Network scheduling method, network device, and computer-readable storage medium

Abstract

The present application provides a network scheduling method, a network device, and a computer-readable storage medium. The network scheduling method comprises: acquiring current terminal feature information of a user terminal UE in a target network, and obtaining a predicted user service requirement on the basis of the current terminal feature information and a preset user portrait (S100); determining a base station energy consumption prediction set on the basis of a preset base station energy consumption portrait and the predicted user service requirement (S200); and performing energy-saving scheduling processing on a base station in the target network on the basis of the base station energy consumption prediction set (S300).

Description

Network scheduling method, network device and computer-readable storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202310802957.7 and application date June 30, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The embodiments of the present application relate to, but are not limited to, the field of communication technology, and in particular to a network scheduling method, a network device, and a computer-readable storage medium. Background Art

[0004] With the continuous development of wireless network technology, wireless network transmission rates are constantly improving, transmission delays are constantly decreasing, transmission bandwidth is also constantly increasing, and the types of services that can be carried are also constantly enriching. As a result, users' requirements for service perception are increasing. In some cases, in order to provide users with a better user experience, only attention is paid to whether the user's perception requirements are met, and the energy consumption problem of the network is often ignored, resulting in high energy consumption of the existing network and failure to achieve the goal of energy conservation and emission reduction.

[0005] Summary of the Invention

[0006] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0007] Embodiments of the present application provide a network scheduling method, a network device, and a computer-readable storage medium.

[0008] In the first aspect, an embodiment of the present application provides a network scheduling method, which includes: obtaining current terminal feature information of a user terminal UE in a target network, and obtaining predicted user service needs based on the current terminal feature information and a preset user portrait; determining a base station energy consumption prediction set according to a preset base station energy consumption portrait and the predicted user service needs; and performing energy-saving scheduling processing on the base stations in the target network according to the base station energy consumption prediction set.

[0009] In a second aspect, an embodiment of the present application further provides a network device comprising: at least one processor; at least one memory for storing at least one program; and implementing the network scheduling method described above when at least one of the programs is executed by at least one of the processors.

[0010] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the network scheduling method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0012] FIG1 is a schematic diagram of a system architecture for executing a network method provided by one embodiment of the present application;

[0013] FIG2 is a flow chart of a network scheduling method provided by an embodiment of the present application;

[0014] FIG3 is a flowchart of determining a base station energy consumption prediction set provided by one embodiment of the present application;

[0015] FIG4 is a flowchart of constructing a user portrait according to an embodiment of the present application;

[0016] FIG5 is a schematic diagram of parameters of a user portrait provided by an embodiment of the present application;

[0017] FIG6 is a flowchart of constructing a network-level logical grid portrait according to an embodiment of the present application;

[0018] FIG7 is a parameter diagram of a network-level logical grid portrait provided by one embodiment of the present application;

[0019] FIG8 is a flowchart of determining a cell-level logical grid according to an embodiment of the present application;

[0020] FIG9 is a flowchart of constructing an initial logic grid according to an embodiment of the present application;

[0021] FIG10 is a schematic diagram of the construction of an initial logic grid provided by one embodiment of the present application;

[0022] FIG11 is a schematic diagram of the construction of an initial logic grid provided by another embodiment of the present application;

[0023] FIG12 is a flowchart of determining a cell-level logical grid according to an embodiment of the present application;

[0024] FIG13 is a flowchart of constructing a cell-level grid portrait according to an embodiment of the present application;

[0025] FIG14 is a schematic diagram of a process for constructing a cell-level grid according to an embodiment of the present application;

[0026] FIG15 is a flowchart of constructing a base station energy consumption profile according to an embodiment of the present application;

[0027] FIG16 is a parameter diagram of a base station energy consumption profile provided by one embodiment of the present application;

[0028] FIG17 is a flowchart of determining a base station energy consumption prediction set according to an embodiment of the present application;

[0029] FIG18 is a flowchart of constructing a segmented energy consumption model of a base station according to an embodiment of the present application;

[0030] FIG19 is a schematic diagram of base station energy consumption reasoning provided by one embodiment of the present application;

[0031] FIG20 is a schematic diagram of constructing a base station energy consumption model according to an embodiment of the present application;

[0032] FIG21 is a flowchart of determining a set of cells in which a user can camp, provided by one embodiment of the present application;

[0033] FIG22 is a flowchart of performing segmented prediction on a set of cells where a user can camp, provided by one embodiment of the present application;

[0034] FIG23 is a flowchart of energy-saving scheduling processing for a base station in a target network provided by one embodiment of the present application;

[0035] FIG24 is a scheduling diagram of a network scheduling method provided by an embodiment of the present application;

[0036] Figure 25 is a schematic diagram of the structure of a network device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain this application and are not intended to limit this application.

[0038] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0039] This application provides a network scheduling method, network device, and computer-readable storage medium. These methods obtain current terminal feature information of user terminals in a target network, and determine predicted user service needs based on the current terminal feature information and a preset user profile. Next, a base station energy consumption prediction set is determined based on a preset base station energy consumption profile and predicted user service needs. Finally, energy-saving scheduling is performed on base stations in the target network based on the base station energy consumption prediction set. According to the technical solutions provided in the embodiments of this application, user perception is coordinated with base station energy consumption, reducing base station energy consumption while ensuring user perception, thereby achieving the goal of energy conservation and emission reduction.

[0040] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0041] As shown in Figure 1, Figure 1 is a schematic diagram of a system architecture for executing a network scheduling method provided by an embodiment of the present application. In Figure 1, the apparatus includes at least one base station (BS) and at least one user equipment (UE), which can be connected to each other via a wireless connection. A base station can provide data to multiple user terminals, and multiple user terminals can communicate and obtain related services through the same base station.

