A base station sleep method based on traffic prediction

Abstract

The present application relates to the field of communication technology and provides a base station sleep method based on traffic prediction, the method comprising: extracting features from historical network traffic data to obtain traffic feature vectors, and calculating the temporal similarity between spatial units based on the traffic feature vectors; constructing a spatiotemporal dependency graph based on the temporal similarity; obtaining static environmental features and dynamic environmental features of the target area based on spatial environmental data, and calculating model parameters based on the static environmental features and dynamic environmental features; using the spatiotemporal dependency graph and model parameters, performing traffic prediction on the target area using a traffic prediction model to obtain predicted network traffic for all spatial units; constructing an optimization objective function for describing the status of all base stations in the target area based on the predicted network traffic, and performing minimization on the optimization objective function to obtain a base station sleep control scheme for the target area. The method of the present application can improve the accuracy of sleep control of base stations.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a base station sleep method based on traffic prediction. Background Art

[0002] As the core infrastructure of modern communications, communication base stations receive, demodulate, process, and forward signals, providing a variety of services, including voice, SMS, and data transmission, to end users within their coverage area. In recent years, next-generation communication networks, represented by 5G, have been rapidly integrated into and driven the development of various sectors of the economy and society. However, with the widespread adoption of smart mobile devices and the rapid rise of emerging applications such as virtual reality and the Internet of Vehicles, 5G base stations currently consume up to 70% of the total energy consumed by wireless communication networks, placing higher standards and requirements on base station power consumption and operating costs. Furthermore, the uneven temporal and spatial distribution of communication traffic, resulting in wasted network resources, further exacerbates energy consumption issues. For example, urban areas often face network resource constraints during peak hours, while rural areas experience a significant oversupply of resources during off-peak hours. Implementing dynamic sleep control for communication base stations by predicting future communication traffic is a simple, intuitive, and effective way to improve network resource utilization, reduce base station energy consumption, and promote green communications.

[0003] my country has built the world's largest and most technologically advanced 5G communications network, significantly increasing its data capacity and connection density, laying a solid foundation for large-scale data collection and analysis. At the same time, the ownership of devices such as smartphones, smartwatches, and computers continues to grow, and each terminal device continuously generates a large amount of data during use. This data contains rich temporal and spatial patterns of network traffic during users' daily activities, providing a comprehensive, dynamic, and highly reliable data foundation.

[0004] Base station dormancy strategies aim to minimize energy consumption by dormant base stations while meeting user network traffic demands. However, network traffic is affected by numerous environmental factors, such as holidays, weather, and traffic. Its dynamic patterns are complex and diverse, often varying with time and space. Understanding the spatially heterogeneous dynamics of network traffic and accurately predicting it in real time are crucial prerequisites for implementing base station dormancy strategies. Existing base station dormancy methods for traffic prediction are primarily based on statistical theory and deep learning. Statistical methods employ linear parameterized mathematical models to fit the temporal dependencies of historical network traffic data, offering computational simplicity and interpretable model parameters. However, real-world network traffic exhibits complex nonlinear characteristics and spatial dependencies, which makes the inherent sample independence assumptions of statistical methods untenable and prevents them from accurately and comprehensively fitting the temporal and spatial variations of network traffic. Deep learning methods employ deep neural network models to fit the complex temporal and spatial dependencies inherent in large-scale historical network traffic data, offering the advantages of deep feature extraction and robust fitting capabilities. These methods primarily focus on the overall temporal and spatial dependencies of network traffic, paying less attention to local spatial heterogeneity. Even if some models consider local spatial heterogeneity, their high modeling cost is a major limitation to their practical application. Furthermore, these methods ignore the dynamic nature of spatial heterogeneity, making it difficult to reveal the spatiotemporal dynamics of network traffic under different service environments at different times. This, in turn, affects the efficiency and accuracy of forecasting user network traffic demand, leading to low accuracy in base station sleep control. Summary of the Invention

[0005] The present application provides a base station sleep method based on traffic prediction, which can solve the problem of low accuracy in base station sleep control.

[0006] In a first aspect, an embodiment of the present application provides a base station sleep method based on traffic prediction, the base station sleep method comprising:

[0007] Obtain historical network traffic data and spatial environment data for each spatial unit in the target area; the historical network traffic data includes the network traffic of the spatial unit at multiple historical moments;

[0008] Perform feature extraction on each historical network traffic data to obtain the traffic feature vector of each spatial unit, and calculate the temporal similarity between every two spatial units based on all traffic feature vectors;

[0009] A spatiotemporal dependency graph is constructed based on all temporal similarities; multiple nodes in the spatiotemporal dependency graph correspond one-to-one to multiple spatial units, and the edge between two nodes represents the similarity relationship between the corresponding two spatial units;

[0010] Obtain the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment based on the spatial environmental data of all spatial units, and calculate the model parameters of the traffic prediction model based on the static environmental characteristics and all dynamic environmental characteristics;

[0011] Based on the spatiotemporal dependency graph and model parameters, the traffic prediction model is used to predict the traffic flow in the target area and obtain the predicted network traffic of all spatial units;

[0012] An optimization objective function is constructed based on the predicted network traffic to describe the status of all base stations in the target area. The optimization objective function is minimized to obtain a base station sleep control scheme for the target area. The base station sleep control scheme is used to describe the operating status of all base stations.

[0013] Optionally, feature extraction is performed on each historical network traffic data to obtain a traffic feature vector for each spatial unit, including:

[0014] For each historical network traffic data, Fourier transform is performed on the historical network traffic data to obtain a spectrum sequence. The elements corresponding to the first multiple historical moments in the spectrum sequence are selected as key elements, and the traffic feature vector is constructed based on all key elements.

