Four-probe flying probe test equipment path optimization method and device and storage medium

Abstract

The invention relates to a four-probe flying probe test equipment path optimization method and device and a storage medium. The method comprises the steps that a task input module receives basic constraint conditions and test task information; if a fixed grouping mode is adopted, the four probes are fixedly divided into two groups according to basic constraint conditions, the distances of the two groups of probes from the current test task to the optional next test task are sequentially calculated, and the moving path of each group of probes is planned; if a variable grouping mode that the probes are allowed to change groups in the testing process of each step is adopted, an optimization objective function of each step of each probe is calculated in sequence, and a moving path of each probe is planned; and according to the planned moving path of each probe, the control execution module controls each probe to move in sequence to complete a test task. The constructed mathematical model more comprehensively and accurately reflects the actual situation of the four-probe flying probe test, provides a more reliable theoretical basis for path optimization, and is beneficial to improving the optimization precision of the 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 adjacent probes in the direction is greater than the preset minimum distance value.

[0019] Furthermore, the step S200 includes:

[0020] 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;

[0021] 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;

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

[0023] Further, 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:

[0024] ,

[0025] 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;

[0026] 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:

[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 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;

[0040] 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.

[0041] Furthermore, the step S300 includes:

[0042] 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;

[0043] 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;

[0044] 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 ;

[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: 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.

[0047] Furthermore, in step S340,

[0048] 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.

[0049] 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.

[0050] Furthermore, in step S300, the total moving path of each probe is calculated separately in the variable group, and the optimization objective function of the variable group is:

[0051] ,

[0052] 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.

[0053] Furthermore, the present invention 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 includes

[0054] A task input module, configured to receive test task information, wherein the test task information includes test point coordinates;

[0055] Path planning module, used to calculate the optimal test path for multiple probes;

[0056] The control execution module is used to control the four-probe flying probe test equipment to execute the test task according to the results of the path planning module.

[0057] Furthermore, the present invention also provides a computer-readable storage medium having program instructions stored thereon, and when the program instructions are executed by a processor, the four-probe flying probe test equipment path optimization method is implemented.

[0058] The beneficial effects of the present invention are:

[0059] The described method, device, and storage medium for path optimization of four-probe flying probe test equipment construct a mathematical model that more comprehensively and accurately reflects the actual conditions of four-probe flying probe testing, providing a more reliable theoretical basis for path optimization and helping to improve the optimization accuracy of test paths. The dynamic greedy solution construction method effectively integrates various constraints to find high-quality test paths within a reasonable time, significantly improving test efficiency. Comparative experiments show that, under the same test task, the method of the present invention significantly reduces test time compared to traditional methods, improving production efficiency and reducing production costs. The combination of fixed and variable grouping strategies enables the system to automatically select the optimal grouping method based on different test scenarios, enhancing the system's flexibility and adaptability. In scenarios with uniform distribution of test tasks, the fixed grouping method offers fast computational speed and can meet basic testing requirements. In scenarios with uneven distribution of test tasks or complex test tasks, the variable grouping method can fully consider various possibilities, find a more optimal test path, improve test quality, and reduce test time and resource waste. The technical solution of the present invention is highly scalable and is not only applicable to current four-probe flying probe test equipment but also provides a reference and reference for path optimization in future multi-probe test equipment, promoting the development of printed circuit board testing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The overall flow chart of the path optimization method for four-probe flying probe test equipment.

[0061] Figure 2 Schematic diagram comparing the constraints satisfied and not satisfied by the four probes in the path optimization method of four-probe flying probe test equipment.

[0062] Figure 3 This is a flow chart of the fixed grouping method in the path optimization method of the four-probe flying probe test equipment.

[0063] Figure 4 This is a flow chart of the variable grouping method in the path optimization method of four-probe flying probe test equipment.

[0064] Figure 5 This is a schematic diagram of the structure of the path optimization system for four-probe flying probe test equipment.

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

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

[0067] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict.

[0068] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, terms such as "upper," "lower," "left," "right," "top," and "bottom" used in this disclosure are intended solely to describe the relative positions of the components of the disclosure as shown in the accompanying drawings.

[0069] In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. The terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. The term "and / or" as used herein includes any combination of one or more of the related listed items.

[0070] It should be understood that although the terms first, second, third, etc. may be used to describe various elements in the present disclosure, 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, a first element may also be referred to as a second element, and similarly, a second element may also be referred to as a first element without departing from the scope of the present disclosure.

[0071] Reference Figures 1 to 6 In some embodiments, the technical solution of the present invention is a four-probe flying probe test equipment path optimization method, the four-probe flying probe test equipment path optimization method 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 100, a path planning module 200 and a control execution module 300 connected in sequence, with reference 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 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;

[0074] 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;

[0075] 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.

