Border mobile network signal coverage problem processing method and device and readable storage medium

Abstract

The invention provides a border migration network signal coverage problem processing method and device and a readable storage medium, and the method comprises the steps: carrying out the coverage problem classification of current network index data, building a database based on a data classification result, and enabling the data classification result to at least comprise a weak coverage class, a cross-region coverage class and a problem-free class; acquiring test data of a border area network shifting signal; detecting and analyzing the test data based on the database to obtain a test analysis result; and determining a corresponding signal coverage problem processing strategy according to the test analysis result. According to the border mobile network signal coverage problem processing method provided by the invention, an unmanned aerial vehicle is utilized to carry out flight on a border port and a sea surface to carry out signal testing, and various problems of border area signal coverage are effectively solved through classification of test result signal coverage problems and a matching algorithm.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method, device and readable storage medium for processing border mobile network signal coverage issues. Background Art

[0002] With the popularization and development of wireless communication networks, especially the promotion of 4G and 5G technologies, the market demand for network optimization is increasing. Optimizing the network can not only improve user experience, but also enhance the competitiveness of operators. Network optimization makes the best use of system resources by adjusting equipment and parameters to achieve the best system performance. This is of great significance for reducing waste and improving the return on investment of network operators.

[0003] Network optimization engineers have two responsibilities: front-end testing and back-end optimization. Front-end testing is mainly responsible for on-site data collection and preliminary problem solving, while back-end optimization engineers analyze test results to propose optimization reports and implement optimization plans.

[0004] At present, the data collection work of front-end testing mainly relies on manpower, and there are problems such as low testing efficiency and high cost. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a method, device and readable storage medium for processing the signal coverage problem of border mobile network in view of the above-mentioned deficiencies in the prior art, so as to solve the problems existing in the prior art.

[0006] In a first aspect, the present application provides a method for processing a border migration network signal coverage problem, the method comprising:

[0007] S1. Classify coverage problems of existing network indicator data and establish a database based on the data classification results, wherein the data classification results at least include weak coverage, cross-area coverage, and no-problem categories;

[0008] S2. Acquire test data of mobile network signals in border areas;

[0009] S3. Detect and analyze the test data based on the database to obtain a test analysis result;

[0010] S4. Determine a corresponding signal coverage problem handling strategy based on the test analysis results.

[0011] In some embodiments, S3 includes:

[0012] S31, determining whether the test data occupies a foreign operator signal;

[0013] S32. If the test data does not occupy the overseas operator signal, classify the coverage problem of the test data based on the database to obtain a test analysis result;

[0014] S33: If the test data occupies the overseas operator's signal, signal switching processing is performed according to the number of points in the measurement report MR of the test data.

[0015] In some embodiments, in S32, classifying the coverage issues of the test data based on the database includes:

[0016] Classify the test data according to the indicator parameters to obtain a first classification result;

[0017] Based on the existing classification data in the database, the first classification result is reclassified through a matching algorithm to obtain a test analysis result.

[0018] In some embodiments, the first classification result is obtained by classifying the test data according to the indicator parameters, including at least one of the following:

[0019] If the reference signal received power RSRP of the test data is less than or equal to the first preset threshold, it is determined that the test data has a weak coverage problem;

[0020] If the reference signal received power RSRP of the test data is greater than the first preset threshold, and the signal to interference plus noise ratio SINR is greater than or equal to the second preset threshold, it is determined that the test data has no coverage problem;

[0021] If the reference signal received power RSRP of the test data is greater than a first preset threshold and the signal to interference plus noise ratio SINR is less than a second preset threshold, it is determined that the test data has an out-of-area coverage problem.

[0022] In some embodiments, based on the existing classification data in the database, the first classification result is secondary classified by a matching algorithm to obtain a test analysis result, including:

[0023] Based on the vector matching algorithm, a correlation coefficient formula is used to perform correlation matching on the existing classification data in the database and the first classification result to obtain a test analysis result.