[0015] Optionally, perform Fourier transform on historical network traffic data to obtain a spectrum sequence, including:

[0016] By formula:

[0017]

[0018] Perform Fourier transform on the i-th historical network traffic data to obtain the i-th spectrum sequence S i (u);

[0019] Among them, x i (t) represents the network traffic at the t-th historical moment in the i-th historical network traffic data, L-1 represents the number of the last historical moment, and j represents an imaginary unit;

[0020] Construct a traffic feature vector based on all key elements, including:

[0021] By formula:

[0022]

[0023] Calculate the i-th traffic feature vector F i ;

[0024] Among them, S i (u0) represents the 0th key element of the i-th spectrum sequence, represents the Kth ith spectrum sequencef key elements, Indicates S i The real part of (u0), Indicates S i The imaginary part of (u0), express The real part of express The imaginary part of

[0025] The temporal similarity between every two spatial units is calculated based on all traffic feature vectors, including:

[0026] By formula:

[0027]

[0028]

[0029] Calculate the temporal similarity d(F i ,F g );

[0030] Among them, S g (u0) represents the 0th key element of the gth spectrum sequence, represents the Kth g-th spectrum sequence f key elements, Indicates S g The real part of (u0), express The imaginary part of , i,g∈(1,2,...,N), i≠g, N represents the number of spatial units.

[0031] Optionally, build a spatiotemporal dependency graph based on all temporal similarities, including:

[0032] For each spatial unit, all the time series similarities corresponding to the spatial unit are sorted from large to small, and the first multiple time series similarities in the sorting results are used as the target time series similarities. It is considered that there is a similar relationship between the spatial unit and the spatial unit corresponding to each target time series similarity;

[0033] Multiple spatial units are regarded as multiple nodes. If there is a similar relationship between the two spatial units corresponding to two nodes, an edge is generated between the two nodes to construct a spatiotemporal dependency graph.

[0034] Optionally, the spatial environment data includes static environment data of the spatial unit and dynamic environment data of multiple historical moments;

[0035] Based on the spatial environment data of all spatial units, the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment are obtained, including:

[0036] Extract features from the static environment data of all spatial units to obtain the static environment features of the target area;

[0037] Feature extraction is performed on the dynamic environmental data of all spatial units at each historical moment to obtain the dynamic environmental features corresponding to the target area at each historical moment.

[0038] Optionally, the model parameters of the traffic prediction model are calculated based on the static environmental characteristics and all dynamic environmental characteristics, including:

[0039] Obtain geographic meta-knowledge of the target area at each historical moment based on static environmental characteristics and all dynamic environmental characteristics;

[0040] For each historical moment, model parameters of the traffic prediction model at the historical moment are generated based on the geographic meta-knowledge corresponding to the historical moment.

[0041] Optionally, geographic meta-knowledge of the target area at each historical moment is obtained based on static and dynamic environmental characteristics, including:

[0042] By formula:

[0043]

[0044] Calculate the geographic meta-knowledge MK at the tth historical moment t ;

[0045] in, represents the convolution filter, SF represents the static environment feature, DF t represents the dynamic environment characteristics at the t-th historical moment, || represents the feature connection operator, and Reshape() represents the reorganization function;

[0046] The model parameters of the historical traffic prediction model are generated based on geographic meta-knowledge, including:

[0047] By formula:

[0048] M t =Reshape -1 (MK t )M shared

[0049] Calculate the model parameter M at the tth historical moment t ;

[0050] Among them, M shared represents the shared parameter matrix, Wr,i ,W z,i ,W h,i ,b r,i ,b z,i ,b h,i These are all parameters in the traffic prediction model.

[0051] Optionally, based on the spatiotemporal dependency graph and model parameters, a traffic prediction model is used to predict traffic in the target area, obtaining the predicted network traffic for all spatial units, including:

[0052] Based on all historical network traffic data and all dynamic environment data, the potential time-dependent characteristics of the target area at each historical moment are obtained;

[0053] Calculate the spatiotemporal dependency features of each historical moment using the spatiotemporal dependency graph and the potential temporal dependency features of each historical moment;

[0054] For each historical moment, the model parameters and all spatiotemporal dependency features of the historical moment are used to extract the dynamic heterogeneous spatiotemporal dependency features of the historical moment;

[0055] Traffic prediction is performed based on the dynamic heterogeneous spatiotemporal dependency characteristics of the last historical moment to obtain the predicted network traffic of all spatial units.

[0056] Optionally, based on all historical network traffic data and all dynamic environment data, obtain the potential time-dependent characteristics of the target area at each historical moment, including:

[0057] By formula:

[0058]

[0059] Calculate the potential time-dependent features at the t-th historical moment

[0060] in, represents the potential time-dependent characteristics of the i-th spatial unit at the t-th historical moment, N represents the number of spatial units, X t represents the network traffic of all spatial units at the t-th historical moment, DE t represents the dynamic environment data of all spatial units at the t-th historical moment, GRU() represents the gated recurrent unit, represents the potential time-dependent characteristics at the t-1th historical moment. When t = 0, is the preset initial value;

[0061] The spatiotemporal dependency features of each historical moment are calculated using the spatiotemporal dependency graph and the potential temporal dependency features of each historical moment, including:

[0062] By formula:

[0063]

[0064] Calculate the spatiotemporal dependence characteristics of the t-th historical moment

[0065] in, represents the similarity weight vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, represents the potential time-dependent characteristics of the j-th spatial unit at the t-th historical moment, W g and b g Indicates parameters, represents the attention score vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, N(i) represents the number of the spatial units corresponding to all nodes adjacent to the node corresponding to the i-th spatial unit in the spatiotemporal dependency graph, represents the spatiotemporal dependency feature of the i-th spatial unit at the t-th historical moment, LeakyReLU represents the activation function, x represents the input data of the activation function, and α represents the hyperparameter;

[0066] Using the model parameters and all spatiotemporal dependency features of historical moments, dynamic heterogeneous spatiotemporal dependency features of historical moments are extracted, including:

[0067] By formula:

[0068]

[0069] Calculate the dynamic heterogeneous spatiotemporal dependence characteristics at the t-th historical moment

[0070] in, represents the reset gate output corresponding to the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-1th historical moment, represents the update gate output corresponding to the i-th spatial unit at the t-th historical moment, represents the candidate hidden state of the i-th spatial unit at the t-th historical moment;

[0071] Traffic prediction is performed based on the dynamic heterogeneous spatiotemporal dependency characteristics of the last historical moment, and the predicted network traffic of all spatial units is obtained, including:

[0072] By formula:

[0073]

[0074] Calculate the predicted network traffic for all spatial units

[0075] in, Represents the predicted dynamic environmental data of all spatial units, MK t represents geographic meta-knowledge, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the last historical moment, W p and b p All represent parameters.

