Cloud control method, system and equipment for feeding equipment and medium

Abstract

The invention discloses a cloud control method, system and device for feeding equipment and a medium. Through full-process control of data uploading, cloud storage and data execution, rapid issuing and adaptation of the parameter instruction of the feeding equipment are realized, compared with a traditional scheme, the scheme does not need to re-debug the mechanical structure of the equipment for different materials, rapid switching can be carried out through a universal data compression format packet transmitted by the cloud to adapt to different types of materials, and the efficiency is improved. The method has the advantages of high equipment flexibility, high model changing efficiency and strong visual identification robustness, and is suitable for small-batch and multi-variety production requirements in the electronic industry.

Description

Technical Field

[0001] This application relates to the field of industrial automation technology, specifically to a cloud-based control method, system, device, and medium for a material feeding device. Background Technology

[0002] PCB terminals are essential basic components in the electronics industry, playing an extremely important role.

[0003] Currently, the industry lacks universal automatic feeding equipment for PCB terminals. When encountering complex terminal components, manual insertion is usually used. This method is not only inefficient, but also leads to a decrease in production quality due to human error.

[0004] The relevant material feeding equipment generally suffers from significant defects such as poor equipment flexibility and insufficient robustness of visual recognition, which severely restricts the adaptability and reliability of the production line. Poor equipment flexibility mainly stems from insufficient standardization and adaptation design of mechanical structures, motion control programs, and tooling fixtures, resulting in equipment that can only handle workpieces of fixed specifications and models, unable to flexibly cope with the production needs of multiple varieties and small batches. Insufficient robustness of visual recognition further exacerbates the problem. As the core component for material detection and positioning, the performance of the vision component is significantly reduced by various environmental factors. Due to the limited feature extraction capability of the vision algorithm, imperfect environmental interference resistance design, and insufficient training sample coverage, the system is highly susceptible to changes in lighting, workpiece surface reflection or contamination, camera shooting angle deviation, and background complexity during actual operation. These interferences prevent the vision algorithm from stably and accurately identifying the posture and spatial position of materials, frequently resulting in false detections or missed detections, thus affecting the accuracy of subsequent grasping and conveying operations.

[0005] Based on the above reasons, this application proposes a cloud-based control method, system, equipment, and medium for a material feeding device. Summary of the Invention

[0006] This application provides a cloud-based control method, system, device, and medium for a material feeding device, aiming to solve the problems of poor parameter reusability between different devices, low cross-platform adaptation efficiency, and insufficient visual positioning stability in scenarios involving the feeding of multiple PCB terminal models, where the feeding parameters are highly dependent on the physical reference of a specific device.

[0007] In a first aspect, embodiments of this application provide a control method for a feeding device, which is applied to the feeding device, the feeding device including a feeding information processing component, a visual positioning component, a motion control component and a communication component, and the method is associated with a cloud server; The method includes: Material characteristic data, equipment baseline data, and operating data are collected through visual positioning components and motion control components to generate formula data and general relative displacement vectors. The formula data, general relative displacement vector, and general material information are encapsulated into a general data compression format package. The general data compression format package is uploaded to the cloud server through the communication component, and the cloud server stores it in an indexed format. The communication component obtains the general data compression format package from the cloud service, parses it, and then performs an adaptation operation.

[0008] In a preferred embodiment, the material characteristic data includes: material geometric features, contour morphology, and attitude data; The equipment reference data includes: the material feeding reference coordinates of the feeding equipment and the material picking height reference data; The operational data includes: displacement feedback data and equipment operating status parameter data.

[0009] As a preferred embodiment, the step of generating the universal relative displacement vector includes: The difference between the material pick-up and release coordinates in the formula data and the material release reference coordinates of the feeding equipment is calculated using the benchmark compensation normalization algorithm. The difference between the material pick-up height and the material pick-up height reference of the feeding equipment is also calculated. Based on the above differences, a general relative displacement vector is obtained.

