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

Abstract

The application provides a border network migration signal coverage problem processing method and device and a readable storage medium. The method comprises: classifying coverage problem data of a current network, establishing a database based on the data classification result, and the data classification result at least comprises a weak coverage class, an out-of-coverage class and a no-problem class; obtaining test data of a border area network migration 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. The application provides a border network migration signal coverage problem processing method, which uses a test software carried by a drone to fly over a border port and a sea surface to perform signal testing, classifies signal coverage problems of a test result, and uses a matching algorithm to effectively solve various problems of border area signal coverage.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus and readable storage medium for handling signal coverage problems in border migration networks. Background Technology

[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 constantly increasing. Optimizing the network not only improves user experience but also enhances the competitiveness of operators. Network optimization, through adjustments to equipment and parameters, utilizes system resources to the maximum extent possible, maximizing system performance. This is of great significance for reducing waste and improving the return on investment for network operators.

[0003] Network optimization engineers have two main roles: front-end testing and back-end optimization. Front-end testing primarily involves on-site data collection and initial problem-solving, while back-end optimization engineers analyze test results, submit optimization reports, and implement optimization solutions.

[0004] Currently, data collection for front-end testing mainly relies on manpower, which results in problems such as low testing efficiency and high costs. Summary of the Invention

[0005] The technical problem to be solved by this application is to address the above-mentioned shortcomings of the prior art by providing a method, apparatus and readable storage medium for handling the signal coverage problem of border migration networks, so as to solve the problems existing in the prior art.

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

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

[0008] S2. Obtain test data of mobile network signals in the border area;

[0009] S3. Analyze the test data based on the database to obtain test analysis results;

[0010] S4. Based on the test analysis results, determine the corresponding signal coverage problem handling strategy.

[0011] In some embodiments, S3 includes:

[0012] S31. Determine whether the test data occupies the signal of an overseas operator;

[0013] S32. If the test data does not occupy the signal of overseas operators, the test data is classified for coverage issues based on the database to obtain test analysis results.

[0014] S33. If the test data occupies the signal of an overseas operator, then the signal switching process shall be performed according to the MR point count in the measurement report of the test data.

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

[0016] The test data is classified according to its index parameters to obtain the first classification result;

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

[0018] In some embodiments, the test data is classified according to its index parameters to obtain a first classification result, which includes at least one of the following:

[0019] If the reference signal received power (RSRP) of the test data is less than or equal to a first preset threshold, then 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 a first preset threshold and the signal-to-interference-plus-noise ratio (SINR) is greater than or equal to a second preset threshold, then 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, then it is determined that the test data has an over-coverage problem.

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

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

[0024] In some embodiments, S4 includes:

[0025] If the test analysis results indicate that the test data has a weak coverage problem, then the corresponding signal coverage problem handling strategy is to open a planned station and / or enhance the signal strength of the main coverage cell.

[0026] If the test analysis results indicate that the test data has a cross-coverage problem, then the corresponding signal coverage problem handling strategy is determined to be optimizing the azimuth angle and / or adding new cells.

[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 was obtained by conducting signal tests in the border area using a drone equipped with test software.

[0029] The building analysis data includes at least one of the following: overall building assessment, building indicator analysis, building resident users, building competitor analysis, indoor distribution system failure analysis, building black spot analysis, and indoor distribution system intelligent planning.

[0030] The scenario analysis data includes at least one of the following: scenario indicator analysis, scenario 4G and 5G coverage difference analysis, regional grid analysis, and grid black spot analysis.

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

[0032] Secondly, this application provides a device for handling signal coverage issues during border network migration, the device comprising:

[0033] The database creation module is configured to classify the coverage issues of the existing network indicator data and create a database based on the data classification results. The data classification results include at least weak coverage, cross-regional coverage, and no problem categories.

[0034] The test data acquisition module is configured to acquire test data of mobile network signals in the border area;

[0035] The detection and analysis module is configured to perform detection and analysis on the test data based on the database to obtain test analysis results.

