Industrial cooperative control system and industrial cooperative control method

Abstract

The invention relates to the technical field of industrial automation control, in particular to an industrial cooperative control system and an industrial cooperative control method, and aims to solve the technical problems of resource redundancy, low data collaboration efficiency and insufficient expansibility of a traditional industrial control system. In order to achieve the purpose, the industrial cooperative control system comprises a centralized industrial personal computer cluster and an edge end real-time control unit, and the centralized industrial personal computer cluster virtualizes a plurality of hardware resources including processors and storages into a uniform resource pool; the uniform resource pool comprises a plurality of virtual industrial control units divided based on a virtual container technology, and the virtual industrial control units are used for generating a cooperative control instruction to realize cooperative control of the industrial production line. And the edge end real-time control unit controls the station to complete a real-time task corresponding to a cooperative control instruction based on the cooperative control instruction of the virtual industrial control unit to the station of the industrial production line, so that the problems of resource redundancy, scattered distribution, high cost, data island and the like of a traditional distributed industrial personal computer are solved.

Description

Technical Field

[0001] This application relates to the field of industrial automation control technology, specifically to an industrial collaborative control system and an industrial collaborative control method. Background Technology

[0002] In the traditional vehicle manufacturing industry, industrial control systems have long adopted a distributed industrial computer (IPC) deployment model, where each piece of equipment or workstation is equipped with an independent IPC. While this approach can achieve basic control functions, it has the following significant limitations:

[0003] 1. Resource redundancy and high costs: Dispersed hardware configurations lead to redundant investment in computing and storage resources, resulting in high equipment procurement and maintenance costs;

[0004] 2. Data silos and inefficient collaboration: Data from each workstation is stored independently, lacking global analysis capabilities and making it difficult to support real-time decision-making and process optimization;

[0005] 3. Insufficient scalability and flexibility: Production line adjustments require hardware redeployment, making it difficult to quickly adapt to the needs of multi-model, small-batch customized production.

[0006] Despite the long-standing existence of the aforementioned problems, the potential value of "centralized control" has remained largely unrecognized within the industry, primarily due to the following reasons:

[0007] 1. Technological cognitive inertia: Traditional automobile manufacturing is mainly based on the large-scale production of a single model, with low demand for flexibility and data integration, and distributed systems can still meet the basic requirements;

[0008] 2. Virtual technology threshold: Industrial control scenarios have stringent requirements for real-time performance and reliability, and traditional virtual technologies are unable to meet the requirements for millisecond-level response and high availability;

[0009] 3. Lack of edge collaboration: The maturity of edge computing and cloud collaboration is insufficient, and centralized deployment is prone to communication delays or single points of failure.

[0010] In recent years, with the explosive growth in demand for intelligent manufacturing and flexible production, the bottlenecks of traditional solutions have become increasingly apparent.

[0011] Accordingly, there is a need in this field for a new industrial collaborative control solution to address the aforementioned problems. Summary of the Invention

[0012] In order to overcome the above-mentioned defects, this application is made to solve or at least partially solve the technical problems of resource redundancy, inefficient data collaboration and insufficient scalability in traditional industrial control systems.

[0013] In a first aspect, an industrial collaborative control system is provided, the system comprising a centralized industrial control computer cluster and multiple edge real-time control units; the centralized industrial control computer cluster and the edge real-time control units are connected via a distributed redundant communication protocol.

[0014] The centralized industrial control computer cluster virtualizes multiple hardware resources into a unified resource pool; the unified resource pool includes multiple virtual industrial control units based on virtual container technology; the virtual industrial control units are used to generate collaborative control instructions to achieve collaborative control of the industrial production line; the hardware resources include processors and memory;

[0015] The edge-end real-time control unit is set at the workstation of the industrial production line. Based on the collaborative control instructions of the virtual industrial control unit to the workstation of the industrial production line, it controls the workstation to complete the real-time task corresponding to the collaborative control instructions.

[0016] In one technical solution of the aforementioned industrial collaborative control system, the centralized industrial control computer cluster is further configured to: dynamically allocate computing power to the industrial production line based on the load of the industrial production line, according to the elastic computing mode.

[0017] In one technical solution of the aforementioned industrial collaborative control system, the centralized industrial computer cluster is further configured as follows:

[0018] Dynamic computing power allocation for the industrial production line is performed based on multi-objective mixed-integer linear programming algorithm and / or model predictive control algorithm with dynamic resource allocation and / or reinforcement learning-driven adaptive scheduling algorithm and / or EDF / RMS real-time preemptive scheduling algorithm.

