping four probes, the first group of probes comprising two probes with smaller X coordinate values, the second group of probes comprising two probes with larger X coordinate values;

[0043] S320, a strategy of making the moving path shortest based on greedy task selection, respectively calculating the distance from the current test task to the selectable next test task for the first group of probes and the second group of probes selecting the test task closest to the current test task as the next test task;

[0044] S330, respectively calculating the total time distance path length of the selectable first group of probes and the second group of probes obtaining the first group of probes total time distance path length of the test and the second group of probes total time distance path length of the test ;

[0045] S340, determining the next test task according to whether the optional next task increases the optimization objective function value of the variable grouping;

[0046] S350, selecting the grouping according to the constraint that the probe needs to meet, calculating the optimization objective function value of the new variable grouping, and selecting the group of tasks corresponding to the minimum objective function value to test, so as to achieve local optimization.

[0047] Further, in the step S340,

[0048] The optimization objective function of the variable grouping is calculated, if the total path length of a group of probes is less than another group, without changing the optimization objective function value of the variable grouping, the grouping of the current probe is not changed, and the group of probes continues to test the next test task.

[0049] If the selected task increases the optimization objective function value of the variable grouping, the test task that minimizes the increment of the optimization objective function value of the variable grouping is selected, and the grouping of the four probes is changed.

[0050] Further, in the step S300, in the variable grouping, the total path of the movement of each probe is calculated separately, and the optimization objective function of the variable grouping is:

[0051] ,

[0052] Among them, is the time distance path length of the first probe in a single test task, is the time distance path length of the second probe in a single test task, is the time distance path length of the third probe in a single test task, is the time distance path length of the fourth probe in a single test task.

[0053] Further, the application also provides a four-probe flying probe test equipment path optimization system for executing the four-probe flying probe test equipment path optimization method, and the four-probe flying probe test equipment path optimization system comprises sequentially connected

[0054] A task input module is used to receive test task information, and the test task information includes test point coordinates.

[0055] A path planning module is used to calculate the optimal test path of multiple probes.

[0056] A control execution module is configured to control the four-probe flying probe testing device to perform a testing task according to the result of the path planning module.

[0057] Further, the application also provides a computer readable storage medium, which stores program instructions, and the program instructions are executed by a processor to implement the four-probe flying probe testing device path optimization method.

[0058] The four-probe flying probe testing device path optimization method, device and storage medium have the following beneficial effects:

[0059] The four-probe flying probe testing device path optimization method, device and storage medium can more comprehensively and accurately reflect the actual situation of the four-probe flying probe testing through the constructed mathematical model, provide a more reliable theoretical basis for path optimization, and help improve the optimization accuracy of the testing path. The dynamic greedy solution construction method can effectively integrate various constraint conditions, find a high-quality testing path within a reasonable time, and significantly improve the testing efficiency. Through comparative experiments, the testing time of the method of the application is greatly shortened compared with the traditional method under the same testing task, the production efficiency is improved, and the production cost is reduced. The combination of the fixed grouping and variable grouping strategies enables the system to automatically select the optimal grouping mode according to different testing scenarios, enhances the flexibility and adaptability of the system. In the scenario where the testing tasks are evenly distributed, the fixed grouping mode has a fast calculation speed and can meet the basic testing requirements; in the scenario where the testing tasks are unevenly distributed or there are complex testing tasks, the variable grouping mode can fully consider various possibilities, find a better testing path, improve the testing quality, and reduce the testing time and resource waste. The technical solution of the application has strong scalability, is not only suitable for the current four-probe flying probe testing device, but also can provide a reference for the path optimization of future multi-probe testing devices, and promote the development of printed circuit board testing technology. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a whole flow chart of the four-probe flying probe testing device path optimization method.

[0061] Figure 2 It is a comparison diagram of the four-probe flying probe testing device path optimization method in which the four probes meet and do not meet the constraints.

