CN115226112A - Network planning method, device and equipment based on machine learning and storage medium - Google Patents
Network planning method, device and equipment based on machine learning and storage medium Download PDFInfo
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Abstract
The application discloses a network planning method, a device, equipment and a storage medium based on machine learning, wherein the method comprises the following steps: when a network planning instruction is detected, determining a drive test point corresponding to the base station to obtain azimuth data to be processed corresponding to the network planning instruction; determining to-be-processed environment data based on a preset ray tracking mode, and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data; inputting the input data to be processed into a preset machine learning model, and performing prediction processing on propagation link loss from the base station to the route measurement point on the input data to be processed based on the preset machine learning model to obtain target predicted route loss; the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label. The method and the device accurately predict the path loss of the base station under the condition of small prediction calculation amount, and improve the prediction efficiency.
Description
Technical Field
The present application relates to the field of wireless network technologies, and in particular, to a method, an apparatus, a device, and a storage medium for network planning based on machine learning.
Background
With the continuous development of science and technology, more and more technologies are applied to the field of network construction, but the network construction also puts forward higher requirements on the technologies, for example, higher requirements on the accuracy of the network construction are put forward.
In network construction, the accuracy of base station network planning influences the development process of the whole network, so that the method has very important status and effect, and in order to improve the accuracy of base station network planning, all relevant characteristics of a base station and a drive test point are input into a network prediction model as input data, so that the technical problems of large calculated amount and low efficiency exist in the prediction process.
Disclosure of Invention
The application mainly aims to provide a network planning method, a device, equipment and a storage medium based on machine learning, and aims to solve the technical problems of large network path loss prediction calculation amount and low efficiency of a base station in the prior art.
In order to achieve the above object, the present application provides a network planning method based on machine learning, which is applied to a base station, and the network planning method based on machine learning includes:
when a network planning instruction is detected, determining a drive test point corresponding to the base station to obtain azimuth data to be processed corresponding to the network planning instruction;
determining to-be-processed environment data based on a preset ray tracking mode, and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
inputting the input data to be processed into a preset machine learning model, and performing prediction processing on the propagation link loss from the base station to the road measurement point on the input data to be processed based on the preset machine learning model to obtain target predicted road loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
Optionally, the step of obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data includes:
extracting azimuth characteristic parameters from the network planning instruction, and extracting to-be-processed characteristic data from the to-be-processed azimuth data and the to-be-processed environment data based on the azimuth characteristic parameters;
and performing data dimension reduction processing on the characteristic data to be processed to obtain input data to be processed.
Optionally, before the step of inputting the input data to be processed into a preset machine learning model and performing propagation link loss prediction processing on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method includes:
acquiring historical data, performing data processing on the historical data based on the preset ray tracking mode to obtain training input data, and acquiring an actual path loss label of the training input data;
inputting the training input data into a preset model to be trained, and performing prediction processing on the training input data based on the preset model to be trained to obtain predicted path loss;
comparing the predicted path loss with the actual path loss to obtain a comparison result;
and on the basis of the comparison result, if the preset model to be trained is determined not to complete the preset training condition, returning to the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained.
Optionally, the step of obtaining historical data, and performing data processing on the historical data based on the preset ray tracing manner to obtain training input data includes:
according to a preset ray tracking mode, determining simulation data corresponding to base station information, drive test point information, map information, material configuration information and antenna information in the historical data, and obtaining training environment characteristics based on the simulation data;
determining antenna gain based on the historical data and a preset antenna gain dictionary;
acquiring historical data, and determining Euclidean distance between the base station and the road measuring point based on the historical data;
acquiring historical data, and determining a relative horizontal angle and a relative vertical angle based on the historical data;
setting the training environment feature, the antenna gain, the Euclidean distance, the relative horizontal angle, and the relative vertical angle as the training input data.
Optionally, if it is determined that the preset model to be trained does not complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the step of obtaining the preset machine learning model includes:
based on the comparison result, if the preset model to be trained is determined not to finish the preset training condition, adjusting the weight parameter and the bias parameter in the preset machine learning model;
and returning to the step of inputting the training input data into the adjusted preset model to be trained until the preset machine learning model is obtained.
Optionally, after the step of inputting the input data to be processed into a preset machine learning model and performing prediction processing on propagation link loss on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method includes:
determining whether the target predicted path loss is within a preset path loss range;
and if the coverage area is within the preset path loss range, performing coverage prediction processing on the network based on a preset coverage prediction model.
The application also provides a network planning method based on machine learning, which is applied to road measuring points and comprises the following steps:
when a network planning instruction is detected, performing communication connection with a base station corresponding to the drive test point to receive a wireless signal sent by the base station;
and acquiring drive test point information based on the wireless signals, and sending the drive test point information to the base station so that a preset machine learning model in the base station can predict the propagation link loss based on the drive test point information to obtain the target predicted path loss.
