Cloud-edge collaborative industrial big data acquisition method, device and equipment and medium

Abstract

The invention belongs to the technical field of big data, and discloses a cloud-side collaborative industrial big data acquisition method, device and equipment and a medium, and the method comprises the steps: end-side hardware DTU is connected with industrial equipment; the edge computing module processes end-side hardware DTU and industrial equipment to obtain a data transmission channel; the end-side hardware DTU and the industrial equipment are connected with the cloud virtual gateway module according to a data transmission channel; the edge calculation module judges whether the protocol type of the industrial equipment is matched with a built-in protocol or not; if yes, the edge computing module judges whether the data cache amount is matched with the cloud resources or not; if yes, the edge computing module judges whether the data processing amount is matched with the cloud computing power or not; if yes, the cloud virtual gateway module obtains data in the industrial equipment, and data collection operation is completed. According to the invention, efficient, safe and flexible acquisition and transmission of data can be realized, so that the industrial big data acquisition and processing efficiency is comprehensively improved.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a cloud-edge collaborative industrial big data acquisition method, device, equipment and medium. Background Technology

[0002] In industrial data acquisition technology, the current solution involves embedding industrial protocols into gateway devices for data acquisition. After preprocessing and calculation by the gateway, the data is transmitted to the receiving end via network signals, ensuring that the industrial data acquisition gateway can communicate with industrial equipment of different brands and models, thereby achieving data interoperability and sharing.

[0003] Existing industrial data acquisition gateways typically incorporate numerous industrial protocol stacks to support communication with various industrial devices. However, this places high demands on hardware resources, directly leading to increased equipment costs and high prices. Secondly, because the protocol stacks are built into the gateway device, supporting new industrial protocols or devices usually requires replacing or upgrading the entire gateway, limiting system scalability and flexibility. Furthermore, with large data volumes, insufficient processing power in the gateway device can lead to data loss or delays, affecting the accuracy and real-time performance of data acquisition. In addition, different brands and models of industrial equipment may use different communication protocols, further increasing the complexity of data acquisition and transmission. Therefore, how to efficiently acquire industrial big data has become a problem to be solved. Summary of the Invention

[0004] This application provides a cloud-edge collaborative industrial big data acquisition method, device, equipment, and medium, which can connect the edge hardware DTU with industrial equipment and edge computing modules, thereby separating hardware data acquisition and cloud data processing, reducing hardware costs. The collaborative design of cloud and edge computing makes the dynamic configuration of cloud resources more flexible, and can adjust computing power according to actual needs, eliminating the cumbersome process of replacing the entire gateway device. By using an edge computing module, efficient, secure, and flexible data acquisition and transmission can be further realized, thereby comprehensively improving the efficiency of industrial big data acquisition and processing.

[0005] In a first aspect, embodiments of this application provide a cloud-edge collaborative industrial big data acquisition method, the method comprising: End-side hardware DTU connects to industrial equipment; The edge computing module processes the end-side hardware DTU and industrial equipment to obtain a data transmission channel; The end-side hardware DTU and industrial equipment are connected to the cloud-based virtual gateway module through the data transmission channel; The edge computing module determines whether the protocol type of the industrial equipment matches the built-in protocol. If so, the edge computing module determines whether the data cache size matches the cloud resources; If so, the edge computing module determines whether the data processing volume matches the computing power of cloud computing; If so, the cloud-based virtual gateway module acquires data from the industrial equipment and completes the data acquisition operation.

[0006] Furthermore, the method also includes: The end-side hardware DTU is connected to the industrial equipment via cables; Alternatively, the end-side hardware DTU connects to industrial equipment based on pre-configured wireless communication parameters.

[0007] Furthermore, the cloud-based virtual gateway module includes a cloud-based industrial protocol library; the cloud-based industrial protocol library includes non-standard protocols and vendor SDK protocol packages.

