Communication method and device

Abstract

The present application provides a communication method and device. The method comprises: acquiring multi-domain data information, wherein the multi-domain data information comprises 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 a wireless key performance index of a user service; and determining a digital twin on the basis of the multi-domain data information, wherein the digital twin comprises entity objects and attribute information corresponding to the entity objects, the entity objects include a user entity object, a network function entity, a grid entity object and a service entity object, the attribute information corresponding to the entity objects comprises attribute information of the user entity object, the attribute information of the network function entity, the attribute information of the grid entity object and the attribute information of the service entity object, and the digital twin is used for network optimization. By using the present application, the digital twin can be constructed, and the digital twin is used for network optimization, thereby improving the service experience.

Description

Communication method and device

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on November 13, 2023, with application number 202311510488.8 and application name “A Communication Method and Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communication technology, and in particular to a communication method and device. Background Art

[0003] During network optimization, when users experience a poor service experience, we optimize all key performance indicators (KPIs) related to the issue, thereby improving the user experience. For example, if game freezes occur more frequently in a certain cell or network element over a period of time, the network optimization department will report the issue to the network optimization department, which will then optimize all wireless KPIs related to the issue, such as coverage, interference, and capacity, to improve the service experience.

[0004] Summary of the Invention

[0005] This application proposes a communication method and device that can build a digital twin and use the digital twin for network optimization, thereby improving the service experience.

[0006] In a first aspect, an embodiment of the present application provides a communication method, which includes obtaining multi-domain data information, wherein the multi-domain data information includes first data information and second data information, the first data information is data information related to the user service experience, and the second data information is data information related to the wireless key performance indicators of the user service; a digital twin is determined based on the multi-domain data information, the digital twin includes an entity object and attribute information corresponding to the entity object, the entity object includes a user entity object, a network function entity, a grid entity object and a business entity object, the attribute information corresponding to the entity object 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 business entity object, and the digital twin is used for network optimization.

[0007] The method can be applied to a server, including being executed by the server, or by a component in the server (e.g., a processor, chip, or chip system, etc.), or by a logic module or software that can implement all or part of the server functions.

[0008] In the above method, the digital twin is determined by multi-domain data information, and the multi-domain data information is integrated, so that the data for constructing the digital twin is more diversified. Moreover, the digital twin can be used for network optimization, thereby improving the business experience.

[0009] 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, grid identifier, the user identifier, type information of the network function entity, and at least two wireless key performance indicators, where the wireless key performance indicators include reference signal received power RSRP, signal to interference plus noise ratio SINR, and physical resource block PRB utilization.

[0010] 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 portrait information of the grid, and the portrait information of the grid includes the location information of the grid and the point of interest information of the grid; the attributes of the business entity object include the business type information and the business identifier.

[0011] In another possible implementation, the digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

[0012] In yet another possible implementation, the method further includes: mapping the multi-domain data information to determine the grid entity object.

[0013] 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 longitude and latitude; or mapping the multi-domain data information to determine the grid entity object based on cell entry.

[0014] In the above method, by mapping the multi-domain data information to determine the grid entity object in the above two ways, the grid entity object can be analyzed more conveniently.

[0015] In another possible implementation, a main factor affecting the service experience information is determined from the at least two wireless key performance indicators based on the digital twin, and the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

[0016] Optionally, efficient relationship query and complex relationship insight analysis capabilities can be achieved through graph-based multi-hop query and relationship computing capabilities, and the main factors can be determined based on digital twins through frequent subgraph mining algorithms.

[0017] In the above method, through the above manner, the strongly correlated wireless key performance indicators that cause business problems can be determined through the digital twin, and the accuracy rate is very high, for example, it can be as high as 85%, so as to perform precise optimization. For example, the strongly correlated wireless key performance indicators can be optimized to solve business problems and improve business experience. The problem of insufficient correlation between business problems and wireless key performance indicators is solved. Compared with optimizing all wireless key performance indicators, network optimization resources can be efficiently utilized, thereby avoiding resource waste.

[0018] 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 a score; comparing the at least two scores to determine the main factors affecting the service experience information, the main factor being the wireless key performance indicator with the highest score.

[0019] In another possible implementation, the method further includes: determining value indicators of different types of grids based on the digital twin; and determining value scores of different types of grids based on the value indicators and weights corresponding to the value indicators.

[0020] In the above method, the value scores of different types of grids are determined, and the value scores of different types of grids can be sorted. For example, different types of grids include schools, business districts, hospitals, and governments. 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, so network optimization can be performed and network optimization resources can be efficiently utilized.

[0021] In another possible implementation, the multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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.

[0022] In the above method, through the above manner, the multi-domain data information can be made non-single and the data can be made more comprehensive, thereby making the constructed digital twin more accurate.

[0023] In a second aspect, an embodiment of the present application provides a communication device, which may be a server, or a component in a server (for example, a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the server functions, including: an acquisition unit and a first determination unit, the acquisition unit being used to 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 the user service experience, and the second data information being data information related to the wireless key performance indicators of the user service; the first determination unit being used to determine a digital twin based on the multi-domain data information, the digital twin including an entity object and attribute information corresponding to the entity object, the entity object including a user entity object, a network function entity, a grid entity object, and a business entity object, the attribute information corresponding to the entity object including 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 business entity object, and the digital twin is used for network optimization.

[0024] 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, grid identifier, the user identifier, type information of the network function entity, and at least two wireless key performance indicators, where the wireless key performance indicators include reference signal received power RSRP, signal to interference plus noise ratio SINR, and physical resource block PRB utilization.

[0025] 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 portrait information of the grid, and the portrait information of the grid includes the location information of the grid and the point of interest information of the grid; the attributes of the business entity object include the business type information and the business identifier.

[0026] In another possible implementation, the digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

[0027] In yet another possible implementation, the apparatus further includes a mapping unit configured to map the multi-domain data information to determine the grid entity object.

