Electronic equipment data real-time transmission method and system based on edge computing

Abstract

The invention provides an electronic equipment data real-time transmission method and system based on edge computing, belongs to the technical field of data transmission, and is used for avoiding data redundancy in a data transmission process. The method comprises the following steps: an edge node respectively obtains respective service data of a plurality of electronic devices from the plurality of electronic devices; the edge node constructs the respective service data of the plurality of electronic devices into a first data set and a second data set, the first data set comprises the same service data in the respective service data of the plurality of electronic devices, and the same service data in the respective service data of the plurality of electronic devices is one piece of service data in the first data set; the second data set comprises different business data in respective business data of the plurality of electronic devices; the edge node sends the first data set, the second data set and the corresponding relation between the first data set and the second data set to the center node; and the central node processes the first data set and the second data set according to the corresponding relation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for real-time data transmission in electronic devices based on edge computing. Background Technology

[0002] In the industrial production sector, taking smartphone screen production lines as an example, with the deep application of industrial internet technology, a large number of identical or similar electronic devices are deployed along the entire production line, such as optical inspection instruments, laminating machines, and dispensing equipment. These devices collectively form a highly collaborative equipment group, serving the same business process in screen manufacturing. During operation, these devices continuously generate massive amounts of business data, including image detection results, lamination pressure parameters, and dispensing volume control signals. This data is not only of the same type but also often highly similar or even identical in content, such as the environmental temperature and vibration frequency experienced by the same batch of screens. Traditional data processing relies on cloud computing models, where all terminal devices directly upload the collected raw data to a remote cloud data center for processing. However, in scenarios like screen production, which have extremely high real-time requirements, this centralized processing model faces significant bottlenecks. On the one hand, the high-frequency reporting of highly similar data by a large number of devices drastically consumes network bandwidth, leading to increased transmission latency. On the other hand, real-time quality control and process adjustments during production require millisecond-level responses, and the time cost of data traveling to and from the cloud is insufficient to meet these real-time control needs.

[0003] To address these challenges, edge computing technology has been introduced into industrial settings. By deploying nodes with computing and storage capabilities at the edge of the production line, raw data can be processed and preliminarily analyzed locally. For example, in screen inspection, edge nodes can process image data in real time, uploading only anomalies or statistical summaries to the cloud, thus significantly reducing network load and response latency. However, current edge computing-based data transmission methods typically involve edge nodes performing independent data filtering, aggregation, or packaging operations for single devices, failing to fundamentally solve the data redundancy problem. Summary of the Invention

[0004] This invention provides a method and system for real-time data transmission of electronic devices based on edge computing, in order to avoid data redundancy during data transmission.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for real-time data transmission of electronic devices based on edge computing is provided, applied to a real-time data transmission system for electronic devices based on edge computing. The system includes a central node and edge nodes. The method includes: the edge nodes acquiring business data from multiple electronic devices, where the multiple electronic devices belong to the same device group, the same device group is oriented towards the same business scenario, and the business data of any two electronic devices are of the same type; the edge nodes construct a first dataset and a second dataset from the business data of the multiple electronic devices, where the first dataset includes the same business data from the business data of the multiple electronic devices, and the same business data from the business data of the multiple electronic devices is considered as one set of business data in the first dataset; the second dataset includes the different business data from the business data of the multiple electronic devices; the edge nodes send the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node; and the central node processes the first dataset and the second dataset according to the correspondence.

[0006] Optionally, the edge node constructs the business data of multiple electronic devices into a first dataset and a second dataset, including: the edge node extracts the annotation information from the business data of multiple electronic devices as the first dataset, and the business data of multiple electronic devices annotated by the same annotation information as the second dataset, and the correspondence between the first dataset and the second dataset indicates that the business data in the second dataset is annotated by the annotation information in the first dataset.

[0007] Optionally, the annotation information includes a timestamp and a data type information. The timestamp indicates that the business data of each of the multiple electronic devices in the second dataset is data collected at the timestamp, and the data type information indicates a corresponding data type. The business data of each of the multiple electronic devices in the second dataset is business data of the corresponding data type.

[0008] Optionally, the edge node extracts annotation information from the business data of each of the multiple electronic devices as a first dataset, and extracts business data from the business data of each of the multiple electronic devices that are annotated with the same annotation information as a second dataset. This includes: the edge node using the security context of the edge node and each of the multiple electronic devices to perform security verification on the business data of the corresponding electronic devices in the multiple electronic devices to obtain the plaintext business data of each of the multiple electronic devices; the edge node extracts annotation information from the plaintext business data of each of the multiple electronic devices as a first dataset, and extracts business data from the plaintext business data of each of the multiple electronic devices that are annotated with the same annotation information as a second dataset.

[0009] Optionally, the edge node is a user plane entity in the network where the edge node is located. For any target electronic device among multiple electronic devices, the method further includes: the user plane entity obtaining a temporary public key of the target electronic device from the network, the temporary public key being derived from the target electronic device's root key and sent to the network; the user plane entity deriving a temporary key on the network side based on the temporary public key and the network's preset private key; the service data of the target electronic device is obtained by the target electronic device using the temporary key on its side to securely protect the plaintext service data of the target electronic device, the temporary key on the target electronic device side being derived from the target electronic device's temporary private key and the network's preset public key, and the temporary private key being derived from the target electronic device's root key; correspondingly, the edge node uses the security context of the edge node and each electronic device among the multiple electronic devices to perform security verification on the service data of the corresponding electronic device among the multiple electronic devices, obtaining the plaintext service data of each of the multiple electronic devices, including: the user plane entity using the temporary key in the security context of the user plane entity and the target electronic device to perform security verification on the service data of the target electronic device, obtaining the plaintext service data of the target electronic device.

