Control method for base station energy saving, storage medium, and electronic apparatus

Abstract

Embodiments of the present disclosure provide a control method for base station energy saving, a storage medium, and an electronic apparatus. Model training is performed on the basis of historical user behavior data to obtain a user behavior prediction model, the user behavior prediction model having time characteristics; user behavior data at a current moment is inputted into the user behavior prediction model to obtain a predicted behavior result at a next moment; and a base station energy-saving policy is adjusted on the basis of the predicted behavior result at the next moment.

Description

Base station energy-saving control method, storage medium and electronic device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on Chinese patent application CN202311692553.3, filed on December 8, 2023, entitled “Base station energy-saving control method, storage medium and electronic device”, and claims the priority of the patent application. All the contents disclosed therein are incorporated into this application by reference. Technical Field

[0003] The embodiments of the present disclosure relate to the field of communications, and in particular to a base station energy-saving control method, a storage medium, and an electronic device. Background Art

[0004] Dedicated networks exist in wireless communications. These networks don't need to be constantly operational, operating only when a specific user terminal requires service. Furthermore, normal networks typically exhibit a "tidal" phenomenon, resulting in varying demand for network usage at different times. During periods of low demand, base stations can implement energy-saving measures to reduce power consumption.

[0005] In related technologies, energy saving is generally performed based on the load situation of the base station. When the load is low, the base station will prepare to enter the energy-saving state. If there is a short-term abnormal load increase at this time, the base station will recalculate the energy-saving time. However, this method is greatly affected by the load situation, and may also cause the base station to be unable to enter energy saving. It is also impossible to wake up the base station in advance to cope with the sudden arrival of users and business impacts, resulting in a poor user experience.

[0006] In summary, the related technologies still have problems such as poor energy-saving effect of base stations and poor user experience.

[0007] Summary of the Invention

[0008] The embodiments of the present disclosure provide a base station energy-saving control method, a storage medium, and an electronic device to at least solve the problems of poor base station energy-saving effect and poor user experience in related technologies.

[0009] According to one embodiment of the present disclosure, a base station energy-saving control method is provided, comprising: performing model training based on historical user behavior data to obtain a user behavior prediction model, wherein the user behavior prediction model has a time characteristic; inputting user behavior data at a current moment into the user behavior prediction model to obtain a predicted behavior result at the next moment; and adjusting the base station energy-saving strategy based on the predicted behavior result at the next moment.

[0010] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0011] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG1 is a hardware structure block diagram of a computer terminal for a base station energy-saving control method according to an embodiment of the present disclosure;

[0013] FIG2 is a flow chart of a base station energy saving control method according to an embodiment of the present disclosure;

[0014] FIG3 is a structural block diagram of a base station energy-saving control system according to an embodiment of the present disclosure;

[0015] FIG4 is a schematic diagram of a training process of a user behavior model learning module according to an embodiment of the present disclosure;

[0016] FIG5 is a schematic diagram of a base station energy-saving control system workflow according to an embodiment of the present disclosure;

[0017] FIG6 is a schematic diagram of the working principle of a base station energy-saving control system according to an embodiment of the present disclosure;

[0018] FIG7 is a grayscale diagram of high-speed rail access probability at different time periods according to an embodiment of the present disclosure;

[0019] FIG8 is a schematic diagram of the process principle of the energy-saving deactivation strategy according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0020] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0022] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking operation on a computer terminal as an example, FIG1 is a hardware structure block diagram of a computer terminal of a base station energy-saving control method according to an embodiment of the present disclosure. As shown in FIG1 , the computer terminal may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It will be understood by those skilled in the art that the structure shown in FIG1 is for illustration only and does not limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

[0023] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the base station energy saving control method in the embodiment of the present disclosure. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0024] The transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a communications provider of a computer terminal. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0025] In this embodiment, a base station energy-saving control method running on the above-mentioned computer terminal is provided. FIG2 is a flow chart of the base station energy-saving control method according to an embodiment of the present disclosure. As shown in FIG2 , the flow chart includes the following steps:

[0026] Step S202 : Perform model training based on historical user behavior data to obtain a user behavior prediction model. The user behavior prediction model has a time characteristic.

[0027] In an exemplary embodiment, model training is performed based on historical user behavior data to obtain a user behavior prediction model, including: obtaining user behavior data for a preset historical period, and classifying the user behavior data for the preset historical period to obtain classified user behavior data; using the classified user behavior data as training data to perform model training to obtain a user behavior prediction model.