[0062] Figure 3 It is a flow chart of the four-probe flying probe testing device path optimization method in which the fixed grouping mode.

[0063] Figure 4 It is a flow chart of the four-probe flying probe testing device path optimization method in which the variable grouping mode.

[0064] Figure 5 It is a structure diagram of the four-probe flying probe testing device path optimization system.

[0065] Figure 6 Figure 1 is a schematic diagram of a four-probe structure of an upper module of a four-probe module.

[0066] Reference signs: 100, task input module; 200, path planning module; 300, control execution module. DETAILED DESCRIPTION

[0067] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with embodiments and drawings to fully understand the purpose, scheme and effects of the present application. It should be noted that the embodiments and features in the embodiments in the present application can be combined with each other without conflict.

[0068] It should be noted that, unless otherwise specified, when a certain feature is referred to as being "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. In addition, the up, down, left, right, top, bottom and the like used in the present application are only relative to the relative position relationship of the components of the present application in the drawings.

[0069] In addition, 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. The terms used in the specification of the present application are only for describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any combination of one or more related listed items.

[0070] It should be understood that although the terms first, second, third, etc. can be used in the present disclosure to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, without departing from the scope of the present disclosure, the first element can also be referred to as the second element, and similarly, the second element can also be referred to as the first element.

[0071] Reference Figures 1 to 6 In some embodiments, the technical scheme of the present application is a four-probe flying probe test equipment path optimization method, which is applied to a four-probe flying probe test equipment path optimization system. The four-probe flying probe test equipment path optimization system comprises a task input module 100, a path planning module 200 and a control execution module 300 connected in sequence. Referring to Figure 1 , the four-probe flying probe test equipment path optimization method comprises the following steps:

[0072] S100, the task input module 100 receives basic constraint conditions and test task information;

[0073] S200, if the fixed grouping mode is adopted, the four probes are fixedly divided into two groups according to the basic constraint condition, each group includes two probes, the distance of the two groups of probes from the current test task to the optional next test task is calculated in turn respectively, and the moving path of each group of probes is planned;

[0074] S300, if the variable grouping mode that allows the probes to change groups in each step of the test process is adopted, the optimization objective function of each step of each probe is calculated in turn respectively, and the moving path of each probe is planned;

[0075] S400, according to the moving path of each probe planned, the control execution module 300 controls each probe to move in turn to complete the test task.

[0076] The beneficial effects of the present application are:

[0077] The four-probe flying probe test equipment path optimization method, device and storage medium, the mathematical model constructed more comprehensively and accurately reflects the actual situation of four-probe flying probe test, provides a more reliable theoretical basis for path optimization, and helps to improve the optimization accuracy of the test path. The dynamic greedy solution construction method can effectively integrate various constraint conditions and find a high-quality test path within a reasonable time, significantly improving the test efficiency. Through comparative experiments, under the same test task, the test time of the method of the present application is greatly shortened compared with the traditional method, the production efficiency is improved, and the production cost is reduced. The combination of the fixed grouping and the variable grouping makes the system able to automatically select the optimal grouping mode according to different test scenes, enhances the flexibility and adaptability of the system. In the scene where the test tasks are evenly distributed, the fixed grouping mode has fast calculation speed and can meet the basic test requirements; in the scene where the test tasks are unevenly distributed or there are complex test tasks, the variable grouping mode can fully consider various possibilities, find a better test path, improve the test quality, and reduce the test time and resource waste. The technical scheme of the present application has strong scalability, is not only suitable for the current four-probe flying probe test equipment, but also can provide reference and reference for the path optimization of future multi-probe test equipment, and promote the development of printed circuit board test technology.

[0078] Specifically, the present application defines the four-probe flying probe test path optimization problem as a cooperative task multi-traveling salesman problem with dynamic position constraints, and constructs a corresponding mathematical model. A dynamic greedy solution construction method is designed to effectively integrate various constraints of the problem into the solution construction process. By comparing the fixed grouping and the variable grouping, the appropriate grouping mode is selected according to different test scenes to optimize the probe path and improve the test efficiency.

