CN111491307A - Mobile broadband network signal strength grade determination method and device - Google Patents

Mobile broadband network signal strength grade determination method and device Download PDF

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CN111491307A
CN111491307A CN202010336644.3A CN202010336644A CN111491307A CN 111491307 A CN111491307 A CN 111491307A CN 202010336644 A CN202010336644 A CN 202010336644A CN 111491307 A CN111491307 A CN 111491307A
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preset
signal intensity
position information
prediction model
model
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程强
刘姿杉
党梅梅
李少晖
王泽珏
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China Academy of Information and Communications Technology CAICT
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China Academy of Information and Communications Technology CAICT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3913—Predictive models, e.g. based on neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services

Abstract

The application provides a method and a device for determining the signal intensity level of a mobile broadband network, wherein the method comprises the following steps: performing model training through a preset classification algorithm to obtain a preset signal intensity level prediction model; acquiring position information; determining a signal intensity grade corresponding to the position information according to the position information by using the preset signal intensity grade prediction model; the method comprises the steps of carrying out model training through a preset classification algorithm to obtain a preset signal strength grade prediction model, wherein the step of obtaining position information and signal receiving strength of a terminal is carried out; mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule; and performing model training based on a preset classification algorithm by taking the position information and the corresponding signal intensity grade as samples to obtain a preset signal intensity grade prediction model. The method can acquire the signal level strength of any position covered by the mobile broadband network.

Description

Mobile broadband network signal strength grade determination method and device
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for determining a signal strength level of a mobile broadband network.
Background
Mobile broadband technology, also known as "Wireless Wide Area Network (WWAN)" technology, provides wireless high-speed Internet access through portable devices. Using mobile broadband, you can connect to the Internet from any location that provides mobile telephony services for mobile Internet connections.
The network signal strength under the coverage of the mobile broadband network is of great significance for improving the efficiency of the process of construction, maintenance, optimization, diagnosis and the like of the mobile broadband network by operators, network service providers and customers, and therefore, obtaining the network signal strength level under the coverage of the mobile broadband network is a technical problem to be solved urgently.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for determining a signal strength level of a mobile broadband network, which can obtain a signal strength level at any position under coverage of the mobile broadband network.
In order to solve the technical problem, the technical scheme of the application is realized as follows:
in one embodiment, a method for determining a signal strength level of a mobile broadband network is provided, and the method comprises:
performing model training through a preset classification algorithm to obtain a preset signal intensity level prediction model;
acquiring position information;
determining a signal intensity grade corresponding to the position information according to the position information by using the preset signal intensity grade prediction model;
the method comprises the following steps of carrying out model training through a preset classification algorithm to obtain a preset signal intensity grade prediction model, wherein the model training comprises the following steps:
acquiring position information and signal receiving strength of a terminal;
mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule;
and performing model training based on a preset classification algorithm by taking the position information and the corresponding signal intensity grade as samples to obtain a preset signal intensity grade prediction model.
In another embodiment, there is provided a mobile broadband network signal strength level determination apparatus, comprising: a generating unit, an acquiring unit and a determining unit;
the generating unit is used for carrying out model training through a preset classification algorithm to obtain a preset signal intensity grade prediction model; the method comprises the steps of carrying out model training through a preset classification algorithm to obtain a preset signal strength grade prediction model, wherein the step of obtaining position information and signal receiving strength of a terminal is carried out; mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule; taking the position information and the corresponding signal intensity grade as samples, and carrying out model training based on a preset classification algorithm to obtain a preset signal intensity grade prediction model;
the acquisition unit is used for acquiring position information;
the determining unit is configured to determine, according to the position information acquired by the acquiring unit, a signal intensity level corresponding to the position information by using the preset signal intensity level prediction model generated by the generating unit.
In another embodiment, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the mobile broadband network signal level strength determination method as described when executing the program.
In another embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the mobile broadband network signal level strength determination method.