[0028] In the actual implementation process, the user behavior prediction model has a time characteristic. The model is trained based on historical user behavior data to obtain the user behavior prediction model. Based on the user behavior data at the current moment, the user behavior prediction model is used to predict the user behavior data at the next moment, that is, to predict the behavior results.

[0029] In an exemplary embodiment, user behavior data of a preset historical period is classified to obtain classified user behavior data, including: identifying the network type corresponding to the user behavior data of the preset historical period; determining characteristic parameters of different user behaviors within the preset historical period according to the network type; and classifying the user behavior data of the preset historical period based on the characteristic parameters of different user behaviors within the preset historical period to obtain classified user behavior data.

[0030] In actual implementation, the preset historical period is a randomly selected historical time period to obtain historical user behavior data for that time period. The network type may include different application scenarios such as high-speed rail scenarios. The characteristic parameters of different user behaviors include at least user behavior characteristics (such as speed characteristics, etc.) and base station resource usage characteristics.

[0031] In an exemplary embodiment, classified user behavior data is used as training data for model training to obtain a user behavior prediction model, including: adding data labels based on the time sequence and base station resource characteristics corresponding to each behavior in the classified user behavior data to obtain training data; and performing model training based on the training data to obtain a user behavior prediction model.

[0032] In actual implementation, when the data (i.e., user behavior data) reaches a certain amount (the initial amount required is larger), it is weighted according to chronological order, and then data recognition and training are performed. Based on the acquired user behavior statistics, the data is classified and cleaned to remove interfering data. This data is then trained according to pre-set categories to obtain user behavior characteristics and base station resource usage characteristics within a specific time period.

[0033] Step S204: input the user behavior data at the current moment into the user behavior prediction model to obtain the predicted behavior result at the next moment.

[0034] Step S206: adjusting the base station energy-saving strategy based on the predicted behavior result at the next moment.

[0035] In an exemplary embodiment, after adjusting the base station energy-saving strategy based on the predicted behavior result at the next moment, it also includes: determining the accuracy of the predicted behavior result at the next moment based on the strategy implementation result corresponding to the adjusted base station energy-saving strategy; when the accuracy of the predicted behavior result at the next moment is less than a preset threshold, and the number of times the accuracy of the predicted behavior result fails to meet the standard exceeds a preset number, triggering update training of the user behavior prediction model.

[0036] In actual implementation, accuracy is determined by the degree of consistency between energy-saving results and behavior predictions. As mentioned above, updates are only made if a certain number of non-compliances have occurred. In actual implementation, a preset update cycle can also be set, where the user behavior prediction model is updated at regular intervals to ensure the accuracy of the energy-saving strategy.

[0037] In an exemplary embodiment, the energy-saving strategy of the base station is adjusted based on the predicted behavior result at the next moment, including: judging the energy-saving strategy or energy-saving withdrawal strategy of the base station at the next moment based on the predicted behavior result at the next moment, and changing the energy-saving state or energy-saving type of the base station according to the energy-saving strategy or energy-saving withdrawal strategy.

[0038] In an exemplary embodiment, the base station energy-saving strategy is adjusted based on the predicted behavior result at the next moment, including: in the case of receiving an external energy-saving instruction, determining whether the user behavior data at the current moment is consistent with entering energy-saving, and if not, requesting confirmation information from an external device, and entering energy-saving upon receiving the confirmation information, otherwise not entering energy-saving; in the case of entering energy-saving, determining the energy-saving state or energy-saving type of the base station based on the predicted behavior result at the next moment and the user behavior data at the current moment.

[0039] During the actual implementation process, if energy saving is to be implemented, the system first checks whether there is a setting command. If necessary, it then checks whether the real-time data of user behavior meets the energy saving status. If not, it returns an alarm and requires confirmation. Upon receiving the confirmation, it enters the energy saving process, otherwise it does not enter. Then, it checks the predicted data of user behavior and combines it with the real-time data of user behavior to determine which level of energy saving is required.

[0040] In an exemplary embodiment, the base station energy-saving strategy is adjusted based on the predicted behavior result at the next moment, including: pre-regulating the base station to enter the energy-saving state according to the predicted behavior result at the next moment; judging whether the base station should exit energy-saving according to the user behavior data at the current moment, and exiting energy-saving when the user behavior data at the current moment is consistent with the predicted behavior result at the next moment; and not exiting energy-saving and maintaining the energy-saving state when the user behavior data at the current moment is inconsistent with the predicted behavior result at the next moment.

