Line compensation control method and device, electronic equipment and storage medium

Abstract

The application provides a production line compensation control method and device, electronic equipment and a storage medium. The production line compensation control method comprises the following steps: obtaining detection data of a production object, wherein the detection data at least comprises a measured value and a measurement point; determining target reference data corresponding to the production object and target assembly equipment corresponding to a target compensation object based on a binding relationship between the production object and the target compensation object; performing distortion screening on the detection data based on the target reference data to obtain effective sample data; determining an average offset based on the measured value in the effective sample data and a standard value corresponding to the measurement point in the target reference data; and generating a compensation instruction based on the average offset and sending the compensation instruction to the target assembly equipment to make the target assembly equipment perform a fine adjustment operation in a case where the average offset is outside a preset interval. The application can improve the stability and traceability of production line compensation control.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing technology, and in particular to a production line compensation control method, device, electronic device and storage medium. Background Technology

[0002] As manufacturing evolves towards higher cycle times, greater variety, and greater flexibility, positioning deviations, clamping deviations, fixture wear deviations, and equipment execution deviations often exist between products, carriers, jigs, workstations, and assembly equipment on the production line. Traditionally, inspection data is primarily used to determine product quality after the fact, making it difficult to adjust assembly equipment in a timely manner.

[0003] The current production line setup process often relies on human experience. Personnel infer the source of deviation based on test results and then manually modify equipment parameters or adjust fixture positions. This method heavily depends on human experience and suffers from slow response times, inconsistent performance, and lack of traceability during parameter adjustments. Furthermore, it is difficult to distinguish the source of deviation between different carriers, fixtures, or assembly equipment for the same product model. Summary of the Invention

[0004] In view of the above, it is necessary to propose a production line compensation control method, device, electronic equipment and storage medium to solve the technical problem of low stability in adjusting the position of fixtures in the production line.

[0005] This application provides a production line compensation control method, the method comprising: acquiring detection data of a production object, the detection data including at least measured values ​​and measurement points; determining target reference data corresponding to the production object and target assembly equipment corresponding to the target compensation object based on the binding relationship between the production object and the target compensation object; performing distortion filtering on the detection data based on the target reference data to obtain valid sample data; determining an average offset based on the measured values ​​in the valid sample data and the standard values ​​corresponding to the measurement points in the target reference data; generating a compensation instruction based on the average offset when the average offset is outside a preset range; sending the compensation instruction to the target assembly equipment, causing the target assembly equipment to perform a fine-tuning operation based on the compensation instruction; and writing back the detection results and execution results after the target assembly equipment performs the fine-tuning operation to a database.

[0006] In some embodiments, determining the binding relationship between the production object and the target compensation object includes: obtaining the production object identifier of the production object, wherein the production object identifier includes one or more of a product identifier, a product serial number, a two-dimensional identification code, or a three-dimensional identification code; obtaining the compensation object identifier of the target compensation object, wherein the compensation object identifier includes one or more of a vehicle identifier, a jig identifier, an equipment identifier, or a workstation identifier; and determining the binding relationship between the production object and the target compensation object based on the production object identifier and the compensation object identifier.

[0007] In some embodiments, determining the target reference data corresponding to the production object and the target assembly equipment corresponding to the target compensation object based on the binding relationship between the production object and the target compensation object includes: determining the product model and measurement point corresponding to the production object based on the binding relationship; determining target reference data including standard values ​​corresponding to the measurement points based on the product model and the measurement points, wherein the target reference data includes one or more of the following: standard values ​​corresponding to the product model, measurement points, product design references, carrier positioning references, fixture basic parameters, or adjustable execution parameters of the assembly equipment; determining equipment association information associated with the target compensation object based on the binding relationship, wherein the equipment association information includes one or more of the following: equipment identifier, workstation identifier, correspondence between fixtures and equipment, or correspondence between carriers and equipment; and determining the target assembly equipment for receiving the compensation instruction from candidate assembly equipment in the production line based on the equipment association information.

[0008] In some embodiments, the step of filtering out distortion in the detection data based on the target reference data to obtain valid sample data includes: determining an image matching result based on the currently acquired image in the detection data and the standard comparison image in the target reference data; determining a size comparison result based on the measured value in the detection data and the tolerance range or distortion threshold in the target reference data; determining a local feature determination result based on image features or size features in a preset local detection area; and filtering out detection data that meets the distortion condition according to at least one of the image matching result, the size comparison result, and the local feature determination result, and determining the unfiltered detection data as the valid sample data.

[0009] In some embodiments, determining the average offset based on the measured values ​​in the valid sample data and the standard values ​​corresponding to the measurement points in the target reference data includes: grouping the valid sample data based on at least one of the production object, target compensation object, or measurement points in the detection data corresponding to the binding relationship; determining the difference between the measured values ​​of each valid sample data in each group and the standard values ​​corresponding to the measurement points in the target reference data; and determining the average offset corresponding to each group based on the difference and the number of valid samples in the corresponding group.

[0010] In some embodiments, generating the compensation instruction based on the average offset when the average offset is outside a preset range includes: obtaining the lower limit and upper limit corresponding to the preset range; determining that the average offset is outside the preset range when the average offset is less than or equal to the lower limit, or the average offset is greater than or equal to the upper limit; and generating the compensation instruction based on the average offset that is outside the preset range.

[0011] In some embodiments, the method further includes: determining an initial compensation amount based on the average offset and the compensation coefficient; performing amplitude limiting processing on the initial compensation amount based on the maximum allowable compensation amount of the target assembly equipment to obtain an amplitude limiting compensation amount; limiting the amplitude limiting compensation amount by step size based on the maximum single adjustment step size of the target assembly equipment to obtain a target compensation amount; generating the compensation instruction based on the target compensation amount, the adjustment direction, and the equipment identifier of the target assembly equipment; outputting an abnormality prompt message when the average offset exceeds the preset interval consecutively in the same production object, the same target compensation object, or the same measurement point; and generating dashboard data for tracing product yield, fixture abnormalities, equipment drift, or carrier positioning abnormalities based on the detection results and execution results written back to the database.

[0012] This application embodiment also provides a production line compensation control device, the device comprising: a data acquisition module for acquiring detection data of a production object, the detection data including at least measured values ​​and measurement points; a binding module for providing a binding relationship between the production object and a target compensation object; a determination module for determining, based on the binding relationship, target reference data corresponding to the production object and target assembly equipment corresponding to the target compensation object; a screening module for performing distortion screening on the detection data based on the target reference data to obtain valid sample data; the determination module is further configured to determine an average offset based on the measured values ​​in the valid sample data and the standard values ​​corresponding to the measurement points in the target reference data; an execution module for generating a compensation instruction based on the average offset when the average offset is outside a preset range, and sending the compensation instruction to the target assembly equipment, causing the target assembly equipment to perform a fine-tuning operation based on the compensation instruction; and a write-back module for writing back the detection results and execution results of the target assembly equipment after performing the fine-tuning operation to a database.

[0013] This application also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the production line compensation control method.

[0014] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the production line compensation control method.

[0015] As can be seen from the above technical solutions, the embodiments of this application link the production object, the target compensation object, the target reference data and the target assembly equipment through binding relationships, so that the detection data can be transformed into the basis for compensation control along the binding link; effective sample data is obtained by distortion screening, reducing the interference of abnormal detection data on the average offset; and a compensation closed loop is formed by preset intervals, compensation instructions and execution write-back, reducing the frequency of manual machine adjustment and improving the stability and traceability of compensation. Attached Figure Description

[0016] Figure 1 This is an application scenario diagram of a production line compensation control method provided in an embodiment of this application.

[0017] Figure 2 This is a flowchart of a production line compensation control method provided in an embodiment of this application.

[0018] Figure 3 This is a flowchart of a method for determining a binding relationship provided in an embodiment of this application.

