CN115529601A

CN115529601A - Data processing method and device based on wireless propagation model and electronic equipment

Info

Publication number: CN115529601A
Application number: CN202211198914.4A
Authority: CN
Inventors: 邢文彪
Original assignee: Agricultural Bank of China
Current assignee: Agricultural Bank of China
Priority date: 2022-09-29
Filing date: 2022-09-29
Publication date: 2022-12-27

Abstract

The application provides a data processing method and device based on a wireless propagation model and electronic equipment. The method comprises the following steps: acquiring a first grid where a base station is located and a second grid where a receiving point is located based on rasterized map data; fitting according to the first grid and the second grid to obtain a signal propagation route; determining N sampling points based on the signal propagation route and a preset sampling interval distance, wherein N is not less than 1; acquiring a third grid to which the N sampling points belong respectively, wherein the number of the third grids is less than or equal to N; and acquiring the feature information between the base station and the receiving point according to the feature type of each third grid, and acquiring training data according to the feature information, wherein the training data is used for training the wireless propagation model. According to the method, when the training data used for training the wireless propagation model is obtained, the environmental characteristics between the base station and the receiving point are fully considered, and the accuracy of predicting the receiving signal intensity of the receiving point based on the wireless propagation model is improved.

Description

Data processing method and device based on wireless propagation model and electronic equipment

Technical Field

The present disclosure relates to wireless communication technologies, and in particular, to a data processing method and apparatus based on a wireless propagation model, and an electronic device.

Background

The wireless propagation model is designed for better and more accurate research of wireless propagation, and is the basis of cell planning of a mobile communication network. The existing characteristics are modeled through a wireless propagation model, and the optimal position of the base station is determined by estimating the effective signal strength transmitted to the user by the candidate base station, so that a large amount of actual measurement time can be saved, and a large amount of manpower and material resources are saved.

When the wireless propagation model is trained, firstly, the measured data of each signal receiving point is determined, and the measured data of each signal receiving point comprises the position information of a transmitting base station, the position information of a receiving point, the ground object type of the transmitting base station, the ground object type of the receiving point and the like. Secondly, the measured data is preprocessed to obtain training data. And finally, training the original wireless propagation model based on the training data to obtain the trained wireless propagation model. When the base station position is determined based on the wireless propagation model, the candidate base station position information, the candidate base station feature type, the transmitting point position information, the transmitting point feature type and the like are input to the trained wireless propagation model as input data, and the trained wireless propagation model obtains the received signal intensity of each receiving point under the candidate base station position based on each input data. And finally determining the position of the target base station according to the received signal strength of each receiving point of different candidate base station positions.

The training data used for training the original wireless propagation model in the process does not fully consider the environmental characteristics between the transmitting point and the receiving point, and the actual signal propagation process is deeply influenced by the environmental characteristics, so that the original signal propagation model is trained through the training data, the accuracy of the wireless propagation model is not favorably guaranteed, and the optimal position of the base station is not favorably determined based on the wireless propagation model.

Disclosure of Invention

The application provides a data processing method and device based on a wireless propagation model and electronic equipment, which fully consider the environmental characteristics between a base station and each receiving point when determining training data for training an original wireless propagation model so as to improve the accuracy of the trained wireless propagation model, thereby being beneficial to determining the optimal position of the base station based on the trained wireless propagation model.

In one aspect, the present application provides a data processing method based on a wireless propagation model, including:

acquiring a first grid where a base station is located and a second grid where receiving points are located based on rasterized map data, wherein the receiving points are used for receiving transmitting signals of the base station;

fitting to obtain a signal propagation route according to the first grid and the second grid;

determining N sampling points based on the signal propagation route and a preset sampling interval distance, wherein N is not less than 1;

acquiring a third grid to which the N sampling points respectively belong, wherein the number of the third grids is less than or equal to N;

and acquiring the feature information between the base station and the receiving point according to the feature type of each third grid, and acquiring training data according to the feature information, wherein the training data is used for training the wireless propagation model.

