A multi-station cooperative type precision assembly adaptive assembly control method and system

Abstract

The application discloses a kind of multi-station collaborative precision assembly adaptive assembly control method and system, it is related to mechanical manufacturing technical field, the system includes execution unit group, communication control network module and cloud computing server;The execution unit group contains at least first active execution unit and first driven execution unit, forms multi-station execution unit set, and first active execution unit, first driven execution unit and all extension station units are connected with cloud computing server by communication control network module, this method includes the following specific steps: S1, initialization configuration;S2, real-time monitoring;S3, distributed collaborative decision-making;S4, execution feedback;S5, cloud correction and S6, system iteration optimization.The multi-station collaborative precision assembly adaptive assembly control method and system support multi-station extension, configure edge node for each station, and equal data broadcast and information sharing can be realized between each station.

Description

Technical Field

[0001] This invention relates to the field of mechanical manufacturing technology, specifically to a multi-station collaborative precision assembly adaptive assembly control method and system. Background Technology

[0002] Machinery manufacturing is an industrial production process that uses mechanical equipment and technical means to process raw materials into various mechanical products. It is an important foundation and pillar industry of the national economy. In the field of precision machinery assembly manufacturing, multi-station assembly lines are used to complete continuous assembly operations.

[0003] Currently, traditional multi-station assembly control systems mostly adopt a centralized cloud control architecture, with information isolated between each station. They mostly adopt a control mode of independent detection and correction for each station, lacking data communication and collaborative linkage capabilities between stations. Therefore, they can only complete single deviation correction and cannot identify the coupling deviations and cumulative errors generated during multi-station assembly, which can easily lead to problems such as out-of-tolerance precision assembly and poor product consistency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-station collaborative precision assembly adaptive assembly control method and system, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-station collaborative precision assembly adaptive assembly control system, the system comprising an execution unit group, a communication control network module, and a cloud computing server; The execution unit group includes at least a first active execution unit and a first passive execution unit, forming a multi-station execution unit set, and the first active execution unit, the first passive execution unit and all extended station units are connected to the cloud computing server through a communication control network module; The first active execution unit, the first passive execution unit, and all extended workstation units within the execution unit group each have a built-in edge computing module. Each edge computing module stores basic assembly process rules, workstation collaborative constraint logic, and communication protocols, forming an independent distributed control node. Each edge node completes self-organizing network through the communication control network module, establishes low-latency broadcast communication links between workstations, and maintains connection with the cloud computing server; The communication control network serves as a data transmission bridge between the execution unit group and the cloud computing server, enabling real-time information interaction. The cloud computing server includes a monitoring module and an adaptive control module. The cloud computing server is responsible for receiving the initial and real-time operation data of the active execution unit, performing task planning and operation status correction, and issuing instructions to the passive execution unit.

[0006] Furthermore, the monitoring module is used to monitor the operating status of the execution unit group; the adaptive control module includes an adjustment control module and a coordinated operation module; the adjustment control module is used to dynamically adjust the action parameters of the driven unit according to the operating status of the active unit to ensure assembly accuracy and efficiency; the coordinated operation module realizes collaborative operation among multiple workstations and optimizes the production process through a distributed control strategy.

[0007] A method for a multi-station collaborative precision assembly adaptive assembly control system, comprising the following specific steps: S1. Initialization Configuration The system collects initial assembly data from the execution unit group, including but not limited to part pose, assembly datum, and initial workstation status, and transmits it to the cloud computing server via the communication control network. After receiving the data, the cloud computing server constructs an initial assembly task model, generates initial multi-workstation collaborative operation instructions, and issues the initial assembly task model, workstation process timing constraints, and deviation compensation datum to all edge nodes within the execution unit group, thus completing the system initialization configuration. S2, Real-time Monitoring After assembly starts, the edge nodes of each workstation autonomously collect the initial and real-time operating data of their workstation. All workstation nodes receive and store the status data of other workstations, forming a local global assembly status view, and upload it to the cloud computing server. S3, Distributed Collaborative Decision Making The cloud computing server employs a distributed control strategy, optimizing the timing and action connections between multiple workstations based on the real-time status and process logic of each workstation. When an edge node at a workstation detects assembly deviations or changes in operating conditions, a collaborative decision-making process is triggered. Based on locally stored process rules and received data from other workstations, the node autonomously analyzes the scope and extent of the deviation's impact on subsequent processes, enabling collaborative operations between workstations and avoiding process conflicts and cycle time delays. S4, Execution Feedback Each workstation executes the adjusted action parameters and feeds back the execution results to its own edge node. At the same time, it broadcasts the status data after execution to other workstations and uploads it to the cloud computing server through the communication control network module.