[0076] Optionally, the optimization objective function is:

[0077]

[0078] Among them, E represents the coverage energy consumption matrix, e m,i represents the energy consumption when the mth base station serves the i-th spatial unit, M represents the number of base stations in the target area, N represents the number of spatial units, S t′ represents the base station state matrix at the future time, Represents the state of the mth base station. When , it means that the mth base station is in working state at the future time. When , it means that the mth base station is in sleep state at the next moment, E 0 represents the fixed energy consumption matrix of the base station, represents the fixed energy consumption of the mth base station, E s represents the base station state switching energy consumption matrix, represents the energy consumption of the mth base station switching state, S t′-1 represents the base station state matrix at the previous moment in the future, F represents the coverage flow matrix, f m,i It represents the traffic load that the m-th base station can provide when serving the i-th spatial unit.

[0079] In a second aspect, an embodiment of the present application provides a base station sleep device based on traffic prediction, including:

[0080] The first acquisition module acquires historical network traffic data and spatial environment data of each spatial unit in the target area; the historical network traffic data includes the network traffic of the spatial unit at multiple historical moments;

[0081] The feature extraction module extracts features from each historical network traffic data to obtain the traffic feature vector of each spatial unit, and calculates the temporal similarity between every two spatial units based on all traffic feature vectors;

[0082] The construction module constructs a spatiotemporal dependency graph based on all temporal similarities. Multiple nodes in the spatiotemporal dependency graph correspond one-to-one to multiple spatial units, and the edge between two nodes represents the similarity relationship between the corresponding two spatial units.

[0083] The second acquisition module obtains the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment according to the spatial environmental data of all spatial units, and calculates the model parameters of the traffic prediction model based on the static environmental characteristics and all dynamic environmental characteristics;

[0084] The traffic prediction module uses the traffic prediction model to predict the traffic flow in the target area based on the spatiotemporal dependency graph and model parameters, and obtains the predicted network traffic flow of all spatial units;

[0085] The minimization solution module constructs an optimization objective function based on the predicted network traffic to describe the status of all base stations in the target area, and minimizes the optimization objective function to obtain the base station sleep control plan for the target area; the base station sleep control plan is used to describe the operating status of all base stations.

[0086] In a third aspect, an embodiment of the present application provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned base station sleep method based on traffic prediction when executing the above-mentioned computer program.

[0087] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned base station sleep method based on traffic prediction.

[0088] The above solution of the present application has the following beneficial effects:

[0089] In an embodiment of the present application, historical network traffic data and spatial environment data of each spatial unit in the target area are obtained, and then feature extraction is performed on each historical network traffic data to obtain a traffic feature vector for each spatial unit, and the temporal similarity between every two spatial units is calculated based on all traffic feature vectors, and then a spatiotemporal dependency graph is constructed based on all temporal similarities, and then the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment are obtained based on the spatial environment data of all spatial units, and the model parameters of the traffic prediction model are calculated based on the static environmental characteristics and all dynamic environmental characteristics, and then based on the spatiotemporal dependency graph and model parameters, the traffic prediction model is used to predict the traffic of the target area to obtain the predicted network traffic of all spatial units, and finally, an optimization objective function for describing the status of all base stations in the target area is constructed based on the predicted network traffic, and the optimization objective function is minimized to obtain a base station sleep control scheme for the target area. Among them, traffic prediction is performed based on spatial environment data and historical network traffic data, the characteristics of network traffic in the two dimensions of space and time are analyzed, the impact of space and time on network traffic is characterized, and the accuracy of traffic prediction is effectively improved. The optimization objective function of the base station is constructed based on the highly accurate predicted network traffic, so that the optimization objective function can reasonably and accurately describe the state of the base station at future moments. The base station is put into sleep control based on a reasonable and accurate optimization objective function, which effectively improves the accuracy of sleep control of the base station.

[0090] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0092] Figure 1 A flowchart of a base station sleep method based on traffic prediction provided in one embodiment of the present application;

[0093] Figure 2 A schematic diagram of a specific flow chart of a base station sleep method based on traffic prediction provided in one embodiment of the present application;

[0094] Figure 3 A schematic diagram of the structure of a base station sleep device based on traffic prediction provided in one embodiment of the present application;

[0095] Figure 4 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0096] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0097] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0098] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0099] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0100] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0101] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0102] In response to the problem of low accuracy of existing base station sleep control, an embodiment of the present application provides a base station sleep method based on traffic prediction. The base station sleep method obtains historical network traffic data and spatial environment data of each spatial unit in the target area, and then performs feature extraction on each historical network traffic data to obtain a traffic feature vector for each spatial unit. The temporal similarity between every two spatial units is calculated based on all traffic feature vectors, and then a spatiotemporal dependency graph is constructed based on all temporal similarities. The static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment are obtained based on the spatial environment data of all spatial units, and the model parameters of the traffic prediction model are calculated based on the static environmental characteristics and all dynamic environmental characteristics. The traffic prediction model is then used to predict the traffic of the target area based on the spatiotemporal dependency graph and the model parameters to obtain the predicted network traffic of all spatial units. Finally, an optimization objective function is constructed based on the predicted network traffic to describe the status of all base stations in the target area, and the optimization objective function is minimized to obtain a base station sleep control scheme for the target area. Among them, traffic prediction is performed based on spatial environment data and historical network traffic data, the characteristics of network traffic in the two dimensions of space and time are analyzed, the impact of space and time on network traffic is characterized, and the accuracy of traffic prediction is effectively improved. The optimization objective function of the base station is constructed based on the highly accurate predicted network traffic, so that the optimization objective function can reasonably and accurately describe the state of the base station at future moments. The base station is put into sleep control based on a reasonable and accurate optimization objective function, which effectively improves the accuracy of sleep control of the base station.

[0103] Next, an exemplary description is given of the base station sleep method based on traffic prediction provided in this application.

[0104] like Figure 1 As shown, the base station sleep method based on traffic prediction provided by this application includes the following steps:

[0105] Step 11: Obtain historical network traffic data and spatial environment data for each spatial unit in the target area.