[0019] In one technical solution of the above-mentioned industrial collaborative control system, the centralized industrial control computer cluster and the edge real-time control unit are connected through a dual-channel communication connection; and / or, the communication connection between the centralized industrial control computer cluster and the edge real-time control unit includes a data pre-caching mechanism.

[0020] In one technical solution of the above-mentioned industrial collaborative control system, the system further includes a cloud data analysis center, which is communicatively connected to the edge real-time control unit;

[0021] The cloud-based data analysis center is used to perform global optimization of the collaborative control commands and / or analyze the historical data corresponding to the collaborative control commands.

[0022] In one technical solution of the aforementioned industrial collaborative control system, the system further includes a unified data platform; the unified data platform and the edge real-time control unit are connected and communicated via multi-source heterogeneous device access and protocol standardization technology.

[0023] The unified data platform is used to collect production data from the industrial production line through the edge terminal real-time control unit and to store the production data.

[0024] In one technical solution of the aforementioned industrial collaborative control system, the unified data platform is further configured as follows:

[0025] Based on a lossless compression algorithm, the timestamp difference of the production data is divided into a common time base and an offset, and the production data is stored using bit packing technology.

[0026] In one technical solution of the aforementioned industrial collaborative control system, the system further includes an API interface;

[0027] The API interface is used to enable the addition and / or replacement of workstations and / or equipment in the industrial production line.

[0028] In one technical solution of the above-mentioned industrial collaborative control system, the API interface includes a protocol converter; the protocol converter is used to convert the industrial communication protocols of the workstation and / or the equipment.

[0029] In one technical solution of the aforementioned industrial collaborative control system, the centralized industrial computer cluster also includes redundant fault-tolerant nodes;

[0030] The redundant fault-tolerant nodes are used to achieve redundant backup and / or fault self-healing for single-point failures in the centralized industrial control computer cluster.

[0031] In one technical solution of the aforementioned industrial collaborative control system, the redundant fault-tolerant node is further configured as follows:

[0032] Monitoring single-point faults in the centralized industrial control computer cluster based on heartbeat monitoring; and / or,

[0033] Seamless failover of faulty nodes in the centralized industrial control computer cluster is achieved using pre-copy real-time migration and / or post-copy migration technologies.

[0034] In a second aspect, an industrial collaborative control method is provided, the method being based on any one of the above-described industrial collaborative control system technical solutions; the method includes:

[0035] Based on the virtual industrial control unit in the centralized industrial control computer cluster, system control instructions for industrial production lines are generated.

[0036] Based on the edge real-time control unit, the workstations of the industrial production line are controlled to complete the real-time tasks corresponding to the collaborative control commands according to the collaborative control commands.

[0037] Solution 1. An industrial collaborative control system, characterized in that the system includes a centralized industrial control computer cluster and multiple edge real-time control units; the centralized industrial control computer cluster and the edge real-time control units are connected via a distributed redundant communication protocol;

[0038] The centralized industrial control computer cluster virtualizes multiple hardware resources into a unified resource pool; the unified resource pool includes multiple virtual industrial control units based on virtual container technology; the virtual industrial control units are used to generate collaborative control instructions to achieve collaborative control of the industrial production line; the hardware resources include processors and memory;

[0039] The edge-end real-time control unit is set at the workstation of the industrial production line. Based on the collaborative control instructions of the virtual industrial control unit to the workstation of the industrial production line, it controls the workstation to complete the real-time task corresponding to the collaborative control instructions.

[0040] Option 2. The industrial collaborative control system according to Option 1, characterized in that,

[0041] The centralized industrial control computer cluster is further configured to: dynamically allocate computing power to the industrial production line based on the load of the industrial production line, using an elastic computing mode.

[0042] Option 3. The industrial collaborative control system according to Option 2, characterized in that the centralized industrial control computer cluster is further configured as follows:

[0043] Dynamic computing power allocation for the industrial production line is performed based on multi-objective mixed-integer linear programming algorithm and / or model predictive control algorithm with dynamic resource allocation and / or reinforcement learning-driven adaptive scheduling algorithm and / or EDF / RMS real-time preemptive scheduling algorithm.

[0044] Option 4. The industrial collaborative control system according to Option 1, characterized in that,

[0045] The centralized industrial control computer cluster and the edge real-time control unit are connected via a dual-channel communication connection; and / or, the communication connection between the centralized industrial control computer cluster and the edge real-time control unit includes a data pre-caching mechanism.