[0010] Optionally, the central node is deployed in the network's management domain, and the method further includes: the central node instructing a user plane entity to establish end-to-end security with a target electronic device; the user plane entity obtaining a temporary public key of the target electronic device from the network, including: the user plane entity obtaining the temporary public key of the target electronic device from the network in response to the instruction of the central node.

[0011] Optionally, the central node processes the first dataset and the second dataset according to the correspondence, including: the central node restores the first dataset and the second dataset to plaintext business data of each of the multiple electronic devices according to the correspondence; for any target electronic device among the multiple electronic devices, the central node analyzes the plaintext business data of the target electronic device through a lightweight model to determine whether the target electronic device is abnormal.

[0012] Optionally, the plaintext business data of the target electronic device includes: multiple business data of the same data type collected by the target electronic device at multiple timestamps. The central node analyzes the plaintext business data of the target electronic device through a lightweight model to determine whether the target electronic device is abnormal. This includes: the central node dividing the multiple business data into multiple groups of business data by using interval difference, each of the multiple groups of business data including one business data in each of multiple intervals; and the central node analyzing the multiple groups of business data through a lightweight model to determine whether the target electronic device is abnormal.

[0013] Optionally, the central node divides multiple business data into multiple groups of business data by using interval difference. These groups consist of M consecutive intervals, where M is an integer greater than 1. For the first group of business data: the central node determines whether any of the multiple business data sets, based on multiple timestamps from earliest to latest, contains a data set whose value falls within the first interval of the M intervals. If a data set contains a data set whose value falls within the first interval, then that data set is considered the first business data set in the first group. Otherwise, the data set is determined to be the first business data set. The data is used as preset interpolation data. The central node determines whether there is any business data whose value falls within the second interval of M intervals, according to the order of multiple timestamps from first to last. If there is a second business data whose value falls within the second interval, then the business data in the first group whose value falls within the second interval is determined to be the second business data. Otherwise, the business data in the first group whose value falls within the second interval is determined to be the preset interpolation data. This process continues until the business data in the first group whose value falls within the Mth interval of M intervals is determined, and the business data that has been placed in the first group is then considered as the second business data. The business data is deleted, resulting in updated business data. For the second set of business data: the central node determines whether there is any business data in the updated business data whose value falls within the first interval, based on multiple timestamps in chronological order. If there is a third business data whose value falls within the first interval, then the business data in the second set of business data whose value falls within the first interval is determined to be the third business data; otherwise, the business data in the second set of business data whose value falls within the first interval is determined to be the preset interpolated data. The central node determines whether there is any third business data in the updated business data, based on multiple timestamps in chronological order. If a set of business data has a value within the second interval, and one of the business data has a value within the second interval (i.e., a fourth business data), then the business data in the second set of business data with a value within the second interval is determined to be the fourth business data. Otherwise, the business data in the second set of business data with a value within the second interval is determined to be the preset interpolated data. This process continues until a business data in the second set of business data with a value within the Mth interval is determined, and the business data already placed in the second set of business data is deleted from the updated business data, resulting in updated business data. This process continues until all business data have been placed in their corresponding intervals.

[0014] Secondly, a real-time data transmission system for electronic devices based on edge computing is provided. The system includes a central node and edge nodes. The system is configured as follows: the edge nodes acquire the business data of multiple electronic devices from each of the multiple electronic devices. The multiple electronic devices belong to the same device group, the same device group is oriented towards the same business scenario, and the business data of any two electronic devices are of the same type; the edge nodes construct a first dataset and a second dataset from the business data of the multiple electronic devices. The first dataset includes the same business data from the business data of the multiple electronic devices, and the same business data from the business data of the multiple electronic devices is considered as one set of business data in the first dataset. The second dataset includes the different business data from the business data of the multiple electronic devices; the edge nodes send the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node; the central node processes the first dataset and the second dataset according to the correspondence.

[0015] It should be understood that the specific implementation of the system can also refer to the relevant introduction of the above methods, and will not be repeated here.

[0016] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the method described in the first aspect.

[0017] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0018] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0019] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the method described in the first aspect.

[0020] In summary, the technical solution of this application effectively solves the core problem of "low transmission efficiency caused by data redundancy within a device group" mentioned in the background technology through innovative data construction and transmission mechanisms, and brings a series of significant technical effects: 1. Reduce network bandwidth usage and improve transmission efficiency: In traditional models, identical data generated by N devices within a group is transmitted N times repeatedly. This solution, by constructing a first dataset, compresses N identical business data entries into a single dataset, fundamentally eliminating redundant transmission. This is the most direct and core technical effect. It ensures that in scenarios with a large number of devices and high data homogeneity (such as sensor networks), the pressure on network bandwidth is no longer linearly related to the number of devices, thus achieving a qualitative leap in transmission efficiency.

[0021] 2. Reduce data transmission latency and enhance real-time performance: The reduction in the amount of data transmitted leads to a shorter transmission time. Edge nodes no longer need to wait for and send the complete raw data streams from all devices; instead, they first perform localized data deduplication and reassembly to generate smaller first and second datasets. This allows data packets to reach the central node faster, better meeting the stringent requirements of low latency and high real-time performance in business scenarios such as industrial control and vehicle-to-everything (V2X) collaboration.

[0022] 3. Reduce the processing burden on the central node and optimize resource utilization: The central node no longer receives massive, repetitive raw data streams, but rather refined datasets that have been preprocessed and structured by the edge nodes. This eliminates the need for the central node to perform cumbersome data deduplication and correlation comparison operations, allowing for efficient processing based directly on the received correspondences. Furthermore, the total amount of stored data is significantly reduced due to redundancy elimination, saving storage costs. Consequently, the central node's computing and storage resources can be more focused on high-value business logic analysis, global situational awareness, and intelligent decision-making, improving the overall system architecture's processing power and cost-effectiveness.

