User perception optimization method and device, server and storage medium

Abstract

The invention provides a user perception optimization method and device, a server and a storage medium, relates to the field of communication, and can solve the problem that the user perception condition cannot be reflected. The method comprises the following steps: acquiring user perception data and antenna configuration information of a target cell; and adjusting the antenna of the target cell based on the user perception data and the antenna configuration information to optimize the service perception of the user in the target cell. According to the method and the device, the user perception data of the target cell is obtained, so that the perception condition of the user for the network service can be obtained, an operator can know the network service quality of the user, and the antenna of the target cell can be adjusted according to the perception condition of the user for the network service, so that the user experience is improved. The service awareness condition of the user in the target cell can be optimized, and the user experience in the target cell can be improved.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a user perception optimization method, device, server and storage medium. Background Art

[0002] With the rapid construction and operation of the fifth generation mobile communication technology (5G) network, users have increasingly higher requirements for the quality of network services provided by operators.

[0003] Operators usually use indicators such as network key performance indicators (KPI) to evaluate network service quality.

[0004] However, indicators such as KPI usually represent the network operation status and are not completely equivalent to the network service quality. There may be a situation where the network operation is good but the user perception is poor. Summary of the invention

[0005] The present application provides a user perception optimization method, device, server and storage medium, which can reflect user perception and optimize user perception.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a user perception optimization method, the method comprising: obtaining user perception data and antenna configuration information of a target cell, the user perception data being used to indicate the user's perception of network services, and the antenna configuration information being used to indicate the configuration information of the antenna and the base station to which the antenna belongs; based on the user perception data and the antenna configuration information, adjusting the antenna of the target cell to optimize the service perception of users in the target cell.

[0008] In combination with the first aspect, in a possible implementation manner, adjusting the antenna of the target cell based on the user perception data and the antenna configuration information includes:

[0009] Based on the user perception data, a user perception score is determined, and the user perception score is used to indicate the user's perception score for network services; based on the user perception score, a cell perception score is determined, and the cell perception score is used to indicate the proportion of user perception scores in the cell that are greater than a user perception score threshold; based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted.

[0010] In combination with the first aspect above, in a possible implementation manner, after determining the user perception score based on the user perception data, the method further includes:

[0011] Based on the service type and service timestamp of the network service, select the user perception score belonging to the preset service type and in the preset time period from the user perception scores;

[0012] Based on the cell sensing score and antenna configuration information, adjust the antenna of the target cell, including:

[0013] The antenna of the target cell is adjusted based on the user perception score and antenna configuration information of the preset service type and in the preset time period.

[0014] In combination with the first aspect, in a possible implementation manner, adjusting the antenna of the target cell based on the cell perception score and the antenna configuration information includes:

[0015] Based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted through reinforcement learning.

[0016] In combination with the first aspect, in a possible implementation manner, based on the cell sensing score and the antenna configuration information, the antenna of the target cell is adjusted through reinforcement learning, including:

[0017] With the goal of optimizing the cell perception score, the antenna adjustment operation is an action set, the cell perception score is a state set, and the antenna of the target cell is adjusted. The antenna adjustment operation includes adjusting at least one of the antenna azimuth angle, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

[0018] In combination with the first aspect, in a possible implementation manner, before adjusting the antenna of the target cell based on the cell perception score and the antenna configuration information, the method further includes:

[0019] When the cell perception score is less than the cell perception score threshold, the antenna of the target cell is adjusted based on the cell perception score and the antenna configuration information.

[0020] In combination with the above-mentioned first aspect, in a possible implementation method, the user perception data includes at least one of extended detailed record XDR data, performance measurement PM data, measurement report MR data, and minimized drive test MDT data, and the antenna configuration information includes at least one of the base station number, antenna number, base station longitude, base station latitude, antenna azimuth, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

[0021] In a second aspect, the present application provides a user perception optimization device, the device comprising:

[0022] An acquisition unit is used to acquire user perception data and antenna configuration information of a target cell, where the user perception data is used to indicate the user's perception of network services, and the antenna configuration information is used to indicate the configuration information of the antenna and the base station to which the antenna belongs; an optimization unit is used to adjust the antenna of the target cell based on the user perception data and the antenna configuration information to optimize the service perception of users in the target cell.

[0023] In conjunction with the second aspect, in a possible implementation manner, the optimization unit is configured to:

[0024] Based on the user perception data, a user perception score is determined, and the user perception score is used to indicate the user's perception score for network services; based on the user perception score, a cell perception score is determined, and the cell perception score is used to indicate the proportion of user perception scores in the cell that are greater than a user perception score threshold; based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted.

[0025] In combination with the second aspect above, in a possible implementation manner, the device further includes:

[0026] A selection unit, configured to select, from the user perception scores, a user perception score that belongs to a preset service type and is in a preset time period based on the service type and service timestamp of the network service;

[0027] The optimization unit is used to adjust the antenna of the target cell based on the user perception score and antenna configuration information belonging to the preset service type and in the preset time period.

