A communication method and apparatus

Abstract

This application provides a communication method and apparatus. The method includes: acquiring multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to wireless key performance indicators of user services. Based on the multi-domain data information, a digital twin is determined. The digital twin includes entity objects and corresponding attribute information. The entity objects include user entity objects, network function entities, grid entity objects, and service entity objects. The attribute information corresponding to the entity objects includes attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the service entity objects. The digital twin is used for network optimization. Using this application, a digital twin can be constructed and used for network optimization, thereby improving the service experience.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method and apparatus. Background Technology

[0002] During network optimization, when users experience poor service, all key performance indicators related to the problem can be optimized to improve the user experience. For example, if the number of times a game lags increases in a certain cell / network element within a certain period, and this service problem is assigned to the network optimization department, the department can improve the service experience by optimizing all key wireless performance indicators related to the problem, such as coverage, interference, and capacity. Summary of the Invention

[0003] This application proposes a communication method and apparatus that can construct a digital twin and use the digital twin for network optimization, thereby improving the service experience.

[0004] In a first aspect, embodiments of this application provide a communication method, the method comprising acquiring multi-domain data information, the multi-domain data information including first data information and second data information, the first data information being data information related to user service experience, and the second data information being data information related to wireless key performance indicators of user services; determining a digital twin based on the multi-domain data information, the digital twin including entity objects and attribute information corresponding to the entity objects, the entity objects including user entity objects, network function entities, grid entity objects, and service entity objects, the attribute information corresponding to the entity objects including attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the service entity objects, the digital twin being used for network optimization.

[0005] This method can be applied to servers, including execution by the server itself, execution by components within the server (e.g., processors, chips, or chip systems), or execution by logic modules or software capable of implementing all or part of the server's functions.

[0006] In the above method, the digital twin is determined by multi-domain data information, which integrates multi-domain data information, thereby making the data for constructing the digital twin more diverse. Moreover, the digital twin can be used for network optimization, thereby improving the business experience.

[0007] In one possible implementation, the first data information includes one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information; the second data information includes one or more of the following: network function entity identifier, mesh identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators, wherein the wireless key performance indicators include reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and physical resource block (PRB) utilization.

[0008] In another possible implementation, the attribute information of the user entity object includes the user identifier; the attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier; the attributes of the grid entity object include the grid identifier and the grid profile information, the grid profile information including the grid location information and the grid point of interest information; the attributes of the service entity object include service type information and the service identifier.

[0009] In another possible implementation, the digital twin also includes the relationships between the entity objects and the time at which the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, the relationship between the network function entity and the business entity object, and the relationship between the grid entity object and the business entity object.

[0010] In another possible implementation, the method further includes: mapping the multi-domain data information to determine the grid entity object.

[0011] In another possible implementation, mapping the multi-domain data information to determine the grid entity object includes: mapping the multi-domain data information to determine the grid entity object based on latitude and longitude; or mapping the multi-domain data information to determine the grid entity object based on cell-to-cell grid integration.

[0012] In the above methods, the grid entity objects are determined by mapping multi-domain data information through the two methods mentioned above, which makes it easier to analyze the grid entity objects.

[0013] In another possible implementation, the main factors affecting the service experience information are determined from the at least two wireless key performance indicators based on the digital twin, wherein the main factor is one of the at least two wireless key performance indicators.

[0014] Optionally, efficient relationship querying and complex relationship insight analysis can be achieved through graph-based multi-hop query and relationship calculation capabilities, and key factors can be identified based on digital twins through frequent subgraph mining algorithms.

[0015] In the above method, the digital twin can be used to identify strongly relevant key wireless performance indicators that cause service problems with a very high accuracy rate, such as up to 85%. This allows for precise optimization, such as optimizing the strongly relevant key wireless performance indicators to solve service problems and improve service experience. This addresses the problem of insufficient correlation between service problems and key wireless performance indicators. Compared to optimizing all key wireless performance indicators, this method enables efficient use of network optimization resources, thereby avoiding resource waste.

[0016] In another possible implementation, determining the main factors affecting user service experience information from the at least two wireless key performance indicators based on the digital twin includes: determining at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; comparing the at least two scores to determine the main factors affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

[0017] In another possible implementation, the method further includes: determining value indicators for different types of grids based on the digital twin; and determining value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0018] In the above method, the value scores of different types of grids are determined and sorted. For example, different grid types include schools, business districts, hospitals, and government. Determining the value scores of different types of grids can be understood as determining the value scores of schools, business districts, hospitals, and governments, and sorting them according to the value scores to determine the value areas. For example, areas with high value scores are high-value areas, thereby optimizing the network and making efficient use of network optimization resources.

[0019] In another possible implementation, the multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether the user is a very important VVIP; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

[0020] The above methods can make multi-domain data information less singular and more comprehensive, thereby making the constructed digital twin more accurate.

[0021] Secondly, embodiments of this application provide a communication device, which can be a server, a component within a server (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the server's functions. The device includes: an acquisition unit and a first determination unit. The acquisition unit is used to acquire multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to wireless key performance indicators of the user service. The first determination unit is used to determine a digital twin based on the multi-domain data information. The digital twin includes entity objects and corresponding attribute information. The entity objects include user entity objects, network function entities, grid entity objects, and service entity objects. The attribute information corresponding to the entity objects includes attribute information of the user entity object, attribute information of the network function entity, attribute information of the grid entity object, and attribute information of the service entity object. The digital twin is used for network optimization.

[0022] In one possible implementation, the first data information includes one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information; the second data information includes one or more of the following: network function entity identifier, mesh identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators, wherein the wireless key performance indicators include reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and physical resource block (PRB) utilization.

[0023] In another possible implementation, the attribute information of the user entity object includes the user identifier; the attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier; the attributes of the grid entity object include the grid identifier and the grid profile information, the grid profile information including the grid location information and the grid point of interest information; the attributes of the service entity object include service type information and the service identifier.

[0024] In another possible implementation, the digital twin also includes the relationships between the entity objects and the time at which the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, the relationship between the network function entity and the business entity object, and the relationship between the grid entity object and the business entity object.

[0025] In another possible implementation, the apparatus further includes a mapping unit for mapping the multi-domain data information to determine the grid entity object.