[0008] Furthermore, the multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S5, Cloud-based Correction The monitoring module of the cloud computing server periodically receives the status data of all workstation nodes. The adaptive control module performs a second-level in-depth deviation analysis on the data of this assembly process, compares the theoretical assembly benchmark with the actual assembly deviation residual, identifies the coupling deviation and cumulative error that cannot be eliminated by a single adjustment, and performs a second correction on the control parameters and coordination strategies of each execution unit to compensate for the cumulative deviation generated by multi-workstation assembly.

[0009] Furthermore, the multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S6, System Iterative Optimization The cloud computing server continuously collects assembly data and collaborative control results from all workstations in each round, iteratively updates multi-workstation collaborative control rules, deviation compensation thresholds, and action adjustment weights, and continuously optimizes the distributed collaborative operation logic to achieve adaptive iterative optimization of the entire precision assembly process, ensuring long-term assembly accuracy consistency and system operation stability. The optimized strategy is then distributed to each edge node to update the control logic stored locally, enabling continuous iteration of edge distributed decision-making and cloud-based global optimization.

[0010] Furthermore, in step S2, during real-time monitoring, the edge nodes of the first active execution unit are used to collect the initial pose, dimensional deviation, and assembly reference data of the parts, and broadcast them to all associated workstations through the communication control network module; the edge nodes of each passive execution unit are used to synchronously collect the real-time status data of their own workstation, including but not limited to assembly progress, pressing force, displacement deviation, and equipment operating status, and periodically broadcast them to other workstation nodes.

[0011] Furthermore, in step S3, during the distributed collaborative decision-making process, if the deviation only affects this workstation, the action parameters are directly corrected through the adjustment control module of this unit; if the deviation is a cross-workstation coupling effect, a collaborative adjustment request is automatically sent to the associated workstation, including the deviation type, compensation requirements, and constraints; after receiving the adjustment request, the edge node of the associated workstation autonomously calculates the appropriate action parameter adjustment scheme based on its own workstation status and process constraints, ensuring the continuity of the production process without waiting for instructions from the cloud computing server.

[0012] Furthermore, in step S5, during the cloud-based correction process, if the multi-station collaborative control results meet the assembly accuracy and efficiency requirements, only the global process model is updated, without interfering with the autonomous decision-making of edge nodes.

[0013] Furthermore, in step S5, during the cloud-based correction process, if cross-workstation collaborative deviations or abnormal working conditions occur, such as the inability to meet accuracy requirements even after multi-workstation synchronous adjustments, the cloud computing server generates a global state correction instruction based on the deviation analysis results. This instruction performs secondary corrections on the control parameters and collaborative strategies of each execution unit and issues a global correction instruction to distribute the corrected optimized parameters to each execution unit. This compensates for the accumulated deviations generated by multi-workstation assembly and uniformly optimizes the process rules and collaborative strategies of each edge node.

[0014] This invention provides a multi-station collaborative precision assembly adaptive assembly control method and system, which has the following beneficial effects: This multi-station collaborative precision assembly adaptive assembly control method and system, based on general control systems and methods, is applicable to precision assembly scenarios in multiple fields. It configures edge nodes for each station, enabling peer-to-peer data broadcasting and information sharing between stations. Local deviation judgment and autonomous adjustment can be completed without relying on cloud computing server commands. When cross-station coupling deviations occur, the associated stations can autonomously initiate collaborative adjustment requests and complete parameter adaptation, avoiding the cloud computing power bottlenecks and high command latency issues of traditional centralized control. For instantaneous, small assembly deviations, the stations quickly correct locally, significantly improving assembly response speed and ensuring smooth assembly cycle. Furthermore, the cloud computing server performs secondary in-depth deviation analysis based on feedback data from all stations, identifying cumulative errors and residual coupling deviations generated by multi-station linkage, and completing global parameter correction. Compared to traditional single-adjustment methods, this approach can eliminate assembly deviations step-by-step, strictly controlling precision indicators such as assembly coaxiality and fit clearance, and adapting to the requirements of high-precision assembly production.

