Data processing method and device, network equipment, readable storage medium and program product

Abstract

The invention relates to a data processing method and device, network equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a data analysis request from a first network entity; under the condition that a data analysis parameter corresponding to the data analysis request is matched with a local capability parameter, obtaining to-be-analyzed data, and analyzing the to-be-analyzed data to obtain a data analysis result; sending a data analysis response containing the data analysis result to the first network entity; sending an analysis cooperation request containing the data analysis parameter to a second network entity under the condition that the data analysis parameter is not matched with the local capability parameter; the analysis cooperation request is used for the second network entity to obtain the to-be-analyzed data under the condition that the capability parameter is matched with the data analysis parameter, analyze the to-be-analyzed data and then return a data analysis result; and if a data analysis result from the second network entity is received, sending a data analysis response to the first network entity. By adopting the method, the data processing efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a data processing method, apparatus, network device, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of mobile communication networks, artificial intelligence technology has been gradually introduced into these networks to achieve intelligent data analysis and processing, thereby improving the quality of communication services. One related technology involves deploying intelligent modules within the communication network to collect and analyze data. However, the lack of efficient collaboration mechanisms between different intelligent modules within a communication network often leads to low overall data processing efficiency. Summary of the Invention

[0003] Therefore, it is necessary to provide a data processing method, apparatus, network device, computer-readable storage medium, and computer program product to address the aforementioned technical problems.

[0004] In a first aspect, this application provides a data processing method, including:

[0005] Receive a data analysis request from the first network entity;

[0006] If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the data to be analyzed is obtained according to the data analysis parameters and the data to be analyzed is analyzed to obtain the data analysis result; a data analysis response containing the data analysis result is sent to the first network entity.

[0007] If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, an analysis collaboration request containing the data analysis parameters is sent to the second network entity; the analysis collaboration request is used by the second network entity, when the capability parameters match the data analysis parameters, to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results;

[0008] If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

[0009] In one embodiment, after sending the analysis collaboration request containing the data analysis parameters to the second network entity, the process includes: receiving an analysis collaboration response from the second network entity; and, if the analysis collaboration response indicates that the capability parameters of the second network entity match the data analysis parameters, sending an analysis initiation request to the second network entity; the analysis initiation request instructs the second network entity to obtain data to be analyzed according to the data analysis parameters and to analyze the data to be analyzed.

[0010] In one embodiment, the number of second network entities is multiple; sending the analysis start request to the second network entities includes: obtaining the matching degree between each second network entity and the data analysis parameters based on the analysis collaboration response of each second network entity; and sending the analysis start request to the second network entity whose capability parameters match the data analysis parameters and whose matching degree is the highest.

[0011] In one embodiment, sending the analysis collaboration request containing the data analysis parameters to the second network entity includes: sending the analysis collaboration request to the second network entity within the core network; receiving the analysis collaboration response from the second network entity within the core network; and, if the analysis collaboration response indicates that the capability parameters of the second network entity within the core network do not match the data analysis parameters, sending the analysis collaboration request to a second network entity outside the core network.

[0012] In one embodiment, before sending the analysis collaboration request containing the data analysis parameters to the second network entity, the process includes: obtaining capability configuration information of each candidate collaboration entity; and determining the second network entity among the candidate collaboration entities based on the matching between the capability configuration information of each candidate collaboration entity and the data analysis parameters.

[0013] In one embodiment, obtaining the capability configuration information of each candidate collaborative entity includes: sending a configuration acquisition request to each candidate collaborative entity; receiving a configuration acquisition response from each candidate collaborative entity; and obtaining the capability configuration information of each candidate collaborative entity based on the configuration acquisition response; wherein the capability configuration information includes the data acquisition permissions and data acquisition frequency of the candidate collaborative entity.

[0014] Secondly, this application also provides a data processing apparatus, comprising:

[0015] An analysis request receiving module is used to receive data analysis requests from the first network entity;

[0016] The first analysis response module is configured to, when the data analysis parameters corresponding to the data analysis request match the local capability parameters, obtain the data to be analyzed according to the data analysis parameters and analyze the data to be analyzed to obtain the data analysis result; and send a data analysis response containing the data analysis result to the first network entity.

[0017] The collaboration request sending module is used to send an analysis collaboration request containing the data analysis parameters to a second network entity when the data analysis parameters corresponding to the data analysis request do not match the local capability parameters; the analysis collaboration request is used by the second network entity to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results when the capability parameters match the data analysis parameters.

[0018] The second analysis response module is used to send a data analysis response containing the data analysis results to the first network entity if it receives data analysis results from the second network entity.

[0019] Thirdly, this application also provides a network device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0020] Receive a data analysis request from the first network entity;

[0021] If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the data to be analyzed is obtained according to the data analysis parameters and the data to be analyzed is analyzed to obtain the data analysis result; a data analysis response containing the data analysis result is sent to the first network entity.

[0022] If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, an analysis collaboration request containing the data analysis parameters is sent to the second network entity; the analysis collaboration request is used by the second network entity, when the capability parameters match the data analysis parameters, to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results;

[0023] If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

[0024] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0025] Receive a data analysis request from the first network entity;

[0026] If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the data to be analyzed is collected according to the data analysis parameters and analyzed to obtain the data analysis result; a data analysis response containing the data analysis result is sent to the first network entity.