[0010] In one preferred embodiment, the general basic information of the material includes: material geometric feature parameters, feeder model information, and software version number information.

[0011] In a preferred embodiment, the steps of performing the adaptation operation include: The communication component downloads the general data compression format package from the cloud server to the local storage, parses the general data compression format, and extracts the general relative displacement vector. By combining the equipment's own material feeding reference coordinates and material picking height reference, reverse compensation calculation is performed on the general relative displacement vector; The driving instructions adapted to the device are obtained by matrix operations, and the device actuator is driven to complete the adaptation process based on the driving instructions.

[0012] In a preferred embodiment, the method further includes: By presetting various stable thresholds through the material feeding information processing component, the material feeding equipment is controlled to continuously produce a preset amount of material, and the difference between the finished product qualification rate of the trial production and the preset qualification rate threshold is obtained. The fluctuation range of vacuum adsorption pressure and motion positioning error of the material feeding equipment when conveying materials are also checked. The material feeding information processing component controls the material feeding equipment to sequentially perform parameter compatibility verification, production stability verification, and finished product qualification rate verification. If the results of each verification meet the stability threshold, the formula data is locked.

[0013] In a preferred embodiment, the method further includes a formula fine-tuning step: Trial production is carried out based on the restored drive command. If the finished product qualification rate of the trial production does not reach the stable threshold, or the equipment operating parameters exceed the preset error range, the material feeding information processing component adaptively adjusts the material picking and placing coordinate parameters or vacuum adsorption pressure parameters until the production status of the trial production reaches the stable threshold.

[0014] Secondly, embodiments of this application also provide a cloud-based control system for a feeding device. It includes a material supply control module, a transfer module, a communication module, and a cloud server, wherein the cloud server is equipped with a cloud management module; The feeding control module includes a recipe construction module and a data packaging module. The transfer module includes a local reconstruction module, a local memory, and an actuator. The feeding control module is communicatively connected to the visual positioning component and the motion control component. The collaborative working logic of each module is as follows: The formula construction module acquires target material feature data and feeding equipment baseline data through visual positioning component and motion control component, performs material feature modeling on target material, divides material feature hierarchy and configures formula data accordingly, and locks the formula data after trial production verification until the production state reaches the stable threshold. The data packaging module is used to perform difference calculation on the formula data and the reference data of the feeding equipment based on the benchmark compensation normalization algorithm, extract the general relative displacement vector, and encapsulate the general relative displacement vector and the general basic information of the material into a general data compression format package. The cloud management module is used to receive the general data compression format package through the communication module, index and store it, and record the full life cycle management data of the formula data and the equipment operation status data of the feeding control module. The local reconstruction module is used to download a matching general data compression format package from the cloud server through the communication module, parse it, and perform reverse compensation calculation in combination with the equipment reference data of the transfer module itself to restore and adapt the drive instructions of the transfer module, and drive the actuator to complete the feeding operation. The system is used to execute the cloud control method for the feeding equipment as described in any one of claims 1-7.

[0015] Thirdly, embodiments of this application also provide a computer device, including a processor, a memory, and a network interface. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor is communicatively connected to the memory and the network interface, and the processor executes the machine-readable instructions to perform all the steps of the cloud control method for the feeding device as described in any one of claims 1 to 7.

[0016] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer-executable non-volatile program code that causes the processor to execute all the steps of the cloud control method for the feeding device according to any one of claims 1 to 7.

[0017] This application achieves rapid issuance and adaptation of material feeding equipment parameter commands through full-process control of data upload, cloud storage, and data execution. Compared with traditional solutions, this solution does not require re-adjustment of the equipment's mechanical structure for different materials. It can quickly switch and adapt to different models of materials through a universal data compression format package transmitted in the cloud. It has the advantages of high equipment flexibility, fast changeover efficiency, and strong visual recognition robustness, and is suitable for the small-batch, multi-variety production needs of the electronics industry. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0021] Figure 1 A flowchart illustrating the cloud-based control method for a feeding device provided in this application embodiment; Figure 2 The adaptation operation flowchart provided for the embodiments of this application; Figure 3 A flowchart of the fine-tuning steps provided for embodiments of this application; Figure 4 Schematic diagram of the structure of the feeding device provided in the embodiments of this application Figure 1 ; Figure 5 Schematic diagram of the structure of the feeding device provided in the embodiments of this application Figure 2 .