[0015] This multi-station collaborative precision assembly adaptive assembly control method and system supports multi-station expansion and allows for adjustment of execution unit configurations according to actual needs. It continuously collects operational data from each station through a real-time monitoring module, dynamically identifies changes in operating conditions, employs autonomous station compensation for ordinary deviations, and initiates cloud-based global correction for extreme out-of-tolerance conditions. It handles different levels of anomalies in a tiered manner, preventing single deviations from causing line downtime or assembly scrap. This effectively solves the problems of poor adaptability to part tolerance fluctuations and low fault tolerance in traditional assembly systems. When a deviation adjustment action occurs at a station, related stations automatically delay, advance, or fine-tune their work rhythm, avoiding process interference and cycle time interruptions between stations. While ensuring assembly accuracy, it stabilizes station intervals, reduces ineffective waiting time, and improves the smoothness and efficiency of the entire assembly line. Furthermore, the cloud computing server continuously aggregates assembly data from the entire process, iteratively updates collaborative control rules, deviation compensation thresholds, and action adjustment weights, and continuously optimizes the distributed collaborative operation logic. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating the multi-station collaborative precision assembly adaptive assembly control method and system of the present invention. Figure 2 This is a system framework diagram of a multi-station collaborative precision assembly adaptive assembly control method and system according to the present invention. Detailed Implementation

[0017] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0018] like Figures 1-2 As shown, the present invention provides a technical solution: a multi-station collaborative precision assembly adaptive assembly control system, the system comprising an execution unit group, a communication control network module, and a cloud computing server; The execution unit group includes at least a first active execution unit and a first passive execution unit, forming a multi-station execution unit set, and the first active execution unit, the first passive execution unit and all extended station units are connected to the cloud computing server through a communication control network module; The first active execution unit, the first passive execution unit, and all extended workstation units within the execution unit group each have a built-in edge computing module. Each edge computing module stores basic assembly process rules, workstation collaborative constraint logic, and communication protocols, forming an independent distributed control node. Each edge node completes self-organizing network through the communication control network module, establishes low-latency broadcast communication links between workstations, and maintains connection with the cloud computing server; The communication control network serves as a data transmission bridge between the execution unit group and the cloud computing server, enabling real-time information interaction. The cloud computing server includes a monitoring module and an adaptive control module. The cloud computing server is responsible for receiving the initial and real-time running data of the active execution unit, performing task planning and running status correction, and sending instructions to the passive execution unit. The monitoring module is used to monitor the operating status of the execution unit group; the adaptive control module includes an adjustment control module and a coordinated operation module; the adjustment control module is used to dynamically adjust the action parameters of the driven unit according to the operating status of the active unit to ensure assembly accuracy and efficiency; the coordinated operation module realizes collaborative operation among multiple workstations and optimizes the production process through a distributed control strategy.

[0019] A method for a multi-station collaborative precision assembly adaptive assembly control system, comprising the following specific steps: S1. Initialization Configuration The system collects initial assembly data from the execution unit group, including but not limited to part pose, assembly datum, and initial workstation status, and transmits it to the cloud computing server via the communication control network. After receiving the data, the cloud computing server constructs an initial assembly task model, generates initial multi-workstation collaborative operation instructions, and issues the initial assembly task model, workstation process timing constraints, and deviation compensation datum to all edge nodes within the execution unit group, thus completing the system initialization configuration. S2, Real-time Monitoring After assembly starts, the edge nodes of each workstation autonomously collect the initial and real-time operating data of their workstation. All workstation nodes receive and store the status data of other workstations, forming a local global assembly status view, and upload it to the cloud computing server. S3, Distributed Collaborative Decision Making The cloud computing server employs a distributed control strategy, optimizing the timing and action connections between multiple workstations based on the real-time status and process logic of each workstation. When an edge node at a workstation detects assembly deviations or changes in operating conditions, a collaborative decision-making process is triggered. Based on locally stored process rules and received data from other workstations, the node autonomously analyzes the scope and extent of the deviation's impact on subsequent processes, enabling collaborative operations between workstations and avoiding process conflicts and cycle time delays. S4, Execution Feedback Each workstation executes the adjusted action parameters and feeds back the execution results to its own edge node. At the same time, it broadcasts the status data after execution to other workstations and uploads it to the cloud computing server through the communication control network module.