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

[0037] Thirdly, this application provides a border migration signal coverage problem processing device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement the border migration signal coverage problem processing method described in the first aspect.

[0038] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the border relocation signal coverage problem processing method described in the first aspect.

[0039] This application provides a method, apparatus, and readable storage medium for handling signal coverage issues during border network migration. The method includes: classifying existing network indicator data for coverage problems; establishing a database based on the data classification results, wherein the data classification results include at least weak coverage, cross-regional coverage, and no problem categories; acquiring 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 handling strategies based on the test analysis results. This application provides a method for handling signal coverage issues during border network migration, utilizing a drone equipped with testing software to conduct signal tests at border ports and over the sea. By classifying the signal coverage problems in the test results and using a matching algorithm, various signal coverage problems in border areas are effectively solved. Attached Figure Description

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

[0041] Figure 1 A flowchart illustrating a method for handling signal coverage issues during border network relocation, provided in an embodiment of this application;

[0042] Figure 2 This is a schematic diagram illustrating the detection and analysis of test data provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram of a border relocation network signal coverage problem processing device provided in this application embodiment;

[0044] Figure 4 This is a schematic diagram of another border relocation network signal coverage problem processing device provided in an embodiment of this application.

[0045] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0046] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0047] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0048] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0049] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

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

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

[0052] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0053] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

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

[0055] Currently, existing technologies have the following drawbacks in signal testing:

[0056] 1. Low testing efficiency

[0057] Current technologies primarily rely on manual labor for the front-end testing phase. Front-end testers carry testing equipment to the required locations to conduct tests, with the vast majority of tests performed on foot, resulting in low testing efficiency. Furthermore, manual testing generally depends on ground-based testing equipment, making it impossible to perform signal testing in three-dimensional space and providing more comprehensive data for signal distribution analysis.

[0058] 2. High labor costs

[0059] Traditional signal testing typically requires a large workforce for equipment handling, installation, and debugging, resulting in high labor intensity for personnel. It heavily relies on specialized technical personnel, frequently leading to staff shortages and consequently high labor costs.

[0060] 3. Low test accuracy

[0061] During the testing process, the division of test areas mainly relies on the experience of front-end personnel. Sometimes, test areas may be unavoidably missed, which means that front-end testers feel that "there is no need to test" these areas. Furthermore, errors may be introduced during the manual operation of the test software, which may indirectly affect the accuracy and reliability of the test.

[0062] 4. Low testing flexibility

[0063] Manual testing is difficult to operate flexibly in various complex environments, such as densely populated urban areas, mountainous regions, and sea surfaces. Furthermore, manual testing struggles to easily cover large testing areas, especially when testing signal distribution and strength over a wide range.

[0064] 5. Low testing security

[0065] In high-risk or harsh environments, test personnel are directly exposed to potential dangers, such as high-voltage lines and chemically contaminated areas, making it impossible to guarantee their safety.

[0066] To address the aforementioned issues, this application employs drones to perform on-site data collection for front-end testing.

[0067] The main concept of this application is:

[0068] This invention utilizes drones equipped with testing software to test signals at borders and on the sea surface. The test data transmitted back by the drones is matched and calculated. This invention includes the analysis and solution of weak coverage, cross-regional coverage, and border roaming issues for mobile network signals. Ultimately, it accurately classifies and solves signal coverage problems.

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

[0070] This application provides a method for handling signal coverage issues in border network migration. The workflow of this method can be implemented by electronic devices, such as computers and handheld smart terminals. For ease of explanation, the embodiments of this application are described with the computer as the subject of the method execution.