[0008] Furthermore, the method also includes: If the edge computing module determines that the protocol type of the industrial equipment does not match the built-in protocol, the edge computing module will parse the protocol type of the industrial equipment to obtain the current protocol type; The edge computing module matches the current protocol type with the cloud-based industrial protocol library, obtains the corresponding protocol packet, and sends it to the cloud-based virtual gateway module. The cloud-based virtual gateway module receives the corresponding protocol packets and completes the connection with the industrial equipment.

[0009] Furthermore, the method also includes: The edge computing module acquires and analyzes the data volume, collection frequency, and data dimensions of industrial equipment to obtain the data load change trend; The edge computing module processes the CPU utilization, memory usage, and network bandwidth of the edge computing nodes and the cloud-based virtual gateway module to obtain the current resource load capacity. The edge computing module calculates the real-time data cache size based on the data load change trend and the current resource load capacity. The edge computing module determines whether the real-time data cache size matches the cloud resources.

[0010] Furthermore, the method also includes: The edge computing module processes the current data volume based on the machine learning ARIMA model to obtain the predicted data volume. If the predicted data processing volume is much greater than the current data processing volume, the edge computing module sends a resource upgrade message to the cloud virtual gateway module. The cloud-based virtual gateway module receives and processes resource upgrade information, and performs increase operations on cloud computing instances, CPU cores, and memory.

[0011] Furthermore, the method also includes: The cloud-based virtual gateway module obtains the collection parameter groups and collection frequency according to the industrial equipment type, and sends the collection parameter groups and collection frequency to the virtual gateway; The virtual gateway receives and configures the collection parameter packets and collection frequency.

[0012] Secondly, embodiments of this application provide a cloud-edge collaborative industrial big data acquisition device, the device comprising: The end-side hardware DTU is used to connect to industrial equipment; it connects to the cloud-based virtual gateway module according to the data transmission channel. The edge computing module is used to process end-side hardware DTUs and industrial equipment to obtain data transmission channels; it determines whether the protocol type of the industrial equipment matches the built-in protocol; if so, it determines whether the data cache size matches the cloud resources; if so, it determines whether the data processing volume matches the cloud computing power. Industrial equipment used to connect to cloud-based virtual gateway modules based on data transmission channels; The cloud-based virtual gateway module is used to acquire data from industrial equipment and complete data acquisition operations.

[0013] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps of a cloud-edge collaborative industrial big data acquisition method as described in any of the above embodiments.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a cloud-edge collaborative industrial big data acquisition method as described in any of the above embodiments.

[0015] In summary, compared with the prior art, the beneficial effects of the technical solution provided in this application include at least the following: This application provides a cloud-edge collaborative industrial big data acquisition method that connects the edge hardware DTU with industrial equipment and edge computing modules, thereby separating hardware data acquisition from cloud data processing and reducing hardware costs. The collaborative design of cloud and edge computing makes the dynamic configuration of cloud resources more flexible, allowing for adjustments to computing power according to actual needs, eliminating the cumbersome process of replacing the entire gateway device. By using an edge computing module, efficient, secure, and flexible data acquisition and transmission can be further achieved, thereby comprehensively improving the efficiency of industrial big data acquisition and processing. Attached Figure Description

[0016] Figure 1A flowchart illustrating a cloud-edge collaborative industrial big data acquisition method provided as an exemplary embodiment of this application.

[0017] Figure 2 This is a structural diagram of a cloud-edge collaborative industrial big data acquisition device provided as an exemplary embodiment of this application. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Please see Figure 1 This application provides a cloud-edge collaborative industrial big data acquisition method, which specifically includes the following steps: Step S1: Connect the end-side hardware DTU to the industrial equipment.

[0021] This application employs a hardware-software decoupling approach to design the interaction mode between the end-side hardware DTU and the cloud. Existing data acquisition gateways, due to their built-in numerous protocol stacks to support communication with various industrial devices, often place high demands on hardware resources (such as memory and processors), directly leading to increased equipment costs and high prices. This solution, through hardware-software decoupling, designs the end-side hardware DTU as basic hardware focused on data acquisition and transparent transmission, while moving advanced functions such as protocol parsing and data processing to the cloud platform. This reduces hardware costs, enabling more economical deployment in a wide range of industrial environments and improving system flexibility and scalability.

