Intelligent recommendation method and system for 5G base station energy saving scheme

Abstract

The invention relates to an intelligent recommendation method and system for a 5G base station energy saving scheme, and belongs to the technical field of base station energy saving, and the method comprises the steps: obtaining an application scene of a cell corresponding to a base station and feature data of the application scene; under the condition of a first model, a scene data set is obtained according to the feature data of the application scene and a turn-off threshold value, a prediction model is adopted to predict the scene data set, and the energy consumption rising and falling trend of the application scene is obtained; a second model is adopted to train the energy consumption rising and falling trend, and a turn-off optimal solution of the application scene is obtained; and executing intelligent recommendation of a turn-off scheme based on the optimal turn-off solution of the application scene. According to the method, accurate recommendation and optimal configuration of the 5G base station energy-saving scheme are realized through intelligent and automatic means, the network performance is guaranteed, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of base station energy saving, and in particular, relates to an intelligent recommendation method and system for a 5G base station energy saving solution. Background Art

[0002] In related technologies, with the advent of the 5G era, various new services emerge in an endless stream, application scenarios continue to emerge, and the development trend of device connections towards massive connections is irreversible, which continues to promote the explosive growth of mobile data traffic. In order to meet the development requirements of the rapid growth of 5G services and data traffic in the future, the power consumption of 5G networks has also doubled, and is expected to be 3 to 4 times that of 4G networks. The contradiction between the growth of mobile communication network data and the high energy consumption problem has further intensified. Energy saving and consumption reduction are important issues that the future mobile communication industry will face in the sustainable development. From the perspective of intelligent operation and maintenance, energy saving is not just a single goal of reducing energy consumption. The purpose of energy saving is to achieve a compromise between the energy consumption generated by network operation and the performance of the services provided by the network, or to reduce energy consumption without affecting the key performance indicators of the network's service provision. Therefore, how to better achieve energy saving in 5G base stations has become an urgent problem to be solved. Summary of the invention

[0003] In view of the above shortcomings of the prior art, the purpose of the invention is to provide a method and system for intelligently recommending 5G base station energy-saving solutions. The method achieves accurate recommendation and optimized configuration of 5G base station energy-saving solutions through intelligent and automated means, ensuring network performance and reducing energy consumption.

[0004] The first aspect of the present invention proposes a method for intelligently recommending energy-saving solutions for 5G base stations, comprising: S1, obtaining application scenarios of cells corresponding to base stations and characteristic data of the application scenarios; S2, in the case of a first model, obtaining a scenario data set according to the characteristic data and a shutdown threshold of the application scenario, and using a prediction model to predict the scenario data set to obtain an energy consumption increase and decrease trend of the application scenario; S3, using a second model to train the energy consumption increase and decrease trend to obtain an optimal shutdown solution for the application scenario; S4, performing intelligent recommendation of a shutdown solution based on the optimal shutdown solution for the application scenario.

[0005] Furthermore, the application scenarios include multi-carrier deployment scenarios, symbol shutdown scenarios, channel shutdown scenarios, cell shutdown scenarios, and deep sleep scenarios, and the characteristic data of the application scenarios include uplink and downlink measurement reports, service category characteristics, user level information, and resource occupancy distribution.

[0006] Furthermore, the method also includes: determining overlapping data and non-overlapping data of the characteristic data of the application scenario, and using the overlapping data as the first data set and the non-overlapping data as the second data set; determining a historical fault set in the second data set, and marking the historical fault set.

[0007] Furthermore, a prediction model is used to predict the scenario data set to obtain the energy consumption increase and decrease trend of the application scenario, including: determining the probability of this energy consumption increase and decrease trend, wherein the probability of this energy consumption increase and decrease trend includes an upward probability and a downward probability; converting the probability of this energy consumption increase and decrease trend into the current energy consumption state vector; according to X(k+1)=X(k)×P, obtaining the probability of the next energy consumption trend, wherein X(k) represents the state vector of the predicted object at time t=k, P represents the one-step transfer probability matrix, and X(k+1) represents the state vector of the predicted object at time t=k+1; and using the probability of the next energy consumption trend as the energy consumption increase and decrease trend of the application scenario.

