Base station load adjusting method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a base station load adjusting method and device, electronic equipment and a computer readable medium. The method comprises the following steps: in an unmanned aerial vehicle cluster aggregation operation process, acquiring base station data of an Internet of Things base station in communication connection with unmanned aerial vehicles in an unmanned aerial vehicle cluster; preprocessing the base station data to obtain preprocessed data; inputting the preprocessed data into a pre-trained base station load value prediction model, and predicting the load value of the Internet of Things base station through the base station load value prediction model; and determining a load solution based on the load value, and executing the load solution to adjust the load of the Internet of Things base station. According to the embodiment, the problems of network congestion and the like in an unmanned aerial vehicle cluster aggregation operation process are solved, and the efficiency and the accuracy of adjusting the load of the Internet of Things base station are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and specifically to a base station load adjustment method, device, electronic device, and computer-readable medium. Background Art

[0002] In the scenario where drone clusters perform collective operations, multiple drones move quickly at low altitudes. In order to avoid network congestion, there are higher requirements for the rapid balancing and adjustment of the load of IoT base stations.

[0003] In the prior art, load optimization and adjustment of IoT base stations mainly rely on manual modification of various base station parameters, which is inefficient and prone to errors. Summary of the invention

[0004] The embodiments of the present application propose a base station load adjustment method, device, electronic device and computer-readable medium to solve the technical problems existing in the prior art.

[0005] In the first aspect, an embodiment of the present application provides a base station load adjustment method, the method comprising: during a drone cluster aggregation operation, obtaining base station data of an Internet of Things base station that is communicatively connected to drones in the drone cluster; preprocessing the base station data to obtain preprocessed data; inputting the preprocessed data into a pre-trained base station load value prediction model, and predicting the load value of the Internet of Things base station through the base station load value prediction model; determining a load solution based on the load value, and executing the load solution to adjust the load of the Internet of Things base station.

[0006] In the second aspect, an embodiment of the present application provides a base station load adjustment device, which includes: an acquisition unit, used to acquire base station data of an Internet of Things base station that is communicatively connected to the drones in the drone cluster during the drone cluster aggregation operation; a preprocessing unit, used to preprocess the base station data to obtain preprocessed data; a processing unit, used to input the preprocessed data into a pre-trained base station load value prediction model to obtain the load value of the Internet of Things base station; an execution unit, used to determine a load solution based on the load value, and execute the load solution to adjust the load of the Internet of Things base station.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any embodiment of the first aspect.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the first aspect.

[0009] The base station load adjustment method, device, electronic device and computer-readable medium provided in the embodiment of the present application obtain the base station data of the Internet of Things base station that is connected to the drones in the drone cluster during the drone cluster aggregation operation; then pre-process the base station data to obtain pre-processed data; then input the pre-processed data into a pre-trained base station load value prediction model, and predict the load value of the Internet of Things base station through the base station load value prediction model; finally, based on the load value, determine the load solution, and then execute the load solution to adjust the load of the Internet of Things base station. Therefore, the method can automatically predict the load value of the Internet of Things base station by using the base station data and the base station load value prediction model of the Internet of Things base station during the drone cluster aggregation operation, and automatically determine the load solution and perform load optimization adjustment based on the composite value, thereby, on the one hand, solving the problems such as network congestion during the drone cluster aggregation operation, ensuring the stable operation of the network, and improving the communication quality; on the other hand, because the process of manual load optimization adjustment based on experience is omitted, the efficiency and accuracy of load adjustment are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0011] Figure 1 is a flow chart of an embodiment of a base station load adjustment method of the present application;

[0012] Figure 2 It is a structural schematic diagram of a base station load value prediction model of the base station load adjustment method of the present application;

[0013] Figure 3 It is a structural schematic diagram of an embodiment of a base station load adjustment device of the present application;

[0014] Figure 4 It is a schematic diagram of the structure of an electronic device used to implement an embodiment of the present application. DETAILED DESCRIPTION

[0015] All actions to obtain signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0016] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0017] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0018] Please refer to Figure 1 , which shows a process 100 of an embodiment of a base station load adjustment method according to the present application. The base station load adjustment method can be applied to various electronic devices with data processing functions. For example, the above electronic devices may include but are not limited to: servers, tablet computers, laptop portable computers, PDAs, desktop computers, etc. The execution subject of the base station load adjustment method may be a processor in the above electronic devices.