[0079] Further, with reference to Figure 1 and Figure 2 , in the step S100,

[0080] The basic constraints include that the probe must not exceed the boundaries of the printed circuit board PCBA and the boundaries of the product model when working and avoiding;

[0081] Constraint 1: The probe must not exceed the boundaries of the PCBA during operation and avoidance. This is because different PCBA models have different shapes and sizes, with maximum and minimum size restrictions. Since the test points are all located within the PCBA boundary, this constraint is always met during probe operation. However, during probe avoidance, the probe's movement trajectory must be considered to avoid exceeding the PCBA boundary.

[0082] Constraint 2: The probe must not exceed the product model boundaries during operation and avoidance. Different product models have different maximum travel limits in the X and Y directions. Product boundaries are fixed, and the appropriate product model must be selected based on the PCBA being tested. PCBAs that exceed the product boundaries cannot be tested using that product. Therefore, Constraint 2 is included in Constraint 1.

[0083] Reference Figure 2 The basic constraint condition also includes that at any working moment or the next moment, the coordinate relationship constraints between the probes are satisfied, specifically: let the probe At some point in The position coordinates in the plane rectangular coordinate system are ( , ), then the coordinate constraints of each probe in the 𝑥 direction are:

[0084] ≤ ≤ ≤ ,

[0085] The equal sign is only Adjacent probes are allowed to In the same projection position;

[0086] Also included are the various probes The direction constraints are The distance between adjacent probes in the direction is greater than the preset minimum distance value.

[0087] Specifically, the four-probe flying probe test path optimization problem is defined as a cooperative task multi-traveling salesman problem with dynamic position constraints. Considering that all test tasks only have two test points, the four probes are divided into two groups for testing to speed up the testing speed. The optimization objective is to minimize the time for the device to complete the test work, i.e., min(max(L1, L2, L3, L4)), where L1, L2, L3, and L4 are the total time distances of the four probes, respectively. During the testing process, the four probes need to satisfy the position constraint x(p1)≤x(p2)≤x(p3)≤x(p4), and according to the test point coordinate constraint, the probe grouping mode will change dynamically.

[0088] Each probe is in One specific embodiment of the constraint in the direction is:

[0089] |Y1-Y2|≥0.3,

[0090] |Y2-Y3|≥0.2,

[0091] |Y3-Y4|≥0.3.

[0092] Further, with reference to Figure 1 and Figure 3 , the step S200 comprises:

[0093] S210, fix the four probes in groups, the first group of probes including two probes with smaller X coordinate values, and the second group of probes including two probes with larger X coordinate values;

[0094] S220, based on the greedy task selection strategy to make the moving path shortest, respectively calculate the distance from the current test task to the next selectable test task for the first group of probes and the second group of probes select the test task closest to the current test task as the next test task;

[0095] S250, repeat step S220, update the moving path of the first group of probes and the second group of probes until all test tasks are completed.

[0096] Specifically, the dynamic greedy strategy is adopted for task selection, and the constraint conditions of the probes are directly integrated into the task selection process. In each decision-making step, the current constraints and conditions are dynamically considered, and the optimal or approximate optimal next action is selected.

[0097] In the fixed grouping mode, four probes are fixed into two groups, such as p1 and p2 are in one group, and p3 and p4 are in another group. The total time distance path length of each group is denoted as LG1 and LG2, respectively. The objective function is max(LG1, LG2). When one group completes the test, the nearest test task is selected for further test under the condition that the selected test task has a smaller x-axis coordinate than the test task of the other group. In this way, the four-probe case is converted into a two-probe case with task limitation for optimization. A task selection strategy based on greediness is adopted to minimize the moving path, and the local optimum is used to approximate the global optimum to shorten the idle time of the probes as much as possible.