[0071] Figure 1 This is a schematic diagram of a method for handling signal coverage issues during border network migration provided in an embodiment of this application, as shown below. Figure 1 As shown, this application provides a method for handling signal coverage issues in border network migration, the method comprising S1-S4, as follows:

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

[0073] In some embodiments, the existing network indicator data is first acquired and classified. The existing network indicator data refers to the three-dimensional coverage data on the wireless network digital operation platform. The existing network indicator data includes at least one of building analysis data, scenario analysis data, and network element analysis data.

[0074] The building analysis data includes at least one of the following: overall building assessment, building indicator analysis, building resident users, building competitor analysis, indoor distribution system failure analysis, building black spot analysis, and indoor distribution system intelligent planning.

[0075] The scenario analysis data includes at least one of the following: scenario indicator analysis, scenario 4G and 5G coverage difference analysis, regional grid analysis, and grid black spot analysis.

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

[0077] In this step, the existing network indicator data is categorized for coverage issues. This involves dividing the acquired data into three categories: weak coverage, cross-regional coverage, and no issues, and assigning them the following codes:

[0078]

[0079] This step uses Python to convert the data into arrays. First, an empty array is created. Then, the query results for each of the three types of questions are iterated over. Finally, the data is added to the array, resulting in three new arrays: weakly covered arrays, etc. Cross-regional coverage No problem category :

[0080]

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

[0082] S2. Obtain test data of mobile network signals in the border area;

[0083] In this application, the test data is obtained by conducting signal tests in border areas using test software carried by a drone;

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

[0085] The test was conducted by using a drone equipped with the MoonPhone testing software system to fly over the sea within 500 meters of the port. The testing software consists of one MoonPhone master controller and multiple MoonPhone slave controllers. The master controller is configured with the test plan, and the slave controllers only need to connect to the master controller's hotspot MoonPadXXX via WiFi and open the MoonPhone software to conduct the test.

[0086] Before testing, install the corresponding drone app for your drone model. After installation, create a new flight plan. On the flight plan page, select "Flight Area" in the left menu and draw the desired flight area. Import the pre-set test route; this test route involves marking points on the sea surface within 500 meters of the shore. After editing, save the flight plan locally and transfer it to the drone via SD card or data cable. Once ready, press the start flight button on the remote controller, and the drone will begin autonomous flight according to the preset test route.

[0087] After the test is completed, the MoonPhone testing software will, based on the selected test data, synchronously replay the test data trajectory map, measurement information, signaling messages, events, and related statistical information in the relevant window in conjunction with the electronic map.

[0088] In this application, using drones for signal testing offers significant advantages over traditional manual operations, including increased testing efficiency, reduced labor costs, improved testing accuracy, enhanced testing safety, and increased testing flexibility. These advantages make drones an indispensable tool for modern signal testing.

[0089] S3. Analyze the test data based on the database to obtain test analysis results;

[0090] After the test is completed, all test results are discussed separately based on the test data returned by the drone.

[0091] In some embodiments, S3 includes:

[0092] S31. Determine whether the test data occupies the signal of an overseas operator;

[0093] S32. If the test data does not occupy the signal of overseas operators, the test data is classified for coverage issues based on the database to obtain test analysis results.

[0094] S33. If the test data occupies the signal of an overseas operator, then the signal switching process shall be performed according to the MR point count in the measurement report of the test data.

[0095] The measurement report (MR) is the measurement report uploaded by the user terminal to the base station. The number of MR points can be understood as the number of terminals under the current base station, that is, the number of users. The more MR points a China Unicom user has, the more users are currently using China Unicom, so the cell will switch to China Unicom as the primary coverage cell; the same logic applies to China Telecom. An MR number of 0 indicates that there are currently no users and no coverage.

