Base station coverage radius analysis system based on artificial intelligence

Abstract

The present invention relates to a base station coverage radius analysis system based on artificial intelligence, including: a radius analysis device, which adopts a coverage analysis model to intelligently analyze the signal coverage radius of the target base station at the next moment of the current moment according to various base station association information of the target base station, multiple signal coverage radiuses corresponding to multiple historical moments before the current moment of the target base station, and the signal coverage radius at the current moment; a target marking device, which performs marking processing on whether the residential building is the coverage target of the target base station based on the distance from each residential building to the target base station. Through the present invention, it is possible to introduce multiple basic data and coverage analysis models for the intelligent analysis of the coverage radius of the target base station in the future, and when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment of the current moment, the residential building is marked as the coverage target of the target base station at the next moment of the current moment.

Description

Technical Field

[0001] The present invention relates to the field of base station management, and in particular to an artificial intelligence-based base station coverage radius analysis system. Background Art

[0002] Base station management (BTS management; BTSM) follows the principles of clear positioning, unified planning, reasonable layout, resource sharing, and environmental protection. Base station management is to strengthen the management of public mobile communication base stations, promote the harmonious development of public mobile communications, and safeguard public interests.

[0003] Generally, base station resource information management mainly completes the entry, modification and maintenance of various indicators of base stations (including base station controllers, base stations, compression encoders, etc.) on electronic maps, including: base station calendar card management, base station data information management, base station graphic data, base station attribute data, base station links, signaling data, resource information retrieval and base station data information statistics, etc. With the rapid development of mobile Internet, mobile communications have become an indispensable part of people's lives, and as an important infrastructure of mobile communications, how to standardize the management of base stations is becoming increasingly important.

[0004] However, since the coverage radius of a base station is affected by many factors, such as meteorological factors, base station performance, and the number of receiving terminals around the base station, it is difficult to accurately analyze the coverage radius of a base station, and can only be roughly judged based on historical experience. Obviously, the accuracy of this rough judgment benchmark is unsatisfactory. Summary of the invention

[0005] In order to solve the technical problems in the related fields, the present invention provides a base station coverage radius parsing system based on artificial intelligence, which introduces a number of basic data for the intelligent analysis of the coverage radius of the target base station at future moments, including the antenna height, feeder loss, set output power and operating frequency of the target base station, and multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, and introduces a targeted coverage analysis model for the intelligent analysis of the coverage radius of the target base station at future moments. Specifically, the autoencoder neural network is trained multiple times to obtain the autoencoder neural network that has completed the multiple trainings and output it as the coverage analysis model. The autoencoder neural network is trained multiple times to obtain the autoencoder neural network that has completed the multiple trainings and output it as the coverage analysis model. The number of trainings performed by the encoder neural network is proportional to the maximum user capacity of the target base station, thereby constructing coverage analysis models with different structures for different base stations, and introducing a target marking device for marking whether each residential building is a coverage target of the target base station based on the distance from the target base station. Specifically, when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment from the current moment, the residential building is marked as the coverage target of the target base station at the next moment from the current moment, thereby notifying the base station equipment in various residential buildings that can communicate in advance, and providing reliable reference data for the debugging of base station signals.

[0006] According to the present invention, a base station coverage radius parsing system based on artificial intelligence is provided, the system comprising:

[0007] A first input mechanism is used to obtain various associated data corresponding to the target base station, wherein the various associated data of the target base station are the antenna height, feeder loss, set output power and operating frequency of the target base station;

[0008] The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced in pairs on the time axis;

[0009] An object building mechanism, configured to perform multiple trainings on the autoencoder neural network to obtain an autoencoder neural network that has completed multiple trainings and output the autoencoder neural network as a coverage analysis model, wherein the number of trainings performed by the autoencoder neural network is proportional to the maximum user capacity of the target base station;

