Cloud platform-based management method for cluster operation status of resistor adjustment equipment

Abstract

The present invention belongs to the technical field of equipment cluster operation status management, and specifically discloses a cloud platform-based resistance trimming machine equipment cluster operation status management method, which includes: screening qualified equipment through self-test parameters for initial task allocation, dynamically adjusting the parameters of the qualified resistance trimming machine cluster based on the fusion analysis results of the ablation coordinates, ablation line width and ablation depth of the ablation point and their corresponding standard ablation parameters, and judging the equipment status based on path coincidence, line width and depth thresholds, and finally realizing the redistribution of abnormal equipment tasks through load rate sorting; the present invention uses a dynamic screening mechanism to compare the self-test parameters of each resistance trimming machine equipment with preset self-test standard parameters, combines dynamic weight analysis to calculate the equipment reliability and stability, screens qualified resistance trimming machine clusters for initial task allocation, avoids the problem of resistance value tolerance, and at the same time uses quantitative analysis of coordinate offset, line width deviation and depth error.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment cluster operation status management, and relates to a method for managing the operation status of a resistance regulating machine equipment cluster based on a cloud platform. Background Art

[0002] Resistor trimmers are core equipment used to adjust the resistance of resistors. They achieve resistance matching by physically or chemically modifying the resistor structure. Laser resistor trimmers use a focused laser beam to target the resistor body (e.g., thick-film or thin-film materials), adjusting the resistance by changing the resistor geometry. The machine measures the resistance change in real time during the process and automatically stops cutting when the target value is reached, ensuring precision.

[0003] There are still several deficiencies in the operation status management of the resistor trimmer equipment cluster: 1. The current task allocation of the resistor trimmer cluster mostly relies on the fixed parameters of the equipment, thereby ignoring the dynamic performance changes of the equipment during the self-test process. At the same time, during the task execution of the resistor trimmer equipment, it is impossible to detect the operating parameters of the equipment in real time for operation regulation, resulting in a lag in operation optimization.

[0004] 2. Currently, in the operation qualification judgment of the resistance trimming machine equipment cluster, the final resistance value of the resistor is generally used as the core evaluation indicator. Insufficient attention is paid to the operation path of the resistance trimming machine equipment during operation, and the impact of the dynamic operation path accuracy of the resistance trimming machine equipment on product quality is ignored, and thus the resistance uniformity and long-term stability of the resistor cannot be guaranteed. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a cloud platform-based method for managing the operating status of a resistance regulating machine cluster is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a cloud platform-based resistor trimmer equipment cluster operation status management method, including: S1, initial task allocation: comparing the self-test parameters of each resistor trimmer equipment with the preset self-test standard parameters, screening qualified resistor trimmer clusters and assigning initial tasks.

[0007] S2. Task operation acquisition: During the execution of the initial task, ablation images and ablation depths are collected in a time series, and the ablation coordinates and ablation line width of the ablation point are calculated through edge detection and contour extraction.

[0008] S3. Task operation optimization: Dynamically adjust the parameters of the qualified resistor adjustment machine cluster based on the fusion analysis results of the ablation coordinates, ablation line width and ablation depth of the ablation point and its corresponding standard ablation parameters.

[0009] S4. Operation qualification judgment: After adjustment, the ablation path including ablation depth and ablation line width is generated in real time through time stamp alignment. At the same time, the resistance value of the corresponding resistor of each resistance adjustment device is combined to determine whether the device is operating abnormally.

[0010] S5. Secondary task distribution: according to the equipment operating parameters of the resistance regulating machine, the abnormal resistance regulating machine is shut down, and at the same time, the unfinished task points of the abnormal resistance regulating machine are secondary distributed.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention uses a dynamic screening mechanism to compare the self-test parameters of each resistor trimmer device with the preset self-test standard parameters, combines dynamic weight analysis to calculate the reliability and stability of the device, and screens out qualified resistor trimmer clusters for initial task allocation, thereby reducing the problem of resistance value deviation caused by fluctuations in the dynamic performance of the equipment.

[0012] (2) The present invention collects ablation images and ablation depths in a time series, uses an edge detection algorithm to extract the contour of the ablation point, and combines the standard ablation path to perform quantitative analysis of coordinate offset, line width deviation and depth error, thereby generating dynamic parameter adjustments including trajectory correction parameters, power compensation values ​​and speed compensation values, thereby improving the response speed of operation optimization.