[0022] The attached figures are labeled as follows: 100. Material feeding information processing component; 200. Transfer component; 300. Vision positioning component; 301. Vision sensor; 400. Feeder; 500. Motion control component; 501. Robotic arm; 502. Nozzle component. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0025] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0029] Please see Figure 1 This application proposes a cloud-based control method for a feeding device. Its overall architecture comprises a feeding information processing component 100, a relay component 200, a vision positioning component 300, a motion control component 500, and a communication module. The method is associated with a cloud server. The feeding information processing component 100 integrates a recipe construction module and a data packaging module. The relay component 200 deploys a local reconstruction module, a local memory, and an actuator. The feeding information processing component 100, the vision positioning component 300, and the motion control component 500 are connected via electrical circuits or an internal bus to acquire and process relevant data and operating parameters in real time.

[0030] The cloud control method of this application consists of two core stages: data uploading and data execution. The specific implementation process is as follows: S100, Data processing, packaging, and uploading stage; S101. Collect material characteristic data, equipment reference data and operating data through visual positioning components and motion control components, and generate formula data and general relative displacement vector; In the initial stage of material modeling and data acquisition performed by the material information processing component, the formula construction module first obtains target material characteristic data, equipment reference data and operating data through the visual positioning component 300 and motion control component 500, and completes physical reference calibration and material feature extraction.

[0031] The physical reference calibration process involves the motion control component 500 driving the nozzle module 502 to move in three-dimensional space, and using the high-precision alignment camera in the vision positioning component 300 to scan the reference Mark point on the feeder 400. The vision positioning component 300 acquires image data containing the Mark point, identifies the center geometric position of the Mark point through the edge detection operator, extracts its pixel coordinate data in the image coordinate system, and then combines it with the calibration intrinsic parameter matrix pre-stored by the camera to convert the pixel coordinates into the feeding reference coordinates (i.e., the core parameters of the equipment reference data) in the physical coordinate system of the feeding device.

[0032] Meanwhile, the material picking height reference is determined by driving the Z-axis module downward through the motion control component 500. When the suction nozzle contacts the material bearing surface, the pressure sensor installed at the end of the suction nozzle module 502 senses the instantaneous pressure change and generates an electrical signal. The central controller of the material feeding information processing component captures the Z-axis encoder value at the moment the signal is triggered, thereby locking the material picking height reference.

[0033] The acquisition of operational data is achieved through the displacement sensor of the motion control component 500, which provides real-time feedback on the motion trajectory data and acceleration / deceleration time parameters of the nozzle module 502. At the same time, the device sensor network collects equipment operating status parameters such as vacuum adsorption pressure and servo motor operating current, forming a complete operational dataset.

[0034] After data acquisition is completed, the formula construction module performs material feature layer division based on the material feature data (including material geometric features, contour morphology, and attitude data) collected by the vision positioning component 300. The physical attribute layer stores material geometric features (such as the length and width dimensions of the minimum bounding rectangle of the material terminals and the pin spacing distribution) and contour morphology (the outer contour of the terminals extracted by the Canny operator); the process control layer defines the dynamic parameters of the nozzle action, such as the trigger voltage threshold of the negative pressure sensor; and the coordinate logic layer stores the initial offset data of the material's center of gravity relative to the geometric center of the nozzle (i.e., attitude data).

[0035] S102. Encapsulate the formula data, general relative displacement vector and general material basic information into a general data compression format; When configuring formula data, the formula construction module sets motion control parameters (such as the pulse output frequency of each motion axis, the slope of the acceleration and deceleration curve), vacuum adsorption pressure parameters, and material pick-up and drop coordinate parameters to form initial formula data.