[0106] The above-mentioned historical network traffic data includes the network traffic of the spatial unit at multiple historical moments (such as the number of incoming calls, outgoing calls, text message reception, text message transmission, etc.). The above-mentioned target area is the area where the base station needs to be put under sleep control, and the spatial unit is the sub-area divided by the target area. For example, the target area can be divided into a grid, and the area corresponding to each grid is a spatial unit. The spatial environment data includes the static environment data of the spatial unit (such as the location of the spatial unit, different road densities, building coverage ratio, number of buildings, impervious surface area, night light intensity, number of points of interest, intensity of social activities, number of base stations, etc.) and dynamic environment data at multiple historical moments (such as temperature, relative humidity, total radiation, net radiation, atmospheric pressure, precipitation intensity, precipitation coverage, precipitation type, week corresponding to the historical moment, hour corresponding to the historical moment, etc.).

[0107] In some embodiments of the present application, historical network traffic data can be obtained by accessing a system that monitors the network traffic of the target area (such as the communication data open API interface provided by the three major communication operators), and spatial environment data can be obtained by accessing a website that discloses relevant data of the target area.

[0108] Step 12: extract features from each historical network traffic data to obtain a traffic feature vector for each spatial unit, and calculate the temporal similarity between every two spatial units based on all traffic feature vectors.

[0109] In some embodiments of the present application, the steps of extracting features from each historical network traffic data, obtaining a traffic feature vector for each spatial unit, and calculating the temporal similarity between every two spatial units based on all traffic feature vectors are specifically as follows:

[0110] In the first step, Fourier transform is performed on each historical network traffic data to obtain a spectrum sequence. The elements corresponding to the first multiple historical moments in the spectrum sequence are selected as key elements, and the traffic feature vector is constructed based on all key elements.

[0111] Specifically, through the formula:

[0112]

[0113] Perform Fourier transform on the i-th historical network traffic data to obtain the i-th spectrum sequence S i (u).

[0114] Among them, x i (t) represents the network traffic at the t-th historical moment in the i-th historical network traffic data, L-1 represents the number of the last historical moment, and j represents an imaginary unit.

[0115] By formula:

[0116]

[0117] Calculate the i-th traffic feature vector F i .

[0118] Among them, S i (u0) represents the 0th key element of the i-th spectrum sequence, represents the Kth ith spectrum sequence f key elements, Indicates S i The real part of (u0), Indicates S i The imaginary part of (u0), express The real part of express The imaginary part of .

[0119] In the second step, the temporal similarity between every two spatial units is calculated based on all traffic feature vectors.

[0120] Specifically, through the formula:

[0121]

[0122] Calculate the temporal similarity d(F i ,F g ).

[0123] Among them, S g (u0) represents the 0th key element of the gth spectrum sequence, represents the Kth g-th spectrum sequence f key elements, Indicates S g The real part of (u0), express The imaginary part of , i,g∈(1,2,...,N), i≠g, N represents the number of spatial units.

[0124] It should be noted that the number of key elements is a preset number.

[0125] Step 13: Construct a spatiotemporal dependency graph based on all temporal similarities.

[0126] The multiple nodes in the above-mentioned spatiotemporal dependency graph correspond one-to-one to the multiple spatial units, and the edge between two nodes is the similarity relationship between the corresponding two spatial units.

[0127] In some embodiments of the present application, the above-mentioned construction of the spatiotemporal dependency graph based on all temporal similarities is specifically as follows:

[0128] In the first step, for each spatial unit, all the temporal similarities corresponding to the spatial unit are sorted from large to small, and the first multiple temporal similarities in the sorting results are used as target temporal similarities. It is considered that there is a similar relationship between the spatial unit and the spatial unit corresponding to each target temporal similarity.

[0129] For example, for the first spatial unit, the temporal similarities between it and the second spatial unit, the third spatial unit, the fourth spatial unit, and the fifth spatial unit are 1.4, 2.5, 0.2, and 1.3, respectively. Among them, the number of target temporal similarities is preset to 3, and it is considered that the spatial units corresponding to 2.5, 1.4, and 1.3 (the third spatial unit, the second spatial unit, and the fifth spatial unit) have a similar relationship with the first spatial unit.

[0130] In the second step, multiple spatial units are used as multiple nodes. If there is a similar relationship between the two spatial units corresponding to two nodes, an edge is generated between the two nodes to construct a spatiotemporal dependency graph.

[0131] It is worth mentioning that by calculating the temporal similarity between spatial units based on historical network traffic data, it is possible to spatially analyze the status of historical network traffic in the target area and analyze the correlation between network traffic between spatial units.

[0132] Step 14: Obtain static environmental features of the target area and dynamic environmental features corresponding to each historical moment according to the spatial environmental data of all spatial units, and calculate model parameters of the traffic prediction model based on the static environmental features and all dynamic environmental features.

[0133] In some embodiments of the present application, the steps of obtaining the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment based on the spatial environmental data of all spatial units, and calculating the model parameters of the traffic prediction model based on the static environmental characteristics and all dynamic environmental characteristics are specifically as follows:

[0134] In the first step, feature extraction is performed on the static environment data of all spatial units to obtain the static environment features of the target area.

[0135] For example, two sequentially connected fully connected layers can be used to extract features from the static environment data of all spatial units to obtain the static environment features of the target area, which can be expressed as:

[0136]

[0137] Among them, SF represents the static environment characteristics, and They all represent learnable parameters, σ() represents the activation function, x represents the input of the activation function, and SE represents the static environment data of all spatial units.

[0138] In the second step, feature extraction is performed on the dynamic environmental data of all spatial units at each historical moment to obtain the dynamic environmental features corresponding to the target area at each historical moment.

[0139] For example, the gated recurrent units and fully connected layers connected in sequence can be used to extract features from all dynamic environmental data of all spatial units to obtain the dynamic environmental features corresponding to the target area at each historical moment. The expression is:

[0140]

[0141] Among them, DF t represents the dynamic environment characteristics at the t-th historical moment, Denotes DE t The output hidden state, DE t Represents the dynamic environmental data of all spatial units at the t-th historical moment, Denotes DE t-1 The output hidden state, DE t-1 represents the dynamic environment data of all spatial units at the t-1th historical moment, σ() represents the activation function, and GRU() represents the calculation of the gated recurrent unit. The expression is:

[0142]

[0143] Among them, R t Represents the reset gate, Z t represents the update gate, represents the candidate hidden state, X t represents the input data of the gated recurrent unit (i.e., the dynamic environment data of all spatial units at the t-th historical moment), W xr 、W hr 、W xz 、W hz 、W xh 、W hh 、b r 、b z 、b h These are parameters in the gated neural unit.