[0046] Option 5. The industrial collaborative control system according to Option 4, characterized in that the system further includes a cloud data analysis center, which is communicatively connected to the edge real-time control unit;

[0047] The cloud-based data analysis center is used to perform global optimization of the collaborative control commands and / or analyze the historical data corresponding to the collaborative control commands.

[0048] Option 6. The industrial collaborative control system according to Option 1, characterized in that the system further includes a unified data platform; the unified data platform and the edge real-time control unit are connected through multi-source heterogeneous device access and protocol standardization technology;

[0049] The unified data platform is used to collect production data from the industrial production line through the edge terminal real-time control unit and to store the production data.

[0050] Option 7. The industrial collaborative control system according to Option 6, characterized in that the unified data platform is further configured as follows:

[0051] Based on a lossless compression algorithm, the timestamp difference of the production data is divided into a common time base and an offset, and the production data is stored using bit packing technology.

[0052] Option 8. The industrial collaborative control system according to Option 1, characterized in that the system further includes an API interface;

[0053] The API interface is used to enable the addition and / or replacement of workstations and / or equipment in the industrial production line.

[0054] Option 9. The industrial collaborative control system according to Option 8, characterized in that the API interface includes a protocol converter; the protocol converter is used to convert the industrial communication protocols of the workstation and / or the equipment.

[0055] Scheme 10. The industrial collaborative control system according to Scheme 1, characterized in that the centralized industrial control computer cluster further includes redundant fault-tolerant nodes;

[0056] The redundant fault-tolerant nodes are used to achieve redundant backup and / or fault self-healing for single-point failures in the centralized industrial control computer cluster.

[0057] Option 11. The industrial collaborative control system according to Option 10, characterized in that the redundant fault-tolerant node is further configured as follows:

[0058] Monitoring single-point faults in the centralized industrial control computer cluster based on heartbeat monitoring; and / or,

[0059] Seamless failover of faulty nodes in the centralized industrial control computer cluster is achieved using pre-copy real-time migration and / or post-copy migration technologies.

[0060] Scheme 12. An industrial collaborative control method, characterized in that the method is based on the industrial collaborative control system of any one of Schemes 1 to 11; the method includes:

[0061] Based on the virtual industrial control unit in the centralized industrial control computer cluster, system control instructions for industrial production lines are generated.

[0062] Based on the edge real-time control unit, the workstations of the industrial production line are controlled to complete the real-time tasks corresponding to the collaborative control commands according to the collaborative control commands.

[0063] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0064] In implementing the industrial collaborative control system technical solution provided in this application, the industrial collaborative control system includes a centralized industrial control computer cluster and multiple edge real-time control units. The centralized industrial control computer cluster and the edge real-time control units are connected via a distributed redundant communication protocol. The centralized industrial control computer cluster virtualizes multiple hardware resources, including processors and memory, into a unified resource pool. The unified resource pool includes multiple virtual industrial control units (ICUs) partitioned based on virtual container technology. These virtual ICUs are used to generate collaborative control commands to achieve collaborative control of the industrial production line. The edge real-time control units are located at workstations on the industrial production line. Based on the collaborative control commands issued by the virtual ICUs to the workstations, the control units complete the real-time tasks corresponding to the collaborative control commands. Through the above configuration, this application can achieve centralized deployment of multiple hardware resources based on virtual technology, thereby effectively solving many problems of traditional distributed industrial control computers, such as resource redundancy, dispersed distribution, high cost, data silos, and insufficient flexible expansion capabilities. Meanwhile, this application utilizes virtual container technology to partition virtual industrial control units, enabling real-time optimized virtual layers, supporting millisecond-level response and deterministic task scheduling, overcoming the latency bottleneck of traditional virtual technologies, and effectively improving the usability of virtual technology in industrial control scenarios. Furthermore, the centralized industrial control computer cluster and the edge real-time control unit of this application are connected via a distributed redundant communication protocol, effectively solving the communication latency problem between the centralized industrial control computer cluster and the edge real-time control unit, and effectively improving the stability and reliability of industrial production line system control.

[0065] Furthermore, the centralized industrial control computer cluster of this application can dynamically adjust the computing power generated by the industry according to the load of the industrial production line, which can effectively avoid the waste of computing power and response delay.

[0066] Furthermore, the industrial system control system of this application also includes a unified data platform, which can realize the collection and storage of production data from industrial production lines and achieve real-time processing, effectively breaking down data silos in industrial production lines and providing a faster data foundation for intelligent collaborative control of industrial production lines. Simultaneously, the unified data platform of this application uses a lossless compression algorithm to divide the timestamp difference of production data into a common time base and an offset, and utilizes bit-packing technology to store the production data, effectively improving the storage efficiency of production data. Attached Figure Description

[0067] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Wherein:

[0068] Figure 1 This is a schematic diagram of the main components of an industrial collaborative control system according to an embodiment of this application;

[0069] Figure 2 This is a schematic diagram of the main components of a centralized industrial control computer cluster according to one embodiment of the present application.