[0010] A radius parsing device, connected to the first entry mechanism, the second entry mechanism and the object forming mechanism respectively, for using the coverage analysis model to intelligently parse the signal coverage radius of the target base station at the next moment after the current moment according to the antenna height and feeder loss of the target base station, the output power and the operating frequency, the multiple signal coverage radii corresponding to the multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment;

[0011] A target marking device, connected to the radius parsing device, for performing a marking process of whether each residential building is a coverage target of the target base station based on the distance from each residential building to the target base station;

[0012] Among them, performing a marking process of whether the residential building is a coverage target of the target base station based on the distance from each residential building to the target base station includes: when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment of the current moment, marking the residential building as a coverage target of the target base station at the next moment of the current moment;

[0013] The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced on the time axis, including: the number of the multiple historical moments is positively correlated with the working frequency of the target base station;

[0014] Among them, the coverage analysis model is used to set the output power and operating frequency, multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment according to the antenna height and feeder loss of the target base station, and intelligently analyze the signal coverage radius of the target base station at the next moment of the current moment, including: the next moment of the current moment, the current moment, and multiple historical moments before the current moment are evenly spaced in pairs on the time axis.

[0015] It can be seen that the present invention has at least the following three important invention points:

[0016] Important invention point 1: multiple basic data are introduced for intelligent analysis of the coverage radius of the target base station at future moments, including the antenna height, feeder loss, set output power and operating frequency of the target base station, and multiple signal coverage radii corresponding to multiple historical moments before the current moment;

[0017] The second important invention point: a targeted coverage analysis model is introduced for intelligent analysis of the coverage radius of the target base station at future moments. Specifically, the autoencoder neural network is trained multiple times to obtain an autoencoder neural network that has completed multiple trainings and outputs it as a coverage analysis model. The number of trainings performed by the autoencoder neural network is proportional to the maximum user capacity of the target base station, thereby constructing coverage analysis models with different structures for different base stations.

[0018] Important invention point three: Introducing a target marking device for marking whether each residential building is a coverage target of the target base station based on the distance from the residential building to the target base station. Specifically, when the distance from a residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment after the current moment, the residential building is marked as the coverage target of the target base station at the next moment after the current moment, thereby notifying the base station equipment in various residential buildings that can communicate in advance, and providing reliable reference data for the debugging of the base station signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0020] Figure 1 Schematic diagram of the internal structure of a base station coverage radius parsing system based on artificial intelligence according to implementation scheme A of the present invention.

[0021] Figure 2 Schematic diagram of the internal structure of a base station coverage radius parsing system based on artificial intelligence according to implementation scheme B of the present invention.

[0022] Figure 3 Schematic diagram of the internal structure of the base station coverage radius parsing system based on artificial intelligence according to Implementation Scheme C of the present invention. DETAILED DESCRIPTION

[0023] The implementation scheme of the base station coverage radius parsing system based on artificial intelligence of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 The internal structure diagram of the base station coverage radius parsing system based on artificial intelligence according to Embodiment A of the present invention is shown, and the system includes:

[0025] A first input mechanism is used to obtain various associated data corresponding to the target base station, wherein the various associated data of the target base station are the antenna height, feeder loss, set output power and operating frequency of the target base station;

[0026] For example, an FPGA chip is selected to implement the first input mechanism, which is used to obtain various associated data corresponding to the target base station, wherein the various associated data of the target base station are the antenna height, feeder loss, set output power and operating frequency of the target base station;

[0027] The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced in pairs on the time axis;

[0028] An object building mechanism, configured to perform multiple trainings on the autoencoder neural network to obtain an autoencoder neural network that has completed multiple trainings and output the autoencoder neural network as a coverage analysis model, wherein the number of trainings performed by the autoencoder neural network is proportional to the maximum user capacity of the target base station;

[0029] A radius parsing device, connected to the first entry mechanism, the second entry mechanism and the object forming mechanism respectively, for using the coverage analysis model to intelligently parse the signal coverage radius of the target base station at the next moment after the current moment according to the antenna height and feeder loss of the target base station, the output power and the operating frequency, the multiple signal coverage radii corresponding to the multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment;