[0019] The base station load adjustment method comprises the following steps:

[0020] Step 101, during the drone cluster aggregation operation, obtain base station data of an Internet of Things base station that is communicatively connected to drones in the drone cluster.

[0021] In this embodiment, during the drone cluster aggregation operation, the execution subject of the base station load adjustment method can obtain the base station data of the Internet of Things base station that is connected to the drones in the drone cluster. The above base station data may include current data and historical data of the Internet of Things base station.

[0022] In some optional implementations of this embodiment, the base station data includes service data and network environment data. The service data includes but is not limited to at least one of the following: historical load data, real-time network traffic data, number of users, and user service habit data. The network environment data includes but is not limited to at least one of the following: network frequency band, network standard, and surrounding base station operation data.

[0023] Step 102: pre-process the base station data to obtain pre-processed data.

[0024] In this embodiment, the base station data may be preprocessed to obtain preprocessed data to ensure the validity and consistency of the data and facilitate subsequent data processing and analysis. The above deprocessing may include but is not limited to at least one of the following: deduplication, cleaning, correction, normalization, etc.

[0025] In some optional implementations of this embodiment, the following steps may be performed:

[0026] First, the base station data is detected to determine the problematic data in the above base station data, and the above problematic data includes invalid data and erroneous value data. For example, the above invalid data may include but is not limited to useless data that does not need to be used, missing value data, data when the base station fails, etc. The above erroneous value data is data that has errors and the errors can be corrected. For example, when the RSRP (Reference Signal Receiving Power) / SINR (Signal to Interference plus Noise Ratio) value on the wireless side is abnormal, it can be considered that the base station is faulty, and the base station data during this period can be deleted as invalid data.

[0027] Then, the erroneous data in the base station data is corrected, and the invalid data in the base station data is deleted to obtain valid data. The valid data may include the data after the erroneous data is corrected and other data in the base station data except the problematic data.

[0028] Finally, the valid data is normalized to obtain the preprocessed data. Specifically, the valid data can be firstly processed by minimum centering, and then scaled according to the range (i.e., the maximum value minus the minimum value) so that the valid data is converged to the value interval [0,1].

[0029] Preprocessing the base station data in the above manner can improve the validity and consistency of the data and facilitate subsequent data processing and analysis.

[0030] Step 103: input the preprocessed data into a pre-trained base station load value prediction model, and predict the load value of the Internet of Things base station through the base station load value prediction model.

[0031] In this embodiment, the above-mentioned execution body may store a pre-trained base station load value prediction model. The base station load value prediction model may be pre-trained by a machine learning method. The basic model structure used to train the base station load value prediction model may be a neural network, etc. The base station load value prediction model may be used to predict the load value of the base station based on the base station data. Here, the pre-processed data may be input into the pre-trained base station load value prediction model, and the load value of the IoT base station may be predicted by the base station load value prediction model.

[0032] In some optional implementations of this embodiment, the above-mentioned base station load value prediction model can be trained through the following steps: first, the historical base station data and historical real load values ​​of the Internet of Things base stations connected to the drones in the historical demonstration of the drone cluster are obtained; then, the historical base station data are preprocessed to obtain sample data; finally, the historical real load values ​​are used as annotations for the sample data, and the basic prediction model is trained through the sample data to obtain the base station load value prediction model. Among them, the acquisition and preprocessing process of the historical base station data can refer to the acquisition and preprocessing process of the base station data in the above steps, which will not be repeated here.