[0019] Figure 4This is a flowchart of a method for determining target reference data and target assembly equipment according to an embodiment of this application.

[0020] Figure 5 This is a flowchart of a method for determining valid sample data provided in an embodiment of this application.

[0021] Figure 6 This is a flowchart of a method for determining the average offset of groups according to an embodiment of this application.

[0022] Figure 7 This is a flowchart of a method for determining compensation instructions provided in an embodiment of this application.

[0023] Figure 8 This is a flowchart of a method for determining compensation instructions based on security restrictions provided in an embodiment of this application.

[0024] Figure 9 This is a functional block diagram of a production line compensation control device provided in an embodiment of this application.

[0025] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] To better understand the purpose, features, and advantages of this application, a detailed description of the application is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other. Numerous specific details are set forth in the following description to provide a thorough understanding of this application; the described embodiments are only a part of the embodiments of this application, and not all of them.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] This application provides a production line compensation control method that can be applied to one or more electronic devices. An electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0030] Electronic devices can be any electronic product that allows human-computer interaction with a customer, such as personal computers, tablets, smartphones, personal digital assistants (PDAs), game consoles, interactive network television (IPTV), smart wearable devices, etc.

[0031] Electronic devices may also include network devices and / or client devices. The network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0032] The networks in which electronic devices are located include, but are not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and virtual private networks (VPNs).

[0033] like Figure 1 The diagram illustrates an application scenario of a production line compensation control method provided in an embodiment of this application. This method can be applied to an electronic device 100. The electronic device 100 is communicatively connected to a database 200 and a target assembly device 300.

[0034] Database 200 is used to store and manage structured data such as binding relationships, target baseline data, distortion filtering rules, interval configurations, detection results, and execution results. The product design baseline, vehicle positioning baseline, fixture basic parameters, and adjustable execution parameters of the assembly equipment in the target baseline data can be stored as configuration tables, supporting multi-dimensional indexing and fast retrieval by product model, measurement point, fixture type, and other dimensions.

[0035] The target assembly equipment 300 is a production device that receives compensation commands and performs fine-tuning operations. Exemplarily, the target assembly equipment 300 may include, but is not limited to: screw fastening equipment, pressing equipment, dispensing equipment, welding equipment, mounting equipment, riveting equipment, and assembly actuators driven by multi-axis motion platforms. The target assembly equipment 300 typically includes one or more adjustable actuators, such as adjustable clamping mechanisms, adjustable positioning platforms, adjustable assembly heads, adjustable pressure mechanisms, or adjustable angle mechanisms. After receiving a compensation command, the target assembly equipment 300 parses the compensation direction, compensation amount, and actuator identifier in the command, converting them into control signals for the corresponding servo motor, stepper motor, cylinder, or piezoelectric actuator.

[0036] like Figure 2 The diagram shown is a flowchart of a production line compensation control method according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The production line compensation control method provided in this embodiment includes the following steps.

[0037] S20, acquire the detection data of the production object, the detection data including at least the measured value and the measurement point.

[0038] In one implementation, the production object can be a product to be assembled, a semi-finished component, work-in-process that has completed some processes, or a carrier (e.g., a pallet, a clamp base plate, or a conveyor chain) used to carry the aforementioned product. Electronic equipment acquires inspection data generated when the production object passes through the inspection station via a communication interface with the inspection equipment.

[0039] The inspection data includes at least two core fields: measured values ​​and measurement points. Measured values ​​refer to the quantitative results obtained after the inspection equipment measures the production object. Depending on the type of inspection equipment and the application scenario, measured values ​​can be: dimensional values ​​(such as length, width, hole diameter, hole spacing), positional values ​​(such as X / Y / Z coordinates of feature points), angle values ​​(such as assembly surface inclination angle, edge angle), gap values ​​(such as assembly gap, step difference), and height values ​​(such as flatness, step height) obtained through visual inspection, or measured values ​​obtained by conversion from image features (such as converting pixel distance to physical distance using calibration coefficients). Measurement points are used to characterize the inspection location, inspection item, or inspection feature corresponding to the measured value. For example, measurement points can be product edge points, positioning hole center points, assembly surface corner points, carrier positioning reference points, jig clamping points, or specific geometric feature identifiers (such as [hole diameter D1], [hole spacing L3], [flatness F2], etc.).

[0040] In one embodiment of this application, the detection data may further include one or more of the following auxiliary information: detection timestamp (for subsequent time-series analysis of offset trends), detection equipment identifier (for distinguishing data sources from multiple detection devices), original acquired images or point cloud data (for image matching in distortion removal), local contour feature vectors (for local feature determination), detection station number, detection batch number, ambient temperature and humidity (for auxiliary analysis of environmental factor correlation), and detection status identifiers (such as [normal], [suspected abnormal], [forced marking]). This auxiliary information provides a richer data foundation for subsequent distortion removal, data grouping, result traceability, and anomaly analysis.

[0041] For example, taking a mobile phone mid-frame assembly line as an example: the production object is the mobile phone mid-frame component, and the inspection equipment consists of a 2D industrial camera and a laser rangefinder sensor installed at the inspection station. The inspection data includes: the measured values ​​of the mid-frame length (e.g., 150.02mm), mid-frame width (e.g., 70.05mm), and screw hole diameter (e.g., 2.48mm) obtained from image processing by the camera, and the measured value of the assembly surface height (e.g., 5.12mm) obtained from the laser sensor; the corresponding measurement points are [mid-frame long side], [mid-frame short side], [screw holes #1-#4], and [assembly surface corners]. The inspection timestamp records the time of each inspection, and the inspection equipment identifier records the current inspection station number.

[0042] S21, based on the binding relationship between the production object and the target compensation object, determine the target reference data corresponding to the production object and the target assembly equipment corresponding to the target compensation object.

[0043] In one embodiment of this application, a production object is sequentially associated with different carriers, jigs, equipment, and workstations throughout the production process; the binding relationship is a structured record of these associations. For example, before entering the testing workstation, a mobile phone frame is placed on a carrier numbered [Tray#037] and positioned by a jig numbered [Jig#12]; these correspondences are recorded as binding relationships. Based on this binding relationship, the electronic device can retrieve target reference data matching the production object from the database and simultaneously determine the assembly equipment associated with the carrier or jig as the target assembly equipment. For details on how to determine the binding relationship, please refer to [link to relevant documentation]. Figure 3 The corresponding detailed explanation.

[0044] The target reference data serves as the basis for subsequent distortion filtering and offset calculation. In one implementation, the target reference data includes at least the standard values ​​corresponding to the measurement points (i.e., the theoretical or nominal values ​​of the product design), and may further include: product design references (such as reference surfaces, reference holes, and reference edges), carrier positioning references (such as the coordinates of positioning pins on the carrier and the height of the bearing surface), fixture basic parameters (such as the clamping position of the fixture and the coordinates of the limiting surface), and adjustable execution parameters of the assembly equipment (such as the default coordinates of the assembly head, the default pressure value, and the stroke range). By linking the above data through binding relationships, the electronic equipment can select the most suitable reference data for a specific production object-carrier-fixture-equipment combination, rather than using a one-size-fits-all universal reference.

[0045] The target assembly equipment is one of the candidate assembly equipment determined based on the target compensation object, used to receive compensation instructions and perform fine-tuning operations. In one implementation, the logic for determining the target assembly equipment is as follows: first, the target compensation object is determined according to the binding relationship (e.g., the jig used by the current product in the binding record); then, according to the configuration correspondence between the jig and the equipment, the equipment responsible for performing compensation in the workstation where the jig is located is found. When there are multiple assembly equipment in the same workstation, the system can accurately locate the equipment based on the specific correspondence in the equipment association information (e.g., [Jig#12 -> Device#3]), thereby ensuring that the compensation instruction is sent to the equipment that actually performs the product assembly action, rather than other unrelated equipment in the workstation.