In another possible implementation manner, the obtaining, according to the type of the terrestrial object of each third grid, terrestrial object information between the base station and the receiving point includes:

quantizing the ground object type of each third grid to obtain a sub-ground object type vector corresponding to each third grid;

and acquiring the feature information between the base station and the receiving point according to the sub-feature type vector corresponding to each third grid.

In another possible implementation manner, the quantizing the surface feature type of each third grid to obtain a sub-surface feature type vector corresponding to each third grid includes:

and constructing a sub-ground feature type vector according to the ground feature type to which the third grid belongs, wherein the vector dimension of the sub-ground feature type vector is determined according to the number of the ground feature types, each element in the sub-ground feature type vector corresponds to one ground feature type, the value of the element of the ground feature type to which the third grid belongs is 1, and the values of other elements are 0.

In another possible implementation manner, the obtaining, according to the sub-feature type vector corresponding to each third grid, feature information between the base station and the receiving point includes:

and summing the sub-feature type vectors to acquire feature information between the base station and the receiving point.

In another possible implementation manner, the obtaining training data according to the feature information includes:

acquiring original data items corresponding to the base station and the receiving point, wherein the original data items comprise base station engineering parameters, geographic data parameters and signal receiving intensity, and the geographic data parameters comprise base station geographic data and receiving point geographic data information;

and obtaining a target data item according to the feature information and the original data item, and taking the target data item as the training data.

In another possible implementation manner, the obtaining a target data item according to the feature information and the original data item includes:

acquiring signal transmission information; wherein the signal transmission information comprises at least one of: the signal emission angle, the signal transmission distance or the height of a receiving point relative to a signal line of the base station;

and obtaining a target data item according to the ground feature information, the signal emission information and the original data item.

In another aspect, the present application provides a data processing method based on a wireless propagation model, including:

aiming at each candidate base station corresponding to the receiving point, acquiring data items corresponding to the candidate base stations and the receiving point;

inputting the data item into a wireless propagation model, and obtaining a signal receiving intensity output by the wireless propagation model, wherein the wireless propagation model is obtained by training according to the training data obtained according to any one of the first aspect;

and determining a target base station in the candidate base stations according to the signal receiving strength corresponding to each candidate base station.

In a third aspect, the present application provides a data processing apparatus based on a wireless propagation model, including a first obtaining module, a fitting module, a determining module, a second obtaining module, and a third obtaining module. Wherein the content of the first and second substances,

a first obtaining module, configured to obtain, based on rasterized map data, a first grid in which a base station is located and a second grid in which a receiving point is located, where the receiving point is a receiving point for receiving a transmission signal of the base station;

the fitting module is used for fitting according to the first grid and the second grid to obtain a signal propagation route;

the determining module is used for determining N sampling points based on the signal propagation route and a preset sampling interval distance, wherein N is not less than 1;

a second obtaining module, configured to obtain a third grid to which the N sampling points belong, where the number of the third grids is less than or equal to N;

and the third acquisition module is used for acquiring the ground feature information between the base station and the receiving point according to the ground feature type of each third grid, and acquiring training data according to the ground feature information, wherein the training data is used for training the wireless propagation model.

In another possible implementation manner, the third obtaining module is specifically configured to:

In another possible implementation manner, the quantizing the feature type of each third grid to obtain a sub-feature type vector corresponding to each third grid includes:

In another possible implementation manner, the third obtaining module is further configured to:

acquiring signal transmission information; wherein the signal transmission information comprises at least one of: the signal transmitting angle, the signal transmission distance or the height of a receiving point relative to a signal line of the base station;

In a fourth aspect, the present application provides a data processing apparatus based on a wireless propagation model, comprising an obtaining module, an inputting module and a determining module, wherein,

the acquisition module is used for acquiring data items corresponding to the candidate base stations and the receiving points aiming at each candidate base station corresponding to the receiving points;

an input module, configured to input the data item into a wireless propagation model, so as to obtain a signal reception intensity output by the wireless propagation model, where the wireless propagation model is obtained by training according to the training data obtained according to any one of the first aspect;

and the determining module is used for determining a target base station in the candidate base stations according to the signal receiving strength corresponding to each candidate base station.