[0028] In another possible implementation, the mapping unit is configured to map the multi-domain data information based on longitude and latitude to determine the grid entity object; or map the multi-domain data information based on cell entry to determine the grid entity object.

[0029] In another possible implementation, the device also includes a second determination unit, which is used to determine the main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, and the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

[0030] In another possible implementation, the second determination 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 a score; the second determination unit is used 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.

[0031] In another possible implementation, the device also includes a third determination unit, which is used to determine the value indicators of different types of grids based on the digital twin; the third determination unit is used to determine the value scores of different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0032] In another possible implementation, the multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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.

[0033] Regarding the technical effects brought about by the second aspect or possible implementation methods, reference may be made to the introduction to the technical effects of the first aspect or corresponding implementation methods.

[0034] In a third aspect, an embodiment of the present application provides a communication device, which may be a server, or a component in a server (for example, a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the server functions. The communication device includes at least one processor and a communication interface, and the at least one processor calls a computer program or instruction stored in a memory to perform the following operations: obtaining 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 the user service experience, and the second data information being data information related to the wireless key performance indicators of the user service; determining a digital twin based on the multi-domain data information, the digital twin including an entity object and attribute information corresponding to the entity object, the entity object including a user entity object, a network function entity, a grid entity object, and a business entity object, the attribute information corresponding to the entity object including 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 business entity object, and the digital twin is used for network optimization.

[0035] 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, grid identifier, the user identifier, type information of the network function entity, and at least two wireless key performance indicators, where the wireless key performance indicators include reference signal received power RSRP, signal to interference plus noise ratio SINR, and physical resource block PRB utilization.

[0036] 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 portrait information of the grid, and the portrait information of the grid includes the location information of the grid and the point of interest information of the grid; the attributes of the business entity object include the business type information and the business identifier.

[0037] In another possible implementation, the digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

[0038] In yet another possible implementation, the processor is further configured to map the multi-domain data information to determine the grid entity object.

[0039] In another possible implementation, the processor is configured to map the multi-domain data information based on longitude and latitude to determine the grid entity object; or map the multi-domain data information based on cell entry to determine the grid entity object.

[0040] In another possible implementation, the processor is further used to determine a main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, and the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

[0041] In another possible implementation, the processor is further 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 a score; and 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.

[0042] In another possible implementation, the processor is further used to determine value indicators of different types of grids based on the digital twin; and determine value scores of different types of grids based on the value indicators and weights corresponding to the value indicators.

[0043] In another possible implementation, the multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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.

[0044] Regarding the technical effects brought about by the third aspect or possible implementation methods, reference may be made to the introduction to the technical effects of the first aspect or corresponding implementation methods.

[0045] In a fourth aspect, an embodiment of the present application provides a chip device, comprising at least one processor, wherein the at least one processor is configured to execute computer programs or instructions to implement the method described in any one of the above aspects.

[0046] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction runs on a processor, the method described in any one of the above aspects is implemented.

[0047] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are run on a computer, the method described in any one of the above aspects is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] FIG1 is a schematic diagram of the architecture of a communication system provided in an embodiment of the present application;

[0049] FIG2 is a schematic diagram of a collaborative processing method for optimizing network problems provided by an embodiment of the present application;

[0050] FIG3 is a schematic diagram of a communication method provided in an embodiment of the present application;

[0051] FIG4 is a schematic diagram of a user entity object provided in an embodiment of the present application;

[0052] FIG5 is a schematic diagram of a network function entity provided in an embodiment of the present application;

[0053] FIG6 is a schematic diagram of a grid entity object provided in an embodiment of the present application;

[0054] FIG7 is a schematic diagram of a business entity object provided in an embodiment of the present application;

[0055] FIG8 is a schematic diagram of a digital twin model provided in an embodiment of the present application;

[0056] FIG9 is a schematic diagram of a digital twin provided in an embodiment of the present application;

[0057] FIG10 is a schematic diagram of a grid entity object and TAZ-level indicators provided in an embodiment of the present application;

[0058] FIG11 is a schematic diagram of determining main factors provided by an embodiment of the present application;

[0059] FIG12 is a schematic diagram of determining a value score provided in an embodiment of the present application;

[0060] FIG13 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0061] FIG14 is a schematic structural diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of this application.

[0063] References to "one embodiment" or "some embodiments" in this application mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in 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 "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0064] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "plurality" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a, b, and c. Among them, a, b, and c can be single or multiple.

[0065] It is understood that in this application, "indication" can include direct indication, indirect indication, explicit indication, and implicit indication. When describing that a certain indication information is used to indicate A, it can be understood that the indication information carries A, directly indicates A, or indirectly indicates A.

[0066] In this application, the information indicated by the indication information is referred to as the information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc., or the information to be indicated can be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved with the help of the arrangement order of each information agreed in advance (such as specified in the protocol), thereby reducing the indication overhead to a certain extent.

[0067] The information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately. The transmission period and / or transmission timing of these sub-information can be the same or different. The specific transmission method is not limited in this application. The transmission period and / or transmission timing of these sub-information can be predefined, for example, according to a protocol, or can be configured by the transmitting device through sending configuration information to the receiving device.

[0068] It can be understood that "sending" and "receiving" in this application indicate the direction of signal transmission. For example, "sending information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receiving information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface.

[0069] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0070] It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0071] The communication method provided in the embodiment of the present application can be applied to cellular communication systems related to the third generation partnership project (3GPP), for example, fourth generation (4G) communication systems, such as long term evolution (LTE) communication systems, and can also be applied to fifth generation (5G) communication systems, such as 5G new radio (NR) communication systems, or to various future communication systems, such as sixth generation (6G) communication systems. The method provided in the embodiment of the present application can also be applied to Bluetooth systems, wireless fidelity (WiFi) systems, LoRa systems or Internet of Vehicles systems, communication systems that support the integration of multiple wireless technologies, and device-to-device (D2D) systems. The method provided in the embodiment of the present application can also be applied to satellite communication systems, wherein the satellite communication system can be integrated with the above-mentioned communication system. The wireless communication systems involved in this application also include but are not limited to: narrowband Internet of Things (NB-IoT) system, 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).