The present application further provides a network planning device based on machine learning, which is applied to a base station, the network planning device based on machine learning includes:
the first determining module is used for determining a drive test point corresponding to the base station when a network planning instruction is detected so as to obtain azimuth data to be processed corresponding to the network planning instruction;
the second determination module is used for determining to-be-processed environment data based on a preset ray tracking mode and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
the first input module is used for inputting the input data to be processed into a preset machine learning model, and performing prediction processing on propagation link loss from the base station to the route measuring point on the input data to be processed based on the preset machine learning model to obtain target predicted route loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
Optionally, the second determining module includes:
the first determination unit is used for extracting azimuth characteristic parameters from the network planning instruction and extracting to-be-processed characteristic data from the to-be-processed azimuth data and the to-be-processed environment data based on the azimuth characteristic parameters;
and the dimension reduction unit is used for carrying out data dimension reduction processing on the characteristic data to be processed to obtain input data to be processed.
Optionally, the network planning apparatus based on machine learning further includes:
the acquisition module is used for acquiring historical data, processing the historical data based on the preset ray tracking mode to obtain training input data and acquiring an actual path loss label of the training input data;
the second input module is used for inputting the training input data into a preset model to be trained, and predicting the training input data based on the preset model to be trained to obtain predicted path loss;
the comparison module is used for comparing the predicted path loss with the actual path loss to obtain a comparison result;
and the returning module is used for returning the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained if the preset model to be trained is determined not to finish the preset training condition based on the comparison result.
Optionally, the obtaining module includes:
the first acquisition unit is used for determining simulation data corresponding to the base station information, the road test point information, the map information, the material configuration information and the antenna information in the historical data together according to a preset ray tracking mode, and acquiring training environment characteristics based on the simulation data;
the second determining unit is used for determining the antenna gain based on the historical data and a preset antenna gain dictionary;
a second acquisition unit, configured to acquire history data, and determine a euclidean distance between the base station and the route measurement point based on the history data;
a third acquisition unit configured to acquire history data, and determine a relative horizontal angle and a relative vertical angle based on the history data;
a setting unit configured to set the training environment characteristic, the antenna gain, the euclidean distance, the relative horizontal angle, and the relative vertical angle as the training input data.
Optionally, the return module comprises:
the adjusting unit is used for adjusting the weight parameters and the bias parameters in the preset machine learning model if the preset model to be trained is determined not to finish the preset training conditions based on the comparison result;
and the returning unit is used for returning the step of inputting the training input data into the adjusted preset model to be trained until the preset machine learning model is obtained.
Optionally, the network planning apparatus based on machine learning further includes:
a third determining module, configured to determine whether the target predicted path loss is within a preset path loss range;
and the coverage prediction module is used for performing coverage prediction processing on the network based on a preset coverage prediction model if the coverage prediction module is within the preset path loss range.
The application also provides a network planning device based on machine learning, which is applied to a road measuring point, and comprises the following components:
the detection module is used for carrying out communication connection with a base station corresponding to the drive test point when a network planning instruction is detected so as to receive a wireless signal sent by the base station;
and the acquisition module is used for acquiring the drive test point information based on the wireless signals and sending the drive test point information to the base station so that a preset machine learning model in the base station can predict the transmission link loss based on the drive test point information to obtain the target predicted drive loss.
The present application further provides a network planning device based on machine learning, the network planning device based on machine learning is an entity node device, the network planning device based on machine learning includes: a memory, a processor and a program of the machine learning based network planning method stored on the memory and executable on the processor, which program, when executed by the processor, may implement the steps of the machine learning based network planning method as described above.
The present application also provides a storage medium having a program stored thereon for implementing the above-mentioned network planning method based on machine learning, wherein the program of the network planning method based on machine learning implements the steps of the above-mentioned network planning method based on machine learning when being executed by a processor.
The present application also provides a computer program product, comprising a computer program which, when executed by a processor, performs the steps of the above-described machine learning based network planning method.
The application provides a network planning method, a device, equipment and a storage medium based on machine learning, compared with the prior art that the quantity of features extracted by a machine learning algorithm model is large, so that the network path loss prediction calculation quantity of a base station is large, and the efficiency is low, in the application, when a network planning instruction is detected, only the to-be-processed azimuth data and the to-be-processed environment data obtained based on a preset ray tracing mode can be obtained, so that the to-be-processed input data can be obtained, and then the to-be-processed input data can be input to a preset machine learning model to obtain the target prediction path loss.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a first embodiment of a network planning method based on machine learning according to the present application;
fig. 2 is a schematic flowchart of a detailed step of step S20 in the machine learning-based network planning method of the present application;
fig. 3 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
The implementation of the objectives, functional features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In a first embodiment of the network planning method based on machine learning, referring to fig. 1, the network planning method based on machine learning includes:
step S10, when a network planning instruction is detected, determining a drive test point corresponding to the base station to obtain azimuth data to be processed corresponding to the network planning instruction;
s20, determining to-be-processed environment data based on a preset ray tracking mode, and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
step S30, inputting the input data to be processed into a preset machine learning model, and based on the preset machine learning model, performing prediction processing on the propagation link loss from the base station to the route measurement point on the input data to be processed to obtain a target prediction route loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
The method comprises the following specific steps:
step S10, when a network planning instruction is detected, determining a drive test point corresponding to the base station to obtain azimuth data to be processed corresponding to the network planning instruction;
in this embodiment, it should be noted that the network planning method based on machine learning may be applied to site planning (site planning of a base station), where the base station and the corresponding drive test point belong to a network planning system based on machine learning, and the network planning system based on machine learning belongs to a network planning device based on machine learning.