[0042] In some embodiments of the present application, the terminal may be a mobile phone, a tablet computer, an Internet of Things device, etc., and the above devices may be connected to the base station via a wireless connection.

[0043] The system architecture and application scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of the system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are equally applicable to similar technical problems.

[0044] Those skilled in the art will understand that the system architecture shown in FIG1 does not constitute a limitation on the embodiments of the present application, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0045] Based on the structure of the above system architecture, various embodiments of the scheduling method of the present application are proposed.

[0046] As shown in Figure 2, Figure 2 is a flow chart of a network scheduling method provided by an embodiment of the present application. The method includes but is not limited to step S100, step S200 and step S300.

[0047] Step S100: obtaining current terminal feature information of a user terminal UE in the target network, and obtaining predicted user service needs based on the current terminal feature information and a preset user profile;

[0048] Step S200: determining a base station energy consumption prediction set based on a preset base station energy consumption profile and predicted user service needs;

[0049] Step S300 : performing energy-saving scheduling processing on base stations in the target network according to the base station energy consumption prediction set.

[0050] In an embodiment of the present application, current terminal feature information of user terminals in the target network is obtained, and predicted user service needs are determined based on the current terminal feature information and a preset user profile. A base station energy consumption prediction set is then determined based on a preset base station energy consumption profile and the predicted user service needs. Finally, energy-saving scheduling is performed on base stations in the target network based on the base station energy consumption prediction set. According to the technical solution provided in the embodiment of the present application, user perception and base station energy consumption are coordinated to reduce base station energy consumption while ensuring user perception, thereby achieving the goal of energy conservation and emission reduction.

[0051] It is worth noting that the target network can be a network that needs to be scheduled. The target network can be composed of multiple base stations, each base station can include multiple cells, and each cell can include multiple user terminals; the current terminal feature information of the user terminal in the target network is collected and processed, wherein the current terminal feature information can be understood as the real-time terminal status information of the user terminal, and the terminal status information can include real-time user attribute information, real-time user service attribute information and real-time user behavior attribute information.

[0052] It can be understood that the user portrait can be a database model obtained based on deep learning of historical user terminal feature information. The model has the function of inference and prediction. Therefore, the current terminal feature information obtained can be matched and inferred with the user portrait, and the prediction result of the user's business needs can be obtained, which is the predicted user business needs in the embodiment of the present application; in some embodiments, the current terminal feature information obtained includes moving speed information, and then the user's moving speed information and user portrait can be combined to predict the cell location information of the user terminal at the next moment; or, within a fixed time period every day, a user terminal will play video, so it can be inferred based on the combination of the user portrait and the collected current terminal feature information to obtain the user's need to perform video playback processing and maintain a large bandwidth business requirement.

[0053] It is understood that the base station energy consumption profile can be a database model obtained through deep learning based on historical base station energy consumption data. This model also has the function of inference and prediction. Based on the base station energy consumption profile and predicted user business needs, a base station energy consumption prediction set can be determined. Finally, based on the base station energy consumption prediction set, energy-saving scheduling can be implemented for the base stations in the target network. Among them, in the process of implementing energy-saving scheduling for the base stations in the target network, it is ensured that all user terminals in the target network meet their respective business needs, and the energy consumption of each base station in the target network can be minimized, thereby achieving the goal of energy conservation and emission reduction.

[0054] As shown in FIG3 , the above step S200 may include but is not limited to step S210 and step S220 .

[0055] Step S210, obtaining a set of cells where the user can reside based on the predicted user service needs and a preset network-level logical grid profile;

[0056] Step S220 , inferring the set of cells where the user can reside based on the base station energy consumption profile to obtain a base station energy consumption prediction set.

[0057] In an embodiment of the present application, in the process of determining the base station energy consumption prediction set, the user's available cell set is first obtained based on the predicted user business needs and the preset network-level logical grid portrait; then, the energy consumption of the user's available cell set is inferred by combining the business volume prediction of the user's available cell set and the base station energy consumption portrait to obtain the base station energy consumption prediction set of the base station where the user's available cell is located.

[0058] It can be understood that the network-level logical grid portrait can be a database model obtained based on deep learning of historical network-level logical grid data, which has the function of inference and prediction; the user-residence cell set can be a set of cells where the user terminal may reside when the business needs are met; the base station energy consumption prediction set can be obtained by inferring the user-residence cell set based on the base station energy consumption portrait, wherein the base station energy consumption prediction set is to perform base station energy consumption prediction processing on each user-residence cell information in the user-residence cell set while meeting the business needs of the user terminal, so as to obtain multiple base station energy consumption prediction information, and the combination of multiple base station energy consumption prediction information is the base station energy consumption prediction set.

[0059] As shown in FIG4 , the user portrait may be obtained through, but not limited to, steps S110 , S120 , S130 , S140 , and S150 .

[0060] Step S110, obtaining user attribute information, user service attribute information, and user behavior attribute information of the UE;

[0061] Step S120, performing data cleaning and classification on the user attribute information, the user service attribute information, and the user behavior attribute information to obtain a first training set and a first test set;

[0062] Step S130: adjusting the model parameters of the preset first portrait model based on the first training set;

[0063] Step S140: verifying the adjusted first portrait model based on the first test set;

[0064] Step S150: If the verification passes, the first portrait model is determined as the user portrait.