[0027] If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, an analysis collaboration request containing the data analysis parameters is sent to the second network entity; the analysis collaboration request is used by the second network entity, when the capability parameters match the data analysis parameters, to collect and obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results;

[0028] If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

[0029] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0030] Receive a data analysis request from the first network entity;

[0031] If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the data to be analyzed is collected according to the data analysis parameters and analyzed to obtain the data analysis result; a data analysis response containing the data analysis result is sent to the first network entity.

[0032] If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, an analysis collaboration request containing the data analysis parameters is sent to the second network entity; the analysis collaboration request is used by the second network entity, when the capability parameters match the data analysis parameters, to collect and obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results;

[0033] If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

[0034] The aforementioned data processing method, apparatus, network device, computer-readable storage medium, and computer program product first receive a data analysis request from a first network entity. If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the system obtains the data to be analyzed based on the data analysis parameters, analyzes the data to be analyzed to obtain a data analysis result, and sends a data analysis response containing the data analysis result to the first network entity. If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, the system sends an analysis collaboration request containing the data analysis parameters to a second network entity. This analysis collaboration request is used by the second network entity to obtain the data to be analyzed based on the data analysis parameters when the capability parameters match the data analysis parameters, analyzes the data to be analyzed, and then returns a data analysis result. Finally, if a data analysis result is received from the second network entity, the system sends a data analysis response containing the data analysis result to the first network entity. This scheme, upon receiving a data analysis request from a first network entity, first determines whether the data analysis parameters corresponding to the request match the local capability parameters. If they match, the data to be analyzed is acquired and analyzed locally. If they do not match, the data to be analyzed is acquired and analyzed through collaboration with a second network entity whose capabilities match. Finally, the data analysis results are returned to the first network entity. This approach fully considers the data processing requirements and the actual processing capabilities and conditions of the intelligent network entities in the communication network when executing data processing tasks. Data processing is performed locally or collaboratively, thus fully utilizing existing services and data through mutual collaboration between network entities to complete data processing tasks. This helps avoid redundant construction of network entity capabilities and improves the overall data processing efficiency. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a diagram illustrating the application environment of a data processing method in one embodiment.

[0037] Figure 2 This is a flowchart illustrating a data processing method in one embodiment;

[0038] Figure 3 This is a schematic diagram of the process for obtaining capability configuration information of each candidate collaborative entity in one embodiment.

[0039] Figure 4This is a schematic diagram of the structure of an intelligent communication network module interaction system in one embodiment;

[0040] Figure 5 This is a flowchart illustrating the intelligent query matching mechanism in one embodiment;

[0041] Figure 6 This is a flowchart illustrating the first scenario of the intelligent query matching mechanism in one embodiment;

[0042] Figure 7 This is a flowchart illustrating the second scenario of the intelligent query matching mechanism in one embodiment;

[0043] Figure 8 This is a schematic diagram illustrating the process of establishing collaborative relationships between intelligent modules in one embodiment;

[0044] Figure 9 This is a structural block diagram of a data processing device in one embodiment;

[0045] Figure 10 This is a diagram of the internal structure of a network device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various objects, but these objects are not limited by these terms. These terms are only used to distinguish the first object from the second object. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0048] Specifically, in the construction of mobile communication networks, artificial intelligence (AI) technology is gradually being introduced to achieve intelligent data analysis and processing, thereby improving the quality of communication services. In existing network construction, AI technology has been gradually incorporated into various modules, such as the Network Data Analytics Function (NWDAF) in the core network and the data acquisition, control, and network management systems in the non-core network. Specifically, the intelligent modules in the core network can collect and analyze data after receiving requests from other network function modules, while the intelligent modules in the non-core network can also collect data from the network functions in the core network at certain time intervals and process it using AI technology. Thus, multiple intelligent modules can exist in the communication network, fully utilizing data in real time. However, the collaboration between these intelligent modules is not close, which can easily lead to resource waste and redundant construction, increasing operating and data processing costs and resulting in low overall data processing efficiency.

[0049] The data processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the third network entity 106 can communicate with the first network entity 102 and the second network entity 104. The first network entity 102, the second network entity 104, and the third network entity 106 can be network elements in a communication network or deployed on existing network elements. The second network entity 104 and the third network entity 106 can be equipped with intelligent modules based on artificial intelligence technology, which can acquire and process data in the communication network. The first network entity 102 can send a data analysis request to the third network entity 106. The third network entity 106 can receive the data analysis request. If the data analysis parameters corresponding to the data analysis request match the local capability parameters of the third network entity 106, it can obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, obtain the data analysis result, and send a data analysis response containing the data analysis result to the first network entity 102. If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters of the third network entity 106, it can send an analysis collaboration request containing the data analysis parameters to the second network entity 104. After receiving the analysis collaboration request, if the capability parameters of the second network entity 104 match the data analysis parameters, it can obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis result to the third network entity 106. After receiving the data analysis result from the second network entity 104, the third network entity 106 can send a data analysis response containing the data analysis result to the first network entity 102.

[0050] In one exemplary embodiment, such as Figure 2 As shown, a data processing method is provided, which can be applied to... Figure 1 Taking the third network entity 106 as an example, the explanation includes the following steps:

[0051] Step S201: Receive a data analysis request from the first network entity.

