Data interaction method, device and system of edge computing platform and network equipment

Abstract

The invention relates to the technical field of communication, in particular to a data interaction method, device and system of an edge computing platform and network equipment. The data interaction method of the edge computing platform comprises the steps of performing data acquisition on a target scene through at least one piece of Internet of Things equipment in an edge equipment layer to obtain a first data set, and sending a second data set to a first edge node in an edge node layer; controlling the first edge node to process the second data set to obtain a third data set, and sending the third data set to the central node; and under the condition that the third data set meets the first data requirement, the control center node performs identification processing on the third data set by adopting a data analysis algorithm corresponding to the target scene, obtains first decision information corresponding to the target scene, and sends the first decision information to the first edge node. According to the invention, the collection equipment can be managed in a unified manner, and the data transmission efficiency can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a data interaction method, apparatus, system and network device for an edge computing platform. Background Technology

[0002] With the development of science and technology, data transmission efficiency has become a key focus. In scene monitoring, edge computing, as a core supplementary technology to cloud computing, effectively solves problems such as transmission latency and network congestion faced by centralized cloud processing by pushing computing and storage resources to the network edge. For example, edge computing devices can use multi-dimensional data to determine regulatory decisions. However, due to the significant heterogeneity of access protocols for these devices, unified management is impossible, and different data corresponds to different fixed transmission links, resulting in low data transmission efficiency. Summary of the Invention

[0003] This disclosure provides a data interaction method, apparatus, system, and network device for an edge computing platform. This can reduce the inability to achieve unified management of devices due to the significant heterogeneity of access protocols, and can improve data transmission efficiency. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, a data interaction method for an edge computing platform is provided, comprising: The target scene is collected by at least one IoT device in the edge device layer to obtain a first data set, and a second data set is sent to a first edge node in the edge node layer. The second data set is a data set obtained by preprocessing the first data set. The first edge node is controlled to process the second data set, obtain the third data set, and send the third data set to the central node; When the third data set meets the first data requirements, the central node is controlled to use a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

[0004] According to some embodiments, the method further includes: Based on the scene information of the target scene, obtain the data transmission method corresponding to the target scene; The control network layer controls the data transmission method for the at least one IoT device, each edge node in the edge node layer, and the central node.

[0005] According to some embodiments, obtaining the data transmission method corresponding to the target scene based on the scene information of the target scene includes at least one of the following: If the scenario information of the target scenario includes a delay greater than a delay threshold and a bandwidth greater than a bandwidth threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using 5G (5th Generation Mobile Networks) network. If the scenario information of the target scenario includes that the available resources of the IoT device are less than the resource threshold and the network stability is less than the stability threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using a lightweight communication protocol.

[0006] According to some embodiments, the method further includes: Based on data traffic and network conditions, the network layer is controlled to adjust the network topology and routing strategy.

[0007] According to some embodiments, the method further includes: If the third data set satisfies the second data requirements, the first edge node is controlled to acquire the second decision information corresponding to the third data set. The first edge node is controlled to perform the operation corresponding to the second decision information, and the operation result is fed back to the at least one IoT device.

[0008] According to some embodiments, before controlling the first edge node to process the second data set and obtain the third data set, the method further includes: Based on the business growth information corresponding to the target scenario, adjust the node information in the edge node layer, wherein the node information includes the number of nodes and the node type; The first edge node is obtained based on the adjusted node information and the call volume corresponding to the second data set.

[0009] According to some embodiments, sending the second data set to the first edge node in the edge node layer includes: A high-performance distributed message processing engine is used, combined with a Redis Remote DictionaryServer (Redis) session cluster and a Kafka high-throughput streaming pipeline to send the second data set to the first edge node in the edge node layer.

[0010] According to a second aspect of the present disclosure, a data interaction device for an edge computing platform is provided, comprising: A data collection and transmission unit is configured to acquire a first data set by collecting data from a target scene through at least one IoT device in the edge device layer, and to send a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; A data processing unit is used to control the first edge node to process the second data set, obtain the third data set, and send the third data set to the central node; The decision acquisition unit is configured to, when the third data set meets the first data requirements, control the central node to perform identification processing on the third data set using the data analysis algorithm corresponding to the target scene, acquire the first decision information corresponding to the target scene, and send the first decision information to the second edge node, wherein the first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

