Data management method and device for intelligent data element, equipment and storage medium

Abstract

The invention provides a data management method and device for intelligent data elements, equipment and a storage medium, and relates to the technical field of big data. The method comprises: generating a multi-layer packaged intelligent data element, the intelligent data element comprising: a first layer storing interfaces communicating with a plurality of demand nodes; the second layer is used for storing an analysis and recommendation algorithm for recommending a corresponding target demand node for the intelligent data element; according to the interface and the analysis and recommendation algorithm, obtaining the target demand node with the data demand in the plurality of demand nodes; determining a transmission path for transmitting the intelligent data element to the target demand node according to the current network state information of each demand node; transmitting the intelligent data element to the target demand node according to the transmission path and the transmission priority of the intelligent data element; according to the scheme, the data circulation efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and specifically to a data management method, device, equipment and storage medium for intelligent data elements. Background Art

[0002] With the rapid development of the big data era, data has become a key factor in driving innovation and decision-making. Traditional data management methods view data as a static, passive carrier of information, failing to fully unlock its potential value. Currently, the industry has proposed a variety of data management solutions, such as data lakes, data warehouses, and metadata management systems. However, most of these solutions are still based on static data management models, and data lacks "autonomy." Although existing big data technologies have explored data management, production security, and storage optimization, most existing data management solutions are limited to a single organization or system and lack effective mechanisms to support the efficient flow of data across different organizations and systems, resulting in inefficient data flow. Summary of the Invention

[0003] At least one embodiment of the present application provides a data management method, apparatus, device, and storage medium for intelligent data elements, which are used to solve the problem of low data flow efficiency in the prior art.

[0004] In order to solve the above technical problems, this application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a data management method for intelligent data elements, the method comprising:

[0006] Generate a multi-level encapsulated intelligent data element, the intelligent data element comprising: a first layer storing interfaces for communicating with multiple demand nodes; a second layer storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element;

[0007] According to the interface and the analysis and recommendation algorithm, obtaining the target demand node having a data demand from the plurality of demand nodes, wherein the data demand is used to instruct transmission of the smart data element to the target demand node;

[0008] Determining a transmission path for transmitting the intelligent data element to the target demand node according to the current network status information of each demand node;

[0009] The smart data element is transmitted to the target demand node according to the transmission path and the transmission priority of the smart data element.

[0010] Optionally, the data management method for the intelligent data element further comprises:

[0011] Obtaining a value assessment level corresponding to the smart data element based on the value assessment factors corresponding to the smart data element, the value assessment factors including at least one of data usage frequency, user feedback information, network resource occupancy, and the life cycle stage in which the smart data element is located; if at least one of the life cycle stage is not a decline stage or an extinction stage, the data usage frequency is high, the user feedback value is high, and the network resource occupancy is low, the value assessment level corresponding to the smart data element is high;

[0012] According to the value evaluation level, the transmission priority of the corresponding intelligent data element is obtained, and a higher value evaluation level corresponds to a higher transmission priority.

[0013] Optionally, the data management method for the smart data element, wherein the determining, based on the current network status information of each demand node, a transmission path for transmitting the smart data element to the target demand node, comprises: determining, based on the network status information and historical transmission information of the demand node, a transmission path for transmitting the smart data element to the target demand node;

[0014] The network status information includes at least one of computing capability, storage capacity, network bandwidth, and transmission delay;

[0015] The historical transmission information is used to indicate the transmission delay of the historical transmission path and / or the historical processing efficiency of the demand node.

[0016] Optionally, in the data management method for the intelligent data element, the step of transmitting the intelligent data element to the target demand node includes:

[0017] Adjusting the transmission path according to the network environment change information to obtain an adjusted transmission path;

[0018] The intelligent data element is transmitted to the target demand node according to the adjusted transmission path.

[0019] Optionally, in the data management method for the smart data element, wherein the number of the target demand nodes is multiple; transmitting the smart data element to the target demand node according to the transmission path and the transmission priority of the smart data element includes:

[0020] According to the transmission path and the transmission priority of the intelligent data element, basic data in the intelligent data element that conforms to a target data type is transmitted to each target demand node, where the target data type is the data type required by the target demand node.

[0021] Optionally, the data management method for the intelligent data element further comprises:

[0022] Acquiring the data requirement according to the task requirement information and / or data usage of the target requirement node;

[0023] According to the data requirement, the data type required by each target requirement node is determined.

[0024] Optionally, in the data management method for the intelligent data element, the number of the target demand nodes is multiple; the method further comprises:

[0025] Copying the smart data element to generate multiple smart data elements;

[0026] According to the transmission path and the transmission priority of the smart data element, one smart data element is transmitted to each target demand node.

[0027] Optionally, in the data management method for the intelligent data element, the intelligent data element further comprises: a third layer storing a dynamic adjustment strategy related to the life cycle stage of the intelligent data element; the method further comprises:

[0028] Obtaining the life cycle stage of the intelligent data element;

[0029] The data or information stored in the smart data element is adjusted using a dynamic adjustment strategy related to the life cycle stage.

[0030] In a second aspect, an embodiment of the present application further provides a data management device for intelligent data elements, including:

[0031] A generation module is used to generate a multi-level encapsulated intelligent data element, wherein the intelligent data element includes: a first layer storing interfaces for communicating with multiple demand nodes; a second layer storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element;

[0032] an acquisition module, configured to acquire, according to the interface and the analysis and recommendation algorithm, the target demand node having a data demand from among the plurality of demand nodes, wherein the data demand is used to instruct the transmission of the intelligent data element to the target demand node;

[0033] a determination module, configured to determine a transmission path for transmitting the intelligent data element to the target demand node based on the current network status information of each demand node;

[0034] A transmission module is used to transmit the intelligent data element to the target demand node according to the transmission path and the transmission priority of the intelligent data element.