[0046] In one embodiment of this application, the specific method for determining the target reference data and the target assembly equipment can be found in [link to relevant documentation]. Figure 4 The corresponding detailed explanation.

[0047] S22, Based on the target benchmark data, the detection data is filtered to remove distortions, thereby obtaining valid sample data.

[0048] In one embodiment of this application, each piece of detection data can be evaluated in multiple dimensions based on the standard values ​​in the target reference data, the standard comparison image, the tolerance range corresponding to the measurement point, and the preset distortion threshold. Detection data that meets the distortion conditions is marked as distorted data and is not included in the subsequent average offset calculation; detection data that is not screened out is determined as valid sample data.

[0049] In one embodiment of this application, distortion removal can employ a cascaded decision-making strategy: first, a size comparison decision with low computational cost is performed to quickly remove obviously abnormal detection records; then, image matching and local feature determination are performed on the data that has passed the first-level screening to identify hidden distortions caused by poor imaging quality, partial occlusion, or feature recognition failure. This cascaded strategy can control computational overhead while ensuring the accuracy of the removal when processing massive detection data from high-cycle production lines.

[0050] For example, when the camera lens of the inspection station is contaminated with oil, causing localized blurry areas in the acquired image, the measured values ​​at the measurement points involving these blurry areas may exhibit random fluctuations. Traditional out-of-tolerance rejection might treat these fluctuations as normal deviations and include them in the calculation. However, this step can use image matching results (low matching degree between the blurry area and the standard control image) or local feature judgment results (edge ​​gradient of the blurry area is below a preset threshold) to mark the detection data corresponding to that point as distorted data and filter it out. This avoids the distorted data from skewing the average offset and causing incorrect compensation by the assembly equipment.

[0051] For details on the method for determining valid sample data in one embodiment of this application, please refer to [link to relevant documentation]. Figure 5 The corresponding detailed explanation.

[0052] S23, Based on the measured values ​​in the effective sample data and the standard values ​​corresponding to the measurement points in the target reference data, determine the average offset.

[0053] In one embodiment of this application, offset calculation is performed based on valid sample data. Specifically, this includes two processes: grouping and calculation. The purpose of grouping is to categorize the valid sample data according to meaningful dimensions, so that the calculated average offset can reflect the systematic offset trend under a specific dimension, rather than being masked by the mixing of data from different dimensions (such as different fixtures or different equipment). The grouping dimension can be based on any one or more combinations of production objects corresponding to the binding relationship, target compensation objects, or measurement points in the detection data.

[0054] For each valid sample data within each group, the difference (i.e., deviation value) between the measured value and the corresponding standard value in the target baseline data can be calculated. The deviation value retains directional information: a positive deviation indicates a positive shift when the measured value is greater than the standard value; a negative deviation indicates a negative shift when the measured value is less than the standard value. The electronic device determines the average offset corresponding to the group based on the sum of the deviation values ​​and the number of valid samples in the corresponding group. In an optional implementation, to enhance the response speed to recent changes in production line status, the electronic device can introduce a sliding window mechanism—selecting only the most recent N (e.g., the most recent 20) valid sample data to calculate the average offset; or it can introduce a weighted average mechanism—assigning a higher weight coefficient (e.g., exponential decay weight) to the most recently collected valid sample data.

[0055] For example, using the same jig [Jig#12] and the same measurement point [screw hole #1 diameter] as the grouping dimension, 30 valid sample data are obtained for this group after step S22. The electronic device calculates the difference (-0.02mm, -0.01mm, +0.02mm, ...) between the measured diameter value (e.g., 2.48mm, 2.49mm, 2.52mm, ...) of each valid sample and the standard value (2.50mm), and calculates the arithmetic mean of all differences to obtain the average offset of this group within the current sampling window. If the sliding window is set to the most recent 20 samples, only the most recent 20 differences are used for calculation, and the earlier 10 samples are not included.

[0056] For a specific method of determining the average offset in one embodiment of this application, please refer to [link to relevant documentation]. Figure 6 The corresponding detailed explanation.

[0057] S24, if the average offset is outside the preset range, generate the compensation command based on the average offset.

[0058] In one embodiment of this application, a preset interval is used to distinguish between acceptable random fluctuations and systematic offsets that require triggering equipment fine-tuning. In actual production line operation, even when equipment and fixtures are in ideal condition, the test data will still exhibit slight fluctuations due to measurement noise, individual product differences, and environmental disturbances. Without an interval mechanism, the system may frequently generate compensation commands for these minor fluctuations, causing the target assembly equipment to repeatedly fine-tune near the standard position (i.e., [oscillation compensation]), which is detrimental to equipment lifespan and production line stability.

[0059] In one embodiment of this application, the preset interval is defined by a lower limit and an upper limit. The lower limit and upper limit can be pre-configured based on factors such as the accuracy level of the product model, the process criticality of the measurement point, the stability history of the target compensation object, and the positioning accuracy of the assembly equipment. For example, for a critical assembly dimension of a certain product model, the interval can be set to [-0.03mm, +0.03mm]; while for non-critical dimensions or points with naturally large detection fluctuations, the interval can be widened to [-0.08mm, +0.08mm]. The calculated average offset is compared with the lower limit and the upper limit, and a compensation command is triggered only when the average offset is less than or equal to the lower limit, or greater than or equal to the upper limit.

[0060] In one embodiment of this application, the interval can be a non-fixed value, dynamically adjusted based on statistical analysis of historical data. For example, electronic equipment can periodically (e.g., per shift or daily) statistically analyze the historical offset distribution of each measurement point, and determine the interval (e.g., + / -2 sigma or + / -3 sigma) based on its standard deviation (sigma), thereby achieving adaptive updating of the interval. This dynamic adjustment mechanism can appropriately narrow the interval to improve accuracy when the process is stable, and appropriately widen the interval to avoid overcompensation when the process fluctuates significantly.

[0061] For details on how to generate compensation instructions in one embodiment of this application, please refer to [link to relevant documentation]. Figure 7 The corresponding detailed explanation.

[0062] In one embodiment of this application, a security constraint mechanism and an anomaly alert mechanism may also be applied during the generation of the compensation amount to ensure the security, stability, and traceability of the compensation control. Specifically, for details on how to generate compensation instructions based on security constraints, please refer to [link to relevant documentation]. Figure 8 The corresponding detailed explanation.

[0063] S25, the compensation instruction is sent to the target assembly equipment, causing the target assembly equipment to perform a fine-tuning operation based on the compensation instruction.

[0064] In one embodiment of this application, a compensation instruction is sent to the target assembly equipment via a device communication network. The compensation instruction may include the following fields: target assembly equipment identifier (such as equipment number, IP address, or MAC address), actuating component identifier (such as [clamping mechanism X-axis][assembly head Z-axis][pressure regulating valve #2]), compensation direction (such as [positive increase][negative decrease]), compensation amount (target compensation amount after amplitude limiting and step size processing), measurement point (used to associate compensation operation with detection data), and instruction generation timestamp.

[0065] After receiving the compensation command, the target assembly equipment's controller (such as a PLC, motion controller, or embedded controller) parses the command content and converts it into control signals for the actuators. The specific form of the fine-tuning operation depends on the type of target assembly equipment and the adjustable execution parameters. For example, the fine-tuning operation may include: lateral position offset adjustment driven by a servo motor (e.g., offsetting the X-axis zero position of the clamping mechanism by +0.05mm), angle adjustment driven by a stepper motor (e.g., rotating the assembly head around the Z-axis by -0.1 degrees), pressure adjustment by a cylinder or electric cylinder, coordinate offset adjustment of the clamping position, updating the target coordinate point in the assembly trajectory, or updating the register value of the adjustable execution parameters.