[0028] In conjunction with the second aspect, in a possible implementation manner, the optimization unit is configured to:

[0029] Based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted through reinforcement learning.

[0030] In conjunction with the second aspect, in a possible implementation manner, the optimization unit is configured to:

[0031] With the goal of optimizing the cell perception score, the antenna adjustment operation is an action set, the cell perception score is a state set, and the antenna of the target cell is adjusted. The antenna adjustment operation includes adjusting at least one of the antenna azimuth angle, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

[0032] In combination with the second aspect above, in a possible implementation manner, the device further includes:

[0033] The comparison unit is used to adjust the antenna of the target cell based on the cell perception score and the antenna configuration information when the cell perception score is less than the cell perception score threshold.

[0034] In combination with the above-mentioned second aspect, in a possible implementation method, the user perception data includes at least one of extended detailed record XDR data, performance measurement PM data, measurement report MR data, and minimized drive test MDT data, and the antenna configuration information includes at least one of the base station number, antenna number, base station longitude, base station latitude, antenna azimuth, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

[0035] In a third aspect, the present application provides a server, comprising: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the user perception optimization method described in the first aspect and any possible implementation method of the first aspect.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program or instructions. When the computer program or instructions are run on a server, the server executes the user perception optimization method described in the first aspect and any possible implementation of the first aspect.

[0037] In a fifth aspect, the present application provides a computer program product comprising a computer program or instructions, which, when executed on a server, enables the server to execute the user perception optimization method as described in the first aspect and any possible implementation of the first aspect.

[0038] In a sixth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the user perception optimization method described in the first aspect and any possible implementation method of the first aspect.

[0039] Specifically, the chip provided in the present application also includes a memory for storing computer programs or instructions.

[0040] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor of the device, or may be packaged separately from the processor of the device, which is not limited in this application.

[0041] The descriptions of the second to sixth aspects of the present application can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second to sixth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0042] In this application, the name of the above-mentioned user perception optimization device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0043] These and other aspects of the present application will become more apparent from the following description.

[0044] The above scheme brings at least the following beneficial effects: the user perception optimization method provided in the present application can obtain the user's perception of network services by acquiring the user perception data of the target cell, so that the operator can know the network service quality at the user's place, and can adjust the antenna of the target cell according to the user's perception of the network service, thereby optimizing the service perception of users in the target cell and improving the user experience in the target cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the architecture of a user perception optimization method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the hardware structure of a server provided in an embodiment of the present application;

[0047] Figure 3 A flowchart of a user perception optimization method provided in an embodiment of the present application;

[0048] Figure 4 A schematic diagram of a process for determining a user perception score provided in an embodiment of the present application;

[0049] Figure 5 A flowchart of another user perception optimization method provided in an embodiment of the present application;

[0050] Figure 6 A schematic diagram of adjusting a target cell antenna through reinforcement learning provided in an embodiment of the present application;

[0051] Figure 7 A schematic diagram of the structure of a user perception optimization device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0054] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0055] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0056] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0057] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0058] The following explains the terms involved in the embodiments of the present application to facilitate the reader's understanding.

[0059] (1) Reinforcement learning, as an important branch of the development of artificial intelligence technology, has made great progress in the fields of games, robots, natural language processing recommendations, etc. Reinforcement learning includes five variables: agent, environment, state, action and reward. An agent is an entity that can perform certain actions according to the environment to obtain rewards; the environment refers to the dynamically changeable scene in which the agent is located; the state refers to the scene type in which the environment is located at a certain moment; the action refers to the action strategy taken by the agent; the reward is the feedback that measures the agent's performance of a certain action. Reinforcement learning is an optimization process that learns how to maximize the cumulative reward through interaction with the environment, in which the agent observes the environment, takes actions, and then obtains rewards from the environment. Its purpose is to maximize the cumulative reward, that is, the return. In order to obtain the maximum return, the agent tries to choose the "correct" action from the start time 0 to the end time T to maximize the return. In the embodiment of the present application, the antenna of the target cell is adjusted through reinforcement learning.

[0060] As the 5G wave advances, the people's communication needs are becoming more and more extensive, and the application of network services is becoming more and more complex. As a provider of network services, operators also bear the important social responsibility of building a digital China, promoting information development, and providing high-quality network services. The core purpose of technological progress is to better serve the people and improve their quality of life. Therefore, in the process of network construction and optimization, operators need to give priority to the needs of the people and adhere to the service principle of people-oriented and people-oriented.