[0011] According to a third aspect of the present disclosure, a data interaction system for an edge computing platform is provided, comprising: an edge device layer, an edge node layer, a network layer, and a central node, wherein at least one Internet of Things (IoT) device in the edge device layer is connected to each node in the edge node layer through the network layer, and each node in the edge node layer is connected to the central node through the network layer, wherein: The edge device layer is used to collect data from the target scene through at least one IoT device in the edge device layer to obtain a first data set, and send a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; The edge node layer is used to process the second data set through the first edge node, obtain the third data set, and send the third data set to the central node; The central node is used to identify and process the third data set using a data analysis algorithm corresponding to the target scene when the third data set meets the first data requirements, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node, wherein the first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

[0012] According to a fourth aspect of the present disclosure, a network device is provided, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the data interaction method of the edge computing platform as described in any of the preceding aspects.

[0013] According to a fifth aspect of the present disclosure, a storage medium is provided that, when instructions in the storage medium are executed by a processor of a network device, enables the network device to perform the data interaction method of an edge computing platform as described in any of the preceding aspects.

[0014] According to a sixth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described in any one of the preceding aspects.

[0015] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: In some or related embodiments, at least one IoT device in the edge device layer collects data from the target scene to obtain a first data set, and sends a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; the first edge node is controlled to process the second data set to obtain a third data set, and sends the third data set to the central node; if the third data set meets the first data requirements, the central node is controlled to use a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain first decision information corresponding to the target scene, and send the first decision information to a second edge node, wherein the first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information. Therefore, data collected from at least one IoT device can be collected, reducing the situation where the heterogeneity of device access protocols makes standardized access management impossible. At least one IoT device can be managed in a unified manner, and data can be processed through edge nodes. Data is only sent to the central node when the first data requirement is met. This can shorten the data processing transmission link and reduce the amount of data transmitted, improve response performance, reduce the energy consumption required for excessive data transmission, simplify the operation process, reduce system integration costs and operation and maintenance complexity, and improve the convenience of data interaction management.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0018] Figure 1This is a flowchart of a data interaction method for a first edge computing platform provided in this embodiment of the disclosure; Figure 2 This is a flowchart of a second data interaction method for an edge computing platform provided in this embodiment of the disclosure; Figure 3 This is an interactive schematic diagram of a data interaction method for an edge computing platform provided in an embodiment of this disclosure; Figure 4 This is an example schematic diagram of a data interaction system of an edge computing platform provided in an embodiment of this disclosure; Figure 5 This is an example schematic diagram of the core functional framework of an edge computing platform provided in an embodiment of this disclosure; Figure 6 This is a block diagram illustrating a data interaction device for an edge computing platform according to an exemplary embodiment; Figure 7 This is an example schematic diagram of a network device according to an exemplary embodiment. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0020] This disclosure provides data interaction methods, apparatus, network devices, and storage media for edge computing platforms. In some embodiments, the terms "data interaction method" and "information processing method" and "communication method" for edge computing platforms can be used interchangeably; the terms "data interaction apparatus" and "information processing apparatus" for edge computing platforms can be used interchangeably; and the terms "information processing system" and "communication system" can be used interchangeably.

[0021] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0022] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0023] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0024] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular or a plural expression.

[0025] In the embodiments disclosed herein, "multiple" refers to two or more.

[0026] In some embodiments, the terms “at least one of,” “one or more,” “a plurality of,” and “multiple” may be used interchangeably.

[0027] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0028] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," etc.

[0029] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0030] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0031] Figure 1 This is a flowchart of a data interaction method for a first edge computing platform provided in this disclosure embodiment, such as... Figure 1 As shown, it includes the following steps: In step S11, at least one IoT device in the edge device layer collects data from the target scene to obtain a first data set, and sends a second data set to the first edge node in the edge node layer. The second data set is a data set obtained by preprocessing the first data set. In some embodiments, the executing entity of this disclosure may be a network device, for example. This network device does not specifically refer to a particular fixed network device. For example, when the device identifier changes, the network device may also change accordingly. For example, when the structure of the network device changes, the network device may also change accordingly. Furthermore, the executing entity of this disclosure may also be a server, which may be a single server or a server cluster; this disclosure does not limit this. The network device may also be referred to as the data interaction system of an edge computing platform.

[0032] In some embodiments, the edge device layer may be a collection of at least one edge device. This edge device layer does not specifically refer to a fixed device layer. The edge device may also be referred to as an Internet of Things (IoT) device. The edge device layer does not specifically refer to a fixed device layer. For example, the edge device layer may change when the type of devices it includes changes. For example, the edge device layer may change when the number of devices it includes changes.