[0144] The third step is to obtain the geographic meta-knowledge of the target area at each historical moment based on static and dynamic environmental characteristics.

[0145] Specifically, through the formula:

[0146]

[0147] Calculate the geographic meta-knowledge MK at the tth historical moment t .

[0148] in, represents the convolution filter, SF represents the static environment feature, DF t represents the dynamic environment characteristics at the t-th historical moment, || represents the feature connection operator, and Reshape() represents the reorganization function.

[0149] The fourth step is to generate the model parameters of the traffic prediction model at each historical moment based on the geographic meta-knowledge corresponding to the historical moment.

[0150] Specifically, through the formula:

[0151] M t =Reshape -1 (MK t )M shared

[0152] Calculate the model parameter M at the tth historical moment t .

[0153] Among them, M shared represents the shared parameter matrix, W r,i ,W z,i ,W h,i ,b r,i ,b z,i ,b h,i These are all parameters in the traffic prediction model.

[0154] It is worth mentioning that obtaining static environmental characteristics and dynamic environmental characteristics can describe the environmental information of the target area in terms of static environment and dynamic environment respectively, and calculating model parameters based on static environmental characteristics and dynamic environmental characteristics makes the calculation of model parameters have information of static environment and dynamic environment, thereby improving the practicality and accuracy of model parameters.

[0155] Step 15: Based on the spatiotemporal dependency graph and model parameters, the traffic prediction model is used to perform traffic prediction on the target area to obtain the predicted network traffic of all spatial units.

[0156] The above-mentioned predicted network traffic is the network traffic of the spatial unit at a future time (such as incoming phone calls, outgoing phone calls, received text messages, sent text messages, etc.).

[0157] In some embodiments of the present application, the steps of performing traffic prediction for the target area using the traffic prediction model based on the spatiotemporal dependency graph and model parameters to obtain the predicted network traffic for all spatial units are specifically as follows:

[0158] In the first step, based on all historical network traffic data and all dynamic environment data, the potential time-dependent characteristics of the target area at each historical moment are obtained.

[0159] Specifically, through the formula:

[0160]

[0161] Calculate the potential time-dependent features at the t-th historical moment

[0162] in, represents the potential time-dependent characteristics of the i-th spatial unit at the t-th historical moment, N represents the number of spatial units, X t represents the network traffic of all spatial units at the t-th historical moment, DE t represents the dynamic environment data of all spatial units at the t-th historical moment, GRU() represents the gated recurrent unit, represents the potential time-dependent characteristics at the t-1th historical moment. When t = 0, The default initial value.

[0163] In the second step, the spatiotemporal dependency features of each historical moment are calculated using the spatiotemporal dependency graph and the latent temporal dependency features of each historical moment.

[0164] Specifically, through the formula:

[0165]

[0166]

[0167] Calculate the spatiotemporal dependence characteristics of the t-th historical moment

[0168] in, represents the similarity weight vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, represents the potential time-dependent characteristics of the j-th spatial unit at the t-th historical moment, W g and b g Indicates parameters, represents the attention score vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, N(i) represents the number of the spatial units corresponding to all nodes adjacent to the node corresponding to the i-th spatial unit in the spatiotemporal dependency graph, represents the spatiotemporal dependency feature of the i-th spatial unit at the t-th historical moment, LeakyReLU represents the activation function, x represents the input data of the activation function, and α represents the hyperparameter.

[0169] In the third step, for each historical moment, the model parameters and all spatiotemporal dependency features of the historical moment are used to extract the dynamic heterogeneous spatiotemporal dependency features of the historical moment.

[0170] Specifically, through the formula:

[0171]

[0172] Calculate the dynamic heterogeneous spatiotemporal dependence characteristics at the t-th historical moment

[0173] in, represents the reset gate output corresponding to the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-1th historical moment, represents the update gate output corresponding to the i-th spatial unit at the t-th historical moment, Represents the candidate hidden state of the i-th spatial unit at the t-th historical moment. When t = 0, The default initial value.

[0174] In the fourth step, traffic prediction is performed based on the dynamic heterogeneous spatiotemporal dependency characteristics of the last historical moment to obtain the predicted network traffic of all spatial units.

[0175] Specifically, through the formula:

[0176]

[0177] Calculate the predicted network traffic for all spatial units

[0178] in, Represents the predicted dynamic environmental data of all spatial units, MK t represents geographic meta-knowledge, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the last historical moment, W p and b p All represent parameters.

[0179] It is understood that all of the above calculation formulas (the formula for calculating potential time-dependent features, the formula for calculating spatiotemporal-dependent features, the formula for calculating dynamic heterogeneous spatiotemporal-dependent features, and the formula for calculating predicted network traffic) are expressions of the prediction model. If the current time is 9:00, the multiple historical moments can be the previous moments including the current time, such as 6:00, 7:00, 8:00, and 9:00. The future moment is the moment after the current time, such as 10:00.

[0180] Exemplarily, before performing the traffic prediction in the fourth step, the above-mentioned formula for predicting network traffic can be used to predict traffic based on the dynamic heterogeneous spatiotemporal dependency characteristics of each historical moment, and the predicted network traffic at the next moment of each historical moment can be obtained to form training data. Then, a loss function is constructed based on the training data, and the prediction model is trained based on the loss function. If the calculated loss function value is less than the preset value, the prediction model is used to perform the above-mentioned fourth step to obtain the predicted network traffic required for this step; if the value of the loss function is greater than or equal to the preset value, the back propagation algorithm is used to pass the value of the loss function to each learnable parameter of the prediction model, and the learnable parameters of the prediction model are updated based on the Adam optimizer, and the step of obtaining the potential time dependency characteristics of the target area at each historical moment based on all historical network traffic data and all dynamic environmental data is returned until the value of the loss function is less than the preset value. The loss function is:

[0181]

[0182] in, represents the value of the loss function, represents the predicted network traffic of all spatial units at the i-th historical moment, X' i represents the network traffic of all spatial units at the i-th historical moment, N T represents the number of historical moments used as training samples, Denotes the predicted dynamic environmental data of all spatial units at the i-th historical moment, DE' i represents the dynamic environment data of all spatial units at the i-th historical moment, and λ represents an adjustable hyperparameter.