[0061] At present, there are more and more types of network services and the wireless environment is becoming more and more complex. However, the traditional evaluation of network service quality is often based on network indicator parameters. For example, operators will use KPI and other indicators to evaluate network service quality. However, KPI and other indicators usually represent the network operation status and are not completely equivalent to network service quality. This leads to a disconnect between actual user perception and network indicator parameters, and the actual problems on the user side are not reflected clearly on the network side. In addition, in the actual application process, there is a lack of standards for the evaluation system of user perception and its corresponding tools. The traditional evaluation method of network service quality can no longer meet the current user perception.

[0062] The user perception optimization method provided in the present application can obtain the user's perception of network services by acquiring the user perception data of the target cell, so that the operator can know the network service quality at the user's place, and can adjust the antenna of the target cell according to the user's perception of the network service, thereby optimizing the service perception of users in the target cell and improving the user experience in the target cell.

[0063] The implementation of the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

[0064] Figure 1 The following is a schematic diagram of the architecture of a user perception optimization method provided in an embodiment of the present application. Figure 1 As shown, the architecture includes: user equipment 101, base station 102 and server 103.

[0065] The user equipment 101 may be a network element device close to the user side, used to monitor the usage of the user terminal.

[0066] In some embodiments, the user equipment 101 may be provided with a communication function, such as a communication function between the user equipment 101 and the base station 102, and a communication function between the user equipment 101 and the server 103. In other embodiments, the user equipment 101 may be provided with data generated by a user terminal, such as voice data, video data, Internet data, etc. The user equipment 101 may process the data generated by the user terminal to obtain user perception data.

[0067] It should be noted that the number of user equipment 101 may be one or more, which is not limited in the embodiment of the present application.

[0068] Base station 102 can be a base station or an evolved base station (eNB or eNodeB) in long term evolution (LTE), long term evolution advanced (LTEA), a base station device in a fifth generation mobile communication technology (5G) network, or a base station in a future communication system, etc. The base station can include various macro base stations, micro base stations, home base stations, wireless remote stations, reconfigurable intelligent surfaces (RIS), wireless fidelity (WIFI) devices and other network side devices, which are not limited to the embodiments of the present disclosure.

[0069] In some embodiments, the base station 102 is used to provide wireless access services for multiple user equipment 101. Specifically, a base station provides a cell (i.e., a service coverage area, also referred to as a service cell). The user equipment 101 entering the cell can communicate with the base station 102 via wireless signals to receive the wireless access services provided by the base station 102.

[0070] It should be noted that the antenna is an important component of the base station 102. The base station 102 sends wireless signals through the antenna to achieve communication with the user equipment 101. At the same time, the antenna can also receive wireless signals and transmit them to the base station 102 for processing.

[0071] The server 103 may be an independent physical server, or a server cluster or distributed file system composed of multiple physical servers, or at least one of cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data or artificial intelligence platforms, etc., which is not limited in the embodiments of the present application. Of course, the server 103 can also include other functions to provide more comprehensive and diversified services.

[0072] In some embodiments, the server 103 has a communication function, for example, the server 103 may send a perception data request to the user equipment 101, and the perception data request is used to obtain user perception data. For another example, the server 103 may send a configuration request to the base station 102, and the configuration request is used to obtain configuration information of the base station 102 and configuration information of the antenna in the base station 102.

[0073] In the embodiment of the present application, the server 103 is used to send a control instruction to the antenna of the base station 102, and the control instruction is used to instruct the antenna of the base station 102 to adjust. The corresponding process may be that the server 103 obtains the user perception data of the user equipment 101 and the antenna configuration information of the base station 102, and the server 103 adjusts the antenna of the base station 102 according to the user perception data of the user equipment 101 and the antenna configuration information of the base station 102.

[0074] The user equipment 101, the base station 102 and the server 103 are connected via a communication link. The communication link may be a wired communication link or a wireless communication link, which is not limited in the present application.

[0075] When implemented in hardware, the server may be provided as follows Figure 2 The hardware structure shown is as follows: Figure 2 As shown in the figure, the basic hardware structure of the server is introduced.

[0076] Figure 2 A schematic diagram of the hardware structure of a server provided in an embodiment of the present application. Figure 2 As shown, the server includes at least one processor 201, a communication line 202, and at least one communication interface 204, and may also include a memory 203. The processor 201, the memory 203 and the communication interface 204 may be connected via the communication line 202.

[0077] The processor 201 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA).

[0078] The communication line 202 is used to transmit information between the above components.

[0079] The communication interface 204 is used to communicate with other devices or communication networks, and can use any transceiver-like device, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0080] The memory 203 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0081] In one possible design, the memory 203 can exist independently of the processor 201, that is, the memory 203 can be a memory outside the processor 201. In this case, the memory 203 can be connected to the processor 201 through the communication line 202 to store execution instructions or application code, and the processor 201 controls the execution to implement the user perception optimization method provided in the following embodiment of the present application. In another possible design, the memory 203 can also be integrated with the processor 201, that is, the memory 203 can be the internal memory of the processor 201. For example, the memory 203 is a high-speed cache that can be used to temporarily store some data and instruction information.