[0033] In some embodiments, an IoT device may be a device for collecting various data from the physical world, wherein different IoT devices may be used to collect different data. The IoT device may include, for example, a camera, a sensor, or a smart terminal, but is not limited to these. The IoT device does not specifically refer to a particular fixed device. For example, when the device type corresponding to the IoT device changes, the IoT device may also change accordingly. For example, when the device identifier of the IoT device changes, the IoT device may also change accordingly.

[0034] In some embodiments, the target scene may be, for example, the scene currently to be monitored. The name of the target scene is not limited. For example, the target scene may also be called the scene to be monitored, the first scene, etc. The target scene does not specifically refer to a particular fixed scene. For example, when the objects included in the target scene change, the target scene may also change accordingly. For example, when the scene identifier of the target scene changes, the target scene may also change accordingly.

[0035] In some embodiments, the first data set may be, for example, a collection of subsets of data collected by each IoT device in at least one IoT device. This first data set does not specifically refer to a fixed set. For example, the first data set may change when the amount of data it includes changes. Similarly, the first data set may change when the data type it includes changes.

[0036] In some embodiments, the second data set is a data set obtained by preprocessing the first data set. The second data set can also vary accordingly with the other data sets. For example, when the first data set changes, the second data set can also change accordingly. For example, when the preprocessing method changes, the second data set can also change accordingly.

[0037] In some embodiments, the edge node layer may include at least one edge node for receiving data from edge devices and performing further processing and analysis. This edge node layer is not specifically defined as a fixed layer. For example, the edge node layer may change when the number of nodes it includes changes. Similarly, the edge node layer may change when the type of nodes it includes changes.

[0038] In some embodiments, the first edge node may be, for example, an edge node used to process a second dataset. This first edge node does not specifically refer to a particular fixed edge node. For example, the first edge node may change accordingly when its name or type changes.

[0039] According to some embodiments, a first data set is obtained by collecting data from a target scene through at least one IoT device in the edge device layer, and a second data set is sent to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set.

[0040] In step S12, the first edge node is controlled to process the second data set, obtain the third data set, and send the third data set to the central node; According to some embodiments, the third data set may be, for example, a data set obtained after processing the second data set. The "third" in the third data set is used to distinguish it from the other data sets. The third data set does not specifically refer to a fixed data set. For example, when the processing method for the second data set changes, the third data set may also change accordingly.

[0041] In some embodiments, the central node may be a node used to receive data from edge nodes and perform in-depth analysis and processing. This central node may be located, for example, in the cloud or a data center.

[0042] According to some embodiments, the first edge node is controlled to process the second data set, obtain the third data set, and send the third data set to the central node.

[0043] In step S13, if the third data set meets the first data requirements, the control center node uses a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform the operation corresponding to the first decision information.

[0044] According to some embodiments, the first decision information may be, for example, decision information determined based on a third dataset. The "first" in the first decision information is used to distinguish it from other decision information. The first decision information does not specifically refer to any fixed piece of information. For example, when the method of determining the first decision information changes, the first decision information may also change accordingly. For example, when the specific decision corresponding to the first decision information changes, the first decision information may also change accordingly.

[0045] In some embodiments, the second edge node may be the same as the first edge node, or it may be a different node. This disclosure does not limit the scope of the embodiments. The second edge node may be multiple nodes, or it may be a single node.

[0046] According to some embodiments, when the third data set meets the first data requirements, the control center node uses a data analysis algorithm corresponding to the target scenario to identify and process the third data set, obtain the first decision information corresponding to the target scenario, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform the operation corresponding to the first decision information.

[0047] In some or related embodiments, at least one IoT device in the edge device layer collects data from the target scene to obtain a first data set, and sends a second data set to a first edge node in the edge node layer. The second data set is a data set obtained by preprocessing the first data set. The first edge node is controlled to process the second data set to obtain a third data set, and sends the third data set to the central node. If the third data set meets the first data requirements, the central node is controlled to use a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain first decision information corresponding to the target scene, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information. Therefore, data collected from at least one IoT device can be collected, reducing the challenges of standardized access management due to the heterogeneity of device access protocols. This allows for unified management of at least one IoT device, and data processing at the edge node, with data only sent to the central node when the primary data requirement is met, shortens data processing transmission links and reduces data transmission volume. This improves response performance, reduces energy consumption from excessive data transmission, simplifies operational processes, lowers system integration costs and operational complexity, and enhances the convenience of data interaction management. Furthermore, data interaction between edge and central nodes can be determined through scheduling strategies, reducing delays in critical data processing and minimizing unnecessary data bandwidth consumption, thus improving the robustness of the cloud-edge collaboration mechanism. Finally, resource decentralization can reduce energy consumption from massive data uplink transmissions while simplifying operational processes, enabling efficient operation and maintenance for non-professional personnel.