[0041] In actual implementation, the energy-saving exit strategy does not completely exit energy saving in one step: the corresponding energy-saving exit state should be pre-set based on the predicted user behavior data, and then the next user behavior should be judged based on the real-time user behavior data. For example, if the number of users entering and online users is gradually increasing, indicating that users are gradually entering the cell, then the energy-saving state should be exited; otherwise, it can be temporarily maintained in the energy-saving exit state.

[0042] In the actual implementation process, the above-mentioned energy-saving state is to enter the state of pre-energy-saving. When it is determined that energy-saving needs to be withdrawn based on the user's real-time data, the energy-saving action is quickly executed according to the pre-energy-saving state, thereby efficiently completing the energy-saving to better ensure the user experience.

[0043] Through the above steps, a user behavior prediction model is obtained by training the model based on historical user behavior data. The user behavior prediction model has time characteristics. The current user behavior data is input into the user behavior prediction model to obtain the predicted behavior results for the next moment. The base station energy-saving strategy is adjusted based on the predicted behavior results for the next moment. This solves the problem of poor base station energy-saving performance and poor user experience in related technologies, achieving the effect of improving base station energy-saving performance and enhancing user experience.

[0044] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD-ROM), including a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the method described in the embodiment of the present disclosure.

[0045] This embodiment also provides a base station energy-saving control device, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described are omitted. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0046] An embodiment of the present disclosure provides a base station energy-saving control device, including: a training module, used to perform model training based on historical user behavior data to obtain a user behavior prediction model, where the user behavior prediction model has a time characteristic; a prediction module, used to input user behavior data at a current moment into the user behavior prediction model to obtain a predicted behavior result at the next moment; and an adjustment module, used to adjust the base station energy-saving strategy based on the predicted behavior result at the next moment.

[0047] In actual implementation, each module of the above-mentioned base station energy-saving control device is used to execute the specific steps of the base station energy-saving control method in the above-mentioned embodiment, which will not be repeated here.

[0048] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0049] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0050] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0051] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0052] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0053] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0054] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present disclosure can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented using program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module for implementation. Thus, the embodiments of the present disclosure are not limited to any specific combination of hardware and software.

[0055] In order to enable those skilled in the art to better understand the technical solutions of the embodiments of the present disclosure, they are described below in conjunction with different embodiments.

[0056] Example 1

[0057] In this embodiment, a base station energy-saving control system is provided. Figure 3 is a structural block diagram of the base station energy-saving control system according to the embodiment of the present disclosure. As shown in Figure 3, it includes: a behavior recognition module, a policy control module, a user behavior model learning module, an energy-saving execution module, an input interface module, and an output interface module.

[0058] The behavior recognition module mainly identifies the behavior of users of this network service and determines the specific behavior of users; the user behavior model learning module can clean the user behavior data identified by the behavior recognition module, train and obtain the specific behavior of users of this network within a certain period of time, and indicate the trained model to the policy control module, which can more accurately determine the specific energy-saving strategy or energy-saving withdrawal strategy of the base station at the next moment, making the base station behavior more targeted, thereby improving the energy-saving effect while ensuring that the network can provide services normally, timely and accurately.

[0059] According to the above-mentioned base station energy-saving control system, the behavior recognition module is used to identify and judge user behavior. When the user behavior that meets the requirements is identified, the data will be reported to the user behavior model learning module and the policy control module at the same time. The user behavior model learning module makes more accurate user behavior predictions based on the received reported data.

[0060] After receiving the data, the user behavior model learning module will store the data. When the number of data items reaches a certain amount, the data will be classified, analyzed, trained and user behavior prediction will be performed. The prediction results will be transmitted to the behavior recognition module and the policy control module respectively.

[0061] In an embodiment of the present disclosure, the user behavior model learning module trains the user behavior prediction model. FIG4 is a schematic diagram of the training process of the user behavior model learning module according to an embodiment of the present disclosure. As shown in FIG4 , it is necessary to determine whether the energy-saving result is the same as the energy-saving prediction result. If they are different, the number of times needs to be recorded. When the number of differences reaches a preset threshold value, the user behavior prediction model is updated in a timely manner. If they are the same, it is necessary to further determine whether the energy-saving result is consistent with the prediction. When it is consistent with the expectations, the model version is saved, and the model is updated and trained based on the current user behavior data. At the same time, the cycle for updating the model should also be set. When it is determined that the update cycle is met, the model is trained and updated. If the energy-saving prediction result does not meet expectations, it is necessary to count the deviations from the prediction expectations, and jointly decide whether to update the model training together with the number of deviations from the prediction results. When performing update training, the old version of the model should be saved first, and the old version of the model should be trained with the current data to obtain the new version of the model.