[0082] As a possible implementation, the processor 201 may include one or more CPUs, for example Figure 2 As another possible implementation, the server may include multiple processors, such as Figure 2 As another possible implementation, the server may further include an output device 205 and an input device 206.

[0083] It should be pointed out that the various embodiments of the present application can learn from or refer to each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can all refer to each other without limitation.

[0084] Figure 3 A flowchart of a user perception optimization method provided in an embodiment of the present application, which can be applied to Figure 2 As shown in the server. Figure 3 As shown, the method comprises the following steps:

[0085] S301. The server obtains user perception data and antenna configuration information of a target cell.

[0086] The user perception data is used to indicate the user's perception of network services.

[0087] The user perception data includes at least one of extended detail record (XDR) data, performance measurement (PM) data, measurement report (MR) data, and minimization of drive test (MDT) data.

[0088] XDR data may include user terminal information, signal strength, data transmission rate, data transmission volume and other information, which can reflect the actual service usage behavior of the user terminal; PM data may include indicators such as response time, throughput, and number of concurrent users, which can reflect the stability of the network system under high load conditions; MR data contains information on uplink and downlink signals, which can reflect the network quality; MDT data includes user terminal location information, signal strength, signal quality and other indicators.

[0089] In a possible implementation, the user terminal generates corresponding data in the process of using the network service, for example, the user terminal generates voice data in the voice service, and the user terminal generates video data and Internet data in the video playback service. The user equipment processes the data generated by the user terminal to obtain user perception data.

[0090] The antenna configuration information is used to indicate the configuration information of the antenna and the base station to which the antenna belongs.

[0091] The antenna configuration information includes at least one of a base station number, an antenna number, a base station longitude, a base station latitude, an antenna azimuth, a mechanical downtilt angle, an electronic downtilt angle, an antenna height, and an antenna transmission power.

[0092] In this way, by acquiring user perception data of the target cell, the user's perception of network services can be obtained, thereby providing information reference for optimizing the service perception of users in the target cell, and adding reference information for antenna adjustment through user perception data and antenna configuration information.

[0093] S302: The server adjusts the antenna of the target cell based on the user perception data and the antenna configuration information to optimize the service perception of the users in the target cell.

[0094] In a possible implementation, the implementation process of step S302 may be:

[0095] Step 1: The server determines the user perception score based on the user perception data.

[0096] The user perception score is used to indicate the user's perception score for the network service.

[0097] Take the video playback service as an example. Figure 4As shown, the process of determining the user perception score is introduced. The server obtains parameters that affect the user's video playback perception based on the user perception data. For example, in the actual video playback business, the factors that affect the user's video playback perception include whether the video playback is stuck, the video playback waiting time, etc. The server obtains parameters that affect the user's video playback perception based on XDR data, PM data, MR data, MDT data, etc., such as round-trip time (RTT), transmission control protocol (TCP) link establishment delay, etc. Next, the server processes the parameters that affect the user's video playback perception to obtain a user perception score. For example, the server performs weighted processing on parameters such as RTT and TCP link establishment delay to obtain a user perception score. For another example, the server performs standardization and / or regularization processing on parameters such as RTT and TCP link establishment delay to obtain a user perception score.

[0098] In this way, by determining the user perception score through user perception data, the user's real perception of network services can be obtained, and the user's perception of network services can be intuitively reflected in a digital way, providing reference information for antenna adjustment.

[0099] Step 2: The server determines the cell perception score based on the user perception score.

[0100] The cell perception score is used to indicate the proportion of user perception scores greater than the user perception score threshold in the cell. The user perception score threshold is used to determine the current user's perception of network services. For example, if the user perception score is greater than the user perception score threshold, it means that the current user has a good perception of the network service; if the user perception score is less than or equal to the user perception score threshold, it means that the current user has a poor perception of the network service.

[0101] In a possible implementation, the number of user perception scores greater than a user perception score threshold and the proportion of user perception scores in the cell are used as the cell perception score.

[0102] In this way, the cell perception score is determined through the user perception score, and the cell is used as the statistical dimension to obtain the perception of network services in the target cell, further determine the user perception, and avoid misjudgment of poor perception caused by the user himself, providing a data basis for antenna adjustment.

[0103] Step 3: The server adjusts the antenna of the target cell based on the cell perception score and antenna configuration information.

[0104] In a possible implementation, the server adjusts the antenna of the target cell through reinforcement learning based on the cell sensing score and antenna configuration information.

[0105] In this way, through reinforcement learning, corresponding decisions can be made according to the current state, and the antenna can be adjusted in the current complex network services, thereby improving the user experience.