[0048] Figure 2 This is a flowchart of a second data interaction method for an edge computing platform provided in this disclosure embodiment, such as... Figure 2 As shown, it includes the following steps: In step S21, at least one IoT device in the edge device layer collects data from the target scene to obtain a first data set, and sends a second data set to the first edge node in the edge node layer. The second data set is a data set obtained by preprocessing the first data set. In some embodiments, the executing entity of this disclosure may be a network device, for example. This network device does not specifically refer to a particular fixed network device. For example, when the device identifier changes, the network device may also change accordingly. For example, when the structure of the network device changes, the network device may also change accordingly. Furthermore, the executing entity of this disclosure may also be a server, which may be a single server or a server cluster; this disclosure does not limit this.

[0049] The relevant descriptions can be as described above, and will not be repeated here.

[0050] According to some embodiments, the edge device layer is the data source and can include various types of IoT devices, such as sensors, cameras, and smart terminals. These IoT devices are responsible for collecting various data from the physical world, such as temperature, humidity, images, and sound, and converting them into digital signals. The technical solutions of this disclosure can be applied to access control, where sensors monitor the operating status of equipment, such as temperature and vibration parameters; cameras capture images of people, vehicles, and goods in the access lane for identity verification or quality inspection. Because edge devices have limited computing power and storage capacity, they primarily perform simple data preprocessing, such as data filtering and sampling, to remove noise, reduce data volume, and improve data transmission efficiency.

[0051] According to some embodiments, the method further includes: Based on the scene information of the target scene, obtain the data transmission method corresponding to the target scene; The control network layer controls the data transmission method for at least one IoT device, and the data transmission between each edge node and the central node in the edge node layer.

[0052] According to some embodiments, the data transmission mode can be used to indicate the method used when transmitting data, and this data transmission mode is not specifically defined by a fixed method. For example, when the protocol corresponding to the data transmission mode changes, the data transmission mode can also change accordingly. For example, when the two parties involved in the data transmission change, the data transmission mode can also change accordingly.

[0053] According to some embodiments, based on the scene information of the target scene, the data transmission method corresponding to the target scene is obtained, including at least one of the following: If the scenario information of the target scenario includes a delay greater than the delay threshold and a bandwidth greater than the bandwidth threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using a 5G network. If the scenario information for the target scenario includes that the available resources of IoT devices are less than the resource threshold and the network stability is less than the stability threshold, then the data transmission method for the target scenario is determined to be a lightweight communication protocol.

[0054] In some embodiments, the thresholds may be used to indicate the data transmission method, and each threshold is not specifically a fixed threshold. For example, when a modification instruction for a certain threshold is received, the threshold may also change accordingly.

[0055] According to some embodiments, the method further includes: Based on data traffic and network conditions, the control network layer adjusts network topology and routing strategies. Therefore, network load can be optimized, network stability and reliability improved, and network congestion and transmission bottlenecks alleviated.

[0056] In some embodiments, the network layer is responsible for data transmission between edge devices, edge nodes, and central nodes. A combination of various network communication technologies can be used to adapt to different network environments and application requirements. For example, in low-latency, high-bandwidth scenarios, 5G networks can be used for data transmission to ensure fast transmission of data with high real-time requirements. For resource-constrained and network-unstable IoT devices, lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) can be used to reduce data transmission overhead and network dependence.

[0057] According to some embodiments, the network layer can also employ software-defined networking (SDN) technology to achieve centralized control and management of the network, dynamically adjust network topology and routing strategies based on data traffic and network conditions, and improve the utilization of network resources and the efficiency of data transmission.

[0058] According to some embodiments, before controlling the first edge node to process the second data set and obtaining the third data set, the method further includes: Based on the business growth information corresponding to the target scenario, adjust the node information in the edge node layer, including the number of nodes and node type; Based on the adjusted node information and the call volume corresponding to the second data set, obtain the first edge node.