[0033] During the training process, the sample data can be input into the basic prediction model one by one to obtain the prediction result of the load value output by the basic prediction model. Then, the loss value can be determined based on the annotation corresponding to the prediction result and the input sample data. The above loss value is the value of the loss function, which is a non-negative real-valued function that can be used to characterize the difference between the detection result and the true result. In general, the smaller the loss value, the better the robustness of the model. The loss function can be set according to actual needs, for example, it can be set to a cross entropy loss function. After that, the loss value can be used to update the parameters of the basic prediction model. Thus, each time the sample data is input, the parameters of the basic prediction model can be updated based on the loss value corresponding to the sample data until the training is completed. In practice, whether the training is completed can be determined in a variety of ways. For example, when the accuracy of the prediction result output by the basic prediction model reaches a preset value (for example, 99%), the training can be determined to be completed. For another example, if the number of training times of the basic prediction model is equal to the preset number of times, the training can be determined to be completed. For another example, when the loss value of the basic prediction model converges, the training can be determined to be completed. Here, if the basic prediction model training is completed, the trained basic prediction model can be determined as the base station load value prediction model.

[0034] In some optional implementations of this embodiment, the basic prediction model includes a multilayer perceptron (MLP). A multilayer perceptron is a feedforward neural network consisting of multiple neuron layers, each of which is connected to neurons in the previous and next layers. It is a widely used artificial neural network model that is used to solve machine learning tasks such as classification, regression, and pattern recognition.

[0035] In some optional implementations of this embodiment, the base station load value prediction model includes an input layer, at least one hidden layer and an output layer. Figure 2. The input layer is the starting point of the base station load value prediction model, which is responsible for receiving external input information, that is, the above-mentioned pre-processed data, and converting it into processable data. The input layer can also adjust the number and connection method of its neurons according to the needs of specific tasks. The output layer is the end point of the base station load value prediction model, which is used to convert the final processing results into an understandable format. The hidden layer is the core part of the base station load value prediction model, which is used for the transmission and calculation of information in the base station load value prediction model. Each hidden layer is composed of multiple neurons, each of which receives input from the previous layer, performs nonlinear transformation and linear change on it, and passes the calculation result as output to the next layer. Through such a calculation process, the base station load value prediction model can learn the complex characteristics of the input data and make predictions based on these characteristics.

[0036] The load value of the IoT base station is predicted by the base station load value prediction model, including the following steps:

[0037] In the first step, the preprocessed data is output to the first hidden layer of the at least one hidden layer through the input layer.

[0038] In the second step, for each hidden layer in at least one hidden layer, linear processing and nonlinear processing are performed on the data output by the previous layer in sequence through the hidden layer to obtain intermediate data.

[0039] The above linear processing process is shown in the following formula:

[0040]

[0041] Among them, c i is the connection strength weight between neurons, and the size of the weight represents the size of the possibility. i Represents bias, which is set to correctly classify, that is, to ensure that the output value calculated by the input cannot be activated arbitrarily. i Compensate for factor loss. x i is the preprocessed data. z is the intermediate vector matrix obtained after the hidden layer is linearly processed.

[0042] The above nonlinear processing can be performed through an activation function. Taking the Sigmoid function as an example, see as follows:

[0043]

[0044] By introducing nonlinear factors, the base station load value prediction model can arbitrarily approximate any nonlinear function, using the properties of monotonic increase and inverse function monotonic increase.

[0045] Here, the second layer, i.e., the first hidden layer, of the base station load value prediction model is used as an example. Assuming that the hidden layer has three neurons, the outputs of the three neurons can be recorded as The calculation formula is as follows:

[0046]

[0047]

[0048]

[0049] Among them, x1, x2, and x3 are preprocessed data. are the intermediate data output after linear processing of the above three neurons, are the biases corresponding to the above three neurons, are the factor loss compensations corresponding to the above three neurons, are the connection strength weights between the above three neurons and the three neurons in the previous layer.