[0035] In a third aspect, an embodiment of the present application also provides a data management device for an intelligent data element, comprising: a processor, a memory, and a program or instruction stored on the memory and executable on the processor, wherein when the processor executes the program or instruction, the data management method for the intelligent data element as described in the first aspect is implemented.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the data management method for the intelligent data element as described in the first aspect is implemented.

[0037] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the data management method for the intelligent data element as described in the first aspect.

[0038] Compared with the prior art, the embodiments of the present application provide a data management method, apparatus, device and storage medium for an intelligent data element, which adopts a multi-level encapsulated intelligent data element, wherein the first layer stores an interface for communicating with multiple demand nodes, and the second layer stores an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element, so that the intelligent data element has autonomous decision-making capabilities, improves the autonomy and intelligence of the data element, and obtains the target demand node through the interface and the analysis and recommendation algorithm, and determines the transmission path for transmitting the intelligent data element to the target demand node based on the current network status information of each of the demand nodes, thereby transmitting the intelligent data element to the target demand node based on the transmission path and the transmission priority of the intelligent data element, thereby realizing secure cross-organizational and cross-system data flow, improving data flow efficiency, and expanding the application potential of data in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0040] Figure 1 A schematic diagram of the architecture of a big data network provided in an embodiment of the present application;

[0041] Figure 2 A flowchart of a data management method for intelligent data elements provided in an embodiment of the present application;

[0042] Figure 3 A diagram showing the encapsulation hierarchy of the intelligent data elements provided in the embodiments of the present application;

[0043] Figure 4 A life cycle diagram of the intelligent data element provided in the embodiment of the present application;

[0044] Figure 5 A schematic diagram of the structure of a data management device for intelligent data elements provided in an embodiment of the present application;

[0045] Figure 6 This is a hardware block diagram of the data management device for the intelligent data element provided in an embodiment of the present application.

[0046] Description of reference numerals: 501 - generation module; 502 - acquisition module; 503 - determination module; 504 - transmission module; 601 - processor; 602 - memory; 603 - transceiver; 604 - user interface. DETAILED DESCRIPTION

[0047] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0048] Please refer to Figure 1 , an embodiment of the present application provides a big data network that can execute the data management method of the smart data unit (Smart Data Unit, abbreviated as SDU) of the embodiment of the present application. The big data network of the embodiment of the present application is a big data network with high intelligence and dynamic adaptability. It is a distributed system composed of multiple nodes in the entire domain. The multiple nodes include but are not limited to servers, cloud computing clusters, edge devices and Internet of Things devices. Each node has certain computing, storage and communication capabilities, and these nodes need to run interfaces and intelligent flow protocols compatible with smart data elements to ensure that smart data elements can be smoothly transmitted and applied in the network.

[0049] like Figure 1The big data network shown in the figure exhibits a cloud-edge-end topology, with a unified protocol guaranteeing data generation, transmission, and reception between nodes. Statistical analysis terminals and AI training terminal nodes send data demand signals. Data generation terminals (i.e., first nodes), such as mobile phones, collect and initialize data. Data demand matching algorithms determine the location of demand nodes, and then intelligently route data to the demand nodes (i.e., target demand nodes). Data is replicated or split during this process to meet the data needs of multiple demand nodes.

[0050] The construction of the big data network of the embodiment of the present application requires standardized interface protocols so that intelligent data elements can be seamlessly accessed and circulated. These interface protocols provide data transmission, format conversion, and protocol adaptation functions to ensure that different types of nodes can interact with intelligent data elements in a unified manner. Through these interfaces, intelligent data elements can be transmitted between different nodes without manually adjusting the data format or communication protocol. In addition, these interfaces also support the dynamic adaptation function of intelligent data elements, allowing intelligent data elements to automatically adjust their transmission path and communication mode according to the network status and node load conditions to ensure efficient data transmission.

[0051] In order to ensure the normal operation of the big data network of the embodiment of the present application, the construction of the big data network of the embodiment of the present application also requires an intelligent flow protocol. The intelligent flow protocol not only supports the communication between intelligent data elements and network nodes, but also helps the intelligent data elements to be intelligently circulated and optimized in the network. The intelligent flow protocol dynamically adjusts the flow path of the intelligent data element by analyzing the network topology, node load conditions and data requirements to ensure that the data can find the optimal storage and processing nodes in the network. In addition, the intelligent flow protocol also has self-learning and optimization functions, which can continuously optimize the flow strategy based on historical data and flow records, and improve the transmission efficiency and response speed of intelligent data elements in the network.

[0052] In addition, the construction of the big data network of the embodiment of the present application also needs to have sufficient scalability and elasticity to support the flow of intelligent data elements in network environments of different scales and complexities. Through distributed architecture and elastic computing, the network can dynamically adjust its computing and storage resources according to actual needs, ensuring that intelligent data elements can still maintain efficient flow performance in high concurrency and high load environments. At the same time, the construction of the big data network of the embodiment of the present application also needs to have high availability and fault tolerance to cope with possible network failures and node failures, ensuring that the transmission process of intelligent data elements will not be interrupted.

[0053] Please refer to Figure 2, an embodiment of the present application provides a data management method for an intelligent data element, which is applied to a data generation node. The data generation node can be any node in the data management system of the intelligent data element.

[0054] Furthermore, the above method includes:

[0055] Step 201, generating a multi-level encapsulated intelligent data element, the intelligent data element includes: a first layer, storing an interface for communicating with multiple demand nodes; a second layer, storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element.

[0056] It is required to explain that the intelligent data element also includes at least one of the following layers: the third layer stores dynamic adjustment strategies related to the life cycle stage of the intelligent data element; the fourth layer stores basic data; the fifth layer stores descriptive information of the basic data; and the sixth layer stores the security strategies adopted by the intelligent data element during transmission.