[0026] In another possible implementation, the mapping unit is used to map the multi-domain data information to determine the grid entity object based on latitude and longitude; or to map the multi-domain data information to determine the grid entity object based on cell-to-cell grid.

[0027] In another possible implementation, the apparatus further includes a second determining unit, configured to determine, based on the digital twin, a major factor affecting the service experience information from the at least two wireless key performance indicators, wherein the major factor is one of the at least two wireless key performance indicators.

[0028] In another possible implementation, the second determining unit is configured to determine at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; the second determining unit is configured to compare the at least two scores to determine the main factor affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

[0029] In another possible implementation, the apparatus further includes a third determining unit, which is configured to determine value indicators for different types of grids based on the digital twin; the third determining unit is configured to determine value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0030] In another possible implementation, the multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether the user is a very important VVIP; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

[0031] For the technical effects of the second aspect or possible implementation, please refer to the introduction of the technical effects of the first aspect or corresponding implementation.

[0032] Thirdly, embodiments of this application provide a communication device, which can be a server, a component within a server (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the server's functions. The communication device includes at least one processor and a communication interface. The at least one processor invokes a computer program or instructions stored in a memory to perform the following operations: acquiring multi-domain data information, including first data information and second data information, wherein the first data information is data information related to user service experience, and the second data information is data information related to key wireless performance indicators of user services; determining a digital twin based on the multi-domain data information, wherein the digital twin includes entity objects and corresponding attribute information, wherein the entity objects include user entity objects, network function entities, grid entity objects, and service entity objects, and the attribute information corresponding to the entity objects includes attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the service entity objects; and the digital twin is used for network optimization.

[0033] In one possible implementation, the first data information includes one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information; the second data information includes one or more of the following: network function entity identifier, mesh identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators, wherein the wireless key performance indicators include reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and physical resource block (PRB) utilization.

[0034] In another possible implementation, the attribute information of the user entity object includes the user identifier; the attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier; the attributes of the grid entity object include the grid identifier and the grid profile information, the grid profile information including the grid location information and the grid point of interest information; the attributes of the service entity object include service type information and the service identifier.

[0035] In another possible implementation, the digital twin also includes the relationships between the entity objects and the time at which the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, the relationship between the network function entity and the business entity object, and the relationship between the grid entity object and the business entity object.

[0036] In another possible implementation, the processor is further configured to map the multi-domain data information to determine the grid entity object.

[0037] In another possible implementation, the processor is configured to map the multi-domain data information to determine the grid entity object based on latitude and longitude; or to map the multi-domain data information to determine the grid entity object based on cell-to-cell grid mapping.

[0038] In another possible implementation, the processor is further configured to determine, based on the digital twin, a major factor affecting the service experience information from the at least two wireless key performance indicators, wherein the major factor is one of the at least two wireless key performance indicators.

[0039] In another possible implementation, the processor is further configured to determine at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; compare the at least two scores to determine the main factor affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

[0040] In another possible implementation, the processor is further configured to determine value indicators for different types of grids based on the digital twin; and to determine value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0041] In another possible implementation, the multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether the user is a very important VVIP; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

[0042] For information on the technical effects of the third aspect or possible implementation, please refer to the description of the technical effects of the first aspect or corresponding implementation.

[0043] Fourthly, embodiments of this application provide a chip device, the chip device including at least one processor, the at least one processor being configured to execute computer programs or instructions to implement the methods described in any of the above aspects.

[0044] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program or instructions that, when executed on a processor, implement the methods described in any of the above aspects.

[0045] Sixthly, embodiments of this application provide a computer program product, which includes a computer program or instructions that, when executed on a computer, implement the methods described in any of the above aspects. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the architecture of a communication system provided in an embodiment of this application;

[0047] Figure 2 This is a schematic diagram of a network problem optimization and collaborative processing method provided in an embodiment of this application;

[0048] Figure 3 This is a schematic diagram of a communication method provided in an embodiment of this application;

[0049] Figure 4 This is a schematic diagram of a user entity object provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of a network functional entity provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of a mesh entity object provided in an embodiment of this application;

[0052] Figure 7 This is a schematic diagram of a business entity object provided in an embodiment of this application;

[0053] Figure 8 This is a schematic diagram of a digital twin model provided in an embodiment of this application;

[0054] Figure 9 This is a schematic diagram of a digital twin provided in an embodiment of this application;

[0055] Figure 10 This is a schematic diagram of a mesh entity object and TAZ-level metrics provided in an embodiment of this application;

[0056] Figure 11 This is a schematic diagram illustrating the determination of key factors according to an embodiment of this application;

[0057] Figure 12 This is a schematic diagram illustrating a method for determining a value score, as provided in an embodiment of this application.

[0058] Figure 13 This is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0059] Figure 14 This is a schematic diagram of the structure of another communication device provided in the embodiments of this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0061] References to "one embodiment" or "some embodiments" as described in this application mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0062] In the description of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c; a and b; a and c; b and c; or a and b and c. Where a, b, and c can be single or multiple.

[0063] It is understood that in this application, "instruction" can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information to indicate A, it can be understood that the instruction information carries A, directly indicates A, or indirectly indicates A.

[0064] In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementations, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index; indirectly instructing the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed; or instructing only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent.

[0065] The information to be instructed can be sent as a whole or divided into multiple sub-information messages, and the sending period and / or timing of these sub-information messages can be the same or different. This application does not limit the specific sending method. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the transmitting device by sending configuration information to the receiving device.

[0066] It is understood that "send" and "receive" in this application refer to the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which can include direct transmission via the air interface or indirect transmission via the air interface from other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which can include direct reception from YY via the air interface or indirect reception from YY via the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0067] In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.

[0068] It is understandable that information may undergo necessary processing, such as encoding and modulation, between the source and destination, but the destination can understand the valid information from the source. Similar statements in this application can be interpreted in a similar way and will not be elaborated further.