[0020] The multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S5, Cloud-based Correction The monitoring module of the cloud computing server periodically receives the status data of all workstation nodes. The adaptive control module performs a second-level in-depth deviation analysis on the data of this assembly process, compares the theoretical assembly benchmark with the actual assembly deviation residual, identifies the coupling deviation and cumulative error that cannot be eliminated by a single adjustment, and performs a second correction on the control parameters and coordination strategies of each execution unit to compensate for the cumulative deviation generated by multi-workstation assembly.

[0021] The multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S6, System Iterative Optimization The cloud computing server continuously collects assembly data and collaborative control results from all workstations in each round, iteratively updates multi-workstation collaborative control rules, deviation compensation thresholds, and action adjustment weights, and continuously optimizes the distributed collaborative operation logic to achieve adaptive iterative optimization of the entire precision assembly process, ensuring long-term assembly accuracy consistency and system operation stability. The optimized strategy is then distributed to each edge node to update the control logic stored locally, enabling continuous iteration of edge distributed decision-making and cloud-based global optimization.

[0022] In step S2, during real-time monitoring, the edge nodes of the first active execution unit are used to collect the initial pose, dimensional deviation, and assembly reference data of the parts, and broadcast them to all associated workstations through the communication control network module; the edge nodes of each passive execution unit are used to synchronously collect the real-time status data of their own workstation, including but not limited to assembly progress, pressing force, displacement deviation, and equipment operating status, and periodically broadcast them to other workstation nodes.

[0023] In step S3, during the distributed collaborative decision-making process, if the deviation only affects this workstation, the action parameters are directly corrected through the adjustment control module of this unit; if the deviation has a cross-workstation coupling effect, a collaborative adjustment request is automatically sent to the associated workstation, including the deviation type, compensation requirements and constraints; after receiving the adjustment request, the edge node of the associated workstation autonomously calculates the appropriate action parameter adjustment scheme based on its own workstation status and process constraints, ensuring the continuity of the production process without waiting for instructions from the cloud computing server.

[0024] In step S5, during cloud-based correction, if the multi-station collaborative control results meet the assembly accuracy and efficiency requirements, only the global process model is updated, without interfering with the autonomous decision-making of edge nodes.

[0025] In step S5, during cloud-based correction, if cross-workstation collaborative deviations or abnormal working conditions occur, such as the inability to meet accuracy requirements even after multi-workstation synchronous adjustments, the cloud computing server generates a global state correction instruction based on the deviation analysis results. This instruction performs secondary corrections on the control parameters and collaborative strategies of each execution unit and issues a global correction instruction to distribute the corrected optimized parameters to each execution unit. This compensates for the accumulated deviations generated by multi-workstation assembly and uniformly optimizes the process rules and collaborative strategies of each edge node.

