Device data processing method and apparatus based on edge computing and distributed computing

Abstract

The application discloses a device data processing method and device based on edge computing and distributed computing, and belongs to the technical field of Internet of Things. The method comprises the following steps: a cloud server acquires device configuration information corresponding to at least one device node in a target device network; the cloud server sends the device configuration information to a target edge server corresponding to the target device network; the target edge server sends a data acquisition request to a device node corresponding to a device network address; the device node corresponding to the device network address sends device attribute data corresponding to a data point location identifier to the target edge server; the target edge server sends the device attribute data to the cloud server; and the cloud server performs a data processing operation on the device attribute data to obtain a data processing result. According to the technical scheme provided in the application, the edge end requests device data from the device node according to the configuration information issued by the cloud end and reports the cloud end to perform data processing, so that the configuration flexibility of the Internet of Things system is improved.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a device data processing method and apparatus based on edge computing and distributed computing. Background Technology

[0002] The Internet of Things (IoT) is an extension and expansion of the Internet, forming a vast network that combines various information sensing devices with the network, enabling interconnection and interoperability of people, machines, and things anytime, anywhere.

[0003] In related technologies, the data acquisition end in the Internet of Things (IoT) system is realized through an industrial gateway that integrates preset device protocols and device configuration information. After receiving the device data, the industrial gateway forwards the data to the server, thereby realizing the data acquisition and transmission of IoT devices.

[0004] However, the industrial gateways in related technologies are deeply bound to pre-set device protocols and device configuration information, resulting in poor configuration flexibility and scalability of IoT systems. Summary of the Invention

[0005] This application provides a device data processing method and apparatus based on edge computing and distributed computing, which can effectively reduce the coupling between cloud servers, edge servers and device nodes, improve the configuration flexibility and scalability of IoT systems, and also reduce the deployment cost of IoT systems.

[0006] According to a first aspect of the embodiments of this application, a device data processing method based on edge computing and distributed computing is provided. The method is applied to a distributed Internet of Things (IoT) system, the distributed IoT system including a cloud server, an edge server, and device nodes. The method includes: the cloud server acquiring device configuration information corresponding to at least one device node in a target device network, the device configuration information including a device network address and a data point identifier; the cloud server sending the device configuration information to a target edge server corresponding to the target device network; the target edge server sending a data acquisition request to the device node corresponding to the device network address, the data acquisition request including the data point identifier; the device node corresponding to the device network address sending device attribute data corresponding to the data point identifier to the target edge server; the target edge server sending the device attribute data to the cloud server; and the cloud server performing data processing operations on the device attribute data to obtain a data processing result.

[0007] In one possible design, the distributed IoT system further includes a message relay server, wherein the target edge server sends the device attribute data to the cloud server, including: the target edge server determining the network connection status; the target edge server sending the device attribute data to the message relay server when the network connection status meets the target network status conditions; and the message relay server sending the device attribute data to the cloud server.

[0008] In one possible design, after the target edge server determines the network connection status, the method further includes: if the network connection status does not meet the target network status conditions, the target edge server stores the device attribute data in a target database; and in response to the network connection status meeting the target network status conditions, the target edge server sends the device attribute data to the message relay server.

[0009] In one possible design, sending the device attribute data to the message relay server includes: the target edge server determining a message topic corresponding to the target device network; the target edge server sending the device attribute data to a data partition corresponding to the message topic, wherein the message relay server includes the data partition.

[0010] In one possible design, the method further includes: the target edge server sending server address information and token information to the cloud server; the cloud server generating verification information based on the server address information and the token information; the cloud server sending the verification information to the target edge server; the target edge server performing verification processing on the verification information to obtain a verification result; the target edge server sending response information to the cloud server if the verification result indicates that the verification passed; and the cloud server sending the device configuration information to the target edge server corresponding to the target device network, including: the cloud server sending the device configuration information to the target edge server in response to receiving the response information.

[0011] In one possible design, the cloud server generates verification information based on the server address information and the token information, including: in response to receiving the server address information and the token information, the cloud server generates signature information, a timestamp, an event identifier, and a verification message; the cloud server performs a fusion process on the signature information, the timestamp, the event identifier, and the server address information to obtain fused address information; and the cloud server generates the verification information based on the fused address information and the verification message information.

[0012] In one possible design, the edge server performs verification processing on the verification information to obtain a verification result, including: the edge server determining the interval between the timestamp and the target time; if the interval is less than a duration threshold, the edge server encrypts the token information, the timestamp, and the event identifier to obtain encrypted information; the edge server compares the encrypted information with the signature information to obtain the verification result.

[0013] According to a second aspect of the embodiments of this application, a device data processing method based on edge computing and distributed computing is provided. The method is applied to a cloud server in a distributed Internet of Things (IoT) system. The method includes: obtaining device configuration information corresponding to at least one device node in a target device network, the device configuration information including a device network address and a data point identifier; sending the device configuration information to a target edge server corresponding to the target device network; receiving device attribute data corresponding to the data point identifier, the device attribute data being data returned by the device node corresponding to the device network address in response to a data acquisition request sent by the target edge server; and performing data processing operations on the device attribute data to obtain a data processing result.

[0014] In one possible design, obtaining device configuration information corresponding to at least one device node in the target device network includes: receiving device protocol information and device configuration information corresponding to the at least one device node sent by a terminal, wherein the device configuration information matches the device protocol information, and the device protocol information and device configuration information are configuration information entered by the terminal.

[0015] In one possible design, receiving the device attribute data corresponding to the data point identifier includes: receiving subscription information sent by a message relay server, the subscription information indicating that the message relay server has received device attribute data sent by the target edge server, the device attribute data being sent by the target edge server to the message relay server when the network connection status meets the target network status conditions; sending a subscription data acquisition request to the message relay server; and receiving the device attribute data sent by the message relay server.

[0016] In one possible design, the method further includes: receiving server address information and token information sent by the target edge server; generating verification information based on the server address information and the token information; sending the verification information to the target edge server; the step of sending the device configuration information to the target edge server corresponding to the target device network includes: in response to receiving a response information sent by the target edge server, sending the device configuration information to the target edge server, wherein the response information is returned by the target edge server when the verification result corresponding to the verification information indicates that the verification has passed.

[0017] In one possible design, generating verification information based on the server address information and the token information includes: in response to receiving the server address information and the token information, generating signature information, timestamp, event identifier, and verification message information; fusing the signature information, timestamp, event identifier, and server address information to obtain fused address information; and generating the verification information based on the fused address information and the verification message information.

[0018] According to a third aspect of the embodiments of this application, a device data processing method based on edge computing and distributed computing is provided. The method is applied to an edge server in a distributed Internet of Things (IoT) system. The method includes: receiving device configuration information sent by a cloud server, wherein the device configuration information is configuration information corresponding to at least one device node in a target device network, the target device network being the device network corresponding to the edge server, and the device configuration information includes a device network address and a data point identifier; sending a data acquisition request to the device node corresponding to the device network address, the data acquisition request including the data point identifier; receiving device attribute data corresponding to the data point identifier sent by the device node; and sending the device attribute data to the cloud server.

[0019] In one possible design, sending the device attribute data to the cloud server includes: determining the network connection status; if the network connection status meets the target network status conditions, sending the device attribute data to a message relay server, the message relay server being used to send the data message subscribed to by the cloud server to the cloud server, the data message including the device attribute data; if the network connection status does not meet the target network status conditions, storing the device attribute data in a target database; and in response to the network connection status meeting the target network status conditions, sending the device attribute data to the message relay server.

[0020] In one possible design, sending the device attribute data to the message relay server includes: determining the message topic corresponding to the target device network; and sending the device attribute data to the data partition corresponding to the message topic, wherein the message relay server includes the data partition.

[0021] In one possible design, the method further includes: sending server address information and token information to the cloud server; receiving verification information sent by the cloud server, the verification information being generated based on the server address information and the token information, the verification information including response information; performing verification processing on the verification information to obtain a verification result; and, if the verification result indicates that the verification passed, sending the response information to the cloud server.

[0022] In one possible design, the verification information includes signature information, timestamp, and event identifier. The verification processing of the verification information to obtain the verification result includes: determining the interval between the timestamp and the target time; if the interval is less than a duration threshold, encrypting the token information, the timestamp, and the event identifier to obtain encrypted information; and comparing the encrypted information with the signature information to obtain the verification result.

[0023] According to a fourth aspect of the embodiments of this application, a device data processing apparatus based on edge computing and distributed computing is provided. The apparatus is applied to a cloud server in a distributed Internet of Things (IoT) system. The apparatus includes: a configuration information acquisition module, configured to acquire device configuration information corresponding to at least one device node in a target device network, the device configuration information including a device network address and a data point identifier; a configuration information sending module, configured to send the device configuration information to a target edge server corresponding to the target device network; a device data receiving module, configured to receive device attribute data corresponding to the data point identifier, the device attribute data being data returned by the device node corresponding to the device network address in response to a data acquisition request sent by the target edge server; and a device data processing module, configured to perform data processing operations on the device attribute data to obtain a data processing result.