[0072] Please refer to Figure 1, which is a schematic diagram of the architecture of a communication system provided in an embodiment of the present application. The application scenario used in the present application is described by taking the communication system shown in Figure 1 as an example. The communication system can be deployed on a single server or in a server cluster consisting of multiple servers. Among them, 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, and the multi-domain data information includes first data information and second data information, wherein the first data information is data information related to the user service experience, and the second data information is data information related to the wireless key performance indicators of the user service. The CEM system 101 obtains the multi-domain data information. Optionally, the CEM system 101 can obtain the multi-domain data information from the data acquisition system 102, and then determine the digital twin based on the multi-domain data information. The digital twin includes a physical object and the physical object corresponding to the physical object. The attribute information of the entity objects includes 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. The CEM system 101 can also determine the main factors affecting the service experience information from the at least two wireless key performance indicators based on the digital twin. The CEM system 101 can also determine value indicators of different types of grids based on the digital twin, and determine value scores of different types of grids based on the value indicators and the weights corresponding to the value indicators. The network optimization system 103 can obtain the main factors affecting the service experience information from the CEM system 101, thereby performing network optimization on the services corresponding to the main factors. The network optimization system 103 can also obtain the value scores of different types of grids from the CEM system 101 and perform network optimization based on the value scores of different types of grids.

[0073] The communication method provided in the embodiment of the present application is described in detail below in conjunction with the communication system shown in FIG1 .

[0074] In order to better understand the solutions provided by the embodiments of the present application, some terms, concepts or processes involved in the embodiments of the present application are first introduced below.

[0075] During the network optimization process, please refer to Figure 2, which is a schematic diagram of collaborative processing of network problem optimization provided by an embodiment of the present application.

[0076] Step 1: Determine the business model based on business experience.

[0077] For example, the number of times a game detects freezes.

[0078] Step 2: Identify business problems based on the business model.

[0079] For example, the service problem may be that at a certain moment, the number of game freezes in a certain cell / network element increases.

[0080] Step 3: Define the business problem.

[0081] It can be understood that the service problem is a problem with the wireless cell, a transmission problem, or a core network problem.

[0082] Step 4: If the business issue is not related to wireless, send the ticket to the corresponding department for processing.

[0083] For example, if the service issue is a core network issue, the order will be sent to the operator's core network department for processing.

[0084] Step 5: If the service issue is a wireless problem, send the ticket to the network optimization department for processing.

[0085] The dispatch information includes the cell and business problem corresponding to the business problem.

[0086] Step 6: The network optimization department determines whether there are any abnormalities in the wireless key performance indicators (KPIs) of the cell corresponding to the service problem.

[0087] If anomalies are detected, all wireless KPIs for the cell are optimized. These KPIs can include one or more of the following: signal to interference plus noise ratio (SINR), reference signal received 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 detected, an on-site optimization can be dispatched to the district / county.

[0088] 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 cause the service problem. However, this method makes it impossible to optimize the corresponding service. In other words, it is impossible to determine the main factor causing the service problem, that is, the most relevant wireless key performance indicator among all wireless key performance indicators, and then accurately optimize it, resulting in a waste of resources.

[0089] Please refer to FIG3 , which is a schematic diagram of a communication method provided in an embodiment of the present application. The method includes but is not limited to the following steps:

[0090] Step S301: Acquire multi-domain data information.

[0091] Among them, the multi-domain data information includes first data information and second data information, the first data information is data information related to the user's service experience, and the first data information may include one or more of the following: user identification, service identification, service type information, service experience information, and network quality data information, wherein the user identification and service identification are required items, and the service type information, service experience information, and network quality data information are optional items. The service type information can be the category information of the service initiated by the user, for example, it can be video, game, instant messaging (IM), live broadcast, etc., which is not limited in the embodiment of this application. The service experience information can be data information obtained by measuring the key quality indicator (KQI) of the service experience after the user initiates the service, for example, it can be effective download rate, freeze, etc., which is not limited in the embodiment of this application. The network quality data information can be data information obtained by measuring the key performance indicator (KPI) of the network after the user initiates the service, for example, it can be network delay, uplink and downlink packet loss rate, etc., which is not limited in the embodiment of this application.

[0092] The second data information is data information related to wireless key performance indicators (KPIs) of user services. For example, the second data information may include a measurement report (MR), which is raw network data measured by a user terminal. The measurement report carries information related to uplink and downlink radio links and may include received signal channel power (RSCP), interference signal code power (ISCP), bit error rate (BLER), transmit power, etc. For example, the second data information may include a call history record (CHR), which is a log file used by network equipment to record user problems during calls. The second data information may include one or more of the following: a network function entity identifier, a mesh identifier, the user identifier, type information of the network function entity, and at least two KPIs. The network function entity identifier, mesh identifier, and user identifier are mandatory, while the type information of the network function entity and the at least two KPIs are optional. The type information of the network function entity may be a cell, a base station, a core network element, etc. The KPIs may include RSRP, SINR, and PRB utilization. Optionally, the first data information and the second data information are referred to as data information in the operation support system (OSS) domain, and may be referred to as OSS domain data information. Optionally, the first data information and the second data information in the multi-domain data information may be obtained from a data collection system or a network management system. Optionally, the data collection system may be a probe deployed between core network elements for real-time network data collection.