[0026] Example: This embodiment selects an automotive precision transmission assembly as the assembly object. The assembly system includes four assembly stations: the first active pressing station (first active execution unit), the second clearance detection station, the third bearing docking station, and the fourth locking and fixing station (three driven execution units). Each station has a built-in edge computing node and uses industrial Ethernet as the communication control network module to specifically illustrate the implementation of this control method. S1: Initialization Configuration: The No. 1 active pressing station collects initial assembly data such as the pose of the gearbox housing parts, the position of the assembly reference holes, and the initial tooling clamping status of each station, and transmits it to the cloud computing server in real time through the communication control network. After receiving the initial data, the cloud computing server establishes a three-dimensional initial assembly task model of the gearbox assembly, presets the coaxiality tolerance of the assembly reference holes to ≤0.02mm, the standard range of pressing force to 3500N~4200N, and the station process interval to 2.5s. The cloud computing server generates a four-station collaborative operation instruction, sends the initial assembly task model, process timing constraints, and deviation compensation benchmarks to the edge nodes of the four stations, clarifies the order of operations of each station, and the allowable deviation threshold between stations, and completes the system initialization configuration. S2. Real-time Monitoring: After assembly starts, each workstation's edge node autonomously collects its own real-time operating data. Specifically, the edge node of the No. 1 active pressing workstation collects the gearbox housing dimensional deviation and installation reference surface offset, and after preprocessing, broadcasts the data to the other three driven workstations. Driven workstations No. 2, 3, and 4 periodically collect real-time status data such as pressing force, assembly displacement, tooling vibration, and equipment operating current, and broadcast this data to each other. All workstation nodes locally store all status data from the four workstations, forming a local global assembly status view, and simultaneously upload the aggregated data to the cloud computing server. S3. Distributed Collaborative Decision-Making: The edge node of the No. 2 gap detection station detects a gap detection deviation caused by the shell offset. It is determined that this deviation not only affects the gap detection accuracy of this station, but also causes the coaxiality of the subsequent bearing docking to exceed the tolerance, which belongs to the cross-station coupling deviation. The No. 2 station sends a collaborative adjustment request to the No. 3 bearing docking station, indicating that the deviation type is shell reference offset, the compensation requirement is coaxiality correction, and the constraint condition is maximum adjustment range ≤ 0.02mm. After receiving the request, the edge node of the No. 3 station does not need to wait for instructions from the cloud computing server. It independently calculates the trajectory compensation scheme by combining its own tooling position and bearing assembly process constraints, and fine-tunes the lateral displacement of the bearing docking by 0.012mm. The No. 4 locking and fixing station simultaneously optimizes the operation sequence, delays the locking action by 0.8s, avoids process interference, and ensures the continuity of the entire assembly production line without any interruption. S4. Execution Feedback: Each workstation executes the adjustment actions generated by the distributed collaborative decision-making: Workstation 2 completes the gap retest, Workstation 3 completes the bearing offset compensation docking, and Workstation 4 completes the delayed locking fixation; Each workstation stores the assembly execution results, corrected posture deviations, and real-time pressing force data to its own edge node, and broadcasts the updated workstation status data to other workstations; All workstations upload the complete assembly data of this round to the cloud computing server through the communication control network module to complete the assembly operation feedback; S5. Cloud-based Correction: The monitoring module built into the computing server aggregates all feedback data from the four workstations, and the adaptive control module performs a second-level in-depth deviation analysis. By comparing the theoretical assembly benchmark with the actual assembly residual, it is found that after the edge nodes autonomously adjust, the assembly still has a cumulative coaxiality error of 0.007mm, which is a residual coupling deviation caused by multi-workstation linkage. The current coaxiality of the assembled product is 0.018mm, which meets the preset accuracy requirement of ≤0.02mm. The cloud computing server only updates the global process model and records the deviation compensation parameters for this time, without interfering with the autonomous decision-making logic of each edge node. If extreme working conditions occur, such as the shell offset reaching 0.025mm, and the coaxiality still exceeds the tolerance after the autonomous adjustment of each workstation, the cloud computing server generates a global state correction command, uniformly corrects the motion compensation coefficient and pressing force threshold of each workstation, and sends the optimization parameters to all edge nodes to forcibly eliminate cross-workstation abnormal deviations and optimize workstation collaboration strategies. S6. System Iteration and Optimization: The cloud computing server continuously collects current and historical gearbox assembly data, calculates the workstation compensation effect corresponding to different housing offsets, and iteratively updates the multi-workstation collaborative control rules: the compensation threshold for the housing lateral offset range of 0.01mm-0.02mm is optimized to 0.015mm, and the bearing workstation displacement adjustment weight is corrected; the cloud computing server distributes the optimized collaborative strategy to the four workstation edge nodes and updates the control logic stored locally on the nodes; This embodiment achieves rapid deviation compensation through edge-distributed autonomous decision-making and relies on cloud-based global optimization and iteration of process parameters. After assembling 500 sets of gearbox assemblies, the assembly coaxiality defect rate decreased from 0.85% to 0.12%, and the workstation cycle time connection error stabilized within 0.3s. This realizes adaptive iterative optimization of the entire precision assembly process, ensuring long-term assembly accuracy consistency and system operation stability.