[0070] Figure 3 This is a schematic diagram of the main deployment steps of a centralized industrial control computer cluster according to one embodiment of the present application.

[0071] Figure 4 This is a schematic flowchart of the main steps of an industrial collaborative control method according to an embodiment of this application. Detailed Implementation

[0072] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0073] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components, such as program code, or a combination of software and hardware. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular forms of the terms "a" and "this" can also include plural forms.

[0074] Here we will first explain some of the terms used in this application.

[0075] In computer science, virtualization is a technology that combines or partitions existing computer resources (CPU, memory, disk space, etc.) to make these resources appear as one or more operating environments, thereby providing access methods superior to the original resource configuration. Virtualization transforms physical resources into logically manageable resources, breaking down barriers between physical structures.

[0076] Virtual containers (container technology) are a lightweight virtualization technology that enables cross-platform compatibility, consistency, and rapid deployment by packaging an application and all its dependencies (such as code, libraries, configuration files, etc.) into an independent runtime environment (called a container image).

[0077] Elastic computing is a computing resource management model based on cloud computing technology, allowing users to dynamically adjust resource scale according to business needs. Its core feature is to pool physical server resources through virtualization technology and use an automated monitoring system to analyze indicators such as CPU utilization and memory load in real time, triggering preset thresholds to automatically increase or decrease the number of virtual machine instances.

[0078] Gorilla is a high-efficiency lossless compression algorithm used in the Prometheus time-series database. It achieves zero-loss compression of data through Delta encoding and XOR compression, significantly reducing storage space and query latency.

[0079] A high-availability cluster is a computer cluster that ensures service continuity through redundant nodes. A high-availability cluster (HACluster) is a group of computer systems that eliminates single points of failure through a multi-node redundant architecture; its core objective is to minimize service downtime.

[0080] Live migration is a key technology in the field of computer virtualization, referring to the operation of transferring virtual machines from a source physical host to a target physical host without interrupting service. This technology achieves continuous transfer of runtime state through a memory pre-copy mechanism.

[0081] Post-copy migration is a technique for migrating virtual machines, primarily used to move a running virtual machine from one physical server (or hypervisor) to another without interrupting its operation. This technique is particularly suitable for scenarios requiring high availability and low downtime.

[0082] Distributed redundancy communication protocols are communication protocols designed for network devices to prevent single points of failure.

[0083] Multi-objective mixed-integer programming (MIP) is a linear programming algorithm that requires some decision variables to take integer values. Its mathematical model consists of a linear objective function, linear constraints, and a specified set of integer variables.

[0084] Model Predictive Control (MPC) is a control method that combines system modeling, rolling optimization, and feedback correction to adjust resource allocation in real time to cope with dynamic load changes.

[0085] The reinforcement learning-driven adaptive scheduling algorithm is a scheduling algorithm that generates the optimal task sequence by balancing time, energy and computing resources in real time, based on reinforcement learning.

[0086] The real-time preemptive scheduling algorithm based on EDF / RMS is a strategy that ensures tasks are completed within time constraints.

[0087] Industrial PCs (IPCs) belong to the category of digital control instruments in industrial automation control devices. Together with distributed control systems (DCS) and programmable logic controllers (PLCs), they constitute basic automation control equipment. Their core function is to achieve continuous control, batch control, and sequential control through microprocessor technology. In closed-loop control systems, they undertake the functions of signal reception, processing, and command transmission.

[0088] See appendix Figure 1 , Figure 1 This is a schematic diagram of the main structural components of an industrial collaborative control system according to an embodiment of this application. Figure 1 As shown, the industrial collaborative control system in this application embodiment mainly includes a centralized industrial control computer cluster and multiple edge real-time control units.

[0089] In this embodiment, the centralized industrial control computer cluster can communicate with the edge real-time control unit (ERCU) via a distributed redundant communication protocol. The centralized industrial control computer cluster can virtualize multiple hardware resources into a unified resource pool. This unified resource pool can include multiple virtual industrial control units partitioned based on virtual container technology. The virtual industrial control units can be used to generate collaborative control instructions to achieve collaborative control of the industrial production line. Hardware resources can include processors and memory. The edge real-time control unit can be located at workstations on the industrial production line. Based on the collaborative control instructions from the virtual industrial control units to the workstations, it controls the workstations to complete the real-time tasks corresponding to the collaborative control instructions.