For a network planning system based on machine learning corresponding to a certain station planning area, a preset machine learning model is built in, or preset machine learning models in other systems can be called, so that the path loss of a base station is predicted, and the coverage prediction of the planning area is further performed.
It should be noted that, the network planning system based on machine learning corresponding to the site planning region may also be configured with or call other models, such as a coverage prediction model or an orientation characteristic parameter determination model, and the type of the specific model is not limited herein.
In this embodiment, the specific application scenarios may be:
the network in a certain area needs to be planned, and in the planning process, the actual network coverage effect needs to be simulated, so that the rationality of the station site planning is verified, and the actual network coverage effect is related to the network path loss. Therefore, the predicted network path loss needs to be determined, specifically, after the base station position is determined, the wireless network path loss at each path measurement point needs to be determined, and if the wireless network path loss is within a preset acceptable range, the path measurement point can be covered by the network, or the design of the base station is reasonable, so that the accurate determination of the wireless network path loss of the base station is the key point for network planning.
Specifically, for example, a base station is arranged at a, a route point is designed at B, and when a wireless network signal of the base station reaches B, if a route loss is within a preset range, subsequent network coverage is performed, or it is determined that the setting of the base station is reasonable.
Specifically, in this embodiment, when a network planning instruction of a base station is detected, a drive test point or a drive test terminal (which may be one or multiple drive test points or multiple drive test terminals) corresponding to the base station is determined, and to-be-processed azimuth data corresponding to the network planning instruction is obtained, where obtaining to-be-processed azimuth data corresponding to the network planning instruction may be:
the first method is as follows: when a network planning instruction of a base station is detected, the azimuth data to be processed is directly extracted from the network planning instruction, that is, in this embodiment, the azimuth data to be processed is carried in the instruction (the azimuth data to be processed is tested in advance);
in this embodiment, the triggering manner of the network planning instruction may be a voice manner or a touch manner. That is, in this embodiment, the network planning instruction may be triggered on a machine learning based network planning system interface.
The second method comprises the following steps: when a network planning instruction of a base station is detected, triggering a preset drive test flow, and acquiring azimuth data to be processed, which is measured in real time by a drive test point, based on the preset drive test flow;
in this embodiment, it should be noted that the orientation data to be processed may be raw orientation data to be processed or orientation features to be processed, where the orientation features to be processed may be in a vector form or a matrix form.
That is, in this embodiment, when a network planning instruction of a base station is detected, acquiring to-be-processed azimuth data corresponding to the network planning instruction includes:
when a network planning instruction of a base station is detected, acquiring the azimuth vector characteristics to be processed corresponding to the network planning instruction;
or when a network planning instruction of the base station is detected, acquiring the azimuth matrix characteristic to be processed corresponding to the network planning instruction.
In this embodiment, if the to-be-processed azimuth data is to-be-processed azimuth raw data, the to-be-processed azimuth raw data is preprocessed, so as to obtain to-be-processed azimuth vector features or to-be-processed azimuth matrix features. The preprocessing includes a vectorization preprocessing or a matrixing preprocessing process.
Step S20, determining to-be-processed environment data based on a preset ray tracking mode, and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
in this embodiment, the to-be-processed environment data is determined based on a preset ray tracing manner, and the specific process may be: the method comprises the steps of predicting the signal strength of a base station based on a preset ray tracking algorithm in a preset ray tracking mode, base station information, drive test point information, map information, material configuration information, antenna information and the like, correspondingly generating a simulation intermediate file in the process of predicting the signal strength of a receiving point, extracting environment data to be processed from the simulation intermediate file, and specifically extracting and obtaining environment features (in a matrix or vector form) to be processed.
In this embodiment, the to-be-processed azimuth data and the to-be-processed environment data are combined to obtain to-be-processed input data, and specifically, the to-be-processed azimuth data and the to-be-processed environment data have the same data form, such as a matrix form or a vector form.
In this embodiment, the azimuth data to be processed specifically includes a relative horizontal angle (alpha), a relative vertical angle, a gain of the antenna corresponding to the direct path, a gain of the antenna corresponding to the transmission path, and a gain of the antenna corresponding to the diffraction path.
In this embodiment, after the orientation feature to be processed (orientation data to be processed) is obtained, the environment feature to be processed of the base station is obtained based on the preset ray tracing manner, that is, the obtained input data is input into the preset machine learning model, instead of inputting all the features (including invalid features) into the preset machine learning model, so that the data processing amount can be reduced, and the data processing efficiency can be improved.