In a fifth aspect, the present invention provides an electronic device, comprising:

at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method for wireless propagation model-based data processing as described in any of the first and second aspects above.

In a sixth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method for processing data based on a wireless propagation model according to any one of the first and second aspects is implemented.

The method includes the steps that when training data used for training an original wireless propagation model are obtained, for a data item corresponding to each receiving point, the ground object type between a base station and the corresponding receiving point is fully considered, the ground object type information is added into the content of an original data item, a new data item of each receiving point is obtained, and the training data used for training the original wireless propagation model are obtained based on the new data item of each receiving point.

Training an original wireless propagation model based on the training data obtained in the process can obtain the wireless propagation model which fully considers the actual environmental characteristics between the base station and each receiving point, so that the received signal strength of each receiving point can be analyzed accurately, the optimal position of the base station can be determined finally according to the received signal strength of each receiving point.

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.

FIG. 1 is a diagram of the data item content of a receiving point relative to a reference base station;

fig. 2 is a schematic flowchart of a data processing method based on a wireless propagation model according to an embodiment of the present application;

fig. 3a is a flowchart of a method for acquiring surface feature information between a base station and a receiving point according to an embodiment of the present disclosure;

FIG. 3b is a schematic diagram of a signal propagation path according to an embodiment of the present application;

fig. 4 is a schematic flowchart of determining a target base station based on a wireless propagation model according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a data processing apparatus based on a wireless propagation model according to an embodiment of the present application;

fig. 6 is an electronic device according to an embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

In some currently known technologies, when training data of a user training an original wireless propagation model is obtained, a reference base station and a receiving point for receiving a signal transmitted by the reference base station are first determined, where the reference base station is located in a preset area, and there is only one reference base station in the preset area for transmitting a signal to each receiving point. Secondly, performing preset rasterization processing on the map data in the preset area, and recording the actual measurement data of the reference base station and each receiving point at the moment. And finally, constructing training data based on the measured data of the reference base station and each receiving point.

Specifically, a data item of each receiving point relative to the reference base station is constructed according to the reference base station and the measured data of each receiving point, and training data is constructed according to the data item of each receiving point. Fig. 1 shows the data item contents of a receiving point relative to a reference base station, as shown in fig. 1, each raw data item includes base station engineering parameters, geographic data parameters and received signal strength, wherein the base station engineering parameters include base station height, signal horizontal transmission angle, signal vertical transmission angle, signal transmission frequency and signal transmission strength, and the geographic data parameters include area identifier, base station coordinates, receiving point coordinates, base station location altitude, receiving point altitude, base station type, receiving point type, base station building height, and receiving point building height. In fig. 1, x represents the rasterization specification.

The original data item corresponding to each receiving point in the training data only includes the surface feature type of the grid where the reference base station is located and the surface feature type of the grid where the receiving point is located, but does not include the surface feature type between the reference base station and the receiving point, that is, in the prior art, when a wireless propagation model is trained, the environmental characteristics between the transmitting point and the receiving point are not fully considered. However, in the actual signal propagation process, the received signal strength of the receiving point is deeply influenced by the environmental characteristics, and therefore, training the original signal propagation model through the above training data is not favorable for guaranteeing the accuracy of the wireless propagation model, and is not favorable for determining the optimal position of the base station based on the wireless propagation model.