[0183] It is worth mentioning that traffic prediction is performed based on spatial environment data and historical network traffic data, and the characteristics of network traffic in both spatial and temporal dimensions are analyzed to characterize the impact of space and time on network traffic, effectively improving the accuracy of traffic prediction.

[0184] Step 16: construct an optimization objective function for describing the status of all base stations in the target area based on the predicted network traffic, and minimize the optimization objective function to obtain a base station sleep control solution for the target area.

[0185] The above base station sleep control scheme is used to describe the operating status of all base stations. For example, at a future moment, the first base station is in a sleep state and the second base station is in an operating state.

[0186] Specifically, the above optimization objective function is:

[0187]

[0188] Among them, E represents the coverage energy consumption matrix, e m,i represents the energy consumption when the mth base station serves the i-th spatial unit, M represents the number of base stations in the target area, N represents the number of spatial units, S t′ represents the base station state matrix at the future time, Represents the state of the mth base station. When , it means that the mth base station is in working state at the future time. When , it means that the mth base station is in sleep state at the next moment, E 0 represents the fixed energy consumption matrix of the base station, represents the fixed energy consumption of the mth base station, E s represents the base station state switching energy consumption matrix, represents the energy consumption of the mth base station switching state, S t′-1 represents the base station state matrix at the previous moment in the future, F represents the coverage flow matrix, f m,i It represents the traffic load that the m-th base station can provide when serving the i-th spatial unit.

[0189] For example, a genetic algorithm can be used to minimize the optimization objective function to obtain a base station sleep control plan for the target area. After obtaining the base station sleep control plan, at a future time, the states of all base stations in the target area are controlled according to the base station sleep control plan.

[0190] It is worth mentioning that traffic prediction is performed based on spatial environment data and historical network traffic data, and the characteristics of network traffic in both spatial and temporal dimensions are analyzed. The impact of space and time on network traffic is characterized, which effectively improves the accuracy of traffic prediction. The optimization objective function of the base station is constructed based on the highly accurate predicted network traffic, so that the optimization objective function can reasonably and accurately describe the state of the base station at future moments. The sleep control of the base station is performed based on a reasonable and accurate optimization objective function, which effectively improves the accuracy of the sleep control of the base station.

[0191] The base station sleep method based on traffic prediction in this application applies meta-learning technology to the prediction model to characterize the impact of the temporal and spatial environment on network traffic, solve the dynamic spatial heterogeneity problem of network traffic modeling, and achieve efficient and high-precision prediction of user network traffic demand, which can meet the dynamic and intelligent needs of base station sleep strategies.

[0192] The method of the present application is illustrated below with reference to a specific example.

[0193] like Figure 2 As shown in Figure 1, historical network traffic data and historical dynamic environment data are input into the spatiotemporal learning unit (i.e., the prediction model mentioned above) composed of sequentially connected gated neural units (GRU, Gate Recurrent Unit), graph attention networks (GAT, Graph Attention Networks) and meta-gated neural units (MetaGRU, Meta Gate Recurrent Unit). The data corresponding to each historical moment is sequentially input into the moment learning unit. At the same time, in the meta-learning module, the static environment data is input into the two sequentially connected multi-layer perceptrons (MLP, Multi-Layer Perceptron), the historical dynamic environment data is input into the sequentially connected GRU and MLP, and both outputs are input into the convolution ConV, combined with the shared parameter matrix M shared Calculations are performed to obtain model parameters, which are then input into the spatiotemporal learning model to output predicted network traffic. Based on the predicted network traffic, a future base station sleep plan is obtained. For multiple moments after the future moment, the future network traffic data and future dynamic environment data are input into the spatiotemporal learning unit for step-by-step prediction, resulting in the predicted traffic for the next moment at each moment. The base station sleep control is then performed based on the predicted traffic. represents the potential time-dependent features at the t-th historical moment, Represents the dynamic heterogeneous spatiotemporal dependence characteristics at the t-th historical moment.

[0194] In an example of the present application, the network traffic data of a city center (including 1,600 spatial units, the time range is from November 3, 2013 to December 5, 2013, and the time interval is 1 hour) and spatial environment data (including weather data, precipitation data, date data, spatial location, frequency domain characteristics, road network data, building data, artificial impervious surface data, night light data, point of interest data, social media data and communication base station data) are used as real data sets of the embodiment to illustrate the implementation process of the present application.

[0195] Traffic flow prediction was performed on the above-mentioned real-world dataset using the method of this application. Mean absolute error (MAE) and root mean square error (RMSE) were selected as evaluation indicators for the prediction results. The models compared included the Historical Average Model (HA), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory Network (ConvLSTM), Temporal Graph Convolutional Network (T-GCN), Spatio-Temporal Graph Convolutional Network (STGNN), and Spatio-Temporal Cross-Domain Network (STCNet). The accuracy comparison results for the single-step prediction task are shown in Table 1.

[0196]

[0197] Table 1

[0198] Call-in indicates incoming calls, Call-out indicates outgoing calls, SMS-in indicates received text messages, and SMS-out indicates sent text messages.

[0199] The accuracy comparison results of the five-step prediction task are shown in Table 2.

[0200]

[0201]

[0202] Table 2

[0203] This shows that the prediction results obtained by the method of this application are more accurate than those of other methods, whether for single-step or five-step predictions. This method can be applied to actual base station sleep control scenarios to predict the most accurate future user network traffic demand, thereby serving the planning of base station dynamic sleep control solutions and the green and low-carbon development needs of the information and communications industry.

[0204] The following is an exemplary description of the base station sleep device based on traffic prediction provided in this application.