[0098] Further, referring to Figure 1 and Figure 3 , in the step S220, in one test task sequence , the total time distance path length of the first group of probes or the second group of probes is:

[0099] ,

[0100] wherein, is the path length of the two probes of the group from the test task to the test task , is the path length of the two probes of the group from the test task to the test task , is the path length of the two probes of the group from the test task to the test task , is the path length of the two probes of the group from the test task to the test task ;

[0101] wherein, in a single test task, the path length is the larger value of the distance generated in the process of moving the two probes of the group from the test task to the test task , i.e.:

[0102] ,

[0103] is the moving distance of the first probe of the group from the test task to the test task , the second probe of the set of probes from the test task to the test task the moving distance,

[0104] set the test task the coordinates of the two test points are respectively:

[0105] and , and < ,

[0106] When the device tests the test task to the test task continue testing, the coordinates of the two test points of the test task are respectively:

[0107] and , and < ,

[0108] probe from to , the moving distance of the corresponding probe is:

[0109] ,

[0110] probe from to , the moving distance of the corresponding probe is:

[0111] .

[0112] In particular, the present study aims to improve the efficiency of the testing process, so the optimization goal is the total time of the testing process, including the time to obtain the test path and the time required for the probe to move. Assuming that the movement of all probes in the same device in the x-y plane is uniform linear motion, and the speed is the same, the test time can be shortened by reducing the path length of the probe movement.

[0113] According to actual conditions, different test tasks require different numbers of probes. For example, 2 probes are required for inductance, open / short circuit, jumper, voltage / current test, general, Zener, and stabilizing diode test tasks, 3 probes are required for transistor, MOS tube, and silicon-controlled rectifier test tasks, 4 probes are required for optocoupler test tasks, 2-3 probes are required for resistance and capacitance test tasks, and sometimes an additional probe is required as an auxiliary isolation. In addition, 2-4 probes are required for burning test tasks, and 4-6 probes are required for JTAG test tasks.

[0114] For ≤ ≤ ≤ , assuming that the two test points of the test task are and , and < , after the device is tested , the probes are moved to the next test task and , and < . Due to device limitations, the positions of the probes must satisfy the following relationship: , which means that the movement of the probes must be: probe moving from to , and probe moving from to . The distance corresponding to the movement of probe is , and the distance corresponding to the movement of probe is . From test task to test task , the total path length of the movement of the two probes is . Even if probe is moved from to , and probe is moved from to , the total path length of the movement of the probes is shorter, but in order to satisfy the constraint condition, the longer path must still be selected.

[0115] However, in actual testing, both probes need to reach and contact the test points at the same time for the test task to begin. This means that when one probe reaches point , the other probe must also reach point Since the moving speed of the probes is the same, to make the probes arrive at the same time means that the path length of the two probes needs to be equal, but this is usually impossible. Therefore, only the path length of the probe moving longer is calculated, when this probe reaches the test point, the other probe must have reached the corresponding test point. For this, the moving path of the two probes moving to the test task is defined as Even if the actual moving path of the probe is less than this distance, its requirement in the time layer is equal to the size of this distance, so it is defined as a time distance. This distance definition can convert the point-to-point distance into the time cost between test tasks.

[0116]

[0117] Therefore, when the test task sequence is , the total length of the moving path of the probe can be expressed as formula

[0118]

[0119] Further, with reference to Figure 1 and Figure 3 , the optimization objective function of the fixed group in the step S200 is:

[0120] ,

[0121] When < , it means that the test time of the first group of probes is shorter than that of the second group of probes , after the first group of probes finish the current test task, the second group of probes is still testing, at this time, the first group of probes that have completed the test immediately start testing the next task, the next test task selected by the first group of probes needs to meet the non-collision condition of the probe, so the selected test task needs to be The test task whose axis coordinate is less than the axis coordinate of the current test task of the second group of probes and is closest to it as the next test task;

[0122] When > , it means that the test time of the second group of probes is shorter than that of the first group of probes , after the second group of probes ​After the current test task is completed, the first group of probes is still being tested, at this time let the second group of probes which have completed the test immediately start testing the next task The next test task selected needs to meet the non-collision condition of the probe, so the test task selected needs to be in the axial coordinate is greater than the first group of probes the current test task The axial coordinate of the test task closest to the current test task is taken as the next test task.