[0027] Based on the above, the multi-station collaborative precision assembly adaptive assembly control method of the present invention includes the following specific steps: S1: Initialization Configuration: Collect initial assembly data of the execution unit group, including but not limited to part pose, assembly datum, and initial workstation status, and transmit it to the cloud computing server through the communication control network; After receiving the data, the cloud computing server constructs the initial assembly task model, generates the initial multi-workstation collaborative operation instruction, and issues the initial assembly task model, workstation process timing constraints and deviation compensation datum to all edge nodes in the execution unit group to complete the system initialization configuration; S2. Real-time monitoring: After assembly starts, the edge nodes of each workstation autonomously collect the initial and real-time operating data of their workstation. All workstation nodes receive and store the status data of other workstations, forming a local global assembly status view, and upload it to the cloud computing server. The edge nodes of the first active execution unit are used to collect the initial pose, dimensional deviation, and assembly reference data of the parts, and broadcast them to all related workstations through the communication control network module. The edge nodes of each passive execution unit are used to synchronously collect the real-time status data of their own workstation, including but not limited to assembly progress, pressing force, displacement deviation, and equipment operating status, and periodically broadcast them to other workstation nodes. S3. Distributed Collaborative Decision-Making: The cloud computing server adopts a distributed control strategy. Based on the real-time status and process logic of each workstation, it optimizes the operation sequence and action connection between multiple workstations. When an edge node of a workstation detects an assembly deviation or change in working conditions, it triggers a collaborative decision-making process. The node autonomously analyzes the scope and degree of the deviation's impact on subsequent processes based on locally stored process rules and received data from other workstations, realizing collaborative operation between workstations and avoiding process conflicts and cycle time delays. If the deviation only affects this workstation, the action parameters are directly corrected through the adjustment control module of this unit. If the deviation has a cross-workstation coupling effect, a collaborative adjustment request is automatically sent to the associated workstation, including the deviation type, compensation requirements, and constraints. After receiving the adjustment request, the edge node of the associated workstation autonomously calculates an appropriate action parameter adjustment scheme based on its own workstation status and process constraints, ensuring the continuity of the production process without waiting for instructions from the cloud computing server. S4. Execution Feedback: Each workstation executes the adjusted action parameters and feeds back the execution results to its own edge node. At the same time, it broadcasts the status data after execution to other workstations and uploads it to the cloud computing server through the communication control network module. S5. Cloud-based Correction: The monitoring module of the cloud computing server periodically receives the status data summary of all workstation nodes. The adaptive control module performs a second in-depth deviation analysis on the data of this assembly process, compares the theoretical assembly benchmark with the actual assembly deviation residual, identifies coupling deviations and cumulative errors that cannot be eliminated by a single adjustment, and performs a second correction on the control parameters and collaborative strategies of each execution unit to compensate for the cumulative deviations generated by multi-workstation assembly. If the multi-workstation collaborative control results meet the assembly accuracy and efficiency requirements, only the global process model is updated, without interfering with the autonomous decision-making of edge nodes. If cross-workstation collaborative deviations or abnormal working conditions occur, such as when the accuracy requirements cannot be met even after multi-workstation synchronous adjustment, the cloud computing server generates a global status correction instruction based on the deviation analysis results, performs a second correction on the control parameters and collaborative strategies of each execution unit, and issues a global correction instruction to distribute the corrected optimized parameters to each execution unit to compensate for the cumulative deviations generated by multi-workstation assembly and uniformly optimize the process rules and collaborative strategies of each edge node. S6. System Iterative Optimization: The cloud computing server continuously collects assembly data and collaborative control results from all workstations in each round, iteratively updates multi-workstation collaborative control rules, deviation compensation thresholds, and action adjustment weights, and continuously optimizes the distributed collaborative operation logic to achieve adaptive iterative optimization of the entire precision assembly process, ensuring long-term assembly accuracy consistency and system operation stability; and distributes the optimized strategy to each edge node to update the locally stored control logic, realizing continuous iteration of edge distributed decision-making and cloud-based global optimization.