[0065] In an embodiment of the present application, in the process of constructing a user portrait, the user attribute information, user business attribute information and user behavior attribute information of the user terminal are first obtained; then the user attribute information, user business attribute information and user behavior attribute information are subjected to data cleaning and classification processing to obtain a first training set and a first test set; then the model parameters of the preset first portrait model are adjusted based on the first training set, and then the adjusted first portrait model is verified based on the first test set. If the verification passes, the first portrait model can be determined as the user portrait.

[0066] It is worth noting that by mining and deep learning the user attribute information, user business attribute information and user behavior attribute information of the user terminal, it is possible to build a user portrait, manage the characteristics of the user terminal, and facilitate refined management of the user terminal.

[0067] In some embodiments, as shown in Figure 5, the user portrait may include but is not limited to contract information, service cell information, user service attributes, user location information and user mobility attributes; wherein, the contract information may include user level, user number and contracted service, the service cell information may include wireless standard and wireless bandwidth, the user service attributes may include service type, service rate, service duration and service quality, the user location information may include geographic location, cell and grid, and the user mobility attributes may include moving direction and moving speed.

[0068] In some embodiments, in the process of building a user profile, through the accumulation and learning of data on the user's own attributes, user business attributes, and user behavior attributes; the entire process can be as follows: first, data collection is carried out, mainly the collection and preliminary preprocessing of definition data; then data sorting is carried out, mainly including the elimination of abnormal data and data with extremely low probability. Clustering is then performed to calculate the converged center position data set. The legal image dataset U in the grid satisfies the distance from the center location dataset is (μU , σ U ) probability distribution; where, refers to the “center of mass” of the data set, (μ U , σ U ) represent the mean and variance of the distance from each set of data to the centroid, and the distance here can be calculated using the second-order moment; then perform baseline data verification, and The error analysis is performed with the subsequently collected image data U to satisfy P(U|μ U ,σ U )≥η U (η U is a pre-defined probability threshold), the baseline profile data is successfully constructed; user profile data also needs to be updated periodically to avoid changes in user profiles caused by changes in user behavior and network changes.

[0069] As shown in FIG6 , the network-level logical grid portrait can be obtained through, but not limited to, steps S211 , S212 , S213 , and S214 .

[0070] Step S211, obtaining wireless signal strength information between the base station and the UE;

[0071] Step S212, determining a cell-level logical grid according to the wireless signal strength information;

[0072] Step S213, obtaining a cell-level grid portrait based on the cell information corresponding to the cell-level logical grid and the UE behavior data;

[0073] Step S214: construct a network-level logical grid portrait based on the cell-level logical grid and the cell-level grid portrait.

[0074] In an embodiment of the present application, in the process of constructing a network-level logical grid portrait, the wireless signal strength information between the base station and the user terminal is first obtained; then the cell-level logical grid is determined based on the wireless signal strength information; then the cell-level grid portrait is determined based on the cell information corresponding to the cell-level logical grid and the user terminal behavior data; finally, the network-level logical grid portrait can be constructed based on the cell-level logical grid and the cell-level grid portrait.

[0075] In some embodiments, as shown in Figure 7, the network-level logical grid portrait may include but is not limited to cell information and portrait information, wherein the cell information may include service cell, same-frequency neighboring cell, different-frequency neighboring cell and service cell load information; the portrait information may include service portrait, channel portrait and mobility portrait; the service portrait includes service delay and service rate, the channel portrait includes spectrum efficiency and channel quality, and the mobility portrait includes the target cell switching success rate.

[0076] It is worth noting that the network-level logical grid portrait divides the target network into grids, and each grid is regarded as an independent wireless resource scheduling and user management unit to achieve refined management and control of network resources and users. The data mining, data maintenance, grid construction, and grid update of the network-level grid portrait can be deployed on an open application model or on a dedicated server. Taking into account the hardware capabilities of the base station and the required computing resources, the maintenance and calculation of the logical grid and portrait data involved in the embodiments of the present application can be deployed on a base station, an open application model, or a separate server.

[0077] It should be noted that the wireless signal strength information between the base station and the user terminal may be wireless signal strength information reported by the user terminal.

[0078] A network-level grid portrait is constructed based on the cell-level logical grids, cell-level grid portraits, and cell coverage relationships within the target network area. The network-level logical grid and network-level logical grid portrait also need to be updated periodically to avoid deviations in the logical grid and grid portrait caused by network changes.

[0079] As shown in FIG8 , the above-mentioned step S212 may include but is not limited to step S2121 and step S2122 .

[0080] Step S2121, constructing an initial logical grid according to wireless signal strength information;

[0081] Step S2122: Determine a cell-level logical grid according to the measurement signal in the initial logical grid.

[0082] In an embodiment of the present application, during the process of constructing a cell-level logical grid, an initial logical grid is first constructed according to wireless signal strength information, and then the cell-level logical grid is determined according to the measurement signal in the initial logical grid.

[0083] It should be noted that the measurement signal may be obtained through, but is not limited to, measurement of a downlink broadcast of a base station by a user terminal, or measurement of an uplink signal of a user terminal by a base station.

[0084] As shown in FIG9 , the above-mentioned step S2121 may include but is not limited to step S2123 and step S2124 .

[0085] Step S2123: When the number of cells in which the UE is located is not less than three, the wireless signal strength information is the first signal strength of the serving cell and the neighboring cell, and the first signal strength of the serving cell and the neighboring cell is divided according to a preset strength interval range to obtain an initial logical grid;

[0086] Step S2124: When the number of cells where the UE is located is less than three, the wireless signal strength information is the second signal strength of the serving cell, and the second signal strength of the serving cell and the radial direction of the serving cell are divided according to a preset strength interval to obtain an initial logical grid.