[0013] (3) The present invention breaks through the limitation of traditional single resistance value judgment by making a multi-dimensional fusion judgment of the ablation path including ablation depth and ablation line width and the resistance value, and increases the attention to the accuracy of the operation path of the resistance adjustment machine, thereby improving the resistance uniformity and long-term stability of the resistor.

[0014] (4) The present invention collects the number of unfinished task points of abnormally operating resistors in real time, calculates the load rate based on the operating parameters of qualified resistors, and sorts qualified devices in ascending order of load rate, preferentially assigning tasks to devices with high remaining processing capacity, thereby achieving dynamic load balancing of resources within the cluster and avoiding overloading or idleness of some devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention.

[0017] Figure 2 This is a connection diagram of the steps for screening a cluster of qualified resistor trimmers according to the present invention.

[0018] Figure 3 Schematic diagram of the connection of the dynamic parameter adjustment analysis steps of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides a cloud platform-based method for managing the operating status of a resistor trimmer device cluster, the method comprising: S1, initial task allocation: comparing the self-test parameters of each resistor trimmer device with preset self-test standard parameters, screening qualified resistor trimmer clusters and allocating initial tasks.

[0021] It's important to note that self-test parameters include resistance value and self-test path. Resistance value is measured using the built-in resistance measurement module installed in the trimming head of the resistor trimmer, while the self-test path is detected using a laser displacement sensor located next to the trimming head. While resistance value reflects the accuracy of the machine's results, it cannot reveal hidden defects in the manufacturing process (such as localized stress concentration caused by path deviation). Self-test path analysis, on the other hand, focuses on process reliability and can proactively identify potential risks (such as potential future resistance deviations caused by path deviation).

[0022] It should be noted that the standard self-test parameters include a standard resistance value and a standard self-test path. The standard resistance value is set by collecting historical self-test resistance values ​​of the resistor trimmer over a recent period (e.g., a three-day period), calculating the average of these historical self-test resistance values, and using this average as the standard resistance value. The standard self-test path is generated by collecting path data from multiple self-tests of the resistor trimmer during a recent period (synchronized with the resistance value statistical period) while the device was operating without any faults. The path is then aligned to identify common trajectory features, such as the locations of key points and path curvature, to generate a standard self-test path.

[0023] It should be added that the allocation steps for initial task allocation include: dividing the resistor trimmer equipment into a first-level resistor trimmer cluster, a second-level resistor trimmer cluster and a third-level resistor trimmer cluster based on the self-inspection qualification of the resistor trimmer equipment in the qualified resistor trimmer cluster.

[0024] The task type of each task is extracted from the cloud platform, and the task type includes: high-precision task, high-speed task and ordinary task.

[0025] It should be noted that high-precision tasks require extremely high machining accuracy and stability, requiring equipment with submicron control capabilities and a low tolerance for errors. For example, resistance adjustment error ≤ ±0.1% and repeatability ≤ 1μm. High-speed tasks emphasize processing speed and throughput, allowing for a moderate sacrifice in precision. They require equipment with high-frequency response capabilities, such as single-task processing time ≤ 50ms, task throughput per unit time ≥ 200 pieces / minute, and dynamic response latency < 10ms. Standard tasks have no special requirements for accuracy and speed, focusing on cost-effectiveness and resource utilization, and allowing for a certain degree of performance fluctuation. For example, resistance adjustment error ≤ ±1%, single-task processing time ≤ 200ms, and equipment load balancing ≥ 80%.

[0026] All high-precision tasks are evenly distributed to each resistor trimmer device in the first-level resistor trimmer cluster, all high-speed tasks are then distributed to each resistor trimmer device in the second-level resistor trimmer cluster, and finally ordinary tasks are distributed to each resistor trimmer device in the third-level resistor trimmer cluster.

[0027] See also Figure 2 As shown, illustratively, the screening of qualified resistance trimmer clusters includes: Q1, calculating the deviation between the resistance value of each resistance trimmer device in each self-test and the standard resistance value in the self-test parameters to obtain the reliability qualification of each resistance trimmer device.