[0090] In one implementation, the processor may include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an accelerator card, etc. A centralized industrial PC (IPC) cluster can be used to replace the traditional distributed industrial PCs, using virtualization technology to abstract hardware resources, including CPUs, GPUs, memory, etc., into a unified resource pool.

[0091] In one implementation, the industrial production line can be an automobile manufacturing production line.

[0092] In one implementation, real-time tasks may include visual guidance, trajectory control, data acquisition and monitoring, etc.

[0093] In one implementation, such as Figure 2 As shown, a centralized industrial control computer cluster can be implemented through the following steps:

[0094] S1: Hardware preparation and network planning. That is, server selection, storage configuration, and network design for a centralized industrial control computer cluster.

[0095] S2: Virtualization platform setup and domain environment. This involves configuring the operating system for a centralized industrial control computer cluster, deploying domain controllers, and adding node servers to the domain.

[0096] S3: Storage system deployment domain cluster configuration. This refers to configuring storage connection and failover clusters for centralized industrial control computer clusters.

[0097] S4: Virtual machine creation for domain industrial control application deployment. This involves enabling templated deployment of virtual machines, installation of industrial control software, and high-availability configuration for centralized industrial control computer clusters.

[0098] S5: Data Acquisition Integration and Remote Management. This means enabling centralized industrial control computer data acquisition configuration and remote access and monitoring.

[0099] S6: System Testing and Optimization Maintenance. This involves implementing centralized industrial control functions, performing regular maintenance, and optimizing performance.

[0100] In one implementation, the centralized industrial control computer cluster can be further configured to: dynamically allocate computing power to the industrial production line based on the load conditions of the industrial production line using an elastic computing model.

[0101] In this embodiment, the centralized industrial control computer cluster can dynamically allocate computing power to industrial production lines based on multi-objective mixed-integer linear programming algorithms, model predictive control algorithms for dynamic resource allocation, reinforcement learning-driven adaptive scheduling algorithms, and EDF / RMS real-time preemptive scheduling algorithms. Specifically, since the centralized industrial control computer cluster is based on virtual container technology to divide multiple virtual industrial control units, it can achieve industrial real-time optimization, support millisecond-level response, and support deterministic task scheduling, effectively overcoming the latency bottleneck of traditional virtual technologies. Furthermore, a dynamic resource allocation engine can be deployed in the centralized industrial control computer cluster. This engine is used to dynamically allocate computing power based on the load of the industrial production line, thereby enabling multi-task parallelism and priority preemption. It deeply integrates the resource scheduling based on elastic computing models in the cloud computing field with the strong real-time requirements of industrial control. Based on the task priority and resource requirements of the industrial production line, it realizes real-time optimization of computing power allocation strategies, which solves the contradiction between the rigid resource configuration of traditional distributed architecture and dynamic production needs. This ensures the real-time requirements of key tasks in the industrial production line and can effectively improve the overall resource efficiency of the industrial production line by 20%-40%. It provides an effective foundation for intelligent manufacturing of industrial production lines with "software and hardware decoupling and on-demand resource supply".

[0102] In one implementation, the centralized industrial control computer cluster can be connected to the edge real-time control unit via a dual-channel communication connection; the communication connection between the centralized industrial control computer cluster and the edge real-time control unit can include a data pre-caching mechanism.

[0103] In this embodiment, the centralized industrial control computer cluster can establish a dual-channel communication connection with the edge real-time control unit, thereby ensuring the reliability of communication between the centralized industrial control computer cluster and the edge real-time control unit. Furthermore, the communication connection between the centralized industrial control computer cluster and the edge real-time control unit includes a data pre-caching mechanism, which effectively avoids the communication latency risk when the centralized industrial control computer cluster is centrally deployed, thus ensuring the real-time performance of the communication process between the centralized industrial control computer cluster and the edge real-time control unit.

[0104] In some specific examples, the communication latency between the centralized industrial control computer cluster and the edge real-time control unit can be controlled to within 10ms.

[0105] In one embodiment, the industrial system control system may further include a cloud-based data analysis center, which can communicate with the edge-end real-time control unit; the cloud-based data analysis center can be used to perform global optimization of collaborative control commands and analyze historical data corresponding to collaborative control commands.

[0106] In this embodiment, the cloud-based data analysis center can collaborate with the edge-based real-time control unit. The edge-based real-time control unit can be responsible for controlling the workstation to complete the real-time tasks corresponding to the collaborative control commands, while the cloud-based data analysis center can realize the global optimization of the collaborative control commands and analyze the historical data corresponding to the collaborative control commands.