[0008] Furthermore, a second model is used to train the energy consumption rising and falling trends to obtain the optimal shutdown solution for the application scenario, including: taking the characteristic data of the application scenario, the shutdown threshold and the energy consumption rising and falling trends as input parameters of the second model; and training the energy consumption rising and falling trends based on the input parameters to obtain the optimal shutdown solution for the application scenario.

[0009] Furthermore, intelligent recommendation of shutdown schemes is performed based on the optimal shutdown solutions of the application scenarios, including: determining ranking results of the optimal shutdown solutions; and determining a global optimal solution for the application scenarios based on the ranking results.

[0010] Furthermore, it also includes: according to π t+1 =Α((x1,σ1,c1),....(x t , σ t , c t ), update the shutdown scheme, where x1 represents the shutdown information of the main scene at the first moment, σ1 represents the sorting position at the first moment, c1 represents the feedback information at the first moment, and x t represents the main scene shutdown information at time t, σ t represents the sorting position at time t, c t represents the feedback information at the tth moment, and A represents the function.

[0011] The second aspect of the present invention proposes an intelligent recommendation system for 5G base station energy-saving solutions, including: an acquisition module, used to obtain the application scenarios of the cells corresponding to the base stations and the characteristic data of the application scenarios; a first acquisition module, used to obtain a scenario data set according to the characteristic data and the shutdown threshold of the application scenarios under the first model, and use a prediction model to predict the scenario data set to obtain the energy consumption increase and decrease trend of the application scenario; a second acquisition module, used to use a second model to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution for the application scenario; an execution module, used to execute intelligent recommendation of shutdown solutions based on the optimal shutdown solution of the application scenario.

[0012] According to a third aspect of the present invention, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the methods described in the first aspect of the present invention.

[0013] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods according to the first aspect of the present invention.

[0014] The beneficial effects of the present invention are as follows:

[0015] The intelligent recommendation method and system of the 5G base station energy-saving solution described in the present invention obtains the application scenario of the cell corresponding to the base station and the characteristic data of the application scenario; in the case of the first model, the scenario data set is obtained according to the characteristic data and the shutdown threshold of the application scenario, and the prediction model is used to predict the scenario data set to obtain the energy consumption increase and decrease trend of the application scenario; the second model is used to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution of the application scenario; based on the optimal shutdown solution of the application scenario, the intelligent recommendation of the shutdown solution is performed. This method realizes the accurate recommendation and optimal configuration of the 5G base station energy-saving solution through intelligent and automated means, ensuring network performance and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1is a flow chart of a method for intelligently recommending a 5G base station energy-saving solution according to an embodiment of the present invention;

[0018] Figure 2 is a flow chart of an intelligent recommendation method for a 5G base station energy saving solution according to a specific embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of an intelligent recommendation system for a 5G base station energy-saving solution according to an embodiment of the present invention;

[0020] Figure 4 It is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0022] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0023] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0025] The present invention proposes an intelligent recommendation method, system and related equipment for a 5G base station energy-saving solution. Specifically, the intelligent recommendation method, system and related equipment for a 5G base station energy-saving solution of an embodiment of the present invention are described below with reference to the accompanying drawings.

[0026] Figure 1 It is a flow chart of a method for intelligently recommending a 5G base station energy-saving solution according to an embodiment of the present invention. It should be noted that the method for intelligently recommending a 5G base station energy-saving solution according to an embodiment of the present invention can be applied to a system for intelligently recommending a 5G base station energy-saving solution according to an embodiment of the present invention. The system for intelligently recommending a 5G base station energy-saving solution can be configured on an electronic device or in a server. This embodiment of the present application does not limit this.