[0024] According to a fifth aspect of the embodiments of this application, a device data processing apparatus based on edge computing and distributed computing is provided. The apparatus is applied to an edge server in a distributed Internet of Things (IoT) system. The apparatus includes: a configuration information receiving module, configured to receive device configuration information sent by a cloud server, wherein the device configuration information is configuration information corresponding to at least one device node in a target device network, the target device network being the device network corresponding to the edge server, and the device configuration information includes a device network address and a data point identifier; a data request sending module, configured to send a data acquisition request to the device node corresponding to the device network address, the data acquisition request including the data point identifier; a device data receiving module, configured to receive device attribute data corresponding to the data point identifier sent by the device node; and a device data sending module, configured to send the device attribute data to the cloud server.

[0025] According to a sixth aspect of the embodiments of this application, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the device data processing method based on edge computing and distributed computing of the second aspect described above.

[0026] According to a seventh aspect of the present application, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the device data processing method based on edge computing and distributed computing described in the third aspect above.

[0027] According to an eighth aspect of the present application, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored in the storage medium, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the device data processing method based on edge computing and distributed computing described in the second aspect above.

[0028] According to a ninth aspect of the present application, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the device data processing method based on edge computing and distributed computing described in the third aspect above.

[0029] According to a tenth aspect of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the device data processing method based on edge computing and distributed computing described in the second aspect above.

[0030] According to an eleventh aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the device data processing method based on edge computing and distributed computing described in the third aspect above.

[0031] The technical solution provided in this application can bring the following beneficial effects:

[0032] A distributed IoT system based on a distributed edge computing architecture comprises cloud servers, edge servers, and device nodes in a device network. The cloud server can obtain and distribute device configuration information corresponding to the device nodes in the device network, achieving unified management of device configurations. The edge server, based on the distributed device configuration information, can request device attribute data from the corresponding device nodes and report it to the cloud server. The cloud server then processes the device attribute data to obtain the data processing results. Therefore, the cloud server can achieve multi-source data acquisition, transmission, and processing across networks and nodes at the edge by simply distributing device configuration information. This effectively reduces the coupling between the cloud server, edge server, and device nodes, improves the configuration flexibility and scalability of the IoT system, and also reduces the deployment cost of the IoT system. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the hardware environment of a distributed Internet of Things system provided in one embodiment of this application;

[0035] Figure 2 This is a schematic diagram of the technical architecture of a distributed Internet of Things system provided in one embodiment of this application;

[0036] Figure 3 This is a schematic diagram of the data flow of a distributed Internet of Things system provided in one embodiment of this application;

[0037] Figure 4 This is a flowchart of a device data processing method based on edge computing and distributed computing provided in one embodiment of this application. Figure 1 ;

[0038] Figure 5 This is a flowchart of a device data processing method based on edge computing and distributed computing provided in one embodiment of this application. Figure 2 ;

[0039] Figure 6 An exemplary diagram illustrates the interaction process for verification between a cloud server and an edge server;

[0040] Figure 7 An exemplary diagram illustrates the process of data transfer between cloud servers and edge servers via message topics and data partitions;

[0041] Figure 8 An exemplary diagram illustrates a process for configuration distribution and data reporting between a cloud server and an edge server;

[0042] Figure 9 An exemplary diagram illustrates a data processing flow of a cloud server;

[0043] Figure 10 This application provides a framework for a device data processing apparatus based on edge computing and distributed computing, according to one embodiment of the present application. Figure 1 ;

[0044] Figure 11 This application provides a framework for a device data processing apparatus based on edge computing and distributed computing, according to one embodiment of the present application. Figure 2 ;

[0045] Figure 12 This is a structural block diagram of a computer device provided in one embodiment of this application. Detailed Implementation

[0046] The device data processing method based on edge computing and distributed computing provided in this application relates to cloud technology, which will be described below to facilitate understanding by those skilled in the art.

[0047] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or local area network to achieve data computing, storage, processing, and sharing.

[0048] Cloud computing is a computing model that distributes computing tasks across a large pool of computers, enabling various application systems to access computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, resources in the "cloud" appear infinitely scalable, readily available, on-demand, and expandable, with payment based on usage.

[0049] As a provider of fundamental cloud computing capabilities, a cloud resource pool (referred to as a cloud platform, generally called an IaaS (Infrastructure as a Service) platform) is established. Various types of virtual resources are deployed in the resource pool for external customers to choose from. The cloud resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices.

[0050] Based on logical function, a PaaS (Platform as a Service) layer can be deployed on top of the IaaS (Infrastructure as a Service) layer, and a SaaS (Software as a Service) layer can be deployed on top of the PaaS layer. Alternatively, SaaS can be deployed directly on top of IaaS. PaaS is a platform for running software, such as databases and web containers. SaaS refers to various types of business software, such as web portals and bulk SMS senders. Generally speaking, SaaS and PaaS are upper layers compared to IaaS.

[0051] Cloud storage is a new concept that extends and develops from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as a storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of storage devices of various types (storage devices are also called storage nodes) in the network to work together through application software or application interfaces to provide data storage and business access functions to the outside world.

[0052] Currently, the storage method in storage systems is as follows: Logical volumes are created, and during creation, physical storage space is allocated to each logical volume. This physical storage space may consist of a single storage device or the disks of several storage devices. Clients store data on a logical volume, which means storing the data on the file system. The file system divides the data into many parts, each part being an object. Each object contains not only the data but also additional information such as a data identifier (ID, IDentity). The file system writes each object to the physical storage space of that logical volume, and it records the storage location information of each object. Therefore, when a client requests access to data, the file system can allow the client to access the data based on the storage location information of each object.

[0053] The process by which a storage system allocates physical storage space to a logical volume is as follows: the physical storage space is pre-divided into strips according to the capacity estimate of the objects stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the grouping of Redundant Array of Independent Disks (RAID). A logical volume can be understood as a strip, thus allocating physical storage space to the logical volume.

[0054] A database, simply put, can be viewed as an electronic filing cabinet—a place to store electronic files, where users can perform operations such as adding, querying, updating, and deleting data. A "database" is a collection of data stored together in a certain way, capable of being shared by multiple users, with minimal redundancy, and independent of application programs.

[0055] A Database Management System (DBMS) is a computer software system designed to manage databases, generally possessing basic functions such as storage, retrieval, security, and backup. DBMSs can be classified according to the database model they support, such as relational or XML (Extensible Markup Language); or according to the type of computer they support, such as server clusters or mobile devices; or according to the query language used, such as SQL (Structured Query Language) or XQuery; or according to performance priorities, such as maximum scale or maximum operating speed; or other classification methods. Regardless of the classification method used, some DBMSs can cross categories, for example, supporting multiple query languages ​​simultaneously.

[0056] Big data refers to data sets that cannot be captured, managed, and processed within a certain timeframe using conventional software tools. It represents massive, rapidly growing, and diverse information assets that require new processing models to achieve stronger decision-making, insightful discovery, and process optimization capabilities. With the advent of the cloud era, big data has attracted increasing attention. Big data requires specialized technologies to effectively process large amounts of data within a tolerable timeframe. Technologies suitable for big data include massively parallel processing databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the internet, and scalable storage systems.

[0057] Cloud IoT aims to connect the information sensed and commands received by traditional IoT devices to the Internet, truly achieving networking. It also enables massive data storage and computation through cloud computing technology. Due to the nature of IoT, which involves connecting things to each other and sensing the current operating status of each "object" in real time, a large amount of data information is generated in this process. How to aggregate this information and how to sift out useful information from the massive amount of data to support decision-making for future development have become key issues affecting the development of IoT. As a result, IoT cloud based on cloud computing and cloud storage technology has become a powerful support for IoT technology and applications.

[0058] A private cloud is a cloud infrastructure and hardware / software resources created within a firewall, allowing various departments within an organization or enterprise to share resources within a data center. In addition to hardware resources, creating a private cloud typically involves cloud equipment (IaaS, Infrastructure as a Service) software.

[0059] Private cloud computing also comprises three layers: cloud hardware, cloud platform, and cloud services. The difference is that cloud hardware consists of the user's own personal computer or server, rather than the cloud computing provider's data center. Cloud computing providers build data centers to provide public cloud services to millions of users, thus requiring hundreds of thousands or even millions of servers. Private cloud computing, for individuals, serves only family and friends; for businesses, it serves only their own employees, customers, and suppliers. Therefore, an individual's or business's own personal computer or server is sufficient to provide cloud services.