[0057] Figure 3 This is a diagram of the encapsulation hierarchy of the intelligent data element provided in the embodiment of this application. Figure 3 As shown, intelligent data elements are no longer simply carriers of static raw information, but rather intelligent organisms with multiple encapsulation levels, including the six encapsulation levels mentioned above. These levels, like "capsules," imbue data with "initiative" and "intelligence." Each level has a specific function, and the content and information of each level flow along with the intelligent data element itself and are continuously and dynamically updated, ensuring the intelligence, flexibility, and security of the data. Each level is explained below:

[0058] The fourth layer, called the basic data layer, is the core data carrier of the intelligent data element and stores basic data, such as the original data content or processed data sets. This data can be structured (such as numerical values and tabular data), unstructured (such as text, images, audio), or semi-structured (such as JSON and XML). This basic data layer is the foundation of all functions of the intelligent data element and determines the core content and application scenarios of the intelligent data element. Whether it is passively stored data or data that needs to be actively circulated, the basic data layer provides the basic information that supports the entire intelligent data element. The design of this basic data layer ensures that the intelligent data element can switch freely between various data types and formats and provides basic data support for higher-level intelligent functions.

[0059] The fifth layer, called the metadata layer, stores descriptive information about the underlying data, including its origin, creation time, format, structure, type, semantic information, and version control. This metadata layer not only provides descriptive information for the data but, more importantly, supports data discovery, classification, and organization. In big data networks, the metadata layer provides the necessary information foundation for the intelligent flow of intelligent data elements. By analyzing metadata, intelligent data elements can identify potential application scenarios and node requirements, thereby optimizing their transmission paths. Furthermore, the metadata layer provides essential contextual information for data management and manipulation, ensuring data consistency and operability across different nodes and systems.

[0060] It's important to note that the metadata layer also encapsulates data ownership and authorization management information. Data ownership information clarifies the data owner and documents detailed authorization rules, ensuring that only authorized nodes can access and use data during transmission. The metadata layer is also integrated with an external registry authentication system. Through this system, authorized nodes in the big data network can query their authentication status and obtain the keys for interacting with smart data elements, ensuring the legal flow and use of data. Furthermore, ownership information in the metadata layer is encrypted and stored to prevent unauthorized modification or access, and supports dynamic updates when authorization rules change.

[0061] The first layer, known as the interface layer, serves as a crucial bridge for intelligent data elements to interact with external systems and network environments. It stores standardized interfaces for communicating with multiple demand nodes, enabling intelligent data elements to communicate with different nodes in the big data network in a unified manner. The design of this interface layer ensures seamless data flow across systems and platforms, and enables data format conversion, communication protocol adaptation, and transmission optimization. The interface layer not only handles data transmission and reception but also ensures format compatibility, protocol adaptation, and communication optimization during data transmission, ensuring the efficient operation of intelligent data elements across diverse networks and systems.

[0062] The second layer, called the Analysis and Recommendation Layer, is the core of the intelligent functionality of the smart data element. It stores the analysis and recommendation algorithms that recommend target nodes for the smart data element. It performs real-time or batch analysis of underlying data to generate valuable insights, predictions, or decision-making recommendations. The design of this analysis and recommendation layer enables the smart data element to automatically invoke pre-set analysis and recommendation algorithms based on the network environment and user needs, dynamically generating corresponding analysis results. Based on these analysis results, it proactively searches for the most suitable nodes or application scenarios, ensuring that the smart data element maximizes its value in the appropriate nodes. Furthermore, the analysis and recommendation layer can self-adjust and optimize its analysis algorithms based on environmental changes and user feedback, further enhancing the intelligence level of the smart data element.

[0063] The sixth layer, known as the security and privacy layer, stores the security policies applied throughout the lifecycle of intelligent data elements. It serves as a protective barrier for intelligent data elements, ensuring data security and privacy throughout their lifecycle. This layer provides a variety of security mechanisms tailored to different network environments and data privacy requirements, including data encryption, access control, authentication, privacy protection, and audit tracking. The design of the security and privacy layer effectively prevents unauthorized access, tampering, or leakage of intelligent data elements during transmission across big data networks. Furthermore, the security and privacy layer dynamically adjusts security policies based on data sensitivity to ensure data privacy and integrity during transmission.

[0064] It should be noted that the security and privacy layer is responsible for data encryption and permission control for intelligent data elements. To ensure data security during transmission, intelligent data elements utilize dynamic and layered encryption mechanisms. The dynamic encryption mechanism allows the system to automatically adjust encryption policies based on the data's authorization status and lifecycle stage, ensuring data security as it circulates across different nodes. The layered encryption mechanism encrypts different parts of the data based on the permissions of different authorized nodes, ensuring that sensitive data is accessible only to authorized nodes. The security and privacy layer also incorporates authentication and key management mechanisms. By integrating with the registry, these mechanisms ensure that authorized nodes can obtain the correct keys, while unauthorized access requests are automatically rejected by the system. Furthermore, the security and privacy layer utilizes permission control mechanisms to strictly limit data access and usage rights, preventing data misuse or leakage.

[0065] The third layer, called the lifecycle management layer, stores dynamic adjustment policies related to the lifecycle stage of smart data elements. It manages the entire lifecycle of smart data elements, from data generation, circulation, usage, to final destruction. This lifecycle management layer uses intelligent lifecycle monitoring and dynamic adjustment mechanisms to ensure that smart data elements are continuously optimized throughout their lifecycle and safely exit the system when their value is exhausted. Throughout the lifecycle of a smart data element, the lifecycle management layer automatically performs operations such as data updates, storage optimization, value assessment, and resource release based on data usage and network feedback, ensuring that the data achieves its maximum value at each stage of its lifecycle.

[0066] In summary, through these six levels of encapsulation, intelligent data elements not only possess intelligence and adaptability, but also ensure the security, flexibility, and efficient transmission of data within the big data network. Each encapsulation layer interacts with each other to form a data unit with high intelligence, full lifecycle management, and dynamic transmission capabilities.

[0067] Step 202: According to the interface and the analysis and recommendation algorithm, the target demand node having data demand among the plurality of demand nodes is obtained, where the data demand is used to instruct the transmission of the intelligent data element to the target demand node.