[0027] like Figure 1 As shown, the intelligent recommendation method for 5G base station energy-saving solutions includes:

[0028] S110, obtaining application scenarios and characteristic data of the application scenarios of the cell corresponding to the base station.

[0029] In an embodiment of the present invention, application scenarios include multi-carrier deployment scenarios, symbol shutdown scenarios, channel shutdown scenarios, cell shutdown scenarios, and deep sleep scenarios, and characteristic data of application scenarios include uplink and downlink measurement reports, service category characteristics, user level information, and resource occupancy distribution.

[0030] Among them, in the symbol shutdown scenario, the symbol shutdown function schedules by concentrating resources in specific time slots when the network is under low load, and shuts down the transmission power of time slots without business, so as to reduce the total power consumption of the power amplifier module by discontinuous transmission of business. When the symbol shutdown function is turned on, when there is no user data to be sent in the downlink symbol, the base station equipment can achieve the purpose of energy saving by turning off the transmission power of the power amplifier module in the RF part. When the symbol shutdown function is turned on, it is necessary to transmit the reference signals of the necessary control channels, such as CSI-RS, SSS, PSS and PBCH in the 5G system, and the original transmission cycle and power are still maintained without affecting the 5G network coverage. The power amplifier module can resume normal working state according to the increase in base station business volume to ensure that the performance of the 5G network is not affected.

[0031] In the channel shutdown scenario, 5G base stations can turn off the transmission power of some RF channels in scenarios with low business load, such as idle time at night and non-capacity cell scenarios, to achieve energy saving. When the base station business load increases to a certain value, the base station turns on the closed RF channel and restores the multi-channel transmission state. Enabling the channel shutdown solution will affect the system capacity. At the same time, the reduction in total transmission power may change the downlink antenna beam shape, affecting the original coverage and certain business performance.

[0032] In the cell shutdown scenario, the cell shutdown function refers to shutting down cells with low or no traffic volume according to the traffic volume in the coverage area to achieve energy saving. For example, when the traffic load of the 5G cell in the capacity layer is low, the 5G cell is shut down and the 4G LTE cell providing the coverage layer is retained to reduce the energy consumption of 5G equipment.

[0033] In a multi-carrier deployment scenario, if the traffic volume of a carrier is lower than the preset threshold, the carrier can be shut down by the network management while ensuring the quality of user services. At this time, the AAU / RRU equipment can reduce the power consumption of the equipment to achieve the effect of energy saving. When the network management configures the restart of the AAU / RRU equipment, the AAU / RRU equipment and the neighboring cells should be restored to the configuration before shutdown.

[0034] In the deep sleep scenario, the deep sleep function means that when the base station traffic is very low, most of the components of the AAU equipment will be powered off (except for the basic transmission link). The deep sleep energy-saving function only retains the transmission link between the BBU and the AAU, and supports the AAU equipment to enter / exit deep sleep mode, thereby achieving energy saving to the greatest extent. When AI is used for network energy saving, the accuracy of its prediction results, including network load, will directly affect the performance of the overall network. Accurate load prediction can timely determine the load trend of different nodes in the network, locate sites that can be deactivated, or implement optimal resource allocation, so that network performance is not affected and energy consumption is saved. However, inaccurate predictions will lead to incorrect deactivation of sites, causing a ping-pong effect in the site activation state, resulting in user call drops, affecting cell coverage, and deteriorating overall network performance.

[0035] S120, in the case of the first model, a scenario data set is obtained according to the characteristic data and the shutdown threshold of the application scenario, and the scenario data set is predicted using a prediction model to obtain the energy consumption increase and decrease trend of the application scenario.

[0036] In an embodiment of the present invention, the characteristic data and the shutdown threshold of the application scenario can be input as parameters into the first model to obtain a scene data set, and then the prediction model is used to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scenario. The specific implementation method can refer to the above embodiment.

[0037] S130: Use the second model to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution for the application scenario.