[0028] Furthermore, the analysis of the reliability qualification of each resistance trimming device includes: Q1-1, taking the difference between the resistance value and the standard resistance value as the resistance deviation of each resistance trimming device during each self-test.

[0029] Q1-2. Count the number of self-tests when the resistance deviation is less than the preset resistance deviation and the total number of self-tests, and use the ratio of the two as the reliability qualification of each resistance trimmer.

[0030] Q2. Compare and analyze the self-test path of each resistance trimmer device in the self-test parameters with its standard self-test path to obtain the stability qualification of each resistance trimmer device.

[0031] Furthermore, the analysis of the stability qualification of each resistance trimming device includes: Q2-1, extracting a self-test coordinate sequence and a standard coordinate sequence from a self-test path of each resistance trimming device during each self-test.

[0032] Q2-2. Calculate the minimum cumulative distance of the sequence using a dynamic time warping algorithm.

[0033] Q2-3. If the minimum cumulative distance is less than the reference standard distance, the stability qualification of the self-test is determined to be 1; otherwise, the stability qualification of the self-test is determined to be 0.

[0034] It should be noted that the reference standard distance is obtained by collecting self-test data from multiple stable and qualified resistor trimmers and calculating the cumulative distance between their self-test coordinate sequences and the standard coordinate sequence. Using statistical methods (such as calculating the mean + 2 times the standard deviation), a distance value that covers the vast majority of resistor trimmer data is determined and set as the reference standard distance.

[0035] Q2-4. Calculate the average stability qualification of each self-inspection as the stability qualification of each resistance regulating machine equipment.

[0036] In a specific embodiment, assuming that the coordinates collected during the self-test of a resistor trimmer are: P1 = [(0, 0), (1, 0.5), (2, 1), (3, 1.5)], and the preset standard path is: P2 = [(0, 0), (1, 1), (2, 2), (3, 3)], the analysis steps for its stability qualification are: (1) Calculate the DTW minimum cumulative distance: align the self-test path with the standard path by time stamp and calculate the Euclidean distance D (P1, P2) of each pair of points on the self-test path and the standard path: D (1, 1) = 0, D (1, 2) = 1.41, D (2, 1) = 1.12, D (2, 2) = 0.5, ...

[0037] (2) The cumulative distance matrix is ​​calculated by the recursive formula C(i, j) = D(i, j) + min{C(i-1, j), C(i, j-1), C(i-1, j-1)}, where C(i, j) represents the minimum cumulative distance when the first i points of the self-test path are aligned with the first j points of the standard path, i represents the i-th point in the actual path (P1) (e.g., point (0, 0) in P1 is the first point, (1, 0.5) is the second point, and so on). j represents the j-th point in the standard path (P2) (e.g., point (0, 0) in P2 is the first point, (1, 1) is the second point, and so on).

[0038] Initialize C(1, 1) = 0 and gradually fill the matrix, for example: C(2, 2) = 0.5 + min{1.12, 1.41, 0} = 0.5, and then obtain the final cumulative distance C(4, 4) = 2.0.

[0039] (3) Determine the stability qualification: Assuming that the reference standard distance is d1=3.0, and the final cumulative distance d2=2.0<3.0, the stability qualification of this self-test is determined to be 1.

[0040] (4) Statistical mean of stability qualification: Assuming that the equipment completes 5 self-tests and the stability qualification is [1, 1, 0, 1, 1] respectively, the mean of the stability qualification is calculated to be 0.8.

[0041] Q3. The reliability qualification and stability qualification are calculated by weighted average using preset dynamic weights to obtain the self-inspection qualification of each resistance regulating machine.

[0042] Q4. The resistor trimmer devices whose self-test qualification is greater than or equal to the preset self-test qualification are generated into a candidate resistor trimmer device group.

[0043] Q5. Extract the required number of resistor adjustment machines from the cloud platform, and select a qualified device cluster from the candidate resistor adjustment machines based on the required number of resistor adjustment machines.

[0044] It should be added that the step of selecting a qualified equipment cluster from the alternative resistor trimmers according to the required number of resistor trimmers includes: if the required number of resistor trimmers is less than or equal to the number of alternative resistor trimmer equipment groups, then sorting the resistor trimmer equipment in the alternative resistor trimmer equipment group from large to small according to the self-inspection qualification, and selecting the resistor trimmer equipment with the required number of resistor trimmers before sorting to generate a qualified equipment cluster.