[0052] Specifically, the first network entity can send a data analysis request to a network entity in the communication network that has deployed intelligent modules, so as to collect and analyze data in the communication network through the intelligent modules. The network entity that receives the data analysis request is the third network entity.

[0053] Upon receiving a data analysis request, the third network entity can obtain corresponding data analysis parameters based on the request. For example, these parameters may include, but are not limited to, instance identifiers and data collection frequency. The instance identifier can indicate the source instance of the data required for this analysis, and the data collection frequency can indicate the sampling time interval granularity of the data required for this analysis. For example, the data analysis parameters may also include parameters indicating the specific analysis and processing content to be performed on the data. Optionally, the data analysis parameters may also include parameters indicating data processing requirements, such as the computational resource requirements and processing time limits for this data analysis.

[0054] For example, a data analysis request may include data analysis parameters, which the third network entity can directly obtain from the data analysis request. Alternatively, the data analysis request may also include parameters such as task identifiers, and the third network entity can be configured with a mapping between different task identifiers and corresponding data analysis parameters, allowing it to query the corresponding data analysis parameters based on the task identifier included in the data analysis request. For example, the third network entity can also utilize its deployed intelligent modules to analyze the parameters carried in the data analysis request to obtain the corresponding data analysis parameters.

[0055] After receiving the data analysis parameters corresponding to the data analysis request, the third network entity can determine whether these parameters match its local capability parameters. These local capability parameters can indicate the third network entity's ability to perform data analysis tasks. Examples include, but are not limited to, the third network entity's data collection permissions for different instances within the communication network's network functional modules, and the available data collection frequency. They can also include parameters reflecting the third network entity's current operational status, such as information on currently available resources and currently executing tasks.

[0056] Specifically, when the local capability parameters of the third network entity match the data analysis parameters, meaning the third network entity meets the conditions corresponding to the data analysis request (e.g., the third network entity has the corresponding data collection permissions and can support the data collection frequency), the process can proceed to step S202, allowing the data analysis processing task to be executed locally on the third network entity. However, when the local capability parameters of the third network entity do not match the data analysis parameters, meaning the third network entity does not meet one or more conditions corresponding to the data analysis request, the process can proceed to step S203, allowing the data analysis processing task to be completed through collaboration with other network entities in the communication network that have deployed intelligent modules.

[0057] Step S202: If the data analysis parameters corresponding to the data analysis request match the local capability parameters, obtain the data to be analyzed according to the data analysis parameters and analyze the data to be analyzed to obtain the data analysis result; send a data analysis response containing the data analysis result to the first network entity.

[0058] Specifically, when the data analysis parameters corresponding to the data analysis request match the local capability parameters of the third network entity, the third network entity can obtain the data to be analyzed based on the data analysis parameters and use the intelligent module to analyze the data to obtain the data analysis results.

[0059] For example, the third network entity can first determine whether the data to be analyzed has already been collected based on the data analysis parameters. If the data to be analyzed has already been collected, it can directly obtain the locally stored data to be analyzed. For example, the third network entity can also determine whether the data to be analyzed has already been collected and analyzed previously based on the data analysis parameters. If so, it can directly obtain the data analysis results from the previous analysis as the data analysis results for this analysis.

[0060] For example, the third network entity can also determine the instance requiring data collection and its corresponding network function module based on the instance identifier included in the data analysis parameters, and collect the data to be analyzed from the network function module according to the data collection frequency included in the data analysis parameters. For example, the third network entity can send a data collection request to the network function module according to the data collection frequency, and receive the sampled data returned by the network function module for each data collection request, thereby obtaining the data to be analyzed by collecting the sampled data returned by the network function module each time. For example, the third network entity can also send a data collection request containing the data collection frequency to the network function module. After receiving the data collection request, the network function module can collect data according to the data collection frequency, and after meeting a preset time or data volume requirement, return the collected sampled data to the third network entity, thereby allowing the third network entity to obtain the data to be analyzed based on the sampled data returned by the network function module.

[0061] The third network entity, after receiving the data to be analyzed, can use an intelligent module to analyze the data and obtain the analysis results. Subsequently, it can send a data analysis response containing the results to the first network entity.

[0062] Step S203: If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, send an analysis collaboration request containing the data analysis parameters to the second network entity.

[0063] In cases where the data analysis parameters corresponding to the data analysis request do not match the local capability parameters of the third network entity, the third network entity can send an analysis collaboration request containing the data analysis parameters to the second network entity. The second network entity can be another network entity in the communication network that has deployed intelligent modules. The intelligent modules deployed in the second network entity and the intelligent modules deployed in the third network entity can be different instances of the same type, or different instances of different types.

[0064] The second network entity can obtain data analysis parameters based on the received analysis collaboration request and determine whether these parameters match its capability parameters (i.e., the second network entity's local capability parameters). When the second network entity determines that its capability parameters match the data analysis parameters, meaning it meets the conditions corresponding to the data analysis request, it can acquire the data to be analyzed based on the data analysis parameters and analyze the data to obtain the data analysis results. The method by which the second network entity acquires and processes the data to be analyzed is similar to the method used by the third network entity in the aforementioned steps, and will not be repeated here. After obtaining the data analysis results, the second network entity can send them to the third network entity.