[0062] The policy control module receives the data reporting information from the behavior recognition module, identifies the data reporting information, and decides whether to save energy based on the pre-configured policy parameters. If the decision requires energy saving, it sends a command to the energy-saving execution module to perform energy-saving actions; the module can also accept the preset information of the input interface module to decide whether to save energy at this site; the module can also accept manually set control policy parameter configurations and mandatory execution commands; the module can also send the decision information to the output interface module.

[0063] The energy-saving execution module is responsible for executing the energy-saving commands sent by the policy control module and feeding back the execution results to the policy control module.

[0064] The output interface module is used to interact with other sites and send decision-related information of this site to adjacent sites.

[0065] The input interface module is responsible for parameter configuration, mandatory command input, information transmitted from neighboring cells, and site-related status feedback.

[0066] FIG5 is a schematic diagram of the working process of the base station energy-saving control system according to an embodiment of the present disclosure. As shown in FIG5 , the working principle of the cooperation between different modules of the base station energy-saving control system is as follows:

[0067] In step S501, the behavior recognition module extracts behavior recognition information and performs preliminary data cleaning.

[0068] The behavior recognition module mainly identifies user behavior based on the network attributes of the base station itself, and classifies it according to user behavior. At the same time, it counts the resource usage of the base station and some user-related information, and periodically passes this statistical information to the policy control module and the user behavior model learning module.

[0069] In step S502, the user behavior model learning module labels, copies, and cleans the data, and then trains and learns to obtain a user behavior prediction model.

[0070] After the user behavior model learning module obtains the statistical information of the behavior recognition module, it classifies the data, cleans the data to remove interference data, and then trains the data according to the preset classification to obtain the user's behavior characteristics, base station resource usage characteristics, etc. in a specific time period, so as to further obtain the user behavior prediction model. Based on the model, user behavior in the future can be predicted in advance and the prediction information can be output to the policy control module.

[0071] Step S503: The strategy control module makes a decision based on the prediction model data, behavior recognition module data, and input interface module data.

[0072] The policy control module receives data from the behavior recognition module and first determines which sites need to implement or exit energy conservation, then determines the type of energy conservation to implement. If the user behavior model learning module outputs prediction information, this judgment is combined with the prediction information. Furthermore, the policy control module must promptly respond to the user behavior model learning module's prediction information regarding energy conservation exit to provide more timely service. The policy control module passes this decision information to the energy conservation execution module, which implements or exits energy conservation according to the instructions. Simultaneously, the policy control module sends this decision information to the user behavior model learning module.

[0073] In step S504, after receiving the decision information, the user behavior model learning module determines whether the user behavior prediction model is accurate based on the decision information. On the other hand, it fits the real-time behavior data transmitted by the behavior recognition module to determine whether it meets expectations. If it does not meet expectations, the model needs to be further trained.

[0074] In step S505, the user can input information into the input interface module as needed, and can also obtain data and status information from the output interface module.

[0075] According to the first embodiment of the present disclosure, in actual implementation, the user behavior model learning module may not be used. Instead, data may be acquired through the output interface module, and the data may be trained in the offline "user behavior model learning module" to obtain a user behavior prediction model. Input and settings may be made in the human-computer interaction module based on the obtained prediction model to achieve the same purpose. A human-computer interaction module may also be added. After the user behavior model learning module completes the training of the user behavior prediction model, the user may observe the relevant information about the base station's expected entry into and exit from energy saving in the human-computer interaction module. In case of an emergency, commands may be input to control the relevant content about entry into and exit from energy saving.

[0076] Example 2

[0077] According to the base station energy-saving control system provided in the first embodiment above, in this embodiment, the working principle and process of the base station energy-saving control system are introduced by taking the high-speed rail network scenario as an example.

[0078] FIG6 is a schematic diagram of the working principle of a base station energy-saving control system according to an embodiment of the present disclosure. As shown in FIG6 , the behavior recognition module, the user behavior model learning module, and the policy control module are main modules. The high-speed rail scenario in a general network is used as an example for description. The working principle is as follows:

[0079] Behavior recognition module:

[0080] First, identify the network type as a high-speed rail scenario in a general network. In this scenario, base stations generally only need to provide services at a certain moment. Therefore, focus on identifying the following indicators: users switching in, users switching out, and the number of online users. Other indicators include access users, user speed identification, and uplink and downlink resource utilization.