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

[0077] The described method, device, and storage medium for path optimization of four-probe flying probe test equipment construct a mathematical model that more comprehensively and accurately reflects the actual conditions of four-probe flying probe testing, providing a more reliable theoretical basis for path optimization and helping to improve the optimization accuracy of test paths. The dynamic greedy solution construction method effectively integrates various constraints to find high-quality test paths within a reasonable time, significantly improving test efficiency. Comparative experiments show that, under the same test task, the method of the present invention significantly reduces test time compared to traditional methods, improving production efficiency and reducing production costs. The combination of fixed and variable grouping strategies enables the system to automatically select the optimal grouping method based on different test scenarios, enhancing the system's flexibility and adaptability. In scenarios with uniform distribution of test tasks, the fixed grouping method offers fast computational speed and can meet basic testing requirements. In scenarios with uneven distribution of test tasks or complex test tasks, the variable grouping method can fully consider various possibilities, find a more optimal test path, improve test quality, and reduce test time and resource waste. The technical solution of the present invention is highly scalable and is not only applicable to current four-probe flying probe test equipment but also provides a reference and reference for path optimization in future multi-probe test equipment, promoting the development of printed circuit board testing technology.

[0078] Specifically, this paper proposes defining the four-probe flying probe test path optimization problem as a collaborative multi-traveling salesman problem with dynamic position constraints and constructing a corresponding mathematical model. A dynamic greedy solution construction method is designed to effectively integrate the problem's multiple constraints into the solution construction process. By comparing fixed and variable grouping strategies, the paper selects the appropriate grouping method based on different test scenarios to optimize the probe path and improve test efficiency.

[0079] Further, refer 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 a certain moment 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 includes each probe in 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 collaborative task multi-traveling salesman problem with dynamic position constraints. Considering that all test tasks have only two test points, the four probes are divided into two groups for testing to speed up the test. The optimization goal is to minimize the time it takes for the device to complete the test work, that is, min(max(L1,L2,L3,L4)), where L1, L2, L3, and L4 are the total time distances of the four probes respectively. During the test process, the four probes must meet the position constraints of x(p1)≤x(p2)≤x(p3)≤x(p4), and the probe grouping method will change dynamically according to the test point coordinate constraints.

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

[0089] |𝑌1 − 𝑌2| ≥ 0.3 ,

[0090] |𝑌2 − 𝑌3| ≥ 0.2 ,

[0091] |𝑌3 − 𝑌4| ≥ 0.3 .

[0092] Further, refer to Figure 1 and Figure 3 , the step S200 includes:

[0093] 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;

[0094] 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;

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

[0096] Specifically, a dynamic greedy strategy is used for task selection, directly integrating the probe's constraints into the task selection process. At each decision step, the current constraints and conditions are dynamically considered to select the optimal or near-optimal next action.

[0097] The fixed grouping method divides the four probes into two groups, such as p1 and p2 in one group and p3 and p4 in the other. The total test time and distance path lengths for each group of probes are denoted as LG1 and LG2, respectively, and the objective function is max(LG1, LG2). After one group completes testing, the closest test task is selected to continue testing, provided that the probes do not collide (i.e., the x-axis coordinate of the selected test task is smaller than the x-axis coordinate of the other group of test tasks). In this way, the four-probe case is converted to a two-probe case with task constraints for optimization. A greedy task selection strategy is used to minimize the movement path, approximate the global optimum with the local optimum, and minimize the probe idle time.

[0098] Further, refer to Figure 1 and Figure 3 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:

[0099] ,

[0100] 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;

[0101] 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:

[0102] ,

[0103] 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,

[0104] Set test tasks The coordinates of the two test points are:

[0105] and ,and < ,

[0106] 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:

[0107] and ,and < ,

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

[0109] ,

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

[0111] .

[0112] Specifically, this study aims to improve the efficiency of the test process. Therefore, the optimization target is the total test time, which includes the time to obtain the test path and the time required for probe movement. Assuming that all probes in the same device move in a uniform linear motion in the 𝑥-𝑦 plane with the same speed, the test time can be shortened by reducing the length of the probe movement path.

[0113] Different test tasks require different numbers of probes depending on the specific situation. For example, testing inductance, open / short circuits, jumpers, voltage / current, common, Zener, and Zener diodes requires two probes, while testing transistors, MOS tubes, and thyristors requires three probes. Optocoupler testing requires four probes. Resistance and capacitance testing requires two to three probes, and sometimes an additional probe is needed for auxiliary isolation. Furthermore, programming testing requires two to four probes, while JTAG testing requires four to six probes.