[0065] For example, a third network entity may pre-establish collaborative relationships with multiple second network entities and select one of the second network entities to send an analysis collaboration request in this step. For example, if the third network entity does not receive data analysis results from the second network entity within a preset time after sending the analysis collaboration request, it may send an analysis collaboration request to another second network entity. For example, a second network entity may also send an analysis collaboration response to the third network entity after receiving the analysis collaboration request, indicating whether its capability parameters match the data analysis parameters. Upon receiving the analysis collaboration response indicating a match, the third network entity may send an analysis start request to the second network entity, enabling the second network entity to obtain and analyze the data to be analyzed according to the data analysis parameters. Alternatively, the third network entity may send an analysis collaboration request to another second network entity after receiving an analysis collaboration response indicating a mismatch between its capability parameters and the data analysis parameters.

[0066] For example, in this step, the third network entity may also send analysis collaboration requests to multiple second network entities, and after receiving analysis collaboration responses from one or more of the second network entities, send an analysis start request to one of the responding second network entities, so that the second network entity can obtain the data to be analyzed according to the data analysis parameters and analyze the data to be analyzed.

[0067] Step S204: If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

[0068] When the third network entity receives the data analysis results from the second network entity, it can send a data analysis response containing the data analysis results to the first network entity.

[0069] In the above data processing method, when a data analysis request is received from a first network entity, it is first determined whether the data analysis parameters corresponding to the request match the local capability parameters. If they match, the data to be analyzed is acquired and analyzed locally. If they do not match, the data to be analyzed is acquired and analyzed through the cooperation of a second network entity with matching capabilities. Finally, the data analysis results are returned to the first network entity. This method can fully consider the data processing requirements and the actual processing capabilities and conditions of the intelligent network entities in the communication network when executing data processing tasks. Data processing can be carried out locally or collaboratively. This allows for full utilization of existing services and data through mutual cooperation between network entities to complete data processing tasks, which helps to avoid redundant construction of network entity capabilities and improves the overall data processing efficiency.

[0070] In an exemplary embodiment, after sending an analysis collaboration request containing data analysis parameters to a second network entity, the process may include: receiving an analysis collaboration response from the second network entity; and sending an analysis start request to the second network entity if the analysis collaboration response indicates that the capability parameters of the second network entity match the data analysis parameters. The analysis start request is used to instruct the second network entity to obtain the data to be analyzed according to the data analysis parameters and to analyze the data to be analyzed.

[0071] Specifically, after sending an analysis collaboration request to the second network entity, the third network entity can receive an analysis collaboration response from the second network entity. This response may include information indicating whether the second network entity's capability parameters match the data analysis parameters. For example, after receiving the analysis collaboration request from the third network entity, the second network entity can determine whether its own capability parameters match the data analysis parameters based on the data analysis parameters included in the request, thus obtaining a parameter matching result, and then send an analysis collaboration response containing this matching result to the third network entity. Alternatively, after receiving the analysis collaboration request from the third network entity, the second network entity can also obtain its current capability parameters corresponding to the data analysis parameters and send an analysis collaboration response containing these current capability parameters to the third network entity, allowing the third network entity to determine whether its current capability parameters match the data analysis parameters.

[0072] The third network entity can determine whether the capability parameters of the second network entity match the data analysis parameters based on the analysis collaboration response received from the second network entity. If the capability parameters match, the third network entity can send an analysis start request to the second network entity, instructing it to initiate data collection and analysis. Upon receiving the analysis start request, the second network entity can acquire and analyze the data to be analyzed according to the data analysis parameters, and then send the analysis results to the third network entity.

[0073] For example, a third network entity may pre-establish collaborative relationships with multiple second network entities and select one of the second network entities to send an analysis collaboration request. If, after sending the analysis collaboration request to the second network entity, the third network entity does not receive an analysis collaboration response from the second network entity within a preset time period, or if the received analysis collaboration response indicates that the capability parameters of the second network entity do not match the data analysis parameters, then the third network entity may send an analysis collaboration request to another second network entity.

[0074] For example, the third network entity may also send analysis collaboration requests to multiple second network entities, and after receiving analysis collaboration responses from one or more of the second network entities, send an analysis initiation request to one of the responding second network entities.

[0075] In this embodiment, by sending an analysis collaboration request to the second network entity and then receiving an analysis collaboration response from the second network entity, and by sending an analysis start request to the second network entity only after determining that the capability parameters of the second network entity match the data analysis parameters, the reliability of collaborating with the second network entity to complete the data analysis and processing task can be improved, which is beneficial to improving the overall data processing efficiency.

[0076] In one exemplary embodiment, there are multiple second network entities; sending an analysis start request to the second network entities may include: obtaining the matching degree between each second network entity and the data analysis parameters based on the analysis collaboration response of each second network entity; and sending an analysis start request to the second network entity whose capability parameters match the data analysis parameters and has the highest matching degree.