According to the method and the device, when training data are obtained, actual environmental characteristics between each receiving point and the base station are fully considered, when an original data item of the receiving point relative to the base station is obtained, feature information between the receiving point and the base station is finally determined according to the original data item, a new data item of the receiving point relative to the base station is determined based on the feature information and the original data item, and finally training data used for training an original wireless propagation model are obtained according to the new data item of each receiving point relative to the base station. The wireless propagation model trained based on the training data considers the practical application environment of the base station, so that the accuracy of predicting the received signal strength of each receiving point of the wireless propagation model is improved, and the optimal position of the base station is determined based on the wireless propagation model.

Fig. 2 is a schematic flowchart of a data processing method based on a wireless propagation model according to an embodiment of the present application. Some embodiments of the present application are described in detail below with reference to fig. 2. The features of the embodiments and examples described below may be combined with each other without conflict between the embodiments.

As shown in fig. 2, the method provided by the present embodiment includes step S201, step S202, step S203, step S204 and step S205, wherein,

step S201, based on the rasterized map data, a first grid where the base station is located and a second grid where the receiving point is located are obtained.

The rasterization specification may be 5m × 5m or 2m × 2m, and the specific specification is set manually, which is not limited in this embodiment.

And S202, fitting according to the first grid and the second grid to obtain a signal propagation route.

Specifically, reference points of a first grid and a second grid are set, and a unique signal propagation route is determined according to the reference points of the first grid and the second grid. For example, the first grid and the second grid may each have a point at the upper left corner thereof as their reference point.

Step S203, determining N sampling points based on the signal propagation route and the preset sampling interval distance, wherein N is not less than 1.

Specifically, after the electronic device determines the signal propagation route, for the signal propagation route, a sampling point is set every other preset sampling interval distance, and sampling is performed once. The preset sampling interval distance can be a unique and unchangeable value, namely, the distance between every two adjacent sampling points is consistent. The preset sampling interval distance may also be gradually increased or decreased, and is not limited in this embodiment.

Step S204, a third grid to which the N sampling points belong is obtained.

Specifically, the third grids to which the sampling points belong are the grids to which the sampling points belong, and since the preset sampling interval distance is set manually, after the grid specification is determined, if the preset sampling interval distance is small, at least two sampling points may be located in the same grid, so that the number of the third grids is less than or equal to N.

In this embodiment, in order to improve the accuracy of the finally obtained wireless propagation model, when the rasterization specification and the preset sampling interval distance are set by the electronic device, it should be satisfied that the number of sampling points is consistent with the number of the third grids.

And S205, acquiring the feature information between the base station and the receiving point according to the feature type of each third grid, and acquiring training data according to the feature information, wherein the training data is used for training the wireless propagation model.

Specifically, the feature type of each third grid between the base station and the receiving point is obtained to obtain feature information between the base station and the receiving point, and finally training data is obtained according to the feature information.

Optionally, a target data item of each receiving point is obtained according to the feature information and the original data item of each receiving point, and training data is obtained according to each target data item. The target data item also comprises signal transmission information, and the signal transmission information comprises at least one of a base station signal transmission angle, a signal transmission distance and a height of a receiving point relative to a signal line.

Specifically, the method for determining the signal transmission angle of the base station is as follows: when the angle between the signal line and the reference direction is known, the reference direction, the first grid reference point, and the second grid reference point are set, and the angular deviation between the reference direction and the second grid reference point is calculated based on the first grid reference point. And calculating the angle difference between the signal line and the second grid reference point based on the angle deviation and the included angle between the signal line and the reference direction, and taking the angle difference as the signal transmitting angle of the base station.

The mode of determining the signal transmission distance is as follows: and calculating a plane distance based on the first grid reference point and the second grid reference point, and calculating a signal transmission distance based on the plane distance and the base station altitude, the building height of the base station, the receiving point altitude and the building height of the receiving point in the original data item.