[0081] The simultaneous increase in the number of incoming users and online users indicates that users are gradually entering the cell. However, it could also be users along the high-speed train line. Further identification of the speed of the incoming users is necessary. If they are low-speed users, these users need to be removed from the number of incoming users and online users. Additionally, access users need to be removed from the number of online users. If both the number of incoming users and online users increase after removal, it indicates that the high-speed train is gradually entering the cell.

[0082] The increase in users switching out and the decrease in the number of online users indicate that users are gradually leaving the cell. However, the number of online users may not be reset to zero due to the access of users along the high-speed rail line. Therefore, data cleaning is required. These users can be removed by identifying user speeds. If the number of online users after cleaning is 0 or very small (generally less than 10), it means that the high-speed train has left the cell. At this time, it is also necessary to determine the uplink and downlink resource utilization rate. If the uplink and downlink resource utilization rate is high, it means that some users still need to provide services. At this time, it is not appropriate to enter the energy-saving state. The energy-saving state can be entered after the uplink and downlink resource utilization rate decreases or all online users are migrated out of the cell.

[0083] After the above data collection, we can finally obtain the time trajectory of the high-speed train's activities in the community, and thus obtain the time period when the community should provide services.

[0084] User behavior model learning module:

[0085] The user behavior model learning module will continuously obtain data from the behavior recognition module. When the data reaches a certain amount (the amount of data required for the first time is more), a certain weight will be assigned to the data in chronological order, and then data recognition, classification and training will be performed. Finally, a time map of the cell that needs to provide services and the corresponding user behavior characteristics will be obtained. Since the time when the high-speed rail passes through the cell is different, each time period that needs to provide services will be a grayscale graph according to the probability of high-speed rail entering. Figure 7 is a grayscale graph of the probability of high-speed rail entering in different time periods according to an embodiment of the present disclosure. As shown in Figure 7, different energy-saving strategies can be selected in combination with the grayscale graph. For example: 0% of the time period can enter station-level energy saving; less than 0.01% of the time period can enter carrier-level energy saving, and only the basic carrier is retained for wide-area coverage; less than 0.5% of the time period can perform BWP-level energy saving; less than 10% can perform symbol-level energy saving; 30% and above can completely exit energy saving.

[0086] Furthermore, the user behavior model learning module receives feedback on energy-saving strategies from the policy control module and real-time data from the monitoring behavior recognition module to determine the accuracy of predictions. If significant errors accumulate to a certain level, data recognition and training will be triggered again to generate an updated user behavior prediction model and corresponding user behavior characteristics, ensuring the model's real-time effectiveness.

[0087] Policy control module:

[0088] The information used by the policy control module for decision-making mainly comes from three aspects: 1. Real-time data from the behavior recognition module; 2. Prediction data from the user behavior model learning module; 3. Information from the input interface module, including setting information and information transmitted from neighboring cells.

[0089] FIG8 is a schematic diagram of the process principle of the energy-saving exit strategy according to an embodiment of the present disclosure. As shown in FIG8 , if energy-saving exit is required, the prediction data of the user behavior model learning module, i.e., the prediction result, is first checked. If the prediction result indicates that energy-saving exit is required, energy-saving exit or partial energy-saving is directly performed according to the data. Otherwise, the input interface module data is checked. If there is a setting command to exit energy-saving or an energy-saving exit instruction transmitted from a neighboring cell, energy-saving exit is also required. Otherwise, based on the real-time data transmitted by the behavior recognition module, it is determined whether the data exceeds the set energy-saving exit threshold and energy-saving exit is performed. Otherwise, energy-saving exit is performed. After relevant processing such as the prediction result indication, the cell can enter the energy-saving exit state in advance and prepare for service provision in advance to ensure better service to users.

[0090] The energy-saving exit strategy does not mean a complete one-step exit from energy saving: the corresponding energy-saving exit state should be pre-set based on the predicted data of the user behavior model learning module. At this time, the next user behavior can be judged based on the real-time data transmitted by the behavior recognition module. For example, if the number of users switching in and the number of online users are gradually increasing, it means that users are gradually entering the cell. In this case, the energy-saving state should be further exited. Otherwise, it can be temporarily maintained in the energy-saving exit state.