[0038] In an embodiment of the present invention, when the energy consumption rising and falling trends of the application scenarios are obtained, the characteristic data, shutdown threshold and energy consumption rising and falling trends of the application scenarios are used as input parameters of the second model; the energy consumption rising and falling trends are trained based on the input parameters to obtain the optimal shutdown solution for the application scenarios.

[0039] S140, intelligently recommending a shutdown solution based on the optimal shutdown solution of the application scenario.

[0040] According to the intelligent recommendation method of the 5G base station energy-saving solution of the embodiment of the present invention, the application scenario of the cell corresponding to the base station and the characteristic data of the application scenario are obtained; in the case of the first model, the scenario data set is obtained according to the characteristic data and the shutdown threshold of the application scenario, and the prediction model is used to predict the scenario data set to obtain the energy consumption increase and decrease trend of the application scenario; the second model is used to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution of the application scenario; based on the optimal shutdown solution of the application scenario, the intelligent recommendation of the shutdown solution is performed. This method realizes the accurate recommendation and optimal configuration of the 5G base station energy-saving solution through intelligent and automated means, ensuring network performance and reducing energy consumption.

[0041] In order to make it easier for those skilled in the art to understand the present invention, Figure 2 is an intelligent recommendation method for a 5G base station energy saving solution according to a specific embodiment of the present invention, such as Figure 2 As shown, the intelligent recommendation method of the 5G base station energy-saving solution includes:

[0042] S210, obtaining application scenarios and characteristic data of the application scenarios of the cell corresponding to the base station.

[0043] In the embodiment of the present invention, the implementation of step S210 may refer to the implementation of step S110 described above, and the present invention will not elaborate on this any more.

[0044] S220: In the case of the first model, a scenario data set is obtained according to the characteristic data and the shutdown threshold of the application scenario.

[0045] In an embodiment of the present invention, overlapping data and non-overlapping data of characteristic data of an application scenario are determined, and the overlapping data are used as a first data set, and the non-overlapping data are used as a second data set; a historical fault set in the second data set is determined and the historical fault set is marked, and then the characteristic data and the shutdown threshold of the application scenario are input as parameters into the first model to obtain a scenario data set.

[0046] S230, using a prediction model to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scene.

[0047] In an embodiment of the present invention, the probability of the current energy consumption increase or decrease trend is determined, and the probability of the current energy consumption increase or decrease trend includes an upward probability and a downward probability; the probability of the current energy consumption increase or decrease trend is converted into the current energy consumption state vector; according to X(k+1)=X(k)×P, the probability of the next energy consumption trend is obtained, wherein X(k) represents the state vector of the predicted object at time t=k, P represents the one-step transfer probability matrix, and X(k+1) represents the state vector of the predicted object at time t=k+1; the probability of the next energy consumption trend is used as the energy consumption increase or decrease trend of the application scenario.

[0048] For example, when the upward probability of the energy consumption rise and fall trend is 0.3 and the downward probability is 0.7, the energy consumption state vector converted into this time is [0.3, 0.7].

[0049] For example, given X(k) = [0.6, 0.4] and the state transition probability matrix Right now That is, the upward probability of the next energy consumption increase and decrease trend is 0.48, and the downward probability is 0.52.

[0050] S240: Use the second model to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution for the application scenario.

[0051] In an embodiment of the present invention, characteristic data of the application scenario, the shutdown threshold and the energy consumption rise and fall trend are used as input parameters of the second model; the energy consumption rise and fall trend is trained based on the input parameters to obtain the optimal shutdown solution for the application scenario.

[0052] In an embodiment of the present invention, when the optimal shutdown solution of the application scenario is obtained, the optimal shutdown solution can be sent to the base station network intelligent perception management system (intelligent perception subsystem). The base station network intelligent perception management system has network operation monitoring, resource scheduling and business management functions. The base station network intelligent perception management system consists of an intelligent perception subsystem, an analysis and decision-making subsystem, a configuration execution subsystem, and a cloud storage data center. The intelligent perception subsystem is responsible for "perception", the analysis and decision-making subsystem is responsible for "planning, analysis, and decision-making", the configuration execution subsystem is responsible for "execution and configuration", and the cloud storage data center is responsible for data storage.