[0123] Further, with reference to Figure 1 and Figure 4 , the step S300 comprises:

[0124] S310, initially group four probes, the first group of probes including two probes with smaller X coordinate values, the second group of probes including two probes with larger X coordinate values;

[0125] S320, a greedy-based task selection strategy that minimizes the moving path, respectively calculates the first group of probes and the second group of probes from the current test task to the selectable next test task, selects the test task closest to the current test task as the next test task;

[0126] S330, respectively calculates the total time distance path length of the selectable first group of probes and the second group of probes , obtains the test total time distance path length of the first group of probes and the test total time distance path length of the second group of probes ;

[0127] S340, determines the next test task according to whether the selectable next task increases the optimization objective function value of the variable group;

[0128] S350, according to the constraints that the probe needs to meet, selects the group, calculates the optimization objective function value of the new variable group, selects the group corresponding to the minimum objective function value for testing to achieve local optimization.

[0129] ​​The variable grouping mode allows the probes to change groups in each step of the test process. When selecting a test task, the change in the total distance of all probe movements after the next task selection is considered. When the total path length of a group of probes is less than that of another group, the group of probes continues to test the next task without changing the objective function value. If the selected task increases the objective function value, the test task that minimizes the increase in the objective function value is selected. After determining the test task, the grouping is selected according to the constraints that the probes need to meet, the new objective function value is calculated, and the group of tasks corresponding to the minimum objective function value is selected for testing to achieve local optimization. This mode provides a larger search space and improves the possibility of finding the optimal solution, and performs better in the case of uneven distribution of test tasks or higher difficulty of some test tasks.

[0130] Further, with reference to Figure 1 and Figure 4 , in the step S340,

[0131] The optimization objective function of variable grouping is calculated. If the total path length of a group of probes is less than that of another group, the grouping of the current probes is not changed, and the group of probes continues to test the next test task without changing the optimization objective function value of the variable grouping.

[0132] If the selected task increases the optimization objective function value of the variable grouping, the test task that minimizes the increase in the optimization objective function value of the variable grouping is selected, and the grouping of the four probes is changed.

[0133] Further, with reference to Figure 1 and Figure 4 , in the step S300, in the variable grouping, the total path of each probe movement is calculated separately, and the optimization objective function of the variable grouping is:

[0134] ,

[0135] wherein, is the time-distance path length of the first probe in a single test task, is the time-distance path length of the second probe in a single test task, is the time-distance path length of the third probe in a single test task, is the time-distance path length of the fourth probe in a single test task.

[0136] Further, with reference to Figure 5 , the application also proposes a four-probe flying probe test equipment path optimization system for executing the four-probe flying probe test equipment path optimization method, the four-probe flying probe test equipment path optimization system comprising sequentially connected

[0137] The task input module 100 is configured to receive test task information, wherein the test task information comprises test point coordinates.

[0138] The path planning module 200 is configured to calculate optimal test paths for the plurality of probes.

[0139] The control execution module 300 is configured to control the four-probe flying probe testing device to perform the test task according to the result of the path planning module 200.