According to the technical scheme, the preset signal intensity grade prediction model with the input of the position information and the output of the signal intensity grade as the signal intensity grade is established and trained through the measurement data of the terminal based on the preset classification algorithm in the embodiment; the signal intensity level of any position can be obtained through the model.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic diagram illustrating a process of establishing a prediction model of a preset signal strength level in an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating the determination of signal strength level at any location in the embodiments of the present application;
FIG. 3 is a schematic flow chart illustrating the process of determining the signal strength level of any one of the regions according to the embodiment of the present application;
FIG. 4 is a schematic representation of a thermodynamic diagram of signal strength levels in an embodiment of the present application;
FIG. 5 is a schematic diagram of an apparatus for implementing the above technique in an embodiment of the present application;
fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail with specific examples. Several of the following embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
The embodiment of the application provides a method for determining the signal intensity grade of a mobile broadband network, which comprises the steps of establishing and training a preset signal intensity grade prediction model with input of position information and output of signal intensity grade through measurement data of a terminal based on a preset classification algorithm; the signal intensity level of any position can be obtained through the model.
In the embodiment of the application, model training needs to be performed through a preset classification algorithm to obtain a preset signal intensity level prediction model. Referring to fig. 1, fig. 1 is a schematic diagram illustrating a process of establishing a prediction model of a preset signal strength level in an embodiment of the present application. The method comprises the following specific steps:
step 101, acquiring measurement data of a terminal; wherein the measurement data comprises location information and signal reception strength.
The measurement data of the terminal obtained in the embodiment of the application may be measurement data reported by the terminal, or measurement data actually obtained by the terminal obtained from other devices;
if the measurement data is obtained from the terminal, the terminal needs to have a function of reporting the measurement data to the local determination device.
This implementation method needs to add a function of reporting measurement data to the terminal that needs to report, and specify the reported measurement data.
The timing of the measurement data reported by the terminal may be reported according to a preset time, or may be reported when a reporting instruction is received.
The implementation manner of the measurement data actually acquired by the terminal acquired from the other device may specifically be:
the determining apparatus of the present application obtains the measurement data stored on the other device by copying or by wired or wireless network transmission, but not limited to the other device, such as a storage device, a server, etc.
The content of the measurement data may be set according to actual needs, and the measurement data reported in the embodiment of the present application at least includes, but is not limited to, the following information: location information and signal reception strength.
The position information comprises longitude and latitude coordinates and an altitude; the Signal received strength may be a Reference Signal Receiving Power (RSRP).
Taking the measurement data of N terminals as an example, the position information in the measurement data of the ith terminal is latitude coordinate latiHeight altiLongitude coordinate loniReception strength P of signal received by terminal i from serving base stationi。
In the embodiment of the application, after the measurement data is obtained, whether the measurement data is preprocessed needs to be determined, and if so, the measurement data is used as a training sample; otherwise, the following data preprocessing is carried out on the measurement data: data cleaning, data marking, normalization and one-shot (one-shot) coding processing and data segmentation; and using the preprocessed measurement data as a training sample.
The attribute (feature) for data tagging of sample data includes a latitude coordinate lat of the terminal subjected to normalization processingiLongitude coordinate of terminal loniTerminal height information altiOutput label is discrete base station coverage intensity grade
Figure BDA0002466812160000051
Signal receiving strength PiInto a plurality of levels.
When data marking is performed on sample data, intensity level mapping of signal reception intensity may be included, or may not be included.
And 102, mapping the signal receiving strength to a corresponding signal strength grade according to a preset mapping rule.
The mapping rule may be set according to practical application, or the mapping of the signal strength and the signal strength level may be implemented according to the existing mapping rule.
The following mapping rules are given in the embodiments of the present application:
first signal strength level (corresponding signal strength excellence point): RSRP is larger than-85 dBm;
second signal strength level (corresponding signal strength better point): RSRP is not more than-85 dBm and not less than-95 dBm;
third signal intensity level (corresponding to signal intensity mid-point): the RSRP is less than-95 dBm and not more than-105 dBm;
fourth signal strength level (corresponding to the point of worse signal strength): RSRP is less than-105 dBm and not less than-115 dBm;
fifth signal strength level (corresponding signal strength very poor point): RSRP is less than-115 dBm.