[0050] And so on. Assuming that there are m neurons in the n-1 layer, the output of the jth neuron in the nth layer is See below:

[0051]

[0052] in, is the output of the ith neuron in the n-1th layer, is the intermediate data output after linear processing of the jth neuron in the nth layer, is the bias corresponding to the jth neuron in the nth layer, is the factor loss compensation corresponding to the jth neuron in the nth layer, is the connection strength weight between the jth neuron in the nth layer and the ith neuron in the n-1th layer, m and n are both positive integers, and e is a natural constant.

[0053] In the third step, the intermediate data outputted by the last hidden layer in at least one hidden layer is processed by the output layer to obtain the load value of the IoT base station. Here, the intermediate data outputted by the last hidden layer can be processed by the output layer such as numerical range conversion to obtain the load value of the IoT base station.

[0054] Through the base station load value prediction model with the above structure, it is possible to learn the complex characteristics of the input data and accurately predict the load value of the IoT base station based on these characteristics.

[0055] Step 104: determine a load solution based on the load value, and execute the load solution to adjust the load of the IoT base station.

[0056] In this embodiment, different load values ​​may correspond to different base station load warning levels, and different base station load warning levels may correspond to different load solutions. The load solution may include but is not limited to at least one of the following: adjusting power parameters, switching thresholds, load balancing processing, radio frequency optimization processing or capacity expansion processing, building a new IoT base station, etc. The above-mentioned execution entity may determine the base station load warning level based on the load value, thereby determining the load solution based on the base station load warning level, and executing the load solution to adjust the load of the IoT base station.

[0057] In some optional implementations of this embodiment, the following steps may be included:

[0058] When the load value is within the first numerical range, the IoT base station can be subjected to parameter optimization processing, and the parameter optimization processing includes at least one of the following: adjusting power parameters and switching thresholds. In practice, the first numerical range can be (-∞, 0.5], and the corresponding base station load warning level is level 0.

[0059] When the load value is within the second numerical range, the IoT base station can be load balanced. In practice, the second numerical range can be (0.5, 0.7], and the corresponding base station load warning level is level 1. Load balancing can be achieved by adjusting parameters, for example, by adjusting parameters to switch the range or threshold of the cell covered by the IoT base station, so as to reduce the signal coverage range of the IoT base station and reduce the load of the IoT base station.

[0060] When the load value is within the third numerical range, the IoT base station can be subjected to radio frequency optimization or capacity expansion. In practice, the third numerical range can be (0.7, 0.9], and the corresponding base station load warning level is level 2. Radio frequency optimization can be achieved by adjusting the antenna of the IoT base station. By adjusting the antenna of the IoT base station, the signal coverage range of the IoT base station can be reduced to reduce the load of the IoT base station. Capacity expansion can be achieved by expanding the carrier of the IoT base station, for example, from one carrier to two carriers.

[0061] When the load value is in the fourth numerical range, a new IoT base station can be built to share services and solve the load problem of the IoT base station. In practice, the fourth numerical range can be (0.9,∞), and the corresponding base station load warning level is level 3.

[0062] The method provided by the above-mentioned embodiment of the present application obtains the base station data of the Internet of Things base station that is connected to the drones in the drone cluster during the drone cluster aggregation operation; then pre-processes the base station data to obtain pre-processed data; then inputs the pre-processed data into the pre-trained base station load value prediction model, and predicts the load value of the Internet of Things base station through the base station load value prediction model; finally, based on the load value, determines the load solution, thereby executing the load solution to adjust the load of the Internet of Things base station. As a result, the method can automatically predict the load value of the Internet of Things base station by using the base station data and the base station load value prediction model of the Internet of Things base station during the drone cluster aggregation operation, and automatically determines the load solution and performs load optimization adjustment based on the composite value, thereby, on the one hand, solving the problems such as network congestion during the drone cluster aggregation operation, ensuring the stable operation of the network, and improving the communication quality; on the other hand, since the process of manual load optimization adjustment based on experience is omitted, the efficiency and accuracy of load adjustment are improved.