[0066] Before performing fine-tuning operations, the target assembly equipment can send an acknowledgment message (ACK) to the electronic device. If the compensation command fails to be sent (e.g., communication timeout, equipment offline) or the target assembly equipment returns to an unexecutable state (e.g., equipment malfunction, exceeding safe travel limits), the electronic device will record the execution anomaly and mark the compensation as "[incomplete]" instead of considering it as completed. The electronic device can attempt to resend the command within a preset number of retries; if the timeout or the number of retries is exceeded, an error message will be triggered. This communication acknowledgment mechanism prevents the system from mistakenly believing that compensation has been performed and skipping subsequent necessary adjustments, ensuring the reliability of the compensation loop.

[0067] S26, Write back the detection results and execution results of the target assembly equipment after performing fine-tuning operations to the database.

[0068] In one embodiment of this application, the detection results are used to characterize the data obtained by re-detecting the production object (or subsequent production object) after a fine-tuning operation. The detection results may include: the measured value of the re-detection, the pass / fail judgment result, the measurement point status (e.g., [improved][not improved][out of tolerance]), and the offset improvement amount (i.e., the change in the measured value before and after compensation). The execution results are used to characterize the reception and execution of the compensation command at the target assembly equipment. The execution results may include: command reception status (success / failure / timeout), execution completion status (executed / partially executed / not executed), actual execution amount (the amount of actual equipment movement or adjustment), execution start time and execution completion time, execution component identification, and the cause of the abnormality (if any).

[0069] After the electronic equipment associates the detection results and execution results with the database, a complete closed-loop record chain is obtained: detection data (raw input) -> distortion screening results -> valid sample data -> average offset -> compensation command -> equipment execution results -> write back the detection results. Based on the complete closed-loop record chain, production line managers and process engineers can accurately trace back any compensation operation.

[0070] like Figure 3 The diagram shown is a flowchart of a method for determining a binding relationship according to an embodiment of this application. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining a binding relationship provided in this embodiment of the application includes the following steps.

[0071] S30, Obtain the production object identifier of the production object.

[0072] In one embodiment of this application, the production object identifier is used to uniquely or relatively uniquely distinguish production objects in the production process. In one implementation, the timing of obtaining the production object identifier may include one or more of the following scenarios: it is assigned and recorded by the MES system when the product is registered online; it is read or associated with an RFID reader / writer on the vehicle when the product is loaded onto the vehicle; it is read by a barcode / QR code on the product when the product enters the testing station; and it is read by a visual recognition system when the product flows between stations using a laser-engraved code (such as a Data Matrix code).

[0073] In one embodiment of this application, the type of production object identifier can be flexibly selected according to the informatization level of the production line and the characteristics of the product. For production lines that have implemented full traceability, the production object identifier can be a product serial number (SN) – each product has a globally unique serial number; for production lines that only need to distinguish product models, the production object identifier can be a product ID or model code; for production lines using a visual recognition system, the production object identifier can be a QR code or 3D code pasted or engraved on the product surface; multiple identifiers can also be combined (e.g., using the product serial number as the primary key and the QR code as a quick scanning index) to improve the robustness and convenience of identification.

[0074] S31, Obtain the compensation object identifier of the target compensation object.

[0075] In one embodiment of this application, the compensation object identifier is used to distinguish different instances of compensation objects on the production line. When multiple similar compensation objects exist on the production line (such as multiple sets of fixtures of the same model, or multiple assembly equipment at the same workstation), clearly identifying the compensation object identifier is a prerequisite for accurately locating the source of deviation and the compensation target. In one implementation, the carrier identifier can be obtained through an RFID tag, QR code, or barcode installed on the carrier; the fixture identifier can be obtained through a number registered in the fixture management system or an identification tag installed on the fixture; the equipment identifier can be read from the equipment's PLC or controller using the equipment's serial number or network address (such as a MAC address); and the workstation identifier can be obtained from the MES system or the production line control system.

[0076] In one embodiment of this application, the timing of collecting the compensation object identifier can be synchronous or asynchronous with the collection of the production object identifier. In a preferred embodiment, when a product is loaded onto a carrier, the system simultaneously reads the product serial number and carrier identifier and writes them to the database; when the carrier moves with the product to a workstation, the system reads the workstation identifier and fixture identifier; when the assembly equipment starts performing assembly operations, the system reads the equipment identifier. By embedding the above identifier collection operations into the normal flow rhythm of the production line, binding relationships can be automatically established and maintained without adding extra operation steps.

[0077] S32, Based on the production object identifier and the compensation object identifier, construct a binding relationship between the production object and the target compensation object.

[0078] In one embodiment of this application, the electronic device writes the acquired production object identifier and compensation object identifier into the same binding record in the database, thereby establishing a binding relationship between the production object and the target compensation object. A binding record may contain the following fields: record ID (primary key), production object identifier, compensation object identifier (which may include one or more of the following: carrier identifier, fixture identifier, equipment identifier, and workstation identifier), inspection batch number, binding establishment time, and binding update identifier. This binding relationship can be established or updated at different stages of the product lifecycle: for example, an initial binding is established when the product is loaded onto the carrier (product -> carrier), a supplementary binding is added when the product arrives at the inspection workstation (adding the workstation identifier), a binding is associated after inspection is completed (adding the inspection batch number), and the binding is finally determined before the assembly equipment is executed (adding the equipment identifier and fixture identifier).

[0079] Through binding relationships, the system can associate detection data, target baseline data, compensation instructions, detection results, and execution results along the same [product -> carrier -> fixture -> workstation -> equipment] link. For example, if a batch of products is found to have a persistent deviation at a certain measurement point, the system can trace which carriers, fixtures, and equipment that the batch of products passed through through the binding relationships, thereby quickly locating whether the deviation originates from a problem with the product itself (such as abnormal incoming materials), carrier positioning deviation, fixture wear, or equipment drift. This multi-dimensional traceability capability based on binding relationships is something that traditional methods that only record product detection results lack.

[0080] like Figure 4 The diagram shown is a flowchart of a method for determining target reference data and target assembly equipment according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining target reference data and target assembly equipment provided in this embodiment of the application includes the following steps.

[0081] S40, Based on the binding relationship, determine the product model and measurement point corresponding to the production object.

[0082] In one embodiment of this application, the production object identifier is extracted from the binding record, and the product model corresponding to the production object is determined by querying the product master data table. The product model is the main index for determining the applicable benchmark dataset. Measurement points can be determined in two ways: one is by directly reading the measurement point field from the inspection data; the other is by obtaining all inspection items and the corresponding measurement point list for the product model based on the inspection scheme configuration for that model. In actual production lines, the same inspection station may undertake inspection tasks for multiple product models, and different product models may correspond to different measurement point definitions and different standard values ​​at the same physical inspection location. Therefore, determining the product model before matching measurement points is a necessary prerequisite for ensuring the correct retrieval of benchmark data.

[0083] S41, Based on the product model and the measurement point, determine target benchmark data including standard values ​​corresponding to the measurement point.

[0084] In one embodiment of this application, the electronic device queries target reference data from a database using product model and measurement point location as joint query conditions. The target reference data includes at least the standard value (theoretical design value) corresponding to each measurement point location. Further, the target reference data may also include: product design references—such as the geometric definitions and theoretical coordinates of reference surfaces, reference holes, and reference edges; carrier positioning references—the theoretical positioning coordinates and positioning tolerances of the current product model on the carrier; fixture basic parameters—the clamping position, limit surface coordinates, and clamping force range of the current product model on the fixture; and adjustable execution parameters—the assembly head reference position, pressure reference value, torque reference value, and stroke range of the current product model on the target assembly equipment. By uniformly managing and associating the above multi-level reference data, it can be ensured that the reference basis for subsequent offset calculation and compensation instruction generation is accurate and matches the specific product-carrier-fixture-equipment combination.