[0036] The material supply information processing component enters the trial production verification phase to determine whether the production status has reached the preset stability threshold. The stability threshold is defined as follows: the finished product qualification rate of the material supply equipment continuously producing a preset quantity (fifty to one hundred pieces) under the control of the material supply information processing component is not lower than the preset qualification rate threshold, and the fluctuation range of the vacuum adsorption pressure and the motion positioning error of the material supply equipment during material conveying are both within the preset error range. During the trial production verification process, the material supply information processing component controls the material supply equipment to perform parameter adaptability verification, production stability verification, and finished product qualification rate verification. The visual positioning component 300 captures images of the assembled terminals in real time and calculates the assembly deviation between their actual position and the theoretical design position. The material feeding information processing component simultaneously collects negative pressure fluctuation data fed back by the vacuum pressure sensor and positioning error data from the motion control component 500. When the assembly deviation fluctuates within the preset tolerance range and the negative pressure fluctuation slope and positioning error both meet the preset requirements, the verification is deemed successful, and the formula construction module locks the formula data and transmits it to the data packaging module.

[0037] The data packaging module encapsulates the general relative displacement vector with the material's general basic information (including material model, number of terminal pins, feeder 400 adapter model, and software version number). The module uses binary serialization to convert this heterogeneous data into a continuous data stream and encapsulates it according to the frame structure defined by common industrial communication protocols (such as TCP / IP or MQTT). A data length check bit is added to the frame header, and a cyclic redundancy check (CRC) code is added to the frame tail. To improve transmission efficiency, the data compression component executes the Deflate compression algorithm to generate a general data compression format package (such as a zip archive).

[0038] S103. Upload the general data compression format package to the cloud server through the communication component, and the cloud server will index and store it. The data packaging module, based on a benchmark compensation normalization algorithm, processes the formula data and equipment benchmark data to generate a universal relative displacement vector. Specifically, the module reads the material handling coordinates (X-axis, Y-axis, Z-axis coordinates) and material handling height data from the formula data. Using the benchmark compensation normalization algorithm, it calculates the differences between the material handling coordinates and the material handling benchmark coordinates, and the differences between the material handling height and the material handling height benchmark. Vector subtraction is then used to eliminate accumulated errors generated during the mechanical assembly of the feeding equipment (such as lead screw manufacturing tolerances and guide rail installation tilt angles). This results in a universal relative displacement vector that represents only the pure geometric spatial relationship of the material relative to the equipment benchmark, thus decoupling it from the physical properties of the feeding equipment.

[0039] The material supply information processing component uploads a common data compression format package to the cloud server based on the communication component, where it is indexed and stored. The cloud management module constructs a multi-level index directory structure, using the material model as the primary index key and the feeder 400 model as the secondary index key, mapping the physical storage path of the compressed package to the index keys. Simultaneously, the cloud management module records the entire lifecycle management data of the formula (including the upload time of the material supply information processing component, the feeding device identifier, and the formula update log) and the operating status data of the material supply information processing component (including ambient temperature data, cumulative working time data, and servo motor load curve data). This data is stored in key-value pairs in log files associated with the compressed package, providing data support for subsequent accuracy prediction.

[0040] S200, Data Acquisition and Parsing Execution Phase; S201. Obtain a general data compression format package from the cloud service through the communication component, parse it, and perform an adaptation operation; When the relay component 200 needs to produce PCB terminals of the same model, it sends a data request command to the cloud server through the communication module (which works in conjunction with the communication component of the feeding equipment). The cloud management module retrieves the corresponding general data compression format package based on the material model in the request and distributes it. After the relay component 200 obtains the compressed package based on the communication component, it performs an adaptation operation.