The manner of determining the height of the receiving point relative to the signal line is: and determining the height of the receiving point relative to the signal line according to the signal transmission distance and the vertical signal emission angle in the original data item, wherein the height of the receiving point relative to the signal line = the signal transmission distance multiplied by tan theta, and theta is the vertical signal emission angle.

Illustratively, if the reference direction is known as the north direction, the first grid reference point is (5,5), the second grid reference point is (30, 30), the angle between the signal line and the reference direction is 35 °, and the vertical emission angle of the signal is 0 °. The base station altitude is 50, the base station height is 10, the receiving point altitude is 40, and the building height of the receiving point is 20. At this time, the angle deviation between the reference direction and the second grid reference point is calculated to be 135 ° based on the first reference point, and then the base station signal transmission angle is known to be 100 ° based on the angle deviation of 135 ° and the angle between the signal line and the reference direction of 35 °.

According to the known first grid reference point and the second grid reference point, the plane distance between the base station and the receiving point is known to be

According to the plane distance, the altitude of the base station, the building height of the base station, the altitude of the receiving point and the building height of the receiving point, the signal transmission distance is known to be

From the signal transmission distance and the vertical signal transmission angle, the height of the receiving point with respect to the signal line is 0.

In the method provided by this embodiment, when the electronic device acquires the training data, the electronic device samples the feature type between the base station and each receiving point based on the original data item of the signal propagation route between the base station and each receiving point, so as to add the feature information between the receiving point and the base station to the original data item of each receiving point, and obtain a new data item corresponding to each receiving point. And acquiring training data based on the new data item corresponding to each receiving point.

The training data obtained through the process fully considers the environmental characteristics between the base station and the receiving point, and the wireless propagation model obtained through training based on the training data can reduce the influence of the actual environment between the base station and the receiving point on the accuracy of the model prediction result, so that the accurate optimal base station position can be determined finally based on the wireless propagation model.

Fig. 3a is a flowchart of a method for acquiring terrestrial information between a base station and a receiving point according to an embodiment of the present disclosure, and fig. 3b is a schematic diagram of a signal propagation route according to an embodiment of the present disclosure. The following describes the embodiment of the present application in further detail with reference to fig. 3a and fig. 3b, and specifically, the embodiment describes how to obtain the feature information corresponding to each receiving point in detail.

As shown in fig. 3a, the method of the present embodiment may include steps S301, S302 and S303, wherein,

step S301, determining a base station index coordinate and a receiving point index coordinate according to the first grid and the second grid, and fitting to obtain a signal propagation route based on the base station index coordinate and the receiving point index coordinate.

The base station index coordinate may be a coordinate of any one of four corners of the first grid, and the receiving point index coordinate may be a coordinate of any one of four corners of the second grid. In this embodiment, the coordinate of the upper left corner of the first grid is used as the index coordinate of the base station, and the coordinate of the upper left corner of the second grid is used as the index coordinate of the receiving point.

Specifically, based on the base station index coordinates and the receiving point index coordinates, a unique signal propagation route is determined as shown in fig. 3b, and in fig. 3b, the black dotted line represents the signal propagation route between the base station and the receiving point.

Step S302, based on the signal propagation route and the preset sampling interval distance, determining N sampling points, wherein N is not less than 1.

Specifically, for the definition of the preset sampling interval distance, reference may be made to the description of step S203 in the foregoing embodiment, and details are not repeated here.

In fig. 3b, the black solid triangle represents a sampling point, the distance between two adjacent black solid triangles is the preset sampling interval distance, and the shaded grid is the third grid.

Step S303, a third grid to which the N sampling points belong is obtained, wherein the number of the third grids is less than or equal to N.

Specifically, in this embodiment, the sampling interval distance and the rasterization specification are preset, so that each third grid corresponds to one sampling point, and each grid through which a signal propagation route of the base station and the signal receiving point passes is the third grid. Exemplarily, in this embodiment, in fig. 3b, the index coordinate of the base station is (5,5), the index coordinate of the receiving point is (35, 45), the rasterization specification is 5m × 5m, the preset interval sampling distance is 7m, each grid through which the signal propagation routes of the base station and the signal receiving point obtained based on this pass is a third grid, and there is only one sampling point in each third grid.