[0106] The user perception optimization method provided in the present application can obtain the user's perception of network services by acquiring the user perception data of the target cell, so that the operator can know the network service quality at the user's place, and can adjust the antenna of the target cell according to the user's perception of the network service, thereby optimizing the service perception of users in the target cell and improving the user experience in the target cell.

[0107] Figure 5 A flowchart of another user perception optimization method provided in an embodiment of the present application, which can be applied to Figure 2 As shown in the server. Figure 5 As shown, the method comprises the following steps:

[0108] S501. The server obtains user perception data and antenna configuration information of a target cell.

[0109] Exemplarily, the user perception data includes XDR data, PM data, MR data, MDT data, etc. Antenna configuration information includes base station number (BS_ID), antenna number (A_ID), base station longitude (BS_Lng), base station latitude (BS_Lat), antenna azimuth (Azimuth), mechanical downtilt (Down_Tilt_M), electronic downtilt (Down_Tilt_E), antenna height (Height), and antenna transmit power (Power), etc.

[0110] S502: The server determines a user perception score based on the user perception data.

[0111] It should be noted that the server can also determine user information based on user perception data, such as cell identifier (Cell_ID), user identifier (User_ID), terminal type (Device_Type), service type (Service_Type), service timestamp (Timestamp), service usage duration (Duration), traffic consumption (Traffic_Usage) and other information.

[0112] Based on the above user information, the time series of a single user's network business can be constructed, so as to understand the user's behavior path in the time dimension.

[0113] S503: The server selects, from the user perception scores, a user perception score that belongs to a preset service type and is in a preset time period based on the service type and service timestamp of the network service.

[0114] The service timestamp may be the start time of the network service or the end time of the network service.

[0115] Exemplarily, taking the preset service type as video playback service and the preset time period as 8:00-9:00 as an example, the above step S503 is that the server selects the user perception score belonging to the video playback service and in 8:00-9:00 from the user perception score based on the service type and service timestamp of the network service.

[0116] In this way, by setting the preset service type and the preset time period, the user perception score within a specific service and a specific time period can be selected in a targeted manner, which can reduce the complexity of calculation.

[0117] S504: The server adjusts the antenna of the target cell based on the user perception score and antenna configuration information of the preset service type and the preset time period.

[0118] The implementation process of the above step S504 may be:

[0119] Step 1: The server determines the perception score of the selected cell based on the perception scores of users belonging to a preset service type and in a preset time period.

[0120] Step 2: The server adjusts the antenna of the target cell based on the selected cell perception score and antenna configuration information.

[0121] It should be noted that the server can generate a corresponding associative array based on the selected cell perception score and antenna configuration information, which includes the base station number (BS_ID), antenna number (A_ID), base station longitude (BS_Lng), base station latitude (BS_Lat), antenna azimuth (Azimuth), mechanical downtilt (Down_Tilt_M), electronic downtilt (Down_Tilt_E), antenna height (Height), antenna transmit power (Power), cell identifier (Cell_ID), service type (Service_Type) and the selected cell perception score (Cell_Perception_Score).

[0122] In this way, by selecting the service type and time period, the perception score of the cell under the same service type and in the same time granularity can be obtained, that is, the proportion of user perception scores in the cell. Based on the real perception experience of users in the cell, the antenna can be adjusted to improve the user experience.

[0123] The user perception optimization method provided in the present application can obtain the user's perception of network services by acquiring the user perception data of the target cell, so that the operator can know the network service quality at the user's place, and can adjust the antenna of the target cell according to the user's perception of the network service, thereby optimizing the service perception of users in the target cell and improving the user experience in the target cell.

[0124] The technical solution provided by the embodiment of the present application determines the user perception score through user perception data, and can obtain the user's real perception of network services, and intuitively reflect the user's perception of network services in a digital way; through the user perception score, the cell perception score is determined, and the perception of network services in the target cell can be obtained, and the user perception can be further determined, while avoiding the misjudgment of poor perception caused by the user's own reasons, and providing more reference information for antenna adjustment. By setting the preset service type and preset time period, the user perception score within a specific service and a specific time period can be selected in a targeted manner, which can reduce the complexity of calculation.

[0125] In addition, in the process of adjusting the antenna of the target cell, the server can also adjust the antenna of the target cell through reinforcement learning based on the cell perception score and antenna configuration information.

[0126] The intelligent agent trained by reinforcement learning can make corresponding decisions according to the current state. This mechanism is suitable for optimizing communication networks in complex network environments.

[0127] For example, taking the Q-learning algorithm as an example, in learning how to obtain the maximum reward, from time t to time T, the reward can be obtained based on the following formula (1).

[0128] G t =R t+1 +γR t+2 +…+γ T R T (1)

[0129] In the formula, G t represents the return, γ represents the discount factor, and R is the reward.

[0130] Based on the reward and the following formula (2), the action value function is determined to evaluate the long-term reward expectation of the agent under a given state-action.