[0068] Among them, the demand node is the node in the big data network except the data generation node.

[0069] In an embodiment of the present application, each demand node in the big data network may have different computing tasks and data requirements. When an intelligent data element is transmitted in the big data network, it is necessary to obtain and identify which demand nodes have a demand for the intelligent data element based on the interface, that is, it is necessary to determine the target demand node.

[0070] The analysis and recommendation algorithm includes a data demand matching algorithm for obtaining the target demand node with data demand from the plurality of demand nodes. Through the analysis and recommendation algorithm, the target demand node that can bring out the value of the intelligent data element can be found.

[0071] Step 203: Determine a transmission path for transmitting the intelligent data element to the target demand node according to the current network status information of each demand node.

[0072] The embodiment of the present application provides an intelligent routing and discovery mechanism, which helps intelligent data elements actively find the target demand nodes that are most suitable for storage, processing and application by analyzing network status information, node load conditions and data requirements. In a big data network, each node, such as a data generation node, runs an intelligent routing algorithm. The intelligent routing algorithm can monitor the status of the network in real time and obtain network status information. The network status information includes at least one of computing power, storage capacity, network bandwidth and transmission delay. Based on the network status information, the intelligent routing algorithm can determine the transmission path for transmitting the intelligent data element to the target demand node, and this transmission path is the optimal transmission path, and guide the intelligent data element to migrate to the node on the transmission path. For example, when a node has higher computing power and lower load, the intelligent data element can choose to transfer itself to the node for processing. In this way, the intelligent data element can flow freely in a complex network environment, ensuring the efficient use and transmission of data.

[0073] Step 204: Transmit the smart data element to the target demand node according to the transmission path and the transmission priority of the smart data element.

[0074] In an embodiment of the present application, multiple intelligent data elements may need to be transmitted in a big data network, and each intelligent data element is transmitted in sequence according to the corresponding transmission priority.

[0075] It should be noted that the embodiment of the present application provides an authorization and authentication mechanism for intelligent data elements. The authorization and authentication mechanism is an authentication mechanism based on a registry to ensure the legal transmission of data. Before the intelligent data element is transmitted to the target demand node, it is necessary to verify whether the target demand node has the legal right to use it to ensure that the data during the transmission process will not be accessed by unauthorized nodes. By querying the registry, the target demand node can verify its own authorization status and obtain the key to access the intelligent data element. Only the target demand node that has passed the authentication can decrypt the data of the intelligent data element and perform operations. In addition, the embodiment of the present application also provides a dynamic authorization mechanism. During the data transmission process, the authorization information can be updated in real time according to changes in demand to ensure flexible transmission and security of data.

[0076] In an optional embodiment, the above method further includes:

[0077] Obtaining a value assessment level corresponding to the smart data element based on the value assessment factors corresponding to the smart data element, the value assessment factors including at least one of data usage frequency, user feedback information, network resource occupancy, and the life cycle stage in which the smart data element is located; if at least one of the life cycle stage is not a decline stage or an extinction stage, the data usage frequency is high, the user feedback value is high, and the network resource occupancy is low, the value assessment level corresponding to the smart data element is high;

[0078] According to the value evaluation level, the transmission priority of the corresponding intelligent data element is obtained, and a higher value evaluation level corresponds to a higher transmission priority.

[0079] The present application provides a value assessment and priority management mechanism for intelligent data elements. During transmission, intelligent data elements dynamically assess their own value to obtain a value assessment grade, and adjust their transmission priority in the big data network based on the value assessment grade. Value assessment factors include at least one of the following: data usage frequency, analysis result accuracy, user feedback, network resource occupancy, and the life cycle stage of the intelligent data element. Through a comprehensive analysis of these value assessment factors, the current value of the intelligent data element can be determined, a value assessment grade can be obtained, and resources and transmission paths can be prioritized in the big data network based on the value. It should be noted that the priority management mechanism ensures that high-value intelligent data elements are transmitted first in the big data network, obtaining more computing resources and storage space, thereby maximizing the efficiency of data utilization. Conversely, for intelligent data elements with lower current value, the priority management mechanism automatically lowers their priority, reducing their network resource occupancy, and even in some cases marking them as low-priority data, delaying or suspending their transmission. This mechanism not only effectively improves the utilization of network resources, but also ensures that critical data and high-value data in the big data network are processed and responded to first.

[0080] In an optional embodiment, determining, based on the current network status information of each demand node, a transmission path for transmitting the smart data element to the target demand node includes:

[0081] determining, based on the network status information and the historical transmission information of the demand node, a transmission path for transmitting the smart data element to the target demand node;

[0082] The network status information includes at least one of computing capability, storage capacity, network bandwidth, and transmission delay;

[0083] The historical transmission information is used to indicate the transmission delay of the historical transmission path and / or the historical processing efficiency of the demand node.

[0084] The embodiment of the present application provides a self-optimization mechanism for intelligent data elements, which can continuously adjust and optimize its own transmission strategy based on the current network status information and historical transmission information, and determine the transmission path for transmitting intelligent data elements to the target demand node. For example, by analyzing past flow records, it is possible to identify which nodes have higher processing efficiency or which paths have lower transmission delays, and then give priority to these nodes and paths in future transmission processes. The self-optimization function can also help intelligent data elements automatically adjust their own functional modules and resource consumption at different life cycle stages to ensure that the value of data is maximized within its life cycle.

[0085] In an optional embodiment, transmitting the intelligent data element to the target demand node includes:

[0086] Adjusting the transmission path according to the network environment change information to obtain an adjusted transmission path;

[0087] The intelligent data element is transmitted to the target demand node according to the adjusted transmission path.