[0022] The edge hardware DTU can utilize devices based on RedCap technology, possessing the ability to communicate with industrial equipment interfaces. RedCap technology is a 5G NR (New Radio) technology optimized for the Internet of Things (IoT). By reducing unnecessary 5G functions, it lowers the cost and power consumption of terminals and modules, while ensuring key 5G network native capabilities and coverage. This allows RedCap-based edge hardware DTUs to meet the communication needs of industrial field equipment at a lower cost while ensuring data transmission quality, serving as the technological foundation for large-scale industrial data acquisition.

[0023] In some embodiments, the end-side hardware DTU is connected to the industrial equipment via a cable; or, the end-side hardware DTU is connected to the industrial equipment according to pre-configured wireless communication parameters.

[0024] The device's terminal unit (DTU) is connected to the industrial equipment using either wired or wireless methods. For wired connections, appropriate cables (RS232, RS485, Ethernet, etc.) are required to connect the DTU's corresponding interface to the communication interface of the PLC or CNC device. For wireless connections, the wireless communication parameters (frequency, channel, encryption method, etc.) of both the DTU and the PLC / CNC device need to be configured to ensure proper communication.

[0025] In step S2, the edge computing module processes the end-side hardware DTU and industrial equipment to obtain a data transmission channel.

[0026] The edge computing module may include 5G edge computing. This application can introduce 5G LAN technology to achieve full-duplex communication between the end-side hardware DTU device and the cloud-based virtual data acquisition gateway. Based on the globally unique MAC address of each end-side hardware DTU device and its connected industrial equipment, this unique identifier supports industrial Layer 2 communication.

[0027] In some feasible implementations, 5G LAN technology uses ARP resolution to identify the MAC addresses of data acquisition targets (i.e., industrial equipment) for terminals and connected machine tools registered in the 5G LAN network, and performs edge computing to learn and store the MAC address table. Furthermore, the virtual data acquisition gateway can use edge computing to parse and classify the industrial protocols required for data acquisition from different types of industrial equipment, and divide LAN groups and broadcast / multicast communications according to different devices and protocol types. The virtual data acquisition gateway can identify and manage end-side hardware DTUs and industrial equipment based on MAC addresses, establishing a stable and reliable Layer 2 LAN communication channel. This effectively solves the compatibility issues of Layer 3 communication protocols and communication interruptions caused by IP address changes, ensuring the mechanism and recognizability of data transmission. This makes the virtual data acquisition gateway, end-side hardware DTUs, and industrial equipment form an organic data acquisition system.

[0028] In some embodiments, data security isolation and large-scale cloud migration can be achieved through 5G edge computing, while 5G private network slicing technology ensures the isolation of services and the security of data transmission. This solution utilizes 5G private network slicing technology to isolate data from the internet environment, customizing independent virtual network environments for different enterprises. Each slice has independent resource allocation, security policies, and management permissions, ensuring business isolation and secure control of enterprise data during transmission. 5G private network slicing technology also offers flexible bandwidth adjustment and low latency guarantees to meet the data transmission performance requirements of different application scenarios. Furthermore, through slice management, enterprises can adjust network and cloud resources as needed, optimize cost-effectiveness, and maximize resource utilization. Simultaneously, leveraging the wide-area coverage of 5G networks, it provides an access foundation for the wide-range, large-scale networking of industrial equipment.

[0029] Step S3: The end-side hardware DTU and industrial equipment are connected to the cloud-based virtual gateway module according to the data transmission channel.

[0030] In some embodiments, the cloud-based virtual gateway module includes a cloud-based industrial protocol library; the cloud-based industrial protocol library includes non-standard protocols and vendor SDK protocol packages.

[0031] In some embodiments, the cloud-based virtual gateway module may include a security unit, a protocol unit, a data acquisition unit, a forwarding unit, and a configuration management unit.