[0205] like Figure 3 As shown, an embodiment of the present application provides a base station sleep device based on traffic prediction, and the base station sleep device 300 includes:

[0206] The first acquisition module 301 acquires historical network traffic data and spatial environment data of each spatial unit in the target area; the historical network traffic data includes the network traffic of the spatial unit at multiple historical moments;

[0207] The feature extraction module 302 extracts features from each historical network traffic data to obtain a traffic feature vector for each spatial unit, and calculates the temporal similarity between every two spatial units based on all traffic feature vectors;

[0208] A construction module 303 constructs a spatiotemporal dependency graph based on all temporal similarities; the multiple nodes in the spatiotemporal dependency graph correspond one-to-one to the multiple spatial units, and the edge between two nodes represents the similarity relationship between the corresponding two spatial units;

[0209] The second acquisition module 304 acquires the static environmental characteristics of the target area and the dynamic environmental characteristics corresponding to each historical moment according to the spatial environmental data of all spatial units, and calculates the model parameters of the traffic prediction model based on the static environmental characteristics and all dynamic environmental characteristics;

[0210] The traffic prediction module 305 uses the traffic prediction model to predict the traffic flow of the target area based on the spatiotemporal dependency graph and model parameters to obtain the predicted network traffic flow of all spatial units;

[0211] The minimization solution module 306 constructs an optimization objective function for describing the status of all base stations in the target area based on the predicted network traffic, and minimizes the optimization objective function to obtain a base station sleep control plan for the target area; the base station sleep control plan is used to describe the operating status of all base stations.

[0212] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0213] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0214] like Figure 4 As shown, an embodiment of the present application provides a terminal device, and the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps of any of the above method embodiments when executing the computer program D102.

[0215] Specifically, when the processor D100 executes the computer program D102, it obtains the historical network traffic data and spatial environment data of each spatial unit in the target area, and then performs feature extraction on each historical network traffic data to obtain the traffic feature vector of each spatial unit, and calculates the time series similarity between every two spatial units based on all traffic feature vectors, and then constructs a spatiotemporal dependency graph based on all time series similarities, and then obtains the static environment characteristics of the target area and the dynamic environment characteristics corresponding to each historical moment based on the spatial environment data of all spatial units, and calculates the model parameters of the traffic prediction model based on the static environment characteristics and all dynamic environment characteristics, and then uses the traffic prediction model based on the spatiotemporal dependency graph and model parameters to predict the traffic of the target area, and obtains the predicted network traffic of all spatial units, and finally constructs an optimization objective function for describing the status of all base stations in the target area based on the predicted network traffic, and minimizes the optimization objective function to obtain the base station sleep control scheme for the target area. Among them, traffic prediction is performed based on spatial environment data and historical network traffic data, the characteristics of network traffic in the two dimensions of space and time are analyzed, the impact of space and time on network traffic is characterized, and the accuracy of traffic prediction is effectively improved. The optimization objective function of the base station is constructed based on the highly accurate predicted network traffic, so that the optimization objective function can reasonably and accurately describe the state of the base station at future moments. The base station is put into sleep control based on a reasonable and accurate optimization objective function, which effectively improves the accuracy of sleep control of the base station.

[0216] The processor D100 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0217] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card, etc. equipped on the terminal device D10. Furthermore, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0218] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0219] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0220] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the base station dormancy method device / terminal device based on traffic prediction, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0221] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0222] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0223] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A base station dormancy method based on traffic prediction, characterized in that: include: Obtain historical network traffic data and spatial environment data for each spatial unit in the target area; The historical network traffic data includes the network traffic of the spatial unit at multiple historical moments; Performing feature extraction on each of the historical network traffic data to obtain a traffic feature vector for each of the spatial units, and calculating the temporal similarity between every two spatial units based on all the traffic feature vectors; Build a spatiotemporal dependency graph based on all temporal similarities; The multiple nodes in the spatiotemporal dependency graph correspond one-to-one to the multiple spatial units, and the edge between two nodes represents the similarity relationship between the corresponding two spatial units; Obtaining static environmental characteristics of the target area and dynamic environmental characteristics corresponding to each historical moment according to the spatial environmental data of all spatial units, and calculating model parameters of the traffic prediction model based on the static environmental characteristics and all dynamic environmental characteristics; Based on the spatiotemporal dependency graph and the model parameters, using the traffic prediction model to perform traffic prediction on the target area to obtain predicted network traffic of all spatial units; Constructing an optimization objective function for describing the status of all base stations in the target area based on the predicted network traffic, and minimizing the optimization objective function to obtain a base station sleep control scheme for the target area; the base station sleep control scheme is used to describe the operating status of all base stations; Among them, for each spatial unit, all the time series similarities corresponding to the spatial unit are sorted from large to small, and the first multiple time series similarities in the sorting results are used as target time series similarities, and it is considered that the spatial unit has a similar relationship with the spatial unit corresponding to each target time series similarity.

2. The base station sleep method according to claim 1, characterized in that: The feature extraction of each of the historical network traffic data to obtain the traffic feature vector of each of the spatial units includes: For each of the historical network traffic data, Fourier transform is performed on the historical network traffic data to obtain a spectrum sequence, and elements corresponding to the first multiple historical moments in the spectrum sequence are selected as key elements, and a traffic feature vector is constructed based on all the key elements.

3. The base station sleep method according to claim 2, characterized in that: The performing Fourier transform on the historical network traffic data to obtain a spectrum sequence includes: By formula: Perform Fourier transform on the i-th historical network traffic data to obtain the i-th spectrum sequence S i (u); Among them, x i (t) represents the network traffic at the t-th historical moment in the i-th historical network traffic data, L-1 represents the number of the last historical moment, and j represents an imaginary unit; The traffic feature vector is constructed based on all key elements, including: By formula: Calculate the i-th traffic feature vector F i ; Among them, S i (u0) represents the 0th key element of the i-th spectrum sequence, represents the Kth ith spectrum sequence f key elements, Indicates S i The real part of (u0), Indicates S i The imaginary part of (u0), express The real part of express The imaginary part of The calculating of the time series similarity between every two spatial units based on all traffic feature vectors includes: By formula: Calculate the temporal similarity d(F i ,F g ); Among them, S g (u0) represents the 0th key element of the gth spectrum sequence, represents the Kth g-th spectrum sequence f key elements, Indicates S g The real part of (u0), express The imaginary part of , i,g∈(1,2,...,N), i≠g, N represents the number of spatial units.