[0069] The communication method provided in this application can be applied to cellular communication systems related to the 3rd Generation Partnership Project (3GPP), such as 4th generation (4G) communication systems, including Long Term Evolution (LTE) systems. It can also be applied to 5th generation (5G) communication systems, such as 5G New Radio (NR) systems, or to various future communication systems, such as 6th generation (6G) systems. The method provided in this application can also be applied to Bluetooth systems, Wireless Fidelity (WiFi) systems, LoRa systems, or vehicle-to-everything (V2X) systems, as well as communication systems supporting the integration of multiple wireless technologies, and device-to-device (D2D) systems. The method provided in this application can also be applied to satellite communication systems, wherein the satellite communication system can be integrated with the aforementioned communication systems. The wireless communication systems involved in this application also include, but are not limited to: narrowband Internet of Things (NB-IoT), Global System for Mobile Communications (GSM), Enhanced Data Rate for GSM Evolution (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access 2000 (CDMA2000), or Time Division-Synchronization Code Division Multiple Access (TD-SCDMA).

[0070] Please see Figure 1 , Figure 1 This is a schematic diagram of the architecture of a communication system provided in an embodiment of this application, to Figure 1The application scenario of this application is illustrated using the communication system shown. This communication system can be deployed on a single server or in a server cluster consisting of multiple servers. The communication system may include a customer experience management (CEM) system 101. Optionally, the communication system may also include a data acquisition system 102 and a network optimization system 103. Optionally, the data acquisition system 102 can be used to collect multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to key wireless performance indicators of user services. The CEM system 101 acquires the multi-domain data information. Optionally, the CEM system 101 can acquire the multi-domain data information from the data acquisition system 102, and then determine a digital twin based on the multi-domain data information. The digital twin includes an entity object and its corresponding entity object. The entity object includes user entity objects, network function entities, grid entity objects, and service entity objects. The attribute information corresponding to each entity object includes the attribute information of the user entity object, the network function entity, the grid entity object, and the service entity object. This digital twin is used for network optimization. The CEM system 101 can also determine the main factors affecting the service experience information from at least two wireless key performance indicators based on the digital twin. The CEM system 101 can also determine the value indicators of different types of grids based on the digital twin, and determine the value scores of different types of grids based on the value indicators and their corresponding weights. The network optimization system 103 can obtain the main factors affecting the service experience information from the CEM system 101, thereby optimizing the network for the services corresponding to these main factors. It can also obtain the value scores of different types of grids from the CEM system 101 and perform network optimization based on these value scores.

[0071] The following is combined with Figure 1 The communication system shown herein will be described in detail with reference to the communication method provided in the embodiments of this application.

[0072] To better understand the solutions provided in the embodiments of this application, some terms, concepts or processes involved in the embodiments of this application will be introduced below.

[0073] During network optimization, please refer to Figure 2 , Figure 2 This is a schematic diagram of a network problem optimization and collaborative processing method provided in an embodiment of this application.

[0074] Step 1: Model based on business experience to determine the business model.

[0075] For example, the number of times a game experiences lag.

[0076] Step 2: Identify business problems based on the business model.

[0077] For example, the problem could be that at a certain time, the number of times a game experiences lag increases in a certain cell / network element.

[0078] Step 3: Define the business issues.

[0079] This can be understood as a problem related to the wireless cell, transmission, or core network.

[0080] Step 4: If the business problem is not a wireless problem, then dispatch the order to the corresponding department for processing.

[0081] For example, if the problem is a core network issue, the issue will be assigned to the relevant core network department of the operator for handling.

[0082] Step 5: If the business problem is a wireless problem, then dispatch the order to the network optimization department for handling.

[0083] The dispatch information includes the cell corresponding to the business issue and the business issue itself.

[0084] Step 6: The network optimization department determines whether there are any abnormalities in the key performance indicators (KPIs) of the cell corresponding to the service problem.

[0085] If any anomalies are found, optimize all wireless KPIs for the cell. Wireless KPIs may include one or more of the following: signal-to-interference-plus-noise ratio (SINR), reference signal receiving power (RSRP), and physical resource block (PRB) utilization. The optimized service quality can then be compared to determine if the optimization was successful. If no anomalies are found, a work order can be dispatched to the district / county for on-site optimization.

[0086] When a user's service experience is poor, the goal of improving the user's service experience is achieved by optimizing all wireless key performance indicators that may be related to the service problem. However, this approach results in the optimization effect not being able to accurately optimize the corresponding service, that is, the main factor causing the service problem, which is the most relevant wireless key performance indicator among all wireless key performance indicators, thus leading to a waste of resources.

[0087] Please see Figure 3 , Figure 3This is a schematic diagram of a communication method provided in an embodiment of this application. The method includes, but is not limited to, the following steps:

[0088] Step S301: Obtain multi-domain data information.

[0089] The multi-domain data information includes first data information and second data information. The first data information is data information related to user service experience and may include one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information. User identifier and service identifier are mandatory, while service type information, service experience information, and network quality data information are optional. Service type information can be the category information of the service initiated by the user, such as video, games, instant messaging (IM), live streaming, etc., which is not limited in this embodiment. Service experience information can be data information obtained by measuring key quality indicators (KQI) after the user initiates the service, such as effective download speed, lag, etc., which is not limited in this embodiment. Network quality data information can be data information obtained by measuring key performance indicators (KPI) after the user initiates the service, such as network latency, uplink and downlink packet loss rates, etc., which is not limited in this embodiment.

[0090] The second data information comprises data related to key wireless performance indicators (KPIs) of the user service. For example, this second data information may include a measurement report (MR), which is raw network data measured by the user terminal. The measurement report carries relevant information about the uplink and downlink wireless links, including receive signal channel power (RSCP), interference signal code power (ISCP), block error rate (BLER), and transmit power. Alternatively, the second data information may include a call history record (CHR), which is a log file used by network devices to record problems encountered by users during calls. The second data information may include one or more of the following: network functional entity identifier, mesh identifier, user identifier, network functional entity type information, and at least two key wireless performance indicators. Among these, the network functional entity identifier, mesh identifier, and user identifier are mandatory, while the network functional entity type information and at least two key wireless performance indicators are optional. The network functional entity type information can be cell, base station, core network element, etc. Key wireless performance indicators may include RSRP, SINR, and PRB utilization. Optionally, the first and second data information are referred to as data information in the Operation Support Systems (OSS) domain, or simply OSS domain data information. Optionally, the first and second data information can be obtained from a data acquisition system or network management system. Optionally, the data acquisition system can be a probe deployed among core network elements for real-time network data collection.