[0131] qπ(s,a)=Eπ[G t |S t =s,A t=a] (2)

[0132] Where qπ(s, a) represents the action value function, S t represents the current state, s represents a state in the state set, A t represents the current action, s represents an action in the action set, G t Indicates return, E π represents the expected return under strategy π.

[0133] The goal of the agent is to find the optimal strategy, that is, to choose the best action in a given state to maximize the cumulative reward. To achieve this goal, the agent needs to constantly try and adjust the strategy. In this process, the agent can determine the selected action based on the transition probability. The strategy π can be obtained based on the following formula (3).

[0134] π(a|s)=P(A t =a|S t =s) s∈S, a∈A (3)

[0135] Where P represents the transition probability when the action is a and the state is s.

[0136] For different strategies π and π', when s∈S, there exists q π (s, a)<q π '(s, a), that is, π≤π'. In the Q-learning algorithm, if there is always one or more strategies that are better than other strategies, these one or more strategies are called the optimal strategy. The optimal strategy can be obtained based on the following formula (4).

[0137] π * (s) = argmax a∈A q*(s,a) (4)

[0138] In the formula, π * represents the optimal strategy, and q*(s, a) represents the optimal action-value function.

[0139] The optimal action-value function can be expressed as the following formula (5).

[0140] q*(s,a)=max π (q π (s,a)) s∈S,a∈A (5)

[0141] In the formula, max π Represents the maximum action-value function value under strategy π.

[0142] It should be noted that when there is more than one action that maximizes the value of the optimal action value function, an action is randomly selected for execution.

[0143] In the learning process of selecting actions through the Q-learning algorithm to obtain rewards, the environment feeds back to the agent the rewards generated based on the current state and actions. The agent updates its action value function based on the rewards obtained each time, and obtains the maximum reward after multiple learning cycles.

[0144] In general, the Q-learning algorithm actually outputs a Q-value table, which indicates the reward that can be brought by selecting the optimized action a in each state s. The higher the reward, the better the actual effect after selecting the action.

[0145] Specifically, in the process of adjusting the antenna of the target cell through the Q-learning algorithm, the antenna of the target cell is adjusted with the optimization of the cell perception score as the goal, the antenna adjustment operation as the action set, and the cell perception score as the state set.

[0146] In the embodiment of the present application, the state set includes but is not limited to the cell perception score and antenna configuration information. Exemplarily, each state s∈S can be represented as a vector containing the cell perception score and antenna configuration information. For example, the state set includes the base station number (BS_ID), antenna number (A_ID), service type (Service_Type) and cell perception score (Cell_Perception_Score), and the state set is represented by the following formula (6).

[0147] S={Current BS_ID ,Current A_ID ,Current Service_Type ,Current Cell_Perception_Score} (6)

[0148] In the formula, Current BS_ID Indicates the base station number, Current A_ID Indicates the antenna number, Current Service_Type Indicates the current business type, Current Cell_Perception_Score Indicates the current cell perception score.

[0149] The action set is an antenna adjustment operation, that is, a method of adjusting the antenna of the target cell. Selecting an action in the action set indicates that the antenna of the target cell is adjusted.

[0150] In the embodiment of the present application, the antenna adjustment operation includes adjusting at least one of the antenna azimuth, mechanical downtilt, electronic downtilt, antenna height, and antenna transmit power. Taking the action set including the operation of antenna azimuth, mechanical downtilt (Down_Tilt_M), electronic downtilt (Down_Tilt_E), antenna height, and antenna transmit power as an example, the action set is expressed by the following formula (7).

[0151] A={a Azimuth , a Down_Tilt_M , a Down_Tile_E , a Height , a power} (7)

[0152] In the formula, a Azimuth Indicates the operation of adjusting the antenna azimuth angle. Down_Tilt_M Indicates the operation of adjusting the mechanical downtilt angle, a Down_Tile_E Indicates the operation of adjusting the electronic downtilt angle, a Height Indicates the operation of adjusting the antenna height. power Indicates the operation of adjusting the antenna transmission power.

[0153] It should be noted that the antenna adjustment operation includes adjusting the antenna so that the antenna azimuth, mechanical downtilt angle, electronic downtilt angle, antenna height or antenna transmission power increases, decreases or remains unchanged.

[0154] After adjusting the antenna of the target cell once through the Q-learning algorithm, the state set is updated accordingly. The updated state set includes the adjusted cell perception score and the adjusted antenna configuration information. The update of the state set can accurately and real-time reflect the changes in the antenna adjustment process.

[0155] In the process of adjusting the antenna of the target cell through the Q-learning algorithm, the goal is to optimize the cell perception score. Based on this, a reward function R is designed to guide the agent to take an action sequence that can maximize the return through rewards during the learning process of the Q-learning algorithm. In the embodiment of the present application, the calculation of the reward function is expressed by the following formula (8).