[0088] The embodiments of the present application provide an adaptive mechanism for intelligent data elements. During the transmission of intelligent data elements, the transmission path can be dynamically adjusted based on information about changes in the network environment. The adaptive mechanism of intelligent data elements allows them to automatically adjust the transmission mode and transmission path based on changes in the network environment. For example, when a node in a big data network is congested or fails, the intelligent data element can quickly perceive this change through a feedback mechanism and actively select a new transmission path or node for transmission, avoiding interruptions or delays in data transmission.

[0089] In an optional embodiment, there are multiple target demand nodes; and transmitting the smart data element to the target demand node according to the transmission path and the transmission priority of the smart data element includes:

[0090] According to the transmission path and the transmission priority of the intelligent data element, basic data in the intelligent data element that conforms to a target data type is transmitted to each target demand node, where the target data type is the data type required by the target demand node.

[0091] In an optional embodiment, the number of the target demand nodes is multiple; the method further includes:

[0092] Copying the smart data element to generate multiple smart data elements;

[0093] According to the transmission path and the transmission priority of the smart data element, one smart data element is transmitted to each target demand node.

[0094] The embodiment of the present application provides a data splitting and replication mechanism for intelligent data elements, which is used to improve the flexibility and utilization efficiency of data in big data networks. Different nodes in a big data network may have requirements for different parts of the same data or certain functional modules, and intelligent data elements can dynamically split or replicate data according to these requirements. When the target demand node only needs part of the data or functions of the intelligent data element, the intelligent data element can use the splitting mechanism to separate the required data modules or functions separately and transmit them to the target demand node, while the original intelligent data element continues to retain the remaining part to maintain its integrity and functionality.

[0095] This data splitting mechanism not only optimizes data transmission efficiency but also avoids unnecessary data redundancy and resource waste. For example, when a node only needs image data from an intelligent data element for processing, while another node requires text data for semantic analysis, the intelligent data element can split the corresponding data elements and transmit them to two different nodes. This mechanism is particularly important in large-scale distributed networks because it ensures the refined application of data. In addition, the data replication mechanism of the intelligent data element allows multiple copies to be created and transmitted to different nodes without affecting the flow of the original data when multiple nodes have demand for the same data. This splitting and replication mechanism significantly improves the flexibility and resource utilization efficiency of the intelligent data element.

[0096] In an optional embodiment, the above method further includes:

[0097] Acquiring the data requirement according to the task requirement information and / or data usage of the target requirement node;

[0098] According to the data requirement, the data type required by each target requirement node is determined.

[0099] In an embodiment of the present application, the task requirement information and / or data usage published by the target demand node are analyzed to determine the type of data required by each target demand node. For example, the target demand node may need to perform large-scale image processing, and the intelligent data element happens to carry relevant image data. In this case, the intelligent data element will be preferentially transmitted to the target demand node to ensure that the data can be efficiently applied. In this way, not only the type and matching degree of the data are considered, but also the network status, node load and transmission delay and other factors are comprehensively considered to ensure that the intelligent data unit can reach the target demand node with the most demand in the shortest time.

[0100] In an optional embodiment, the smart data element further includes: a third layer storing a dynamic adjustment strategy related to the life cycle stage of the smart data element; the method further includes:

[0101] Obtaining the life cycle stage of the intelligent data element;

[0102] The data or information stored in the smart data element is adjusted using a dynamic adjustment strategy related to the life cycle stage.

[0103] Figure 4 This is the life cycle diagram of the intelligent data element provided in the embodiment of this application. Figure 4 , explaining the life cycle stages of intelligent data elements.

[0104] The lifecycle of an intelligent data element can be divided into five stages: birth, growth, maturity, decline, and extinction. Each stage has its own unique functions and characteristics. Dynamic adjustment strategies related to the lifecycle stage ensure that data can maximize its effectiveness at the right time and in the right environment.

[0105] The birth phase is the starting point of the life cycle of an intelligent data element and is usually triggered by a node or system based on specific data requirements. This phase includes two processes: generation and initialization. During the generation process, the intelligent data element is given basic data and the initial structure of the encapsulation layer, such as metadata, interfaces, and security mechanisms. During the initialization process, the intelligent data element completes the configuration of its basic functions, including the setting of lifecycle management strategies, to ensure that it can be adjusted and optimized according to needs during future transmission and use. A notable feature of the birth phase is that the initial state of the intelligent data element is relatively simple, and the functional modules have not yet been fully deployed, but it already has the ability to be transmitted independently in the big data network. The main function of this stage is to lay the foundation for the intelligent data element so that it can grow and optimize smoothly in the subsequent lifecycle stages.

[0106] The growth stage is the most critical period in the lifecycle of an intelligent data element. During this phase, intelligent data elements continuously enhance their adaptability and application value within the big data network through network circulation, analysis and recommendation, and functional expansion. As intelligent data elements gradually enter appropriate nodes through intelligent routing, they dynamically adjust their content and structure based on environmental changes and node needs. Intelligent data elements in the growth stage not only exploit the potential value of data through analysis and recommendation algorithms but also distribute portions of their data across different nodes through data splitting and replication mechanisms, improving data utilization efficiency. This growth stage is characterized by the continuous enrichment of intelligent data element functionality and data content, and the gradual strengthening of its self-optimization capabilities. The core role of the growth stage is to continuously enhance the adaptability and value-mining capabilities of intelligent data elements through circulation within the big data network.

[0107] The mature stage marks the peak of the intelligent data element's lifecycle. At this point, the intelligent data element has found its most suitable application scenario and is functioning efficiently within that node or environment. During this stage, the functionality and content of the intelligent data element have stabilized, and the value of the data has been fully exploited and utilized. Furthermore, the intelligent data element may also continue to transmit and utilize some of its content or functionality in other nodes through data splitting or assistance. Intelligent data elements in the mature stage have higher priority and network resource usage, ensuring they have access to sufficient computing resources and storage space to maximize the value of the data. This stage is characterized by the efficiency and stability of the intelligent data element, and its value output reaches its peak.