[0060] The device data processing method based on edge computing and distributed computing provided in this application embodiment can be applied to distributed Internet of Things (IoT) systems. The aforementioned distributed IoT system can be a distributed IoT platform implemented based on a distributed computing architecture and edge computing capabilities. The aforementioned technologies such as cloud computing, cloud storage, database, database management system, big data, cloud IoT, and private cloud can all be applied in the aforementioned distributed IoT system. Specific application methods will be described in detail in specific embodiments.

[0061] Before introducing the embodiments provided in this application, a brief introduction to the relevant terms or nouns that may be involved in the embodiments of this application will be given here so that those skilled in the art can understand them.

[0062] Edge computing refers to an open platform that integrates network, computing, storage, and application capabilities, located close to the source of objects or data, to provide services at the nearest edge. Applications originate at the edge, resulting in faster network service responses and meeting the industry's basic needs in real-time business, application intelligence, security, and privacy protection. Edge computing exists between physical entities and industrial connections, or at the top of physical entities.

[0063] Private cloud: Built for the exclusive use of a target user, thus providing the most effective control over data, security, and quality of service. This target user owns the infrastructure and can control how applications are deployed on that infrastructure. Private clouds can be deployed within the firewall of an enterprise data center or in a secure hosting facility; the core attribute of a private cloud is dedicated resources.

[0064] Industrial protocols provide common application layers and device descriptions for open fieldbus networks such as DeviceNet (a fieldbus standard used in automation technology), ControlNet (a real-time fieldbus network for the control layer), Component (an open network between sensors and controllers), and EtherNet / IP (an industrial Ethernet communication protocol). Built on a single, media-independent platform, they provide seamless communication from the industrial field to enterprise management, allowing users to integrate information related to safety, control, synchronization, motion, messaging, and configuration across different networks. Common industrial connectivity protocols include ModBus and OPC.

[0065] ModBus: ModBus is a serial communication protocol developed for communication using Programmable Logic Controllers (PLCs). ModBus has become an industry standard for communication protocols in the industrial field and is now a common connection method between industrial electronic devices.

[0066] The Internet of Things (IoT) refers to the use of various information sensors, radio frequency identification (RFID) technology, global positioning systems (GPS), infrared sensors, laser scanners, and other devices and technologies to collect real-time information on any object or process that needs to be managed, connected, or interacted with. This information includes sound, light, heat, electricity, mechanics, chemistry, biology, location, and other necessary data. Through various possible network access methods, ubiquitous connections between things and between things and people are achieved, enabling intelligent perception, identification, and management of objects and processes.

[0067] Kafka is a high-throughput distributed publish-subscribe messaging system that can handle all action streams from consumers on a website. A Kafka cluster contains one or more servers, called brokers. Each message published to the Kafka cluster has a category called a topic. (Physically, messages from different topics are stored separately; logically, although messages from a single topic are stored on one or more brokers, users only need to specify the topic to produce or consume data without worrying about where the data is stored). A partition is a physical concept; each topic contains one or more partitions. A producer is responsible for publishing messages to the Kafka broker. A consumer is a client that reads messages from the Kafka broker. Each consumer belongs to a specific consumer group (a groupname can be specified for each consumer; if no group name is specified, it belongs to the default group).

[0068] Redis (Remote Dictionary Server) is an open-source, network-enabled, in-memory or persistent, log-structured key-value database written in ANSI C, and provides APIs (Application Programming Interfaces) in multiple languages.

[0069] MySQL is a relational database management system. Relational databases store data in different tables instead of putting all the data in one large repository, which increases speed and improves flexibility.

[0070] MQTT (Message Queuing Telemetry Transport) is a client-server message publish / subscribe transport protocol. The MQTT protocol is lightweight, simple, open, and easy to implement, making it widely applicable. It is used in many situations, including constrained environments such as machine-to-machine communication and the Internet of Things (IoT). It is widely used in sensors communicating via satellite links, medical devices making occasional dial-up calls, smart homes, and some miniaturized devices.

[0071] EMQ X is a highly scalable and highly available distributed MQTT message server that also supports IoT protocol access. It is suitable for IoT, machine-to-machine communication and mobile applications, and can handle tens of millions of concurrent clients.

[0072] A Virtual Private Cloud (VPC) is a user-created custom private network.

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0074] Please refer to Figure 1 This diagram illustrates the hardware environment of a distributed Internet of Things (IoT) system according to an embodiment of this application. The distributed IoT system includes: a terminal 10, a cloud server 20, an edge server 30, and a device 40.

[0075] Terminal 10 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Optionally, the management application corresponding to the cloud server 20 can run on the terminal 10. The target object can configure the cloud server 20 through the configuration page corresponding to the management application. For example, when adding the device 40 in a distributed IoT system, it is necessary to enter the device protocol corresponding to the device 40 and the device configuration information corresponding to the device protocol, such as the network address and data point of the device 40, through the configuration page corresponding to the terminal 10.

[0076] The cloud server 20 can manage the edge server 30 and the device 40 through the technologies introduced above to implement the above cloud IoT application. The cloud server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the cloud server 20 is a private cloud server. Optionally, the cloud server 20 provides back-end services for multiple edge servers 30 and multiple terminals 10 at the same time, such as configuration distribution, data management, etc.

[0077] Compared with the cloud server 20, the edge server 30 is closer to the device 40, so it can process and analyze data more quickly and achieve linkage control in the first time. Similarly, the edge server 30 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Optionally, the edge server 30 and its corresponding device 40 are in the same device network. The above distributed IoT system can include multiple device networks, and each device network includes the corresponding edge server 30 and device 40.

[0078] The above device 40 can generate device data that needs to be collected and upload the device data to the edge server 30. The device 40 can be any device that can generate data, such as an electronic device including various information sensors (for example: environmental temperature sensors, environmental humidity sensors, gas concentration sensors, etc.). Optionally, the above electronic devices include terminal devices of users, factory production devices, electronic devices in buildings, etc. Optionally, the terminal devices include but are not limited to smart phones, tablets, laptop computers, desktop computers, smart home appliances, smart cars, smart speakers, smart watches and other terminal devices. Optionally, the factory production devices include but are not limited to production line devices, fans, heaters, radiators, measuring devices, chargers, transformers, valves, motors and other devices. Optionally, the electronic devices in buildings include but are not limited to circuit management devices, access control devices.

[0079] Optionally, terminal 10, cloud server 20, edge server 30, and device 40 can communicate with each other via network 50. Optionally, terminal 10, cloud server 20, edge server 30, and device 40 can be directly or indirectly connected to each other via wired or wireless communication; this application does not impose any limitations on this. Optionally, edge server 30 and device 40 can communicate with each other via device network 60, and edge server 30 and device 40 can be directly or indirectly connected via wired or wireless communication; this application does not impose any limitations on this.

[0080] The above embodiments describe the hardware environment of the distributed Internet of Things system provided in this application. The following will combine... Figure 2 The technical architecture of this distributed Internet of Things system will be described. Figure 2 A schematic diagram of the technical architecture of a distributed Internet of Things system provided in one embodiment of this application is shown.

[0081] like Figure 2 As shown, the technical architecture of the aforementioned distributed IoT system can be divided into a cloud side and an edge side. Optionally, the cloud side includes cloud servers, and the edge side includes edge servers and corresponding device clusters. The cloud side provides basic product services for this distributed IoT system, including but not limited to modules such as device management (configuration information such as channels, devices, locations, and rules), data preview and rule calculation, platform management (operation logs, exception records), asset management, configuration management, and data processing. Furthermore, the cloud side also includes PaaS and IaaS, with PaaS including data storage services. Optionally, the data storage services are implemented based on the remote dictionary service Redis, the relational database management system MySQL, the time-series, event, and metric database InfluxDB, the distributed publish-subscribe messaging system Kafka, and the distributed message queue telemetry transmission message server EMQX. Edge-side services include modules such as device configuration management, data acquisition, task scheduling, and data forwarding.

[0082] In some practical IoT application scenarios, such as industrial settings, industrial production groups may comprise multiple production plants. The aforementioned distributed IoT system with its central hub and multiple nodes can be well-suited to this multi-plant production model. The central node corresponds to the entire group, and each plant corresponds to at least one node. The central node can be implemented using a cloud server, while each node can be implemented using an edge server corresponding to its respective plant.

[0083] In the aforementioned distributed architecture with one central hub and multiple nodes, the data flow is quite complex. The following section will discuss this in conjunction with... Figure 3 The data flow in the aforementioned distributed Internet of Things system will be explained. Figure 3This illustration shows a schematic diagram of the data flow of a distributed Internet of Things (IoT) system provided in one embodiment of this application.