[0041] The adaptation process specifically includes: S2011, the transfer component 200 downloads the general data compression format package to the local storage and parses it to extract the general relative displacement vector generated by the material feeding information processing component; S2012, the transfer component 200 starts the self-calibration program, using its own displacement sensor and alignment camera to obtain the current physical reference (its own feeding reference coordinates and picking height reference), and performs reverse compensation calculation in combination with the reference. S2013. A homogeneous transformation matrix is ​​constructed through the built-in matrix operation unit. The general relative displacement vector is added to its own reference data. At the same time, the rotation matrix is ​​used to correct the possible horizontal rotation deviation between the two devices, and the material pick-up and put-down coordinates of the physical space of the transfer component 200 are restored to obtain the material pick-up and put-down coordinates of the transfer component 200. The drive command adapted to itself is generated. Finally, the transfer component 200 drives the actuator (such as the robotic arm 501 and the suction module 502) to complete the material feeding operation based on the drive command.

[0042] S300: After generating the drive command, the transfer component 200 does not immediately enter mass production, but instead performs a formula fine-tuning step. S301, the transfer component 200 performs trial production based on the restored drive command, and the vision positioning component 300 immediately performs imaging after picking up the material and calculates the actual offset of the material center relative to the nozzle center. S302. If the finished product qualification rate does not reach the stable threshold or the equipment operating parameters exceed the error range, adjust the material pick-up and drop-out coordinates or vacuum adsorption pressure parameters based on the material feeding information processing component, without regenerating the formula data, until the production status is stable.

[0043] Throughout the entire process described above, the visual positioning component 300 plays a crucial closed-loop feedback role: during the data processing, packaging, and uploading stage, the material characteristic data it collects provides core input for formula construction; during the data acquisition, parsing, and execution stage, it is not only used for the initial benchmark calibration of the transfer component 200, but also for dynamic compensation during the production process—when material position drift caused by the vibration of the feeder 400 is detected, the local reconstruction module will adjust the target position register of the motion controller in real time to achieve dynamic alignment.

[0044] The data transmission link, formed by the collaborative communication components and modules, adopts a publish-subscribe model based on a common industrial communication protocol (such as MQTT). The material supply information processing component acts as the publisher, pushing general data compression format packages to specific topics. The cloud server acts as the agent, forwarding messages and persistently storing them. The relay component 200 acts as the subscriber, obtaining updates in real time. In industrial environments with limited network bandwidth, general data compression format packages (such as zip format) can reduce the size of formula data to less than 30% of its original size, reducing delivery latency.

[0045] This system decouples control parameters, which were originally coupled to the equipment hardware, into independent logic modules through deep division of material characteristics. Combined with benchmark compensation normalization algorithm and reverse compensation calculation, it realizes cross-device reuse of parameters. The introduction of cloud server enables full life cycle traceability of formula data. This technical path based on "data upload-cloud storage-data execution" provides highly reliable technical support for flexible material supply for multiple models in the field of electronic manufacturing.

[0046] Before uploading data, the central controller of the material supply information processing component performs a compatibility check on the hardware version of the relay component 200. When the data packaging module encapsulates a general data compression format package, it writes the minimum required firmware version number into the general basic information of the material. When the relay component 200 parses the compressed package, the local reconstruction module first reads its own firmware version and compares it with the requirements in the compressed package. If the versions do not match, the local reconstruction module will request the cloud server through the communication component to call the corresponding logic conversion plugin to perform cross-version mapping of motion control parameters, ensuring that the generated drive instructions can be correctly recognized and executed by the underlying hardware of the relay component 200.

[0047] Furthermore, for PCB terminals, which are sensitive to static electricity and pressure, the vacuum adsorption pressure parameters in the process control layer have dynamic adjustment characteristics: at the moment of suction, the system generates an extremely high vacuum by increasing the duty cycle of the negative pressure pump to overcome the adhesion between the material and the strip; during the high-speed movement phase, the system finely adjusts the negative pressure in real time according to the acceleration curve to prevent material from falling off due to high-speed inertia; during the unloading phase, the system switches to positive pressure blowing mode by controlling the solenoid valve, using airflow to quickly break the vacuum layer and ensure that the terminal can accurately land on the PCB pad. These fine control logics are all encapsulated in a process parameter package outside of the general relative displacement vector, and flow between different devices along with the normalized coordinates.