[0108] During the decline phase, the usage and value of a smart data element may gradually decrease over time, entering the decline phase. During this phase, the smart data element uses a self-assessment mechanism to determine its continued existence based on its current usage and network feedback. A notable characteristic of a smart data element during the decline phase is that its resource usage gradually decreases, and its circulation path and priority may be automatically lowered. A smart data element in the decline phase gradually releases its occupied computing and storage resources and prepares for its eventual destruction. However, the smart data element does not completely lose its value during this phase; it may still provide limited functional support to other nodes in specific scenarios through recommendation mechanisms or data demand matching algorithms.

[0109] In the extinction phase, when a smart data element confirms that its value has been completely exhausted and there is no need for it to continue to exist, it enters the extinction phase. This phase includes two processes: self-destruction and persistent archiving. Self-destruction means that the smart data element automatically triggers the destruction mechanism, safely removing all data and metadata to ensure the cleanliness and security of the system. Persistent archiving means that in special circumstances, part of the content of the smart data element may be selectively archived by the system for future reference or analysis. The technical feature of the extinction phase is that the smart data element can autonomously and securely exit the system, and a strict destruction mechanism is used to prevent unauthorized data recovery or leakage.

[0110] Therefore, an intelligent data element is not merely a static information carrier, but an intelligent entity capable of self-management, self-optimization, and self-propagation. The lifecycle management of an intelligent data element encompasses the entire process from data generation, circulation, use, optimization, and destruction, giving data a "lifecycle" similar to that of an organism. In traditional big data management, data is often a passive resource whose value often depends on the invocation and processing of external systems and lacks the ability to self-manage and optimize. However, through intelligent management throughout its entire lifecycle, intelligent data elements transform data from a static resource into an intelligent unit that can proactively adapt to its environment and optimize its own value. This enables intelligent data elements to not only efficiently circulate within the big data network but also continuously mine and unlock the maximum value of data at different stages through lifecycle management.

[0111] It should be noted that at each stage of the smart data element's lifecycle, intelligent monitoring, evaluation, and corresponding dynamic adjustment strategies are required to ensure that the smart data element continuously optimizes its value at each stage. Dynamic adjustment strategies can include at least one of the following: value assessment strategy, resource optimization strategy, priority management strategy, and automatic destruction strategy.

[0112] The value assessment strategy dynamically adjusts the lifecycle phase and priority of intelligent data elements through a comprehensive evaluation of factors such as data usage frequency, node feedback, and the effectiveness of analysis results. These assessments determine the current lifecycle phase of an intelligent data element and, based on the results, optimize resource allocation and flow accordingly.

[0113] Resource optimization strategies ensure that the computing, storage, and network resources occupied by intelligent data elements are dynamically adjusted at different stages of their lifecycle. For example, during the growth and maturity stages, intelligent data elements will occupy more resources to maximize their value, while during the decline stage, resources will be gradually released to improve overall network efficiency.

[0114] The priority management strategy can dynamically adjust the transmission path and resource allocation of intelligent data elements in the big data network according to the life cycle stage of the intelligent data elements, ensuring that high-value intelligent data elements can obtain resources first, while low-value intelligent data elements are automatically downgraded.

[0115] An automatic destruction policy ensures that data is safely exited from the system at the end of its lifecycle. This strict destruction policy ensures that the data and metadata of intelligent data elements are securely removed after their value is exhausted, preventing any leakage or unauthorized recovery. Furthermore, a persistent archiving mechanism provides selective backup for data retention in specific scenarios, ensuring that critical data can be retrieved or analyzed when necessary.

[0116] In this way, through these intelligent dynamic adjustment strategies, intelligent data elements can be evaluated and optimized throughout their entire life cycle and safely exited when their value is exhausted, ensuring maximum data utilization and efficient operation of the system.

[0117] In summary, the data management method for intelligent data elements provided in the embodiments of the present application involves three aspects: the encapsulation layer design of intelligent data elements, the transmission method of intelligent data elements in a big data network, and the lifecycle management method of intelligent data elements.

[0118] The encapsulation layer design of intelligent data elements organically combines data with metadata, interfaces, analysis and recommendation, security and privacy, and lifecycle management through multi-level encapsulation. This not only enhances the intelligence of the data but also ensures its flexibility and security in complex network environments. Each layer of encapsulation performs a specific function: the basic data layer serves as the core carrier, the metadata layer provides descriptive information, the interface layer ensures cross-platform compatibility, the analysis and recommendation layer empowers data to make autonomous decisions, the security and privacy layer ensures data security during transfer, and the lifecycle management layer is responsible for the full lifecycle management of data. Through this multi-dimensional encapsulation, intelligent data elements have the ability to perceive the environment and proactively optimize their behavior. They can adapt to different application scenarios and achieve efficient transfer and value enhancement in complex big data networks.

[0119] The intelligent data element flow method in big data networks is based on intelligent routing, data demand matching algorithms, and adaptive optimization mechanisms. This ensures that data can find the most suitable nodes and application scenarios in the distributed network based on actual needs. Through intelligent routing algorithms, intelligent data elements can analyze network topology, node load, and data demand to select the optimal flow path, avoiding network congestion and resource waste. Furthermore, intelligent data elements have splitting and replication capabilities, which can separate and transmit data or functional modules to multiple nodes based on the needs of different nodes, thereby improving data utilization efficiency in the network. Furthermore, the adaptive mechanism of intelligent data elements allows them to adjust flow paths and strategies in real time according to environmental changes, ensuring efficient data transmission and application in dynamic network environments. This intelligent and dynamic flow method significantly improves data circulation efficiency and maximizes the application value of data in different scenarios.