[0023] 4. Achieve a balance between eliminating data redundancy and ensuring information integrity: This solution doesn't simply discard "duplicate" data. Instead, it cleverly achieves lossless data reconstruction through the construction of a "first dataset (common data) + second dataset (unique data) + correspondence." The central node can fully reconstruct the original data context of each electronic device (i.e., "who" generated "what" data "when") based on the correspondence. This design maximizes data compression while completely preserving the effective information and relevance of the data, avoiding information loss due to over-compression.

[0024] 5. Fully leverage the advantages of edge computing and optimize system architecture: This solution is a perfect example of the edge computing concept of "decentralizing computing power to the network edge." It places computationally intensive but logically relatively deterministic tasks such as data deduplication, classification, and reconstruction on edge nodes, while leaving tasks like global management and complex analysis at the center. This division of labor—compression at the edge and analysis at the center—allows edge and center nodes to perform their respective functions, forming an efficient collaborative computing model that optimizes the overall system architecture and resource allocation.

[0025] Therefore, the technical effect of this application's solution is systematic and multi-layered. It is not a simple optimization of the existing transmission process, but rather solves the inherent redundancy problem of group data transmission from an information theory perspective by changing the way data is organized and expressed at edge nodes. Ultimately, while ensuring information integrity, it achieves a simultaneous and significant reduction in three key indicators: bandwidth usage, transmission latency, and central load, thereby greatly improving the overall performance and practicality of the edge computing-based real-time data transmission system. Attached Figure Description

[0026] Figure 1 A schematic diagram of the architecture of a real-time data transmission system for electronic devices based on edge computing provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the real-time data transmission method for electronic devices based on edge computing provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0028] The technical solutions of this invention can be applied to various edge computing-based real-time data transmission systems for electronic devices, such as Wi-Fi wireless network systems, vehicle-to-everything (V2X) edge computing-based real-time data transmission systems for electronic devices, device-to-device (D2D) edge computing-based real-time data transmission systems for electronic devices, vehicle-to-everything (V2X) edge computing-based real-time data transmission systems for electronic devices, fourth-generation (4G) mobile edge computing-based real-time data transmission systems for electronic devices, such as Long Term Evolution (LTE) systems, Worldwide Interoperability for Microwave Access (WiMAX) edge computing-based real-time data transmission systems for electronic devices, fifth-generation (5G) systems, such as New Radio (NR) systems, and future edge computing-based real-time data transmission systems for electronic devices.

[0029] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0030] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0031] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0032] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0033] In the embodiments of this invention, "protocol" may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to a future real-time data transmission system for electronic devices based on edge computing. The embodiments of this invention do not specifically limit this.

[0034] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0035] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0036] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0037] To facilitate understanding of the embodiments of the present invention, firstly, using Figure 1 The following describes in detail, using an edge computing-based real-time data transmission system for electronic devices as an example, a real-time data transmission system for electronic devices applicable to embodiments of the present invention. For example, Figure 1 This is a schematic diagram of the architecture of a real-time data transmission system for electronic devices based on edge computing, which is applicable to the real-time data transmission method for electronic devices based on edge computing provided in the embodiments of the present invention.

[0038] like Figure 1As shown, the real-time data transmission system for electronic devices based on edge computing may include: edge nodes, central nodes, and optionally, multiple electronic devices.

[0039] Edge nodes can be user plane entities within a network. The network can be a mobile communication network, and the user plane entity can be a User Plane Function (UPF). Central nodes can be deployed within the network's management domain, specifically an Element Management System (EMS) or a Network Management System (NMS). Any one of the multiple electronic devices can be a terminal, or a terminal device.

[0040] The terminal device can be a terminal with wireless transceiver capabilities or a chip or chip system that can be installed on the terminal. This terminal device can also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device. In the embodiments of this application, the terminal device can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal in industrial control, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, vehicle-mounted terminal, RSU with terminal functionality, etc. The terminal device of this application may also be an on-board module, on-board component, on-board chip, or on-board unit that is built into a vehicle as one or more components or units. The vehicle can implement the method provided in this application through the built-in on-board module, on-board component, on-board chip, or on-board unit.

[0041] Multiple electronic devices belong to the same device group, and this device group is geared towards the same business scenario. The business data of any two electronic devices are of the same type. The business data of each electronic device can include multiple sets of data. Each set of data includes a timestamp, a data type indicator, and the business data itself, indicating that the business data uses the corresponding data type at that timestamp. Any two sets of data may have different timestamps, but the data type indicator may show the same data type.

[0042] The following uses a mobile phone screen manufacturing business scenario to illustrate the specific implementation of this system: 1) Equipment groups and equipment types: Group definition: 20 industrial robotic arms of the same model on a mobile phone screen automated assembly line.

[0043] Specific equipment: Electronic devices are sensors built into each robotic arm controller, which can report the operating status.

[0044] 2) The specific composition of business data: Each robotic arm reports a set of data per second. A set of data may be: {Device ID: Robot_07, Time: 2023-11-01 10:00:00, Data type: motor_current, Data value: [Current1, Current2, ...]}.

[0045] In this communication system, edge nodes acquire service data from multiple electronic devices, which belong to the same device group and are geared towards the same service scenario. The service data of any two electronic devices are of the same type. The edge nodes construct a first dataset and a second dataset from the service data of the multiple electronic devices. The first dataset includes the same service data from the service data of the multiple electronic devices, and the same service data from the service data of the multiple electronic devices is considered as one set of service data in the first dataset. The second dataset includes the different service data from the service data of the multiple electronic devices. The edge nodes send the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node. The central node processes the first dataset and the second dataset according to the correspondence. For details, please refer to the relevant introduction of the method below, which will not be repeated here.