[0091] Optionally, the multi-domain data information may also include one or more of the following: customer management and service-related data information, and third-party data information. Optionally, the customer management and service-related data information may include one or more of the following: call plan, whether the user is a VVIP (Very Important Person), average revenue per user (ARPU), complaint information, roaming information, data package information, whether the user has left the network, whether the user is a derogatory user, whether the user is in arrears, and whether the user is subject to speed throttling. Here, ARPU refers to the average revenue contributed by each user to communication services over a period of time (usually one month or one year). Optionally, this customer management and service-related data information can be obtained from the operator. Optionally, this customer management and service-related data information can be referred to as business support system (BSS) domain data information, or simply B-domain data information. Third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads. For example, POI information can be universities, factories, residential areas, etc., which is not limited in this embodiment. Optionally, this third-party data can be purchased from a third party, such as a map company for POI information. Optionally, this third-party data can be simply referred to as S-domain data. By using the above methods, multi-domain data can be made less singular, more comprehensive, and thus the constructed digital twin can be more accurate.

[0092] Step S302: Determine the digital twin based on multi-domain data information.

[0093] The digital twin includes entity objects and their corresponding attribute information. Entity objects include user entity objects, network function entities, grid entity objects, and business entity objects. The attribute information for each entity object includes the attribute information of the user entity object, the network function entity, the grid entity object, and the business entity object. The digital twin is used for network optimization. Determining the digital twin based on multi-domain data information can specifically include: generating entity objects based on the identification information in the first and second data information of the multi-domain data information. For example, generating user entity objects based on the user identifier in the first data information; generating business entity objects based on the business identifier and business type information in the first data information; generating network function entities based on the network function entity identifier and network function entity type information in the second data information; and determining grid entity objects based on multi-domain data information. Specific determination methods are described below. Optionally, the digital twin can be represented using a graphical model.

[0094] The user entity object's attribute information includes a user identifier, and optionally, user information. The user identifier can be the user's mobile phone number, and the user information can be the user's age, phone plan, business preferences, etc. This user information can be obtained from the BSS, or it can be obtained from the OSS user data using machine learning methods. For an example, please refer to... Figure 4 , Figure 4 This is a schematic diagram of a user entity object provided in an embodiment of this application. The attribute information of the user entity object includes: user identifier is 13XXX, age is 28 years old, business hobby is short video, and phone plan is 168 plan.

[0095] The attribute information of a network function entity includes its type information and its identifier. The type information can be cell, base station, core network element, etc. Different types of network function entities can use different identifiers; for example, a cell can use its cell identifier. Optionally, the attribute information may also include parameter information, such as the network vendor and the serving Internet Protocol address (IP). See the example provided. Figure 5 , Figure 5 This is a schematic diagram of a network function entity provided in an embodiment of this application. The attribute information of the network function entity includes: the type information of the network function entity is a cell, the network function entity identifier is uid, and the network vendor is operator 1.

[0096] The attribute information of a grid entity object includes a grid identifier and a grid profile. The grid profile includes the grid's location information and its Point of Interest (POI) information. The location information can be understood as the size and position of the grid's coverage area. The POI information can be understood as a category, such as a university, factory, residential area, or commercial district. For an example, please refer to [link to example]. Figure 6 , Figure 6 This is a schematic diagram of a grid entity object provided in an embodiment of this application. The attribute information of the grid entity object includes: grid identifier is grid identifier 1, grid POI information is XX school, grid location information includes XX road XX number, grid coverage area size is 20 acres, business type is mainly game business, latency sensitivity, and commercial attribute is the number of VVIP users.

[0097] The attribute information of the business entity object includes business type information and business identifier, and may also include business attributes. The business type information can be the category of the user-initiated business, such as video, game, IM, live streaming, etc., but this embodiment does not limit this. Business attributes can be the business operator, the business IP address, etc. In one example, please refer to... Figure 7 , Figure 7 This is a schematic diagram of a business entity object provided in an embodiment of this application. The attribute information of the business entity object includes: business identifier is business identifier 1, operator is operator 1, IP is 10.XX.XX.XX, and business type information is live streaming.

[0098] The digital twin also includes relationships between entity objects and the timing of these relationships. Relationships between entity objects include one or more of the following: relationships between user entity objects and network function entities; relationships between user entity objects and grid entity objects; relationships between user entity objects and business entity objects; relationships between network function entities and grid entity objects; relationships between network function entities and business entity objects; and relationships between grid entity objects and business entity objects. Optionally, the relationship between user objects and network function entities can include network performance or load. For example, if the network function entity is a cell, network performance can refer to latency, bandwidth, jitter, packet loss, etc., for a user in that cell. Load can be understood as cell load, such as the maximum number of users in the cell. The relationship between user entity objects and grid entity objects can include user location or user distribution, such as whether a user is in that grid and the user distribution within that grid. The relationship between user entity objects and business entity objects can include user experience, such as service experience information including buffering, effective download rate, etc. The relationship between network function entities and grid entity objects includes location information, or the number of network function entities serving within that grid. For example, a network function entity might be a cell; the location information could be whether the cell is located within the grid. The number of network function entities serving within that grid could refer to the number of cells serving within that grid. The relationship between network function entities and service entity objects includes the service network model or the service distribution within the grid. The service network model can be understood as services and the network; services could be, for example, video services or gaming services, and the network could refer to network transmission metrics such as latency and speed. In one example, please refer to... Figure 8 , Figure 8 This is a schematic diagram of a digital twin model, which includes four entity objects and the relationships between the four entity objects.

[0099] Optionally, when the digital twin is represented by a graph model, the relationships between entity objects can be described using edges between nodes. That is, edges are used to periodically describe the associations occurring between various entity objects, for example, when a service XX occurred, the network quality data was as follows, and the wireless key performance indicators were as follows. Optionally, each edge can have multiple attributes. Optionally, the attribute information of an edge can include the time when the relationship between entity objects occurs. Optionally, the attribute information of an edge can also include service experience information and network quality data from the first data information, and can also include at least two wireless key performance indicators from the second data information. The time when the relationship between entity objects occurs can refer to the moment or time period during which the relationship occurs. Optionally, the relationships between various entity objects can be constructed based on data information in the OSS domain (e.g., what service occurred at time XX, what the service experience information was, what the network quality data was, and what the wireless key performance indicators were as follows). Each time a service is initiated, the first and second data information are generated, but the values ​​of the data collected each time are different. The first and second data information can be associated using user identifiers and time. For example, a user with user ID 13XX initiates a game service at time T1. The service ID of the game service is service ID 2, the duration is x hours, the service experience information is lag, and the network quality data information includes an effective downlink rate of XX and a packet loss rate of XX. At least two wireless key performance indicators include SRSP of XX, SINR of XX, and PRB utilization of XX. When the collected data includes the identifiers of entity A and entity B, A and B are related. In this example, the user ID is 13XX and the service ID is service ID 2, meaning that the user entity and the service entity are related.