[0120] The lifecycle management approach for intelligent data elements is divided into five stages: birth, growth, maturity, decline, and extinction. Each stage has specific management rules and operational procedures. Through intelligent management throughout its entire lifecycle, intelligent data elements can dynamically adjust their lifecycle stages based on usage and value changes, continuously optimizing the value output of data throughout their lifecycle. The lifecycle management layer uses mechanisms such as value assessment, resource optimization, and priority management to ensure that data occupies appropriate resources at different lifecycle stages and is safely exited from the system when its value is exhausted. Furthermore, lifecycle management is proactive, enabling proactive adjustments to the transmission path and resource allocation of intelligent data elements based on analysis and predictions to avoid resource waste and data value loss. Through this dynamic and intelligent lifecycle management approach, intelligent data elements not only maximize their value throughout their entire lifecycle, but also ensure data security and efficient resource utilization.

[0121] The data management method of the intelligent data element in the embodiment of the present application has the following beneficial effects:

[0122] Improving the autonomy and intelligence of data units. This is significantly enhanced through multi-level encapsulation of intelligent data elements. Intelligent data elements can sense environmental changes and autonomously adjust their behavior and attributes, achieving adaptive optimization in complex and ever-changing big data environments. Furthermore, the analysis and recommendation layer of intelligent data elements possesses autonomous decision-making capabilities, selecting the optimal transmission path and application scenario based on current network status and historical transmission information. This maximizes the value of intelligent data elements and significantly improves data utilization efficiency and value creation.

[0123] This enables efficient and secure data transfer across organizations and systems. Through intelligent routing and data demand matching algorithms, intelligent data elements can autonomously locate the most suitable nodes and application scenarios within a global big data network, transcending organizational and system boundaries. Furthermore, the security and privacy layers of intelligent data elements ensure the security and privacy of data during transfer, significantly reducing the risks and costs of data sharing. This mechanism not only improves data transfer efficiency but also promotes the full release and sharing of data value.

[0124] Intelligent data element lifecycle management achieves dynamic and forward-looking data lifecycle management. The lifecycle management approach of intelligent data elements overcomes the static and passive nature of existing data units. Through continuous value assessment and dynamic adjustment mechanisms, intelligent data elements can flexibly adjust their lifecycle stages and management strategies based on actual usage and value changes. Furthermore, the analysis and recommendation layer of intelligent data elements has the ability to predict future value and usage trends, enabling forward-looking resource optimization and value enhancement. This dynamic and intelligent lifecycle management approach not only improves resource utilization efficiency but also maximizes the value creation of data throughout its entire lifecycle.

[0125] Implementing efficient and accurate data rights confirmation, authorization, and security management mechanisms. By embedding data rights confirmation information and authorization mechanisms in the metadata layer of intelligent data elements, data ownership and usage rights are clarified. Dynamic authorization rules ensure that authorization information remains accurate and valid when data flows between different nodes. The registry authentication system supports querying the registry to obtain the authorization status and authentication keys of intelligent data elements, thereby ensuring the legal use of data. In addition, dynamic encryption and layered encryption mechanisms are implemented to ensure that data is always encrypted during transmission. Encryption policies are dynamically adjusted based on the permissions of different authorized nodes to achieve refined data protection.

[0126] Please refer to Figure 5 , the embodiment of the present application further provides a data management device for intelligent data elements, including:

[0127] The generation module 501 is used to generate a multi-level encapsulated intelligent data element, which includes: a first layer storing interfaces for communicating with multiple demand nodes; a second layer storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element;

[0128] An acquisition module 502 is configured to acquire, based on the interface and the analysis and recommendation algorithm, the target demand node having a data demand from the plurality of demand nodes, wherein the data demand is used to instruct the transmission of the intelligent data element to the target demand node;

[0129] A determination module 503 is configured to determine a transmission path for transmitting the intelligent data element to the target demand node based on the current network status information of each demand node;

[0130] The transmission module 504 is configured to transmit the smart data element to the target demand node according to the transmission path and the transmission priority of the smart data element.

[0131] Optionally, the data management device for the intelligent data element further comprises:

[0132] An evaluation module is configured to obtain a value evaluation grade corresponding to the smart data element based on a value evaluation factor corresponding to the smart data element, the value evaluation factor including at least one of data usage frequency, user feedback information, network resource occupancy, and life cycle stage; the value evaluation grade corresponding to the smart data element is high if at least one of the life cycle stage is not a decline stage or an extinction stage, the data usage frequency is high, the user feedback value is high, and the network resource occupancy is low is satisfied;

[0133] The priority acquisition module is used to acquire the transmission priority of the corresponding intelligent data element according to the value evaluation level.

[0134] Optionally, in the data management device for the intelligent data element, the determining module 503 is specifically configured to:

[0135] determining, based on the network status information and the historical transmission information of the demand node, a transmission path for transmitting the smart data element to the target demand node;

[0136] The network status information includes at least one of computing capability, storage capacity, network bandwidth, and transmission delay;

[0137] The historical transmission information is used to indicate the transmission delay of the historical transmission path and / or the historical processing efficiency of the demand node.

[0138] Optionally, in the data management device for the intelligent data element, the transmission module 504 is specifically configured to:

[0139] Adjusting the transmission path according to the network environment change information to obtain an adjusted transmission path;

[0140] The intelligent data element is transmitted to the target demand node according to the adjusted transmission path.

[0141] Optionally, in the data management device for the intelligent data element, the number of the target demand nodes is multiple; the transmission module 504 is specifically configured to:

[0142] According to the transmission path and the transmission priority of the intelligent data element, basic data in the intelligent data element that conforms to a target data type is transmitted to each target demand node, where the target data type is the data type required by the target demand node.

[0143] Optionally, the data management device for the intelligent data element further comprises:

[0144] A demand acquisition module, configured to acquire the data demand based on the task demand information and / or data usage of the target demand node;

[0145] The type determination module is used to determine the data type required by each target demand node according to the data demand.

[0146] Optionally, in the data management device for the intelligent data element, the number of the target demand nodes is multiple; the device further comprises:

[0147] a replication module, configured to replicate the smart data element to generate a plurality of the smart data elements;

[0148] The data element transmission module is used to transmit one of the smart data elements to each of the target demand nodes according to the transmission path and the transmission priority of the smart data element.