[0024] In some embodiments, S4 includes:

[0025] If the test analysis result is that the test data has a weak coverage problem, determining the corresponding signal coverage problem processing strategy is to open a planned station and / or enhance the signal strength of the main coverage cell;

[0026] If the test analysis result is that the test data has an out-of-area coverage problem, then the corresponding signal coverage problem processing strategy is determined to be optimizing the direction angle and / or adding a new cell.

[0027] In some embodiments, the existing network indicator data includes at least one of building analysis data, scenario analysis data, and network element analysis data;

[0028] The test data is obtained by conducting signal tests in border areas using drones equipped with test software;

[0029] The building analysis data includes at least one of overall building assessment, building index analysis, building resident users, building competition analysis, room fault analysis, building black spot analysis, and room intelligent planning;

[0030] The scene analysis data includes at least one of scene index analysis, scene 45G coverage difference analysis, regional grid analysis, and grid black spot analysis;

[0031] The network element analysis data includes at least one of 5G quality assessment, cell coverage assessment, weak coverage analysis, cross-area coverage analysis, engineering parameter longitude and latitude anomaly analysis, and 5G weight recommendation.

[0032] In a second aspect, the present application provides a device for processing a border migration network signal coverage problem, the device comprising:

[0033] A database establishment module, which is configured to classify coverage problems of existing network indicator data and establish a database based on data classification results, wherein the data classification results at least include a weak coverage class, an out-of-area coverage class, and a no-problem class;

[0034] A test data acquisition module configured to acquire test data of mobile network signals in border areas;

[0035] A detection and analysis module, which is configured to perform detection and analysis on the test data based on the database to obtain a test and analysis result;

[0036] A processing strategy determination module is configured to determine a corresponding signal coverage problem processing strategy based on the test analysis results.

[0037] In a third aspect, the present application provides a device for processing signal coverage problems in border network migration, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the method for processing signal coverage problems in border network migration described in the first aspect above.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for processing the signal coverage problem of border migration network described in the first aspect above is implemented.

[0039] The present application provides a method, device and readable storage medium for processing signal coverage problems of mobile network at borders. The method includes: classifying coverage problems of existing network indicator data, establishing a database based on data classification results, wherein the data classification results include at least weak coverage, cross-region coverage, and no-problem categories; obtaining test data of mobile network signals in border areas; performing detection and analysis on the test data based on the database to obtain test analysis results; and determining corresponding signal coverage problem processing strategies based on the test analysis results. The present application provides a method for processing signal coverage problems of mobile network at borders, which uses a drone equipped with test software to fly over border ports and on the sea to perform signal tests. By classifying the signal coverage problems of the test results and using matching algorithms, various signal coverage problems in border areas can be effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] Figure 1 A flowchart of a method for processing a border migration network signal coverage problem provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of detecting and analyzing test data provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of a device for processing border migration network signal coverage problems provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the structure of another device for processing border network migration signal coverage problems provided in an embodiment of the present application.

[0045] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solution of the present application, the implementation mode of the present application will be further described in detail below with reference to the accompanying drawings.

[0047] It should be understood that the specific embodiments and drawings described herein are only used to explain the present application, rather than to limit the present application.

[0048] It can be understood that, in the absence of conflict, the various embodiments in the present application and the various features in the embodiments can be combined with each other.

[0049] It can be understood that, for the convenience of description, the drawings of the present application only show the parts related to the present application, while the parts unrelated to the present application are not shown in the drawings.

[0050] It can be understood that each unit and module involved in the embodiments of the present application may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0051] It can be understood that the terms "first", "second", etc. in the embodiments of the present application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0052] It is understandable that, in the absence of conflict, the functions and steps marked in the flowcharts and block diagrams of the present application may occur in an order different from that marked in the drawings.

[0053] It is understood that the flowcharts and block diagrams of the present application illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present application. Among them, each box in the flowchart or block diagram may represent a unit, module, program segment, code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or by a combination of hardware and computer instructions.

[0054] It can be understood that the units and modules involved in the embodiments of the present application can be implemented by software or hardware, for example, the units and modules can be located in a processor.