[0087] In an embodiment of the present application, when the number of cells where the user terminal is located is not less than three, the wireless signal strength information is the first signal strength of the serving cell and the neighboring cell, and then the first signal strength of the serving cell and the neighboring cell is divided and processed according to a preset strength interval range to obtain an initial logical grid; when the number of cells where the user terminal is located is less than three, the wireless signal strength information is the second signal strength of the serving cell, and then the second signal strength of the serving cell and the radial direction of the serving cell are divided and processed according to a preset strength interval range to obtain an initial logical grid.

[0088] In some embodiments, as shown in Figure 10, the wireless signal coverage areas of serving cell A, neighboring cell A1, and neighboring cell A2 are divided into continuous intervals with a step size of ΔdB in the direction of decreasing signal strength. The signal intervals of serving cell A, neighboring cell A1, and neighboring cell A2 with a step size of ΔdB can form a polygonal grid, each of which is referred to as an initial logical grid. As shown in Figure 11, the wireless signal coverage area of ​​serving cell A is similarly divided into continuous intervals with a step size of ΔdB in the direction of decreasing signal strength. The coverage area of ​​serving cell A is also divided radially into multiple sector-shaped areas. The axial and radial intervals of serving cell A also form multiple initial logical grids. For Figure 10, assuming that the cell ID of serving cell A is 101, the cell ID of neighboring cell A1 is 201, and the cell ID of neighboring cell A2 is 301, after the initial logical grid is constructed, the cell IDs of the serving cell and neighboring cell corresponding to different initial logical grids can be as shown in the table below.

[0089] As shown in FIG. 12 , the above-mentioned step S2122 may include but is not limited to step S2125 and step S2126 .

[0090] Step S2125, collecting measurement signals in the initial logic grid within a preset time interval;

[0091] Step S2126: When the number of times that the measured signal is not lower than the preset signal threshold reaches a set number, the corresponding initial logical grid is determined as a cell-level logical grid.

[0092] In an embodiment of the present application, after constructing the initial logical grid, in the subsequent process of determining to construct the cell-level logical grid, the measurement signal in the initial logical grid can be collected within a preset time interval. When the number of times the measurement signal is not lower than the preset signal threshold reaches a set number, the corresponding initial logical grid will be determined as a cell-level logical grid.

[0093] In some embodiments, the measurement signal may be a reference signal received power signal covered by a base station. The measurement signal is collected continuously for N days (N ≥ 14), and only when the number of valid measurement results (signal strength not less than a signal threshold) collected is not less than a preset number of times, the corresponding initial logical grid is determined as a cell-level logical grid. The signal threshold and the preset number of times can be determined through experimental verification, or based on experience or theoretical analysis.

[0094] As shown in FIG. 13 , the above-mentioned step S213 may include but is not limited to step S2131 , step S2132 , step S2133 , step S2134 and step S2135 .

[0095] Step S2131, obtaining cell information and UE behavior data corresponding to the cell-level logical grid;

[0096] Step S2132: sorting the cell information and UE behavior data to obtain a second training set and a second test set;

[0097] Step S2133: adjusting the model parameters of the preset second portrait model based on the second training set;

[0098] Step S2134: Verify the adjusted second portrait model based on the second test set;

[0099] Step S2135: If the verification passes, the second portrait model is determined as a cell-level grid portrait.

[0100] In an embodiment of the present application, in the process of constructing a cell-level grid portrait, the cell information and user terminal behavior data corresponding to the cell-level logical grid are first obtained; then the cell information and user terminal behavior data are sorted to obtain a second training set and a second test set; then the model parameters of the preset second portrait model are adjusted based on the second training set; then the adjusted second portrait model is verified based on the second test set; if the verification passes, the second portrait model is determined as a cell-level grid portrait.

[0101] In some embodiments, as shown in FIG14 , in the process of constructing a cell-level grid portrait, the cell information and user terminal behavior data corresponding to each cell-level logical grid are first collected; then the data is filtered, cleaned, and abnormal data is removed, and clustering is performed to calculate the converged center position data set. The legal portrait dataset D in the grid satisfies the distance from the center location dataset. is (μ D , σ D ), that is, the probability density function is During the baseline data verification process, Do deviation analysis with the subsequently collected portrait data D to satisfy P(D|μ D ,σ D )≥η D (η D is a predefined probability threshold), the baseline profile data is constructed successfully, where refers to the “center of mass” of the data set, (μ D , σ D ) represent the mean and variance of the distance from each data set to the centroid, respectively. After the above steps, each cell has multiple logical grids, and each grid has also completed the logical grid portrait data construction. The cell-level logical grid data and grid portrait data of each cell are uploaded to the logical grid / portrait server. The cell-level logical grid and grid portrait data need to be updated periodically to avoid deviations in the logical grid and grid portrait caused by network changes. The logical grid and raster portrait are updated once during each update window.

[0102] As shown in FIG15 , the energy consumption profile of the base station may be obtained through, but not limited to, steps S221 , S222 , S223 , S224 , and S225 .

[0103] Step S221, obtaining hardware feature data, configuration data, and operation data of the base station;

[0104] Step S222: Arrange the hardware feature data, configuration data, and operation data to obtain a third training set and a third test set;

[0105] Step S223: adjusting the model parameters of the preset third portrait model based on the third training set;

[0106] Step S224: verifying the adjusted third portrait model based on the third test set;

[0107] Step S225: If the verification passes, the third portrait model is determined as the base station energy consumption portrait.