[0085] S42, Based on the binding relationship, determine the device association information associated with the target compensation object.

[0086] In one embodiment of this application, the path for determining equipment association information is: binding relationship -> target compensation object (e.g., a jig) -> workstation where the jig is located -> equipment configuration table of the workstation -> candidate assembly equipment set. If the target compensation object is a vehicle, then candidate equipment is further determined through the workstation where the vehicle is currently located. In another embodiment, the system maintains a [compensation object-equipment mapping table] to directly store the correspondence between compensation objects and equipment, thereby improving query efficiency. Equipment association information may also include equipment type, number of axes capable of performing compensation, whether remote command control is supported, and the current online status of the equipment, for subsequent target assembly equipment screening.

[0087] S43, Based on the equipment association information, determine the target assembly equipment for receiving the compensation instruction from the candidate assembly equipment in the production line.

[0088] In one embodiment of this application, when there are multiple devices in the candidate assembly equipment set, the electronic device determines the final target assembly equipment based on preset screening rules. The screening rules can comprehensively consider the following factors: whether the device identifier matches the device identifier in the device association information, whether the workstation where the device is located matches the workstation in the binding relationship, whether the device has adjustable execution parameters to execute the compensation item corresponding to the current measurement point, whether the device is online and in a state where it can receive compensation instructions, and whether the device currently has a queue of compensation tasks to be executed. For example, when the same workstation contains multiple assembly devices responsible for different processes (such as screw fastening equipment and pressing equipment deployed at different workstations in the same workstation), the electronic device determines that the current product is being assembled by the pressing equipment based on the correspondence between the fixture and the equipment. Therefore, the pressing equipment is identified as the target assembly equipment, rather than mistakenly sending the compensation instruction to the screw fastening equipment. In a preferred embodiment, if the target assembly equipment is offline due to a fault or maintenance, the electronic device can automatically select a replacement device with the same adjustable execution parameters that is online within the workstation and send an equipment switching prompt to the management personnel.

[0089] like Figure 5 The diagram shown is a flowchart of a method for determining valid sample data according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining valid sample data provided in this embodiment includes the following steps.

[0090] S50, based on the acquired image in the detection data and the standard comparison image in the target reference data, determine the image matching result.

[0091] In one embodiment of this application, image matching is used to determine the degree of consistency between the currently acquired image and a standard state image. Specifically, the region of interest (ROI) in the standard reference image can be used as a template, and normalized cross-correlation (NCC) matching calculation is performed on the corresponding region in the currently acquired image to obtain the matching score of each ROI. When the matching score of any key ROI is lower than a preset matching threshold, the image matching result is considered abnormal. In another embodiment, image matching can be implemented based on a feature point matching method: local feature descriptors such as SIFT, SURF, or ORB are extracted from the standard reference image and the currently acquired image respectively, and the number and quality of feature point matching are calculated; when the ratio of the number of matching points to the total number of feature points in the standard reference image is lower than a preset ratio threshold, it is determined that there is a risk of image distortion. In yet another embodiment, if the electronic device has sufficient computing resources (such as GPU acceleration), a deep learning-based image anomaly detection model (such as an autoencoder-based reconstruction error method or a similarity measurement method based on a Siamese network) can also be used to more robustly adapt to changes in illumination and reasonable variations in product appearance.

[0092] In one embodiment of this application, the image matching result can be represented as a Boolean value (normal match / abnormal match), a continuous score (such as a similarity score between 0 and 1), or a multi-level classification (such as [excellent][good][suspicious][abnormal]). In a cascaded decision strategy, image matching is typically used as a secondary decision with moderate computational cost to further filter the detection data that has passed size comparison. Specific distortion modes include: local blurring caused by lens oil or moisture, perspective distortion caused by product tilt, overexposure or underexposure caused by drastic changes in ambient light, and motion blur (taking pictures before the product has completely stopped in a high-speed production line).

[0093] S51, Based on the measured values ​​in the detection data and the tolerance range or distortion threshold in the target reference data, determine the size comparison result.

[0094] In one embodiment of this application, the measured value in the detection data can be compared with the standard value in the target reference data to calculate the absolute deviation. This absolute deviation is then compared with a preset distortion threshold: if the absolute deviation exceeds the distortion threshold, and the degree of deviation exceeds the compensable range of the target assembly equipment (i.e., even with maximum compensation, it cannot be corrected), then the detection data is determined to meet the distortion condition. It is important to emphasize that the distortion threshold in dimensional comparison is a different concept from production tolerance (the tolerance range for pass / fail determination): the distortion threshold is typically significantly larger than the production tolerance, and its purpose is to identify [extreme measurement values ​​that cannot be generated by normal process fluctuations] (such as abnormal readings caused by sensor failure or foreign object obstruction), rather than to determine whether the product is qualified. In a preferred embodiment, the distortion threshold can be set to + / - 20% of the standard value or 3-5 times the tolerance range; the specific value can be determined based on the statistical distribution of historical data.

[0095] In one embodiment of this application, the size comparison may further include verifying the physical reasonableness of the measured values. For example, if the measured value at a certain measuring point indicates that a certain feature dimension of the product is negative, zero, or exceeds the overall external dimensions of the product, then the measured value obviously contains a measurement error and should be directly marked as distorted data. Physical reasonableness verification can eliminate obviously unreasonable data caused by sensor reading errors, data communication errors, or measurement software malfunctions.

[0096] S52, determine the local feature determination result based on the image features or size features in the preset local detection area.

[0097] In one embodiment of this application, local feature determination focuses on the feature integrity assessment of specific key regions to identify distortions caused by local occlusion, local deformation, or local recognition failure. The preset Local Area of ​​Interest (ROI) can be a positioning hole region, edge contour region, assembly surface region, key corner point, adhesive path region, pressing region, or any key location that significantly affects detection reliability. The electronic device extracts image features (such as edge gradient, contour shape, texture features) or size features (such as local dimensions within the ROI) within this ROI and determines whether they meet preset feature conditions. For example, for the positioning hole region, the local feature condition can be: [the circularity of the hole edge >= 0.85 and the ratio of the hole area to the standard area is between 0.9 and 1.1]; for the assembly surface region, the local feature condition can be: [the grayscale standard deviation of pixels within the region >= a preset value (indicating the presence of effective texture / features) and no saturated pixels within the region (indicating no overexposure due to reflection)]. When the local features do not meet the preset conditions, it indicates that the ROI may have occlusion, severe deformation, or recognition failure, and the corresponding detection data is determined to meet the distortion conditions.

[0098] S53, based on at least one of the image matching result, the size comparison result, and the local feature determination result, after deleting the detection data that meets the distortion condition from the detection data, the effective sample data is obtained.

[0099] In one embodiment of this application, a comprehensive distortion determination can be performed on the detection data based on the determination result. The logic for the comprehensive determination can include any of the following: Any data deemed distorted by any criterion is filtered out, which is suitable for scenarios with strict data quality requirements; All enabled decision dimensions are considered to be distorted before being filtered out, which is suitable for scenarios where more samples need to be retained; Only those dimensions deemed distorted are filtered out, providing a compromise solution; The summation is calculated based on the confidence weights of each dimension and then compared with the overall threshold.

[0100] In one embodiment of this application, the rejected detection data can be stored in a database as distortion data records. The distortion data record may include: original detection data, rejection reason (e.g., [image blurring], [size exceeding distortion threshold], [positioning hole occlusion]), rejection time, associated production object identifier and compensation object identifier, and the judgment dimension that triggered the rejection and the corresponding judgment value (e.g., matching score 0.23, deviation 0.87mm). These records provide a basis for subsequent detection equipment maintenance (e.g., lens cleaning, light source calibration), detection scheme optimization (e.g., adjusting ROI definition or distortion threshold), and manual review. Detection data that is not rejected is determined as valid sample data and directly proceeds to step S23 for offset calculation.