[0059] According to some embodiments, sending the second data set to a first edge node in the edge node layer includes: A high-performance distributed message processing engine is used, combined with a Redis session cluster and a Kafka high-throughput streaming pipeline, to send the second data set to the first edge node in the edge node layer.

[0060] In step S22, the first edge node is controlled to process the second data set, obtain the third data set, and send the third data set to the central node; The relevant descriptions can be as described above, and will not be repeated here.

[0061] According to some embodiments, the edge node layer can consist of computing devices distributed at the network edge, such as edge servers and gateways. Edge nodes, for example, can be responsible for receiving data from edge devices and performing further processing and analysis. Edge nodes possess certain computing power and storage resources, enabling them to run lightweight applications and algorithms, achieving real-time data processing and local decision-making.

[0062] In step S23, if the third data set meets the first data requirements, the control center node uses a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform the operation corresponding to the first decision information. The relevant descriptions can be as described above, and will not be repeated here.

[0063] In some embodiments, the central node layer can be located in the cloud or a data center, possessing powerful computing capabilities and storage resources. The central node is responsible for receiving data from edge nodes and performing in-depth analysis and processing. The central node can run complex big data analytics and artificial intelligence algorithms to uncover the potential value of the data and support decision-making. In intelligent port entry and exit systems, the central node can comprehensively analyze reported data, customs clearance data, and collected data from various edge nodes to determine whether customs clearance conditions are met and issue release or detention notices. The central node can also feed back the analysis results to the edge nodes, guiding their decision-making and operations, and achieving cloud-edge collaboration.

[0064] In step S24, if the third data set meets the second data requirements, the first edge node is controlled to acquire the second decision information corresponding to the third data set; The relevant descriptions can be as described above, and will not be repeated here.

[0065] In step S25, the first edge node is controlled to perform the operation corresponding to the second decision information, and the operation result is fed back to at least one Internet of Things device.

[0066] The relevant descriptions can be as described above, and will not be repeated here.

[0067] For example, in smart vehicle access control systems, edge nodes can analyze video data collected by cameras to identify vehicle speed, location, license plate number, and other information. Based on this information, they can intelligently control the gates to optimize traffic flow. Edge nodes can also cache and store data, temporarily saving it when the network fails or becomes congested, and then transmitting it again once the network is restored, ensuring data integrity and reliability.

[0068] According to some embodiments, Figure 3 This is an interactive schematic diagram of a data interaction method for an edge computing platform provided in an embodiment of this disclosure, such as... Figure 3 As shown, it includes: (1) After the edge device collects data, it performs preprocessing and then sends the processed data to the edge node. (2) The edge node receives the processed data, performs further processing and analysis, and decides whether to send the data to the central node according to the data type and application requirements. For example, for some data with high real-time requirements and that need to be processed immediately, the edge node can process it locally and feed the processing results directly back to the edge device or actuator. In the intelligent port of entry and exit, after the edge node receives the equipment fault alarm data, it immediately performs fault diagnosis and analysis, and sends control commands to the equipment to adjust the operating status of the equipment and avoid the fault from expanding. For some data that requires in-depth analysis and comprehensive processing, the edge node sends it to the central node. (3) After the central node receives the data from the edge node, it performs in-depth analysis and processing and feeds the analysis results back to the edge node. (4) The edge node adjusts its local decisions and operations according to the feedback information from the central node and sends the relevant information to the edge device or actuator. At the same time, the edge device can also directly receive the user's control commands and perform the corresponding operations.

[0069] In one or related embodiments, when the third data set meets the second data requirements, the first edge node is controlled to acquire second decision information corresponding to the third data set; the first edge node is controlled to execute the operation corresponding to the second decision information, and the operation result is fed back to at least one IoT device. This can reduce data processing time, reduce the acquisition time of control commands (i.e., decision information), reduce the adjustment time of device operating status, reduce the probability of fault escalation, and improve data processing efficiency and decision information determination efficiency.