[0093] Optionally, the multi-domain data information may also include one or more of the following: data information related to customer management and services, and third-party data information. Optionally, the data information related to customer management and services includes one or more of the following: call package, whether it is a very, very important person VVIP, average revenue per user (ARPU), complaint information, roaming information, traffic package information, whether it is off-network, whether it is a derogatory user, whether it is in arrears, and whether it is speed-restricted. Among them, ARPU refers to the average communication service revenue contributed by each user in a period of time (usually one month or one year). Optionally, the data information related to customer management and services can be obtained from the operator. Optionally, the data information related to customer management and services can be called data information in the business support system (BSS) domain, which can be referred to as 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 colleges, factories, residential areas, etc., which is not limited in the embodiments of this application. Optionally, this third-party data can be purchased from a third party, for example, POI information can be purchased from a map company. Alternatively, the third-party data can be referred to as S-domain data. This approach allows for more comprehensive data across multiple domains, making the constructed digital twin more accurate.

[0094] Step S302: Determine the digital twin based on multi-domain data information.

[0095] 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 user entity objects, attribute information of network function entities, attribute information of grid entity objects, and attribute information of business entity objects. 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 data information and the second data information in the multi-domain data information, for example, generating a user entity object based on the user identification in the first data information, generating a business entity object based on the business identification and business type information in the first data information, generating a network function entity based on the network function entity identification and type information of the network function entity in the second data information, and determining the grid entity object based on the multi-domain data information. The specific determination method is described below. Optionally, the digital twin can be represented by a graphical model.

[0096] The attribute information of the user entity object includes a user ID and, optionally, user information. Optionally, the user ID can be the user's mobile phone number, and the user information can be the user's age, call package, service preferences, etc. The user information can be obtained from the BSS, or it can be obtained from user data in the OSS using machine learning methods. In one example, see Figure 4, which is a schematic diagram of a user entity object provided in an embodiment of the present application, wherein the attribute information of the user entity object includes: user ID is 13XXX, age is 28, service preference is short video, and call package is 168 package.

[0097] The attribute information of the network function entity includes the type information of the network function entity and the network function entity identifier. The type information of the network function entity can be a cell, a base station, a core network element, etc. Different types of network function entities can use different identifiers as network function entity identifiers. For example, a cell can use a cell identifier as a network function entity identifier. Optionally, the attribute information of the network function entity can also include parameter information of the network function entity, such as the network vendor, the service Internet Protocol address (IP), etc. In an example, please refer to Figure 5, which is a schematic diagram of a network function entity provided in an embodiment of the present application, wherein 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.

[0098] Among them, the attribute information of the grid entity object includes the grid identification and the portrait information of the grid, wherein the portrait information of the grid includes the location information of the grid and the POI information of the grid, wherein the location information of the grid can be understood as the coverage size and location of the grid. The POI information of the grid can be understood as a classification, for example, it can be a college, a factory, a residential area, a business district, etc. In an example, please refer to Figure 6, which is a schematic diagram of a grid entity object provided by an embodiment of the present application, wherein the attribute information of the grid entity object includes: the grid identification is network label 1, the grid POI information is XX school, the location information of the grid includes XX Road XX No., the coverage size of the grid is 20 acres, the business type is mainly game business, delay sensitivity, and the commercial attribute is the number of VVIP users.

[0099] Among them, the attribute information of the business entity object includes business type information and business identification, and may also include business attributes, wherein the business type information may be the category information of the business initiated by the user, for example, it may be video, game, IM, live broadcast, etc., which is not limited in the embodiment of the present application. The business attributes may be the operating manufacturer of the business, the IP of the business, etc. In an example, please refer to Figure 7, which is a schematic diagram of a business entity object provided in an embodiment of the present application, wherein the attribute information of the business entity object includes: the business identification is business mark 1, the operating manufacturer is manufacturer 1, the IP is 10.XX.XX.XX, and the type information of the business is live broadcast.

[0100] The digital twin also includes relationships between entity objects and the time when relationships between entity objects occur. 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 service entity objects, relationships between network function entities and grid entity objects, relationships between network function entities and service entity objects, and relationships between grid entity objects and service entity objects. Optionally, relationships between user objects and network function entities may include network performance or load. For example, if the network function entity is a cell, network performance may refer to latency, bandwidth, jitter, packet loss, etc. for users in that cell. Load may be understood as cell load, such as the maximum number of users in a cell. Relationships between user entity objects and grid entity objects may include user location or user distribution, such as whether a user is in that grid and the distribution of users in that grid. Relationships between user entity objects and service entity objects may include user experience, such as service experience information including lag and effective download rate. The relationship between a network function entity and a grid entity object includes location information, or the number of network function entities serving under the grid. For example, if the network function entity is a cell, the location information can be understood as whether the cell location is within the grid. The number of network function entities serving under the grid can refer to the number of cells served under the grid. The relationship between a network function entity and a business entity object includes a business network model or the distribution of services under the grid. The business network model can be understood as services and networks. Services can be, for example, video services or gaming services. The network can refer to, for example, the network's pipeline transmission indicators, latency rates, etc. In one example, see Figure 8, which is a schematic diagram of a digital twin model including four entity objects and the relationships between them.

[0101] Optionally, when the digital twin is represented by a graph model, the relationship between entity objects can be described by edges between nodes, that is, edges are used to periodically describe the associations between entity objects, for example, XX service occurred at XX time, what is the network quality data information, and what is the wireless key performance indicator. Optionally, each edge can have multiple attributes. Optionally, the attribute information of the edge can include the time when the relationship between entity objects occurs. Optionally, the attribute information of the edge can also include the service experience information, network quality data information in the first data information, and at least two wireless key performance indicators in the second data information. The time when the relationship between entity objects occurs can refer to the moment or time period when the relationship between entity objects occurs. Optionally, the relationship between entity objects can be constructed based on the data information of the OSS domain (at XX time, what service occurred, what is the service experience information, what is the network quality data information, and what is the wireless key performance indicator). Each time a service is initiated, the first data information and the second data information will be generated, but the value of the data collected each time is different. The first data information and the second data information can be associated by user identification and time. For example, a user with a user ID of 13XX initiates a gaming service at time T1, where the gaming service has a service ID of service label 2 and lasts for 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 identifier of entity object A and the identifier of entity object B, A and B have a relationship, which in this example includes the user ID of 13XX and the service ID of service label 2, that is, the user entity object and the service entity object have a relationship.