[0077] In this embodiment, a third network entity can send analysis collaboration requests to multiple second network entities and receive analysis collaboration responses from each second network entity. Subsequently, the third network entity can determine whether the capability parameters of each second network entity match the data analysis parameters based on the analysis collaboration responses, and obtain the matching degree between each second network entity and the data analysis parameters. A higher matching degree indicates that the second network entity is more suitable for performing the analysis processing task corresponding to the data analysis request (e.g., having more abundant computing resources and higher processing efficiency). For example, the matching degree between the second network entity and the data analysis parameters can be calculated by the second network entity based on its own capability parameters and data analysis parameters and included in the analysis collaboration response. For example, this matching degree can also be calculated by the third network entity based on the data analysis parameters and information such as the current capability parameters included in the analysis collaboration response.

[0078] After obtaining the matching degree between each second network entity and the data analysis parameter, the second network entity with the highest matching degree between its capability parameter and the data analysis parameter can be selected from multiple second network entities as the collaboration object for this time, and an analysis start request can be sent to it.

[0079] In this embodiment, by selecting the second network entity with the highest matching degree between the capability parameters and data analysis parameters to send the analysis start request, the data collection and analysis processing can be carried out by utilizing the second network entity most suitable for the task, thereby improving the execution efficiency and accuracy of the data analysis and processing task.

[0080] In one exemplary embodiment, sending an analysis collaboration request containing data analysis parameters to a second network entity may include: sending the analysis collaboration request to a second network entity within the core network; receiving an analysis collaboration response from a second network entity within the core network; and, if the analysis collaboration response indicates that the capability parameters of the second network entity within the core network do not match the data analysis parameters, sending the analysis collaboration request to a second network entity outside the core network.

[0081] Specifically, the third network entity can be a network entity within the core network, which can establish cooperative relationships with second network entities both within the core network and outside the core network. When it needs to send an analysis cooperation request to a second network entity, the third network entity can first send the analysis cooperation request to the second network entity within the core network and then receive the analysis cooperation responses from those second network entities.

[0082] Specifically, when the analysis collaboration requests from all second network entities within the core network indicate that the capability parameters of the corresponding second network entity do not match the data analysis parameters, the third network entity can send an analysis collaboration request to a second network entity outside the core network. Conversely, when the analysis collaboration requests from one or more second network entities within the core network indicate that the capability parameters of the corresponding second network entity match the data analysis parameters, one of these second network entities within the core network can be selected to send an analysis initiation request.

[0083] In this embodiment, by prioritizing cooperation with the second network entity within the core network and then seeking cooperation with the second network entity outside the core network when a mismatch occurs, the efficiency and security of data processing tasks can be improved by fully utilizing the intelligent agent modules inside and outside the core network.

[0084] In an exemplary embodiment, before sending an analysis collaboration request containing data analysis parameters to the second network entity, the process may include: obtaining capability configuration information of each candidate collaboration entity; and determining the second network entity among the candidate collaboration entities based on the matching between the capability configuration information and the data analysis parameters of each candidate collaboration entity.

[0085] Specifically, candidate collaborating entities can be network entities in the communication network that have pre-established a collaborative relationship with a third network entity, and these entities may be equipped with intelligent modules. The third network entity may pre-acquire and store the capability configuration information of each candidate collaborating entity. This capability configuration information may include, but is not limited to, the type and instance identifier of the intelligent modules deployed in the candidate collaborating entity, the data acquisition frequency supported by the candidate collaborating entity, its data acquisition permissions for various types of network function modules in the communication network, and may also include the basic capability configuration of the candidate collaborating entity, such as maximum computing power and maximum task queue length.

[0086] When a third network entity needs to collaborate with other network entities in the communication network to complete a data analysis and processing task, it can first determine whether the capability configuration information of each candidate collaborating entity matches the data analysis parameters, and then select one or more candidate collaborating entities whose capability configuration information matches the data analysis parameters as the second network entity.

[0087] In this embodiment, by acquiring the capability configuration information of each candidate collaborative entity and selecting the second network entity from the candidate collaborative entities based on the matching of the capability configuration information and the data analysis parameters, the matching of the second network entity and the data analysis parameters can be ensured through preliminary screening. This helps to reduce the probability of the third network entity seeking collaboration with the mismatched second network entity, reduce unnecessary interactions, and thus improve the execution efficiency and resource utilization of data processing tasks.

[0088] In one exemplary embodiment, such as Figure 3 As shown, obtaining the capability configuration information of each candidate collaborative entity may include:

[0089] Step S301: Send configuration retrieval requests to each candidate collaborative entity.

[0090] Specifically, the third network entity can obtain the capability configuration information of each candidate collaborating entity through interaction with them. In this step, the third network entity can send a configuration retrieval request to each candidate collaborating entity. For example, the configuration retrieval request may include the type and instance identifier of the intelligent module deployed by the third network entity. Optionally, the configuration retrieval request may also include the capability configuration information of the third network entity.

[0091] Step S302: Receive configuration acquisition responses from each candidate collaborative entity.

[0092] Upon receiving a configuration retrieval request from a third network entity, a candidate collaborating entity can obtain its own capability configuration information and send a configuration retrieval response containing that information to the third network entity. Thus, the third network entity can receive configuration retrieval responses from each candidate collaborating entity.

[0093] Optionally, when the configuration acquisition request from the third network entity contains the capability configuration information of the third network entity, the candidate cooperating entity may also store the capability configuration information of the third network entity (that is, for the candidate cooperating entity, the third network entity may be a candidate cooperating entity of the entity).