[0114] for ≤ ≤ ≤ ,Assume that the test task The two measuring points are and ,and < When the equipment is tested After that, the probe moves to the next test task and ,and < Due to equipment limitations, the position of the probe must satisfy the following relationship: , which means that the probe movement must be: from Move to , probe from Move to . Corresponding probe The distance moved is , probe The distance moved is From the test task To the test task , the total path length of the two probes is Even if the probe from Move to , probe from Move to , the total path length of the probe movement is shorter, but in order to meet the constraints, a longer path must still be selected.

[0115] However, in the actual test process, the two probes need to reach and touch the test point at the same time before the test task can start. This means that when one probe reaches the point When the other probe reaches the point Since the probes move at the same speed, the length of the paths they move must be equal in order for them to arrive at the same time, but this is usually impossible. Therefore, we only need to calculate the length of the path moved by the probe with the longer travel distance. When this probe arrives at the test point, the other probe must have already arrived at the corresponding test point. To this end, we define the test task Move to Test Task The moving paths of the two probes are Even if the actual movement path of the probe is less than this distance, its time requirement is equivalent to the distance, so it is defined as the time distance. This distance definition method can convert the point-to-point distance into the time spent between test tasks.

[0116]

[0117] So, when the test task sequence is When the total length of the probe's moving path can be expressed as

[0118]

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

[0120] ,

[0121] 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;

[0122] 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.

[0123] Further, refer to Figure 1 and Figure 4 , the step S300 includes:

[0124] 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;

[0125] 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;

[0126] 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 ;

[0127] S340, determining the next test task according to whether the optional next task increases the optimization objective function value of the variable grouping;

[0128] 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.

[0129] The variable grouping method allows probes to change groups during each step of the test. When selecting a test task, the change in the total distance moved by all probes after the next task is selected is considered. When the total path length of one group of probes is smaller than that of another group, the group of probes is allowed to continue testing the next task without changing the objective function value. If the selected task will increase the objective function value, the test task that minimizes the increment of the objective function value is selected. After determining the test task, group selection is performed based on the constraints that the probes need to meet, and the new objective function value is calculated. The group of tasks corresponding to the minimum objective function value is selected for testing to achieve a local optimum. This method provides a larger search space, increases the possibility of finding the optimal solution, and performs better when the test tasks are unevenly distributed or some test tasks are more difficult.

[0130] Further, refer to Figure 1 and Figure 4 In the step S340,

[0131] 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.

[0132] 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.

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

[0134] ,

[0135] 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.

[0136] Further, refer to Figure 5 The present invention 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 includes sequentially connected

[0137] The task input module 100 is used to receive test task information, wherein the test task information includes test point coordinates;

[0138] A path planning module 200 is used to calculate the optimal test path of multiple probes;

[0139] 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.

[0140] Reference Figure 6 Flying probe testers can perform a variety of tests, including measurements on components such as resistors, capacitors, inductors, and diodes, as well as other functional tests such as voltage and current testing, open / short circuit testing, jumper testing, and programming. When performing these tests, flying probe testers typically utilize a variety of methods, including open circuit testing, short circuit testing, and capacitance testing. Flying probe testers typically consist of two upper and lower modules, each equipped with up to four probes. The number of probes configured varies depending on the model. For example, a simpler version only features two to four probes in the upper module; a standard version, in addition to the four probes in the upper module, also includes two probes in the lower module; and a high-end version features eight probes in both modules. Because the upper and lower modules independently control their probes, there are no constraints on probe movement between the two modules. Therefore, this paper focuses on optimizing the probe movement path within a single module, not the relationship between the two modules. By optimizing the probe movement path, the testing efficiency of the flying probe tester can be improved, thereby enhancing PCBA production efficiency.

[0141] Furthermore, the present invention also provides a computer-readable storage medium having program instructions stored thereon, and when the program instructions are executed by a processor, the four-probe flying probe test equipment path optimization method is implemented.

[0142] Some of the terms in this invention are explained as follows:

[0143] ① Flying Probe Test (FPT): A method used for PCB testing that uses a motor to control the rapid movement of the probe 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 journey.

[0145] ③Multiple 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 flying probe testing, in order to ensure that the two probes reach the test point at the same time, a distance measurement method is defined. The maximum value of the moving distances of the two probes is taken as the time distance, which is used to measure the time spent between test tasks.

[0147] The above is only a preferred embodiment of the present invention. The present invention is not limited to the above-mentioned implementation mode. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this disclosure should be included in the scope of protection of this 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.

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 the probe At a certain moment in 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 probes are allowed to In the same projection position; 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: 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 until all test tasks are completed.

4. The four-probe flying probe test equipment path optimization method according to claim 3, 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 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, 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: 。 5. 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.

6. The four-probe flying probe test equipment path optimization method according to claim 1, characterized in that: 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.

7. The four-probe flying probe test equipment path optimization method according to claim 6, 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.

8. 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.

9. 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 8, 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).

10. 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 8 is implemented.

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