[0032] The security unit verifies identity information by obtaining the unique hardware identifier of the access device. Through 5G private network connection and network slicing, it ensures that all operations, including connection, data acquisition, and uploading, run within a secure network environment. The protocol unit incorporates over a hundred common industrial protocols, enabling data acquisition from CNC equipment, PLC devices, various I / O acquisition cards, sensors, meters, and robotic industrial equipment. The acquisition unit drives connected devices according to different protocols, collects device parameters based on subscription messages sent from the cloud, and stores the collected data in the virtual gateway's data cache according to defined rules. The forwarding unit supports forwarding the collected device data externally according to certain rules, supporting, but not limited to, communication methods such as HTTP, MQTT, and OPC UA, to third-party systems. The configuration management unit uses a virtual gateway to provide web services, supporting quick device access configuration via a browser. It also supports centralized configuration of the virtual gateway by the cloud-based virtual gateway module, including grouping acquisition parameters, setting acquisition frequency, and distributing acquisition policies to the virtual gateway.

[0033] In some embodiments, a series of virtual gateway services can be deployed in the cloud or on the MEC service side using Docker containers. Kubernetes is used to manage the virtual gateway containers, enabling service status monitoring, resource expansion, and recycling. Furthermore, the cloud-based virtual gateway module establishes communication with industrial equipment. The 5G DTU is configured with dedicated SIM card connection information, including APN, port mapping, and data pass-through. The cloud-based virtual gateway module can be configured with a fixed IP address and communication port assigned by the 5G DTU to establish a secure bidirectional communication connection with the industrial equipment.

[0034] Furthermore, the cloud-based virtual gateway module can also be deployed and run on the server side. It can dynamically expand computing resources according to the number of connected devices, realize one-to-many access, migrate the functions of multiple physical gateways to run on the server, realize resource sharing, and thus improve hardware utilization.

[0035] In some embodiments, a cloud-edge collaboration mechanism can be implemented, coupling 5G edge computing with a cloud-based virtual gateway module to operate within the same service framework. Through the application of 5G edge computing technology, including the deployment of intelligent protocol engines, resource loading, and computing power scheduling on edge computing nodes, collaboration between edge computing and the cloud-based virtual gateway module is achieved. This includes, but is not limited to, intelligent protocol adaptation and dynamic loading, efficient data caching, and intelligent scheduling of resource computing power, thereby comprehensively optimizing resource allocation on the cloud-based virtual gateway platform. This enables coordinated communication, identification, data caching, data parsing, and data acquisition actions throughout the data acquisition system. The cloud-edge collaboration mechanism can save cloud resources, reduce cloud storage and cloud computing costs, and improve data acquisition efficiency, thereby increasing overall resource utilization.

[0036] Step S4: The edge computing module determines whether the protocol type of the industrial equipment matches the built-in protocol.

[0037] In some embodiments, the method further includes: If the edge computing module determines that the protocol type of the industrial equipment does not match the built-in protocol, the edge computing module will parse the protocol type of the industrial equipment to obtain the current protocol type; The edge computing module matches the current protocol type with the cloud-based industrial protocol library, obtains the corresponding protocol packet, and sends it to the cloud-based virtual gateway module. The cloud-based virtual gateway module receives the corresponding protocol packets and completes the connection with the industrial equipment.

[0038] The edge computing module incorporates a built-in intelligent protocol engine to identify the communication protocol types supported by the currently connected devices. The cloud-based industrial protocol library contains hundreds of industrial protocols, including non-standard protocols and vendor-specific SDK protocol packages such as Focus and S7. When the protocol type in the cloud-based virtual gateway module does not match the current device type, the virtual data acquisition gateway invokes the protocol parsing algorithm in the edge computing intelligent protocol engine to decapsulate and parse the data packet frame format transmitted by the DTU, such as VLAN tags and specific byte sequences, thereby quickly identifying the protocol type and matching it with the industrial parsing protocols and their data frame format definitions in the cloud-based industrial protocol library. Subsequently, a protocol loading scheduling mechanism is activated to automatically retrieve and load the corresponding protocol package from the cloud-based industrial protocol library into the cloud-based virtual gateway module, ensuring seamless integration with various devices. Simultaneous loading and activation of multiple protocols are supported, ensuring that the system can process data streams from different devices simultaneously in complex multi-device environments.