[0102] In one example, see Figure 9, which is a schematic diagram of a digital twin provided by an embodiment of the present application. This 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 ID 13XXX, age 28, short video hobby, and 168 phone plan. The network function entity's attribute information includes: network function entity type information is cell, network function entity ID uid, and network vendor is operator 1. The grid entity object's attribute information includes: grid ID 1, grid POI information is XX school, grid location information includes XX Road, No. XX, grid coverage area is 20 mu, service type is primarily gaming, latency sensitivity, and commercial attribute is the number of VVIP users. The service entity object's attribute information includes: service ID 1, operator is vendor 1, IP address 10.XX.XXXX, and service type information is live streaming. This digital twin also includes relationships between entity objects, for details, please refer to the description in Figure 9. It should be noted that the description of the relationship between entity objects in FIG9 is merely an example, and the relationship between entity objects, that is, the attributes of the edges, may vary depending on different analysis scenarios.

[0103] In one possible implementation, before determining the twin based on the multi-domain data information, the multi-domain data information can be mapped to determine the mesh entity object. Specifically, there are two mapping methods:

[0104] The first mapping method: multi-domain data information can be mapped in the form of longitude and latitude to determine the grid entity object.

[0105] Specifically, the first data information can be associated with the second data information through the five-tuple information (AMF Region ID, AMF Set ID, AMFPointer, AMF_UE_NGAP_ID) to fill in the longitude and latitude information.<AMF Region ID> Identify the area,<AMF Set ID> Uniquely identifies the set of authentication management functions (AMFs) within an AMF area.<AMF Pointer> Identifies one or more AMFs in the AMF set. The AMF UE NGAP ID is used to identify the UE in the AMF at the N2 reference point. For example, the first data information may include a user identifier, a 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 a network function entity identifier, a grid identifier, the user identifier, network function entity type information, and at least two wireless key performance indicators. The number of users in the 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 rate of the university at a certain time point can be determined. The longitude and latitude information can be used to more accurately map multi-domain data information to determine the grid entity object.

[0106] The second mapping method: mapping multi-domain data information based on the cell-to-grid method to determine the grid entity object.

[0107] Specifically, multi-domain data information can be mapped to determine grid entity objects based on the number of wireless MRs in a cell and the quality of the MRs. For example, base station 1 covers two grid entity objects, Grid 1 and Grid 2. Grid 1's network functional entity identifier is Network Label 1, its POI information is XX School, its location information includes No. 01, XX Road, and its coverage area is 20 mu. Grid 2's network functional entity identifier is Network Label 2, its POI information is XX Shopping Mall, its location information includes No. 02, XX Road, and its coverage area is 3,000 square meters. For example, if user 1's total traffic volume under base station 1 is XX, the number of MRs under Grid 1 and the number of MRs under Grid 2 can be determined using the base station's configuration information. Therefore, based on the number of MRs under Grid 1 and the number of MRs under Grid 2, the traffic proportions of user 1 under Grid 1 and Grid 2 can be determined, ultimately determining user 1's traffic volume under Grid 1 and user 1's traffic volume under Grid 2.

[0108] In general, through the above two mapping methods, after multi-domain data information is mapped to determine the grid entity object, the traffic autonomous zone (TAZ) level indicator can be determined from the grid entity object. Optionally, the TAZ level indicator can be the attribute information of the grid entity object. For example, please refer to Figure 10, which is a schematic diagram of a grid entity object and TAZ-level indicators proposed in an embodiment of the present application. For example, the grid entity object is a central business district (CBD), and the TAZ-level indicators are the number of users who have subscribed to a certain package, the number of speed-limited users, and the number of users in arrears under the CBD; for example, the grid entity object is a business district, and the TAZ-level indicators can be the number of high-ARPU users, high-definition video traffic of high-ARPU users, instant mobile game time of high-ARPU users, the number of speed-limited users, 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 under the business district; for example, if the grid entity object is a university, the TAZ-level indicators can be the number of users who are offline, high-definition video traffic, high-definition video downloads, the number of users with poor gaming experience, and the instant mobile game time of users under the university; for example, if the grid entity object is a government, the TAZ-level indicators can be the number of users who complain; for example, if the grid entity object is a hospital, the TAZ-level indicators can be the number of derogatory users; for example, if the grid entity object is an airport, the TAZ-level indicators can be the number of roaming users. Optionally, high-value services, such as high-definition video traffic and real-time mobile game duration, can be determined based on TAZ-level indicators. Optionally, a grid that includes TAZ-level indicators can be referred to as a TAZ grid. The TAZ grid is an optimized block unit for business-based network planning. It is also the source of user business needs, the basis for calculating network infrastructure resource requirements, and the basis for space-based differentiation strategies. Multi-scale grids can be provided for different business needs (planning, construction, operation and maintenance, optimization, etc.). A differentiated strategy is implemented for each grid. The grid seamlessly covers the planned area, carries diverse information, and includes multiple business forms.

[0109] In one example, the POI information in the third-party data information and the grid information with commercial attributes divided by roads can be used to determine that the grid identifier of the grid entity object is network mark 1, the POI information of the grid is XX school, the location information of the grid includes No. XX, XX Road, and the coverage size of the grid is 20 acres. Then, through the population information in the third-party data and the statistics of the first data information and the second data information, it is determined that the business type of the XX school is mainly gaming business, the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and users' real-time mobile game time.