In this embodiment, the to-be-processed environmental characteristics of the base station are obtained based on a preset ray tracing manner instead of measuring complex environmental characteristics, so that the time for obtaining environmental data can be reduced, and the data processing efficiency is improved.
In this embodiment, the direct path corresponds to the antenna gain, the transmission path corresponds to the antenna gain, and the diffraction path corresponds to the antenna gain, and the euclidean distance, the relative horizontal angle (alpha), and the relative vertical angle (beta) belong to the azimuth data to be processed; the order of the direct path and the order of the transmission path are environmental data to be processed, that is, the order of the direct path and the order of the transmission path, and the order of the diffraction path is an environmental parameter (also called a multi-path order, which represents the number of intersection points of the ray and a scene) introduced by a preset ray tracing manner, that is, RT.
Referring to fig. 2, the step of obtaining the input data to be processed based on the azimuth data to be processed and the environment data to be processed includes the following steps S21 to S22:
step S21, extracting orientation characteristic parameters from the network planning instruction, and extracting to-be-processed characteristic data from the to-be-processed orientation data and the to-be-processed environment data based on the orientation characteristic parameters;
in this embodiment, first, an orientation feature parameter is extracted from the network planning instruction, where the orientation feature parameter may be preset in the network planning instruction, or the orientation feature parameter is determined based on a configuration file.
In this embodiment, the to-be-processed feature data is extracted from the to-be-processed orientation data and the to-be-processed environment data by the name of the orientation feature parameter, that is, the parameter content corresponding to the orientation feature parameter is extracted from the to-be-processed orientation data and the to-be-processed environment data.
And S22, performing data dimension reduction processing on the characteristic data to be processed to obtain input data to be processed.
In this embodiment, the feature data to be processed is subjected to data dimension reduction processing to obtain input data to be processed, where a dimension reduction mode may be a k-nearest neighbor dimension reduction mode, or the like.
In this embodiment, after the feature data to be processed is obtained, data dimension reduction processing is further performed, so that the data processing amount can be further reduced, and the data processing efficiency is improved.
Step S30, inputting the input data to be processed into a preset machine learning model, and performing prediction processing on the propagation link loss from the base station to the road measurement point on the input data to be processed based on the preset machine learning model to obtain target predicted road loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
In this embodiment, the input data to be processed is input into a preset machine learning model, and prediction processing of propagation link loss from a base station to the route measurement point is performed on the input data to be processed based on the preset machine learning model to obtain a target predicted route loss, that is, since the preset machine learning model is a model capable of accurately predicting route loss obtained by iteratively training a preset model to be trained based on training data with a preset loss label, the target predicted route loss of the input data to be processed can be accurately predicted.
In this embodiment, it should be noted that the training data based on the preset loss label may also be processed based on a preset ray tracing manner, that is, in this embodiment, the training data is processed by a preset ray tracing algorithm.
Before the step of inputting the input data to be processed into a preset machine learning model and performing propagation link loss prediction processing on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method includes the following steps S01-S04:
step S01, acquiring historical data, performing data processing on the historical data based on the preset ray tracing mode to obtain training input data, and acquiring an actual road loss label of the training input data;
in this embodiment, history data (including base station information, drive test point information, map information, material configuration information, antenna information, and the like) is acquired, the history data is base data that is not processed, the history data is subjected to data processing based on the preset ray tracing manner, training input data is acquired, and after the training input data is acquired, an actual road loss label of the training input data is acquired, where the actual road loss label of the training input data may be a label recorded in history or a label labeled manually after a manual test.
The method comprises the following steps of acquiring historical data, carrying out data processing on the historical data based on a preset ray tracing mode to obtain training input data, and acquiring an actual path loss label of the training input data, wherein the steps comprise:
acquiring historical data, and performing data processing on the historical data based on a preset ray tracking mode to obtain initial input data;
and randomly extracting data with a preset proportion, such as 30% of data, from the initial input data to serve as training input data, and acquiring an actual path loss label of the training input data. In other words, during training, only 30% of the data set is taken as training data, and the corresponding target model can be obtained.
The method comprises the following steps of A1-A5, wherein the base station is in communication connection with the base station, the step of obtaining historical data and the step of performing data processing on the historical data based on a preset ray tracing mode to obtain training input data comprises the following steps:
a1, determining simulation data corresponding to base station information, drive test point information, map information, material configuration information and antenna information in the historical data together according to a preset ray tracking mode, and obtaining training environment characteristics based on the simulation data;
step A2, determining antenna gain based on the historical data and a preset antenna gain dictionary;
the antenna gain is one of the most important parameters of the antenna, and the magnitude of the antenna gain is directly proportional to the signal coverage range, which affects the operation quality of wireless communication, so that the antenna gain between the base station and the route point needs to be determined.
Specifically, in this embodiment, the antenna gain is determined based on the history data and a preset antenna gain dictionary.