[0028] This multi-station collaborative precision assembly adaptive assembly control method and system, based on general control systems and methods, is applicable to precision assembly scenarios in multiple fields. It configures edge nodes for each station, enabling peer-to-peer data broadcasting and information sharing between stations. Local deviation judgment and autonomous adjustment can be completed without relying on cloud computing server commands. When cross-station coupling deviations occur, the associated stations can autonomously initiate collaborative adjustment requests and complete parameter adaptation, avoiding the cloud computing power bottlenecks and high command latency issues of traditional centralized control. For instantaneous, small assembly deviations, the stations quickly correct locally, significantly improving assembly response speed and ensuring smooth assembly cycle time. Furthermore, the cloud computing server performs secondary in-depth deviation analysis based on feedback data from all stations, identifying cumulative errors and residual coupling deviations generated by multi-station linkage, and completing global parameter correction. Compared to traditional single-adjustment methods, this system can eliminate assembly deviations step by step, strictly control precision indicators such as assembly coaxiality and fit clearance, and adapt to the production requirements of high-precision assembly. It also supports multi-station expansion, allowing for adjustments to the execution unit configuration based on actual needs. Real-time monitoring modules continuously collect operational data from each station, dynamically identifying changes in operating conditions. For ordinary deviations, station-based autonomous compensation is used; for extreme out-of-tolerance conditions, cloud-based global correction is initiated. Different levels of anomalies are handled in a tiered manner, preventing single deviations from causing line-wide downtime or assembly scrap. This effectively solves the problems of poor adaptability to part tolerance fluctuations and low fault tolerance in traditional assembly systems. When a deviation adjustment action occurs at a station, related stations automatically delay, advance, or fine-tune their work rhythm, avoiding process interference and cycle time interruptions between stations. While ensuring assembly accuracy, it stabilizes station intervals, reduces ineffective waiting time, and improves the smoothness and production efficiency of the entire assembly line. Furthermore, the cloud computing server continuously aggregates assembly data throughout the entire process, iteratively updating collaborative control rules, deviation compensation thresholds, and action adjustment weights, continuously optimizing the distributed collaborative operation logic.

[0029] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A multi-station collaborative precision assembly adaptive assembly control system, characterized in that: The system includes an execution unit group, a communication control network module, and a cloud computing server. The execution unit group includes at least a first active execution unit and a first passive execution unit, forming a multi-station execution unit set, and the first active execution unit, the first passive execution unit and all extended station units are connected to the cloud computing server through a communication control network module; The first active execution unit, the first passive execution unit, and all extended workstation units within the execution unit group each have a built-in edge computing module. Each edge computing module stores basic assembly process rules, workstation collaborative constraint logic, and communication protocols, forming an independent distributed control node. Each edge node completes self-organizing network through the communication control network module, establishes low-latency broadcast communication links between workstations, and maintains connection with the cloud computing server; The communication control network serves as a data transmission bridge between the execution unit group and the cloud computing server, enabling real-time information interaction. The cloud computing server includes a monitoring module and an adaptive control module. The cloud computing server is responsible for receiving the initial and real-time operation data of the active execution unit, performing task planning and operation status correction, and issuing instructions to the passive execution unit.

2. A multi-station collaborative precision assembly adaptive assembly control system, characterized in that: The monitoring module is used to monitor the operating status of the execution unit group; the adaptive control module includes an adjustment control module and a coordinated operation module; the adjustment control module is used to dynamically adjust the action parameters of the driven unit according to the operating status of the active unit to ensure assembly accuracy and efficiency; the coordinated operation module realizes collaborative operation among multiple workstations and optimizes the production process through a distributed control strategy.