[0101] like Figure 6 The diagram shown is a flowchart of a method for determining the average offset of groups according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining the average offset of groups provided in this application includes the following steps.

[0102] S60, group the valid sample data based on at least one of the production object, target compensation object, or measurement point in the detection data corresponding to the binding relationship.

[0103] In one embodiment of this application, the purpose of grouping is to categorize valid sample data with the same or similar sources of deviation, so that the calculated average offset can reflect the systematic deviation in a specific dimension. In one implementation, the grouping dimension can be a single dimension, such as: grouping by production object—analyzing multiple test results of the same product (suitable for scenarios where the same product is tested multiple times); grouping by target compensation object—analyzing the test results of all products processed by the same fixture (suitable for determining whether the fixture produces a systematic deviation); grouping by measurement points in the test data—analyzing the test results of different products at the same measurement point (suitable for determining the deviation trend of a workstation or equipment).

[0104] In one embodiment of this application, the grouping dimension can be a combination of multiple dimensions to achieve more precise location of the source of deviation. For example, grouping by the triple dimension of [product model + fixture identifier + measurement point] can determine the specific deviation of [the screw hole diameter measurement point of product model A in fixture Jig#12]. Multi-dimensional combination grouping is particularly important in complex production lines—the production line may produce multiple product models simultaneously, using multiple sets of fixtures and multiple devices. Single-dimensional grouping may mix deviations from different sources together, masking the true offset pattern.

[0105] In one embodiment of this application, the valid sample data can be sorted (e.g., arranged in ascending order by detection timestamp) before grouping, so that the latest sample can be correctly selected when a sliding window mechanism is used subsequently. In addition, when the number of valid samples in a certain group is lower than a preset minimum sample number threshold (e.g., less than 5), the electronic device can temporarily not generate a compensation instruction for that group and output a [insufficient samples] prompt to avoid making compensation decisions based on a very small amount of accidental data.

[0106] S61, determine the difference between the measured value and the standard value of each valid sample data in each group.

[0107] In one embodiment of this application, for each valid sample data within the same group, the difference between its measured value and the corresponding standard value in the target benchmark data can be calculated. The sign of the difference indicates the direction of the offset (a positive value indicates the measured value is too large, and a negative value indicates the measured value is too small), and the absolute value of the difference indicates the magnitude of the offset.

[0108] In one implementation, the difference calculation maintains a consistent definition for different types of measured values ​​(such as dimensions, angles, and position coordinates), enabling the system to handle multi-dimensional offsets with a unified data structure. For position coordinate measured values, the difference can be Euclidean distance or component difference, depending on the compensation direction (single-axis compensation or multi-axis linkage compensation) supported by the adjustable execution parameters of the target assembly equipment.

[0109] S62, based on the difference and the number of valid samples, determine the average offset corresponding to each group.

[0110] In one embodiment of this application, the average offset is the arithmetic mean of the offsets of each group of samples.

[0111] In one embodiment of this application, a sliding window strategy can also be used to calculate only the arithmetic mean of the most recent N valid samples (e.g., the most recent 20 samples or samples from the most recent hour), while earlier samples automatically slide out of the window and are not included in the calculation. The sliding window mechanism can use a longer window to smooth noise during the process stabilization period and automatically shorten the window to improve response speed when a change in offset trend is detected.

[0112] In one embodiment of this application, the most recently collected samples can be assigned a higher weight based on an exponentially weighted moving average algorithm, with the weight decaying exponentially over time.

[0113] This approach allows for the extraction of statistically significant systematic offsets from valid sample data, rather than reflecting random biases in individual samples. By comprehensively characterizing the offset information through both direction and magnitude, it provides a data foundation for subsequent interval comparisons and compensation direction determination.

[0114] like Figure 7 The diagram shown is a flowchart of a method for determining a compensation instruction according to an embodiment of this application. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining a compensation instruction provided in this embodiment includes the following steps.

[0115] S70, obtain the lower limit and upper limit corresponding to the preset interval.

[0116] In one embodiment of this application, a lower limit (typically negative or zero) and an upper limit (typically positive or zero) jointly define the upper and lower limits of the allowable fluctuation range. The configuration of the range can originate from multiple levels: The product design layer can be set according to the product's functional requirements and assembly precision level; The process specification layer can be set according to the process control limits defined by the process engineer; The historical statistics layer can be automatically calculated based on the historical offset statistical distribution of the measurement point (such as mean + / - k*sigma).

[0117] In one embodiment of this application, for measurement points with high accuracy requirements (such as the height of the assembly surface that affects sealing), a narrower range (such as + / -0.02mm) can be configured; for measurement points with naturally large fluctuations but small functional impacts (such as appearance gaps), a wider range (such as + / -0.10mm) can be configured.

[0118] S71, determine whether the average offset is outside the preset range.

[0119] In one embodiment of this application, if the average offset is less than or equal to the lower limit, or the average offset is greater than or equal to the upper limit, it is determined that the average offset is outside the preset range, and the compensation instruction generation process is triggered; otherwise, the average offset is within the range, which is within the allowable normal fluctuation range, and compensation is not triggered.

[0120] In one embodiment of this application, compensation can be triggered only when the average offset is outside the range for N consecutive times (e.g., 3 or 5 consecutive times). This mechanism can further filter out single, occasional out-of-range occurrences (e.g., single fluctuations caused by incomplete screening of individual abnormal samples), reducing unnecessary compensation frequency. The continuous out-of-range counting, used in conjunction with the range, provides a flexible adjustment space between control sensitivity and stability.

[0121] S72, generate the compensation command based on the average offset that is outside the preset range.

[0122] In one embodiment of this application, the specific content of the compensation instruction depends on the direction and magnitude of the average offset. The compensation direction is opposite to the direction of the average offset: if the average offset is positive (the measured value is too large), the compensation direction is negative (causing the device to fine-tune in the direction of decreasing the measured value); if the average offset is negative (the measured value is too small), the compensation direction is positive (causing the device to fine-tune in the direction of increasing the measured value).

[0123] The compensation amount can be determined based on the magnitude of the average offset. For example, the compensation amount can be the absolute value of the average offset; alternatively, a compensation coefficient can be determined based on the equipment and process, and the product of the absolute value of the average offset and the compensation coefficient can be used to determine the compensation amount.

[0124] In one embodiment of this application, the data structure of the compensation instruction may include the following fields: instruction ID (unique identifier), target assembly equipment identifier, executing component identifier, compensation direction (enumerated values: positive / negative), compensation amount (numerical value and unit), associated measurement point, generation timestamp, expiration time (the instruction becomes invalid if it is not executed after this time), and instruction priority (normal / high priority). The instruction is encapsulated in JSON, XML, or binary protocol format and sent to the target assembly equipment via TCP / IP, UDP, serial port, or fieldbus protocol.

[0125] like Figure 8 The diagram shown is a flowchart of a method for determining compensation instructions based on security restrictions according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements. The method for determining compensation instructions based on security restrictions provided in this embodiment includes the following steps.

[0126] S80, determine the initial compensation amount based on the average offset and the compensation coefficient.

[0127] In one embodiment of this application, a compensation coefficient is used to convert the average offset into an initial compensation amount that the equipment can execute. The compensation coefficient is obtained through calibration: for linear relationships, the system executes a calibration process to assemble the target assembly equipment under several known offsets, measures the corresponding deviation values ​​of the products, and determines the proportional coefficient between the offset and the compensation amount through linear regression. For nonlinear relationships, the system can establish a lookup table (LUT) or a polynomial fitting model to more accurately describe the mapping relationship between the compensation amount and the offset. The calibration of the compensation coefficient can be performed periodically or re-executed after equipment maintenance or fixture replacement to ensure compensation accuracy.