In this embodiment, the specific process of obtaining the antenna gain may be: and traversing according to an interpolation method to obtain antenna gain dictionaries in the horizontal direction (180 ) and the vertical direction (90, 90), then respectively obtaining EOD (projection angle) and AOD (pitch angle) under the direct path, the projection path and the diffraction path, obtaining antenna gain under the corresponding path according to EOD and AOD table lookup (presetting the antenna gain dictionary), converting the antenna gain from a log domain to a linear domain, and normalizing to obtain the antenna gain characteristic in the characteristic.
The specific method comprises the following steps: let Gain ue Antenna gain obtained by interpolation in the dB domain (log domain);
Gain max maximum antenna gain for the dB domain;
let P ue Antenna gain in the linear domain;
P max maximum antenna gain in the linear domain;
the way of converting the antenna gain from the dB domain to the linear domain is:
then based on linear domain, the antenna gain is normalized
Step A3, acquiring historical data, and determining Euclidean distance between the base station and the road measuring point based on the historical data;
if n is the number of the road measurement points or the receiving points, the Euclidean Distance (Distance) between the base station and the road measurement points is as follows:
Step A4, acquiring historical data, and determining a relative horizontal angle and a relative vertical angle based on the historical data;
obtaining historical data, and obtaining Tx and Rx based on the historical data, wherein Tx (X0, y0, z 0) is an emitting point coordinate, rx (X1, y1, z 1) is a receiving point coordinate, and the relative horizontal angle is the included angle between the projection line segment of Rx to the horizontal plane and the X axis
The relative vertical angle is the included angle between the line segment between the receiving and sending points and the Rx projected line segment to the horizontal plane
And step A5, setting the training environment characteristics, the antenna gain, the Euclidean distance, the relative horizontal angle and the relative vertical angle as the training input data.
In this embodiment, the training environment feature is obtained by introducing the environment parameter in combination with the preset three-dimensional map corresponding to the base station in the preset ray tracing manner, and the specific process of obtaining the training environment feature may be:
determining base station information and drive test point information, such as transmission power, frequency and other information (determined by inputting an original file or setting);
determining map information of the base station, such as landform type, ground elevation, building height and the like (determined by inputting an original file or setting);
determining material configuration information, such as loss of radiation through obstacles, etc. (determined by inputting an original file or setting);
antenna information, such as antenna configuration information and antenna patterns, etc., is determined (either by entering an original file or setting up the determination).
In this embodiment, after determining the drive test base station information, the map information, the material configuration information, and the antenna information, the signal strength of the receiving point is predicted by using a ray tracing algorithm, in the process of predicting the signal strength of the receiving point, a simulation intermediate file is correspondingly generated, and the to-be-processed environment data is extracted from the simulation intermediate file, specifically, the to-be-processed environment feature (in a matrix or vector form) is extracted. Overall, in this embodiment, the original file corresponding to the input by the ray tracing method is: the base station, the drive test, the map information, the antenna information and the like obtain a result matrix (containing some parameters) and a simulation result file based on the original file, and further obtain the environmental characteristics to be processed. I.e. the type of the path between the transmitting and receiving points and the corresponding direct path order, transmission path order, diffraction path order (the multipath order represents the number of times of the ray colliding with the scene).
S02, inputting the training input data into a preset model to be trained, and performing prediction processing on the training input data based on the preset model to be trained to obtain predicted path loss;
in this embodiment, after obtaining training input data, that is, after obtaining historical data processed in a preset ray tracing manner, the training input data is input into a preset model to be trained, and prediction processing is performed on the training input data based on the preset model to be trained, so as to obtain predicted road loss.
In this embodiment, if the model to be trained is a three-layer fully-connected neural network structure, that is, the model to be trained only includes one hidden layer, one input layer, and one output layer, where the number of neurons in the input layer may be 9, the number of neurons in the hidden layer may be 10, and the number of neurons in the output layer is 1. Before training, initializing parameters of a preset model to be trained, for example, weight initialization adopts truncation positive-too distribution, standard deviation is initially set to 0.1, bias initialization is constant, the size is 0.1, and corresponding learning rate is adjusted according to actual conditions.
The algorithm training process sends training input data to the network in a batch mode, and the batch size is 256.
Step S03, comparing the predicted path loss with the actual path loss label to obtain a comparison result;
wherein, the specific training process is as follows:
the network input feature vector (training input data) is X = (X1, X2, X3, X4, X5, X6, X7, X8, X9), and corresponds to a direct path order, a direct path-corresponding antenna gain, a transmission path order, a transmission path-corresponding antenna gain, a diffraction path order, a diffraction path-corresponding antenna gain, a euclidean distance, a relative horizontal angle (alpha), and a relative vertical angle (beta).
The output of the hidden layer is: f (X) = ReLU (W) 1 X+b 1 ) Wherein W is 1 And b 1 Representing the weights and biases, respectively, of the network first layer neurons. The hidden layer uses an activation function of ReLU.
The output of the output layer is: y = W 2 f(X)+b 2 Wherein, W 2 And b 2 Representing the weights and biases of the second layer of the network. The output layer has no activation function.