The signal strength corresponding to the signal strength levels from the first level to the fifth level is gradually decreased.
And 103, performing model training based on a preset classification algorithm by taking the position information and the corresponding signal intensity grade as samples to obtain a preset signal intensity grade prediction model.
In the embodiment of the present application, the input of the obtained preset signal strength level prediction model is position information, and the output is a signal strength level.
The model training may be performed by using a classification algorithm suitable for the communication network, and in the embodiment of the present application, a K-nearest neighbor (KNN) classification algorithm is taken as an example.
The model relation of the input and the output of the established model is as follows:
Figure BDA0002466812160000061
wherein the content of the first and second substances,
Figure BDA0002466812160000062
is the signal strength level.
In the embodiment of the application, the KNN classifier is subjected to cross validation and the super-parameters are adjusted in the training process, so that the accuracy of the established model is improved.
The adjusted hyper-parameters comprise the value of K, the characteristic weight and the weights of different adjacent nodes.
Performing model training based on a preset classification algorithm, namely determining whether the classification accuracy of a trained classification model is greater than a preset threshold value when adjusting and optimizing the hyper-parameter, and if so, taking the trained classification model as a preset signal intensity level prediction model; otherwise, continuing to perform model training based on the preset classification algorithm, and optimizing model parameters until the classification accuracy of the trained classification model is greater than a preset threshold value, and taking the trained classification model as a preset signal intensity level prediction model.
And at this point, generating a preset signal strength level prediction model.
Because the general assumed condition of the KNN algorithm on the overall distribution has a wide range, the classification model established based on the KNN has better robustness and applicability.
Hereinafter, a signal strength level determination process in the embodiment of the present application will be described in detail with reference to the drawings.
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating the determination of the signal strength level at any position in the embodiment of the present application. The method comprises the following specific steps:
step 201, position information is acquired.
The position information here is latitude and longitude coordinates and altitude of the corresponding position: latitude coordinate lat0Altitude alt0Longitude coordinate lon0。
Step 202, determining a signal intensity level corresponding to the position information according to the position information by using the preset signal intensity level prediction model.
Based on the position information, the signal intensity level of the corresponding position can be obtained
Figure BDA0002466812160000063
The embodiment of the application can quickly, accurately and conveniently acquire the signal intensity level of any position.
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating the process of determining the signal strength level of any one of the regions in the embodiment of the present application. The method comprises the following specific steps:
step 301, dividing a preset area by using a grid with a preset step length, and acquiring a centroid coordinate of the divided grid as position information of the grid.
The preset step length can be set according to actual needs, or can be selected according to topological characteristics of the preset area, if the meshes of the area division are expected to be dense, the set value of the preset step length is smaller, and if the meshes of the area division are expected to be sparse, the set value of the preset step length is larger.
And selecting a longitude and latitude coordinate corresponding to the center of the grid according to the latitude coordinate and the longitude coordinate in the centroid coordinate of the grid, and using an average value of the altitude in a terrain area actually corresponding to the grid according to the altitude determination.
And 302, determining a signal intensity level corresponding to the grid according to the position information of the grid by using a preset signal intensity level prediction model.
And acquiring a signal intensity grade corresponding to each grid based on the position information, wherein the signal intensity grades of all the grids form the signal intensity grade of the preset area.
The embodiment of the application can quickly, accurately and conveniently acquire the signal intensity level of any region.
In the specific embodiment of the application, the signal intensity level can be visually displayed, and in the specific implementation, the signal intensity level corresponding to the centroid coordinate of each grid of the preset area can be represented by using a thermodynamic diagram technology and output. The visual display of the signal quality distribution under the whole network coverage range can be realized.