[0128] For example, if the system detects an average offset of -0.04mm in the screw hole diameter (i.e., the diameter is too small), the target assembly equipment indirectly affects the hole diameter by adjusting the torque of the screw fastener. The compensation coefficient between the hole diameter deviation and the torque compensation is determined to be 0.5Nm / 0.01mm through calibration. Therefore, the initial compensation amount = 0.04mm x (0.5Nm / 0.01mm) = 2.0Nm, in the positive direction (increasing torque to enlarge the hole diameter).

[0129] S81, based on the maximum allowable compensation amount of the target assembly equipment, the initial compensation amount is limited.

[0130] In one embodiment of this application, the maximum permissible compensation amount is the inherent safety limit of the target assembly equipment or the maximum adjustment range allowed by the process. The purpose of the amplitude limiting process is to prevent the single compensation amount from exceeding the physical travel of the equipment, the safe operating range, or the boundary of the process rationality. The electronic device compares the absolute value of the initial compensation amount with the maximum permissible compensation amount: if the initial compensation amount is greater than the maximum permissible compensation amount, the absolute value of the compensation amount is truncated to the maximum permissible compensation amount, retaining the original direction, to obtain the amplitude limiting compensation amount; otherwise, the initial compensation amount is directly used as the amplitude limiting compensation amount.

[0131] In some embodiments, the limiting process can also generate a [limiting triggered] flag. When the limiting is triggered, the event can be recorded and marked in the subsequent dashboard with [[Single compensation has reached the limit, there may be a systemic problem]], prompting manual intervention for inspection.

[0132] S82, based on the single maximum adjustment step size of the target assembly equipment, the step size limit of the amplitude compensation amount is imposed.

[0133] In one embodiment of this application, the maximum single adjustment step size is used to reduce the risks of over-adjustment, oscillation, and large one-time adjustments. Unlike amplitude limiting, the goal of step size limiting is to control the compensation amount within a small adjustment range, allowing the device to gradually approach the target state through multiple incremental adjustments, rather than a single large change.

[0134] In one implementation, the electronic device compares the limiting compensation amount with the maximum single adjustment step size: if the limiting compensation amount is less than or equal to the maximum single adjustment step size, then the limiting compensation amount is the target compensation amount; if the limiting compensation amount is greater than the maximum single adjustment step size, then the target compensation amount is set to the maximum single adjustment step size (retaining the direction), and the remaining uncompensated amount (residual) will be recalculated and compensated in subsequent compensation cycles after the write-back detection results confirm the compensation effect of the current step, based on the new offset. This gradual compensation strategy is similar to incremental control in control theory, which can ensure the compensation effect while avoiding product scrapping due to overcompensation.

[0135] S83, based on the target compensation amount, adjustment direction and equipment identifier of the target assembly equipment, generate the compensation instruction.

[0136] In one embodiment of this application, the target compensation amount can be converted from floating-point format to a data format that the target assembly equipment controller can parse (such as an integer multiple of the minimum control resolution), the adjustment direction can be mapped to a direction flag bit in the device protocol, and the device identifier can be mapped to a target address in the communication protocol.

[0137] In some embodiments, the compensation instruction also includes a verification field (such as a CRC checksum) to ensure that the instruction has not been tampered with or damaged during transmission.

[0138] S84 outputs an error message when the average offset exceeds the preset range consecutively.

[0139] In one embodiment of this application, a continuous anomaly alert mechanism is used to identify persistent systemic problems and prompt manual intervention. In one implementation, the electronic device maintains a set of counters to count the number of times the average offset exceeds the range consecutively under the same production object, the same target compensation object, or the same measurement point dimension. When the count value of any counter reaches a preset consecutive anomaly count threshold (e.g., 5 or 10 times, configurable according to management requirements), the system outputs anomaly alert information in one or more of the following ways: displaying the anomaly event with a red highlight or flashing mark on the traceability dashboard; sending alarm notifications to relevant responsible persons via enterprise instant messaging tools or email; outputting visual or audible warnings via production line three-color lights (Andon system); and pushing the anomaly event to the MES system or equipment maintenance management system via an interface. The anomaly alert information may include: the anomaly dimension (e.g., [Jig #12 has exceeded the offset limit 8 times consecutively]), suggested troubleshooting directions (e.g., [It is suggested to check if the positioning pins of the jig are worn], [It is suggested to check if the guide rails of the equipment are loose], [It is suggested to check if the lens of the detection equipment needs cleaning]), associated dashboard data links, and the most recent related detection data and compensation records.

[0140] S85, Based on the detection results and execution results written back to the database, generate traceability dashboard data.

[0141] In one embodiment of this application, the write-back detection results and execution results can be extracted from the database periodically (e.g., every 5 minutes or in real time), and generated into dashboard data after aggregation and statistical calculation. The dashboard may include the following views: an offset trend graph, which displays the average offset change curve of each measurement point with time as the horizontal axis, supplemented by reference lines for the upper and lower limits of the interval; a compensation execution record table, which lists the triggering reason, compensation amount, executing device, and execution result of each compensation in reverse chronological order; a fixture health panel, which displays the cumulative number of compensations, the number of consecutive compensations in the same direction, and the slope of the offset trend for each fixture, to help determine whether the fixture needs maintenance or replacement; a device drift monitoring graph, which displays the trend of the compensation amount received by the target assembly equipment over time, to help determine whether the equipment has progressive drift; and a yield correlation analysis, which compares the number of compensations with the product yield to help evaluate the effectiveness of the compensation strategy.

[0142] Specifically, dashboard data can be presented in the form of charts, tables, and dashboards on workshop screens, management PCs, or mobile terminals using web front-end frameworks (such as ECharts, Grafana, or a self-developed dashboard system). In one implementation, the dashboard also provides an interactive drill-down function: users can click on abnormal data points in the trend chart to drill down and view the original detection data that triggered the compensation, distortion screening records, and compensation instruction details, achieving end-to-end traceability from macro trends to micro records.

[0143] Please see Figure 9 , Figure 9 This is a functional block diagram of a production line compensation control device according to an embodiment of this application. The production line compensation control device 91 includes a data acquisition module 910, a binding module 911, a determination module 912, a screening module 913, an execution module 914, and a write-back module 915. The module / unit referred to in this application refers to a series of computer-readable instruction segments that can be executed by the processor 13 and perform a fixed function, and are stored in the memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0144] The acquisition module 910 is used to acquire the detection data of the production object, and the detection data includes at least the measured value and the measurement point.

[0145] The binding module 911 is used to provide the binding relationship between the production object and the target compensation object.

[0146] The determining module 912 is used to determine the target baseline data corresponding to the production object and the target assembly equipment corresponding to the target compensation object based on the binding relationship.

[0147] The screening module 913 is used to screen out distortions in the detection data based on the target benchmark data to obtain valid sample data.

[0148] The determining module 912 is further configured to determine the average offset based on the measured values ​​in the effective sample data and the standard values ​​corresponding to the measurement points in the target reference data.

[0149] The execution module 914 is used to generate the compensation instruction based on the average offset when the average offset is outside the preset range, and send the compensation instruction to the target assembly equipment, so that the target assembly equipment performs a fine-tuning operation based on the compensation instruction.

[0150] The write-back module 915 is used to write back the detection results and execution results of the target assembly equipment after fine-tuning operations to the database.

[0151] Please see Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 100 includes a memory 12 and a processor 13. The memory 12 is used to store computer-readable instructions, and the processor 13 executes the computer-readable instructions stored in the memory to implement a production line compensation control method as described in any of the above embodiments.

[0152] In one embodiment of this application, the electronic device 100 further includes a bus and a computer program stored in the memory 12 and executable on the processor 13, such as a production line compensation control program.