[0149] Optionally, the data management device for the intelligent data element further comprises: a third layer storing dynamic adjustment strategies related to the life cycle stage of the intelligent data element; the device further comprises:

[0150] An acquisition stage module is used to acquire the life cycle stage of the smart data element;

[0151] An adjustment module is used to adjust the data or information stored in the smart data element by adopting a dynamic adjustment strategy related to the life cycle stage.

[0152] It should be noted that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0153] The present application also provides a data management device for intelligent data elements, such as Figure 6 As shown, including:

[0154] Processor 601, memory 602, transceiver 603 and programs or instructions stored on the memory 602 and executable on the processor 601; when the processor 601 executes the programs or instructions, each process of the above-mentioned embodiment of the data management method for intelligent data elements is implemented, and the same technical effect can be achieved. To avoid repetition, they will not be described here.

[0155] The transceiver 603 is configured to receive and send data under the control of the processor 601 .

[0156] Among them, Figure 6 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically connecting various circuits of one or more processors represented by processor 601 and memory represented by memory 602. The bus architecture may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not described further herein. The bus interface provides an interface. The transceiver 603 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 604 may also be an interface capable of connecting external or internal devices as required, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0157] The processor 601 is responsible for managing the bus architecture and general processing, and the memory 602 can store data used by the processor 601 when performing operations.

[0158] The present application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-mentioned intelligent data element data management method embodiment and achieves the same technical effect. To avoid repetition, the description is omitted here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0159] An embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned data management method embodiment of the intelligent data element are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0160] It should be noted that, in this document, the terms "include," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0161] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0162] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A data management method for intelligent data elements, characterized in that: The method comprises: Generate a multi-level encapsulated intelligent data element, the intelligent data element comprising: a first layer storing interfaces for communicating with multiple demand nodes; a second layer storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element; According to the interface and the analysis and recommendation algorithm, obtaining the target demand node having a data demand from the plurality of demand nodes, wherein the data demand is used to instruct transmission of the smart data element to the target demand node; Determining a transmission path for transmitting the intelligent data element to the target demand node according to the current network status information of each demand node; The smart data element is transmitted to the target demand node according to the transmission path and the transmission priority of the smart data element.

2. The data management method of intelligent data element according to claim 1, characterized in that: The method further comprises: Obtaining a value assessment level corresponding to the smart data element based on the value assessment factors corresponding to the smart data element, the value assessment factors including at least one of data usage frequency, user feedback information, network resource occupancy, and the life cycle stage in which the smart data element is located; if at least one of the life cycle stage is not a decline stage or an extinction stage, the data usage frequency is high, the user feedback value is high, and the network resource occupancy is low, the value assessment level corresponding to the smart data element is high; According to the value evaluation level, the transmission priority of the corresponding intelligent data element is obtained, and a higher value evaluation level corresponds to a higher transmission priority.

3. The data management method of intelligent data element according to claim 1, characterized in that: Determining, based on the current network status information of each demand node, a transmission path for transmitting the smart data element to the target demand node, includes: determining, based on the network status information and historical transmission information of the demand node, a transmission path for transmitting the smart data element to the target demand node; The network status information includes at least one of computing capability, storage capacity, network bandwidth, and transmission delay; The historical transmission information is used to indicate the transmission delay of the historical transmission path and / or the historical processing efficiency of the demand node.

4. The data management method of intelligent data element according to claim 1, characterized in that: The transmitting the intelligent data element to the target demand node includes: Adjusting the transmission path according to the network environment change information to obtain an adjusted transmission path; The intelligent data element is transmitted to the target demand node according to the adjusted transmission path.

5. The data management method of intelligent data element according to claim 1, characterized in that: There are multiple target demand nodes; transmitting the intelligent data element to the target demand node according to the transmission path and the transmission priority of the intelligent data element includes: transmitting basic data in the intelligent data element that conforms to the target data type to each target demand node according to the transmission path and the transmission priority of the intelligent data element, and the target data type is the data type required by the target demand node.

6. The data management method of intelligent data element according to claim 5, characterized in that: The method further comprises: Acquiring the data requirement according to the task requirement information and / or data usage of the target requirement node; According to the data requirement, the data type required by each target requirement node is determined.

7. The data management method of intelligent data element according to claim 1, characterized in that: The number of the target demand nodes is multiple; and the method further includes: Copying the smart data element to generate multiple smart data elements; According to the transmission path and the transmission priority of the smart data element, one smart data element is transmitted to each target demand node.

8. The data management method of intelligent data element according to claim 1, characterized in that: The smart data element further includes: a third layer storing a dynamic adjustment strategy related to the life cycle stage of the smart data element; the method further includes: Obtaining the life cycle stage of the intelligent data element; The data or information stored in the smart data element is adjusted using a dynamic adjustment strategy related to the life cycle stage.

9. A data management device for intelligent data elements, characterized in that: include: A generation module is used to generate a multi-level encapsulated intelligent data element, wherein the intelligent data element includes: a first layer storing interfaces for communicating with multiple demand nodes; a second layer storing an analysis and recommendation algorithm for recommending corresponding target demand nodes for the intelligent data element; an acquisition module, configured to acquire, according to the interface and the analysis and recommendation algorithm, the target demand node having a data demand from among the plurality of demand nodes, wherein the data demand is used to instruct the transmission of the intelligent data element to the target demand node; a determination module, configured to determine a transmission path for transmitting the intelligent data element to the target demand node based on the current network status information of each demand node; A transmission module is used to transmit the intelligent data element to the target demand node according to the transmission path and the transmission priority of the intelligent data element.

10. A data management device for intelligent data elements, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the processor implements the data management method for the intelligent data element according to any one of claims 1 to 8 when executing the program or instruction.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the data management method for intelligent data elements according to any one of claims 1 to 8.

12. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implements the data management method for intelligent data elements according to any one of claims 1 to 8.

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