[0053] Among them, when the intelligent sensing subsystem obtains the optimal shutdown solution, the optimal shutdown solution can be reported to the analysis and decision-making subsystem. The above analysis and decision-making subsystem performs intelligent recommendation of shutdown solutions based on the optimal shutdown solution of the application scenario.

[0054] S250, intelligently recommending a shutdown solution based on the optimal shutdown solution of the application scenario.

[0055] In an embodiment of the present invention, the ranking results of the optimal shutdown solutions are determined; based on the ranking results, the global optimal solution of the application scenario is determined. Then, the global optimal solution is recommended to the network operator as a shutdown solution. This solution includes but is not limited to detailed information such as specific energy-saving strategies, schedules, and parameter settings.

[0056] Among them, when the ranking result of the shutdown optimal solution is 1, it is the global optimal solution.

[0057] In an embodiment of the present invention, according to π t+1 =Α((x1,σ1,c1),....(x t , σ t , c t ), update the shutdown scheme, where x1 represents the shutdown information of the main scene at the first moment, σ1 represents the sorting position at the first moment, c1 represents the feedback information at the first moment, and x t represents the main scene shutdown information at time t, σ t represents the sorting position at time t, c t Represents the feedback information at the tth moment, and A represents the function. That is, the update mechanism is a process of continuous learning and optimization. After recommending the shutdown solution to the network operator, the system continuously updates and optimizes the recommendation algorithm and model by collecting and analyzing feedback information (such as energy saving effect, user satisfaction and network performance). This helps to ensure that the recommended solution is always consistent with the current network status and user needs, and can still be accurately pushed even with less training data, while making up for the lack of intelligence and comprehensiveness of the first model operation.

[0058] According to the intelligent recommendation method of the 5G base station energy-saving solution of the embodiment of the present invention, through the comprehensive application of the first model and the second model, the energy consumption rise and fall trend can be accurately predicted based on the characteristic data of the application scenario and the shutdown threshold, so as to recommend the optimal shutdown solution. This not only helps to achieve energy saving and consumption reduction of base station equipment, but also ensures that network performance is not affected; it can intelligently recommend the optimal shutdown solution according to the current network status and user needs, including detailed information such as energy-saving strategy, schedule and parameter setting. At the same time, through continuous learning and optimization update mechanism, the system can continuously adapt to network changes to ensure that the recommended solution is always consistent with the current situation; through the flexible application of multiple application scenarios such as multi-carrier deployment scenario, symbol shutdown scenario, channel shutdown scenario, cell shutdown scenario and deep sleep scenario, the system can dynamically adjust the transmission power and state of the base station equipment according to the traffic situation of different scenarios, thereby improving resource utilization efficiency; accurate energy consumption prediction and intelligent shutdown solution recommendation help reduce the operating cost of the base station. By reducing unnecessary energy consumption, the system can significantly reduce electricity bills while improving the reliability and service life of the equipment; while ensuring energy saving and consumption reduction, the system can also maintain the stability of network performance and user experience. Through refined control and optimization, the system can avoid adverse effects such as incorrect site deactivation and user call drops, ensuring that network coverage and service performance are not affected.

[0059] 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 a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, 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, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0060] According to one aspect of an embodiment of the present invention, an intelligent recommendation system for a 5G base station energy-saving solution is also proposed. Figure 3 is a schematic diagram of an intelligent recommendation system for a 5G base station energy-saving solution according to an embodiment of the present invention; Figure 3 As shown, including:

[0061] An acquisition module 310 is used to acquire an application scenario of a cell corresponding to a base station and feature data of the application scenario;

[0062] A first obtaining module 320 is used to obtain a scene data set according to the characteristic data and the shutdown threshold of the application scene in the case of the first model, and use the prediction model to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scene;

[0063] A second obtaining module 330 is used to train the energy consumption rise and fall trend using a second model to obtain an optimal shutdown solution for the application scenario;

[0064] The execution module 340 is used to perform intelligent recommendation of a shutdown solution based on the optimal shutdown solution of the application scenario.