[0140] Reference Figure 6 A flying probe tester can perform various types of tests, including measurements of resistors, capacitors, inductors, diodes, and other components, as well as voltage and current tests, open and short circuit tests, jumpering, and other functional tests. In performing these tests, the flying probe tester typically uses multiple test methods such as open test method, short test method, and capacitance test method. The flying probe testing device is typically composed of an upper module and a lower module, each of which can be equipped with up to four probes. Different models of devices have different numbers of probes. For example, a simple version of the device is equipped with only two to four probes in the upper module; a standard version of the device is equipped with two probes in the lower module in addition to the four probes in the upper module; and a high-end version of the device is equipped with eight probes in the upper and lower modules. Since the control of the probes in the upper and lower modules is independent, there is no constraint relationship between the probe movements of the two modules. Therefore, the main research content of this paper is the optimization of the probe movement path in a single module, and does not involve the relationship between the two modules. By optimizing the movement path of the probes, the test efficiency of the flying probe tester can be improved, thereby improving the production efficiency of the PCBA.

[0141] Further, the present application also provides a computer readable storage medium having program instructions stored thereon, wherein the program instructions are executed by a processor to implement the four-probe flying probe testing device path optimization method.

[0142] Some terms in the present application are explained as follows:

[0143] ① Flying Probe Test (FPT): a method for testing PCBs, which uses motor control to move probes quickly to test components on the circuit board.

[0144] ② Traveling Salesman Problem (TSP): a classic combinatorial optimization problem that aims to find a path that traverses all cities with the shortest total distance.

[0145] ③ Multi-Traveling Salesman Problem (MTSP): an extension of TSP, involving multiple traveling salesmen traveling simultaneously, with the goal of optimizing the total path of all traveling salesmen or the longest path of a single traveling salesman.

[0146] (4) Time distance: In the flying probe test, in order to ensure that two probes reach the test point at the same time, a distance measurement method is defined, taking the maximum value of the moving distance of the two probes as the time distance, which is used to measure the time cost between test tasks.

[0147] The above only describes preferred embodiments of the present application, and the present application is not limited to the above-described embodiments. As long as the same means achieve the technical effects of the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A four-probe flying probe test equipment path optimization method, wherein the four-probe flying probe test equipment path optimization method is applied to a four-probe flying probe test equipment path optimization system, wherein the four-probe flying probe test equipment path optimization system comprises a task input module (100), a path planning module (200) and a control execution module (300) connected in sequence, and is characterized in that: The four-probe flying probe test equipment path optimization method comprises the following steps: S100, the task input module (100) receives basic constraint conditions and test task information; S200: If a fixed grouping method is used, the four probes are fixedly divided into two groups according to the basic constraints, each group including two probes, and the distances of the two groups of probes from the current test task to the optional next test task are calculated respectively, and the movement paths of each group of probes are planned; S300, if a variable grouping method is used that allows probes to change groups during each step of the test, the optimization objective function of each probe is calculated in turn for each step, and the moving path of each probe is planned; S400, according to the planned moving path of each probe, the control execution module (300) controls each probe to move in sequence to complete the test task; Wherein, the step S200 includes: S210, fixed group of four probes, the first group of probes The second set of probes includes two probes with smaller X coordinate values. Includes two probes with larger X coordinate values; S220, based on the greedy task selection strategy to make the moving path shortest, calculate the first group of probes respectively and the second set of probes The distance from the current test task to the optional next test task, and the test task closest to the current test task is selected as the next test task; S250, repeat step S220 to update the first set of probes and the second set of probes Move along the path until all test tasks are completed; Wherein, the step S300 includes: S310, initial grouping of four probes, the first group of probes The second set of probes includes two probes with smaller X coordinate values. Includes two probes with larger X coordinate values; S320, based on the greedy task selection strategy to make the moving path shortest, calculate the first group of probes respectively and the second set of probes The distance from the current test task to the optional next test task, and the test task closest to the current test task is selected as the next test task; S330, respectively calculating the optional first group of probes and the second set of probes The total time distance path length is obtained by the first set of probes The total test time distance path length and the second set of probes The total test time distance path length ; S340, determining the next test task according to whether the optional next task increases the optimization objective function value of the variable grouping; S350: Select a group based on the constraints that the probes need to meet, calculate the optimization objective function value of the new variable group, and select a group of tasks corresponding to the minimum objective function value for testing to achieve local optimality.

2. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: In the step S100, The basic constraints include that the probe must not exceed the boundaries of the printed circuit board PCBA and the boundaries of the product model when working and avoiding; The basic constraint condition also includes that at any working moment or the next moment, the coordinate relationship constraints between the probes are satisfied, specifically: suppose probe 𝑖∈{1,2,3,4} is at a certain moment The position coordinates in the plane rectangular coordinate system are ( , ), then the coordinate constraints of each probe in the 𝑥 direction are: ≤ ≤ ≤ ， The equal sign is only Adjacent locations hold true, meaning that adjacent probes are allowed to be at the same projection position in the X direction; Also includes each probe in The direction constraints are The distance between adjacent probes in the direction is greater than the preset minimum distance value.

3. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: In step S220, in a test task sequence The first set of probes or a second set of probes Total time distance path length for: ， in, For the two probes in this group of probes, Move to Test Task Length of the journey, For the two probes in this group of probes, Move to Test Task Length of the journey, For the two probes in this group of probes, Move to Test Task Length of the journey, For the two probes in this group of probes, Return to Test Tasks Length of the journey; Among them, in a single test task, the distance For the two probes in this group of probes, Move to Test Task In the process, the larger value of the distance generated is: ， The first probe in the set From the test task Move to Test Task The moving distance, The second probe in the probe group From the test task Move to Test Task The moving distance, Set test tasks The coordinates of the two test points are: and ,and < , When the equipment test completes the test task Then move to the test task Continue testing, test tasks The coordinates of the two test points are: and ,and < , probe from Move to , corresponding to the probe The moving distance is: ， probe from Move to , corresponding to the probe The moving distance is: 。 4. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: In step S200, the optimization objective function of the fixed group is: ， when < When , it indicates the first group of probes The test time is longer than that of the second set of probes The test time is short, and the first set of probes After completing the current test task, the second set of probes Testing is still ongoing, and the first set of probes has been completed. Immediately begin testing the next task, the first set of probes The next test task selected must meet the probe non-collision condition, so the selected test task must be Axis coordinates are smaller than the second set of probes Current test task The test task with the closest axis coordinate is selected as the next test task; when > When , it indicates the second set of probes The test time is longer than the first set of probes The test time is short, in the second set of probes After completing the current test task, the first set of probes The test is still in progress, at this time let the second set of probes that have completed the test Immediately begin testing the next task, the second set of probes The next test task selected must meet the probe non-collision condition, so the selected test task must be Axis coordinates greater than the first set of probes Current test task The test task with the closest axis coordinates is selected as the next test task.

5. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: In the step S340, Calculate the optimization objective function of the variable grouping. If the total path length of one group of probes is smaller than that of another group, the current grouping of probes is not changed without changing the optimization objective function value of the variable grouping. This group of probes will continue to test the next test task. If the selected task increases the optimization objective function value of the variable grouping, then the test task that minimizes the increment of the optimization objective function value of the variable grouping is selected, and the grouping of the four probes is changed at the same time.

6. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: In step S300, the total moving path of each probe is calculated separately in the variable group. The optimization objective function of the variable group is: ， in, is the time distance path length of the first probe in a single test task, is the time distance path length of the second probe in a single test task, is the time distance path length of the third probe in a single test task, is the time distance path length of the fourth probe in a single test task.

7. A four-probe flying probe test equipment path optimization system, configured to execute the four-probe flying probe test equipment path optimization method according to any one of claims 1 to 6, characterized in that: The four-probe flying probe test equipment path optimization system includes sequentially connected A task input module (100) is used to receive test task information, wherein the test task information includes test point coordinates; A path planning module (200) is used to calculate an optimal test path for multiple probes; The control execution module (300) is used to control the four-probe flying probe test equipment to execute the test task according to the result of the path planning module (200).

8. A computer-readable storage medium, characterized in that Program instructions are stored thereon, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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