[0100] In one example, see Figure 9 , Figure 9This is a schematic diagram of a digital twin provided in an embodiment of this application. The digital twin includes a user entity object, a network function entity, a grid entity object, and a service entity object. The user entity object's attribute information includes: user identifier 13XXX, age 28, hobby short video, and phone plan 168. The network function entity's attribute information includes: network function entity type information is community, network function entity identifier is uid, and network vendor is operator 1. The grid entity object's attribute information includes: grid identifier is network identifier 1, grid POI information is XX school, grid location information is XX road XX number, grid coverage area is 20 acres, service type is mainly game service, latency sensitivity, and commercial attribute is the number of VVIP users. The service entity object's attribute information includes: service identifier is service identifier 1, operator is vendor 1, IP is 10.XX.XXXX, and service type information is live streaming. The digital twin also includes the relationships between the entity objects; details can be found in [reference needed]. Figure 9 The description in the text. It should be noted that... Figure 9 The description of the relationships between entity objects in this example is merely an illustration. The relationships between entity objects, i.e., the attributes of the edges, can vary depending on the analysis scenario.

[0101] In one possible implementation, before determining the twin based on multi-domain data information, the multi-domain data information can be mapped to determine the mesh entity object. Specifically, there are two mapping methods:

[0102] The first mapping method: Multi-domain data information can be mapped using latitude and longitude to determine grid entity objects.

[0103] Specifically, the first data information can be linked to the second data information through a 5-tuple (AMF Region ID, AMF Set ID, AMFPointer, AMF_UE_NGAP_ID) to fill in the latitude and longitude information.<AMF RegionID> Identification area<AMF Set ID> The AMF set within the unique identifier authentication management function (AMF) area.<AMF Pointer> The AMF (Application Functions) in the AMF set is identified by an AMF UENGAP ID, which is used to identify the UE within the AMF at the N2 reference point. For example, the first data information may include user identifier, service identifier, service type information, service experience information, network quality data information, the user's region, and the user's location information. The second data information may include network function entity identifier, grid identifier, the user identifier, network function entity type information, and at least two key radio performance indicators. The number of users within a grid entity object can be determined using the first and second data information. For example, if the grid entity object is a university, the number of users, high-frequency video traffic, high-frequency video download volume, and download speed at a specific time point can be determined. Furthermore, latitude and longitude information allows for more accurate mapping of multi-domain data information to identify the grid entity object.

[0104] The second mapping method is to map multi-domain data information to determine grid entity objects based on the method of cell-based grid entry.

[0105] Specifically, multi-domain data information can be mapped to determine grid entity objects based on the number and quality of wireless MRs in the cell. For example, base station 1 covers two grid entity objects, namely grid 1 and grid 2. The network function entity identifier of grid 1 is network label 1, the network POI information is XX school, the grid location information includes XX Road 01, and the grid coverage area is 20 acres; the network function entity identifier of grid 2 is network label 2, the network POI information is XX shopping mall, the grid location information includes XX Road 02, and the grid coverage area is 3000 square meters. For example, if user 1's total traffic under base station 1 is XX, the number of MRs under grid 1 and grid 2 can be determined through the base station's configuration information. Therefore, based on the number of MRs under grid 1 and grid 2, the traffic ratio of user 1 under grid 1 and grid 2 can be determined, ultimately determining the traffic of user 1 under grid 1 and the traffic of user 1 under grid 2.

[0106] In summary, using the two mapping methods described above, after mapping multi-domain data information to determine grid entity objects, traffic autonomous zone (TAZ) level indicators can be determined from the grid entity objects. Optionally, these TAZ level indicators can be attribute information of the grid entity objects. For example, please refer to... Figure 10 , Figure 10 This is a schematic diagram illustrating a grid entity object and TAZ-level metrics proposed in an embodiment of this application. For example, if the grid entity object is a central business district (CBD), the TAZ-level metrics could be the number of users subscribed to a specific package, the number of users with speed limits, and the number of users in arrears within that CBD. If the grid entity object is a business district, the TAZ-level metrics could be the number of high-ARPU users, the high-definition video traffic of high-ARPU users, the real-time mobile game time of high-ARPU users, the number of users with speed limits, the number of users in arrears, the number of users with poor voice quality, and the number of users with poor WeChat voice / video quality. If the grid entity object is a university, the TAZ-level metrics could be the number of users offline, the high-definition video traffic, the high-definition video download volume, the number of users with poor game experience, and the users' real-time mobile game time within that university. If the grid entity object is a government, the TAZ-level metrics could be the number of users who file complaints. If the grid entity object is a hospital, the TAZ-level metrics could be the number of users who engage in derogatory practices. If the grid entity object is an airport, the TAZ-level metrics could be the number of roaming users. Optionally, high-value services, such as high-definition video traffic and real-time mobile game time, can be identified based on the TAZ-level metrics. Optionally, grids including TAZ-level metrics can be simply referred to as TAZ grids. TAZ grids are optimized block units for business infrastructure network planning, sources of user service requirements, the basis for calculating network infrastructure resource requirements, and the foundation for spatially differentiated strategies. Multi-scale grids can be provided for different service needs (planning, construction, operation, optimization, etc.). Differentiated strategies are implemented on a grid-by-grid basis. The grid provides seamless coverage within the planned area, carrying diverse information and encompassing various service models.

[0107] In one example, the grid entity object can be identified as grid identifier 1, grid POI information as XX school, grid location information including XX road XX number, and grid coverage area of ​​20 acres by using POI information from third-party data and grid information with commercial attributes divided by roads. Then, by using population information from third-party data and by statistically analyzing the first and second data information, it can be determined that the business type of XX school is mainly game business, the number of users with poor game experience, high-definition video traffic, high-definition video download volume, and users' real-time mobile game time.

[0108] In the above methods, the grid entity objects are determined by mapping multi-domain data information through the two methods mentioned above, which makes it easier to analyze the grid entity objects.

