A solution generation, network testing method and device

Abstract

The embodiment of the present invention provides a solution generation, network testing method and device, which relates to the field of mobile communication technology, and in particular to the field of data collection and processing technology. The solution generation method is applied to a server, and the method includes: receiving a solution generation request sent by a client and carrying network data of a target network area, parsing the solution generation request, and obtaining the network data of the target network area, wherein the network data is: data obtained by the client performing a network test on the target network area; analyzing the network data, and determining the target problem type of the network problem in the target network area based on the analysis result; and determining the solution corresponding to the target problem type based on the corresponding relationship between the solution and the problem type as the target solution for solving the network problem. Applying the solution provided by the embodiment of the present invention improves the efficiency of solution generation.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to the field of data acquisition and processing technology. Background Art

[0002] With the development of communication technology, network coverage is getting higher and higher. People can use the network to work, study and entertain themselves no matter where they are. However, the network quality of some covered network areas is not ideal and it is difficult to meet people's needs.

[0003] In order to improve network quality, in the prior art, staff usually manually analyze network problems in a network area and generate improvement solutions for solving network problems based on experience. However, since the improvement solutions for solving network problems are manually generated by staff, the efficiency of solution generation is low. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a solution generation and network testing method and device to improve the efficiency of solution generation. The specific technical solution is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a solution generation method, which is applied to a server, and the method includes:

[0006] Receiving a solution generation request carrying network data of a target network area from a client, parsing the solution generation request, and obtaining the network data of the target network area, wherein the network data is data obtained by performing a network test on the target network area by the client;

[0007] Analyze the network data, and determine the target problem type of the network problem in the target network area based on the analysis result;

[0008] Based on the correspondence between the solution and the problem type, a solution corresponding to the target problem type is determined as a target solution for solving the network problem.

[0009] In a second aspect, an embodiment of the present invention provides a network testing method, which is applied to a client, and the method includes:

[0010] Obtain a test route and a test script for a target network area to be tested;

[0011] According to the test route, the test script is used to perform a network test on the target network area to obtain network data of the target network area;

[0012] A solution generation request including the network data of the target network area is generated, and the solution generation request is sent to the server.

[0013] In a third aspect, an embodiment of the present invention provides a solution generation device, which is applied to a server, and the device includes:

[0014] A data acquisition module, configured to receive a solution generation request carrying network data of a target network area sent by a client, parse the solution generation request, and obtain the network data of the target network area, wherein the network data is data obtained by the client performing a network test on the target network area;

[0015] A type determination module, configured to analyze the network data and determine a target problem type of the network problem in the target network area based on the analysis result;

[0016] The solution generation module is used to determine the solution corresponding to the target problem type based on the corresponding relationship between the solution and the problem type, as the target solution for solving the network problem.

[0017] In a fourth aspect, an embodiment of the present invention provides a network testing device, applied to a client, the device comprising:

[0018] A script acquisition module is used to obtain a test route and a test script for a target network area to be tested;

[0019] A network testing module, configured to perform a network test on the target network area according to the test route and using the test script to obtain network data of the target network area;

[0020] The request sending module is used to generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

[0021] In a fifth aspect, an embodiment of the present invention provides a server, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus;

[0022] Memory, used to store computer programs;

[0023] The processor is used to implement the method steps described in the first aspect when executing the program stored in the memory.

[0024] In a sixth aspect, an embodiment of the present invention provides a client, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0025] Memory, used to store computer programs;

[0026] The processor is used to implement the method steps described in the second aspect when executing the program stored in the memory.

[0027] In a seventh aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of the first aspect or the second aspect are implemented.

[0028] As can be seen from the above, when the solution generation scheme provided by this embodiment is applied, after receiving the solution generation request sent by the client, the server analyzes the network data obtained by network testing the target network area, determines the target problem type of the network problem in the target network area based on the analysis result, and then determines the target solution for solving the above network problem based on the target problem type. Compared with the prior art in which the staff generates solutions based on experience, the automation of solution generation is realized, which does not rely on manual generation of solutions, and significantly improves the efficiency of solution generation.

[0029] Furthermore, the server determines the target solution corresponding to the target problem type of the network problem in the target network area based on the correspondence between the solution and the problem type. When the solution has a correspondence with the problem type, it means that the solution can effectively repair the network problem of the problem type corresponding to the solution. Therefore, it is determined that the obtained target problem solution can perform network repair on the network problem of the target problem type. Therefore, the target solution is generated by adopting this embodiment, which effectively guarantees the accuracy of the generated solution.