[0055] At present, the shortcomings of the existing technology in signal testing are as follows:

[0056] 1. Low testing efficiency

[0057] The existing front-end testing phase mainly relies on manpower. Front-end testers carry test equipment to the places where they need to test. Most tests rely on front-end testers to walk, which makes the test efficiency low. Moreover, manual testing generally relies on ground test equipment, which cannot realize signal testing in three-dimensional space and cannot provide more comprehensive data for signal distribution analysis.

[0058] 2. High labor costs

[0059] Traditional signal testing usually requires a lot of manpower for equipment transportation, installation and debugging, and the labor intensity of practitioners is very high. It relies heavily on professional technicians, often resulting in a shortage of manpower, which leads to high labor costs.

[0060] 3. Low test accuracy

[0061] During the testing process, the division of test areas mainly depends on the experience of the front-line staff. Sometimes, test areas will inevitably be omitted, that is, areas that the front-line testers feel "do not need to be tested". In addition, errors may be introduced during the manual operation of the test software, which will indirectly affect the accuracy and reliability of the test.

[0062] 4. Low testing flexibility

[0063] Manual testing is difficult to operate flexibly in a variety of complex environments, such as densely populated urban areas, mountainous areas, and on the sea. In addition, manual testing is difficult to easily cover a large test area, especially when a large-scale signal distribution and strength test is required.

[0064] 5. Low test security

[0065] In high-risk or harsh environments, testers are directly exposed to potential dangers, such as high-voltage lines, chemically contaminated areas, etc., and their safety cannot be guaranteed.

[0066] In response to the above problems, this application uses drones to undertake on-site data collection for front-end testing.

[0067] The main ideas of this application are:

[0068] The present invention utilizes a drone equipped with test software to test border and sea surface signals, and performs matching calculations on the test data sent back by the drone. The present invention includes analysis and solutions to mobile network signals with weak coverage, cross-region coverage, and border roaming problems, and ultimately accurately classifies and solves signal coverage problems.

[0069] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0070] The present application provides a method for processing the signal coverage problem of border mobile networks. The working process of the method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For the convenience of explanation, the embodiments of the present application are described with the method execution subject being a computer.

[0071] Figure 1 A schematic diagram of a method for processing a border migration signal coverage problem provided in an embodiment of the present application, such as Figure 1 As shown, the present application provides a method for processing the signal coverage problem of border migration network, and the method includes S1-S4, which are specifically as follows:

[0072] S1. Classify coverage problems of existing network indicator data and establish a database based on the data classification results, wherein the data classification results at least include weak coverage, cross-area coverage, and no-problem categories;

[0073] In some embodiments, first, existing network indicator data is obtained and data classification is performed, wherein the existing network indicator data refers to data of a three-dimensional coverage category on a wireless network digital operation platform, and the existing network indicator data includes at least one of building analysis data, scene analysis data, and network element analysis data;

[0074] The building analysis data includes at least one of overall building assessment, building index analysis, building resident users, building competition analysis, room fault analysis, building black spot analysis, and room intelligent planning;

[0075] The scene analysis data includes at least one of scene index analysis, scene 45G coverage difference analysis, regional grid analysis, and grid black spot analysis;

[0076] The network element analysis data includes at least one of 5G quality assessment, cell coverage assessment, weak coverage analysis, cross-area coverage analysis, engineering parameter longitude and latitude anomaly analysis, and 5G weight recommendation.

[0077] In this step, the coverage problem classification of the existing network indicator data is mainly to divide the acquired data into three categories, namely, weak coverage, cross-area coverage, and no problem, and assign codes as follows:

[0078]

[0079] This step converts the data into an array through Python. First, an empty array is created. Then, the query results of the three types of questions are traversed. Finally, the data is added to the array to obtain three new arrays. The three types of arrays are: weak coverage class z r , cross-area coverage class z q , No problem class w :

[0080]

[0081] The above three arrays together construct the database of this application.