[0109] In another possible implementation, after determining the digital twin, the method further includes: identifying key factors affecting service experience information from at least two wireless key performance indicators based on the digital twin.

[0110] Optionally, the primary factor is one of at least two wireless key performance indicators.

[0111] Optionally, at least two scores corresponding to at least two wireless key performance indicators (KPIs) can be determined based on the digital twin, and the at least two scores can be compared to determine the main factors affecting service experience information. Each KPI corresponds to one score, and the main factor affecting service experience information is the KPI with the highest score. Optionally, determining the main factor affecting service experience information from at least two KPIs based on the digital twin can be understood as determining the root cause relationship between service experience information in the first data information and at least two KPIs in the second data information, or determining a strongly correlated KPI between service experience information in the first data information and the KPI affecting service experience information, or determining the main and non-main factors between service experience information in the first data information and the factors affecting service experience information. Optionally, after determining the main factor, service experience can be improved by optimizing the main factor.

[0112] Optionally, based on the relational computation capabilities of the digital twin, namely the multi-hop query and relational computation capabilities of the graph, the frequent subgraph mining algorithm can be used to determine the main factors affecting service experience information from at least two wireless key performance indicators. In other words, it can be understood as determining the correlation between service experience information and wireless key performance indicators. For example, if the service experience information is game lag, the main factor is RSRP, that is, the main factor causing game lag is RSRP.

[0113] In one example, see Figure 11 , Figure 11This is a schematic diagram illustrating the determination of key factors proposed in this application embodiment. Assume the first data information includes a user with identifier 13XX who initiated a game service at time T1, where the game service identifier is identifier 2, the duration is x hours, and the service experience information includes poor video download speed. Network quality data information includes an effective downlink speed of XX and a packet loss rate of XX. The second data information includes a user with identifier 13XX and at least two wireless key performance indicators, including SRSP of XX, SINR of XX, and PRB utilization of XX. Optionally, the wireless key performance indicators affecting the poor video download speed can be determined based on the digital twin using a frequent term mining algorithm of the subgraph: SRSP with a corresponding score of 1, SINR with a corresponding score of 2, and PRB utilization with a score of 3. Score 1 is greater than score 2, and score 1 is greater than score 3. Therefore, SRSP is the key factor affecting the poor video download speed. Optionally, SINR and PRB utilization are non-key factors affecting the poor video download speed.

[0114] In the above method, the digital twin can be used to identify strongly relevant key wireless performance indicators that cause service problems with a very high accuracy rate, such as up to 85%. This allows for precise optimization, such as optimizing the strongly relevant key wireless performance indicators to solve service problems and improve service experience. This addresses the problem of insufficient correlation between service problems and key wireless performance indicators. Compared to optimizing all key wireless performance indicators, this method enables efficient use of network optimization resources, thereby avoiding resource waste.

[0115] In another possible implementation, after determining the digital twin, the method further includes: determining value indicators for different types of grids based on the digital twin, and determining value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0116] Optionally, the value indicator can be one of the aforementioned TAZ-level indicators. Alternatively, different types of grids can be understood as having different POI information. In one example, based on a digital twin, the grid identifier is identified as grid label 1, the grid POI information is XX school, and the grid location information includes XX Road No. 01. Value indicators can include the number of users with poor game experience quality, high-definition video traffic, high-definition video downloads, and users' real-time mobile game time. That is, the value indicators corresponding to XX school can be determined based on the digital twin. In another example, based on a digital twin, the grid identifier is identified as grid label 2, the grid POI information is XX business district, and the grid location information includes XX Road No. 02. Value indicators can include the number of high ARPU users in this business district, the high-definition video traffic of high ARPU users, the real-time mobile game time of high ARPU users, the number of users with poor voice quality, and the number of users with poor WeChat voice / video quality. That is, the value indicators corresponding to XX business district can be determined based on the digital twin. Alternatively, when the grid is of other types, the corresponding value indicators can include the number of VVIP users, the number of complaining users, etc.

[0117] Optionally, the weights corresponding to the value indicators can be determined using the entropy weight method. For detailed steps, please refer to [link to documentation / reference]. Figure 12 , Figure 12 This is a schematic diagram illustrating a method for determining value scores provided in an embodiment of this application. First, the value indicators are standardized. Then, the information entropy of the value indicators is determined. Next, the weights corresponding to the value indicators are determined, and finally, the value scores for different types of grids are determined. In one example, determining the value indicators for school XX based on a digital twin may include the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and users' real-time mobile game time. The value indicators are standardized; for example, the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and users' real-time mobile game time are standardized as X1, X2, X3, and X4, respectively. Then, the weights corresponding to the value indicators are determined based on the entropy weight method; for example, the weights corresponding to the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and users' real-time mobile game time are w1, w2, w3, and w4, respectively. Finally, the value score for school XX is determined as (w1*X1 + w2*X2 + w3*X3 + w4*X4). The above is merely an example of determining the value score of one type of grid, namely, determining the value score of XX school. Of course, the above description can be used to determine the value scores of other types of grids. For example, the value scores of XX business district, hospital, etc. Correspondingly, after determining the value scores of different types of grids, they can be sorted according to the value scores to determine the value areas. For example, areas with high value scores are high-value areas, thereby performing network optimization, such as generating optimization schemes and selecting optimization areas, etc.

[0118] In the above method, the value scores of different types of grids are determined and sorted. For example, different grid types include schools, business districts, hospitals, and government. Determining the value scores of different types of grids can be understood as determining the value scores of schools, business districts, hospitals, and governments, and sorting them according to the value scores to determine the value areas. For example, areas with high value scores are high-value areas, thereby optimizing the network and making efficient use of network optimization resources.

[0119] exist Figure 3 The described method determines the digital twin by integrating multi-domain data information, thereby making the data used to construct the digital twin more diverse. Furthermore, the digital twin can be used for network optimization, thereby improving the business experience.

[0120] The methods of the embodiments of this application have been described in detail above, and the apparatus of the embodiments of this application is provided below.

[0121] Please see Figure 13 , Figure 13 This is a schematic diagram of the structure of a communication device 1300 provided in an embodiment of this application. The communication device 1300 can be a server, a component of a server (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the server functions. The communication device 1300 may include an acquisition unit 1301 and a first determination unit 1302. The specific details of each unit are as follows: The acquisition unit 1301 is used to acquire multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to wireless key performance indicators of user services. The first determination unit 1302 is used to determine a digital twin based on the multi-domain data information. The digital twin includes entity objects and attribute information corresponding to the entity objects. The entity objects include user entity objects, network function entities, grid entity objects, and service entity objects. The attribute information corresponding to the entity objects includes attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the service entity objects. The digital twin is used for network optimization.