[0048] In addition, the system has a comprehensive anomaly handling mechanism: during data upload or download, if the cyclic redundancy check fails or decompression fails, the communication component and communication module will immediately trigger a retransmission request; if, during the formula fine-tuning stage, the visual positioning component 300 continuously detects an offset exceeding the compensation range, the feeding information processing component 100 will automatically lock the movement of the robotic arm 501 and send a fault diagnosis request to the cloud management module. The cloud management module will compare the operating condition data of the equipment, analyze whether the positioning failure is caused by guide rail wear or camera loosening, and push specific maintenance suggestions to maintenance personnel, further improving the robustness of the system.

[0049] Data Upload: The formula construction module of the material supply information processing component uses the industrial camera of the vision positioning component 300 to image the new model PCB terminal, extract the geometric features, contour shape and posture data of the material, and combine it with the displacement feedback data of the motion control component 500, the material feeding reference coordinates, the material picking height reference data and the equipment operating status parameter data to complete the material modeling and configure the formula data; after trial production verification (parameter adaptability verification, production stability verification, finished product qualification rate verification) and reaching the preset stability threshold, the formula data is locked.

[0050] The data packaging module uses a benchmark compensation normalization algorithm to calculate the difference between the material picking and releasing coordinates and the material releasing benchmark coordinates, as well as the difference between the material picking height and the material picking height benchmark, in the formula data to obtain a general relative displacement vector. This vector and the general basic information of the materials are then packaged into a zip format compressed package that conforms to the TCP / IP protocol and uploaded to the cloud server through the communication component, where the cloud server performs indexed storage.

[0051] Data execution: The relay component 200 sends a data request to the cloud server through the communication module, obtains the corresponding general data compression format package and downloads it to the local storage; it parses the compressed package to extract the general relative displacement vector, and obtains its own feeding reference coordinates and picking height reference through the self-calibration program.

[0052] The transfer component 200 performs reverse compensation calculations and matrix operations to restore and adapt the drive instructions to itself, and drives the actuator to complete the material feeding operation. Based on the drive instructions, trial production is carried out. If the finished product qualification rate does not reach the stable threshold or the equipment operating parameters exceed the error range, the material feeding information processing component finely adjusts the material picking and unloading coordinates or vacuum adsorption pressure parameters until the production status is stable, locks the localized formula, and starts mass production.

[0053] In summary, through the full-process control of data uploading, cloud storage, and data execution, the rapid issuance and adaptation of parameter commands for the material feeding equipment are achieved, eliminating the need to readjust the mechanical structure of the equipment for different materials, and adapting to the small-batch, multi-variety production needs of the electronics industry.

[0054] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0057] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0059] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0060] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Since these modifications and variations fall within the scope of the claims and their equivalents, this application also intends to include these modifications and variations.

[0061] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cloud-based control method for a material feeding device, characterized in that, The method is applied to a feeding device, which includes a feeding information processing component, a visual positioning component, a motion control component, and a communication component, and is associated with a cloud server. The method includes: Material characteristic data, equipment baseline data, and operating data are collected through visual positioning components and motion control components to generate formula data and general relative displacement vectors. The formula data, general relative displacement vector, and general material information are encapsulated into a general data compression format package. The general data compression format package is uploaded to the cloud server through the communication component, and the cloud server stores it in an indexed format. The communication component obtains the general data compression format package from the cloud service, parses it, and then performs an adaptation operation.

2. The cloud-based control method for the feeding equipment as described in claim 1, characterized in that, The material characteristic data includes: material geometric features, contour shape and posture data; The equipment reference data includes: the material feeding reference coordinates of the feeding equipment and the material picking height reference data; The operational data includes: displacement feedback data and equipment operating status parameter data.