[0156]

[0157] In the formula, Current Cell_Perception_Score Indicates the current cell perception score, Past Cell_Perception_Score Indicates the last cell perception score, where n is a positive integer greater than 1, such as 5.

[0158] When the current cell perception score is greater than the previous cell perception score, positive feedback is given to the agent to indicate that the antenna adjustment for the target cell is a positive and effective adjustment this time; when the current cell perception score is less than the previous cell perception score, negative feedback is given to the agent to indicate that the antenna adjustment for the target cell is a negative and erroneous adjustment this time; when the current cell perception score is equal to the previous cell perception score, a smaller negative feedback is given to the agent to indicate that the antenna adjustment for the target cell is an invalid adjustment this time. The design of the reward function can effectively reduce the convergence process of the Q-learning algorithm.

[0159] In the process of adjusting the antenna of the target cell through the Q-learning algorithm, the optimal strategy is continuously learned through continuous interaction with the environment, thereby achieving the maximum reward. In the initial stage of the Q-learning algorithm, the values ​​in the Q table are randomly assigned. When the agent selects the optimized action a and executes it in each state s, a corresponding reward will be generated. At this time, the agent updates the values ​​in the Q table based on the action value function. The update of the action value function can be expressed by the following formula (9).

[0160] q t+1 (s, a) = (1-α t )q t (s,a)+α t (r t +γmax a' (q t (s', a'))) (9)

[0161] In the formula, q t (s, a) represents the reward when the state is s and the action is a at time t, α t ∈[0, 1] represents the learning factor, which is used to control its convergence speed, r t represents the instantaneous reward value, γ represents the discount factor, max a' (q t (s', a')) represents the maximum action-value function value.

[0162] like Figure 6 As shown in the figure, a schematic diagram of adjusting the antenna of the target cell through reinforcement learning is introduced. During the learning process, the agent is given feedback through the reward function, and the antenna of the target cell is adjusted to achieve the goal of optimizing the cell perception score. The adjusted antenna configuration information is applied to the new environment to interact with the agent.

[0163] In this way, through the reinforcement learning algorithm, the intelligent agent can learn autonomously without relying on manually labeled data, can adapt to complex network environments, explore better strategies, maintain stable learning effects and performance, and thus improve user experience.

[0164] It should be noted that, when the cell perception score is less than the cell perception score threshold, the server adjusts the antenna of the target cell based on the cell perception score and the antenna configuration information.

[0165] The cell perception score threshold is used to determine the overall perception of the network service cell. For example, if the cell perception score is greater than the cell perception score threshold, it means that the overall perception of the network service cell is good, that is, the network service quality of the cell is good; if the cell perception score is less than or equal to the cell perception score threshold, it means that the overall perception of the network service cell is poor, that is, the network service quality of the cell is poor.

[0166] In this way, by setting the cell perception score threshold, the target cells with poor network service quality can be accurately screened out, so that the antennas of the target cells can be adjusted in a targeted manner to improve user perception.

[0167] The user perception optimization method provided in the present application can obtain the user's perception of network services by acquiring the user perception data of the target cell, so that the operator can know the network service quality at the user's place, and can adjust the antenna of the target cell according to the user's perception of the network service, thereby optimizing the service perception of users in the target cell and improving the user experience in the target cell.

[0168] The embodiment of the present application can divide the user perception optimization device into functional modules or functional units according to the above method example. For example, each functional module or functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic, which is only a logical function division, and there may be other division methods in actual implementation.

[0169] like Figure 7 FIG. 7 is a schematic diagram of a user perception optimization device provided in an embodiment of the present application. The user perception optimization device 70 includes:

[0170] The acquisition unit 701 is used to acquire user perception data and antenna configuration information of the target cell, where the user perception data is used to indicate the user's perception of the network service, and the antenna configuration information is used to indicate the configuration information of the antenna and the base station to which the antenna belongs.

[0171] The optimization unit 702 is configured to adjust the antenna of the target cell based on the user perception data and the antenna configuration information to optimize the service perception of the users in the target cell.

[0172] In a possible implementation, the optimization unit 702 is configured to:

[0173] Based on the user perception data, a user perception score is determined, and the user perception score is used to indicate the user's perception score for network services; based on the user perception score, a cell perception score is determined, and the cell perception score is used to indicate the proportion of user perception scores in the cell that are greater than a user perception score threshold; based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted.

[0174] In a possible implementation, the device further includes:

[0175] A selection unit, configured to select, from the user perception scores, a user perception score that belongs to a preset service type and is in a preset time period based on the service type and service timestamp of the network service;

[0176] The optimization unit 702 is configured to adjust the antenna of the target cell based on the user perception score and antenna configuration information of the preset service type and the preset time period.