[0030] A target marking device, connected to the radius parsing device, for performing a marking process of whether each residential building is a coverage target of the target base station based on the distance from each residential building to the target base station;

[0031] Among them, performing a marking process of whether the residential building is a coverage target of the target base station based on the distance from each residential building to the target base station includes: when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment of the current moment, marking the residential building as a coverage target of the target base station at the next moment of the current moment;

[0032] The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced on the time axis, including: the number of the multiple historical moments is positively correlated with the working frequency of the target base station;

[0033] Among them, the coverage analysis model is used to set the output power and the operating frequency, the multiple signal coverage radii corresponding to the multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment according to the antenna height and the feeder loss of the target base station. The signal coverage radius of the target base station at the next moment of the current moment is intelligently analyzed, including: the next moment of the current moment, the current moment, and the multiple historical moments before the current moment are evenly spaced in pairs on the time axis;

[0034] Among them, the second input mechanism is used to obtain multiple signal coverage radii corresponding to the target base station at multiple historical moments before the current moment, and the current moment and the multiple historical moments before it are evenly spaced on the time axis. It also includes: using a data conversion function to represent the data conversion relationship in which the number of moments in the multiple historical moments is positively correlated with the working frequency of the target base station.

[0035] Figure 2 Schematic diagram of the internal structure of a base station coverage radius parsing system based on artificial intelligence according to implementation scheme B of the present invention.

[0036] Compared with implementation scheme A, the base station coverage radius parsing system based on artificial intelligence shown in implementation scheme B may also include the following components:

[0037] A voltage conversion device is arranged near the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism and is connected to the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism respectively;

[0038] Among them, the voltage conversion device is arranged near the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism and is respectively connected to the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism, including: the voltage conversion device is used to provide the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism with the required working voltages respectively.

[0039] Figure 3 Schematic diagram of the internal structure of the base station coverage radius parsing system based on artificial intelligence according to Implementation Scheme C of the present invention.

[0040] Compared with implementation scheme A, the base station coverage radius parsing system based on artificial intelligence shown in implementation scheme C may also include the following components:

[0041] A quartz oscillator device is arranged near the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism and is connected to the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism respectively;

[0042] Among them, the quartz oscillator device is arranged near the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism and is respectively connected to the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism, including: the quartz oscillator device is used to provide the required reference clock pulses for the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0043] Next, the specific structure of the base station coverage radius parsing system based on artificial intelligence of the present invention is further described.

[0044] In the base station coverage radius parsing system based on artificial intelligence according to any embodiment of the present invention:

[0045] A programmable logic device is used to perform image data processing on the output data of the radius parsing device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain output processing data corresponding to the radius parsing device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0046] In the base station coverage radius parsing system based on artificial intelligence according to any embodiment of the present invention:

[0047] Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image sharpening processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0048] In the base station coverage radius parsing system based on artificial intelligence according to any embodiment of the present invention:

[0049] Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image enhancement processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0050] In the base station coverage radius parsing system based on artificial intelligence according to any embodiment of the present invention:

[0051] Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image filtering processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0052] And in the base station coverage radius parsing system based on artificial intelligence according to any embodiment of the present invention:

[0053] Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing distortion correction processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

[0054] In addition, in the artificial intelligence-based base station coverage radius analysis system, the marking process of whether the residential building is a coverage target of the target base station is performed based on the distance from each residential building to the target base station, including: when the distance from a residential building to the target base station is greater than the signal coverage radius of the target base station at the next moment after the current moment, the residential building is marked as an uncovered target of the target base station at the next moment after the current moment.

[0055] The artificial intelligence-based base station coverage radius parsing system of the present invention is used to address the technical problem in the prior art that the coverage radius of the target base station at the future moment is difficult to accurately parse. This system introduces multiple basic data and coverage analysis models for the intelligent parsing of the coverage radius of the target base station at the future moment, and when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment from the current moment, the residential building is marked as the coverage target of the target base station at the next moment from the current moment, thereby solving the above technical problem.