Expressing the target predicted path loss of the network output as y (i) The actual path loss is expressed as
And calculating the error between the network output and the actual path loss, selecting an MSE loss function, and expressing the MSE loss function as follows:
in this embodiment, the predicted path loss is compared with the actual path loss label to obtain a comparison result, and the prediction accuracy is obtained based on the comparison result.
And S04, if the preset model to be trained is determined not to be completed with the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained.
And if the preset model to be trained is determined not to complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained.
Wherein, if it is determined that the preset model to be trained does not complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the step of obtaining the preset machine learning model includes:
if the preset model to be trained is determined not to complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained;
and if the preset model to be trained is determined to complete the preset training conditions, verifying the trained model based on the initial input data and the verification path loss label of the initial input data, and obtaining the preset machine learning model when the verification is passed.
The application provides a network planning method, a device, equipment and a storage medium based on machine learning, compared with the prior art that the quantity of features extracted by a machine learning algorithm model is large, so that the network path loss prediction calculation quantity of a base station is large, and the efficiency is low, in the application, when a network planning instruction is detected, only the to-be-processed azimuth data and the to-be-processed environment data obtained based on a preset ray tracing mode can be obtained, so that the to-be-processed input data can be obtained, and then the to-be-processed input data can be input to a preset machine learning model to obtain the target prediction path loss.
Further, based on the first embodiment of the present application, another embodiment of the present application is provided, in which if it is determined that the preset model to be trained does not complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the step of obtaining the preset machine learning model includes:
b1, based on the comparison result, if the preset model to be trained is determined not to complete the preset training condition, adjusting the weight parameter and the bias parameter in the preset machine learning model;
and B2, returning to the step of inputting the training input data into the adjusted preset model to be trained until the preset machine learning model is obtained.
In this embodiment, if it is determined that the preset model to be trained does not complete the preset training condition, the weight parameter and the bias parameter in the preset machine learning model are adjusted, and particularly, the weight parameter and the bias parameter in the preset machine learning model are directionally adjusted based on the comparison result and the actual path loss label, and training is continued based on the adjusted model until the preset machine learning model is obtained.
In this embodiment, based on the comparison result, if it is determined that the preset model to be trained does not complete the preset training condition, the weight parameter and the bias parameter in the preset machine learning model are adjusted, and the step of inputting the training input data into the adjusted preset model to be trained is returned until the preset machine learning model is obtained. In the embodiment, the preset model to be trained is accurately adjusted, so that the preset machine learning model is quickly obtained.
Further, based on the first embodiment and the second embodiment in the present application, another embodiment of the present application is provided, in this embodiment, after the step of inputting the input data to be processed into a preset machine learning model, and performing prediction processing on propagation link loss on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method includes:
step S40, determining whether the target predicted path loss is within a preset path loss range;
and S50, if the road loss is within the preset road loss range, performing coverage prediction processing on the network based on a preset coverage prediction model.
In this embodiment, it may also be determined whether the target predicted path loss is within a preset path loss range, specifically, if the target predicted path loss is within the preset path loss range, the coverage prediction processing is performed on the base station based on a preset coverage prediction model, specifically, an integrated file is generated based on the target predicted path loss, and the integrated file is sent to the middle station, and the coverage prediction is performed on the planned base station based on the trained machine learning model.
In this embodiment, the step of determining whether the target predicted path loss is within the preset path loss range may not be performed, and the coverage prediction processing may be directly performed on the network based on the target predicted path loss and a preset coverage prediction model.
In this embodiment, it is further determined whether the target predicted path loss is within a preset path loss range; and if the current path loss is within the preset path loss range, performing coverage prediction processing on the network based on a preset coverage prediction model. Therefore, a foundation is laid for network construction.
Further, based on the above embodiments in the present application, another embodiment of the present application is provided, in which the method is applied to a road measuring point, and the method for network planning based on machine learning includes:
step C1, when a network planning instruction is detected, performing communication connection with a base station corresponding to the drive test point to receive a wireless signal sent by the base station;
and step C2, acquiring the information of the drive test points based on the wireless signals, and sending the information of the drive test points to the base station so that a preset machine learning model in the base station can predict the transmission link loss based on the information of the drive test points to obtain the target predicted path loss.
In this embodiment, the network planning method based on machine learning is further applied to a road test point, which may be a road test terminal, and when the road test terminal detects a network planning instruction, the road test terminal performs communication connection with a base station corresponding to the road test point to receive a wireless signal sent by the base station, and further, based on the wireless signal, acquires road test point information such as the position of the road test point, and sends the road test point information to the base station, so that a preset machine learning model in the base station performs propagation link loss prediction processing based on the road test point information to obtain a target predicted road loss. In this embodiment, since the drive test point accurately obtains the drive test point information, a foundation is laid for accurately obtaining the target predicted path loss by a preset machine learning model in the base station.