[0096] Specifically, Figure 2 This is a schematic diagram illustrating the detection and analysis of test data provided in an embodiment of this application, such as... Figure 2 As shown, this embodiment discusses all test results in different cases based on the test data transmitted back by the UAV:

[0097] Scenario 1: Using overseas carrier signals

[0098] If the test data transmitted by the drone uses the signal of an overseas operator, it needs to be switched to the signal of a local inland operator. Near-site analysis uses data from the wireless network digital operation platform as the basis for analysis, and the following analysis is performed on building-level data (<500 meters):

[0099] Output the MR (Match Point) counts for each building's Unicom L1650 and Telecom L1850 networks, compare the differences in the counts, and categorize them into three types:

[0100] (1) If there are more Unicom coverage points, that is, the ratio of Unicom MR number to Telecom MR number is greater than 2:1, then the signal will be switched to the Unicom main coverage cell;

[0101] (2) There are more telecom coverage points, that is, the ratio of telecom MR number to Unicom MR number is greater than 2:1. At this time, the signal will be switched to the main coverage cell of telecom.

[0102] (3) If the MR number is 0, it is classified as no coverage, and a new indoor distribution system needs to be built.

[0103] The MR mark count represents the degree to which a user occupies the signal. The more MR marks, the better the signal quality and the more users. Therefore, the MR mark count is used here to determine which signal to switch.

[0104] Scenario 2: Not using overseas carrier signals

[0105] Now that it has been confirmed that the signal is not from overseas, we will continue to discuss it in three categories:

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

[0107] S321. Classify the test data according to the index parameters to obtain the first classification result;

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

[0109] In some embodiments, the test data is classified according to its index parameters to obtain a first classification result, which includes at least one of the following:

[0110] If the Reference Signal Receiving Power (RSRP) of the test data is less than or equal to a first preset threshold, then 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) is greater than or equal to a second preset threshold, then 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, then it is determined that the test data has an over-coverage problem.

[0113] Specifically, this embodiment discusses three cases, with specific examples as follows:

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

[0115] (1) Weak coverage problem - there is a problem with coverage, the decision condition is: RSRP < -105;

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

[0117] (3) Over-coverage problem - coverage is not a problem but the quality is poor. Judgment conditions: RSRP>-105 and SINR<0.

[0118] Then, based on the existing classification data in the database, a matching algorithm is used to perform a secondary classification on the first classification result:

[0119] In some embodiments, based on the existing classification data in the database, a secondary classification is performed on the first classification result using a matching algorithm to obtain test analysis results. This includes: 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 test analysis results.

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

[0121]

[0122] Referring to the vector matching method, the test array is compared with the existing classification data in the database. , , After matching each element separately, a new array is obtained:

[0123]

[0124] The above three new arrays are a reclassification of the test data returned by the drone. Based on the database, the data classification is similar to the database classification. Of course, it is difficult to obtain an accurate data classification by performing a single match, so it is necessary to repeat the match until the new array is infinitely close to the database array.

[0125] To ensure the new array closely approximates the database array, a correlation coefficient is used to determine their similarity. The correlation coefficient formula is used to calculate the classification accuracy after matching the arrays.

[0126] .

[0127] Taking the weak coverage problem as an example, when and The closer they are, the better. The more accurate the classification, the better the test results. With database The more similar, when r+ and If the similarity reaches 99%, it can be considered completely... In other words, at this time the array The drone test data included in the report all fall under the category of weak coverage issues.

[0128]

[0129] in, yes and covariance, , They are and The mean, , Each refers to and The variance.

[0130] then:

[0131] when hour, , The categories represented by the array belong to the weak covering problem;

[0132] when hour, , The categories represented by the array belong to the problem of overlapping and overwriting;

[0133] when hour, , The categories represented by the array are without problems.

[0134] S4. Based on the test analysis results, determine the corresponding signal coverage problem handling strategy.

[0135] In some embodiments, S4 includes:

[0136] If the test analysis results indicate that the test data has a weak coverage problem, then the corresponding signal coverage problem handling strategy is to open a planned station and / or enhance the signal strength of the main coverage cell.

[0137] If the test analysis results indicate that the test data has a cross-coverage problem, then the corresponding signal coverage problem handling strategy is determined to be optimizing the azimuth angle and / or adding new cells.