[0039] Step S5: If yes, the edge computing module determines whether the data cache size matches the cloud resources.

[0040] In some embodiments, the method further includes: The edge computing module acquires and analyzes the data volume, collection frequency, and data dimensions of industrial equipment to obtain the data load change trend; The edge computing module processes the CPU utilization, memory usage, and network bandwidth of the edge computing nodes and the cloud-based virtual gateway module to obtain the current resource load capacity. The edge computing module calculates the real-time data cache size based on the data load change trend and the current resource load capacity. The edge computing module determines whether the real-time data cache size matches the cloud resources.

[0041] The edge computing module incorporates a resource loading engine that collects information such as data volume, collection frequency, and data dimensions from data acquisition devices (i.e., industrial equipment), and identifies data load trends through statistical analysis. Simultaneously, it monitors performance metrics of edge computing nodes and the cloud-based virtual gateway module, such as CPU utilization, memory usage, and network bandwidth, to assess current resource load capacity. It employs the Least Recently Used (LRU) algorithm for data caching to achieve fast data access and efficient utilization. The Dynamic Capacity Growth (DCG) algorithm automatically adjusts computing resource allocation based on real-time load, intelligently evaluating the real-time data cache size. If the cache size approaches a threshold and the cloud resources allocated to the current cloud-based virtual gateway module are strained, the module can be expanded; if the cache status is good, computing resources can be reallocated.

[0042] In some embodiments, Kubernetes container orchestration tools can dynamically deploy or scale Pods (container groups) based on the actual needs of current data caching and load conditions to adjust the cloud resources occupied by the cloud virtual gateway module. Each Pod can carry a portion of the functions of the cloud virtual gateway module, such as data caching and protocol conversion. When the resource loading engine in the edge computing module preprocesses the transparent data and finds that the data collection target device has a large data volume, increased device data collection frequency, and multiple data dimensions, Kubernetes can automatically increase the number of Pods and increase cache cloud resources to ensure that cloud resource allocation and cache load are optimally matched, improving resource utilization efficiency. If a Pod fails, Kubernetes will automatically restart or reschedule the Pod to ensure service continuity and reliability.

[0043] Step S6: If yes, the edge computing module determines whether the data processing volume matches the cloud computing power.

[0044] In some embodiments, the method further includes: The edge computing module processes the current data volume based on the machine learning ARIMA model to obtain the predicted data volume. If the predicted data processing volume is much greater than the current data processing volume, the edge computing module sends a resource upgrade message to the cloud virtual gateway module. The cloud-based virtual gateway module receives and processes resource upgrade information, and performs increase operations on cloud computing instances, CPU cores, and memory.

[0045] Specifically, the ARIMA machine learning model is used to predict the current data processing volume and allocate computing resources in advance. If the data processing volume increases significantly and the current cloud computing resources are scarce, a resource upgrade request can be sent to the cloud virtual gateway module; if cloud computing resources are abundant, step S7 can be performed.

[0046] Specifically, based on real-time changes in data processing volume, the cloud computing resources configured in the cloud-based virtual gateway module can be intelligently allocated and expanded. When the edge computing module predicts a large data processing volume through its model, it will coordinate with the virtual data acquisition gateway of the cloud-based virtual gateway module to increase resources such as cloud computing instances, CPU cores, and memory. When the data processing volume decreases, resources are automatically reduced to save costs. While continuously repeating this process, the edge computing module and the cloud-based virtual gateway module can combine real-time data acquisition computing capabilities to continuously learn and correct themselves, performing precise DCG quantification of computing resources. At the same time, edge computing collaboratively enhances data forwarding capabilities, ensuring the smooth operation of the data acquisition workflow. This ensures that data processing capabilities are closely matched with actual needs, thereby guaranteeing the efficient and stable operation of the system.

[0047] Step S7: If yes, the cloud-based virtual gateway module acquires data from the industrial equipment and completes the data acquisition operation.