[0082] S2. Acquire test data of mobile network signals in border areas;

[0083] In this application, the test data is obtained by conducting signal tests in border areas using drones equipped with test software;

[0084] The following is a specific example of obtaining test data:

[0085] The test was conducted by using a drone equipped with the Spider MoonPhone test software system to fly over the sea within 500 meters from the port. The test software included a MoonPhone master machine + multiple MoonPhone slave machines. The master machine was configured with a test plan, and the slave phones only needed to connect to the master phone's hotspot MoonPadXXX via WiFi and then open the MoonPhone software for testing.

[0086] Before testing, install the drone app corresponding to the drone model. After installation, create a new flight plan. After entering the flight plan page, select "Flight Area" in the left menu bar and draw the area you want to fly. Import the test route set in advance. This test route is a dot test on the sea surface within 500 meters from the shore. After editing, save the flight plan locally and transfer it to the drone via an SD card or data cable. After preparation, press the start flight button on the remote control, and the drone can start autonomous flight according to the preset test route.

[0087] After the test is completed, MoonPhone test software will play back the test data trajectory diagram, measurement information, signaling messages, events and related statistical information in the relevant window in combination with the electronic map according to the selected test data.

[0088] In this application, drones have significant advantages over traditional manual operations in signal testing, including improving test efficiency, reducing labor costs, improving test accuracy, enhancing test safety, and improving test flexibility. These advantages make drones one of the indispensable tools for modern signal testing.

[0089] S3. Detect and analyze the test data based on the database to obtain a test analysis result;

[0090] After the test is completed, all test results will be discussed case by case based on the test data sent back by the drone.

[0091] In some embodiments, S3 includes:

[0092] S31, determining whether the test data occupies a foreign operator signal;

[0093] S32. If the test data does not occupy the overseas operator signal, classify the coverage problem of the test data based on the database to obtain a test analysis result;

[0094] S33: If the test data occupies the overseas operator's signal, signal switching processing is performed according to the number of points in the measurement report MR of the test data.

[0095] Among them, the measurement report (MR) is the measurement report uploaded by the user terminal to the base station. The MR number can be understood as the number of terminals under the current base station, that is, the number of users. The more MRs of China Unicom, the more users are currently using China Unicom, so it switches to China Unicom's main coverage cell; the same is true for China Telecom. An MR number of 0 means there are no users and no coverage.

[0096] Specifically, Figure 2 A schematic diagram of performing detection and analysis on test data provided in an embodiment of the present application, such as Figure 2 As shown, this embodiment discusses all test results by situation based on the test data sent back by the drone:

[0097] Case 1: Occupying overseas operator signals

[0098] If the test data sent back by the drone occupies the signal of an overseas operator, it is necessary to switch it to the signal of an inland local operator. The near-site analysis uses the data of the wireless network digital operation platform as the analysis basis, and uses the building-level data (<500 meters) for the following analysis:

[0099] Output the MR points of China Unicom L1650 and China Telecom L1850 in each building, and compare the differences in the points, which are divided into three categories:

[0100] (1) China Unicom has more points, that is, the ratio of China Unicom MR to China Telecom MR is greater than 2:1. In this case, the signal will be switched to the main coverage cell of China Unicom.

[0101] (2) If there are more points covered by China Telecom, that is, the ratio of China Telecom MR to China Unicom MR is greater than 2:1, then the signal will be switched to the main coverage area of ​​China Telecom;

[0102] (3) If the MR number is 0, it is classified as no coverage, and a new room division needs to be built;

[0103] Among them, the MR number represents the degree of user occupation of the signal. The more MR points there are, the better the signal quality and the more users there are. Therefore, the MR number is used here to determine which signal to switch to.

[0104] Case 2: Not occupying the overseas operator's signal

[0105] At this point, it has been determined that it is not an overseas signal, and we will continue to discuss it in three categories:

[0106] In some embodiments, in S32, classifying the coverage issues of the test data based on the database includes:

[0107] S321, classifying according to the indicator parameters of the test data to obtain a first classification result;

[0108] S322: Based on the existing classification data in the database, the first classification result is reclassified by a matching algorithm to obtain a test analysis result.