[0177] In a possible implementation, the optimization unit 702 is configured to:

[0178] Based on the cell perception score and antenna configuration information, the antenna of the target cell is adjusted through reinforcement learning.

[0179] In a possible implementation, the optimization unit 702 is configured to:

[0180] With the goal of optimizing the cell perception score, the antenna adjustment operation is an action set, the cell perception score is a state set, and the antenna of the target cell is adjusted. The antenna adjustment operation includes adjusting at least one of the antenna azimuth angle, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

[0181] In a possible implementation, the device further includes:

[0182] The comparison unit is used to adjust the antenna of the target cell based on the cell perception score and the antenna configuration information when the cell perception score is less than the cell perception score threshold.

[0183] In a possible implementation, the user perception data includes at least one of extended detailed record XDR data, performance measurement PM data, measurement report MR data, and minimized drive test MDT data, and the antenna configuration information includes at least one of a base station number, an antenna number, a base station longitude, a base station latitude, an antenna azimuth, a mechanical downtilt angle, an electronic downtilt angle, an antenna height, and an antenna transmit power.

[0184] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0185] An embodiment of the present application provides a computer program product comprising instructions. When the computer program product is run on a server, the server executes the user perception optimization method in the above method embodiment.

[0186] An embodiment of the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a server, the server executes the user perception optimization method in the method flow shown in the above method embodiment.

[0187] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a 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 a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0188] Since the server, computer-readable storage medium, and computer program product in the embodiments of the present application can be applied to the above method, the technical effects that can be obtained can also refer to the above method embodiments, and the embodiments of the present application will not be repeated here.

[0189] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0192] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A user perception optimization method, characterized in that: The method comprises: Acquire user perception data and antenna configuration information of the target cell, wherein the user perception data is used to indicate the user's perception of the network service, and the antenna configuration information is used to indicate the configuration information of the antenna and the base station to which the antenna belongs; Based on the user perception data and the antenna configuration information, the antenna of the target cell is adjusted to optimize the service perception of the users in the target cell.

2. The method according to claim 1, characterized in that The adjusting the antenna of the target cell based on the user perception data and the antenna configuration information includes: Determining a user perception score based on the user perception data, wherein the user perception score is used to indicate a user's perception score for the network service; Determine a cell perception score based on the user perception score, where the cell perception score is used to indicate a proportion of user perception scores in the cell that are greater than a user perception score threshold; The antenna of the target cell is adjusted based on the cell perception score and the antenna configuration information.

3. The method according to claim 2, characterized in that After determining the user perception score based on the user perception data, the method further includes: Based on the service type and service timestamp of the network service, selecting, from the user perception scores, a user perception score belonging to a preset service type and in a preset time period; The adjusting the antenna of the target cell based on the cell perception score and the antenna configuration information includes: The antenna of the target cell is adjusted based on the user perception score of the preset service type and in the preset time period and the antenna configuration information.

4. The method according to claim 2, characterized in that: The adjusting the antenna of the target cell based on the cell perception score and the antenna configuration information includes: Based on the cell perception score and the antenna configuration information, the antenna of the target cell is adjusted through reinforcement learning.

5. The method according to claim 4, characterized in that The adjusting the antenna of the target cell by reinforcement learning based on the cell sensing score and the antenna configuration information includes: With the goal of optimizing the cell perception score, the antenna adjustment operation is an action set, the cell perception score is a state set, and the antenna of the target cell is adjusted. The antenna adjustment operation includes adjusting at least one of the antenna azimuth angle, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

6. The method according to claim 2, characterized in that Before adjusting the antenna of the target cell based on the cell perception score and the antenna configuration information, the method further includes: When the cell perception score is less than a cell perception score threshold, the antenna of the target cell is adjusted based on the cell perception score and the antenna configuration information.

7. The method according to claim 1, characterized in that The user perception data includes at least one of extended detailed record XDR data, performance measurement PM data, measurement report MR data, and minimized drive test MDT data, and the antenna configuration information includes at least one of base station number, antenna number, base station longitude, base station latitude, antenna azimuth, mechanical downtilt angle, electronic downtilt angle, antenna height, and antenna transmit power.

8. A user perception optimization device, characterized in that: The device comprises: an acquisition unit, configured to acquire user perception data and antenna configuration information of a target cell, wherein the user perception data is used to indicate a user's perception of a network service, and the antenna configuration information is used to indicate configuration information of an antenna and a base station to which the antenna belongs; The optimization unit is used to adjust the antenna of the target cell based on the user perception data and the antenna configuration information to optimize the service perception of the users in the target cell.

9. A server, characterized in that: include: A processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the user perception optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or instruction. When the server executes the computer program or instruction, the server executes the user perception optimization method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The computer program product comprises a computer program or instructions, and when the computer program or instructions are executed on a server, the server is enabled to execute the user perception optimization method according to any one of claims 1 to 7.

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