[0108] In an embodiment of the present application, in the process of constructing a base station energy consumption profile, the hardware feature data, configuration data and operation data of the base station can be first obtained; then, the hardware feature data, configuration data and operation data can be sorted to obtain a third training set and a third test set; then, based on the third training set, the model parameters of the preset third portrait model are adjusted; then, based on the third test set, the adjusted third portrait model is verified; finally, if the verification passes, the third portrait model is determined as the base station energy consumption profile.

[0109] In some embodiments, as shown in Figure 16, the base station energy consumption profile may include parameters of the baseband processing unit and parameters of the radio frequency unit; wherein, the parameters of the baseband processing unit and the radio frequency unit may include hardware parameters, wireless configuration, energy-saving functions and energy consumption data; the parameters of the baseband processing unit may also include single-board load indicators and cell loads.

[0110] As shown in FIG. 17 , the above-mentioned step S220 may include but is not limited to step S226 and step S227 .

[0111] Step S226: constructing a segmented energy consumption model of the base station based on the base station energy consumption profile;

[0112] Step S227 : performing segmented prediction processing on the set of cells where the user can camp based on the segmented energy consumption model of the base station to obtain a base station energy consumption prediction set.

[0113] In an embodiment of the present application, after obtaining the base station energy consumption portrait, it is also necessary to construct a base station energy consumption model based on the base station energy consumption portrait; then, based on the base station segmented energy consumption model, a segmented prediction processing is performed on the user's resident cell set to obtain a base station energy consumption prediction set.

[0114] It's worth noting that the base station (including the baseband processing unit and radio frequency unit) collects characteristic parameters, configuration parameters, and runtime parameters, and applies data mining and deep learning techniques to analyze and process the collected data set to construct a base station energy consumption profile. Compared to constructing user profiles, base station energy consumption profiles are more complex, so relying solely on profile data sets cannot fully satisfy base station power consumption prediction. This is primarily because the radio frequency unit's power amplifier (PA) consumes 40%-65% of the RF unit's power consumption. Under different traffic load conditions, the PA's operating range varies, resulting in varying PA efficiency (PA efficiency = PA output power / PA input power). To ensure PA output power, the PA input power must be adjusted. Different interference environments and varying user distributions affect the base station's spatial division multiplexing capabilities and scheduling performance, which in turn affects the air interface traffic load and, in turn, the energy consumption of the RF unit and baseband processing unit. Different traffic loads also affect the energy-saving features employed by the baseband and radio frequency processing units, which in turn impacts the overall energy consumption of the baseband and radio frequency processing units.

[0115] As shown in FIG. 18 , the above-mentioned step S226 may include but is not limited to step S2261 , step S2262 , step S2263 , step S2264 , step S2265 , step S2266 and step S2267 .

[0116] Step S2261, obtaining hardware feature data, configuration data, and operation data of the base station;

[0117] Step S2262, determining energy consumption prediction influencing parameters from the hardware feature data, configuration data, and operation data;

[0118] Step S2263, determining multiple power consumption inference models according to the energy consumption prediction influencing parameters and the preset segment division range;

[0119] Step S2264: obtaining a fourth training set and a fourth test set based on the base station energy consumption profile;

[0120] Step S2265: adjusting model parameters of multiple power consumption inference models based on the fourth training set;

[0121] Step S2266: Verify the multiple power consumption inference models after adjustment based on the fourth test set;

[0122] Step S2267: If the verification is passed, multiple power consumption inference models are determined as base station segment energy consumption models.

[0123] In an embodiment of the present application, in the process of constructing a base station segmented energy consumption model based on the base station energy consumption portrait, the hardware feature data, configuration data and operation data of the base station are first obtained, and then the energy consumption prediction influencing parameters are determined from the hardware feature data, configuration data and operation data; then, multiple power consumption reasoning models are determined based on the energy consumption prediction influencing parameters and the preset segmentation range; then, a fourth training set and a fourth test set are obtained based on the base station energy consumption portrait; model parameters of multiple power consumption reasoning models are adjusted based on the fourth training set; then, the multiple power consumption reasoning models after adjustment are verified based on the fourth test set; if the verification passes, the multiple power consumption reasoning models are determined as base station segmented energy consumption models.

[0124] In some embodiments, as shown in FIG19 , the factors that have the greatest impact on base station energy consumption are selected (such as downlink PRB occupancy, number of downlink spatial division multiplexing layers, downlink modulation and coding method, etc.), and the impact of these factors or a combination of these factors on base station energy consumption under different value ranges is determined through laboratory / commercial field test analysis. Combined with the characteristics of the base station hardware, the segmentation range is reasonably delineated and the judgment conditions for the segmentation are determined. In one embodiment provided in the present application, segmentation is performed according to load, and energy consumption reasoning is performed by dividing the load into three segments: high load, medium load, and low load. As shown in Figure 20, in the process of building a segmented energy consumption model of a base station, the present application provides an embodiment, which collects a sufficient amount of base station energy consumption portrait data within different energy consumption reasoning segment ranges, and builds energy consumption models for different load segments based on machine learning; this process can be functionally divided into four sub-functions: "data maintenance, model training, model maintenance, and model distribution"; data maintenance module: mainly supports data collection, data maintenance, training set data / test set data provision, acquisition and maintenance of base station data reporting and energy consumption prediction feedback, providing training set data to the model training module, and providing test set data to the model maintenance module; model training module: The main functions include using a neural network algorithm to perform deep learning based on training set data, sending the generated energy consumption model to the model maintenance module, receiving error feedback from the model maintenance module's predictions based on test set data, and optimizing the neural network algorithm based on the error feedback from the model maintenance module. The model maintenance module receives model updates from the model training module, uses the base station energy consumption model based on test set data to infer base station energy consumption, and feeds back the inference error to the model training module, sending the determined energy consumption inference model to the model delivery module. The model delivery module receives the energy consumption model from the model determination module and delivers it to the base station equipment. During subsequent operation, the base station equipment will input the energy consumption profile parameter sets to be predicted into the energy consumption inference paths corresponding to different loads. After inference based on the corresponding energy consumption model, the predicted base station energy consumption under the corresponding conditions is obtained.