[0109] In some embodiments, the first classification result is obtained by classifying the test data according to the indicator parameters, including at least one of the following:

[0110] If the reference signal receiving power RSRP (Reference Signal Receiving Power) of the test data is less than or equal to the first preset threshold, it is determined that the test data has a weak coverage problem;

[0111] If the reference signal received power RSRP of the test data is greater than a first preset threshold, and the signal to interference plus noise ratio SINR (Signal to Interference plus Noise Ratio) is greater than or equal to a second preset threshold, it is determined that the test data has no coverage problem;

[0112] If the reference signal received power RSRP of the test data is greater than a first preset threshold and the signal to interference plus noise ratio SINR is less than a second preset threshold, it is determined that the test data has an out-of-area coverage problem.

[0113] Specifically, this embodiment is discussed in three categories, and specific examples are as follows:

[0114] First, the test data is classified according to the index parameters to obtain the first classification result:

[0115] (1) Weak coverage problem - coverage problem, judgment condition: RSRP < -105;

[0116] (2) No problem to be solved - both coverage and quality are fine. Decision conditions: RSRP>-105 and SINR>0;

[0117] (3) Cross-area coverage problem: coverage is good but quality is poor. Decision conditions: RSRP>-105 and SINR<0.

[0118] Then, based on the existing classification data in the database, the first classification result is reclassified by a matching algorithm:

[0119] In some embodiments, based on the existing classification data in the database, the first classification result is reclassified through a matching algorithm to obtain a test analysis result, including: based on a vector matching algorithm, using a correlation coefficient formula, performing correlation matching on the existing classification data in the database and the first classification result to obtain a test analysis result.

[0120] Specifically, use Python to convert the first classification results into three test arrays:

[0121]

[0122] Referring to the vector matching method, the test array is matched with the existing classification data z in the database r 、z q 、z w After matching respectively, we get a new array:

[0123]

[0124] The above three new arrays are the reclassification of the test data sent back by the drone. They are data classifications similar to the database classifications obtained based on the database. Of course, it is difficult to obtain accurate data classifications by matching only once, so repeated matching is required until the new array is infinitely close to the database array.

[0125] In order to make the new array as close as possible to the database array, the correlation calculation method is used here to determine the similarity between the two. The classification accuracy after matching between the arrays is calculated according to the following correlation coefficient formula:

[0126]

[0127] Taking the weak coverage problem as an example, when r + With z r The closer it is, the more + The more accurate the classification, the better the test result r + With database z r The more similar, when r+ and z r When the similarity reaches 99%, it can be completely considered that + =z r , that is to say, at this time, the drone test data contained in the array r[] all belong to the weak coverage problem.

[0128]

[0129] Where COV(X, Y) is the covariance of X and Y, μ X , μ Y are the means of X and Y, respectively, and D(X) and D(Y) refer to the variances of X and Y, respectively.

[0130] then:

[0131] when When + =z r , r + The classification represented by the array belongs to the weak covering problem;

[0132] when When + =z q ,q + The classification represented by the array belongs to the cross-area coverage problem;

[0133] when When + =z W , w + The categories represented by the array are non-problematic.

[0134] S4. Determine a corresponding signal coverage problem handling strategy based on the test analysis results.

[0135] In some embodiments, S4 includes:

[0136] If the test analysis result is that the test data has a weak coverage problem, determining the corresponding signal coverage problem processing strategy is to open a planned station and / or enhance the signal strength of the main coverage cell;

[0137] If the test analysis result is that the test data has an out-of-area coverage problem, then the corresponding signal coverage problem processing strategy is determined to be optimizing the direction angle and / or adding a new cell.

[0138] Specifically, different response strategies are adopted for different types of test analysis results, as follows:

[0139] (1) No problem: no need to solve;

[0140] (2) Weak coverage: If the signal strength in a certain area is lower than the weak coverage standard, resulting in unstable signal strength received by the terminal, poor air interface quality, and easy call drops, it is considered a weak coverage area.

[0141] Solution:

[0142] ① Open the planned site. If there is a nearby planned site or the cell is not activated, there is no need to adjust the RF solution.