[0046] Figure 2 This is a flowchart illustrating the method provided in an embodiment of the present invention. This real-time data transmission method for electronic devices based on edge computing is applicable to the aforementioned system and involves interaction between edge nodes and central nodes. The specific process is as follows: S201, the edge node obtains the business data of each of the multiple electronic devices.

[0047] Taking any one of multiple electronic devices (denoted as the target electronic device) as an example, this electronic device can sequentially collect each set of data. Each set of data includes a timestamp, a data type indicator, and the business data itself. For details, please refer to the relevant introduction of the system mentioned above. Each set of data is also plaintext business data. Therefore, the business data of the target electronic device is obtained by the target electronic device using a temporary key on its side to perform security protection (such as encryption and integrity protection) on the plaintext business data of the target electronic device. The temporary key on the target electronic device side is derived from the target electronic device's temporary private key and the network's pre-set public key. The temporary private key is derived from the target electronic device's root key. The target electronic device's root key can be pre-set in the target electronic device's Universal Subscriber Identity Module (USIM). The USIM derives a pair of temporary private keys and temporary public keys based on the root key using an elliptic hyperbolic algorithm, and then sends the pair of temporary private keys and temporary public keys to the target electronic device itself, thus ensuring that the root key does not leave the USIM. Therefore, the target electronic device itself can derive a temporary key on the target electronic device side based on the temporary private key and the network's preset public key, and then further derive an encryption key and an integrity protection key to encrypt and protect the plaintext business data of the target electronic device, respectively, to obtain the business data of the target electronic device, i.e., the ciphertext data.

[0048] Then, the target electronic device can send encrypted data to the edge node (i.e., the user plane entity deployed near the target electronic device, which may be selected by the network as a whole for multiple electronic devices).

[0049] S202, the edge node constructs the first dataset and the second dataset from the business data of multiple electronic devices.

[0050] S203, the edge node sends the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node.

[0051] The first dataset includes the same business data from the business data of multiple electronic devices. The same business data from the business data of multiple electronic devices is considered as one set of business data in the first dataset. The second dataset includes the different business data from the business data of multiple electronic devices.

[0052] For example, an edge node extracts the annotation information from the business data of multiple electronic devices as a first dataset, and the business data of multiple electronic devices that are annotated by the same annotation information as a second dataset. The correspondence between the first dataset and the second dataset indicates that the business data in the second dataset is annotated by the annotation information in the first dataset.

[0053] In this system, edge nodes can use the security context of each of the multiple electronic devices to perform security verification on the corresponding service data of the electronic devices, obtaining the plaintext service data of each electronic device. For example, for any target electronic device among the multiple electronic devices, the edge node is a user plane entity (also called an edge user plane entity) in the network where the edge node is located. The user plane entity obtains the temporary public key of the target electronic device from the network (such as the corresponding network element in the network, the specific network element is not limited). The temporary public key is derived by the target electronic device based on its root key and sent to the network. For example, the target electronic device can send the temporary public key to the network element for storage during the initial registration process with the network. Thus, in one implementation, the central node can instruct the user plane entity to establish end-to-end security with the target electronic device, so that the user plane entity can obtain the temporary public key of the target electronic device from the network (such as the corresponding network element in the network) in response to the instruction of the central node. Then, the user plane entity can derive a temporary key on the network side based on the temporary public key and the network's preset private key, and save it in the security context between the user plane entity and the target electronic device. In this way, when a user plane entity receives service data from an electronic device, it uses a temporary key from the security context between the user plane entity and the target electronic device to perform security verification on the service data, thus obtaining the plaintext service data from the target electronic device. For example, the user plane entity uses this temporary key to further derive a decryption key and an integrity verification key, which are used to decrypt and verify the integrity of the service data from the target electronic device, respectively, to obtain the plaintext service data from the target electronic device.

[0054] Therefore, edge nodes can extract annotation information from the plaintext business data of multiple electronic devices as a first dataset, and collect business data annotated with the same annotation information from the plaintext business data of multiple electronic devices as a second dataset. For example, the annotation information includes a timestamp and data type information. The timestamp indicates that the business data of multiple electronic devices in the second dataset was collected at the timestamp, and the data type information indicates the corresponding data type. The business data of multiple electronic devices in the second dataset is business data of the corresponding data type.

[0055] Continuing with the above business scenario, we will illustrate the specific implementation of this application as follows: The first dataset is: {timestamp: 2023-11-01 10:00:01, data type: motor_current}. This represents "the motor current readings of all robotic arms at the time 10:00:01".

[0056] The second dataset: [1.5A, 1.6A, 1.2A, ...]. This is an array storing the current values ​​of the main motors of 20 robotic arms at that moment. These values ​​are different and reflect the real-time load of each robotic arm.

[0057] Correspondence: Similarly, through the mapping between array index and device list, the central node can know that the current value "1.5A" corresponds to the data of the robotic arm numbered Robot_01 at a specified time.

[0058] S204, the central node processes the first and second datasets according to the corresponding relationship.

[0059] The central node can restore the first and second datasets into plaintext business data for each of the multiple electronic devices based on the correspondence, that is, perform the reverse process of the edge nodes.

[0060] It should be understood that, for any one of the multiple electronic devices, the plaintext business data of the target electronic device includes: multiple business data of the same data type collected by the target electronic device at multiple timestamps. In other words, S201-S203 are executed multiple times, and each execution enables the central node to obtain one business data of the same data type collected by the target electronic device at a corresponding timestamp.