[0110] In the above method, by mapping the multi-domain data information to determine the grid entity object in the above two ways, the grid entity object can be analyzed more conveniently.

[0111] In yet another possible implementation, after determining the digital twin, the method further includes: determining, based on the digital twin, a main factor affecting the service experience information from at least two wireless key performance indicators.

[0112] Optionally, the main factor is one wireless key performance indicator among at least two wireless key performance indicators.

[0113] Optionally, at least two scores corresponding to at least two wireless key performance indicators can be determined based on the digital twin, and the at least two scores can be compared to determine the main factors affecting the service experience information. Among them, each key performance indicator corresponds to a score, and the main factor affecting the service experience information is the wireless key performance indicator with the highest score. Optionally, determining the main factor affecting the service experience information from at least two wireless key performance indicators based on the digital twin can be understood as determining the root cause relationship between the service experience information in the first data information and at least two wireless key performance indicators in the second data information based on the digital twin, or in other words, determining the service experience information in the first data information and a wireless key performance indicator that is strongly correlated with the service experience information based on the digital twin, or determining the service experience information in the first data information and the main factors, non-main factors, etc. that affect the service experience information based on the digital twin. Optionally, after determining the main factor, the service experience can be improved by optimizing the main factor.

[0114] Optionally, based on the relational computing capabilities of the digital twin, that is, the multi-hop query and relational computing capabilities of the graph, the frequent item mining of the subgraph, that is, the frequent subgraph mining (Frequent Subgraph Mining) algorithm, can be used to determine the main factors affecting the service experience information from at least two wireless key performance indicators. This can be understood as determining the correlation between the service experience information and the wireless key performance indicators. For example, the service experience information is game lag, and the main factor is RSRP, that is, the main factor causing game lag is RSRP.

[0115] In an example, please refer to Figure 11, which is a schematic diagram of determining the main factors proposed in an embodiment of the present application. Assume that the first data information includes a user with a user identifier of 13XX initiating a gaming service at time T1, wherein the service identifier of the gaming service is service identifier 2, the duration is x hours, the service experience information is the video download rate difference, the network quality data information includes the effective downlink rate of XX, the packet loss rate of XX, the second data information includes the user identifier of 13XX, at least two wireless key performance indicators including SRSP of XX, SINR of XX, and PRB utilization of XX. Optionally, the wireless key performance indicator SRSP corresponding to the video download rate difference can be determined based on the digital twin through the frequent item mining algorithm of the subgraph, the score 1, the SINR corresponding to the score 2, and the PRB utilization of the score 3, wherein the score 1 is greater than the score 2, and the score 1 is greater than the score 3. Therefore, SRSP is the main factor affecting the video download rate difference. Optionally, SINR and PRB utilization are non-main factors affecting the video download rate difference.

[0116] In the above method, through the above manner, the strongly correlated wireless key performance indicators that cause business problems can be determined through the digital twin, and the accuracy rate is very high, for example, it can be as high as 85%, so as to perform precise optimization. For example, the strongly correlated wireless key performance indicators can be optimized to solve business problems and improve business experience. The problem of insufficient correlation between business problems and wireless key performance indicators is solved. Compared with optimizing all wireless key performance indicators, network optimization resources can be efficiently utilized, thereby avoiding resource waste.

[0117] In another possible implementation, after determining the digital twin, the method further includes: determining value indicators of different types of grids based on the digital twin, and determining value scores of different types of grids based on the value indicators and weights corresponding to the value indicators.

[0118] Optionally, the value indicator can be the aforementioned TAZ-level indicator. Optionally, different types of grids can be understood as having different POI information. In one example, based on the digital twin, if the grid identifier is determined to be Network Mark 1, the grid POI information is XX School, and the grid location information includes No. 01, XX Road, the value indicators can include the number of users with poor gaming experience, HD video traffic, HD video downloads, and the user's real-time mobile game duration. In other words, the value indicator corresponding to XX School can be determined based on the digital twin. In another example, based on the digital twin, if the grid identifier is determined to be Network Mark 2, the grid POI information is XX Business District, and the grid location information includes No. 02, the value indicators can include the number of high-ARPU users in the business district, the HD video traffic of high-ARPU users, the real-time mobile game duration of high-ARPU users, the number of users with poor voice quality, and the number of users with poor WeChat voice / video quality. In other words, the value indicator 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.

[0119] Optionally, the weight corresponding to the value index can be determined by the entropy weight method. For specific steps, see Figure 12. Figure 12 is a schematic diagram of a method for determining a value score provided by an embodiment of the present application. First, the value index is data standardized, and then the information entropy of the value index is determined. Then, the weight corresponding to the value index is determined, and finally the value score of different types of grids is determined. In one example, the value index corresponding to the XX school determined based on the digital twin can include the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and the user's instant mobile game duration. The value index is data standardized. For example, the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and the user's instant mobile game duration are data standardized, respectively X1, X2, X3, and X4. Then, the weight corresponding to the value index is determined based on the entropy weight method. For example, the number of users with poor gaming experience, high-definition video traffic, high-definition video downloads, and the user's instant mobile game duration are weighted w1, w2, w3, and w4, respectively. Finally, the value score of the XX school is determined to be (w1*X1+w2*X2+w3*X3+w4*X4). The above is only an example of determining the value score of one type of grid, that is, an example of determining the value score of XX school. Of course, the value scores of other types of grids can be determined by referring to the above. For example, the value score of XX business district, the value score of hospital, etc. can also be determined. Accordingly, 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, so as to perform network optimization, such as generating optimization plans and selecting optimization areas.

[0120] In the above method, the value scores of different types of grids are determined, and the value scores of different types of grids can be sorted. For example, different types of grids include schools, business districts, hospitals, and governments. 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, so network optimization can be performed and network optimization resources can be efficiently utilized.