[0094] Step S303: Obtain the response based on the configuration to get the capability configuration information of each candidate collaborative entity.

[0095] The third network entity can obtain the capability configuration information of each candidate collaborating entity by receiving the configuration response from each candidate collaborating entity, and then store it. Thus, the third network entity can configure a list of intelligent modules that includes the capability configuration information of multiple candidate collaborating entities.

[0096] It is understandable that each network entity in a communication network that deploys intelligent modules can act as a third network entity, and establish cooperative relationships with other network entities that deploy intelligent modules to designate other network entities as candidate cooperative entities relative to itself. Specifically, a third network entity can obtain the capability configuration information of a candidate cooperative entity by sending a configuration retrieval request to its corresponding candidate cooperative entity and receiving a configuration retrieval response, or by receiving a configuration retrieval request carrying the capability configuration information of a candidate cooperative entity.

[0097] In this embodiment, the third network entity can obtain the capability configuration information of the candidate cooperative entity by interacting with the candidate cooperative entity, and provide its own capability configuration information to the candidate cooperative entity. This enables each intelligent module in the communication network to obtain the capability configuration information of other intelligent modules and configure the intelligent module set list accordingly. Subsequently, the intelligent module set list can be used to realize cooperative interaction with other intelligent modules.

[0098] In one exemplary embodiment, a data processing method is provided.

[0099] Specifically, the data processing method in this embodiment can be applied to, for example... Figure 4The intelligent module interaction system for the communication network shown includes a consumer network function module, core network intelligent modules (including core network intelligent module 1 and core network intelligent module 2), and non-core network intelligent modules. The consumer network function module can be deployed on the first network entity of the communication network. The core network intelligent modules can be deployed on network entities within the core network, and the non-core network intelligent modules can be deployed on network entities within the non-core network. Each intelligent module can establish intelligent network data analysis collaboration relationships with other intelligent modules. Each intelligent module can be configured with a list of intelligent module sets, which may include the capability configuration information of other intelligent modules that have established collaboration relationships with it. For example, the consumer network function module may include, but is not limited to, modules such as the Authentication Management Function (AMF); the core network intelligent module may include, but is not limited to, modules such as the Network Data Analytics Function (NWDAF); and the non-core network intelligent modules may include, but are not limited to, modules such as the data acquisition and control system and the network management system. The network entity where the core network intelligent module that receives the data analysis request from the consumer network function module is located can be a third network entity, and the network entity where the intelligent module that has established a collaboration relationship with the core network intelligent module is located can be a second network entity. Specifically, the consumer network function module can send requests to the core network intelligent module to obtain and analyze data from other network function modules in the communication network. For example, the network function modules whose data is obtained and analyzed may include, but are not limited to, modules such as the Session Management Function (SMF).

[0100] For example, a third network entity can follow the procedure as follows: Figure 5 The intelligent query matching mechanism shown executes a data processing method. This mechanism can include two scenarios: in the first scenario, the third network entity can independently complete the data collection and analysis tasks; in the second scenario, the third network entity can seek collaboration from other intelligent modules to complete the data collection and analysis tasks. The following sections will describe these scenarios in conjunction with... Figure 6 and Figure 7 This section explains two scenarios for the intelligent query matching mechanism.

[0101] Please refer to the following: Figure 6 This is a flowchart illustrating the first scenario corresponding to the intelligent query matching mechanism. For example... Figure 6As shown, the consumer network function module (corresponding to the first network entity) can send a data analysis request to the core network intelligent module 1 (corresponding to the third network entity). The core network intelligent module 1 can obtain the corresponding data analysis parameters (including the type of network function module to be collected and analyzed, instance identifier, sampling time interval granularity, etc.) based on the data analysis request, and determine whether the data analysis parameters match the local capability parameters. If the match is successful, it means that the core network intelligent module 1 can meet all conditions, that is, the core network intelligent module 1 has the sampling permission of the network function module and the sampling time interval granularity meets the requirements, and the core network intelligent module can currently work normally. Specifically, when the core network intelligent module 1 determines that the data analysis parameters match the local capability parameters, it can send a data collection request to the network function module corresponding to the analyzed instance identifier, which includes the sampling time interval granularity. The network function module can send a data collection request reply to the core network intelligent module 1, which may contain the data to be analyzed sampled according to the sampling time interval granularity. Subsequently, the core network intelligent module 1 can analyze the received data to be analyzed, obtain the data analysis results, and then send a data analysis response containing the data analysis results to the consumer network function module.

[0102] Please refer to the following: Figure 7 This is a flowchart illustrating the first scenario corresponding to the intelligent query matching mechanism. For example... Figure 7 As shown, the consumer network function module (corresponding to the first network entity) can send a data analysis request to the core network intelligent module 1 (corresponding to the third network entity). The core network intelligent module 1 can obtain the corresponding data analysis parameters based on the data analysis request and determine whether the data analysis parameters match the local capability parameters. If the match is unsuccessful, it indicates that the core network intelligent module 1 does not meet certain conditions, such as the core network intelligent module 1 not having the sampling permission for the network function module, the sampling time interval granularity not meeting the requirements, or the core network intelligent module 1 being currently busy. Therefore, it can seek cooperation from other intelligent modules. Specifically, the core network intelligent module 1 can query its internally stored list of intelligent modules to select a suitable module and seek cooperation from it. The core network intelligent module 1 can prioritize matching with other intelligent modules within the core network. If a match is successful, it can seek cooperation from other intelligent modules within the core network (corresponding to...). Figure 7 If steps 4a to 8a in the above steps fail to match, then a non-core network intelligent module can be matched, and cooperation can be sought from it (corresponding to...). Figure 7 Steps 4b to 8b in the document.