And step S304, constructing a sub-feature type vector according to the feature type to which the third grid belongs.

The vector dimension of the sub-feature type vector is determined according to the number of the feature types, each element in the sub-feature type vector corresponds to one feature type, the element of the feature type to which the third grid belongs takes a value of 1, and the values of other elements are 0.

Illustratively, if the number of ground object types is five, specifically A, B, C, D, E, the vector dimension of the sub-ground object type vector is 5. Further, if the feature type of the third grid is a, the sub-feature type vector is represented as: (1,0,0,0,0).

When setting the rasterization specification, each grid only contains one ground type as far as possible. Optionally, in this embodiment, if a certain grid includes two or more feature types, the feature type with the highest proportion is determined as the feature type of the grid.

Step S305, summing up the sub-feature type vectors to obtain the feature information between the base station and the receiving point.

Specifically, all the third grids between the receiving point and the base station are counted, and the sub-feature type vector corresponding to each third grid is summed to obtain a total feature type vector, that is, the feature information between the base station and the receiving point.

Illustratively, if there are three third grids on the signal propagation path between the base station and a certain receiving point, the number of ground object types is five, specifically A, B, C, D, E. The sub-feature type vector of the first third grid is (1,0,0,0,0), the sub-feature type vector of the second third grid is (1,0,0,0,0), the sub-feature type vector of the third grid is (0,1,0,0,0), then the total feature type vector between the base station and the receiving point is (2,1,0,0,0), and the feature information between the base station and the receiving point is obtained according to the total feature type vector: two ground feature types of A, B coexist between the base station and the receiving point, wherein two third grids are of type A, and one third grid is of type B.

In the method provided by this embodiment, when determining the feature information between the base station and a certain receiving point, the electronic device first determines a signal propagation path between the base station and the receiving point, then sequentially sets sampling points along the signal propagation path based on a preset sampling interval distance to obtain third grids and corresponding feature types, and quantizes the feature type of each third grid based on the number of the feature types to obtain corresponding sub-feature type vectors. And finally, determining the feature information between the base station and the receiving point according to the sub-feature type vector of each third grid. When the electronic equipment determines the sampling point and the rasterization specification, each grid through which a signal propagation route passes is determined as a third grid as far as possible.

In the process, sampling is sequentially carried out according to a preset sampling interval distance along a signal propagation route between the base station and a certain receiving point, feature information between the base station and the receiving point is finally determined according to a sub-feature type vector of a third grid determined by sampling, training data used for training an original wireless propagation model is finally determined according to the feature information, and environmental characteristics in the signal propagation process between the base station and the receiving point are fully considered. And when the rasterization specification and the preset sampling interval distance are set, each grid through which the signal propagation route of the base station and the corresponding receiving point passes is ensured as much as possible to be used as a third grid, so that the accuracy of the wireless propagation model obtained through final training is improved.

Fig. 4 is a schematic flowchart of determining a target base station based on a wireless propagation model according to an embodiment of the present application. The following describes a specific implementation process of the embodiment of the present application in detail with reference to fig. 4. Specifically, the present embodiment defines the manner of determining the target base station in detail on the basis of the above-described embodiments.

As shown in fig. 4, the method includes steps S401, S402 and S403, wherein,

step S401, aiming at each candidate base station corresponding to the receiving point, acquiring data items corresponding to the candidate base stations and the receiving point.

Specifically, for each candidate base station, a data item of each reception point with respect to the candidate base station is acquired.

Step S402, inputting the data item into the wireless propagation model to obtain the signal receiving intensity output by the wireless propagation model.

Wherein, the wireless propagation model is trained based on the training data acquired in the foregoing embodiments.