[0153] Figure 10 Only an electronic device 100 with memory 12 and processor 13 is shown; those skilled in the art will understand that... Figure 10 The structure shown does not constitute a limitation on the electronic device 100, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0154] Combination Figure 2 The memory 12 in the electronic device 100 stores a plurality of computer-readable instructions to implement the production line compensation control method. The processor 13 can execute the plurality of instructions to achieve the following: acquiring detection data of a production object, the detection data including at least measured values ​​and measurement points; determining target reference data corresponding to the production object and target assembly equipment corresponding to the target compensation object based on the binding relationship between the production object and the target compensation object; filtering out distortions in the detection data based on the target reference data to obtain valid sample data; determining the average offset based on the measured values ​​in the valid sample data and the standard values ​​corresponding to the measurement points in the target reference data; generating the compensation instruction based on the average offset when the average offset is outside a preset range; sending the compensation instruction to the target assembly equipment, causing the target assembly equipment to perform a fine-tuning operation based on the compensation instruction; and writing back the detection results and execution results after the target assembly equipment performs the fine-tuning operation to the database.

[0155] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 2 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0156] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. The electronic device 100 may be a bus-type structure or a star-type structure. The electronic device 100 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, the electronic device 100 may also include input / output devices, network access devices, etc.

[0157] It should be noted that electronic device 100 is only an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0158] The memory 12 includes at least one type of readable storage medium, which can be non-volatile or volatile. The readable storage medium includes flash memory, portable hard drives, multimedia cards, card-type memory (e.g., SD or DX memory), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 100, such as the portable hard drive of the electronic device 100. In other embodiments, the memory 12 can also be an external storage device of the electronic device 100, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 100. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 100, such as the code of a production line compensation control program, but also to temporarily store data that has been output or will be output.

[0159] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 100, connecting to various components of the electronic device 100 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing production line compensation control programs) and calls data stored in the memory 12 to perform various functions of the electronic device 100 and process data.

[0160] The processor 13 executes the operating system of the electronic device 100 and various installed applications. The processor 13 executes the applications to implement the steps in the various production line compensation control method embodiments described above, for example... Figure 2 The steps are shown.

[0161] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device 100.

[0162] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the production line compensation control method described in the various embodiments of this application.

[0163] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0164] The computer program includes computer program code, which may be in the form of source code, foreground object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, and other memory.

[0165] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0166] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 10 The symbol is represented by only one arrow, but this does not indicate that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0167] This application also provides a computer-readable storage medium (not shown), which stores computer-readable instructions that are executed by a processor in an electronic device to implement the production line compensation control method described in any of the above embodiments.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0170] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the specification may also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A production line compensation control method, characterized in that, The method includes: Acquire the detection data of the production object, wherein the detection data includes at least the measured values ​​and measurement points; Based on the binding relationship between the production object and the target compensation object, the target baseline data corresponding to the production object and the target assembly equipment corresponding to the target compensation object are determined. Based on the target benchmark data, the detection data is filtered to remove distortions, thereby obtaining valid sample data; The average offset is determined based on the measured values ​​in the effective sample data and the standard values ​​corresponding to the measurement points in the target reference data. If the average offset is outside the preset range, a compensation command is generated based on the average offset. The compensation instruction is sent to the target assembly equipment, causing the target assembly equipment to perform a fine-tuning operation based on the compensation instruction.

2. The production line compensation control method as described in claim 1, characterized in that, Determining the binding relationship between the production object and the target compensation object includes: Obtain the production object identifier of the production object, wherein the production object identifier includes one or more of the following: product identifier, product serial number, two-dimensional identification code, or three-dimensional identification code; Obtain the compensation object identifier of the target compensation object, wherein the compensation object identifier includes one or more of the following: vehicle identifier, jig identifier, equipment identifier, or workstation identifier; Based on the production object identifier and the compensation object identifier, a binding relationship is established between the production object and the target compensation object.

3. The production line compensation control method as described in claim 1, characterized in that, The step of determining the target baseline data corresponding to the production object and the target assembly equipment corresponding to the target compensation object based on the binding relationship between the production object and the target compensation object includes: Based on the binding relationship, the product model and measurement point corresponding to the production object are determined; Based on the product model and the measurement point, target reference data including standard values ​​corresponding to the measurement point is determined. The target reference data includes one or more of the following: standard values ​​corresponding to the product model, measurement point, product design reference, vehicle positioning reference, fixture basic parameters, or adjustable execution parameters of the assembly equipment. Based on the binding relationship, the equipment association information associated with the target compensation object is determined. The equipment association information includes one or more of the following: equipment identifier, workstation identifier, correspondence between fixture and equipment, or correspondence between vehicle and equipment. Based on the equipment association information, a target assembly equipment for receiving the compensation instruction is determined from the candidate assembly equipment in the production line.

4. The production line compensation control method as described in claim 1, characterized in that, The process of filtering out distortions in the detection data based on the target benchmark data to obtain valid sample data includes: Based on the acquired images in the detection data and the standard comparison images in the target benchmark data, the image matching results are determined; Based on the measured values ​​in the detection data and the tolerance range or distortion threshold in the target reference data, the size comparison result is determined; Based on the image features or size features in the preset local detection area, the local feature determination result is determined; Based on at least one of the image matching results, the size comparison results, and the local feature determination results, the detection data that meets the distortion conditions is deleted from the detection data to obtain the effective sample data.

5. The production line compensation control method as described in claim 1, characterized in that, The determination of the average offset based on the measured values ​​in the effective sample data and the standard values ​​corresponding to the measurement points in the target benchmark data includes: Based on at least one of the production object, target compensation object, or measurement point in the detection data corresponding to the binding relationship, the effective sample data is grouped. Determine the difference between the measured value of each valid sample data in each group and the standard value corresponding to the measurement point in the target benchmark data; Based on the difference and the number of valid samples in the corresponding group, the average offset corresponding to each group is determined.

6. The production line compensation control method as described in claim 1, characterized in that, When the average offset is outside a preset range, generating the compensation command based on the average offset includes: Based on the average offset and the compensation coefficient, the initial compensation amount is determined; Based on the maximum allowable compensation amount of the target assembly equipment, the initial compensation amount is limited to obtain the limited compensation amount. Based on the maximum single adjustment step size of the target assembly equipment, the step size limit of the amplitude compensation amount is applied to obtain the target compensation amount; The compensation instruction is generated based on the target compensation amount, the adjustment direction, and the equipment identifier of the target assembly equipment.

7. The production line compensation control method as described in claim 1, characterized in that, The method further includes: The test results and execution results after the fine-tuning operation of the target assembly equipment are written back to the database; If the average offset exceeds the preset range consecutively for the same production object, the same target compensation object, or the same measurement point, an abnormal prompt message will be output. Based on the detection and execution results stored in the database, dashboard data is generated for tracing product yield, fixture abnormalities, equipment drift, or carrier positioning abnormalities.

8. A production line compensation control device, characterized in that, The device includes: The data acquisition module is used to acquire the detection data of the production object, and the detection data includes at least the measured values ​​and measurement points; The binding module is used to provide the binding relationship between the production object and the target compensation object; The determination module is used to determine the target baseline data corresponding to the production object and the target assembly equipment corresponding to the target compensation object based on the binding relationship; The screening module is used to screen out distortions in the detection data based on the target benchmark data to obtain valid sample data; The determining module is further configured to determine the average offset based on the measured values ​​in the effective sample data and the standard values ​​corresponding to the measurement points in the target reference data; An execution module is configured to generate a compensation instruction based on the average offset when the average offset is outside a preset range, and send the compensation instruction to the target assembly equipment, so that the target assembly equipment performs a fine-tuning operation based on the compensation instruction. The write-back module is used to write back the detection results and execution results of the target assembly equipment after fine-tuning operations to the database.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the processor is used to implement the production line compensation control method as described in any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the production line compensation control method as described in any one of claims 1 to 7.

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