3. The cloud-based control method for the feeding equipment as described in claim 1, characterized in that, The steps for generating the universal relative displacement vector include: The difference between the material pick-up and release coordinates in the formula data and the material release reference coordinates of the feeding equipment is calculated using the benchmark compensation normalization algorithm. The difference between the material pick-up height and the material pick-up height reference of the feeding equipment is also calculated. Based on the above differences, a general relative displacement vector is obtained.

4. The cloud-based control method for the feeding equipment as described in claim 1, characterized in that, The general basic information of the material includes: material geometric feature parameters, feeder model information, and software version number information.

5. The cloud-based control method for the feeding equipment as described in claim 1, characterized in that, The steps for performing the adaptation operation include: The communication component downloads the general data compression format package from the cloud server to the local storage, parses the general data compression format, and extracts the general relative displacement vector. By combining the equipment's own material feeding reference coordinates and material picking height reference, reverse compensation calculation is performed on the general relative displacement vector; The driving instructions adapted to the device are obtained by matrix operations, and the device actuator is driven to complete the adaptation process based on the driving instructions.

6. The cloud-based control method for the feeding equipment as described in claim 5, characterized in that, The method further includes: By presetting various stable thresholds through the material feeding information processing component, the material feeding equipment is controlled to continuously produce a preset amount of material, and the difference between the finished product qualification rate of the trial production and the preset qualification rate threshold is obtained. The fluctuation range of vacuum adsorption pressure and motion positioning error of the material feeding equipment when conveying materials are also checked. The material feeding information processing component controls the material feeding equipment to sequentially perform parameter compatibility verification, production stability verification, and finished product qualification rate verification. If the results of each verification meet the stability threshold, the formula data is locked.

7. The cloud-based control method for the feeding equipment as described in claim 6, characterized in that, The method also includes a formulation fine-tuning step: Trial production is carried out based on the restored drive command. If the finished product qualification rate of the trial production does not reach the stable threshold, or the equipment operating parameters exceed the preset error range, the material feeding information processing component adaptively adjusts the material picking and placing coordinate parameters or vacuum adsorption pressure parameters until the production status of the trial production reaches the stable threshold.

8. A cloud-based control system for a material feeding device, characterized in that, It includes a material supply control module, a transfer module, a communication module, and a cloud server, wherein the cloud server is equipped with a cloud management module; The feeding control module includes a recipe construction module and a data packaging module. The transfer module includes a local reconstruction module, a local memory, and an actuator. The feeding control module is communicatively connected to the visual positioning component and the motion control component. The collaborative working logic of each module is as follows: The formula construction module acquires target material feature data and feeding equipment baseline data through visual positioning component and motion control component, performs material feature modeling on target material, divides material feature hierarchy and configures formula data accordingly, and locks the formula data after trial production verification until the production state reaches the stable threshold. The data packaging module is used to perform difference calculation on the formula data and the reference data of the feeding equipment based on the benchmark compensation normalization algorithm, extract the general relative displacement vector, and encapsulate the general relative displacement vector and the general basic information of the material into a general data compression format package. The cloud management module is used to receive the general data compression format package through the communication module, index and store it, and record the full life cycle management data of the formula data and the equipment operation status data of the feeding control module. The local reconstruction module is used to download a matching general data compression format package from the cloud server through the communication module, parse it, and perform reverse compensation calculation in combination with the equipment reference data of the transfer module itself to restore and adapt the drive instructions of the transfer module, and drive the actuator to complete the feeding operation. The system is used to execute the cloud control method for the feeding equipment as described in any one of claims 1-7.

9. A computer device comprising a processor, a memory, and a network interface, wherein the memory stores machine-readable instructions executable by the processor, characterized in that: When the computer device is running, the processor is communicatively connected to the memory and the network interface, and the processor executes the machine-readable instructions to perform all the steps of the cloud control method for the feeding device as described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, The computer-readable medium stores computer-executable non-volatile program code that causes the processor to perform all the steps of the cloud control method for the feeding device according to any one of claims 1 to 7.

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