Referring to fig. 3, fig. 3 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 3, the machine learning based network planning apparatus may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the network planning apparatus based on machine learning may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
Those skilled in the art will appreciate that the machine learning based network planning device architecture shown in fig. 3 does not constitute a limitation of machine learning based network planning devices and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 3, a memory 1005 as a storage medium may include an operating system, a network communication module, and a machine learning-based network planning program therein. The operating system is a program that manages and controls the hardware and software resources of the machine learning based network planning device, supporting the operation of the machine learning based network planning program as well as other software and/or programs. The network communication module is used to implement communication between the components within the memory 1005 and with other hardware and software in the machine learning based network planning system.
In the machine learning based network planning apparatus shown in fig. 3, the processor 1001 is configured to execute a machine learning based network planning program stored in the memory 1005 to implement any one of the steps of the machine learning based network planning method described above.
The specific implementation of the network planning device based on machine learning in the present application is basically the same as that of the above network planning method based on machine learning, and is not described herein again.
The present application further provides a network planning device based on machine learning, the network planning device based on machine learning includes:
applied to a base station, the network planning device based on machine learning comprises:
the first determining module is used for determining a drive test point corresponding to the base station when a network planning instruction is detected so as to obtain azimuth data to be processed corresponding to the network planning instruction;
the second determination module is used for determining to-be-processed environment data based on a preset ray tracking mode and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
the first input module is used for inputting the input data to be processed into a preset machine learning model, and predicting the propagation link loss from the base station to the route measurement point on the basis of the preset machine learning model on the input data to be processed to obtain a target predicted route loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
Optionally, the second determining module includes:
a first determining unit, configured to extract an orientation feature parameter from the network planning instruction, and extract feature data to be processed from the orientation data to be processed and the environment data to be processed based on the orientation feature parameter;
and the dimension reduction unit is used for carrying out data dimension reduction processing on the characteristic data to be processed to obtain input data to be processed.
Optionally, the network planning apparatus based on machine learning further includes:
the acquisition module is used for acquiring historical data, processing the historical data based on the preset ray tracking mode to obtain training input data and acquiring an actual path loss label of the training input data;
the second input module is used for inputting the training input data into a preset model to be trained, and carrying out prediction processing on the training input data based on the preset model to be trained to obtain predicted path loss;
the comparison module is used for comparing the predicted path loss with the actual path loss to obtain a comparison result;
and the returning module is used for returning the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained if the preset model to be trained is determined not to finish the preset training condition based on the comparison result.
Optionally, the obtaining module includes:
the first acquisition unit is used for determining simulation data corresponding to the base station information, the road test point information, the map information, the material configuration information and the antenna information in the historical data together according to a preset ray tracking mode, and acquiring training environment characteristics based on the simulation data;
the second determining unit is used for determining the antenna gain based on the historical data and a preset antenna gain dictionary;
a second acquisition unit, configured to acquire history data, and determine a euclidean distance between the base station and the route measurement point based on the history data;
a third acquisition unit configured to acquire history data, and determine a relative horizontal angle and a relative vertical angle based on the history data;
a setting unit configured to set the training environment characteristic, the antenna gain, the euclidean distance, the relative horizontal angle, and the relative vertical angle as the training input data.
Optionally, the return module comprises:
the adjusting unit is used for adjusting the weight parameters and the bias parameters in the preset machine learning model if the preset model to be trained is determined not to finish the preset training conditions based on the comparison result;
and the returning unit is used for returning the step of inputting the training input data into the adjusted preset model to be trained until the preset machine learning model is obtained.
Optionally, the network planning apparatus based on machine learning further includes:
a third determining module, configured to determine whether the target predicted path loss is within a preset path loss range;
and the coverage prediction module is used for performing coverage prediction processing on the network based on a preset coverage prediction model if the coverage prediction module is in a preset path loss range.
The specific implementation of the network planning apparatus based on machine learning in the present application is substantially the same as that of each embodiment of the network planning method based on machine learning, and is not described herein again.
The application also provides a network planning device based on machine learning, which is applied to road measuring points, and comprises:
the detection module is used for carrying out communication connection with a base station corresponding to the drive test point when a network planning instruction is detected so as to receive a wireless signal sent by the base station;
and the acquisition module is used for acquiring the drive test point information based on the wireless signals and sending the drive test point information to the base station so as to predict the propagation link loss based on the drive test point information by a preset machine learning model in the base station and obtain the target predicted drive loss.
The specific implementation of the network planning apparatus based on machine learning in the present application is substantially the same as that of the above network planning method based on machine learning, and is not described herein again.
The present application provides a storage medium, and the storage medium stores one or more programs, which can be further executed by one or more processors for implementing the steps of the machine learning-based network planning method described in any one of the above.
The specific implementation of the storage medium of the present application is substantially the same as that of each embodiment of the network planning method based on machine learning, and is not described herein again.
The present application also provides a computer program product, comprising a computer program which, when executed by a processor, performs the steps of the above-described machine learning based network planning method.