[0084] On the one hand, such as Figure 3 As shown, the cloud-side (group-side) cloud servers include an industrial data acquisition and configuration server and a data management server. The industrial data acquisition and configuration server is used for remote configuration of industrial equipment protocols (such as ModBus), including modules for equipment management (e.g., configuring information such as channels, equipment, and locations), rule calculation, data preview, and platform management. It also distributes configurations through an adaptation service. The data management server manages the acquired and processed data, including core modules such as data visualization (configuration), data models, data assets, data storage, and computation. It provides data display and management services for edge devices, demonstrating the value of the acquired data.

[0085] On the other hand, such as Figure 3 As shown, the edge side belongs to the device-to-source layer. The edge server on the edge side connects with the edge devices and mainly performs industrial protocol parsing, data acquisition, preprocessing, and data forwarding. This includes modules such as device configuration, data acquisition, task scheduling, and data forwarding, supporting distributed scheduling. Furthermore, the edge server and the cloud server forward data via Kafka, supporting data interruption resumption to ensure no data loss.

[0086] In this architecture, the data transmission process mainly includes configuration distribution, data reporting, and data forwarding. The cloud server is responsible for overall configuration management and data management visualization. It uniformly maintains the configuration information of devices in various device networks (such as the device networks corresponding to different factory areas) and distributes the configuration information of devices in different device networks to different device networks through an adaptation service, thus achieving configuration distribution. After receiving the device configuration, the edge servers corresponding to each device network schedule the data and can then interface with the corresponding devices and collect data through edge adapters.

[0087] Specifically, the edge server interfaces with its corresponding device via protocols. For example, it establishes a connection using IP address and port number to achieve device protocol integration. Simultaneously, the edge server collects data from the device and sends the collected data to the corresponding topic in Kafka. If a network failure occurs, the data is stored in Redis, and a background service monitors the connection status, resuming data transmission once the network recovers.

[0088] After receiving the data, the industrial data acquisition and configuration server performs data rule calculations, including data transformation, and then sends the calculated data to a forwarding topic in Kafka for data forwarding. The data management server subscribes to the data in the corresponding forwarding topic and displays it visually.

[0089] The above embodiments illustrate the application of the device data processing method based on edge computing and distributed computing provided in this application to a distributed Internet of Things system. The following describes the method embodiments provided in this application.

[0090] Please refer to Figure 4 It illustrates the flow of a device data processing method based on edge computing and distributed computing provided in one embodiment of this application. Figure 1 This method can be applied to the aforementioned distributed IoT system. Optionally, the distributed IoT system includes a cloud server, an edge server, and device nodes. The method may include the following steps (401-406).

[0091] Step 401: The cloud server obtains the device configuration information corresponding to at least one device node in the target device network.

[0092] Optionally, the device configuration information includes the device network address and data point identifier. The device network address refers to the network address corresponding to the device node. Each device node corresponds to at least one data point, and the data point identifier can characterize the data point. The data point is related to the device attributes of the device corresponding to the device node and can be a data acquisition point. For example, the device corresponding to the device node may include a fan. A fan has multiple attributes such as speed and temperature; therefore, the data points corresponding to the fan would include points corresponding to attributes such as speed and temperature, collecting fan speed data, temperature data, etc. This application embodiment does not limit the type of device corresponding to the device node. The device node can correspond to at least one device; for example, a device node can correspond to the production equipment of an entire production line, or it can correspond to a single, independent device.

[0093] Optionally, the aforementioned device configuration information also includes channel information corresponding to the device nodes, whereby the channel information represents the device category. The classification of device categories can be done in various ways, such as by the network in which the device is located, by the building in which the device is located, by the workshop in which the device's function is located, by the device's function, by the production line in which the device is located, etc. This embodiment of the application does not limit this approach. Accordingly, the aforementioned device categories include network category, building category, workshop category, functional category, production line category, etc.

[0094] Optionally, the cloud servers mentioned above refer to cloud servers corresponding to private clouds, such as corporate private clouds or other private clouds.

[0095] Optionally, the aforementioned target device network includes the device network corresponding to the cloud server. The aforementioned device network is the network corresponding to the IoT device cluster. Optionally, the aforementioned device network includes, but is not limited to, local area networks and private networks.

[0096] In some practical application scenarios, such as industrial scenarios, the aforementioned equipment network includes factory equipment local area networks (LANs). For a group with multiple factories, each factory corresponds to at least one factory equipment LAN; each factory equipment LAN can include at least one IoT device within the factory.

[0097] In an exemplary embodiment, such as Figure 5 As shown, the implementation process of step 401 above includes the following step 4011, Figure 5 This application illustrates a flowchart of a device data processing method based on edge computing and distributed computing according to an embodiment of the present application. Figure 2 .

[0098] Step 4011: The cloud server receives device protocol information and device configuration information corresponding to at least one device node sent by the terminal.

[0099] Optionally, the device configuration information is matched with the device protocol information. Optionally, the device protocol information and device configuration information are configuration information entered through a terminal.

[0100] In practical applications, users can log in to the configuration page corresponding to the cloud server through the terminal. The terminal can obtain the device protocol information corresponding to the device node and the device configuration information that matches the device protocol information based on the configuration page, thereby realizing the configuration of the entire distributed Internet of Things system.

[0101] In one possible implementation, corresponding to the scenario of adding device nodes in a distributed IoT system, the terminal displays the aforementioned configuration page and receives a device node addition instruction based on the configuration page. In response to the device node addition instruction, the terminal displays device protocol selection information, which includes at least one preset device protocol. The user can select the protocol supported by the device corresponding to the newly added device node. In response to the selection instruction for the target device protocol, the terminal determines the device protocol information corresponding to the device node to be added based on the target device protocol. After selecting the device protocol, the user can enter device configuration information such as channel information, address information, and data point information corresponding to the device node to be added through the configuration page. In response to receiving the save instruction corresponding to the aforementioned device configuration information, the terminal sends the device protocol information and device configuration information of the device node to be added to the cloud server.

[0102] If at least one of the aforementioned preset device protocols does not include the target device protocol corresponding to the device node to be added, the user can add the target device protocol. In response to the protocol addition instruction for the target device protocol, the terminal generates device protocol information corresponding to the device node to be added based on the target device protocol. After adding the device protocol, the user can configure device configuration information matching the target device protocol. In response to receiving the device configuration information corresponding to the target device protocol, the terminal generates device configuration information corresponding to the device node to be added. This implementation method enables device node expansion and device protocol expansion without redeploying edge servers, improving the configuration flexibility and scalability of the IoT system and reducing the deployment cost of the IoT system.

[0103] In another possible implementation, for scenarios involving updates to the device configuration information of existing device nodes, the terminal can receive the updated device configuration information corresponding to the existing device nodes based on the aforementioned configuration page, and then send the updated device configuration information to the cloud server. This improves configuration efficiency for device node replacements or updates, enabling updates and modifications without requiring adjustments to the edge devices.

[0104] Optionally, the aforementioned device configuration information also includes data preprocessing rules. Users can enter the data preprocessing rules corresponding to the aforementioned device nodes through the configuration page. In some practical application scenarios, the data generated by the devices corresponding to the device nodes is raw collected data, which has poor readability and universality. After the edge server obtains the aforementioned raw collected data, it can preprocess the aforementioned raw collected data according to the data preprocessing rules corresponding to the device nodes, such as data transformation and data cleaning, to obtain processed device attribute data, and then report the processed device attribute data.

[0105] Step 402: The cloud server sends device configuration information to the target edge server corresponding to the target device network.

[0106] In an exemplary embodiment, the cloud server can distribute the aforementioned device configuration information by calling an adaptation service. The cloud server and edge server can distribute device configuration information synchronously via an interface. During the configuration distribution process, cross-network interface security must be ensured. Therefore, in this exemplary embodiment, the interface call process includes encrypting the main timestamp, event ID, and using SHA256 encryption. Accordingly, as... Figure 5 As shown, the method may further include the following steps (407-411).

[0107] Step 407: The target edge server sends server address information and token information to the cloud server.

[0108] Correspondingly, the cloud server receives the server address information and token information sent by the target edge server.

[0109] Optionally, the aforementioned server address information includes the edge server address information corresponding to the edge server. Optionally, the aforementioned edge server includes at least one interface, and the aforementioned edge server address information includes the interface address corresponding to at least one interface. The aforementioned different interfaces are used to provide different edge services, such as data forwarding, data preprocessing, etc., and this application embodiment does not limit this.

[0110] Step 408: The cloud server generates verification information based on the server address information and token information.

[0111] In one possible implementation, in response to receiving server address information and token information, the cloud server generates signature information, timestamp, event identifier, and verification message information; optionally, the verification message information includes action identifier, request identifier, and response information; after generating the above information, the cloud server performs fusion processing on the signature information, timestamp, event identifier, and server address information to obtain fused address information; based on the fused address information and verification message information, verification information is generated.

[0112] Optionally, the above fusion process includes adding the signature information, timestamp, and event identifier to the server address to obtain the fused address information.