[0045] If the number of required resistor trimmers is greater than the number of candidate resistor trimmer equipment groups, the difference between the number of required resistor trimmers and the number of candidate resistor trimmer equipment groups is taken as the required resistor trimmer difference.

[0046] The resistor trimmer devices whose self-inspection qualification is lower than the preset self-inspection qualification are sorted from large to small according to the self-inspection qualification, and then the resistor trimmer devices with the corresponding number of required resistor trimmer differences are selected, and they are combined with the alternative resistor trimmer devices to form a qualified device cluster.

[0047] The embodiment of the present invention uses a dynamic screening mechanism to compare the self-test parameters of each resistor trimmer device with preset self-test standard parameters, combines dynamic weight analysis to calculate the reliability and stability of the device, and screens out qualified resistor trimmer clusters for initial task allocation, thereby reducing the problem of resistance value deviation caused by fluctuations in the dynamic performance of the equipment.

[0048] S2. Task operation acquisition: During the execution of the initial task, ablation images and ablation depths are collected in a time series, and the ablation coordinates and ablation line width of the ablation point are calculated through edge detection and contour extraction.

[0049] It should be added that the ablation image is collected by a camera installed next to the laser head or on the top of the equipment with the lens aimed at the ablation area of ​​the resistor, and the ablation depth is detected by a laser displacement sensor placed in the laser head assembly or independently installed on the robotic arm perpendicular to the laser ablation direction.

[0050] Exemplarily, the ablation coordinates and ablation line width of the ablation point are calculated by edge detection and contour extraction, including: obtaining an ablation point edge detection image from the ablation image through an edge detection algorithm, and then performing contour extraction on the ablation point edge detection image to obtain the contour of the ablation point and each pixel point within the contour.

[0051] The weighted average calculation is performed on each pixel point within the contour to obtain the ablation coordinates of the ablation point.

[0052] In a specific embodiment, the process of performing weighted averaging calculation on each pixel point within the contour to obtain the ablation coordinates is as follows: (1) If the grayscale matrix of the ablation image is I(x, y), the size is W×H, and the pixel value range is [0, 255], the set of all pixels within the extracted ablation point contour is P = {(x1, y1), (x2, y2), ..., (xN, yN)}.

[0053] (2) Through the grayscale normalization formula Eliminate lighting differences, where 、 and They are the maximum grayscale value, minimum grayscale value and standard maximum value of the ablation image respectively.

[0054] (3) Through Gaussian filtering formula Calculate the pixel value of the pixel point, where and are Gaussian kernel standard deviation and kernel radius, is the horizontal (x-axis) offset of the pixel point, is the vertical offset of the pixel (y axis), is a natural constant.

[0055] (4) Through the formula Calculate the weight of each pixel, where is the pixel number, , is the number of pixels, It has the same meaning as the kernel radius in the Gaussian filter formula and is used to calculate pixel weights.

[0056] (5) Through the formula and , and obtain the ablation coordinates of the ablation point.

[0057] The pixel spacing between the left and right edges of the ablation point contour along the normal direction of the ablation point is extracted as the ablation line width of the ablation point.

[0058] S3. Task operation optimization: Dynamically adjust the parameters of the qualified resistor adjustment machine cluster based on the fusion analysis results of the ablation coordinates, ablation line width and ablation depth of the ablation point and its corresponding standard ablation parameters.

[0059] See also Figure 3As shown, exemplarily, the dynamic parameter adjustment analysis of the qualified resistor adjustment machine cluster includes: W1, extracting the standard ablation path of each qualified resistor adjustment machine device from the cloud platform, obtaining the standard coordinates matching the ablation coordinates in the standard path through the nearest neighbor algorithm, performing the differential operation between the ablation coordinates and the standard coordinates, and generating a real-time path offset.

[0060] It's important to note that the nearest neighbor algorithm is a simple and intuitive approach. It finds the closest coordinate on the standard path for each ablation coordinate as the matching point. It works by calculating the distance (typically using Euclidean distance) between each point on the ablation path and all other points on the standard path. The point with the smallest distance is then selected as the standard coordinate for that ablation point. The nearest neighbor algorithm eliminates the need for complex calculations and directly finds the nearest standard coordinate for the ablation coordinate, improving the responsiveness of trajectory correction. Furthermore, the standard ablation path may contain nonlinear, multi-dimensional, or high-density coordinate points. The nearest neighbor algorithm makes no prior assumptions about data distribution or path morphology, allowing it to flexibly adapt to complex path shapes. Therefore, the nearest neighbor algorithm is chosen to obtain the standard path.