Specifically, the data items of the receiving points corresponding to each candidate base station are respectively input into the infinite propagation model, so as to obtain the signal receiving intensity of the receiving points corresponding to each candidate base station output by the wireless propagation model.

Step S403, determining a target base station among the plurality of candidate base stations according to the signal reception strength corresponding to each candidate base station.

Alternatively, for the signal reception intensity of each receiving point corresponding to each candidate base station, the average signal reception intensity of each candidate base station may be calculated, that is, the signal intensities of the receiving points corresponding to the candidate base station are summed to obtain the total signal reception intensity, and then the total signal reception intensity is divided by the number of receiving points to obtain the average signal reception intensity of the candidate base station. And determining the candidate base station with the highest average signal receiving strength as the target base station in the plurality of candidate base stations.

The foregoing embodiments describe a data processing method based on a wireless propagation model from the perspective of a method flow, and the following embodiments describe a data processing apparatus based on a wireless propagation model from the perspective of a virtual module or a virtual unit, which are described in detail in the following embodiments.

The embodiment of the present application provides a data processing apparatus based on a wireless propagation model, as shown in fig. 5, the apparatus includes a first obtaining module 51, a fitting module 52, a determining module 53, a second obtaining module 54, and a third obtaining module 55, wherein,

a first obtaining module 51, configured to obtain, based on rasterized map data, a first grid in which a base station is located and a second grid in which a receiving point is located, where the receiving point is a receiving point for receiving a transmission signal of the base station;

a fitting module 52, configured to fit to obtain a signal propagation route according to the first grid and the second grid;

the determining module 53 is configured to determine N sampling points based on the signal propagation route and a preset sampling interval distance, where N is not less than 1;

a second obtaining module 54, configured to obtain third grids to which the N sampling points belong, where the number of the third grids is less than or equal to N;

and a third obtaining module 55, configured to obtain feature information between the base station and the receiving point according to the feature type of each third grid, and obtain training data according to the feature information, where the training data is used to train the wireless propagation model.

In another possible implementation manner of the embodiment of the present application, the third obtaining module 55 is specifically configured to:

Another possible implementation manner of the embodiment of the present application, the quantizing the ground object type of each third grid to obtain a sub-ground object type vector corresponding to each third grid, includes:

and constructing a sub-ground feature type vector according to the ground feature type to which the third grid belongs, wherein the vector dimension of the sub-ground feature type vector is determined according to the number of the ground feature types, each element in the sub-ground feature type vector corresponds to one ground feature type, the element value of the ground feature type to which the third grid belongs is 1, and the values of other elements are 0.

Another possible implementation manner of the embodiment of the present application, acquiring the feature information between the base station and the receiving point according to the sub-feature type vector corresponding to each third grid, includes:

and summing the sub-ground feature type vectors to obtain the ground feature information between the base station and the receiving point.

In another possible implementation manner of the embodiment of the present application, the third obtaining module 55 is further configured to:

acquiring an original data item corresponding to a base station and a receiving point, wherein the original data item comprises a base station engineering parameter, a geographic data parameter and signal receiving intensity, and the geographic data parameter comprises base station geographic data and receiving point geographic data information;

and obtaining a target data item according to the ground feature information and the original data item, and taking the target data item as training data.

Another possible implementation manner of the embodiment of the application, obtaining the target data item according to the feature information and the original data item, includes:

The data processing apparatus based on the wireless propagation model provided in the embodiment of the present application is suitable for the above method embodiment, and is not described herein again. In this embodiment, the first obtaining module 51, the second obtaining module 54, and the third obtaining module 55 may be the same obtaining module, may also be different obtaining modules, and may also be partially the same data module, which is not limited in this embodiment.