[0056] Although a certain preferred embodiment of the present invention has been shown and described in detail, it should be understood that various changes and modifications may be made therein without departing from the scope of the appended claims.

Claims

1. A base station coverage radius analysis system based on artificial intelligence, characterized in that: The system comprises: A first input mechanism is used to obtain various associated data corresponding to the target base station, wherein the various associated data of the target base station are the antenna height, feeder loss, set output power and operating frequency of the target base station; The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced in pairs on the time axis; An object building mechanism, configured to perform multiple trainings on the autoencoder neural network to obtain an autoencoder neural network that has completed multiple trainings and output the autoencoder neural network as a coverage analysis model, wherein the number of trainings performed by the autoencoder neural network is proportional to the maximum user capacity of the target base station; A radius parsing device, connected to the first entry mechanism, the second entry mechanism and the object forming mechanism respectively, for using the coverage analysis model to intelligently parse the signal coverage radius of the target base station at the next moment after the current moment according to the antenna height and feeder loss of the target base station, the output power and the operating frequency, the multiple signal coverage radii corresponding to the multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment; A target marking device, connected to the radius parsing device, for performing a marking process of whether each residential building is a coverage target of the target base station based on the distance from each residential building to the target base station; Among them, performing a marking process of whether the residential building is a coverage target of the target base station based on the distance from each residential building to the target base station includes: when the distance from a certain residential building to the target base station is less than or equal to the signal coverage radius of the target base station at the next moment of the current moment, marking the residential building as a coverage target of the target base station at the next moment of the current moment; The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced on the time axis, including: the number of the multiple historical moments is positively correlated with the working frequency of the target base station; Among them, the coverage analysis model is used to set the output power and the operating frequency, the multiple signal coverage radii corresponding to the multiple historical moments before the current moment of the target base station, and the signal coverage radius of the target base station at the current moment according to the antenna height and the feeder loss of the target base station. The signal coverage radius of the target base station at the next moment of the current moment is intelligently analyzed, including: the next moment of the current moment, the current moment, and the multiple historical moments before the current moment are evenly spaced in pairs on the time axis; A programmable logic device is used to perform image data processing on the output data of the radius parsing device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain output processing data corresponding to the radius parsing device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

2. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 1, characterized in that: The second input mechanism is used to obtain multiple signal coverage radii corresponding to multiple historical moments before the current moment of the target base station, wherein the current moment and the multiple historical moments before the current moment are evenly spaced on the time axis and further includes: using a data conversion function to represent a data conversion relationship in which the number of moments of the multiple historical moments is positively correlated with the working frequency of the target base station.

3. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 2, characterized in that: The system further comprises: A voltage conversion device is arranged near the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism and is connected to the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism respectively; Among them, the voltage conversion device is arranged near the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism and is respectively connected to the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism, including: the voltage conversion device is used to provide the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism with the required working voltages respectively.

4. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 2, characterized in that: The system further comprises: A quartz oscillator device is arranged near the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism and is connected to the radius resolution device, the first input mechanism, the second input mechanism and the object formation mechanism respectively; Among them, the quartz oscillator device is arranged near the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism and is respectively connected to the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism, including: the quartz oscillator device is used to provide the required reference clock pulses for the radius resolving device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

5. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 1, characterized in that: Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image sharpening processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

6. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 1, characterized in that: Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image enhancement processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

7. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 1, characterized in that: Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing image filtering processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

8. The base station coverage radius analysis system based on artificial intelligence as claimed in claim 1, characterized in that: Using a programmable logic device to perform image data processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively includes: performing distortion correction processing on the output data of the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism to obtain the output processing data corresponding to the radius resolution device, the first input mechanism, the second input mechanism and the object assembly mechanism respectively.

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