[0113] Optionally, the aforementioned signature information is associated with the aforementioned token information. Optionally, the aforementioned response information is randomly generated and returned after verification.

[0114] Step 409: The cloud server sends verification information to the target edge server.

[0115] Correspondingly, the edge server receives verification information sent by the cloud server. The verification information is generated based on the server address information and token information, and includes response information.

[0116] Step 410: The target edge server performs verification processing on the verification information and obtains the verification result.

[0117] In one possible implementation, the target edge server first determines the interval between the timestamp and the target time, and determines whether the interval is less than a duration threshold. If the interval is less than the duration threshold, the target edge server encrypts the token information, timestamp, and event identifier to obtain encrypted information. The target edge server then compares the encrypted information with the signature information to obtain a verification result.

[0118] Optionally, the target edge server sorts the token information, timestamp, and event identifier lexicographically to obtain a sorted string. The token information, timestamp, and event identifier can all be strings, hence the resulting string. The sorted string is then concatenated to obtain a concatenated string, which is then encrypted using SHA256. The encrypted string is included in the aforementioned encrypted information. The verification result is determined by comparing the encrypted string with the signature string (which includes the signature information). If the encrypted string and the signature string match, the verification passes; otherwise, it fails.

[0119] Step 411: If the verification result indicates that the verification is successful, the target edge server sends a response message to the cloud server.

[0120] Correspondingly, the cloud server receives the response information sent by the target edge server.

[0121] In one example, such as Figure 6 As shown, this example illustrates an interactive process for verification between a cloud server and an edge server. The edge server sends its cloud-configured edge server address (e.g., URL (Uniform Resource Locator)) and token information to the cloud server. When the cloud server calls the relevant interface of the edge server, it adds a signature / timestamp / event ID to the interface URL using URL PARAMS (POST parameters), and appends a request verification message action ID / request ID / echoback to the request body. This generates verification information (e.g., the interface URL for verification request: signature / timestamp / eventid-d action / requestid / echoback), and sends a verification request including the verification information to the edge server. The echoback is randomly generated and returned after verification.

[0122] When the edge server's interface is called, it parses the verification request and performs rule verification. If verification passes, service access is granted; otherwise, service is denied. Optionally, the signature encryption / verification process corresponding to rule verification is as follows: Check if the timestamp has expired (a 30-second timeout is recommended for signatures); sort the three parameters (Token, timestamp, and eventld) lexicographically; concatenate the three parameter strings into a single string, encrypt it using SHA256, and compare the encrypted string with the signature to obtain the verification result. The verification result, including response information, is returned after verification.

[0123] Optionally, such as Figure 5 As shown, after step 412, step 402 can be replaced by step 4021.

[0124] Step 4021: In response to receiving the response information sent by the target edge server, the cloud server sends device configuration information to the target edge server.

[0125] The response information is returned by the target edge server when the verification result corresponding to the verification information indicates that the verification has passed.

[0126] Accordingly, the edge server receives device configuration information sent by the cloud server. This device configuration information refers to the configuration information corresponding to at least one device node in the target device network, which is the device network corresponding to the edge server. The device configuration information includes the device network address and data point identifier.

[0127] Step 403: The target edge server sends a data acquisition request to the device node corresponding to the device network address.

[0128] Optionally, the data acquisition request includes data point identifiers.

[0129] After receiving the above device configuration information, the edge server can connect with the device node corresponding to the device network address in the device configuration information according to the device protocol information, and collect data to obtain the device attribute data in the device node corresponding to the above data point identifier.

[0130] Step 404: The device node corresponding to the device network address sends the device attribute data corresponding to the data point identifier to the target edge server.

[0131] After receiving the device acquisition request, the device node sends the device attribute data corresponding to the data point identifier to the edge server.

[0132] Correspondingly, the target edge server receives device attribute data sent by the device node that corresponds to the data point identifier.

[0133] Step 405: The target edge server sends device attribute data to the cloud server.

[0134] Correspondingly, the cloud server receives the device attribute data corresponding to the data point identifier.

[0135] Optionally, the device attribute data is the data returned by the device node corresponding to the device network address in response to the data acquisition request sent by the target edge server.

[0136] Optionally, the target edge server sends the aforementioned device attribute data to the cloud server via a message relay server. The message relay server provides data forwarding services between the cloud server and the edge server, and can send data messages subscribed to by the cloud server, including device attribute data, to the cloud server.

[0137] Optionally, the message relay server can also provide data forwarding services between different cloud servers, and this application embodiment does not limit this.

[0138] In an exemplary embodiment, such as Figure 5 As shown, the implementation process of step 405 above includes the following steps (4051 to 4055).

[0139] Step 4051: The target edge server determines the network connection status.

[0140] Optionally, the aforementioned network connection status characterizes the network quality of the edge server. Optionally, the edge server acquires network transmission quality data related to network quality, such as network signal strength, and can determine the network connection status of the edge server based on the aforementioned network transmission quality data.

[0141] Step 4052: If the network connection status meets the target network status conditions, the target edge server sends device attribute data to the message relay server.

[0142] Optionally, the message relay server is used to send data messages subscribed to by the cloud server to the cloud server. The data messages include device attribute data.

[0143] Step 4053: If the network connection status does not meet the target network status conditions, the target edge server stores the device attribute data in the target database.

[0144] Step 4054: In response to the network connection status meeting the target network status conditions, the target edge server sends device attribute data to the message relay server.

[0145] In the event of poor network connectivity, the aforementioned target edge server can pause data reporting, store device attribute data in a local database, and resume transmission once the network connection is restored.

[0146] In one possible implementation, the process of the target edge server sending device attribute data to the message relay server includes: the target edge server determining the message topic corresponding to the target device network and sending device attribute data to the data partition corresponding to the message topic, wherein the message relay server includes the data partition.

[0147] Optionally, in the message relay server, there is a correspondence between the device network and the message topic, thereby enabling the differentiation and management of the device attribute data of multiple device networks.

[0148] Optionally, the message topic can have scalable data partitions to handle device attribute data of varying volumes. For example, if a device network has many device nodes and many data points corresponding to those nodes, the corresponding device attribute data will be quite large. Expanding the data partitions can effectively improve the data throughput of the message relay server and enhance data transmission capabilities.

[0149] Step 4055: The message relay server sends device attribute data to the cloud server.

[0150] Correspondingly, the cloud server receives device attribute data sent by the message relay server.

[0151] In some possible implementations, the message relay server sends subscription information to the cloud server. Correspondingly, the cloud server receives the subscription information sent by the message relay server, which represents device attribute data received by the message relay server from the target edge server. Optionally, the device attribute data is sent by the target edge server to the message relay server when the network connection status meets the target network status conditions.

[0152] In practical applications, cloud servers can subscribe to the data they need from message relay servers. Once the subscribed data message is sent to the message relay server, the message relay server can send a subscription message to the cloud server, indicating that the cloud server has received the subscribed data message.

[0153] In response to receiving the aforementioned subscription information, the cloud server sends a subscription data retrieval request to the message relay server. Accordingly, the message relay server receives the subscription data retrieval request.

[0154] After receiving the subscription message, the cloud server can send a subscription data retrieval request to the message relay server if data needs to be retrieved, depending on the actual operating conditions.

[0155] Optionally, the aforementioned subscription data retrieval request is used to instruct the message relay server to send the subscribed data message, i.e., the aforementioned device attribute data, to the cloud server.

[0156] In response to the aforementioned subscription data retrieval request, the message relay server sends device attribute data to the cloud server.

[0157] In response to the aforementioned subscription data retrieval request, the message relay server sends device attribute data to the cloud server. Correspondingly, the cloud server receives the device attribute data sent by the message relay server.

[0158] In one example, such as Figure 7 As shown, this example illustrates a schematic diagram of the data transfer process between cloud servers and edge servers via message topics and data partitions. In a distributed scenario with one central hub and multiple nodes, the core capabilities of a distributed IoT system are reflected in device data acquisition, data forwarding throughput, and data processing capabilities. High data throughput can be achieved through the allocation of message topics and data partitions.

[0159] For the edge side, the edge server's ability to collect data from device clusters in the device network can be improved by expanding data partitions, thereby increasing data reporting throughput. Optionally, each device cluster has a corresponding message topic, and each topic can correspond to multiple data partitions and support data partition expansion. After the edge server collects the device attribute data corresponding to the devices in the device cluster, it can send the device attribute data to the corresponding topic's data partition, thereby improving data throughput. For example, in Figure 7 In this context, device cluster 1 corresponds to message topic 1, device cluster 2 corresponds to message topic 2, and so on. Accordingly, the edge server corresponding to device cluster 1 will send the device attribute data of the device nodes in device cluster 1 to the data partition corresponding to message topic 1; the edge server corresponding to device cluster 2 will send the device attribute data of the device nodes in device cluster 2 to the data partition corresponding to message topic 2; and the edge server corresponding to device cluster 3 will send the device attribute data of the device nodes in device cluster 3 to the data partition corresponding to message topic 3, thereby improving data throughput.