On the basis of the above device embodiments, the present application further provides a data processing device based on a wireless propagation model, specifically for determining a target base station based on the wireless propagation model, the device includes an obtaining module, an input module, and a determining module, wherein,

an input module, configured to input a data item into a wireless propagation model, so as to obtain a signal reception intensity output by the wireless propagation model, where the wireless propagation model is obtained by training according to training data obtained according to any one of the first aspect;

and the determining module is used for determining the target base station in the candidate base stations according to the signal receiving strength corresponding to each candidate base station.

The data processing apparatus based on the wireless propagation model provided in the embodiment of the present application is applicable to the foregoing method embodiments, and is not described herein again.

In an embodiment of the present application, there is provided an electronic device, as shown in fig. 6, the electronic device shown in fig. 6 includes: a processor 61 and a memory 62. Wherein the processor 61 is coupled to the memory 62, such as via a bus 63. Optionally, the electronic device may also include a transceiver 64. It should be noted that the transceiver 64 is not limited to one in practical application, and the structure of the electronic device is not limited to the embodiment of the present application.

The Processor 61 may be a CPU (Central Processing Unit, CPU 61), a general-purpose Processor 61, a dsp (Digital Signal Processor, dsp 61), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or execute the various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein. The processor 61 may also be a combination that performs a computational function, such as comprising one or more of a combination of microprocessors 61, a combination of a DSP and microprocessors 61, and the like.

Bus 63 may include a path that carries information between the aforementioned components. The bus 631002 may be a PCI (Peripheral Component Interconnect) bus 63, an EISA (Extended Industry Standard Architecture) bus 63, or the like. The bus 63 may be divided into an address bus 63, a data bus 63, a control bus 63, etc. For ease of illustration, only one thick line is shown in FIG. 6, but does not indicate only one bus 63 or one type of bus 63.

The Memory 62 may be a ROM (Read Only Memory 62) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory 62) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory 62), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.

The memory 62 is used for storing application program codes for executing the scheme of the application, and is controlled to be executed by the processor 61. The processor 61 is configured to execute application program code stored in the memory 62 to implement the aspects shown in the foregoing method embodiments.

Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. But also a server, etc. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

The present application provides a computer-readable storage medium, on which a computer program is stored, which, when running on a computer, enables the computer to execute the corresponding content in the foregoing method embodiments.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A data processing method based on a wireless propagation model is characterized by comprising the following steps:

2. The method according to claim 1, wherein the obtaining of the feature information between the base station and the receiving point according to the feature type of each third grid comprises:

3. The method according to claim 2, wherein the quantizing the feature type of each third grid to obtain a sub-feature type vector corresponding to each third grid, comprises:

4. The method according to claim 3, wherein the obtaining of the feature information between the base station and the receiving point according to the sub-feature type vector corresponding to each third grid comprises:

5. The method according to any one of claims 1 to 4, wherein the obtaining training data according to the feature information comprises:

acquiring original data items corresponding to the base station and the receiving points, wherein the original data items comprise base station engineering parameters, geographic data parameters and signal receiving intensity, and the geographic data parameters comprise base station geographic data and receiving point geographic data information;

6. The method of claim 5, wherein obtaining a target data item from the feature information and the raw data item comprises:

7. A data processing method based on a wireless propagation model is characterized by comprising the following steps:

inputting the data items into a wireless propagation model, and obtaining the signal receiving intensity output by the wireless propagation model, wherein the wireless propagation model is obtained by training according to the training data obtained according to any one of claims 1 to 6;

8. A data processing apparatus based on a wireless propagation model, comprising:

the system comprises a first acquisition module, a second acquisition module and a first processing module, wherein the first acquisition module is used for acquiring a first grid where a base station is located and a second grid where a receiving point is located based on rasterized map data, and the receiving point is used for receiving a transmitting signal of the base station;

9. An electronic device, comprising:

one or more processors;

a memory;

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: -performing a data processing method based on a wireless propagation model according to any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for data processing based on a wireless propagation model according to any one of claims 1 to 7.