[0061] Continuing with the example above: The restored data format (taking data from Robot_03, the No. 3 robotic arm, over a period of time as an example): The plaintext business data of the target electronic device includes: timestamp T1: value = 1.52A, timestamp T2: value = 1.51A, timestamp T3: value = 1.53A, timestamp T4: value = 1.55A, timestamp T5: value = 1.58A, timestamp T6: value = 1.62A, timestamp T7: value = 1.50A, T8: value = 1.48A, T9: value = 1.45A, T10: value = 1.42A... (and so on. We take data from 10 consecutive time points for example. At this time, this data may indicate that the current of the robotic arm increases due to a slight jamming at time T5-T6, and then the current continues to decrease during T7-T10, which may indicate a potential fault of loosening or poor lubrication of the drive mechanism).

[0062] For any one of the multiple electronic devices, the central node can analyze the plaintext service data of the target electronic device using a lightweight model to determine whether the target electronic device is abnormal.

[0063] A lightweight model can be a small neural network or a decision tree model.

[0064] For example, the central node divides multiple business data into multiple groups of business data using interval interpolation. Each group of business data includes one business data item within each of multiple intervals. These multiple intervals consist of M consecutive intervals, where M is an integer greater than 1. For the first group of business data: the central node determines, according to the multiple timestamps in chronological order, whether there is a business data item whose value falls within the first interval of the M intervals. If there is a first business data item whose value falls within the first interval, then the business data item in the first group whose value falls within the first interval is determined to be the first business data item; otherwise, the business data item in the first group whose value falls within the first interval is determined to be the preset interpolated data. The central node determines, according to the multiple timestamps in chronological order... The process proceeds in sequence to determine if any of the multiple data sets contains a value within the second interval of M intervals. If so, the data set containing the value within the second interval of the first set of data is identified as the second data set; otherwise, the data set containing the value within the second interval of the first set of data is identified as the preset interpolated data. This process continues until a data set containing a value within the Mth interval of the first set of data is identified. The data set containing the value within the first set of data is then deleted, resulting in the updated data set. For the second set of business data: the central node determines whether any of the updated business data has a value within the first interval, based on multiple timestamps in chronological order. If any of the business data has a value within the first interval (a third business data), then the business data in the second set of business data with a value within the first interval is determined to be the third business data; otherwise, the business data in the second set of business data with a value within the first interval is determined to be the preset interpolated data. The central node also determines whether any of the updated business data has a value within the second interval, based on multiple timestamps in chronological order. If, among the multiple business data, one value falls within the second interval, it is identified as the fourth business data. Otherwise, the business data within the second interval is identified as the preset interpolated data. This process continues until the business data within the Mth interval is identified, and the business data already placed in the second interval is deleted from the updated business data, resulting in the updated business data. This process continues until all business data are placed in their corresponding intervals.

[0065] To make it easier to understand, let's continue with the example above: Setting the range: Based on the normal operating current range of this model of robotic arm (e.g., 1.4A-1.7A), the current is divided into M=4 consecutive ranges: Range 1: [1.40, 1.45), Range 2: [1.45, 1.50), Range 3: [1.50, 1.55), Range 4: [1.55, 1.60).

[0066] Perform grouping by "interval difference": First set of business data search: Interval 1 ([1.40, 1.45)): Searching chronologically, the first data point falling into this interval is 1.45 at time T9 (Note: the interval is left-closed and right-open; 1.45 belongs to interval 2). The first value actually falling into this interval is 1.42 at time T10. Therefore, the value of the first group in interval 1 is 1.42. Interval 2 ([1.45, 1.50)): Continuing the search, the first data point falling into this interval is 1.48 at time T8. Therefore, the value of the first group in interval 2 is 1.48. Interval 3 ([1.50, 1.55)): Continuing the search, the first data point falling into this interval is 1.52 at time T1. Therefore, the value of the first group in interval 3 is 1.52. Interval 4 ([1.55, 1.60)): Continuing the search, the first data point falling into this interval is 1.55 at time T4 (belonging to interval 4). Therefore, the value of the first group in interval 4 is 1.55. Complete the first group: The first group of business data is [1.42, 1.48, 1.52, 1.55]. Delete the used data points T10, T8, T1, and T4. The updated sequence is: [1.51, 1.53, 1.58, 1.62, 1.50, 1.45].

[0067] Second set of business data search: Interval 1 ([1.40, 1.45)): Searching in the update sequence, the first data point falling into this interval is 1.45 (belonging to Interval 2). No data falls into Interval 1, so interpolated data 0 is entered. Interval 2 ([1.45, 1.50)): Continuing the search, the data 1.45 falls into this interval. Therefore, the value of the second group in Interval 2 is 1.45. Interval 3 ([1.50, 1.55)): Continuing the search, the first data point falling into this interval is 1.51. Therefore, the value of the second group in Interval 3 is 1.51. Interval 4 ([1.55, 1.60)): Continuing the search, the first data point falling into this interval is 1.58. Therefore, the value of the second group in Interval 4 is 1.58. Complete the second group: The business data of the second group is [0, 1.45, 1.51, 1.58]. Delete the used T9, T2, and T5 data points. The updated sequence is: [1.53, 1.62, 1.50].

[0068] Third set of business data search: Interval 1 ([1.40, 1.45)): No data, enter 0. Interval 2 ([1.45, 1.50)): No data, enter 0. Interval 3 ([1.50, 1.55)): 1.53 is found. Interval 4 ([1.55, 1.60)): 1.62 is found (note, 1.62 > 1.60, not belonging to interval 4). No data falls into interval 4, enter 0. Complete the third group: The business data for the third group is [0, 0, 1.53, 0].