[0143] ② Enhance the signal strength of the main coverage cell. If it is far away from the site, consider increasing the transmit power and downtilt angle.

[0144] (3) Out-of-area coverage: The coverage distance of the cell is too far due to the base station antenna being hung too high or the pitch angle being too small, so the coverage is out-of-area to the area covered by other sites, and the signal level received by the mobile phone in this area is relatively good.

[0145] Solution:

[0146] ① Optimize the direction angle. If the target area is obviously not in the direction of the antenna main lobe, consider adjusting the antenna direction angle; if the coverage is weak near the site and the signal strength is strong far away, consider lowering the tilt angle.

[0147] ②Add new cells. If the coverage gap is large and cannot be completely resolved by adjusting the power, azimuth, or downtilt angle, consider adding new base stations or changing the antenna height to resolve the problem.

[0148] The technology in this application can be applied to a wide range of scenarios, such as densely populated urban areas, mountainous areas, and sea surfaces, where signal testing can be performed using drones equipped with specialized testing software, replacing the previous manual field testing. The classification of the results after benchmarking with the database can also more accurately classify and analyze the signal coverage problem.

[0149] The actual application scenarios of the technical solution of the present application are, for example, in border areas such as Shenzhen Bay and ports, where users may receive operator signals from Hong Kong, China, resulting in frequent switching of mobile phone signals and weak signal coverage, and when users accidentally occupy the Hong Kong operator signal to access the Internet, unnecessary high traffic charges will be incurred. The technical solution of the present application can effectively solve the above problems.

[0150] The present application provides a method for processing signal coverage problems of border mobile networks, which uses a drone equipped with test software to fly over border ports and on the sea to perform signal tests. By classifying the signal coverage problems of the test results and using a matching algorithm, various signal coverage problems in border areas can be effectively solved.

[0151] Through reasonable planning and scientific operation, this application makes the application of drones in the field of signal testing show greater potential and value, and effectively solves the problem of border signal coverage. Drone testing on the sea surface is not limited to traditional testing on the shore at most. After expanding the test range to the sea, it can more accurately capture and solve the signals covered from abroad.

[0152] It should be understood that, although the various steps in the flowcharts in the above-described embodiments are sequentially displayed according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily to be carried out sequentially, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0153] Figure 3 A schematic diagram of a device for processing border migration signal coverage problems provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the present application provides a device for processing border migration network signal coverage problems, the device comprising:

[0154] A database establishment module 11, which is configured to classify coverage problems of the existing network indicator data and establish a database based on the data classification results, wherein the data classification results at least include a weak coverage class, an out-of-area coverage class, and a no-problem class;

[0155] A test data acquisition module 12, which is configured to acquire test data of mobile network signals in border areas;

[0156] A detection and analysis module 13, which is configured to perform detection and analysis on the test data based on the database to obtain a test and analysis result;

[0157] The processing strategy determination module 14 is configured to determine a corresponding signal coverage problem processing strategy based on the test analysis results.

[0158] Regarding the limitation of the device for processing the signal coverage problem of the border migration network, reference may be made to the limitation of the method for processing the signal coverage problem of the border migration network in the above-mentioned embodiments of the present application, and this embodiment will not be repeated here.

[0159] Figure 4 Another schematic diagram of the device for processing the signal coverage problem of border migration network provided by the embodiment of the present application is as follows Figure 4 As shown, in some embodiments, the present application provides a device for processing border network migration signal coverage problems, including a memory 22 and a processor 21, the memory stores a computer program, and the processor is configured to run the computer program to execute the border network migration signal coverage problem processing method in the above-mentioned embodiments of the present application.

[0160] The memory is connected to the processor, the memory may be a flash memory or a read-only memory or other memory, and the processor may be a central processing unit or a single-chip microcomputer.

[0161] In some embodiments, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for processing the border network migration signal coverage problem in the above-mentioned embodiments of the present application is implemented.