[0125] As shown in FIG. 21 , the above-mentioned step S210 may include but is not limited to step S230 and step S240 .

[0126] Step S230, determining information of a plurality of predicted resident cells that meet the predicted user service needs based on the network-level logical grid portrait;

[0127] Step S240 : adjusting the information of the plurality of predicted camped cells according to the cell energy-saving strategy in the target network to obtain a set of cells where the user can camp.

[0128] In an embodiment of the present application, in the process of determining the set of cells where the user can reside, first, multiple predicted cell information that meets the predicted user service needs is determined based on the network-level logical grid portrait; then, the multiple predicted cell information is adjusted and processed according to the cell energy-saving strategy in the target network to obtain the set of cells where the user can reside.

[0129] It is worth noting that some cells in the target network may be configured with energy-saving strategies, and the cell may not be able to provide services at the next moment. Therefore, it is necessary to further screen and process the information of multiple predicted resident cells that meet the predicted user service needs to obtain a set of user-residing cells.

[0130] As shown in FIG. 22 , the above-mentioned step S227 may include but is not limited to step S2271 , step S2272 and step S2273 .

[0131] Step S2271, selecting a corresponding power consumption inference model for each predicted camping cell in the set of camping cells for the user;

[0132] Step S2272: performing prediction processing on the corresponding predicted resident cell information according to the power consumption inference model to obtain predicted power consumption information, wherein the predicted power consumption information corresponds one-to-one to the predicted resident cell information;

[0133] Step S2273: Determine the multiple pieces of predicted power consumption information as a base station energy consumption prediction set.

[0134] In an embodiment of the present application, in the process of calculating the base station energy consumption prediction set, first, a corresponding power consumption reasoning model is selected for each predicted resident cell information in the user's resident cell set to prepare the prerequisite for energy consumption segmentation prediction; then, the corresponding predicted resident cell information is predicted and processed according to the selected power consumption reasoning model to obtain the predicted power consumption information; finally, the obtained multiple predicted power consumption information is determined as the base station energy consumption prediction set.

[0135] It is worth noting that in the process of selecting the corresponding power consumption reasoning model for each predicted resident cell information in the user's resident cell set, each predicted resident cell information will correspond to a power consumption reasoning model. For example, if a certain predicted resident cell information corresponds to a high load situation, the high load power consumption reasoning model will be used for power consumption prediction processing.

[0136] As shown in FIG. 23 , the above-mentioned step S300 may include but is not limited to step S310 and step S320 .

[0137] Step S310, selecting the predicted power consumption information with the lowest power consumption from the base station energy consumption prediction set;

[0138] Step S320 : Scheduling the base station and UE in the target network according to the predicted camping cell information corresponding to the lowest predicted power consumption information.

[0139] In an embodiment of the present application, during the process of energy-saving scheduling of base stations in the target network, the predicted power consumption information with the lowest power consumption is first selected from the base station energy consumption prediction set, and then the base stations and user terminals in the target network can be scheduled based on the predicted resident cell information corresponding to the lowest predicted power consumption information.

[0140] It is worth noting that the process of energy-saving scheduling of base stations in the target network is essentially to achieve scheduling of user terminals and wireless resources in the target network, so that user terminals reside in appropriate cells, meet the service needs of user terminals and keep the energy consumption of base stations in the target network at the lowest state.

[0141] In order to more clearly illustrate the scheduling process of the network scheduling method provided in the embodiment of the present application, an example is given below.

[0142] As shown in Figure 24, base stations A1-A3 have enabled the F1 frequency band, and base stations B1-B2 have enabled the F2 frequency band. At time t1, terminal U1 resides in the F1 frequency band of base station A1, and terminal U2 resides in the F2 frequency band of base station B2.

[0143] Base station A1 predicts the user's location at time t2: At time t1, terminal U1 is in logical grid a1, and terminal U2 is in logical grid b1. It queries the "user profile" to determine the terminal's movement direction and speed. Combined with the terminal's historical profile, it determines the terminal's grid location at time t2. The diagram indicates that terminal U1 is predicted to be in grid a2 at time t2, and terminal U2 is predicted to be in grid b2.

[0144] Base station A1 determines the set of solutions where the user can reside: Based on the user profile, the user's service attributes can be learned. Combined with the user's real-time service characteristics at time t1, the user's service needs at time t2 can be predicted. Based on the user's service needs at time t2 and the user's grid position, the grid a2 and grid b2 positions are searched in the network-level grid profile library. The set of cells {Cell1, Cell2, ..., Cell n}, base station A1 sends prediction information that terminal U1 will reside in each cell in the set of residable cells at time t2. When each cell in the set of residable cells receives the prediction information that terminal U1 will reside in at time t2 sent by base station A1, each cell needs to make a judgment based on its own configured energy-saving strategy. k (1≤k≤n) determines that at time t2 it will be unable to provide services due to energy saving, k (1≤k≤n) will record this information.