[0065] According to the intelligent recommendation system of the 5G base station energy-saving solution of the embodiment of the present invention, the application scenario of the cell corresponding to the base station and the characteristic data of the application scenario are obtained; in the case of the first model, the scenario data set is obtained according to the characteristic data and the shutdown threshold of the application scenario, and the prediction model is used to predict the scenario data set to obtain the energy consumption increase and decrease trend of the application scenario; the second model is used to train the energy consumption increase and decrease trend to obtain the optimal shutdown solution of the application scenario; the intelligent recommendation of the shutdown solution is performed based on the optimal shutdown solution of the application scenario. In this way, the accurate recommendation and optimal configuration of the 5G base station energy-saving solution are realized through intelligent and automated means, which ensures network performance and reduces energy consumption.

[0066] Optionally, the application scenarios include multi-carrier deployment scenarios, symbol shutdown scenarios, channel shutdown scenarios, cell shutdown scenarios, and deep sleep scenarios, and the characteristic data of the application scenarios include uplink and downlink measurement reports, service category characteristics, user level information, and resource occupancy distribution.

[0067] Optionally, the system also includes a marking module, and the method also includes: determining overlapping data and non-overlapping data of the characteristic data of the application scenario, and using the overlapping data as the first data set and the non-overlapping data as the second data set; determining a historical fault set in the second data set, and marking the historical fault set.

[0068] Optionally, the first obtaining module 320 is specifically used to determine the probability of the current energy consumption increase and decrease trend, and the current energy consumption increase and decrease trend probability includes an upward probability and a downward probability; convert the current energy consumption increase and decrease trend probability into the current energy consumption state vector; according to X(k+1)=X(k)×P, obtain the next energy consumption trend probability, wherein X(k) represents the state vector of the predicted object at time t=k, P represents the one-step transfer probability matrix, and X(k+1) represents the state vector of the predicted object at time t=k+1; use the next energy consumption trend probability as the energy consumption increase and decrease trend of the application scenario.

[0069] Optionally, the second obtaining module 330 is specifically used to use the characteristic data of the application scenario, the shutdown threshold and the energy consumption increase and decrease trend as input parameters of the second model; train the energy consumption increase and decrease trend based on the input parameters to obtain the optimal shutdown solution for the application scenario.

[0070] Optionally, the execution module 340 is specifically configured to determine a ranking result of the shutdown optimal solution; and determine a global optimal solution for the application scenario based on the ranking result.

[0071] Optionally, an update module is also included for updating the t+1 =Α((x1,σ1,c1),....(x t , σ t , c t ), update the shutdown scheme, where x1 represents the shutdown information of the main scene at the first moment, σ1 represents the sorting position at the first moment, c1 represents the feedback information at the first moment, and x t represents the main scene shutdown information at time t, σ t represents the sorting position at time t, c t represents the feedback information at the tth moment, and A represents the function.

[0072] According to one aspect of an embodiment of the present invention, an electronic device is provided.

[0073] Figure 4 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more ( Figure 4 Only one is shown in the figure) processor 102 (processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmable logic device, referred to as PLD)) and a memory 104 for storing data. In an exemplary embodiment, the electronic device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only and does not limit the structure of the above terminal device. Figure 4 More or fewer components as shown, or with Figure 4 Equivalent functions or comparisons shown Figure 4 A different configuration with more features is shown.

[0074] 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 intelligent recommendation method for the 5G base station energy saving solution in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement 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 arranged relative to the processor 102, and these remote memories may be connected to the terminal device 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 a combination thereof.

[0075] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the switching device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0076] The present invention proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute an intelligent recommendation method for a 5G base station energy-saving solution.