[0107] In one embodiment, the industrial collaborative control system may further include a unified data platform. The unified data platform can communicate with edge real-time control units via multi-source heterogeneous device access and standardized protocol technologies. The unified data platform can be used to collect and store production data from industrial production lines through edge real-time control units.

[0108] In this embodiment, the industrial collaborative control system can establish a cross-workstation data integration platform, i.e., a unified data platform, to achieve standardized collection, storage, and real-time sharing of production data. Production data can include equipment status at each workstation, production process parameters, quality inspection data, etc. The unified data platform can be one or more virtual industrial control units within a unified resource pool. It can be built using a virtualized resource pool and network architecture, and through multi-source heterogeneous device access and protocol standard technologies, it enables collaborative collection and visualization processing of production data across workstations and different devices in the industrial production line. The unified data platform allows for the flexible support of virtualized resources for the dynamic expansion of production data collection and storage in the industrial production line, enabling real-time processing of industrial-grade production data. This effectively breaks down the data silos of traditional workstations, providing a data foundation for millisecond-level response in intelligent manufacturing.

[0109] In one implementation, the unified data platform can use a lossless compression algorithm to divide the timestamp difference of production data into a common time base and an offset, and use bit packing technology to store the production data.

[0110] In this implementation, a lossless compression algorithm is designed for high-concurrency production data in industrial production lines. For example, a lossless compression algorithm based on Gorilla key data with zero distortion is used. The timestamp differences in the production data are divided into a common time base and an offset, and bit-packing technology is used to store the production data. During production data collection, the unified data platform supports non-uniform sampling. The combination of these technologies enables the production data collection and storage process to dynamically adapt to changes in time intervals, thereby improving storage efficiency by more than 40%. Simultaneously, the production data collected and stored by the unified data platform can also provide a corpus foundation for future large-scale industrial time-series models.

[0111] In one implementation, the industrial collaborative control system may also include an API (Application Programming Interface). The API interface can be used to add or replace workstations and equipment on the industrial production line.

[0112] In this embodiment, the industrial collaborative control system can be designed with hardware expansion slots and API interfaces to support the access of new equipment and new workstations in the industrial production line, as well as the plug-and-play replacement of faulty equipment or workstations, so that the above configuration process can be completed without shutting down the industrial production line or equipment.

[0113] In one implementation, the API interface may include a protocol converter; the protocol converter can be used to convert industrial communication protocols of workstations and equipment.

[0114] In this embodiment, an adaptive protocol converter can be designed. The protocol converter is compatible with mainstream communication protocols (such as PROFINET (an Ethernet-based automation bus standard), EtherCAT (Ethernet control automation technology), etc.). The protocol converter can automatically identify new equipment on the industrial production line, thereby completing the adaptive conversion of industrial communication protocols for workstations and equipment.

[0115] In one implementation, the centralized industrial control computer cluster may further include redundant fault-tolerant nodes. These redundant fault-tolerant nodes can be used to achieve redundant backup and self-healing for single points of failure in the centralized industrial control computer cluster.

[0116] In this embodiment, redundant fault-tolerant nodes can monitor single points of failure in a centralized industrial control computer cluster based on heartbeat monitoring. Simultaneously, redundant fault-tolerant nodes can achieve seamless failover of faulty nodes in the centralized industrial control computer cluster based on pre-copy real-time migration and / or post-copy migration technologies.

[0117] Specifically, in centralized industrial control computer clusters, redundant backup and self-healing can be achieved by setting up multiple redundant fault-tolerant nodes, effectively avoiding the risk of single points of failure. Redundant fault-tolerant nodes can monitor the master node of the centralized industrial control computer cluster based on heartbeat monitoring. When the master node fails, the redundant fault-tolerant node can take over control within a preset time (e.g., within 50ms), thus ensuring the continuous operation of the industrial production line. Simultaneously, redundant fault-tolerant nodes can also support seamless switching of running virtual industrial control units, further mitigating industrial production line interruptions caused by hardware failures. Redundant fault-tolerant nodes can achieve sub-second fault switching and zero-aware migration in centralized industrial control computer clusters through a combination of technologies such as multi-node redundant clusters, distributed redundancy and low-latency communication, pre-copy real-time migration, post-copy migration, health monitoring and fault diagnosis, migration strategy engine, seamless switching assurance technology, methods for determining time windows in migration projects, and edge-cloud collaborative migration. This reduces hardware-induced production line downtime from minutes in traditional architectures to milliseconds, meeting the PLe (highest safety level) requirements of ISO 13849-1 and providing a reliable "never-stop" foundation for intelligent manufacturing in industrial production lines.