[0160] After receiving data, the message relay server forwards it to the data processing side. On the data processing side, the cloud server uses a data consumer group (Group) mode to increase the number of threads within the group for data subscription and reception, thereby increasing data processing capacity and utilizing rebalancing capabilities to achieve load balancing. A core drawback of MQTT is its lack of support for data persistence and grouping. In this embodiment, using Kafka for data relay ensures high data throughput, supports data persistence and grouping, improves data throughput, and guarantees data visualization.

[0161] In one example, such as Figure 8 As shown, it exemplifies a process diagram illustrating configuration distribution and data reporting between a cloud server and an edge server. Figure 8 The distributed IoT system shown includes a cloud server (including an industrial data acquisition and configuration server and a data management server), a message relay server (including a distributed publish-subscribe messaging system), multiple edge servers (edge ​​server 1, edge server 2, and edge server 3), and devices corresponding to each edge server (device 1, device 2, and device 3). Among them, device 1, device 2, and device 3 correspond to different device networks and different device nodes.

[0162] In the cloud-based configuration distribution process, the industrial data acquisition and configuration server sends an HTTP request to the adaptation service to instruct it to distribute device configuration information to the corresponding edge servers. After receiving device configuration information for multiple device nodes (e.g., device configuration information for device 1, device 2, and device 3), the adaptation service, through its corresponding central service, sends this information to the corresponding edge servers. For example, device configuration information for device 1 is sent to edge server 1; device configuration information for device 2 is sent to edge server 2; and device configuration information for device 3 is sent to edge server 3. Upon receiving the device configuration information, each edge server can interface with the corresponding device and collect data, thereby achieving distributed cross-node device data collection and enabling unified cloud-based configuration and management, as well as device parameter distribution.

[0163] After the edge server obtains the device attribute data corresponding to the device, it achieves highly reliable data transmission through Redis+Kafka to realize data reporting. During the data reporting process, the edge server sends the collected device attribute data to the message relay server. The message relay server then reports the data messages subscribed to by the industrial acquisition configuration server (including the aforementioned device attribute data) to the industrial acquisition configuration server, thus realizing data reporting. The data management server can call the relevant interfaces of the industrial acquisition configuration server to obtain the data messages subscribed to by the data management server in the industrial acquisition configuration server, perform relevant calculations, obtain data processing results, and visualize them for user management and viewing.

[0164] Step 406: The cloud server performs data processing operations on the device attribute data to obtain the data processing results.

[0165] The aforementioned cloud server can perform relevant data processing on device attribute data, thereby realizing data management of the distributed Internet of Things system. This application embodiment does not limit the type of data processing, and different data processing methods can be configured according to the actual application scenario.

[0166] Typically, in industrial scenarios, the production equipment corresponding to a device node may be foreign equipment. The data standards of the equipment attribute data it generates, such as data format and data measurement units, may differ from the national standards. Therefore, data processing can be used to convert the data to obtain equipment attribute data that conforms to the national standards.

[0167] Optionally, the cloud processor performs data transformation on the device attribute data to obtain device data that conforms to the target data standard.

[0168] For some data parameters that need to be calculated based on device attribute data, the corresponding data processing operations can also be performed by the cloud processor to obtain the corresponding parameter information.

[0169] For data that needs to be visualized, cloud servers can perform visualization processing on device attribute data to obtain corresponding visualization results.

[0170] The above data processing results include, but are not limited to, the device data, parameter information, and visualization results that conform to the target data standards.

[0171] In one example, such as Figure 9 As shown, this example illustrates a schematic diagram of a data processing flow performed by a cloud server. The general process of data processing by a cloud server is as follows:

[0172] The cloud server subscribes to Kafka topics across different device networks to fetch and consume data, thereby acquiring device data from the edge. Specifically, the cloud server subscribes to topics across all device networks, fetches data through Consumergroups and multiple consumer threads, and after the subscribing process fetches data, it writes it to Redis. Then, multi-threading technology is used to calculate location rules for the device data (one device includes multiple location information). If the calculation is successful, the cached data is deleted; if it fails, a retry is performed. After obtaining the processed location data, it is processed into device data, stored in InfluxDB, and reported to the message relay server for forwarding.

[0173] In the above data processing flow, the system increases message retrieval capability through KafkaConsumer groups; increases thread processing capability through multi-threaded processing; and increases data processing capability by performing Redis failure storage and retries after processing. At the same time, Consumer groups can increase the number of processing thread instances, thereby increasing processing capability and comprehensively improving the data processing volume, processing speed and processing efficiency of the cloud server.

[0174] In summary, the technical solution provided in this application provides a distributed IoT system based on a distributed edge computing architecture, consisting of a cloud server, an edge server, and device nodes in a device network. The cloud server can obtain and distribute device configuration information corresponding to the device nodes in the device network, achieving unified management of device configurations. The edge server can request device attribute data corresponding to the corresponding device node based on the distributed device configuration information and report it to the cloud server. The cloud server can obtain the data processing results by performing data processing operations on the device attribute data. It is evident that the cloud server can achieve multi-source data acquisition, transmission, and processing across networks and nodes at the edge side simply by distributing device configuration information. This effectively reduces the coupling between the cloud server, edge server, and device nodes, improves the configuration flexibility and scalability of the IoT system, and also reduces the deployment cost of the IoT system.

[0175] The industrial production scenario is a typical application scenario corresponding to the technical solution provided in the embodiments of this application. The beneficial effects of the technical solution provided in the embodiments of this application will be explained below in conjunction with the industrial production scenario.

[0176] The technical solution provided in this application decomposes the data acquisition architecture, decoupling the cloud server, edge server, and device nodes in the device network to achieve a distributed edge computing architecture, which is highly compatible with the industrial production model of a group with multiple factories. The cloud server is responsible for core business data processing, while the edge server is located close to the factory equipment, supporting a multi-factory model within a single group. This enables multi-source data acquisition, transmission, and processing under group management, specifically achieving cross-network and cross-node industrial equipment data acquisition, edge processing, distributed scheduling, data reporting, data computation, and data visualization between the group and multiple factories.

[0177] Under this architecture, the cloud server only needs to send device configuration information to the edge server, enabling the edge server to interface with devices using various industrial equipment protocols and achieve data collection from these devices. Furthermore, the configuration sending method does not require replacing the edge computing equipment, supporting industrial protocol expansion and remote configuration. There is also no need to deploy a separate IoT system within the factory; only the edge server for data collection needs to be deployed. This not only enhances the compatibility of the IoT system with industrial equipment but also reduces the deployment cost of the IoT system.

[0178] During data reporting, edge servers and cloud servers relay data via a message relay server (Kafka), decoupling the cloud and edge servers and improving the secure data forwarding capabilities within the group's private cloud and factory area. The combination of edge servers and Redis+Kafka for data forwarding and storage also enables data interruption and resumption in the event of network outages at the edge and cloud, solving the problem of data loss during traditional IoT MQTT data reporting.

[0179] In addition, the message relay server improves the system's data throughput through topic+partition sharding capabilities, supports 100,000 data forwards per second, supports forwarding capacity expansion, and also accelerates data rule calculation capabilities by using thread pool technology to achieve concurrent data calculation at multiple points.

[0180] The following are embodiments of the apparatus of this application, which can be used to execute embodiments of the method of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method of this application.

[0181] Please refer to Figure 10 It illustrates a block diagram of a device data processing apparatus based on edge computing and distributed computing according to an embodiment of this application. Figure 1The device 1000 has the function of implementing the aforementioned device data processing method based on edge computing and distributed computing on the cloud server side. This function can be implemented in hardware or by hardware executing corresponding software. The device 1000 can be a computer device or can be installed within a computer device. Optionally, the device 1000 is applied to a cloud server in a distributed Internet of Things (IoT) system. The device 1000 may include: a configuration information acquisition module 1010, a configuration information sending module 1020, a device data receiving module 1030, and a device data processing module 1040.

[0182] The configuration information acquisition module 1010 is used to acquire device configuration information corresponding to at least one device node in the target device network, wherein the device configuration information includes device network address and data point identifier.

[0183] Configuration information sending module 1020 is used to send the device configuration information to the target edge server corresponding to the target device network;

[0184] The device data receiving module 1030 is used to receive device attribute data corresponding to the data point identifier. The device attribute data is the data returned by the device node corresponding to the device network address in response to the data acquisition request sent by the target edge server.

[0185] The device data processing module 1040 is used to perform data processing operations on the device attribute data to obtain data processing results.

[0186] In an exemplary embodiment, the configuration information acquisition module 1010 includes a configuration information receiving unit.