[0145] Each base station determines its own energy consumption at time t2: After predicting the set of cells where the terminal can reside at time t2, the energy consumption profile data of the base station where the cell resides at time t2 is predicted, and a segmented base station energy consumption inference method is used to predict the energy consumption set of each base station at time t2. In this example, it is {P BS_A1 ,P BS_A2 ,P BS_A3 ,P BS_B1 ,P BS_B2}.

[0146] Determine the lowest energy consumption residency plan: Each base station in the target network area (base stations A1-A3, B1-B2 in this example) will predict the energy consumption information at time t2 {P BS_A1 ,P BS_A2 ,P BS_A3 ,P BS_B1 ,P BS_B2}, and base station power consumption P BS_j The cell information of the terminal corresponding to (j∈{A1,A2,A3,B1,B2}) is sent to the Decision Control Server (DCS). The DCS calculates the power consumption set of the target network area at time t2 based on the power consumption information of each base station predicted at time t2 and the terminal residency information corresponding to the power consumption of each base station. (There are K base stations in the current network area and m optional residency solutions.) DCS selects the residency solution with the lowest energy consumption from the power consumption set. k(1≤k≤n) reported to DCS is that it cannot meet the terminal's residency requirements, then DCS will remove the Cell when selecting the available residency solution. k When selecting an available residency solution, DCS will combine the energy-saving strategies of each cell in the target network to avoid selecting cells that are already in the energy-saving period or about to enter the energy-saving period at time t2; in particular, energy-saving functions that will cause the cell to be out of service, such as carrier shutdown, deep sleep and other energy-saving functions.

[0147] DCS issues control commands: Based on the selected residency plan at time t2, the DCS issues corresponding control instructions to each base station / cell in the area, ensuring that each base station / cell in the area executes the plan selected by the DCS, so that each terminal in the area resides in the designated cell at time t2. This minimizes energy consumption in the area while satisfying user service perception.

[0148] In addition, as shown in FIG25 , an embodiment of the present application further provides a network device 700, which includes:

[0149] The memory 720 , the processor 710 , and computer programs stored in the memory 720 and executable on the processor 710 .

[0150] The processor 710 and the memory 720 may be connected via a bus or other means.

[0151] It should be noted that the network device 700 in this embodiment and the network scheduling method in the above embodiments belong to the same concept, so these embodiments have the same implementation principles and technical effects, which will not be described in detail here.

[0152] The non-transient software program and instructions required to implement the network scheduling method of the above embodiment are stored in the memory 720. When executed by the processor 710, the network scheduling method of the above embodiment is executed, for example, the method steps S100 to S300 in FIG. 2, the method steps S210 to S220 in FIG. 3, the method steps S110 to S150 in FIG. 4, the method steps S211 to S214 in FIG. 6, the method steps S2121 to S2122 in FIG. 8, and the method steps S2100 to S2100 in FIG. 9 are executed. Method steps S2123 to S2124, method steps S2125 to S2126 in Figure 12, method steps S2131 to S2135 in Figure 13, method steps S221 to S225 in Figure 15, method steps S226 to S227 in Figure 17, method steps S2261 to S2267 in Figure 18, method steps S230 to S240 in Figure 21, method steps S2271 to S2272 in Figure 22 and method steps S310 to S320 in Figure 23.

[0153] In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by a processor 710, for example, by a processor 710 in the above-mentioned network device 700 embodiment, which enables the above-mentioned processor 710 to execute the network scheduling method in the above-mentioned embodiment, for example, to execute the method steps S100 to S300 in Figure 2, the method steps S210 to S220 in Figure 3, the method steps S110 to S150 in Figure 4, and the method steps S211 to S220 in Figure 6 described above. S214, method steps S2121 to S2122 in Figure 8, method steps S2123 to S2124 in Figure 9, method steps S2125 to S2126 in Figure 12, method steps S2131 to S2135 in Figure 13, method steps S221 to S225 in Figure 15, method steps S226 to S227 in Figure 17, method steps S2261 to S2267 in Figure 18, method steps S230 to S240 in Figure 21, method steps S2271 to S2272 in Figure 22 and method steps S310 to S320 in Figure 23.

[0154] The embodiments of the present application include: obtaining current terminal feature information of user terminals in the target network, and determining predicted user service needs based on the current terminal feature information and a preset user profile; then determining a base station energy consumption prediction set based on a preset base station energy consumption profile and predicted user service needs; and finally, performing energy-saving scheduling for base stations in the target network based on the base station energy consumption prediction set. According to the technical solution provided in the embodiments of the present application, user perception and base station energy consumption are coordinated to reduce base station energy consumption while ensuring user perception, thereby achieving the goal of energy conservation and emission reduction.

[0155] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0156] The above is an explanation of some implementations of the present application, but the present application is not limited to the above-mentioned implementation methods. Technical personnel familiar with the art can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A network scheduling method, the method comprising: Acquire current terminal feature information of a user terminal UE in the target network, and obtain predicted user service needs based on the current terminal feature information and a preset user portrait; Determine a base station energy consumption prediction set according to a preset base station energy consumption profile and the predicted user service demand; Energy-saving scheduling is performed on base stations in the target network according to the base station energy consumption prediction set.

2. The network scheduling method according to claim 1, wherein: The determining of the base station energy consumption prediction set according to the preset base station energy consumption profile and the predicted user service demand includes: Obtaining a set of cells where the user can reside based on the predicted user service demand and a preset network-level logical grid portrait; The base station energy consumption prediction set is obtained by inferring the user-residence cell set according to the base station energy consumption profile.