[0118] In one implementation, such as Figure 3 As shown, the centralized industrial control computer cluster includes real-time container-1 and real-time container-2, used as virtual industrial control units (vPLC-1, v-PLC-2); non-real-time container-1 serves as a unified data platform, handling production data acquisition and storage; and non-real-time container-2 acts as a cloud data analysis center for data analysis tasks. The container runtime environment of the centralized industrial control computer cluster includes I / O access services, network access services, hardware resource isolation, and operation management. The centralized industrial control computer cluster can be built on an RTOS (Real-Time Operator System) / Linux-RT platform. It can also include general-purpose hardware, which may include API interfaces, protocol converters, etc.

[0119] Based on the above configuration, the industrial collaborative control system of this application embodiment includes a centralized industrial control computer cluster and multiple edge real-time control units. The centralized industrial control computer cluster and the edge real-time control units are connected via a distributed redundant communication protocol. The centralized industrial control computer cluster virtualizes multiple hardware resources, including processors and memory, into a unified resource pool. The unified resource pool includes multiple virtual industrial control units (AVUs) partitioned based on virtual container technology. These AVUs are used to generate collaborative control instructions to achieve collaborative control of the industrial production line. The edge real-time control units are located at workstations on the industrial production line. Based on the collaborative control instructions from the virtual AVUs to the workstations on the industrial production line, the control units complete the real-time tasks corresponding to the collaborative control instructions. Through the above configuration, this application embodiment can achieve centralized deployment of multiple hardware resources based on virtual technology, thereby effectively solving many problems of traditional distributed industrial control computer resources such as redundancy, dispersion, high cost, data silos, and insufficient flexible expansion capabilities. At the same time, this application embodiment uses virtual container technology to partition virtual industrial control units, enabling real-time optimized virtual layers, supporting millisecond-level response and deterministic task scheduling, breaking through the latency bottleneck of traditional virtual technology, and effectively improving the availability of virtual technology in industrial control scenarios. Furthermore, in this embodiment of the application, the centralized industrial control computer cluster and the edge real-time control unit are connected through a distributed redundant communication protocol, which can effectively solve the communication delay problem between the centralized industrial control computer cluster and the edge real-time control unit, and effectively improve the stability and reliability of industrial production line system control.

[0120] Furthermore, the centralized industrial control computer cluster of this application embodiment can realize dynamic adjustment of industrial computing power according to the load of industrial production lines, which can effectively avoid computing power waste and response delay.

[0121] Furthermore, the industrial system control system of this application embodiment also includes a unified data platform, which can realize the collection and storage of production data from industrial production lines and achieve real-time processing, effectively breaking down data silos in industrial production lines and providing a faster data foundation for intelligent collaborative control of industrial production lines. Simultaneously, the unified data platform of this application embodiment uses a lossless compression algorithm to divide the timestamp difference of production data into a common time base and an offset, and utilizes bit-packing technology to store the production data, effectively improving the storage efficiency of production data.

[0122] In one implementation, taking an automobile manufacturing production line as an example, and a centralized industrial control computer cluster consisting of three high-performance industrial control computers forming a unified resource pool, each industrial control computer is equipped with a multi-core CPU, a GPU accelerator card, and high-speed storage. These are divided into multiple independent virtual industrial control units using industrial-grade lightweight virtualization containers. Combined with edge-end real-time control units, a unified data platform, and redundant fault-tolerant nodes, the industrial collaborative control system of this application embodiment achieves the following technical improvements:

[0123] Improvement 1: Increased resource utilization and reduced costs

[0124] The use of a unified resource pool increased hardware utilization from 60% to 90% and reduced equipment procurement costs by 60% (in the case of overall implementation in the final assembly workshop, approximately 50 distributed industrial control computers were originally required, but now only 4 centralized nodes are needed). The modular expansion interface (API interface) supports "plug and play" for adding new workstations, reducing production line adjustment time from 2 hours to 10 minutes.

[0125] Improvement 2: Breakthrough in real-time performance and reliability

[0126] Lightweight virtualized containers reduce task scheduling latency from 100ms in traditional virtualization to 5ms, meeting the real-time requirements of the process; the distributed redundant communication protocol ensures that the edge control command latency is <10ms and the single-point fault switching time is <50ms, which is far below the industry average fault tolerance threshold of 200ms.