[0069] Ultimately, the original 10 current data points were reorganized into 3 groups: Group 1: [1.42, 1.48, 1.52, 1.55], Group 2: [0, 1.45, 1.51, 1.58], and Group 3: [0, 0, 1.53, 0]. Given multiple sets of business data, the central node analyzes these data using a lightweight model to determine if any anomalies exist in the target electronic device.

[0070] For ease of understanding, let's continue with the example above, taking the smart agricultural greenhouse cluster monitoring in Scenario 1 as an example. Other scenarios can be understood similarly: The central node now feeds these three sets of data into a lightweight AI model (such as a small neural network or decision tree model). The model is trained to identify healthy current fluctuation patterns. For a normal robotic arm, the recombined current data should continuously cover multiple intervals, exhibiting a specific pattern even with interpolated zeros. Here, the zero value plays a crucial role. In the second set, a zero appears in the first interval, indicating a "data hole" in the low current interval early in the time series, creating a discontinuous jump compared to the value (1.42) in the first set. In the third set, a large number of zeros ([0,0,...,0]) appear, strongly indicating that within a very short time window, the robotic arm's current value abruptly shrinks to a very narrow range (value only in the third interval), while completely escaping the high load interval (zero in the fourth interval). This pattern is called "data distribution collapse." Therefore, the lightweight model will determine this pattern from "relatively continuous distribution" (set 1) to "holes appearing" (set 2) and then to "distribution collapse" (set 3) as abnormal. It indicates not just a high current at a certain moment (such as 1.62A for T6), but a failure of a dynamic process. That is, the robotic arm may quickly evolve from its initial normal state to being unable to maintain the normal load range. This is very likely a sign of serious wear or damage to mechanical transmission components (such as gears and timing belts), requiring immediate shutdown and maintenance.

[0071] In other words, compared to traditional threshold alarms that may only trigger when the current exceeds 1.7A (absolute upper limit) or falls below 1.3A (absolute lower limit), this application can accurately capture progressive mechanical faults. Specifically, by analyzing the distribution and evolution patterns of data within a preset operating range, this application can issue early warnings before the absolute safety threshold is reached, even when dynamic characteristics have already deteriorated, thus achieving predictive maintenance. Industrial production demands extremely high reliability and continuity from equipment. This solution, through deep pattern mining of the restored individual equipment data, can discover early individual faults that are easily masked by overall average data, preventing them from evolving into serious problems that cause the entire production line to shut down. This represents an advancement from "group monitoring" to "precise individual maintenance," meeting the urgent needs of the industrial sector for high reliability and predictive maintenance.

[0072] Furthermore, this method is executed at the central node, with controllable computational load (lightweight model + normalized data), and does not affect the efficient data transmission responsibilities at the edge. This forms a highly efficient collaboration of "efficient compressed transmission at the edge -> accurate reconstruction and analysis at the center," making it particularly suitable for deployment in resource-constrained industrial edge computing scenarios.

[0073] Furthermore, through preprocessing with "interval interpolation," the variable-length, non-uniform original sequence is transformed into a regular, fixed-length data set, greatly facilitating the access and analysis of lightweight models. Traditional methods may only focus on whether a single data point exceeds a threshold. However, this method, through data recombination, enables the model to focus on the distribution and evolution sequence of data across different numerical intervals. The introduction of interpolated data 0 is crucial. It is not an ordinary numerical value, but a powerful "missing marker." It explicitly tells the model: "No data was captured in this logically expected interval." This artificially introduced "mutation" makes any discontinuous or incomplete patterns more conspicuous, greatly helping the model capture potential, progressive, or intermittent faults. Therefore, the above method is particularly suitable for data streams reconstructed by edge computing, meaning this anomaly detection scheme perfectly matches the front-end data transmission scheme. The central node first efficiently reconstructs the complete time-series data of each device, and then immediately performs in-depth, pattern-based health analysis on individual devices. This forms a complete closed loop from "efficient transmission" to "intelligent analysis," fully tapping the value of the deredundant data and ultimately achieving refined and intelligent operation and maintenance management of electronic equipment groups.

[0074] In summary, the technical solution of this application effectively solves the core problem of "low transmission efficiency caused by data redundancy within a device group" mentioned in the background technology through innovative data construction and transmission mechanisms, and brings a series of significant technical effects: 1. Reduce network bandwidth usage and improve transmission efficiency: In traditional models, identical data generated by N devices within a group is transmitted N times repeatedly. This solution, by constructing a first dataset, compresses N identical business data entries into a single dataset, fundamentally eliminating redundant transmission. This is the most direct and core technical effect. It ensures that in scenarios with a large number of devices and high data homogeneity (such as sensor networks), the pressure on network bandwidth is no longer linearly related to the number of devices, thus achieving a qualitative leap in transmission efficiency.

[0075] 2. Reduce data transmission latency and enhance real-time performance: The reduction in the amount of data transmitted leads to a shorter transmission time. Edge nodes no longer need to wait for and send the complete raw data streams from all devices; instead, they first perform localized data deduplication and reassembly to generate smaller first and second datasets. This allows data packets to reach the central node faster, better meeting the stringent requirements of low latency and high real-time performance in business scenarios such as industrial control and vehicle-to-everything (V2X) collaboration.