[0048] The cloud-based virtual gateway module can integrate functions such as data acquisition, protocol parsing, data caching and storage, enabling data acquisition from various industrial devices.

[0049] In some embodiments, the method further includes: The cloud-based virtual gateway module obtains the collection parameter groups and collection frequency according to the industrial equipment type, and sends the collection parameter groups and collection frequency to the virtual gateway; The virtual gateway receives and configures the collection parameter packets and collection frequency.

[0050] In some feasible implementations, a data acquisition driver is loaded into the virtual gateway, and the corresponding data acquisition protocol is configured to realize data acquisition and parsing, data writing, and file transfer for different industrial devices.

[0051] In some feasible implementations, a portion of the data is stored in a real-time database for rapid processing and computation, while another portion is stored in a relational database for efficient storage of critical data and for resuming interrupted downloads. The computing resources of the virtual gateway can be dynamically expanded, and the data caching is not limited by the control of the physical gateway hardware itself.

[0052] In some feasible implementations, data uploading to the cloud involves establishing a connection between an MQTT client and an MQTT server in the cloud platform, and then uploading JSON-formatted device data to the cloud-based virtual gateway module.

[0053] In some feasible implementations, virtual gateway data forwarding can forward data from various industrial devices to third-party systems according to configured rules, supporting, but not limited to, communication methods such as HTTP, MQTT, and OPC UA.

[0054] In some feasible implementations, the virtual gateway supports the operation of multiple nodes, enabling the access of multiple industrial devices, and supports various device models as well as various IO acquisition cards, sensors, electricity meters, and robotic industrial equipment.

[0055] In some feasible implementations, the data acquisition parameter groups and acquisition frequencies can be flexibly set according to the needs of different devices, and the configuration can be distributed to the virtual gateway. The virtual gateway provides remote automatic upgrade functionality, allowing online updates to the software within the virtual gateway, including basic services, acquisition adapters, etc.; the cloud-based virtual gateway module provides software upgrade and management services, and compared with traditional physical gateways, centralized management is more convenient for software upgrades and maintenance.

[0056] The cloud-edge collaborative industrial big data acquisition method provided in the above embodiments can connect the edge hardware DTU with industrial equipment and edge computing modules, thereby separating hardware data acquisition and cloud data processing, reducing hardware costs. The collaborative design of cloud and edge computing makes the dynamic configuration of cloud resources more flexible, and can adjust computing power according to actual needs, eliminating the cumbersome process of replacing the entire gateway device. By using an edge computing module, efficient, secure, and flexible data acquisition and transmission can be further realized, thereby comprehensively improving the efficiency of industrial big data acquisition and processing.

[0057] Please see Figure 2 Another embodiment of this application provides a cloud-edge collaborative industrial big data acquisition device, which includes: The end-side hardware DTU is used to connect to industrial equipment; it connects to the cloud-based virtual gateway module according to the data transmission channel.

[0058] The edge computing module is used to process the end-side hardware DTU and industrial equipment to obtain data transmission channels; determine whether the protocol type of the industrial equipment matches the built-in protocol; if so, determine whether the data cache size matches the cloud resources; if so, determine whether the data processing volume matches the cloud computing power.

[0059] Industrial equipment used to connect to cloud-based virtual gateway modules based on data transmission channels.

[0060] The cloud-based virtual gateway module is used to acquire data from industrial equipment and complete data acquisition operations.

[0061] The specific limitations of the cloud-edge collaborative industrial big data acquisition device provided in this embodiment can be found in the embodiment of the cloud-edge collaborative industrial big data acquisition method described above, and will not be repeated here. Each module in the above-described cloud-edge collaborative industrial big data acquisition device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0062] This application provides a computer device that may include a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it causes the processor to perform the steps of a cloud-edge collaborative industrial big data acquisition method as described in any of the above embodiments.

[0063] The working process, working details, and technical effects of the computer equipment provided in this embodiment can be found in the embodiment of a cloud-edge collaborative industrial big data acquisition method described above, and will not be repeated here.

[0064] This application provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of a cloud-edge collaborative industrial big data acquisition method as described in any of the above embodiments. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0065] The working process, working details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the embodiment of a cloud-edge collaborative industrial big data acquisition method described above, and will not be repeated here.