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

[0139] (1) No problem: No problem needs to be solved;

[0140] (2) Weak coverage area: If the signal strength of 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 Activate the planned station. If there is a nearby planned station or the community is not yet activated, there is no need to adjust the RF.

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

[0144] (3) Over-coverage type: Due to the base station antenna being mounted too high or the pitch angle being too small, the coverage distance of the cell is too far, thus over-covering the area covered by other sites, and the signal level received by the mobile phone in this area is better.

[0145] Solution:

[0146] ① Optimize the azimuth angle. If the target area is clearly not in the direction of the antenna main lobe, consider adjusting the antenna azimuth angle; if there is weak coverage close to the site while the signal strength is strong at a distance, consider reducing the tilt angle.

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

[0148] The technology in this application can be applied to a wide range of scenarios, such as densely populated urban areas, mountainous regions, and sea surfaces with varying degrees of complexity. It allows for signal testing using drones equipped with specialized testing software, replacing the previous method of manual on-site testing. Furthermore, the results, categorized by benchmarking against a database, enable more accurate classification, analysis, and solutions to signal coverage problems.

[0149] This application provides a method for handling signal coverage issues in border areas. It utilizes a drone equipped with testing software to conduct signal tests at border ports and over the sea. By classifying the signal coverage issues in the test results and using a matching algorithm, it effectively solves various signal coverage problems in border areas.

[0150] This application, through reasonable planning and scientific operation, enables the application of drones in the field of signal testing to demonstrate greater potential and value, and effectively solves the problem of signal coverage at borders. Furthermore, drone testing at sea is no longer limited to the traditional testing that is limited to the shore. By expanding the testing range to the sea, it can more accurately capture and resolve signals coming from overseas.

[0151] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0152] Figure 3A schematic diagram of the border migration signal coverage problem processing device provided in the embodiments of this application is shown below. Figure 3 As shown, this application provides a device for handling signal coverage issues during border network relocation, the device comprising:

[0153] The database establishment module 11 is configured to classify the coverage issues of the existing network indicator data and establish a database based on the data classification results. The data classification results include at least weak coverage, cross-area coverage, and no problem.

[0154] Test data acquisition module 12 is configured to acquire test data of mobile network signals in the border area;

[0155] The detection and analysis module 13 is configured to perform detection and analysis on the test data based on the database to obtain test analysis results;

[0156] The processing strategy determination module 14 is configured to determine the corresponding signal coverage problem processing strategy based on the test analysis results.

[0157] Regarding the limitations on the border migration signal coverage problem processing device, please refer to the limitations on the border migration signal coverage problem processing method in the above embodiments of this application, which will not be repeated here.

[0158] Figure 4 Another schematic diagram of the border migration signal coverage problem processing device provided in the embodiments of this application is shown below. Figure 4 As shown, in some embodiments, this application provides a border migration signal coverage problem processing device, 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 migration signal coverage problem processing method in the above embodiments of this application.

[0159] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0160] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the border migration signal coverage problem handling method in the above embodiments of this application.

[0161] 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 technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0162] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this 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 this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for handling signal coverage issues in border network migration, characterized in that, The method includes: S1. Classify the coverage issues of the existing network indicator data, and establish a database based on the data classification results. The data classification results include at least three categories: weak coverage, cross-area coverage, and no issues. The database includes three types of data: weak coverage. Cross-regional coverage No problem category ; S2. Acquire test data of mobile network signals in border areas. The test data is obtained by using a drone equipped with test software to conduct signal tests at border ports and over the sea. S3. Analyze the test data based on the database to obtain test analysis results; S4. Based on the test analysis results, determine the corresponding signal coverage problem handling strategy; S3 includes: S31. Determine whether the test data occupies the signal of an overseas operator; S32. If the test data does not occupy the signal of overseas operators, the test data is classified for coverage issues based on the database to obtain test analysis results. The classification of coverage issues based on the database for the test data includes: The test data is classified according to its index parameters to obtain the first classification result. Based on the existing classification data in the database, the first classification result is reclassified using a matching algorithm to obtain the test analysis result; S33. If the test data occupies the signal of an overseas operator, then the signal switching process is performed according to the MR point count in the measurement report of the test data, and the signal is switched to the corresponding domestic local operator signal, where the MR point count is the number of terminals under the current base station.