[0076] 3. Reduce the processing burden on the central node and optimize resource utilization: The central node no longer receives massive, repetitive raw data streams, but rather refined datasets that have been preprocessed and structured by the edge nodes. This eliminates the need for the central node to perform cumbersome data deduplication and correlation comparison operations, allowing for efficient processing based directly on the received correspondences. Furthermore, the total amount of stored data is significantly reduced due to redundancy elimination, saving storage costs. Consequently, the central node's computing and storage resources can be more focused on high-value business logic analysis, global situational awareness, and intelligent decision-making, improving the overall system architecture's processing power and cost-effectiveness.

[0077] 4. Achieve a balance between eliminating data redundancy and ensuring information integrity: This solution doesn't simply discard "duplicate" data. Instead, it cleverly achieves lossless data reconstruction through the construction of a "first dataset (common data) + second dataset (unique data) + correspondence." The central node can fully reconstruct the original data context of each electronic device (i.e., "who" generated "what" data "when") based on the correspondence. This design maximizes data compression while completely preserving the effective information and relevance of the data, avoiding information loss due to over-compression.

[0078] 5. Fully leverage the advantages of edge computing and optimize system architecture: This solution is a perfect example of the edge computing concept of "decentralizing computing power to the network edge." It places computationally intensive but logically relatively deterministic tasks such as data deduplication, classification, and reconstruction on edge nodes, while leaving tasks like global management and complex analysis at the center. This division of labor—compression at the edge and analysis at the center—allows edge and center nodes to perform their respective functions, forming an efficient collaborative computing model that optimizes the overall system architecture and resource allocation.

[0079] Therefore, the technical effect of this application's solution is systematic and multi-layered. It is not a simple optimization of the existing transmission process, but rather solves the inherent redundancy problem of group data transmission from an information theory perspective by changing the way data is organized and expressed at edge nodes. Ultimately, while ensuring information integrity, it achieves a simultaneous and significant reduction in three key indicators: bandwidth usage, transmission latency, and central load, thereby greatly improving the overall performance and practicality of the edge computing-based real-time data transmission system.

[0080] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Exemplarily, the electronic device may be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. Figure 3 As shown, the electronic device 400 may include a processor 401. Optionally, the electronic device 400 may also include a memory 402 and / or a transceiver 403. The processor 401 is coupled to the memory 402 and the transceiver 403, for example, via a communication bus.

[0081] The following is combined Figure 3 A detailed description of each component of the electronic device 400 is provided below: The processor 401 is the control center of the electronic device 400. It can be a single processor or a collective term for multiple processing elements. For example, the processor 401 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0082] Optionally, the processor 401 can perform various functions of the electronic device 400 by running or executing software programs stored in the memory 402 and calling data stored in the memory 402, such as performing the aforementioned functions. Figure 2 The method for real-time data transmission of electronic devices based on edge computing is shown.

[0083] In a specific implementation, as one example, processor 401 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0084] In a specific implementation, as one example, the electronic device 400 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0085] The memory 402 is used to store the software program that executes the solution of the present invention, and is controlled by the processor 401 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0086] Optionally, the memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 402 may be integrated with the processor 401 or exist independently, and may be accessed through the interface circuit of the electronic device 400. Figure 3 (Not shown in the image) is coupled to processor 401, but this embodiment of the invention does not specifically limit this.

[0087] Transceiver 403 is used for communication with other electronic devices. For example, if electronic device 400 is a terminal, transceiver 403 can be used to communicate with a network device or with another terminal device. As another example, if electronic device 400 is a network device, transceiver 403 can be used to communicate with a terminal or with another network device.

[0088] Alternatively, transceiver 403 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0089] Alternatively, the transceiver 403 can be integrated with the processor 401, or it can exist independently and be connected via the interface circuit of the electronic device 400. Figure 3 (Not shown in the image) is coupled to processor 401, but this embodiment of the invention does not specifically limit this.

[0090] Understandable, Figure 3 The structure of the electronic device 400 shown does not constitute a limitation on the electronic device. Actual electronic devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0091] Furthermore, the technical effects of the electronic device 400 can be referred to the technical effects of the methods described in the above method embodiments, and will not be repeated here.

[0092] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0093] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0094] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0095] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0096] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0097] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0099] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0100] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0103] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time data transmission in electronic devices based on edge computing, characterized in that, A method for real-time data transmission in electronic devices based on edge computing, the system comprising a central node and edge nodes, the method comprising: The edge node obtains the business data of each of the multiple electronic devices from the multiple electronic devices respectively. The multiple electronic devices belong to the same device group. The same device group is oriented towards the same business scenario. The business data of any two of the multiple electronic devices are of the same type. The edge node constructs the business data of the multiple electronic devices into a first dataset and a second dataset. The first dataset includes the same business data in the business data of the multiple electronic devices, and the same business data in the business data of the multiple electronic devices is considered as one set of business data in the first dataset. The second dataset includes the different business data in the business data of the multiple electronic devices. The edge node sends the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node; The central node processes the first dataset and the second dataset according to the correspondence.

2. The method according to claim 1, characterized in that, The edge node constructs the service data of the multiple electronic devices into a first dataset and a second dataset, including: The edge node extracts the annotation information from the business data of each of the multiple electronic devices as the first dataset, and uses the business data of each of the multiple electronic devices that is annotated by the same annotation information as the second dataset. The correspondence between the first dataset and the second dataset indicates that the business data in the second dataset is annotated by the annotation information in the first dataset.

3. The method according to claim 2, characterized in that, The annotation information includes a timestamp and a data type information. The timestamp indicates that the business data of each of the multiple electronic devices in the second dataset is data collected at the timestamp, and the data type information indicates a corresponding data type. The business data of each of the multiple electronic devices in the second dataset is business data of the data type mentioned above.