[0066] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A cloud-edge collaborative industrial big data acquisition method, characterized in that, The method includes: End-side hardware DTU connects to industrial equipment; The edge computing module processes the end-side hardware DTU and the industrial equipment to obtain a data transmission channel; The end-side hardware DTU and industrial equipment are connected to the cloud-based virtual gateway module through the data transmission channel; The edge computing module determines whether the protocol type of the industrial equipment matches the built-in protocol. If so, the edge computing module determines whether the data cache size matches the cloud resources; If so, the edge computing module determines whether the data processing volume matches the cloud computing power; If so, the cloud-based virtual gateway module acquires data from the industrial equipment and completes the data acquisition operation.

2. The cloud-edge collaborative industrial big data acquisition method according to claim 1, characterized in that, The method further includes: The end-side hardware DTU is connected to the industrial equipment via cables; Alternatively, the end-side hardware DTU connects to the industrial equipment according to pre-configured wireless communication parameters.

3. The cloud-edge collaborative industrial big data acquisition method according to claim 2, characterized in that, The cloud-based virtual gateway module includes a cloud-based industrial protocol library; the cloud-based industrial protocol library includes non-standard protocols and vendor SDK protocol packages.

4. The cloud-edge collaborative industrial big data acquisition method according to claim 3, characterized in that, The method further includes: If the edge computing module determines that the protocol type of the industrial equipment does not match the built-in protocol, the edge computing module parses the protocol type of the industrial equipment to obtain the current protocol type; The edge computing module matches the current protocol type with the cloud-based industrial protocol library to obtain the corresponding protocol packet and sends it to the cloud-based virtual gateway module. The cloud-based virtual gateway module receives the corresponding protocol packet and completes the connection with the industrial equipment.

5. The cloud-edge collaborative industrial big data acquisition method according to claim 1, characterized in that, The method further includes: The edge computing module acquires and analyzes the data volume, collection frequency, and data dimensions of industrial equipment to obtain the data load change trend; The edge computing module processes the CPU utilization, memory usage, and network bandwidth of the edge computing nodes and the cloud-based virtual gateway module to obtain the current resource load capacity. The edge computing module calculates the real-time data cache size based on the data load change trend and the current resource load capacity. The edge computing module determines whether the real-time data cache size matches the cloud resources.

6. The cloud-edge collaborative industrial big data acquisition method according to claim 1, characterized in that, The method further includes: The edge computing module processes the current data volume based on the machine learning ARIMA model to obtain the predicted data volume. If the predicted data processing volume is much larger than the current data processing volume, the edge computing module sends a resource upgrade information to the cloud virtual gateway module. The cloud-based virtual gateway module receives and processes the resource upgrade information, and performs increase operations on the cloud computing instance, the number of CPU cores, and the memory.

7. The cloud-edge collaborative industrial big data acquisition method according to claim 1, characterized in that, The method further includes: The cloud-based virtual gateway module obtains the collection parameter groups and collection frequency according to the industrial equipment type, and sends the collection parameter groups and collection frequency to the virtual gateway. The virtual gateway receives the collection parameter packets and the collection frequency and configures them.

8. A cloud-edge collaborative industrial big data acquisition device, characterized in that, The device includes: The end-side hardware DTU is used to connect to industrial equipment; it is connected to the cloud-based virtual gateway module according to the data transmission channel. The edge computing module is used to process the terminal hardware DTU and the industrial equipment to obtain a data transmission channel; determine whether the protocol type of the industrial equipment matches the built-in protocol; if so, determine whether the data cache size matches the cloud resources; if so, determine whether the data processing volume matches the cloud computing power. Industrial equipment for connecting to a cloud-based virtual gateway module via the data transmission channel; The cloud-based virtual gateway module is used to acquire data from industrial equipment and complete data acquisition operations.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cloud-edge collaborative industrial big data acquisition method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cloud-edge collaborative industrial big data acquisition method as described in any one of claims 1 to 7.

Images