[0121] In the method described in FIG3 , the digital twin is determined by multi-domain data information, and the multi-domain data information is integrated, thereby making the data for constructing the digital twin more diverse. Moreover, the digital twin can be used for network optimization, thereby improving the service experience.

[0122] The above describes in detail the method of the embodiment of the present application, and the following provides an apparatus of the embodiment of the present application.

[0123] Please refer to Figure 13, which is a structural diagram of a communication device 1300 provided in an embodiment of the present application. The communication device 1300 can be a server, or a component in a server (for example, a processor, a chip, or a chip system, etc.), or it can be a logic module or software that can realize all or part of the server functions. The communication device 1300 may include an acquisition unit 1301 and a first determination unit 1302, and each unit is specifically as follows: the acquisition unit 1301 is used to acquire multi-domain data information, and the multi-domain data information includes first data information and second data information, the first data information is data information related to the user service experience, and the second data information is data information related to the wireless key performance indicators of the user service; the first determination unit 1302 is used to determine a digital twin based on the multi-domain data information, the digital twin includes an entity object and attribute information corresponding to the entity object, the entity object includes a user entity object, a network function entity, a grid entity object and a business entity object, the attribute information corresponding to the entity object 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 business entity object, and the digital twin is used for network optimization.

[0124] 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, grid identifier, the user identifier, type information of the network function entity, and at least two wireless key performance indicators, where the wireless key performance indicators include reference signal received power RSRP, signal to interference plus noise ratio SINR, and physical resource block PRB utilization.

[0125] 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 portrait information of the grid, and the portrait information of the grid includes the location information of the grid and the point of interest information of the grid; the attributes of the business entity object include the business type information and the business identifier.

[0126] In another possible implementation, the digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

[0127] In yet another possible implementation, the apparatus further includes a mapping unit configured to map the multi-domain data information to determine the grid entity object.

[0128] In another possible implementation, the mapping unit is configured to map the multi-domain data information based on longitude and latitude to determine the grid entity object; or map the multi-domain data information based on cell entry to determine the grid entity object.

[0129] In another possible implementation, the device also includes a second determination unit, which is used to determine the main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, and the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

[0130] In another possible implementation, the second determination 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 a score; the second determination unit is used 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.

[0131] In another possible implementation, the device also includes a third determination unit, which is used to determine the value indicators of different types of grids based on the digital twin; the third determination unit is used to determine the value scores of different types of grids based on the value indicators and the weights corresponding to the value indicators.

[0132] In another possible implementation, the multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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.

[0133] It should be noted that the implementation and beneficial effects of each module may also correspond to the corresponding description of the method embodiment shown in FIG3 .

[0134] Refer to Figure 14, Figure 14 is a communication device 1400 provided in an embodiment of the present application, and the communication device 1300 can be a server, or a component in a server (for example, a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the server function. The communication device 1400 includes at least one processor 1401 and a communication interface 1403, and optionally, further includes a memory 1402, wherein the processor 1401, the memory 1402 and the communication interface 1403 are interconnected via a bus 1404. The memory 1402 includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or a portable read-only memory (CD-ROM), and the memory 1402 is used for related computer programs and data. The communication interface 1403 is used to receive and send data.

[0135] The processor 1401 may be one or more central processing units (CPUs). When the processor 1401 is a CPU, the CPU may be a single-core CPU or a multi-core CPU.

[0136] The processor 1401 in the communication device 1400 is used to read the computer program stored in the memory 1402 to perform the following operations: obtaining 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 the user service experience, and the second data information being data information related to the wireless key performance indicators of the user service; determining a digital twin based on the multi-domain data information, the digital twin including an entity object and attribute information corresponding to the entity object, the entity object including a user entity object, a network function entity, a grid entity object and a business entity object, the attribute information corresponding to the entity object including 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 business entity object, and the digital twin is used for network optimization.

[0137] 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, grid identifier, the user identifier, type information of the network function entity, and at least two wireless key performance indicators, where the wireless key performance indicators include reference signal received power RSRP, signal to interference plus noise ratio SINR, and physical resource block PRB utilization.

[0138] 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 portrait information of the grid, and the portrait information of the grid includes the location information of the grid and the point of interest information of the grid; the attributes of the business entity object include the business type information and the business identifier.

[0139] In another possible implementation, the digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

[0140] In yet another possible implementation, the processor 1401 is further configured to map the multi-domain data information to determine the grid entity object.

[0141] In another possible implementation, the processor 1401 is configured to map the multi-domain data information based on longitude and latitude to determine the grid entity object; or map the multi-domain data information based on cell entry to determine the grid entity object.

[0142] In another possible implementation, the processor 1401 is further used to determine a main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, and the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

[0143] In another possible implementation, the processor 1401 is further 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 a score; and 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.

[0144] In another possible implementation, the processor 1401 is further configured to determine value indicators of different types of grids based on the digital twin; and determine value scores of different types of grids based on the value indicators and weights corresponding to the value indicators.

[0145] In another possible implementation, the multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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.

[0146] It should be noted that the implementation and beneficial effects of each operation may also correspond to the corresponding description of the method embodiment shown in FIG3 .

[0147] An embodiment of the present application also provides a chip device, which includes at least one processor, and the at least one processor is used to call a computer program or instruction stored in a memory so that the processor executes the method provided in the embodiment shown in Figure 3 above.

[0148] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction runs on a processor, the method provided in the embodiment shown in FIG. 3 is executed.

[0149] An embodiment of the present application further provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are run on a processor, the method provided in the embodiment shown in FIG. 3 is executed.

[0150] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0151] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk, a mobile hard disk, a CD-ROM or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a base station or a terminal. Of course, the processor and the storage medium can also exist in a base station or a terminal as discrete components.