[0122] In one possible implementation, the first data information includes one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information; the second data information includes one or more of the following: network function entity identifier, mesh identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators, wherein the wireless key performance indicators include reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and physical resource block (PRB) utilization.

[0123] In another possible implementation, the attribute information of the user entity object includes the user identifier; the attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier; the attributes of the grid entity object include the grid identifier and the grid profile information, the grid profile information including the grid location information and the grid point of interest information; the attributes of the service entity object include service type information and the service identifier.

[0124] In another possible implementation, the digital twin also includes the relationships between the entity objects and the time at which the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, the relationship between the network function entity and the business entity object, and the relationship between the grid entity object and the business entity object.

[0125] In another possible implementation, the apparatus further includes a mapping unit for mapping the multi-domain data information to determine the grid entity object.

[0126] In another possible implementation, the mapping unit is used to map the multi-domain data information to determine the grid entity object based on latitude and longitude; or to map the multi-domain data information to determine the grid entity object based on cell-to-cell grid.

[0127] In another possible implementation, the apparatus further includes a second determining unit, configured to determine, based on the digital twin, a major factor affecting the service experience information from the at least two wireless key performance indicators, wherein the major factor is one of the at least two wireless key performance indicators.

[0128] In another possible implementation, the second determining unit is configured to determine at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; the second determining unit is configured to compare the at least two scores to determine the main factor affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

[0129] In another possible implementation, the apparatus further includes a third determining unit, which is configured to determine value indicators for different types of grids based on the digital twin; the third determining unit is configured to determine value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0130] In another possible implementation, the multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether the user is a very important VVIP; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

[0131] It should be noted that the implementation and beneficial effects of each module can be found by referring to [the relevant documentation / reference]. Figure 3 The corresponding description of the method embodiments shown.

[0132] Please see Figure 14 , Figure 14 This application provides a communication device 1400, which can be a server, a component within a server (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the server's functions. The communication device 1400 includes at least one processor 1401 and a communication interface 1403. Optionally, it also includes a memory 1402. The processor 1401, memory 1402, and communication interface 1403 are interconnected via a bus 1404. The memory 1402 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM). The memory 1402 is used for related computer programs and data. The communication interface 1403 is used for receiving and sending data.

[0133] Processor 1401 can be one or more central processing units (CPUs). When processor 1401 is a CPU, the CPU can be a single-core CPU or a multi-core CPU.

[0134] The processor 1401 in the communication device 1400 is used to read the computer program stored in the memory 1402 and perform the following operations: acquire multi-domain data information, the multi-domain data information including first data information and second data information, the first data information being data information related to user service experience, and the second data information being data information related to wireless key performance indicators of user services; determine a digital twin based on the multi-domain data information, the digital twin including entity objects and attribute information corresponding to the entity objects, the entity objects including user entity objects, network function entities, grid entity objects, and service entity objects, the attribute information corresponding to the entity objects including attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the service entity objects, the digital twin being used for network optimization.

[0135] In one possible implementation, the first data information includes one or more of the following: user identifier, service identifier, service type information, service experience information, and network quality data information; the second data information includes one or more of the following: network function entity identifier, mesh identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators, wherein the wireless key performance indicators include reference signal received power (RSRP), signal-to-interference-plus-noise ratio (SINR), and physical resource block (PRB) utilization.

[0136] In another possible implementation, the attribute information of the user entity object includes the user identifier; the attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier; the attributes of the grid entity object include the grid identifier and the grid profile information, the grid profile information including the grid location information and the grid point of interest information; the attributes of the service entity object include service type information and the service identifier.

[0137] In another possible implementation, the digital twin also includes the relationships between the entity objects and the time at which the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, the relationship between the network function entity and the business entity object, and the relationship between the grid entity object and the business entity object.

[0138] In another possible implementation, the processor 1401 is further configured to map the multi-domain data information to determine the grid entity object.

[0139] In another possible implementation, the processor 1401 is used to map the multi-domain data information to determine the grid entity object based on latitude and longitude; or to map the multi-domain data information to determine the grid entity object based on cell-to-cell grid.

[0140] In another possible implementation, the processor 1401 is further configured to determine, based on the digital twin, a major factor affecting the service experience information from the at least two wireless key performance indicators, wherein the major factor is one of the at least two wireless key performance indicators.

[0141] In another possible implementation, the processor 1401 is further configured to determine at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; compare the at least two scores to determine the main factor affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

[0142] In another possible implementation, the processor 1401 is further configured to determine value indicators for different types of grids based on the digital twin; and to determine value scores for different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0143] In another possible implementation, the multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether the user is a very important VVIP; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

[0144] It should be noted that the implementation and beneficial effects of each operation can be found by referring to [the relevant documentation / reference]. Figure 3 The corresponding description of the method embodiments shown.

[0145] This application also provides a chip device, which includes at least one processor. The at least one processor is configured to invoke a computer program or instructions stored in a memory, so that the processor performs the above-described... Figure 3 The method provided in the illustrated embodiment.

[0146] This application also provides a computer-readable storage medium storing a computer program or instructions that, when executed on a processor, cause the above-mentioned... Figure 3 The method provided in the illustrated embodiment is performed.

[0147] This application also provides a computer program product, which includes a computer program or instructions that, when executed on a processor, cause the above-mentioned... Figure 3 The method provided in the illustrated embodiment is performed.

[0148] It is understood that the processor in the embodiments of this application may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0149] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a base station or terminal. Of course, the processor and storage medium can also exist as discrete components in the base station or terminal.

[0150] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0151] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0152] In the description of this application, terms such as “first,” “second,” “S301,” or “S302” are used only for the purpose of distinguishing descriptions and for the convenience of context. The different sequence numbers themselves do not have specific technical meanings and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying the order of execution of operations. The order of execution of each process should be determined by its function and internal logic.

[0153] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. A and B can be singular or plural. Additionally, the character " / " in this document indicates that the preceding and following related objects have an "or" relationship.