The specific implementation of the computer program product of the present application is substantially the same as that of each embodiment of the network planning method based on machine learning, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one of 8230, and" comprising 8230does not exclude the presence of additional like elements in a process, method, article, or apparatus comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A network planning method based on machine learning is characterized in that the network planning method based on machine learning is applied to a base station, and comprises the following steps:
when a network planning instruction is detected, determining a road measurement point corresponding to the base station, and determining azimuth data to be processed of the base station and the road measurement point;
determining to-be-processed environment data based on a preset ray tracking mode, and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
inputting the input data to be processed into a preset machine learning model, and performing prediction processing on the propagation link loss from the base station to the road measurement point on the input data to be processed based on the preset machine learning model to obtain target predicted road loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
2. The machine-learning based network planning method of claim 1, wherein the step of obtaining the input data to be processed based on the orientation data to be processed and the environment data to be processed comprises:
extracting azimuth characteristic parameters from the network planning instruction, and extracting to-be-processed characteristic data from the to-be-processed azimuth data and the to-be-processed environment data based on the azimuth characteristic parameters;
and performing data dimension reduction processing on the characteristic data to be processed to obtain input data to be processed.
3. The method for network planning based on machine learning according to claim 1, wherein before the step of inputting the input data to be processed into a preset machine learning model, and performing propagation link loss prediction processing on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method comprises:
acquiring historical data, performing data processing on the historical data based on the preset ray tracking mode to obtain training input data, and acquiring an actual path loss label of the training input data;
inputting the training input data into a preset model to be trained, and performing prediction processing on the training input data based on the preset model to be trained to obtain predicted path loss;
comparing the predicted path loss with the actual path loss to obtain a comparison result;
and if the preset model to be trained is determined not to complete the preset training condition based on the comparison result, returning to the step of inputting the training input data into the preset model to be trained until the preset machine learning model is obtained.
4. The machine learning-based network planning method according to claim 3, wherein the step of obtaining historical data and performing data processing on the historical data based on the preset ray tracing manner to obtain training input data comprises:
according to a preset ray tracking mode, determining simulation data corresponding to base station information, drive test point information, map information, material configuration information and antenna information in the historical data, and obtaining training environment characteristics based on the simulation data;
determining antenna gain based on the historical data and a preset antenna gain dictionary;
acquiring historical data, and determining Euclidean distance between the base station and the route measuring point based on the historical data;
acquiring historical data, and determining a relative horizontal angle and a relative vertical angle based on the historical data;
setting the training environment feature, the antenna gain, the Euclidean distance, the relative horizontal angle, and the relative vertical angle as the training input data.
5. The machine learning based network planning method according to claim 3, wherein the step of returning the training input data to the preset model to be trained until the preset machine learning model is obtained, if it is determined that the preset model to be trained does not complete the preset training condition based on the comparison result, comprises:
based on the comparison result, if the preset model to be trained is determined not to complete the preset training condition, adjusting the weight parameter and the bias parameter in the preset machine learning model;
and returning to the step of inputting the training input data into the adjusted preset model to be trained until the preset machine learning model is obtained.
6. The method for network planning based on machine learning according to any of claims 1-5, wherein after the step of inputting the input data to be processed into a preset machine learning model, and performing propagation link loss prediction processing on the input data to be processed based on the preset machine learning model to obtain a target predicted path loss, the method comprises:
determining whether the target predicted path loss is within a preset path loss range;
and if the current path loss is within the preset path loss range, performing coverage prediction processing on the network based on a preset coverage prediction model.
7. A network planning method based on machine learning is characterized in that the network planning method is applied to road measuring points and comprises the following steps:
when a network planning instruction is detected, performing communication connection with a base station corresponding to the drive test point to receive a wireless signal sent by the base station;
and acquiring the drive test point information based on the wireless signals, and sending the drive test point information to the base station so that a preset machine learning model in the base station can predict the propagation link loss based on the drive test point information to obtain the target predicted drive loss.
8. A machine learning based network planning apparatus, comprising:
the first determining module is used for determining a drive test point corresponding to the base station when a network planning instruction is detected so as to obtain azimuth data to be processed corresponding to the network planning instruction;
the second determining module is used for determining to-be-processed environment data based on a preset ray tracking mode and obtaining to-be-processed input data based on the to-be-processed azimuth data and the to-be-processed environment data;
the first input module is used for inputting the input data to be processed into a preset machine learning model, and performing prediction processing on propagation link loss from the base station to the route measuring point on the input data to be processed based on the preset machine learning model to obtain target predicted route loss;
the preset machine learning model is obtained by performing iterative training on a preset model to be trained based on training data with a preset loss label.
9. A machine learning based network planning apparatus, the machine learning based network planning apparatus comprising: a memory, a processor, and a program stored on the memory for implementing the machine learning based network planning method,
the memory is used for storing a program for realizing the network planning method based on machine learning;
the processor is configured to execute a program for implementing the machine learning based network planning method to implement the steps of the machine learning based network planning method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a program for implementing a machine learning based network planning method, the program being executed by a processor for implementing the steps of the machine learning based network planning method according to any one of claims 1 to 7.
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