2. The method for handling the signal coverage problem of border network migration according to claim 1, characterized in that, Based on the index parameters of the test data, a first classification result is obtained, including at least one of the following: If the reference signal received power (RSRP) of the test data is less than or equal to a first preset threshold, then 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 a first preset threshold and the signal-to-interference-plus-noise ratio (SINR) is greater than or equal to a second preset threshold, then 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, then it is determined that the test data has an over-coverage problem.

3. The method for handling the signal coverage problem of border network migration according to claim 1, characterized in that, Based on the existing classification data in the database, a matching algorithm is used to perform a secondary classification on the first classification result to obtain test analysis results, including: Based on the vector matching algorithm, the correlation coefficient formula is used to perform correlation matching on the existing classification data in the database and the first classification result to obtain the test analysis results.

4. The method for handling signal coverage issues during border network relocation according to claim 1, characterized in that, S4 includes: If the test analysis results indicate that the test data has a weak coverage problem, then the corresponding signal coverage problem handling strategy is to open a planned station and / or enhance the signal strength of the main coverage cell. If the test analysis results indicate that the test data has a cross-coverage problem, then the corresponding signal coverage problem handling strategy is determined to be optimizing the azimuth angle and / or adding new cells.

5. The method for handling the signal coverage problem of border network migration according to any one of claims 1-4, characterized in that, The existing network indicator data includes at least one of building analysis data, scenario analysis data, and network element analysis data; The building analysis data includes at least one of the following: overall building assessment, building indicator analysis, building resident users, building competitor analysis, indoor distribution system failure analysis, building black spot analysis, and indoor distribution system intelligent planning. The scenario analysis data includes at least one of the following: scenario indicator analysis, scenario 4G and 5G coverage difference analysis, regional grid analysis, and grid black spot analysis. The network element analysis data includes at least one of the following: 5G quality assessment, cell coverage assessment, weak coverage analysis, cross-area coverage analysis, engineering parameter latitude and longitude anomaly analysis, and 5G weight recommendation.

6. A device for handling signal coverage issues in border network migration, characterized in that, The device includes: The database creation module is configured to classify coverage issues in live network indicator data and build a database based on the data classification results. These data classification results include at least three categories: weak coverage, cross-regional coverage, and no issues. The database contains three types of data: weak coverage... Cross-regional coverage No problem category ; The test data acquisition module is configured to acquire test data of mobile network signals in border areas. The test data is obtained by conducting signal tests on a drone equipped with test software at border ports and over the sea. The detection and analysis module is configured to perform detection and analysis on the test data based on the database to obtain test analysis results. The processing strategy determination module is configured to determine the corresponding signal coverage problem processing strategy based on the test analysis results. The detection and analysis module is specifically configured as follows: Determine whether the test data occupies signals from overseas operators; If the test data does not occupy the signal of overseas operators, the test data is classified for coverage issues based on the database to obtain test analysis results; the classification of the test data for coverage issues based on the database includes: classifying the test data according to the indicator parameters to obtain a first classification result; and classifying the first classification result for secondary classification based on the existing classification data in the database using a matching algorithm to obtain test analysis results. If the test data occupies the signal of an overseas operator, the signal switching process is performed according to the MR point count in the measurement report of the test data, and the signal is switched to the corresponding domestic local operator signal, where the MR point count is the number of terminals under the current base station.

7. A device for handling signal coverage issues in border network migration, characterized in that, It includes 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 border migration signal coverage problem processing method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for handling the signal coverage problem of border relocation networks as described in any one of claims 1-5.

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