[0154] In this application, "transmission" can include the following three situations: sending data, receiving data, or both sending and receiving data. In this application, "data" can include business data and / or signaling data.

[0155] The terms “comprising” or “having” and any variations thereof in this application are intended to cover a non-exclusive inclusion, such as a process / method that includes a series of steps, or a system / product / equipment that includes a series of units, not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes / methods / products / equipment.

[0156] In the description of this application, unless otherwise specified, the number of nouns refers to "singular nouns or plural nouns," that is, "one or more." "At least one" means one or more. "Including at least one of the following: A, B, C" means that it may include A, or B, or C, or A and B, or A and C, or B and C, or A, B, and C. A, B, and C may be single or multiple.

Claims

1. A communication method, characterized in that, include: Acquire multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to wireless key performance indicators of user services. A digital twin is determined based on the multi-domain data information. The digital twin includes entity objects and corresponding attribute information of the entity objects. The entity objects include user entity objects, network function entities, grid entity objects, and business entity objects. The attribute information corresponding to the entity objects includes the attribute information of the user entity objects, the attribute information of the network function entities, the attribute information of the grid entity objects, and the attribute information of the business entity objects. The digital twin is used for network optimization.

2. The method according to claim 1, characterized in that, The first data information includes: user identifier and service identifier; the second data information includes: network function entity identifier, grid identifier, and the user identifier.

3. The method according to claim 2, characterized in that, The attribute information of the user entity object includes the user identifier; The attribute information of the network functional entity includes the type information of the network functional entity and the identifier of the network functional entity; The attributes of the grid entity object include the grid identifier and the grid's profile information, which includes the grid's location information and the grid's point of interest information. The attributes of the business entity object include business type information and the business identifier.

4. The method according to any one of claims 1-3, characterized in that, The digital twin also includes the relationships between the entity objects and the time when the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, and the relationship between the grid entity object and the business entity object.

5. The method according to any one of claims 1-3, characterized in that, The first data information also includes service experience information, and the second data information also includes: at least two key wireless performance indicators, namely, Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), and Physical Resource Block (PRB) utilization. The method further includes: Based on the digital twin, the main factors affecting the service experience information are determined from the at least two wireless key performance indicators, wherein the main factor is one of the at least two wireless key performance indicators.

6. The method according to claim 5, characterized in that, The determination of key factors affecting user service experience information from at least two wireless key performance indicators based on the digital twin includes: Based on the digital twin, at least two scores corresponding to the at least two wireless key performance indicators are determined, wherein each wireless key performance indicator corresponds to one score. The at least two scores are compared to determine the main factors affecting the service experience information, and the main factors are the wireless key performance indicators with the highest scores.

7. The method according to any one of claims 1-3, characterized in that, The method further includes: Based on the digital twin, determine the value indicators for different types of grids; The value scores of different types of grids are determined based on the value indicators and the weights corresponding to the value indicators.

8. The method according to any one of claims 1-3, characterized in that, The multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether they are very important VVIPs; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

9. A communication device, characterized in that, Includes an acquisition unit and a first determination unit. The acquisition unit is used to acquire multi-domain data information, which includes first data information and second data information. The first data information is data information related to user service experience, and the second data information is data information related to wireless key performance indicators of user services. The first determining unit is used to determine a digital twin based on the multi-domain data information. The digital twin includes entity objects and attribute information corresponding to the entity objects. The entity objects include user entity objects, network function entities, grid entity objects, and business entity objects. The attribute information corresponding to the entity objects includes attribute information of the user entity objects, attribute information of the network function entities, attribute information of the grid entity objects, and attribute information of the business entity objects. The digital twin is used for network optimization.

10. The apparatus according to claim 9, characterized in that, The first data information includes: user identifier and service identifier; the second data information includes: network function entity identifier, grid identifier, and the user identifier.

11. The apparatus according to claim 10, characterized in that, The attribute information of the user entity object includes the user identifier; The attribute information of the network functional entity includes the type information of the network functional entity and the identifier of the network functional entity; The attributes of the grid entity object include the grid identifier and the grid's profile information, which includes the grid's location information and the grid's point of interest information. The attributes of the business entity object include business type information and the business identifier.

12. The apparatus according to any one of claims 9-11, characterized in that, The digital twin also includes the relationships between the entity objects and the time when the relationships between the entity objects occur. The relationships between the entity objects include one or more of the following: the relationship between the user entity object and the network function entity, the relationship between the user entity object and the grid entity object, the relationship between the user entity object and the business entity object, the relationship between the network function entity and the grid entity object, and the relationship between the grid entity object and the business entity object.

13. The apparatus according to any one of claims 9-11, characterized in that, The first data information also includes service experience information, and the second data information also includes at least two key wireless performance indicators, namely, Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), and Physical Resource Block (PRB) utilization. The device also includes a second determining unit. The second determining unit is used to determine, based on the digital twin, the main factors affecting the service experience information from the at least two wireless key performance indicators, wherein the main factor is one of the at least two wireless key performance indicators.

14. The apparatus according to claim 13, characterized in that, The second determining unit is used to determine at least two scores corresponding to the at least two wireless key performance indicators based on the digital twin, wherein each wireless key performance indicator corresponds to one score; The second determining unit is used to compare the at least two scores to determine the main factors affecting the service experience information, wherein the main factor is the wireless key performance indicator with the highest score.

15. The apparatus according to any one of claims 9-11, characterized in that, The device also includes a third determining unit. The third determining unit is used to determine the value indicators of different types of grids based on the digital twin; The third determining unit is used to determine the value score of different types of grids based on the value index and the weight corresponding to the value index.

16. The apparatus according to any one of claims 9-11, characterized in that, The multi-domain data information also includes one or more of the following: customer management and service related data information, third-party data information; the customer management and service related data information includes one or more of the following: user packages, whether they are very important VVIPs; the third-party data information includes one or more of the following: population information, point of interest (POI) information, and grid information with commercial attributes divided by roads.

17. A communication device, characterized in that, The apparatus includes at least one processor and a communication interface, wherein the at least one processor invokes a computer program or instructions stored in a memory to perform the method as described in any one of claims 1-8.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed on a processor, implement the method as described in any one of claims 1-8.

19. A computer program product, characterized in that, The computer program product includes a computer program or instructions that, when run on a computer, implement the method as described in any one of claims 1-8.

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