[0187] A configuration information receiving unit is used to receive device protocol information and device configuration information corresponding to the at least one device node sent by the terminal. The device configuration information matches the device protocol information, and the device protocol information and device configuration information are configuration information entered by the terminal.

[0188] In an exemplary embodiment, the device data receiving module 1030 includes: a subscription message receiving unit, a data request sending unit, and a device data receiving unit.

[0189] A subscription message receiving unit is used to receive subscription information sent by a message relay server. The subscription information indicates that the message relay server has received device attribute data sent by the target edge server. The device attribute data is sent by the target edge server to the message relay server when the network connection status meets the target network status conditions.

[0190] A data request sending unit is used to send a subscription data acquisition request to the message relay server;

[0191] The device data receiving unit is used to receive the device attribute data sent by the message relay server.

[0192] In an exemplary embodiment, the device 1000 further includes: a server information receiving module, a verification information generating module, and a verification information sending module.

[0193] The server information receiving module is used to receive server address information and token information sent by the target edge server;

[0194] The verification information generation module is used to generate verification information based on the server address information and the token information;

[0195] The verification information sending module is used to send the verification information to the target edge server;

[0196] The configuration information sending module 1020 is specifically used to send the device configuration information to the target edge server in response to receiving the response information sent by the target edge server. The response information is returned by the target edge server when the verification result corresponding to the verification information indicates that the verification has passed.

[0197] In an exemplary embodiment, the verification information generation module includes: a verification information generation unit, an address information fusion unit, and a verification information generation unit.

[0198] The verification information generation unit is used to generate signature information, timestamp, event identifier and verification message information in response to receiving the server address information and the token information;

[0199] The address information fusion unit is used to fuse the signature information, the timestamp, the event identifier, and the server address information to obtain the fused address information.

[0200] The verification information generation unit is used to generate the verification information based on the fused address information and the verification message information.

[0201] In summary, the technical solution provided in this application provides a distributed IoT system based on a distributed edge computing architecture, consisting of a cloud server, an edge server, and device nodes in a device network. The cloud server can obtain and distribute device configuration information corresponding to the device nodes in the device network, achieving unified management of device configurations. The edge server can request device attribute data corresponding to the corresponding device node based on the distributed device configuration information and report it to the cloud server. The cloud server can obtain the data processing results by performing data processing operations on the device attribute data. It is evident that the cloud server can achieve multi-source data acquisition, transmission, and processing across networks and nodes at the edge side simply by distributing device configuration information. This effectively reduces the coupling between the cloud server, edge server, and device nodes, improves the configuration flexibility and scalability of the IoT system, and also reduces the deployment cost of the IoT system.

[0202] Please refer to Figure 11 It illustrates a block diagram of a device data processing apparatus based on edge computing and distributed computing according to an embodiment of this application. Figure 2 The device 1100 has the function of implementing the device data processing method based on edge computing and distributed computing on the edge server side, and the function can be implemented by hardware or by hardware executing corresponding software. The device 1100 can be a computer device or can be installed in a computer device. Optionally, the device 1100 is applied to an edge server in a distributed Internet of Things system. The device 1100 may include: a configuration information receiving module 1110, a data request sending module 1120, a device data receiving module 1130, and a device data sending module 1140.

[0203] The configuration information receiving module 1110 is used to receive device configuration information sent by the cloud server. The device configuration information is the configuration information corresponding to at least one device node in the target device network. The target device network is the device network corresponding to the edge server. The device configuration information includes the device network address and data point identifier.

[0204] The data request sending module 1120 is used to send a data acquisition request to the device node corresponding to the device network address, wherein the data acquisition request includes the data point identifier.

[0205] The device data receiving module 1130 is used to receive device attribute data sent by the device node that corresponds to the data point identifier;

[0206] The device data sending module 1140 is used to send the device attribute data to the cloud server.

[0207] In an exemplary embodiment, the device data transmission module 1140 includes: a network status determination unit, a device data transmission unit, and a device data storage unit.

[0208] A network status determination unit is used to determine the network connection status;

[0209] The device data sending unit is configured to send the device attribute data to a message relay server when the network connection status meets the target network status conditions. The message relay server is configured to send the data message subscribed by the cloud server to the cloud server, and the data message includes the device attribute data.

[0210] The device data storage unit is used to store the device attribute data in the target database when the network connection status does not meet the target network status conditions;

[0211] The device data sending unit is further configured to send the device attribute data to the message relay server in response to the network connection status meeting the target network status condition.

[0212] In an exemplary embodiment, the device data transmission unit includes:

[0213] The message topic determination subunit is used to determine the message topic corresponding to the target device network;

[0214] The data sending subunit is used to send the device attribute data to the data partition corresponding to the message topic, and the message relay server includes the data partition.

[0215] In an exemplary embodiment, the apparatus 1100 further includes: a server information sending module, a verification information receiving module, a verification result generating module, and a response information sending module.

[0216] The server information sending module is used to send server address information and token information to the cloud server;

[0217] The verification information receiving module is used to receive verification information sent by the cloud server. The verification information is generated based on the server address information and the token information, and the verification information includes response information.

[0218] The verification result generation module is used to perform verification processing on the verification information to obtain the verification result;

[0219] The response information sending module is used to send the response information to the cloud server when the verification result indicates that the verification is successful.

[0220] In an exemplary embodiment, the verification information includes signature information, timestamp, and event identifier, and the verification result generation module includes: an interval duration determination unit, an encryption information generation unit, and a verification result generation unit.

[0221] An interval duration determination unit is used to determine the interval duration between the timestamp and the target time.

[0222] An encrypted information generation unit is used to encrypt the token information, the timestamp, and the event identifier if the interval duration is less than a duration threshold, thereby obtaining encrypted information.

[0223] The verification result generation unit is used to compare the encrypted information with the signature information to obtain the verification result.

[0224] In summary, the technical solution provided in this application provides a distributed IoT system based on a distributed edge computing architecture, consisting of a cloud server, an edge server, and device nodes in a device network. The cloud server can obtain and distribute device configuration information corresponding to the device nodes in the device network, achieving unified management of device configurations. The edge server can request device attribute data corresponding to the corresponding device node based on the distributed device configuration information and report it to the cloud server. The cloud server can obtain the data processing results by performing data processing operations on the device attribute data. It is evident that the cloud server can achieve multi-source data acquisition, transmission, and processing across networks and nodes at the edge side simply by distributing device configuration information. This effectively reduces the coupling between the cloud server, edge server, and device nodes, improves the configuration flexibility and scalability of the IoT system, and also reduces the deployment cost of the IoT system.

[0225] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0226] Please refer to Figure 12 This diagram illustrates a structural block diagram of a computer device according to an embodiment of this application. The computer device can be a server, such as the aforementioned cloud server or edge server, for executing the aforementioned device data processing method based on edge computing and distributed computing on the cloud server side or the edge server side. Specifically:

[0227] Computer device 1200 includes a central processing unit (CPU) 1201, a system memory 1204 including random access memory (RAM) 1202 and read-only memory (ROM) 1203, and a system bus 1205 connecting the system memory 1204 and the CPU 1201. Computer device 1200 also includes a basic input / output system (I / O system) 1206 to facilitate information transfer between various devices within the computer, and a mass storage device 1207 for storing the operating system 1213, application programs 1214, and other program modules 1215.

[0228] The basic input / output system 1206 includes a display 1208 for displaying information and an input device 1209 for user input, such as a mouse or keyboard. Both the display 1208 and the input device 1209 are connected to the central processing unit 1201 via an input / output controller 1210 connected to the system bus 1205. The basic input / output system 1206 may also include the input / output controller 1210 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 1210 also provides output to a display screen, printer, or other types of output devices.

[0229] Mass storage device 1207 is connected to central processing unit 1201 via a mass storage controller (not shown) connected to system bus 1205. Mass storage device 1207 and its associated computer-readable media provide non-volatile storage for computer device 1200. That is, mass storage device 1207 may include computer-readable media (not shown) such as hard disk or CD-ROM (Compact Disc Read-Only Memory) drive.

[0230] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory or other solid-state storage technologies, CD-ROM, DVD (Digital Video Disc) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 1204 and mass storage device 1207 described above can be collectively referred to as memory.

[0231] According to various embodiments of this application, the computer device 1200 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 1200 can be connected to the network 1212 via the network interface unit 1211 connected to the system bus 1205, or the network interface unit 1211 can be used to connect to other types of networks or remote computer systems (not shown).

[0232] The memory also includes a computer program stored in the memory and configured to be executed by one or more processors to implement the above-mentioned device data processing method based on edge computing and distributed computing on the cloud server side, or the above-mentioned device data processing method based on edge computing and distributed computing on the edge server side.