[0063] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a base station load adjustment device, and the device embodiment is similar to Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0064] like Figure 3 As shown, the base station load adjustment device 300 of this embodiment includes: an acquisition unit 301, which is used to acquire the base station data of the Internet of Things base station that is connected to the drones in the drone cluster during the drone cluster aggregation operation; a preprocessing unit 302, which is used to preprocess the base station data to obtain preprocessed data; a processing unit 303, which is used to input the preprocessed data into a pre-trained base station load value prediction model to obtain the load value of the Internet of Things base station; an execution unit 304, which is used to determine a load solution based on the load value, and execute the load solution to adjust the load of the Internet of Things base station.

[0065] In some optional implementations of this embodiment, the preprocessing unit 302 is further used to: detect the base station data to determine problem data in the base station data, where the problem data includes invalid data and erroneous value data; correct the erroneous value data in the base station data and delete the invalid data in the base station data to obtain valid data; normalize the valid data to obtain preprocessed data.

[0066] In some optional implementations of this embodiment, the execution unit 304 is further used to: when the load value is in a first numerical range, perform parameter optimization processing on the Internet of Things base station, and the parameter optimization processing includes at least one of the following: adjusting power parameters and switching thresholds; when the load value is in a second numerical range, perform load balancing processing on the Internet of Things base station; when the load value is in a third numerical range, perform radio frequency optimization processing or capacity expansion processing on the Internet of Things base station; when the load value is in a fourth numerical range, create a new Internet of Things base station.

[0067] In some optional implementations of the present embodiment, the base station load value prediction model includes an input layer, at least one hidden layer and an output layer; the processing unit 303 is further used to: output the preprocessed data to the first hidden layer in the at least one hidden layer through the input layer; for each hidden layer in the at least one hidden layer, perform linear processing and nonlinear processing on the data output by the previous layer in sequence through the hidden layer to obtain intermediate data; process the intermediate data output by the last hidden layer in the at least one hidden layer through the output layer to obtain the load value of the Internet of Things base station.

[0068] In some optional implementations of the present embodiment, the base station load value prediction model is trained through the following steps: obtaining historical base station data and historical real load values ​​of the Internet of Things base stations connected to the drones in the historical aggregation operation of the drone cluster; preprocessing the historical base station data to obtain sample data; using the historical real load values ​​as labels for the sample data, and training the basic prediction model through the sample data to obtain a base station load value prediction model.

[0069] In some optional implementations of this embodiment, the basic prediction model includes a multi-layer perceptron.

[0070] In some optional implementations of this embodiment, the base station data includes business data and network environment data; the business data includes at least one of the following: historical load data, real-time network traffic data, number of users, user business habit data; the network environment data includes at least one of the following: network frequency band, network standard, and surrounding base station operation status data.

[0071] The device provided by the above-mentioned embodiment of the present application obtains the base station data of the Internet of Things base station that is connected to the drones in the drone cluster during the drone cluster aggregation operation; then pre-processes the base station data to obtain pre-processed data; then inputs the pre-processed data into the pre-trained base station load value prediction model, and predicts the load value of the Internet of Things base station through the base station load value prediction model; finally, based on the load value, determines the load solution, thereby executing the load solution to adjust the load of the Internet of Things base station. Therefore, the method can automatically predict the load value of the Internet of Things base station by using the base station data of the Internet of Things base station and the base station load value prediction model during the drone cluster aggregation operation, and automatically determines the load solution and performs load optimization adjustment based on the composite value, thereby, on the one hand, solving the problems such as network congestion during the drone cluster aggregation operation, ensuring the stable operation of the network, and improving the communication quality; on the other hand, since the process of manual load optimization adjustment based on experience is omitted, the efficiency and accuracy of load adjustment are improved.

[0072] Reference below Figure 4 , which shows a schematic structural diagram of an electronic device for implementing some embodiments of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0073] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0074] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a disk, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 4Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0075] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0076] It should be noted that the computer-readable medium described in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0077] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0078] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: during the drone cluster aggregation operation, obtains the base station data of the Internet of Things base station that is connected to the drones in the drone cluster; pre-processes the base station data to obtain pre-processed data; inputs the pre-processed data into a pre-trained base station load value prediction model, and predicts the load value of the Internet of Things base station through the base station load value prediction model; determines a load solution based on the load value, and executes the load solution to adjust the load of the Internet of Things base station.