[0162] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0163] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present application, but the present application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A method for processing the signal coverage problem of border migration network, characterized in that: The method comprises: S1. Classify coverage problems of existing network indicator data and establish a database based on the data classification results, wherein the data classification results at least include weak coverage, cross-area coverage, and no-problem categories; S2. Acquire test data of mobile network signals in border areas; S3. Detect and analyze the test data based on the database to obtain a test analysis result; S4. Determine a corresponding signal coverage problem handling strategy based on the test analysis results.

2. The method for processing the signal coverage problem of border migration network according to claim 1 is characterized in that: S3, including: S31, determining whether the test data occupies a foreign operator signal; S32. If the test data does not occupy the overseas operator signal, classify the coverage problem of the test data based on the database to obtain a test analysis result; S33: If the test data occupies the overseas operator's signal, signal switching processing is performed according to the number of points in the measurement report MR of the test data.

3. The method for processing the signal coverage problem of border migration network according to claim 2 is characterized in that: In S32, the test data is classified into coverage problems based on the database, including: Classify the test data according to the indicator parameters to obtain a first classification result; Based on the existing classification data in the database, the first classification result is reclassified through a matching algorithm to obtain a test analysis result.

4. The method for processing the signal coverage problem of border migration network according to claim 3 is characterized in that: Classifying the test data according to the indicator parameters to obtain a first classification result includes at least one of the following: If the reference signal received power RSRP of the test data is less than or equal to the first preset threshold, it is determined that the test data has a weak coverage problem; If the reference signal received power RSRP of the test data is greater than the first preset threshold, and the signal to interference plus noise ratio SINR is greater than or equal to the second preset threshold, it is determined that the test data has no coverage problem; If the reference signal received power RSRP of the test data is greater than a first preset threshold and the signal to interference plus noise ratio SINR is less than a second preset threshold, it is determined that the test data has an out-of-area coverage problem.

5. The method for processing the signal coverage problem of border migration network according to claim 3 is characterized in that: Based on the existing classification data in the database, the first classification result is secondary classified by a matching algorithm to obtain a test analysis result, including: Based on the vector matching algorithm, a correlation coefficient formula is used to perform correlation matching on the existing classification data in the database and the first classification result to obtain a test analysis result.

6. The method for processing the signal coverage problem of border migration network according to claim 1 is characterized in that: S4, including: If the test analysis result is that the test data has a weak coverage problem, determining the corresponding signal coverage problem processing strategy is to open a planned station and / or enhance the signal strength of the main coverage cell; If the test analysis result is that the test data has an out-of-area coverage problem, then the corresponding signal coverage problem processing strategy is determined to be optimizing the direction angle and / or adding a new cell.

7. The method for processing the signal coverage problem of border migration network according to any one of claims 1 to 6, characterized in that: The existing network indicator data includes at least one of building analysis data, scene analysis data and network element analysis data; The test data is obtained by conducting signal tests in border areas using drones equipped with test software; The building analysis data includes at least one of overall building assessment, building index analysis, building resident users, building competitor analysis, room fault analysis, building black spot analysis, and room intelligent planning; The scene analysis data includes at least one of scene index analysis, scene 45G coverage difference analysis, regional grid analysis, and grid black spot analysis; The network element analysis data includes at least one of 5G quality assessment, cell coverage assessment, weak coverage analysis, cross-area coverage analysis, engineering parameter longitude and latitude anomaly analysis, and 5G weight recommendation.

8. A device for processing signal coverage problems in border migration networks, characterized in that: The device comprises: A database establishment module, which is configured to classify coverage problems of existing network indicator data and establish a database based on data classification results, wherein the data classification results at least include a weak coverage class, an out-of-area coverage class, and a no-problem class; A test data acquisition module configured to acquire test data of mobile network signals in border areas; A detection and analysis module, which is configured to perform detection and analysis on the test data based on the database to obtain a test and analysis result; A processing strategy determination module is configured to determine a corresponding signal coverage problem processing strategy based on the test analysis results.

9. A device for processing signal coverage problems in border migration networks, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the method for processing the signal coverage problem of border migration network as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for processing the signal coverage problem of border migration network according to any one of claims 1 to 7 is implemented.

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