4. The base station sleep method according to claim 3, characterized in that: The construction of a spatiotemporal dependency graph based on all temporal similarities includes: For each spatial unit, sort all the time series similarities corresponding to the spatial unit from large to small, take the first multiple time series similarities in the sorting result as the target time series similarity, and consider that there is a similarity relationship between the spatial unit and the spatial unit corresponding to each target time series similarity; Multiple spatial units are regarded as multiple nodes. If there is a similar relationship between the two spatial units corresponding to two nodes, an edge is generated between the two nodes to construct a spatiotemporal dependency graph.

5. The base station sleep method according to claim 1, characterized in that: The spatial environment data includes static environment data of the spatial unit and dynamic environment data of multiple historical moments; The step of obtaining the static environmental features of the target area and the dynamic environmental features corresponding to each historical moment based on the spatial environmental data of all spatial units includes: Extracting features from the static environment data of all spatial units to obtain static environment features of the target area; Feature extraction is performed on the dynamic environment data of all spatial units at each historical moment to obtain the dynamic environment features corresponding to the target area at each historical moment.

6. The base station sleep method according to claim 1, characterized in that: The model parameters of the flow prediction model are calculated based on the static environmental characteristics and all dynamic environmental characteristics, including: Acquire geographic meta-knowledge of the target area at each historical moment according to the static environmental features and all dynamic environmental features; For each historical moment respectively, model parameters of the traffic prediction model of the historical moment are generated based on the geographic meta-knowledge corresponding to the historical moment.

7. The base station sleep method according to claim 6, characterized in that: The acquiring of geographic meta-knowledge of the target area at each historical moment according to the static environmental features and the dynamic environmental features includes: By formula: Calculate the geographic meta-knowledge MK at the tth historical moment t ; in, represents the convolution filter, SF represents the static environment feature, DF t represents the dynamic environment characteristics at the t-th historical moment, || represents the feature connection operator, and Reshape() represents the reorganization function; Generating the model parameters of the traffic prediction model at the historical moment based on the geographic meta-knowledge corresponding to the historical moment includes: By formula: M t =Reshape -1 (MK t )M shared Calculate the model parameter M at the tth historical moment t ; Among them, M shared represents the shared parameter matrix, W r,i ,W z,i ,W h,i ,b r,i ,b z,i ,b h,i are all parameters in the traffic prediction model, and N represents the number of spatial units.

8. The base station sleep method according to claim 7, characterized in that: The method of performing traffic prediction on the target area using the traffic prediction model based on the spatiotemporal dependency graph and the model parameters to obtain predicted network traffic of all spatial units includes: Based on all historical network traffic data and all dynamic environment data, obtaining potential time-dependent characteristics of the target area at each historical moment; Calculating the spatiotemporal dependency feature of each historical moment using the spatiotemporal dependency graph and the potential temporal dependency feature of each historical moment; For each historical moment, respectively, the model parameters and all spatiotemporal dependency features of the historical moment are used to extract the dynamic heterogeneous spatiotemporal dependency features of the historical moment; Traffic prediction is performed based on the dynamic heterogeneous spatiotemporal dependency characteristics of the last historical moment to obtain the predicted network traffic of all spatial units; The step of obtaining the potential time-dependent characteristics of the target area at each historical moment based on all historical network traffic data and all dynamic environment data includes: By formula: Calculate the potential time-dependent features at the t-th historical moment in, represents the potential time-dependent characteristics of the i-th spatial unit at the t-th historical moment, N represents the number of spatial units, X t represents the network traffic of all spatial units at the t-th historical moment, DE t represents the dynamic environment data of all spatial units at the t-th historical moment, GRU() represents the gated recurrent unit, represents the potential time-dependent characteristics at the t-1th historical moment. When t = 0, is the preset initial value; The calculating of the spatiotemporal dependency features of each historical moment using the spatiotemporal dependency graph and the potential temporal dependency features of each historical moment includes: By formula: Calculate the spatiotemporal dependence characteristics of the t-th historical moment in, represents the similarity weight vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, represents the potential time-dependent characteristics of the j-th spatial unit at the t-th historical moment, W g and b g Indicates parameters, represents the attention score vector between the i-th spatial unit and the j-th spatial unit at the t-th historical moment, N(i) represents the number of the spatial units corresponding to all nodes adjacent to the node corresponding to the i-th spatial unit in the spatiotemporal dependency graph, represents the spatiotemporal dependency characteristics of the i-th spatial unit at the t-th historical moment, LeakyReLU represents the activation function, x represents the input data of the activation function, and α represents the hyperparameter; The extracting of the dynamic heterogeneous spatiotemporal dependency features of the historical moment by utilizing the model parameters and all spatiotemporal dependency features of the historical moment includes: By formula: Calculate the dynamic heterogeneous spatiotemporal dependence characteristics at the t-th historical moment in, represents the reset gate output corresponding to the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-th historical moment, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the i-th spatial unit at the t-1th historical moment, represents the update gate output corresponding to the i-th spatial unit at the t-th historical moment, represents the candidate hidden state of the i-th spatial unit at the t-th historical moment.

9. The base station sleep method according to claim 8, characterized in that: The traffic prediction is performed based on the dynamic heterogeneous spatiotemporal dependency characteristics of the last historical moment to obtain the predicted network traffic of all spatial units, including: By formula: Calculate the predicted network traffic for all spatial units in, Represents the predicted dynamic environmental data of all spatial units, MK t represents geographic meta-knowledge, represents the dynamic heterogeneous spatiotemporal dependence characteristics of the last historical moment, W p and b p All represent parameters.

10. The base station sleep method according to claim 1, characterized in that: The optimization objective function is: Among them, E represents the coverage energy consumption matrix, e m,i represents the energy consumption when the mth base station serves the i-th spatial unit, M represents the number of base stations in the target area, N represents the number of spatial units, S t′ represents the base station state matrix at the future time, Represents the state of the mth base station. When , it means that the mth base station is in working state at the future time. When , it means that the mth base station is in sleep state at the future time, E 0 represents the fixed energy consumption matrix of the base station, represents the fixed energy consumption of the m-th base station, E s represents the base station state switching energy consumption matrix, represents the energy consumption of the m-th base station switching state, S t′-1 represents the base station state matrix at the previous moment in the future, F represents the coverage flow matrix, f m,i It represents the traffic load that the m-th base station can provide when serving the i-th spatial unit.

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