[0061] W2, analyze the proportional relationship between the real-time path offset and the set reference deviation threshold, and obtain the trajectory correction parameters of each qualified resistor trimmer device through the adjustment value formula. The adjustment value formula is , where is the trajectory correction parameter, and are the deviation thresholds between the real-time path offset and the set reference, is the proportional coefficient.

[0062] W3. Extract the standard ablation depth of each qualified resistor trimmer device from the cloud platform.

[0063] W4. Compare and analyze the ablation depth of each qualified adjustment unit equipment with the standard ablation depth to obtain the power compensation value of each qualified adjustment unit equipment.

[0064] Furthermore, the ablation depth of each qualified resistor trimmer device is compared and analyzed with the standard ablation depth, including: W4-1, subtracting the ablation depth from the standard deviation ablation depth to obtain the ablation depth difference of each qualified resistor trimmer device;

[0065] W4-2, import the ablation depth difference into the power compensation formula to obtain the power compensation value of the qualified resistance trimming equipment, and then obtain the power compensation value of each qualified resistance trimming equipment. The power compensation formula is , where is the power compensation value, is the ablation depth difference, is the power compensation proportional coefficient.

[0066] It should be added that It is obtained through experiments, testing the power adjustment required under different depth differences, and then obtained through data fitting For example, when the depth difference is known, the power is adjusted until the standard ablation depth is reached, and the corresponding power change is recorded to calculate the average value as the power compensation proportional coefficient.

[0067] W5. Import the ablation line width and the standard ablation line width into the line width deviation formula to obtain the speed compensation value of each qualified adjustment machine equipment. The line width deviation formula is , where is the speed compensation value, and are the ablation line width and standard line width, is the initial velocity, is the line width deviation proportional coefficient. The initial speed is obtained based on the initial assignment task setting of the resistor trimmer equipment, and the line width deviation proportional coefficient is verified by performing gradient descent optimization experiments on the historical data set of the equipment cluster's operating status.

[0068] when When , it is a positive deviation, that is, the line width is too wide, and the ablation time can be reduced by increasing the speed. When the deviation is negative, the line width is too narrow, and the ablation time can be increased by reducing the speed. Proportional adjustment mechanism: the relative value of the speed adjustment range and the line width deviation ( For example, if the standard line width is 50μm and the actual line width is 55μm (+10% deviation), the speed will increase by 10%. The adjusted speed is based on the initial speed. Based on this, it avoids vibration or overshoot of the resistance regulating machine caused by sudden speed changes.

[0069] W6. Dynamically adjust parameters based on the trajectory correction parameters, power compensation values ​​and speed compensation values ​​of each qualified adjustment machine equipment.

[0070] The embodiment of the present invention collects ablation images and ablation depths in a time series, uses an edge detection algorithm to extract the contours of the ablation points, and combines the standard ablation path to perform quantitative analysis of coordinate offset, line width deviation, and depth error. Dynamic parameter adjustments including trajectory correction parameters, power compensation values, and speed compensation values ​​are generated to improve the response speed of operation optimization.

[0071] S4. Operation qualification judgment: After adjustment, the ablation path including ablation depth and ablation line width is generated in real time through time stamp alignment. At the same time, the resistance value of the corresponding resistor of each resistance adjustment device is combined to determine whether the device is operating abnormally.

[0072] It should be added that the resistance value of the resistor is acquired through a resistance sensor.

[0073] Exemplarily, the generating of the ablation path including the ablation depth and the ablation line width includes: constructing an actual ablation path of each resistor trimming device based on the ablation coordinates of each resistor trimming device in a time series.

[0074] The ablation coordinates, ablation depth and ablation line width of each ablation point in the actual ablation path are time-stamped and aligned.

[0075] The ablation depth and ablation line width are marked in the actual ablation path of each resistor trimmer device in chronological order, thereby obtaining an ablation path including the ablation depth and ablation line width.