[0091] After the energy saving is completed, the energy saving result needs to be fed back to the user behavior model learning module (i.e., the learning module). The learning module determines whether the real-time data and the user prediction data are consistent. If they are consistent, the current process is terminated. Otherwise, it is necessary to further determine whether the user entry rate and the change rate of the number of online users are higher than the predicted data. If they are not high, the current process is terminated. If they are high, it means that the predicted data is low and cannot meet the actual user needs. Therefore, the energy saving level of the energy saving needs needs to be adjusted. The judgment result and the adjustment result are then fed back to the learning module for the next training and update of the user behavior prediction model.

[0092] If it is to enter energy saving, first check whether there is a setting command in the input interface module. If necessary, check whether the real-time data of the behavior recognition module meets the requirements of the energy saving state. If not, return an alarm and ask for confirmation. After receiving the confirmation information, enter the energy saving process, otherwise do not enter. Then check the predicted data of the user behavior model learning module and combine it with the real-time data of the behavior recognition module to determine which level of energy saving needs to be entered.

[0093] In summary, the base station energy-saving control method provided by the disclosed embodiments uses big data analysis and training to develop a user behavior prediction model, which is used to predict user behavior patterns within the base station. Based on these patterns, the base station determines whether to enter an energy-saving state and wakes up in advance, thereby achieving base station energy conservation while ensuring normal service. The base station automatically saves and deactivates energy based on the identified user behavior, thereby improving both energy conservation and service effectiveness.

[0094] The above description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure should be included within the scope of protection of the present disclosure.

Claims

1. A base station energy saving control method, comprising: Performing model training based on historical user behavior data to obtain a user behavior prediction model, wherein the user behavior prediction model has a time characteristic; Inputting the user behavior data at the current moment into the user behavior prediction model to obtain the predicted behavior result at the next moment; The base station energy saving strategy is adjusted based on the predicted behavior result at the next moment.

2. The method according to claim 1, wherein: The method of performing model training based on historical user behavior data to obtain a user behavior prediction model includes: Acquire user behavior data for a preset historical period, and classify the user behavior data for the preset historical period to obtain classified user behavior data; The classified user behavior data is used as training data for model training to obtain the user behavior prediction model.

3. The method according to claim 2, wherein: Classifying the user behavior data of the preset historical period to obtain classified user behavior data includes: Identify the network type corresponding to the user behavior data in the preset historical period; Determining characteristic parameters of different user behaviors within the preset historical period according to the network type; The user behavior data of the preset historical period is classified based on the characteristic parameters of different user behaviors in the preset historical period to obtain the classified user behavior data.

4. The method according to claim 2, wherein: The step of using the classified user behavior data as training data to perform model training to obtain the user behavior prediction model includes: Add data labels based on the time sequence and base station resource characteristics corresponding to each behavior in the classified user behavior data to obtain the training data; Model training is performed based on the training data to obtain the user behavior prediction model.

5. The method according to claim 1, wherein: After adjusting the base station energy saving strategy based on the predicted behavior result at the next moment, the method further includes: Determining the accuracy of the predicted behavior result at the next moment based on the strategy implementation result corresponding to the adjusted base station energy saving strategy; When the accuracy of the predicted behavior result at the next moment is less than a preset threshold, and the number of times the accuracy of the predicted behavior result fails to meet the standard exceeds a preset number, update training of the user behavior prediction model is triggered.

6. The method according to claim 1, wherein: The adjusting the base station energy saving strategy based on the predicted behavior result at the next moment includes: Determine the energy saving strategy or energy saving withdrawal strategy of the base station at the next moment based on the predicted behavior result at the next moment, The energy-saving state or energy-saving type of the base station is changed according to the energy-saving strategy or the energy-saving deactivation strategy.

7. The method according to claim 1, wherein: The adjusting the base station energy saving strategy based on the predicted behavior result at the next moment includes: In the case of receiving an external energy-saving instruction, determining whether the user behavior data at the current moment meets the requirements for energy saving, and if not, requesting confirmation information from the external device, and entering energy saving upon receipt of the confirmation information, otherwise not entering energy saving; In the case of entering energy saving, the energy saving state or energy saving type of the base station is determined based on the predicted behavior result at the next moment and the user behavior data at the current moment.

8. The method according to claim 1, wherein: The adjusting the base station energy saving strategy based on the predicted behavior result at the next moment includes: According to the predicted behavior result at the next moment, pre-regulating the base station to enter a power-saving deactivation state; Whether the base station exits energy saving is determined based on the user behavior data at the current moment. If the user behavior data at the current moment is consistent with the predicted behavior result at the next moment, energy saving is exited; if the user behavior data at the current moment is inconsistent with the predicted behavior result at the next moment, energy saving is not exited and the energy saving exit state is maintained.

9. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

Images