Referring to fig. 4, fig. 4 is a schematic diagram of a thermodynamic diagram of signal strength levels in an embodiment of the present application. In fig. 4, the correspondence between the position information and the signal intensity level is displayed on the thermodynamic diagram, the position information is the centroid of each grid, for example, the position corresponding to the gray circle in the diagram, and the signal intensity level corresponding to each position information is displayed.
In a specific implementation, each position information and a specific value of the corresponding signal strength level may be directly displayed on the thermodynamic diagram, or the position information and the corresponding signal strength level may be displayed when the mouse moves and selects the corresponding position, which is not limited in the embodiment of the present application.
The thermodynamic diagram can also represent the signal level intensity of different positions according to the corresponding color of the preset signal intensity level.
Based on the same inventive concept, the embodiment of the application also provides a device for determining the signal strength level of the mobile broadband network. Referring to fig. 5, fig. 5 is a schematic structural diagram of an apparatus applied to the above technology in the embodiment of the present application. The device comprises: a generating unit 501, an acquiring unit 502, and a determining unit 503;
the generating unit 501 is configured to perform model training through a preset classification algorithm to obtain a preset signal strength level prediction model; the method comprises the steps of carrying out model training through a preset classification algorithm to obtain a preset signal strength grade prediction model, wherein the step of obtaining position information and signal receiving strength of a terminal is carried out; mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule; taking the position information and the corresponding signal intensity grade as samples, and carrying out model training based on a preset classification algorithm to obtain a preset signal intensity grade prediction model;
an acquisition unit 502 for acquiring position information;
a determining unit 503, configured to determine, according to the position information acquired by the acquiring unit 502, a signal strength level corresponding to the position information by using the preset signal strength level prediction model generated by the generating unit 501.
Preferably, the preset classification algorithm is a KNN classification algorithm.
Preferably, the first and second electrodes are formed of a metal,
the generating unit 501 is specifically configured to, when performing model training based on a preset classification algorithm, determine whether the classification accuracy of a trained classification model is greater than a preset threshold, and if so, take the trained classification model as a preset signal strength level prediction model; otherwise, continuing to perform model training based on the preset classification algorithm, and optimizing model parameters until the classification accuracy of the trained classification model is greater than a preset threshold value, and taking the trained classification model as a preset signal intensity level prediction model.
Preferably, the apparatus further comprises: a processing unit 504;
a processing unit 504, configured to determine whether the measurement data is preprocessed when the obtaining unit 502 obtains the measurement data of the terminal, and if so, take the measurement data as a training sample; otherwise, the following data preprocessing is carried out on the measurement data: data cleaning, data marking, normalization and one-shot coding processing and data segmentation; and using the preprocessed measurement data as a training sample.
Preferably, the apparatus further comprises: a processing unit 504;
the processing unit 504 is further configured to divide a preset area by using a grid with a preset step length, and acquire a centroid coordinate of the divided grid as position information of the grid;
the determining unit 503 is further configured to determine, by using the preset signal strength level prediction model, a signal strength level corresponding to the grid according to the position information of the grid.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 504 is further configured to represent and output a signal intensity level corresponding to the centroid coordinate of each grid of the preset area by using a thermodynamic diagram technique.
The units of the above embodiments may be integrated into one body, or may be separately deployed; may be combined into one unit or further divided into a plurality of sub-units.
In another embodiment, an electronic device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the mobile broadband network signal strength level determination method when executing the program.
In another embodiment, a computer readable storage medium is also provided, on which computer instructions are stored, which when executed by a processor, may implement the steps in the mobile broadband network signal strength level determination method.
Fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 6, the electronic device may include: a Processor (Processor)610, a communication Interface (Communications Interface)620, a Memory (Memory)630 and a communication bus 640, wherein the Processor 610, the communication Interface 620 and the Memory 630 communicate with each other via the communication bus 640. The processor 610 may call logic instructions in the memory 630 to perform the following method:
performing model training through a preset classification algorithm to obtain a preset signal intensity level prediction model;
acquiring position information;
determining a signal intensity grade corresponding to the position information according to the position information by using the preset signal intensity grade prediction model;
the method comprises the following steps of carrying out model training through a preset classification algorithm to obtain a preset signal intensity grade prediction model, wherein the model training comprises the following steps:
acquiring position information and signal receiving strength of a terminal;
mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule;
and performing model training based on a preset classification algorithm by taking the position information and the corresponding signal intensity grade as samples to obtain a preset signal intensity grade prediction model.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for determining a signal strength level of a mobile broadband network, the method comprising:
performing model training through a preset classification algorithm to obtain a preset signal intensity level prediction model;
acquiring position information;
determining a signal intensity grade corresponding to the position information according to the position information by using the preset signal intensity grade prediction model;
the method comprises the following steps of carrying out model training through a preset classification algorithm to obtain a preset signal intensity grade prediction model, wherein the model training comprises the following steps:
acquiring position information and signal receiving strength of a terminal;
mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule;
and performing model training based on a preset classification algorithm by taking the position information and the corresponding signal intensity grade as samples to obtain a preset signal intensity grade prediction model.
2. The method of claim 1, wherein the preset classification algorithm is a K-nearest neighbor KNN classification algorithm.
3. The method of claim 1, further comprising:
when model training is carried out based on a preset classification algorithm, whether the classification accuracy of a trained classification model is larger than a preset threshold value or not is determined, and if yes, the trained classification model is used as a preset signal intensity level prediction model; otherwise, continuing to perform model training based on the preset classification algorithm, and optimizing model parameters until the classification accuracy of the trained classification model is greater than a preset threshold value, and taking the trained classification model as a preset signal intensity level prediction model.
4. The method of claim 1, wherein when obtaining the measurement data of the terminal, the method further comprises:
determining whether the measurement data is preprocessed or not, and if so, using the measurement data as a training sample; otherwise, the following data preprocessing is carried out on the measurement data: data cleaning, data marking, normalization and one-shot coding processing and data segmentation; and using the preprocessed measurement data as a training sample.
5. The method according to any one of claims 1-4, wherein the method further comprises:
dividing a preset area by using a grid with a preset step length, and acquiring a centroid coordinate of the divided grid as position information of the grid;
and determining the signal intensity level corresponding to the grid according to the position information of the grid by using the preset signal intensity level prediction model.
6. The method of claim 5, further comprising:
and representing the signal intensity level corresponding to the centroid coordinate of each grid of the preset area by utilizing a thermodynamic diagram technology, and outputting the signal intensity level.
7. An apparatus for determining a signal strength level of a mobile broadband network, the apparatus comprising: a generating unit, an acquiring unit and a determining unit;
the generating unit is used for carrying out model training through a preset classification algorithm to obtain a preset signal intensity grade prediction model; the method comprises the steps of carrying out model training through a preset classification algorithm to obtain a preset signal strength grade prediction model, wherein the step of obtaining position information and signal receiving strength of a terminal is carried out; mapping the signal receiving intensity into a corresponding signal intensity grade according to a preset mapping rule; taking the position information and the corresponding signal intensity grade as samples, and carrying out model training based on a preset classification algorithm to obtain a preset signal intensity grade prediction model;
the acquisition unit is used for acquiring position information;
the determining unit is configured to determine, according to the position information acquired by the acquiring unit, a signal intensity level corresponding to the position information by using the preset signal intensity level prediction model generated by the generating unit.
8. The apparatus of claim 7, further comprising: a processing unit;
the processing unit is used for dividing a preset area by using a grid with a preset step length and acquiring the centroid coordinate of the divided grid as the position information of the grid;
the determining unit is further configured to determine, by using the preset signal strength level prediction model, a signal strength level corresponding to the grid according to the position information of the grid obtained by the processing unit.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-6 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 6.
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CN116743286A (en) * 2023-06-12 2023-09-12 广东技术师范大学 Wireless signal receiving strength prediction method and device based on self-attention mechanism

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