[0070] According to some embodiments, such as Figure 4 As shown, a data interaction system for an edge computing platform is provided, comprising: an edge device layer, an edge node layer, a network layer, and a central node. At least one IoT device in the edge device layer is connected to each node in the edge node layer via the network layer, and each node in the edge node layer is connected to the central node via the network layer. The edge device layer is used to collect data from the target scene through at least one IoT device in the edge device layer to obtain a first data set, and send a second data set to the first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; The edge node layer is used to process the second data set through the first edge node, obtain the third data set, and send the third data set to the central node; The central node, when the third data set meets the first data requirements, uses a data analysis algorithm corresponding to the target scenario to identify and process the third data set, obtain the first decision information corresponding to the target scenario, and send the first decision information to the second edge node. The first decision information instructs the second edge node to perform the operation corresponding to the first decision information. Therefore, a data interaction system for an edge computing platform that supports multi-protocol compatibility, low-latency interaction, security, efficiency, and ease of use can be constructed. It can achieve standardized access and parallel management of multi-protocol devices such as MQTT, HyperText Transfer Protocol (HTTP), HyperText Transfer Protocol Secure (HTTP(S)), Transmission Control Protocol (TCP), Modicon Bus Communication Protocol (Modbus), and User Datagram Protocol (UDP), meeting the unified management and control requirements of heterogeneous hardware devices.

[0071] Among them, such as Figure 4 As shown, the data interaction system of the edge computing platform adopts a layered distributed architecture, which is divided into edge device layer, edge node layer, network layer and central node layer from top to bottom. Each layer realizes data interaction through standardized interfaces. The architecture design follows the principle of high cohesion and low coupling. Moreover, the architecture supports horizontal expansion and can dynamically add nodes according to business growth to improve the system's processing capacity.

[0072] According to some embodiments, Figure 5 This disclosure illustrates a core functional framework for an edge computing platform provided in an embodiment. The core functional modules of the data interaction system of this edge computing platform comprise seven interconnected modules. Access management provides protocol parsing, device adaptation, and cloud-to-cloud integration functions, supporting rapid access for devices with multiple protocols. Product management supports service configuration, attribute configuration, and event configuration, enabling full lifecycle management of products. Device management includes device registration, debugging, status monitoring, and alarm notification functions, supporting remote device control. The rule engine provides rule configuration, linkage triggering, and automatic execution functions, automating the clearance process. Data management supports data filtering, deduplication, format conversion, and structured extraction, ensuring data quality. Interface management includes interface creation, approval, authorization, and access control functions, enabling secure data sharing. System management provides user management, permission configuration, and dictionary management functions, supporting customized deployment.

[0073] According to some embodiments, when the data interaction system of the edge computing platform performs data processing, it may include: (1) By adopting a high-performance distributed message processing engine and combining a Redis session cluster with a Kafka high-throughput streaming pipeline, a unified device access and communication hub with the capacity to handle tens of millions of concurrent connections was built. This architecture not only enables elastic scaling and efficient message routing for massive device connections, but also supports cross-vendor and cross-protocol devices to achieve service collaboration and full lifecycle management on a unified platform through standardized protocol adaptation and configuration management at the edge.

[0074] (2) By adopting the automatic container orchestration technology of the Kubernetes Container Orchestration System, a flexible IoT platform architecture that can automatically scale according to the call volume was constructed. Dynamic scaling and self-healing are achieved, providing highly available infrastructure support for the management of massive device access. The edge computing platform ensures the stable and efficient operation of IoT services across all regions and time periods through reasonable collaborative scheduling and intelligent operation and maintenance.

[0075] (3) At the platform's backend service layer, a highly cohesive and loosely coupled business capability center was built based on the Spring Cloud Distributed Microservices Framework (Spring Cloud) microservice architecture. Through the automated pipeline deployment of Continuous Integration / Continuous Delivery (CI / CD) via the integration of Jenkins and Docker containerization platform, agile delivery and canary releases throughout the entire process from code submission to containerized deployment were achieved. Service circuit breaking and rate limiting were implemented using Hystrix circuit breaker components and Sentinel flow control circuit breaker components to ensure that a failure caused by a single service interface does not affect other system services. By leveraging the Spring Cloud Gateway unified application programming interface (API) gateway, dynamic routing and security management are provided. It integrates Prometheus monitoring and Grafana visualization (Prometheus + Grafana +) distributed tracing technology to build a three-dimensional monitoring and insight system covering basic equipment, service instances, and the entire business call chain.