[0079] Computer program code for performing the operations of some embodiments of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++; and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, or may be connected to an external computer (e.g., through the Internet using an Internet service provider), including a local area network (LAN) or a wide area network (WAN).

[0080] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0081] The units described in some embodiments of the present application may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a first determination unit, a second determination unit, a selection unit, and a third determination unit. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0082] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0083] The above description is only some preferred embodiments of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with (but not limited to) the technical features with similar functions disclosed in the embodiments of the present application to form a technical solution.

Claims

1. A base station load adjustment method, characterized in that: The method comprises: During the drone cluster aggregation operation, obtaining base station data of an Internet of Things base station that is communicatively connected to drones in the drone cluster; Preprocessing the base station data to obtain preprocessed data; Inputting the preprocessed data into a pre-trained base station load value prediction model, and predicting the load value of the Internet of Things base station through the base station load value prediction model; Based on the load value, a load solution is determined, and the load solution is executed to adjust the load of the Internet of Things base station.

2. The method according to claim 1, characterized in that: The preprocessing of the base station data to obtain preprocessed data includes: Detecting the base station data to determine problematic data in the base station data, wherein the problematic data includes invalid data and erroneous value data; Correcting the erroneous data in the base station data and deleting the invalid data in the base station data to obtain valid data; The valid data is normalized to obtain preprocessed data.

3. The method according to claim 1, characterized in that: The step of determining a load solution based on the load value comprises: When the load value is within the first value range, performing parameter optimization processing on the Internet of Things base station, the parameter optimization processing comprising at least one of the following: adjusting a power parameter and a switching threshold; When the load value is within a second numerical range, load balancing processing is performed on the Internet of Things base station; When the load value is within a third value range, performing radio frequency optimization processing or capacity expansion processing on the Internet of Things base station; When the load value is within the fourth numerical range, a new Internet of Things base station is established.

4. The method according to claim 1, characterized in that: The base station load value prediction model includes an input layer, at least one hidden layer and an output layer; the load value of the Internet of Things base station is predicted by the base station load value prediction model, including: Outputting the preprocessed data to a first hidden layer of the at least one hidden layer through the input layer; For each hidden layer in the at least one hidden layer, sequentially performing linear processing and nonlinear processing on data output by a previous layer through the hidden layer to obtain intermediate data; The intermediate data output by the last hidden layer in the at least one hidden layer is processed by the output layer to obtain the load value of the Internet of Things base station.

5. The method according to claim 1, characterized in that: The base station load value prediction model is trained by the following steps: Obtain the historical base station data and historical real load values ​​of the IoT base stations connected to the drones during the historical aggregation operation of the drone cluster; Preprocessing the historical base station data to obtain sample data; The historical real load value is used as the label of the sample data, and the basic prediction model is trained by the sample data to obtain the base station load value prediction model.

6. The method according to claim 5, characterized in that: The basic prediction model includes a multi-layer perceptron.

7. The method according to any one of claims 1 to 6, characterized in that: The base station data includes business data and network environment data; the business data includes at least one of the following: historical load data, real-time network traffic data, number of users, and user business habit data; the network environment data includes at least one of the following: network frequency band, network standard, and surrounding base station operation status data.

8. A base station load adjustment device, characterized in that: The device comprises: An acquisition unit, used to acquire base station data of an Internet of Things base station that is communicatively connected to drones in the drone cluster during the drone cluster aggregation operation; A preprocessing unit, used for preprocessing the base station data to obtain preprocessed data; A processing unit, used for inputting the pre-processed data into a pre-trained base station load value prediction model to obtain the load value of the Internet of Things base station; An execution unit is used to determine a load solution based on the load value, and execute the load solution to adjust the load of the Internet of Things base station.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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