[0077] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

[0078] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0079] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. An intelligent recommendation method for a 5G base station energy-saving solution, characterized in that: include: S1, obtaining application scenarios of a cell corresponding to a base station and feature data of the application scenarios; S2, in the case of the first model, obtaining a scene data set according to the characteristic data and the shutdown threshold of the application scene, and using the prediction model to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scene; S3, using a second model to train the energy consumption rise and fall trend to obtain an optimal shutdown solution for the application scenario; S4, performing intelligent recommendation of shutdown solutions based on the optimal shutdown solution of the application scenario.

2. The intelligent recommendation method for 5G base station energy saving solution according to claim 1 is characterized in that: The application scenarios include multi-carrier deployment scenarios, symbol shutdown scenarios, channel shutdown scenarios, cell shutdown scenarios, and deep sleep scenarios. The characteristic data of the application scenarios include uplink and downlink measurement reports, service category characteristics, user level information, and resource occupancy distribution.

3. The intelligent recommendation method for a 5G base station energy-saving solution according to claim 1 is characterized in that: The method further comprises: Determine overlapping data and non-overlapping data of the characteristic data of the application scenario, and use the overlapping data as a first data set and the non-overlapping data as a second data set; A historical fault set in the second data set is determined, and the historical fault set is marked.

4. The intelligent recommendation method for 5G base station energy saving solution according to claim 1 is characterized in that: The prediction model is used to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scene, including: Determine the probability of the energy consumption increase or decrease trend of this time, wherein the probability of the energy consumption increase or decrease trend of this time includes an upward probability and a downward probability; Converting the energy consumption increase / decrease trend probability into the energy consumption state vector; According to X(k+1)=X(k)×P, the probability of the next energy consumption trend is obtained, where X(k) represents the state vector of the predicted object at time t=k, P represents the one-step transition probability matrix, and X(k+1) represents the state vector of the predicted object at time t=k+1; The next energy consumption trend probability is used as the energy consumption increase and decrease trend of the application scenario.

5. The intelligent recommendation method for 5G base station energy saving solution according to claim 1 is characterized in that: The second model is used to train the energy consumption rise and fall trend to obtain the optimal shutdown solution for the application scenario, including: Using the characteristic data of the application scenario, the shutdown threshold and the energy consumption rise and fall trend as input parameters of the second model; The energy consumption rise and fall trend is trained based on the input parameters to obtain an optimal shutdown solution for the application scenario.

6. The intelligent recommendation method for 5G base station energy saving solution according to claim 1, characterized in that: Intelligent recommendation of shutdown solutions based on the optimal shutdown solution of the application scenario includes: Determining a ranking result of the optimal shutdown solution; A global optimal solution for the application scenario is determined based on the sorting result.

7. The intelligent recommendation method for 5G base station energy saving solution according to claim 1, characterized in that: Also includes: According to π t+1 =Α((x1,σ1,c1),....(x t , σ t , c t ), update the shutdown scheme, where x1 represents the shutdown information of the main scene at the first moment, σ1 represents the sorting position at the first moment, c1 represents the feedback information at the first moment, and x t represents the main scene shutdown information at time t, σ t represents the sorting position at time t, c t represents the feedback information at the tth moment, and A represents the function.

8. An intelligent recommendation system for 5G base station energy-saving solutions, characterized in that: include: An acquisition module, used to acquire application scenarios of a cell corresponding to a base station and feature data of the application scenarios; A first obtaining module is used to obtain a scene data set according to the characteristic data and the shutdown threshold of the application scene in the case of the first model, and use the prediction model to predict the scene data set to obtain the energy consumption increase and decrease trend of the application scene; A second obtaining module is used to train the energy consumption rise and fall trend using a second model to obtain an optimal shutdown solution for the application scenario; An execution module is used to perform intelligent recommendation of a shutdown solution based on the optimal shutdown solution of the application scenario.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

Images