[0127] Improvement 3: Data-driven process optimization

[0128] The unified data platform integrates process quality data from the entire workshop, and reduces the defect rate from 0.5% to 0.1% through AI analysis; data AI tracking shortens the time for tracing the source of process problems from 2 hours to 5 minutes.

[0129] Improvement 4: Flexible production adaptability

[0130] The efficiency of switching between multiple models on the automobile manufacturing production line has been improved by 80%, supporting random mixed-flow production of multiple models and meeting the needs of small-batch customized orders.

[0131] Furthermore, this application also provides an industrial collaborative control method.

[0132] See appendix Figure 4 , Figure 4 This is a schematic flowchart illustrating the main steps of an industrial collaborative control method according to an embodiment of this application. Figure 4 As shown, the industrial collaborative control method in this application embodiment is based on the industrial collaborative control system in the above-described industrial collaborative control system embodiment. The industrial collaborative control method mainly includes the following steps S101 to S102.

[0133] Step S101: Generate system control instructions for the industrial production line based on the virtual industrial control unit in the centralized industrial control computer cluster.

[0134] Step S102: Based on the edge real-time control unit, control the workstations of the industrial production line to complete the real-time tasks corresponding to the collaborative control instructions according to the collaborative control instructions.

[0135] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0136] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0137] Another aspect of this application provides a computer-readable storage medium.

[0138] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that performs the industrial collaborative control method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described industrial collaborative control method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device including various electronic devices, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0139] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. An industrial collaborative control system, characterized in that, The system includes a centralized industrial control computer cluster and multiple edge real-time control units; the centralized industrial control computer cluster and the edge real-time control units are connected via a distributed redundant communication protocol. The centralized industrial control computer cluster virtualizes multiple hardware resources into a unified resource pool; the unified resource pool includes multiple virtual industrial control units based on virtual container technology; the virtual industrial control units are used to generate collaborative control instructions to achieve collaborative control of the industrial production line; the hardware resources include processors and memory; The edge-end real-time control unit is set at the workstation of the industrial production line. Based on the collaborative control instructions of the virtual industrial control unit to the workstation of the industrial production line, it controls the workstation to complete the real-time task corresponding to the collaborative control instructions.

2. The industrial collaborative control system according to claim 1, characterized in that, The centralized industrial control computer cluster is further configured to: dynamically allocate computing power to the industrial production line based on the load of the industrial production line, using an elastic computing mode.

3. The industrial collaborative control system according to claim 2, characterized in that, The centralized industrial control computer cluster is further configured as follows: Dynamic computing power allocation for the industrial production line is performed based on multi-objective mixed-integer linear programming algorithm and / or model predictive control algorithm with dynamic resource allocation and / or reinforcement learning-driven adaptive scheduling algorithm and / or EDF / RMS real-time preemptive scheduling algorithm.

4. The industrial collaborative control system according to claim 1, characterized in that, The centralized industrial control computer cluster and the edge real-time control unit are connected via a dual-channel communication connection; and / or, the communication connection between the centralized industrial control computer cluster and the edge real-time control unit includes a data pre-caching mechanism.

5. The industrial collaborative control system according to claim 4, characterized in that, The system also includes a cloud-based data analysis center, which is communicatively connected to the edge-end real-time control unit. The cloud-based data analysis center is used to perform global optimization of the collaborative control commands and / or analyze the historical data corresponding to the collaborative control commands.

6. The industrial collaborative control system according to claim 1, characterized in that, The system also includes a unified data platform; the unified data platform and the edge real-time control unit are connected through multi-source heterogeneous device access and protocol standardization technology; The unified data platform is used to collect production data from the industrial production line through the edge terminal real-time control unit and to store the production data.

7. The industrial collaborative control system according to claim 6, characterized in that, The unified data platform is further configured as follows: Based on a lossless compression algorithm, the timestamp difference of the production data is divided into a common time base and an offset, and the production data is stored using bit packing technology.

8. The industrial collaborative control system according to claim 1, characterized in that, The system also includes an API interface; The API interface is used to enable the addition and / or replacement of workstations and / or equipment in the industrial production line.

9. The industrial collaborative control system according to claim 8, characterized in that, The API interface includes a protocol converter; the protocol converter is used to implement the conversion of industrial communication protocols of the workstation and / or the equipment.

10. The industrial collaborative control system according to claim 1, characterized in that, The centralized industrial control computer cluster also includes redundant fault-tolerant nodes; The redundant fault-tolerant nodes are used to achieve redundant backup and / or fault self-healing for single-point failures in the centralized industrial control computer cluster.

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