[0076] (4) To address the complex real-time decision-making and dynamic changes in business rules required in IoT scenarios, the Drools business rule management system and the Groovy dynamic scripting language (Groovy) dynamic rule-driven engine can be introduced. By integrating the Drools rule engine, business rules such as device behavior policies, alarm thresholds, and linkage logic can be designed as hot-deployable configurations, achieving decoupling between business logic and core code. Simultaneously, the platform incorporates the Groovy dynamic scripting engine into the communication services between the north and south sides, supporting real-time script injection and updates for format conversion and lightweight computing logic without downtime. This provides efficient and usable extension solutions for device data reporting, device service access, device command issuance, and device data display from various manufacturers, significantly improving the platform's adaptability and operational efficiency in scenarios such as smart manufacturing and smart alarms.

[0077] (5) To address the dual challenges of massive time-series data and complex business retrieval in the Internet of Things (IoT), a secure, reliable, and high-performance IoT data processing system platform was constructed. The edge computing project adopted a domestically integrated architecture combining the Internet of Things Database (IoTDB+), Elasticsearch distributed search and analysis engine (Elasticsearch+), and DM Database. A unified retrieval center for all businesses was built, supporting multi-dimensional, cross-entity millisecond-level fuzzy queries and correlation analysis. Through full-text search and complex query analysis capabilities, the barriers between "device data" and "business information" were broken down, providing an integrated data foundation and technical support for real-time monitoring, equipment data reporting and reception, fault cause analysis, and exploration of massive historical data storage.

[0078] (6) A two-way authentication message queue telemetry transport (MQTT) security system based on enterprise-level certificates (Certificate SigninMQTTg Request, CRT) can be constructed to provide reliable guarantees for enterprise-level secure connections and device authentication for IoT and edge computing projects. All devices and edge gateways are pre-installed with a unique device certificate issued by the enterprise's private Certificate Authority (CA) before leaving the factory, realizing "one device, one key" hard identification for network access verification. Through the strong binding of certificates and device identities, while realizing secure access control for massive devices, a standardized infrastructure is provided for device group authorization, business permission inheritance, and security audit traceability, meeting the high-level security compliance requirements of information security level protection. Therefore, a local data processing and security protection system can be constructed to realize localized computation and encrypted transmission of sensitive data, avoid leakage of key information, and reduce cloud transmission and storage pressure.

[0079] A block diagram illustrating a data interaction device for an edge computing platform according to an exemplary embodiment. (Refer to...) Figure 6 The device 600 includes: The data collection sending unit 601 is used to collect data from the target scene through at least one IoT device in the edge device layer to obtain a first data set, and send a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; The data processing unit 602 is used to control the first edge node to process the second data set, obtain the third data set, and send the third data set to the central node; The decision acquisition unit 603 is used to, when the third data set meets the first data requirements, use a data analysis algorithm corresponding to the target scenario to identify and process the third data set, acquire the first decision information corresponding to the target scenario, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform the operation corresponding to the first decision information.

[0080] According to some embodiments, the data processing unit 602 is further configured to: Based on the scene information of the target scene, obtain the data transmission method corresponding to the target scene; The control network layer controls the data transmission method for at least one IoT device, and the data transmission between each edge node and the central node in the edge node layer.

[0081] According to some embodiments, when the data processing unit 602 obtains the data transmission method corresponding to the target scene based on the scene information of the target scene, it is specifically used for at least one of the following: If the scenario information of the target scenario includes a delay greater than the delay threshold and a bandwidth greater than the bandwidth threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using a 5G network. If the scenario information for the target scenario includes that the available resources of IoT devices are less than the resource threshold and the network stability is less than the stability threshold, then the data transmission method for the target scenario is determined to be a lightweight communication protocol.

[0082] According to some embodiments, the data processing unit 602 is further configured to: Based on data traffic and network conditions, the control network layer adjusts the network topology and routing strategies.

[0083] According to some embodiments, the data processing unit 602 is further configured to: If the third data set meets the second data requirements, control the first edge node to obtain the second decision information corresponding to the third data set; Control the first edge node to perform the operation corresponding to the second decision information, and feed back the operation result to at least one Internet of Things device.

[0084] According to some embodiments, the data processing unit 602 is configured to control the first edge node to process the second data set, and before obtaining the third data set, it is further configured to: Based on the business growth information corresponding to the target scenario, adjust the node information in the edge node layer, including the number of nodes and node type; Based on the adjusted node information and the call volume corresponding to the second data set, obtain the first edge node.

[0085] According to some embodiments, when the data processing unit 602 sends the second data set to the first edge node in the edge node layer, it is specifically used for: A high-performance distributed message processing engine is used, combined with a Redis session cluster and a Kafka high-throughput streaming pipeline, to send the second data set to the first edge node in the edge node layer.