4. The method according to claim 2, characterized in that, The edge node extracts annotation information from the service data of each of the multiple electronic devices as the first dataset, and extracts service data annotated with the same annotation information from the service data of each of the multiple electronic devices as the second dataset, including: The edge node uses the security context of the edge node and each of the multiple electronic devices to perform security verification on the service data of the corresponding electronic devices in the multiple electronic devices, so as to obtain the plaintext service data of each of the multiple electronic devices. The edge node extracts the annotation information from the plaintext service data of each of the multiple electronic devices as the first dataset, and extracts the service data of each of the multiple electronic devices that is annotated by the same annotation information as the second dataset.

5. The method according to claim 4, characterized in that, The edge node is a user plane entity in the network where the edge node is located. For any one of the plurality of electronic devices as a target electronic device, the method further includes: The user plane entity obtains the temporary public key of the target electronic device from the network. The temporary public key is derived by the target electronic device based on its root key and sent to the network. The user plane entity derives a network-side temporary key based on the temporary public key and the network's preset private key; the service data of the target electronic device is obtained by the target electronic device using its own temporary key to securely protect the plaintext service data of the target electronic device. The temporary key on the target electronic device side is derived by the target electronic device based on its temporary private key and the network's preset public key, and the temporary private key is derived by the target electronic device based on its root key. Accordingly, the edge node uses the security context of the edge node and each of the plurality of electronic devices to perform security verification on the service data of the corresponding electronic devices among the plurality of electronic devices, to obtain the plaintext service data of each of the plurality of electronic devices, including: The user plane entity uses the temporary key in the security context of the user plane entity and the target electronic device to perform security verification on the service data of the target electronic device, thereby obtaining the plaintext service data of the target electronic device.

6. The method according to claim 5, characterized in that, The central node is deployed in the management domain of the network, and the method further includes: The central node indicates that the user plane entity needs to establish end-to-end security with the target electronic device; The user plane entity obtains the temporary public key of the target electronic device from the network, including: The user plane entity, in response to the instruction of the central node, obtains the temporary public key of the target electronic device from the network.

7. The method according to any one of claims 1-6, characterized in that, The central node processes the first dataset and the second dataset according to the correspondence, including: The central node restores the first dataset and the second dataset into plaintext service data for each of the multiple electronic devices according to the correspondence. For any one of the multiple electronic devices, the central node analyzes the plaintext service data of the target electronic device using a lightweight model to determine whether the target electronic device is abnormal.

8. The method according to claim 7, characterized in that, The plaintext service data of the target electronic device includes: multiple service data of the same data type collected by the target electronic device at multiple timestamps. The central node analyzes the plaintext service data of the target electronic device through a lightweight model to determine whether the target electronic device is abnormal, including: The central node divides the multiple business data into multiple groups of business data by using interval difference. Each of the multiple groups of business data includes one business data in each of the multiple intervals. The central node analyzes the multiple sets of business data using the lightweight model to determine whether the target electronic device is abnormal.

9. The method according to claim 8, characterized in that, The central node divides the multiple business data into multiple groups of business data by using interval difference methods, including: The multiple intervals include M consecutive intervals, where M is an integer greater than 1. For the first set of business data: The central node determines whether there is a piece of business data whose value is located in the first interval of the M intervals, according to the multiple timestamps in chronological order. If there is a piece of business data whose value is located in the first interval, then the business data whose value is located in the first interval of the first group of business data is determined to be the first business data; otherwise, the business data whose value is located in the first interval of the first group of business data is determined to be the preset interpolated data. The central node determines whether, according to the multiple timestamps in chronological order, there is a business data whose value is located in the second interval of the M intervals. If there is a second business data whose value is located in the second interval, then the business data in the first group of business data whose value is located in the second interval is determined to be the second business data; otherwise, the business data in the first group of business data whose value is located in the second interval is determined to be the preset interpolated data. This process continues until the business data in the first group of business data is determined to be located in the Mth interval among the M intervals, and the business data that has been placed in the first group of business data is deleted to obtain the updated business data. For the second set of business data: The central node determines whether there is any business data in the updated business data whose value is located in the first interval according to the multiple timestamps in chronological order. If there is a third business data whose value is located in the first interval among the multiple business data, then the business data in the second group of business data whose value is located in the first interval is determined to be the third business data. Otherwise, the business data in the second group of business data whose value is located in the first interval is determined to be the preset interpolated data. The central node determines whether there is any business data in the updated business data whose value is located in the second interval according to the multiple timestamps in chronological order. If there is a fourth business data whose value is located in the second interval among the multiple business data, then the business data in the second group of business data whose value is located in the second interval is determined to be the fourth business data. Otherwise, the business data in the second group of business data whose value is located in the second interval is determined to be the preset interpolated data. This process continues until the business data in the second group of business data is determined to be within the Mth interval, and the business data that has been placed in the second group of business data in the updated business data is deleted to obtain the updated business data again. Then, this process continues until all the multiple business data have been placed into their corresponding intervals.

10. A real-time data transmission system for electronic devices based on edge computing, characterized in that, The system includes a central node and edge nodes, and the system is configured as follows: The edge node obtains the business data of each of the multiple electronic devices from the multiple electronic devices respectively. The multiple electronic devices belong to the same device group. The same device group is oriented towards the same business scenario. The business data of any two of the multiple electronic devices are of the same type. The edge node constructs the business data of the multiple electronic devices into a first dataset and a second dataset. The first dataset includes the same business data in the business data of the multiple electronic devices, and the same business data in the business data of the multiple electronic devices is considered as one set of business data in the first dataset. The second dataset includes the different business data in the business data of the multiple electronic devices. The edge node sends the first dataset, the second dataset, and the correspondence between the first dataset and the second dataset to the central node; The central node processes the first dataset and the second dataset according to the correspondence.

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