[0233] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set, when executed by a processor, implements the above-described device data processing method based on edge computing and distributed computing on the cloud server side.

[0234] In an exemplary embodiment, a computer-readable storage medium is also provided, the storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set, when executed by a processor, implements the above-described device data processing method based on edge computing and distributed computing on the edge server side.

[0235] Optionally, the computer-readable storage medium may include: ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drives), or optical disc, etc. The random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).

[0236] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned device data processing method based on edge computing and distributed computing on the cloud server side.

[0237] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned device data processing method based on edge computing and distributed computing on the edge server side.

[0238] It should be understood that "multiple" as used herein refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the step numbers described herein are merely illustrative of one possible execution order. In some other embodiments, the steps may not be executed in numerical order, such as two steps with different numbers being executed simultaneously, or two steps with different numbers being executed in the reverse order of the illustration. This application does not limit this.

[0239] In addition, in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0240] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A device data processing method based on edge computing and distributed computing, characterized in that, The method is applied to a distributed Internet of Things (IoT) system, which includes a cloud server, an edge server, and device nodes. The method includes: The cloud server receives device protocol information and device configuration information corresponding to at least one device node in the target device network sent by the terminal. The device configuration information includes a device network address and a data point identifier. The device configuration information matches the device protocol information. The device protocol information and the device configuration information are configuration information entered by the terminal. The cloud server sends the device configuration information and the device protocol information to the target edge server corresponding to the target device network; The target edge server performs protocol docking with the device node corresponding to the device network address in the device configuration information according to the device protocol information, and sends a data acquisition request to the device node corresponding to the device network address. The data acquisition request includes the data point identifier. The device node corresponding to the device network address sends the device attribute data corresponding to the data point identifier to the target edge server; The target edge server sends the device attribute data to the cloud server; The cloud server performs data processing operations on the device attribute data to obtain data processing results; The target edge server sends server address information and token information to the cloud server; The cloud server generates verification information based on the server address information and the token information; The cloud server sends the verification information to the target edge server; The target edge server performs verification processing on the verification information to obtain the verification result; If the verification result indicates that the verification is successful, the target edge server sends a response message to the cloud server. The cloud server sends the device configuration information and the device protocol information to the target edge server corresponding to the target device network, including: In response to receiving the response information, the cloud server sends the device configuration information and the device protocol information to the target edge server.

2. The method according to claim 1, characterized in that, The distributed IoT system also includes a message relay server, wherein the target edge server sends the device attribute data to the cloud server, including: The target edge server determines the network connection status; When the network connection status meets the target network status conditions, the target edge server sends the device attribute data to the message relay server. The message relay server sends the device attribute data to the cloud server.

3. The method according to claim 2, characterized in that, After the target edge server determines the network connection status, the method further includes: If the network connection status does not meet the target network status conditions, the target edge server will store the device attribute data in the target database. In response to the network connection status meeting the target network status conditions, the target edge server sends the device attribute data to the message relay server.

4. The method according to claim 2 or 3, characterized in that, Sending the device attribute data to the message relay server includes: The target edge server determines the message topic corresponding to the target device network; The target edge server sends the device attribute data to the data partition corresponding to the message topic, and the message relay server includes the data partition.

5. The method according to claim 1, characterized in that, The cloud server generates verification information based on the server address information and the token information, including: In response to receiving the server address information and the token information, the cloud server generates signature information, timestamp, event identifier and verification message information; The cloud server performs a fusion process on the signature information, the timestamp, the event identifier, and the server address information to obtain the fused address information; The cloud server generates the verification information based on the merged address information and the verification message information.

6. The method according to claim 5, characterized in that, The target edge server performs verification processing on the verification information to obtain a verification result, including: The target edge server determines the interval between the timestamp and the target time. If the interval duration is less than the duration threshold, the target edge server encrypts the token information, the timestamp, and the event identifier to obtain encrypted information. The target edge server compares the encrypted information with the signature information to obtain the verification result.

7. A device data processing method based on edge computing and distributed computing, characterized in that, The method is applied to a cloud server in a distributed Internet of Things (IoT) system, and the method includes: The receiving terminal sends device protocol information and device configuration information corresponding to at least one device node in the target device network. The device configuration information includes a device network address and a data point identifier. The device configuration information matches the device protocol information. The device protocol information and the device configuration information are configuration information entered by the terminal. Send the device configuration information and the device protocol information to the target edge server corresponding to the target device network; Receive device attribute data corresponding to the data point identifier, wherein the device attribute data is the data returned by the device node corresponding to the device network address in response to the data acquisition request sent by the target edge server; Perform data processing operations on the device attribute data to obtain the data processing results; Receive server address information and token information sent by the target edge server; Based on the server address information and the token information, verification information is generated; Send the verification information to the target edge server; Sending the device configuration information and the device protocol information to the target edge server corresponding to the target device network includes: In response to receiving the response information sent by the target edge server, the device configuration information and the device protocol information are sent to the target edge server. The response information is returned by the target edge server when the verification result corresponding to the verification information indicates that the verification has passed.

8. A device data processing method based on edge computing and distributed computing, characterized in that, The method is applied to an edge server in a distributed Internet of Things (IoT) system, and the method includes: The device receives device configuration information and device protocol information sent by a cloud server. The device configuration information and device protocol information are configuration information and device protocol information corresponding to at least one device node in the target device network. The target device network is the device network corresponding to the edge server. The device configuration information includes a device network address and a data point identifier. Based on the device protocol information, the device node corresponding to the device network address in the device configuration information is connected to the protocol, and a data acquisition request is sent to the device node corresponding to the device network address. The data acquisition request includes the data point identifier. Receive device attribute data sent by the device node that corresponds to the data point identifier; Send the device attribute data to the cloud server; Send server address information and token information to the cloud server; Receive verification information generated by the cloud server based on the server address information and the token information; The verification information is processed to obtain the verification result; If the verification result indicates that the verification passed, a response message is sent to the cloud server; The device configuration information and device protocol information received from the cloud server include: The cloud server receives the response information and sends device configuration information and device protocol information.

9. A device data processing apparatus based on edge computing and distributed computing, characterized in that, The device is used in a cloud server within a distributed Internet of Things (IoT) system, and the device includes: The configuration information acquisition module is used to receive device protocol information and device configuration information corresponding to at least one device node in the target device network sent by the terminal. The device configuration information includes a device network address and a data point identifier. The device configuration information is matched with the device protocol information. The device protocol information and the device configuration information are configuration information entered by the terminal. The configuration information sending module is used to send the device configuration information and the device protocol information to the target edge server corresponding to the target device network; The device data receiving module is used to receive device attribute data corresponding to the data point identifier. The device attribute data is the data returned by the device node corresponding to the device network address in response to the data acquisition request sent by the target edge server. The device data processing module is used to perform data processing operations on the device attribute data to obtain data processing results. The server information receiving module is used to receive server address information and token information sent by the target edge server; The verification information generation module is used to generate verification information based on the server address information and the token information; The verification information sending module is used to send the verification information to the target edge server; Sending the device configuration information and the device protocol information to the target edge server corresponding to the target device network includes: In response to receiving the response information sent by the target edge server, the device configuration information and the device protocol information are sent to the target edge server. The response information is returned by the target edge server when the verification result corresponding to the verification information indicates that the verification has passed.

10. A device data processing apparatus based on edge computing and distributed computing, characterized in that, The device is used as an edge server in a distributed Internet of Things (IoT) system, and the device includes: The configuration information receiving module is used to receive device configuration information and device protocol information sent by the cloud server. The device configuration information and the device protocol information are configuration information and device protocol information corresponding to at least one device node in the target device network. The target device network is the device network corresponding to the edge server. The device configuration information includes the device network address and data point identifier. The data request sending module is used to perform protocol interfacing with the device node corresponding to the device network address in the device configuration information according to the device protocol information, and send a data acquisition request to the device node corresponding to the device network address. The data acquisition request includes the data point identifier. The device data receiving module is used to receive device attribute data sent by the device node that corresponds to the data point identifier; The device data sending module is used to send the device attribute data to the cloud server; The server information sending module is used to send server address information and token information to the cloud server; The verification information receiving module is used to receive verification information sent by the cloud server. The verification information is generated based on the server address information and the token information, and the verification information includes response information. The verification result generation module is used to perform verification processing on the verification information to obtain the verification result; The response information sending module is used to send the response information to the cloud server when the verification result indicates that the verification is successful; The device configuration information and device protocol information received from the cloud server include: The cloud server receives the device configuration information and device protocol information in response to the received response information.

11. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, a code set, or an instruction set, the at least one instruction, the at least one program, the code set, or the instruction set being loaded and executed by the processor to implement the device data processing method as described in claim 7 or 8.

12. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the device data processing method as described in any one of claims 7 or 8.

13. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the device data processing method as described in any one of claims 7 or 8.

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