[0086] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0087] Figure 7 This is a block diagram of a network device 700 provided in an embodiment of this disclosure. For example, network device 700 can be provided as a network device. See also... Figure 7 The network device 700 includes a processing component 722, which further includes at least one processor, and memory resources represented by memory 732 for storing instructions, such as application programs, that can be executed by the processing component 722. The application programs stored in memory 732 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 722 is configured to execute instructions to perform any of the methods described above applied to the network device.

[0088] Network device 700 may also include a power supply component 727 configured to perform power management of network device 700, a wired or wireless network interface 750 configured to connect network device 700 to a network, and an input / output (I / O) interface 758. Network device 700 can operate on an operating system stored in memory 732, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0089] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0093] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0094] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is established by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0095] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0096] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data interaction method for an edge computing platform, characterized in that, include: The target scene is collected by at least one IoT device in the edge device layer to obtain a first data set, and a second data set is sent to a first edge node in the edge node layer. The second data set is a data set obtained by preprocessing the first data set. The first edge node is controlled to process the second data set, obtain the third data set, and send the third data set to the central node; When the third data set meets the first data requirements, the central node is controlled to use a data analysis algorithm corresponding to the target scene to identify and process the third data set, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node. The first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

2. The method according to claim 1, characterized in that, The method further includes: Based on the scene information of the target scene, obtain the data transmission method corresponding to the target scene; The control network layer controls the data transmission method for the at least one IoT device, each edge node in the edge node layer, and the central node.

3. The method according to claim 2, characterized in that, The step of obtaining the data transmission method corresponding to the target scene based on the scene information of the target scene includes at least one of the following: If the scenario information of the target scenario includes a delay greater than a delay threshold and a bandwidth greater than a bandwidth threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using a 5G network. If the scenario information of the target scenario includes that the available resources of the IoT device are less than the resource threshold and the network stability is less than the stability threshold, then the data transmission method corresponding to the target scenario is determined to be a data transmission method using a lightweight communication protocol.

4. The method according to claim 2, characterized in that, The method further includes: Based on data traffic and network conditions, the network layer is controlled to adjust the network topology and routing strategy.

5. The method according to claim 1, characterized in that, The method further includes: If the third data set satisfies the second data requirements, the first edge node is controlled to acquire the second decision information corresponding to the third data set. The first edge node is controlled to perform the operation corresponding to the second decision information, and the operation result is fed back to the at least one IoT device.

6. The method according to claim 1, characterized in that, Before controlling the first edge node to process the second data set and obtain the third data set, the method further includes: Based on the business growth information corresponding to the target scenario, adjust the node information in the edge node layer, wherein the node information includes the number of nodes and the node type; The first edge node is obtained based on the adjusted node information and the call volume corresponding to the second data set.

7. The method according to claim 1, characterized in that, Sending the second data set to the first edge node in the edge node layer includes: A high-performance distributed message processing engine is used, combined with a Redis session cluster and a Kafka high-throughput streaming pipeline, to send the second data set to the first edge node in the edge node layer.

8. A data interaction device for an edge computing platform, characterized in that, include: A data collection and transmission unit is configured to acquire a first data set by collecting data from a target scene through at least one IoT device in the edge device layer, and to send a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; A data processing unit is used to control the first edge node to process the second data set, obtain the third data set, and send the third data set to the central node; The decision acquisition unit is configured to, when the third data set meets the first data requirements, control the central node to perform identification processing on the third data set using the data analysis algorithm corresponding to the target scene, acquire the first decision information corresponding to the target scene, and send the first decision information to the second edge node, wherein the first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

9. A data interaction system for an edge computing platform, characterized in that, include: The edge device layer, the first edge node, and the center node, wherein: The edge device layer is used to collect data from the target scene through at least one IoT device in the edge device layer to obtain a first data set, and send a second data set to a first edge node in the edge node layer, wherein the second data set is a data set obtained by preprocessing the first data set; The first edge node is used to process the second data set, obtain the third data set, and send the third data set to the central node; The central node is used to identify and process the third data set using a data analysis algorithm corresponding to the target scene when the third data set meets the first data requirements, obtain the first decision information corresponding to the target scene, and send the first decision information to the second edge node, wherein the first decision information is used to instruct the second edge node to perform an operation corresponding to the first decision information.

10. A network device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the data interaction method of the edge computing platform as described in any one of claims 1 to 7.

Images