[0152] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented 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 the present application are performed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable device. The computer program or instructions may 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 may 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 may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video disk; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

[0153] In the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0154] In the description of this application, words such as "first", "second", "S301", or "S302" are only used to distinguish the description and facilitate the context. Different sequence numbers themselves do not have specific technical meanings and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying the order of execution of operations. The execution order of each process should be determined by its function and internal logic.

[0155] In this application, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. A and B can be singular or plural. Additionally, the character " / " in this document indicates that the related objects are in an "or" relationship.

[0156] In this application, "transmission" may include the following three situations: sending of data, receiving of data, or sending of data and receiving of data. In this application, "data" may include business data and / or signaling data.

[0157] In this application, the terms "comprise" or "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process / method comprising a series of steps, or a system / product / apparatus comprising a series of units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes / methods / products / apparatus.

[0158] In the description of this application, unless otherwise specified, the number of nouns refers to "singular or plural," 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 include B, or include C, or include A and B, or include A and C, or include B and C, or include A, B, and C. A, B, and C can be single or plural.

Claims

1. A communication method, characterized in that: include: Acquire multi-domain data information, where the multi-domain data information includes first data information and second data information, where 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 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 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, and the digital twin is used for network optimization.

2. The method according to claim 1, characterized in that: 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, grid identifier, the user identifier, type information of the network function entity, 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.

3. The method according to claim 1 or 2, characterized in that: The attribute information of the user entity object includes the user identifier; The attribute information of the network function entity includes 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 grid image information, and the grid image information includes grid location information and grid 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 to 3, characterized in that: The digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

5. The method according to any one of claims 2 to 4, characterized in that: The method further comprises: Based on the digital twin, a main factor affecting the service experience information is determined from the at least two wireless key performance indicators, where the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

6. The method according to claim 5, characterized in that The determining, based on the digital twin, the main factors affecting the user service experience information from the at least two wireless key performance indicators includes: 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 at least two scores are compared to determine the main factor affecting the service experience information, where the main factor is the wireless key performance indicator with the highest score.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: determining value indicators of different types of grids based on the digital twin; The value scores of different types of grids are determined based on the value indicators and weights corresponding to the value indicators.

8. The method according to any one of claims 1 to 7, characterized in that: The multi-domain data information also includes one or more of the following: data information related to customer management and services, and third-party data information; the data information related to customer management and services includes one or more of the following: user packages, whether the user is a very, very important person (VVIP), and 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: comprising an acquisition unit and a first determination unit, The acquisition unit is used to acquire multi-domain data information, where the multi-domain data information includes first data information and second data information, where 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 is used to determine a digital twin based on the multi-domain data information, the digital twin including an entity object and attribute information corresponding to the entity object, the entity object including a user entity object, a network function entity, a grid entity object and a business entity object, the attribute information corresponding to the entity object 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 business entity object, and the digital twin is used for network optimization.

10. The device according to claim 9, characterized in that 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, grid identifier, the user identifier, type information of the network function entity, 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.

11. The device according to claim 9 or 10, characterized in that The attribute information of the user entity object includes the user identifier; The attribute information of the network function entity includes 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 grid image information, and the grid image information includes grid location information and grid point of interest information; The attributes of the business entity object include business type information and the business identifier.

12. The device according to any one of claims 9 to 11, characterized in that: The digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

13. The device according to any one of claims 10 to 12, characterized in that: The device further comprises a second determining unit, The second determination unit is used to determine a main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, where the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

14. The method according to claim 13, characterized in that The second determination 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 factor affecting the service experience information, where the main factor is the wireless key performance indicator with the highest score.

15. The device according to any one of claims 9 to 14, characterized in that: The device further comprises a third determining unit, The third determination unit is used to determine value indicators of different types of grids based on the digital twin; The third determining unit is used to determine the value scores of different types of grids based on the value indicators and the weights corresponding to the value indicators.

16. The device according to any one of claims 9 to 15, 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 the user is a very, very important person VVIP, the third-party data information includes one or more of the following: population information, point of interest POI information, grid information with commercial attributes divided by roads, interest.

17. A communication system, characterized in that: Including data collection system and customer experience management system, The data collection system is used to collect multi-domain data information, 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 the second data information is data information related to wireless key performance indicators of user services; Sending the multi-domain data information to the customer experience management system; The customer experience management system is used to receive the multi-domain data information, and determine a digital twin based on the multi-domain data information, wherein the digital twin includes an entity object and attribute information corresponding to the entity object, the entity object includes a user entity object, a network function entity, a grid entity object and a business entity object, and the attribute information corresponding to the entity object 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 business entity object, and the digital twin is used for network optimization.

18. The system according to claim 17, characterized in that 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, grid identifier, the user identifier, type information of the network function entity, 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.

19. The system according to claim 17 or 18, characterized in that The attribute information of the user entity object includes the user identifier; The attribute information of the network function entity includes 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 grid image information, and the grid image information includes grid location information and grid point of interest information; The attributes of the business entity object include business type information and the business identifier.

20. The system according to any one of claims 17 to 19, characterized in that: The digital twin also includes the relationship between the entity objects and the time when the relationship between the entity objects occurs. The relationship between the entity objects includes 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.

21. The system according to any one of claims 18 to 20, characterized in that: The customer experience management system is also used to determine a main factor affecting the service experience information from the at least two wireless key performance indicators based on the digital twin, wherein the main factor is one wireless key performance indicator among the at least two wireless key performance indicators.

22. A communication device, characterized in that: The apparatus comprises at least one processor and a communication interface, wherein the at least one processor calls a computer program or instruction stored in a memory to execute the method according to claims 1-8.

23. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction, which, when executed on a processor, implements the method according to any one of claims 1 to 8.

24. A computer program product, characterized in that The computer program product includes a computer program or instructions, and when the computer program or instructions are executed on a computer, the method according to any one of claims 1 to 8 is implemented.

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