[0076] Exemplarily, determining whether the device operates abnormally includes: comparing the actual ablation path with the starting point of the standard ablation path as the overlap point, extracting the overlap path length and the standard ablation path length, and taking the ratio of the two as the path overlap ratio.

[0077] The maximum ablation line width and the minimum ablation line width are extracted from the ablation line width of each ablation point, and the difference between the two is calculated to obtain the ablation line width difference.

[0078] The ablation depth difference can be obtained by analyzing the ablation line width difference in the same way.

[0079] The required resistance value of the resistor is extracted from the cloud platform, and the absolute difference between the required resistance value and the resistance value is calculated to obtain the resistance difference of the resistor.

[0080] The path overlap ratio, ablation line width difference, ablation depth difference and resistance difference of the corresponding resistor are respectively compared with the set reference thresholds. The qualified resistance trimmer equipment with parameters greater than the reference thresholds is judged to be operating abnormally, otherwise it is judged to be operating normally.

[0081] The embodiment of the present invention breaks through the limitations of traditional single resistance value judgment by performing multi-dimensional fusion judgment on the ablation path including ablation depth and ablation line width and the resistance value, increases the attention to the accuracy of the operation path of the resistance trimmer, and thus improves the resistance uniformity and long-term stability of the resistor.

[0082] S5. Secondary task distribution: according to the equipment operating parameters of the resistance regulating machine, the abnormal resistance regulating machine is shut down, and at the same time, the unfinished task points of the abnormal resistance regulating machine are secondary distributed.

[0083] It should be added that the equipment operating parameters of the resistance regulating machine include: the initial assigned task points, the single task processing time and the total available time, as well as the number of unfinished task points. The initial assigned task points are obtained based on the initial task allocation results. The single task processing time is recorded by the timer equipped with the resistance regulating machine itself. The timer is used to record the time required to complete a single task. After multiple recordings, the average value is taken as the single task processing time.

[0084] The total available time is calculated by analyzing the operation logs of the resistor trimmer to obtain information such as the device's startup time, shutdown time, and maintenance time. For example, if the resistor trimmer is powered on for 8 hours per day, with 1 hour spent on maintenance, the total available time is 7 hours.

[0085] The unfinished task points are obtained by calculating the difference between the initially assigned task points and the completed task points.

[0086] Exemplarily, the secondary task distribution includes: using the unfinished task points of each abnormally operating resistance regulating device as the task points to be reallocated.

[0087] It should be added that the number of task points to be reallocated = the initial task points of the abnormal operation resistor adjustment device - the number of task points already processed by the abnormal operation resistor adjustment device.

[0088] The product of the initial assigned task points in the equipment operating parameters of the qualified resistor trimmer equipment and the single task processing time is calculated as the initial task processing time, and the ratio of the initial assigned task points to the total available time in the equipment operating parameters is used as the load rate of the qualified resistor trimmer equipment.

[0089] The qualified resistance trimmer devices are sorted in ascending order according to the load rate to generate a qualified resistance trimmer device sequence.

[0090] The difference between the total available time and the initial task processing time is calculated as the current available time, and the current available time is compared and analyzed with the single task processing time to obtain the remaining processing capacity of each qualified resistor trimmer equipment.

[0091] Based on the sequence of qualified resistor trimmer equipment, the task points to be reallocated are allocated in sequence, and the task points allocated to the qualified resistor trimmer equipment do not exceed their remaining processing capacity until all the task points to be reallocated are allocated.

[0092] In a specific embodiment, the equipment operating parameters of the resistance trimmer are shown in Table 1:

[0093] Table 1: Schematic table of equipment operating parameters of resistance trimmer equipment

[0094]

[0095] The secondary task allocation steps of the resistance trimmer equipment include:

[0096] (1) Calculate the number of task points to be reallocated: the number of task points to be reallocated = the initial task points of the abnormal operation resistor adjustment device - the number of task points processed by the abnormal operation resistor adjustment device. The number of uncompleted task points of device A is: 50-20=30, and the number of uncompleted task points of device D is: 60-35=25. The number of task points to be reallocated is 30+25=55.

[0097] (2) Calculate the load rate of the qualified resistor adjustment equipment: the load rate of equipment B is 60 / 240=0.25, the load rate of equipment C is 120 / 300=0.40, and the load rate of equipment E is 20 / 180=0.11.