[0103] Specifically, such as Figure 7As shown in steps 4a to 8a, the core network intelligent module 1 can send an analysis collaboration request to the selected core network intelligent module 2 (corresponding to the second network entity within the core network), which includes information such as the type and instance identifier of the network functional module and the sampling time interval granularity. The core network intelligent module 2 can send a data collection request to the network functional module corresponding to the instance identifier, which includes the sampling time interval granularity. The network functional module can send a data collection request reply to the core network intelligent module 2, which may contain the data to be analyzed sampled according to the sampling time interval granularity. Subsequently, the core network intelligent module 2 can analyze the received data to obtain data analysis results, and then send a collaborative analysis request reply to the core network intelligent module 1, which includes the data analysis results.

[0104] Specifically, such as Figure 7 As shown in steps 4b to 8b, the core network intelligent module 1 can send an analysis collaboration request to the selected non-core network intelligent module (corresponding to the second network entity of the non-core network). Upon receiving the analysis collaboration request, the non-core network intelligent module can acquire and analyze the data to be analyzed, and return the analysis results to the core network intelligent module 1. The method by which the non-core network intelligent module acquires and analyzes the data to be analyzed is similar to the processing method of the core network intelligent module 2 in steps 4a to 8a, and will not be repeated here.

[0105] Among them, such as Figure 7 As shown in step 9, after receiving the data analysis results from the core network intelligent module 2 or the non-core network intelligent module, the core network intelligent module 1 can send a data analysis response containing the data analysis results to the consumer network function module.

[0106] The process of establishing collaborative relationships between intelligent modules in a communication network can be described as follows: Figure 8As shown. Exemplarily, the core network intelligent module 1 can send a configuration acquisition request to other intelligent modules (including other intelligent modules within the core network and non-core network intelligent modules). This configuration acquisition request may contain the capability configuration information of the core network intelligent module 1. Other intelligent modules that receive the configuration acquisition request from the core network intelligent module 1 can configure the locally stored list of intelligent modules according to the capability configuration information of the core network intelligent module 1. This configuration may include the types, instance identifiers, supported sampling time interval granularity, and sampling permissions of multiple intelligent modules. Simultaneously, other intelligent modules can also send a configuration acquisition response to the core network intelligent module 1, which may contain the capability configuration information of the other intelligent modules. After receiving the configuration acquisition response from the other intelligent modules, the core network intelligent module 1 can obtain the capability configuration information of the other intelligent modules and use this capability configuration information to configure the locally stored list of intelligent modules.

[0107] This embodiment proposes a collaborative sharing scheme among intelligent modules in a communication network. By enabling these modules to cooperate in completing tasks, and by combining intelligent modules in the core network and non-core network, and based on data processing requirements and the actual data processing and analysis of each module, the intelligent modules in the communication system can assist each other, fully utilize existing services and data, improve the utilization rate of existing intelligent modules, reduce redundant construction, development, and data collection and analysis costs, and alleviate the burden on some intelligent modules. Furthermore, this embodiment employs an intelligent query and matching mechanism based on data security, taking into account data processing requirements and the actual processing capabilities and conditions of each intelligent module (internal or external to the core network). This fully considers the actual situation, makes full use of existing services and data, and performs analysis and processing for different situations, which is beneficial to improving the overall efficiency and security of data analysis and processing.

[0108] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0109] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data processing apparatus embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0110] In one exemplary embodiment, such as Figure 9 As shown, a data processing apparatus is provided, comprising:

[0111] The analysis request receiving module 901 is used to receive data analysis requests from the first network entity;

[0112] The first analysis response module 902 is configured to, when the data analysis parameters corresponding to the data analysis request match the local capability parameters, obtain the data to be analyzed according to the data analysis parameters and analyze the data to be analyzed to obtain the data analysis result; and send a data analysis response containing the data analysis result to the first network entity.

[0113] The collaboration request sending module 903 is used to send an analysis collaboration request containing the data analysis parameters to a second network entity when the data analysis parameters corresponding to the data analysis request do not match the local capability parameters; the analysis collaboration request is used by the second network entity to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis result when the capability parameters match the data analysis parameters.

[0114] The second analysis response module 904 is used to send a data analysis response containing the data analysis result to the first network entity if it receives the data analysis result from the second network entity.

[0115] In an exemplary embodiment, the apparatus further includes: a collaboration response receiving module, configured to receive an analysis collaboration response from the second network entity; and a start request sending module, configured to send an analysis start request to the second network entity when the analysis collaboration response indicates that the capability parameters of the second network entity match the data analysis parameters; the analysis start request instructs the second network entity to obtain data to be analyzed according to the data analysis parameters and to analyze the data to be analyzed.