3. The network scheduling method according to claim 1, wherein: The user portrait is obtained in the following way: Acquire user attribute information, user service attribute information, and user behavior attribute information of the UE; Performing data cleaning and classification on the user attribute information, the user service attribute information, and the user behavior attribute information to obtain a first training set and a first test set; Adjusting model parameters of a preset first portrait model based on the first training set; Performing verification processing on the adjusted first portrait model based on the first test set; If the verification passes, the first portrait model is determined as the user portrait.

4. The network scheduling method according to claim 2, wherein: The network-level logical grid image is obtained by: Acquire wireless signal strength information between the base station and the UE; Determine a cell-level logical grid according to the wireless signal strength information; Obtain a cell-level grid portrait according to the cell information corresponding to the cell-level logical grid and UE behavior data; The network-level logical grid portrait is constructed according to the cell-level logical grid and the cell-level grid portrait.

5. The network scheduling method according to claim 4, wherein: The determining of the cell-level logical grid according to the wireless signal strength information comprises: Constructing an initial logical grid according to the wireless signal strength information; The cell-level logical grid is determined according to the measurement signal in the initial logical grid.

6. The network scheduling method according to claim 5, wherein: The constructing an initial logical grid according to the wireless signal strength information comprises: When the number of cells where the UE is located is not less than three, the wireless signal strength information is the first signal strength of the serving cell and the neighboring cell, and the first signal strength of the serving cell and the neighboring cell is divided and processed according to a preset strength interval range to obtain the initial logical grid; When the number of cells where the UE is located is less than three, the wireless signal strength information is the second signal strength of the serving cell, and the second signal strength of the serving cell and the radial direction of the serving cell are divided according to a preset strength interval range to obtain the initial logical grid.

7. The network scheduling method according to claim 5, wherein: The determining of the cell-level logical grid according to the measurement signal in the initial logical grid comprises: Collecting measurement signals in the initial logic grid within a preset time interval; When the number of times that the measurement signal is not lower than the preset signal threshold reaches a set number, the corresponding initial logical grid is determined as the cell-level logical grid.

8. The network scheduling method according to claim 4, wherein: The obtaining of a cell-level grid portrait according to the cell information corresponding to the cell-level logical grid and the UE behavior data includes: Obtaining cell information and UE behavior data corresponding to the cell-level logical grid; Arranging the cell information and the UE behavior data to obtain a second training set and a second test set; Adjusting model parameters of a preset second portrait model based on the second training set; Performing verification processing on the adjusted second portrait model based on the second test set; If the verification passes, the second portrait model is determined as the cell-level grid portrait.

9. The network scheduling method according to claim 1, wherein: The base station energy consumption profile is obtained in the following manner: Acquiring hardware feature data, configuration data, and operation data of the base station; Arrange the hardware feature data, the configuration data, and the operation data to obtain a third training set and a third test set; Adjusting model parameters of a preset third portrait model based on the third training set; Performing verification processing on the adjusted third portrait model based on the third test set; If the verification passes, the third portrait model is determined as the base station energy consumption portrait.

10. The network scheduling method according to claim 2, wherein: The inference of the user-residenceable cell set according to the preset base station energy consumption profile to obtain a base station energy consumption prediction set includes: Constructing a segmented energy consumption model of a base station based on the energy consumption portrait of the base station; The base station energy consumption prediction set is obtained by performing segmented prediction processing on the user-residenceable cell set based on the segmented energy consumption model of the base station.

11. The network scheduling method according to claim 10, wherein: The constructing of a base station segmented energy consumption model based on the base station energy consumption portrait includes: Acquiring hardware feature data, configuration data, and operation data of the base station; Determining energy consumption prediction influencing parameters from the hardware feature data, the configuration data, and the operation data; Determining multiple power consumption reasoning models according to the energy consumption prediction influencing parameters and the preset segment division range; Obtaining a fourth training set and a fourth test set based on the base station energy consumption portrait; Adjusting model parameters of the plurality of power consumption reasoning models based on the fourth training set; Performing verification processing on the multiple power consumption reasoning models after adjustment based on the fourth test set; If the verification is passed, the multiple power consumption reasoning models are determined as the base station segment energy consumption model.

12. The network scheduling method according to claim 2, wherein: The obtaining of a set of cells where the user can reside according to the predicted user service demand and a preset network-level logical grid profile includes: Determine information of a plurality of predicted resident cells that meet the predicted user service demand according to the network-level logical grid portrait; The plurality of predicted resident cell information are adjusted and processed according to the cell energy saving strategy in the target network to obtain the user resident cell set.

13. The network scheduling method according to claim 10, wherein: The performing segmented prediction processing on the set of cells where the user can reside based on the segmented energy consumption model of the base station to obtain the base station energy consumption prediction set includes: Selecting the corresponding power consumption reasoning model for each predicted resident cell information in the set of resident cells where the user can reside; Performing prediction processing on the corresponding predicted resident cell information according to the power consumption inference model to obtain predicted power consumption information, wherein the predicted power consumption information corresponds to the predicted resident cell information one by one; The plurality of predicted power consumption information are determined as the base station energy consumption prediction set.

14. The network scheduling method according to claim 13, wherein: The performing energy-saving scheduling processing on the base stations in the target network according to the base station energy consumption prediction set includes: Selecting the predicted power consumption information with the lowest power consumption from the base station energy consumption prediction set; The base station and the UE in the target network are scheduled according to the predicted resident cell information corresponding to the lowest predicted power consumption information.

15. A network device comprising: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the network scheduling method according to any one of claims 1 to 14 is implemented.

16. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the network scheduling method according to any one of claims 1 to 14.

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