[0098] (3) Sort the qualified resistor adjustment devices in ascending order according to the load rate: the sequence of qualified resistor adjustment devices after sorting is: device E (0.11) → device B (0.25) → device C (0.40).

[0099] (4) Calculate the remaining processing capacity of each qualified resistor trimmer device: the remaining processing capacity of device B is 180 / 2=90, the load rate of device C is 180 / 3=60, and the load rate of device E is 160 / 1=160.

[0100] (5) The remaining processing capacity of device E before allocation is 160 points, which is greater than the number of task points to be reallocated, 55. Therefore, the number of task points to be reallocated is allocated to device E.

[0101] The embodiment of the present invention collects the number of unfinished task points of abnormally operating resistors in real time, calculates the load rate in combination with the operating parameters of qualified resistors, sorts the qualified devices in ascending order of load rate, and preferentially allocates tasks to devices with high remaining processing capacity, thereby achieving dynamic load balancing of resources within the cluster and avoiding overloading or idleness of some devices.

[0102] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A cloud platform-based method for managing the operation status of a resistor adjustment device cluster, characterized by: The method includes: S1. Initial task allocation: Compare the self-test parameters of each resistor trimmer device with the preset self-test standard parameters, screen qualified resistor trimmer clusters and assign initial tasks; S2. Task operation acquisition: During the execution of the initial task, ablation images and ablation depths are collected in time series, and the ablation coordinates and ablation line width of the ablation point are calculated through edge detection and contour extraction; S3. Task operation optimization: Dynamically adjust the parameters of the qualified resistor trimmer cluster based on the fusion analysis results of the ablation coordinates, ablation line width, and ablation depth of the ablation point and its corresponding standard ablation parameters; Dynamic parameter adjustment analysis of qualified resistor trimmer clusters includes: W1. Extract the standard ablation path of each qualified dispatching machine from the cloud platform, obtain the standard coordinates that match the ablation coordinates in the standard path through the nearest neighbor algorithm, perform the difference operation between the ablation coordinates and the standard coordinates, and generate the real-time path offset; W2. Analyze the proportional relationship between the real-time path offset and the set reference deviation threshold, and obtain the trajectory correction parameters of each qualified dispatching machine through the adjustment value formula; W3. Extract the standard ablation depth of each qualified assembly equipment from the cloud platform; W4. Compare and analyze the ablation depth of each qualified adjustment unit with the standard ablation depth to obtain the power compensation value of each qualified adjustment unit; W5. Import the ablation line width and the standard ablation line width into the line width deviation formula to obtain the speed compensation value of each qualified adjustment machine equipment; W6. Dynamically adjust parameters based on trajectory correction parameters, power compensation values, and speed compensation values ​​of each qualified dispatching machine equipment; S4. Operation qualification determination: After adjustment, the ablation path including ablation depth and ablation line width is generated in real time through time stamp alignment. At the same time, the resistance value of the corresponding resistor of each resistance trimmer device is combined to determine whether the device is operating abnormally; S5. Secondary task distribution: according to the equipment operating parameters of the resistance regulating machine, the abnormal resistance regulating machine is shut down, and at the same time, the unfinished task points of the abnormal resistance regulating machine are secondary distributed.

2. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 1, characterized in that: The screening of qualified resistor trimmer clusters includes: Q1. Calculate the deviation between the resistance value of each resistance trimmer device in each self-test and the standard resistance value in the self-test parameters to obtain the reliability qualification of each resistance trimmer device; Q2. Compare and analyze the self-test path of each resistance trimmer device in the self-test parameters with its standard self-test path to obtain the stability qualification of each resistance trimmer device; Q3. Calculate the weighted average of the reliability qualification and stability qualification using the preset dynamic weight to obtain the self-test qualification of each resistance regulating device; Q4. The resistance trimmer devices whose self-test qualification is greater than or equal to the preset self-test qualification are generated into a candidate resistance trimmer device group; Q5. Extract the required number of resistor adjustment machines from the cloud platform, and select a qualified device cluster from the candidate resistor adjustment machines based on the required number of resistor adjustment machines.

3. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 2, characterized in that: The analysis of the reliability qualification of each resistance trimmer device includes: The difference between the resistance value and the standard resistance value is used as the resistance deviation of each resistance trimmer during each self-test; The number of self-tests with resistance deviation less than the preset resistance deviation and the total number of self-tests are counted, and the ratio of the two is used as the reliability qualification of each resistance trimmer device.

4. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 2, characterized in that: The analysis of the stability qualification of each resistance regulating device includes: Extracting a self-test coordinate sequence and a standard coordinate sequence from the self-test path of each resistance trimmer device during each self-test; Calculating the minimum cumulative distance between the self-checking coordinate sequence and the standard coordinate sequence by a dynamic time warping algorithm; If the minimum cumulative distance is less than the reference standard distance, the stability qualification of the self-test is determined to be 1; otherwise, the stability qualification of the self-test is determined to be 0; The average stability qualification of each self-inspection is calculated as the stability qualification of each resistance regulating machine equipment.

5. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 1, characterized in that: Calculate the ablation coordinates and ablation line width of the ablation point through edge detection and contour extraction, including: Obtaining an edge detection image of the ablation point from the ablation image using an edge detection algorithm, and then performing contour extraction on the edge detection image of the ablation point to obtain the contour of the ablation point and each pixel within the contour; Perform weighted average calculation on each pixel point within the contour to obtain the ablation coordinates of the ablation point; The pixel spacing between the left and right edges of the ablation point contour along the normal direction of the ablation point is extracted as the ablation line width of the ablation point.

6. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 1, characterized in that: Compare and analyze the ablation depth of each qualified adjustment machine with the standard ablation depth, including: The ablation depth is subtracted from the standard ablation depth to obtain the ablation depth deviation of each qualified adjustment machine equipment; The ablation depth deviation is introduced into the power compensation formula to obtain the power compensation value of the qualified group adjustment equipment, and then the power compensation value of each qualified group adjustment equipment is obtained.

7. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 1, characterized in that: The generating of the ablation path including the ablation depth and the ablation line width comprises: Based on the ablation coordinates of each resistor trimmer device in time series, the actual ablation path of each resistor trimmer device is constructed; Align the ablation coordinates, ablation depth and ablation line width of each ablation point in the actual ablation path with the timestamp; The ablation depth and ablation line width are marked in the actual ablation path of each resistor trimmer device in chronological order, thereby obtaining an ablation path including the ablation depth and ablation line width.

8. The cloud platform-based method for managing the operation status of a resistance trimmer cluster according to claim 6, wherein: The determining whether the device operates abnormally includes: The starting points of the actual ablation path and the standard ablation path are used as the coincidence points for overlap comparison, and then the overlap path length and the standard ablation path length are extracted, and the ratio of the two is used as the path overlap ratio; Extract the maximum ablation line width and the minimum ablation line width from the ablation line width of each ablation point, and subtract the two to obtain the ablation line width difference; The maximum ablation depth and the minimum ablation depth are extracted from the ablation line width of each ablation point, and the difference between the two is calculated to obtain the ablation depth difference; Extract the required resistance value of the resistor from the cloud platform, and calculate the absolute difference between it and the resistance value to obtain the resistance difference of the resistor; The path overlap ratio, ablation line width difference, ablation depth difference and resistance difference of the corresponding resistor are compared with the set reference thresholds respectively. The qualified adjustment machine equipment with parameters greater than the reference thresholds is judged to be operating abnormally, otherwise it is judged to be operating normally.

9. The cloud platform-based resistance trimmer equipment cluster operation status management method according to claim 1, characterized in that: The secondary task distribution includes: The unfinished task points of each abnormally operating resistance regulating device are used as the task points to be reallocated; The product of the initial assigned task points in the equipment operating parameters of the qualified resistance trimmer equipment and the single task processing time is calculated as the initial task processing time, and the ratio of the initial assigned task points to the total available time in the equipment operating parameters is used as the load rate of the qualified resistance trimmer equipment; Sort the qualified resistance trimmer devices in ascending order according to the load rate to generate a qualified resistance trimmer device sequence; The difference between the total available time and the initial task processing time is calculated as the current available time, and the current available time is compared and analyzed with the single task processing time to obtain the remaining processing capacity of each qualified resistor trimmer device; Based on the sequence of qualified resistor trimmer equipment, the task points to be reallocated are allocated in sequence, and the task points allocated to the qualified resistor trimmer equipment do not exceed their remaining processing capacity until all the task points to be reallocated are allocated.

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