3. The method for a multi-station collaborative precision assembly adaptive assembly control system according to any one of claims 1-2, characterized in that, The multi-station collaborative precision assembly adaptive assembly control method includes the following specific steps: S1. Initialization Configuration The system collects initial assembly data from the execution unit group, including but not limited to part pose, assembly datum, and initial workstation status, and transmits it to the cloud computing server via the communication control network. After receiving the data, the cloud computing server constructs an initial assembly task model, generates initial multi-workstation collaborative operation instructions, and issues the initial assembly task model, workstation process timing constraints, and deviation compensation datum to all edge nodes within the execution unit group, thus completing the system initialization configuration. S2, Real-time Monitoring After assembly starts, the edge nodes of each workstation autonomously collect the initial and real-time operating data of their workstation. All workstation nodes receive and store the status data of other workstations, forming a local global assembly status view, and upload it to the cloud computing server. S3, Distributed Collaborative Decision Making The cloud computing server employs a distributed control strategy, optimizing the timing and action connections between multiple workstations based on the real-time status and process logic of each workstation. When an edge node at a workstation detects assembly deviations or changes in operating conditions, a collaborative decision-making process is triggered. Based on locally stored process rules and received data from other workstations, the node autonomously analyzes the scope and extent of the deviation's impact on subsequent processes, enabling collaborative operations between workstations and avoiding process conflicts and cycle time delays. S4, Execution Feedback Each workstation executes the adjusted action parameters and feeds back the execution results to its own edge node. At the same time, it broadcasts the status data after execution to other workstations and uploads it to the cloud computing server through the communication control network module.

4. The multi-station collaborative precision assembly adaptive assembly control system according to claim 3, characterized in that, The multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S5, Cloud-based Correction The monitoring module of the cloud computing server periodically receives the status data of all workstation nodes. The adaptive control module performs a second-level in-depth deviation analysis on the data of this assembly process, compares the theoretical assembly benchmark with the actual assembly deviation residual, identifies the coupling deviation and cumulative error that cannot be eliminated by a single adjustment, and performs a second correction on the control parameters and coordination strategies of each execution unit to compensate for the cumulative deviation generated by multi-workstation assembly.

5. The multi-station collaborative precision assembly adaptive assembly control system according to claim 4, characterized in that, The multi-station collaborative precision assembly adaptive assembly control method also includes the following specific steps: S6, System Iterative Optimization The cloud computing server continuously collects assembly data and collaborative control results from all workstations in each round, iteratively updates multi-workstation collaborative control rules, deviation compensation thresholds, and action adjustment weights, and continuously optimizes the distributed collaborative operation logic to achieve adaptive iterative optimization of the entire precision assembly process, ensuring long-term assembly accuracy consistency and system operation stability. The optimized strategy is then distributed to each edge node to update the control logic stored locally, enabling continuous iteration of edge distributed decision-making and cloud-based global optimization.

6. The multi-station collaborative precision assembly adaptive assembly control method according to claim 3, characterized in that: In step S2, during real-time monitoring, the edge nodes of the first active execution unit are used to collect the initial pose, dimensional deviation, and assembly reference data of the parts, and broadcast them to all associated workstations through the communication control network module; the edge nodes of each passive execution unit are used to synchronously collect the real-time status data of their own workstation, including but not limited to assembly progress, pressing force, displacement deviation, and equipment operating status, and periodically broadcast them to other workstation nodes.

7. The multi-station collaborative precision assembly adaptive assembly control method according to claim 3, characterized in that: In step S3, during the distributed collaborative decision-making process, if the deviation only affects this workstation, the action parameters are directly corrected through the adjustment control module of this unit; if the deviation is a cross-workstation coupling effect, a collaborative adjustment request is automatically sent to the associated workstation, including the deviation type, compensation requirements and constraints; after receiving the adjustment request, the edge node of the associated workstation autonomously calculates the appropriate action parameter adjustment scheme based on its own workstation status and process constraints, ensuring the continuity of the production process without waiting for instructions from the cloud computing server.

8. The multi-station collaborative precision assembly adaptive assembly control method according to claim 4, characterized in that: In step S5, during cloud-based correction, if the multi-station collaborative control results meet the assembly accuracy and efficiency requirements, only the global process model is updated, without interfering with the autonomous decision-making of edge nodes.

9. The multi-station collaborative precision assembly adaptive assembly control method according to claim 4, characterized in that: In step S5, during cloud-based correction, if cross-workstation collaborative deviations or abnormal working conditions occur, such as the inability to meet accuracy requirements even after multi-workstation synchronous adjustments, the cloud computing server generates a global state correction instruction based on the deviation analysis results. This instruction performs secondary corrections on the control parameters and collaborative strategies of each execution unit and issues a global correction instruction to distribute the corrected optimized parameters to each execution unit. This compensates for the accumulated deviations caused by multi-workstation assembly and uniformly optimizes the process rules and collaborative strategies of each edge node.

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