[0116] In an exemplary embodiment, the number of second network entities is multiple; the startup request sending module is configured to: obtain the matching degree between each second network entity and the data analysis parameters based on the analysis collaboration response of each second network entity; and send the analysis startup request to the second network entity whose capability parameters match the data analysis parameters and whose matching degree is the highest.

[0117] In an exemplary embodiment, the collaboration request sending module 903 is configured to: send the analysis collaboration request to a second network entity within the core network; receive an analysis collaboration response from the second network entity within the core network; and, if the analysis collaboration response indicates that the capability parameters of the second network entity within the core network do not match the data analysis parameters, send the analysis collaboration request to a second network entity outside the core network.

[0118] In an exemplary embodiment, the apparatus further includes: a configuration information acquisition module, configured to acquire capability configuration information of each candidate collaborative entity; and a network entity matching module, configured to determine a second network entity among the candidate collaborative entities based on the matching of the capability configuration information of each candidate collaborative entity and the data analysis parameters.

[0119] In an exemplary embodiment, the configuration information acquisition module is configured to: send a configuration acquisition request to each of the candidate collaborative entities; receive a configuration acquisition response from each of the candidate collaborative entities; and obtain the capability configuration information of each candidate collaborative entity based on the configuration acquisition response; wherein the capability configuration information includes the data collection permissions and data collection frequency of the candidate collaborative entity.

[0120] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in the network device, or stored in software within the memory of the network device, so that the processor can invoke and execute the operations corresponding to each module.

[0121] In one exemplary embodiment, a network device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown, this network device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores local capability parameters, capability configuration information of candidate cooperative entities, and other data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a data processing method.

[0122] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the network device to which the solution of this application is applied. Specific network devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In one embodiment, a network device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0124] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0125] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0128] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0129] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: Receive a data analysis request from the first network entity; If the data analysis parameters corresponding to the data analysis request match the local capability parameters, the data to be analyzed is obtained according to the data analysis parameters and the data to be analyzed is analyzed to obtain the data analysis result; a data analysis response containing the data analysis result is sent to the first network entity. If the data analysis parameters corresponding to the data analysis request do not match the local capability parameters, an analysis collaboration request containing the data analysis parameters is sent to the second network entity; the analysis collaboration request is used by the second network entity, when the capability parameters match the data analysis parameters, to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results; If a data analysis result is received from the second network entity, a data analysis response containing the data analysis result is sent to the first network entity.

2. The method according to claim 1, characterized in that, After sending the analysis collaboration request containing the data analysis parameters to the second network entity, the process includes: Receive analytical collaboration responses from the second network entity; If the analysis collaboration response indicates that the capability parameters of the second network entity match the data analysis parameters, an analysis initiation request is sent to the second network entity; the analysis initiation request is used to instruct the second network entity to obtain the data to be analyzed according to the data analysis parameters and to analyze the data to be analyzed.

3. The method according to claim 2, characterized in that, The number of the second network entities is multiple; the step of sending the analysis start request to the second network entities includes: Based on the analysis and collaboration response of each of the second network entities, the matching degree between each of the second network entities and the data analysis parameters is obtained; The analysis start request is sent to the second network entity whose capability parameters match the data analysis parameters and whose matching degree is the highest.

4. The method according to claim 1, characterized in that, Sending the analysis collaboration request containing the data analysis parameters to the second network entity includes: Send the analysis collaboration request to the second network entity within the core network; Receive analytical collaboration responses from the second network entity within the core network; If the capability parameters of the second network entity within the core network do not match the data analysis parameters as indicated by the analysis collaboration response, the analysis collaboration request is sent to the second network entity outside the core network.

5. The method according to any one of claims 1 to 4, characterized in that, Before sending the analysis collaboration request containing the data analysis parameters to the second network entity, the process includes: Obtain the capability configuration information of each candidate collaborative entity; Based on the matching of the capability configuration information of each candidate collaborative entity and the data analysis parameters, a second network entity is determined among the candidate collaborative entities.

6. The method according to claim 5, characterized in that, The process of obtaining the capability configuration information of each candidate collaborative entity includes: Send configuration retrieval requests to each of the candidate collaborative entities; Receive configuration acquisition responses from each of the candidate collaborative entities; The response is obtained according to the configuration, and the capability configuration information of each candidate collaborative entity is obtained; the capability configuration information includes the data collection permissions and data collection frequency of the candidate collaborative entity.

7. A data processing apparatus, characterized in that, The device includes: An analysis request receiving module is used to receive data analysis requests from the first network entity; The first analysis response module is configured to, when the data analysis parameters corresponding to the data analysis request match the local capability parameters, obtain the data to be analyzed according to the data analysis parameters and analyze the data to be analyzed to obtain the data analysis result; and send a data analysis response containing the data analysis result to the first network entity. The collaboration request sending module is used to send an analysis collaboration request containing the data analysis parameters to a second network entity when the data analysis parameters corresponding to the data analysis request do not match the local capability parameters; the analysis collaboration request is used by the second network entity to obtain the data to be analyzed according to the data analysis parameters, analyze the data to be analyzed, and return the data analysis results when the capability parameters match the data analysis parameters. The second analysis response module is used to send a data analysis response containing the data analysis results to the first network entity if it receives data analysis results from the second network entity.

8. A network device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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