[0030] Of course, it is not necessary to achieve all of the advantages described above at the same time to implement any product or method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0032] Figure 1 A schematic diagram of a flow chart of a first solution generation method provided in an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a flow chart of a second solution generation method provided in an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a flow chart of a third solution generation method provided in an embodiment of the present invention;

[0035] Figure 4 A schematic diagram of a flow chart of a first network testing method provided by an embodiment of the present invention;

[0036] Figure 5a A schematic diagram of a flow chart of a second network testing method provided by an embodiment of the present invention;

[0037] Figure 5b A schematic diagram of a subway tunnel leaky cable coverage model provided by an embodiment of the present invention;

[0038] Figure 6 A flowchart of a solution generation method provided by an embodiment of the present invention;

[0039] Figure 7 A schematic diagram of the structure of a first solution generating device provided in an embodiment of the present invention;

[0040] Figure 8 A schematic diagram of the structure of a second solution generating device provided in an embodiment of the present invention;

[0041] Fig. 9 A schematic diagram of the structure of a first network testing device provided by an embodiment of the present invention;

[0042] Fig.10 A schematic diagram of the structure of a second network testing device provided by an embodiment of the present invention;

[0043] Fig.11 A schematic diagram of the structure of a server provided in an embodiment of the present invention;

[0044] Fig.12 A schematic diagram of the structure of a client provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field based on this application belong to the scope of protection of the present invention.

[0046] See also Figure 1 , Figure 1 The present invention provides a flowchart of a first solution generation method according to an embodiment of the present invention. The method includes the following steps S101-S103.

[0047] Before describing the above steps, the execution subject of the embodiment of the present invention is first introduced. The execution subject of the embodiment of the present invention is a server.

[0048] The above steps are described below.

[0049] Step S101: receiving a solution generation request carrying network data of a target network area sent by a client, parsing the solution generation request, and obtaining the network data of the target network area.

[0050] The solution generation request is used to request the server to generate a solution for a network problem in a target network area.

[0051] The target network area is a network area where network problems are to be detected. The network area can be an indoor network area or an outdoor network area. For example, the indoor network area can be an indoor shopping mall, a basement, or other network area that has been covered by the network, and the outdoor network area can be a street, a highway, or other network area that has been covered by the network.

[0052] In one embodiment of the present invention, the target network area may be a network area where a subway tunnel where network problem detection is to be performed is located.

[0053] As the radiation range of urban rail transit construction continues to expand, more and more users choose to take the subway. Detecting network problems in subway tunnels and generating problem solutions for subway tunnels, and repairing the network covering subway tunnels based on problem solutions, can effectively improve the network quality of subway tunnels, thereby facilitating users' travel. In addition, the existing technical center conducts network testing and generates problem solutions for subway tunnels by manually inspecting the network and generating solutions through staff, which is inefficient. In addition, the space in the subway tunnel is small, the traffic is unchanged, and the operation is difficult. The solution of this embodiment is adopted to generate network problem solutions for subway tunnels through the server, realize the automation of solution generation, and do not need to rely on staff to generate solutions, which greatly improves the efficiency of solution generation and saves manpower and material costs.

[0054] The above network data is: data obtained by the client performing a network test on the target network area.

[0055] The network data may include network attribute information, network operator information, location information of each target sampling point, etc.

[0056] Among them, the attribute information of the network may include the values ​​of network parameter items, and the above network parameter items may include RSRP (Reference Signal Receiving, reference signal received power), SINR (Signal to Interferenceplus Noise Ratio, signal to interference plus noise ratio), uplink and downlink rates, delay, beam scenario type, MCS (Modulation and Coding Scheme, modulation and coding strategy), BLER (Block Error Rate, block error rate), CQI (Channel Quality Indicator, channel quality indication), RANK (rank), RB / slot (Resource Block / slot, time domain), GRANT (scheduling grant), etc. The operator information of the network may include the operator identification of the network, whether the operator of the network supports 5G, and other information. The location information of the above target sampling point represents the actual geographical location of the target sampling point.

[0057] After parsing the request to obtain the network data of the target network area carried in the request, the server may perform preprocessing operations such as filtering out noise data and removing duplicates on the network data, and use the preprocessed data as the network data for data analysis in the following step S102.

[0058] Step S102: Analyze the network data, and determine the target problem type of the network problem in the target network area based on the analysis result.

[0059] When analyzing network data, the obtained analysis results can reflect abnormal data in the network data. Since the abnormal data is caused by one or more types of network problems, the above analysis results can reflect the target problem type of the network problem.

[0060] In one embodiment, when analyzing network data and determining the target problem type, a statistical analysis can be performed on each value of the same parameter item in the network data, and abnormal data appearing abnormally in the network data can be determined based on the statistical analysis results. According to the correspondence between the preset abnormal parameter items and the problem type, the problem type corresponding to the parameter item corresponding to the abnormal data is determined as the problem type corresponding to the target network area.

[0061] For example: calculate the average value of each value of the same parameter item in the network data. When the calculated average rate of the uplink rate parameter item is less than the preset rate, it means that the network uplink rate in the target network area is slow. Since in the correspondence between the preset abnormal parameter items and the problem types, the problem type corresponding to the uplink rate type is: uplink failure, therefore, the uplink failure can be determined as the target problem type corresponding to the target network area.

[0062] Other implementations for determining the target problem type can be found in the following Figure 3 The corresponding embodiments are not described in detail here.

[0063] Step S103: Based on the correspondence between the solution and the problem type, determine a solution corresponding to the target problem type as a target solution for solving the network problem.

[0064] The above correspondence may be predetermined by the staff, and specifically, the above correspondence may be obtained by the staff summarizing historical network problem solving cases based on experience. When there is a correspondence between solution P and problem type T, it means that solution P can be used to effectively fix the network problem of problem type T.

[0065] Specifically, at least two implementation methods may be used to determine a solution corresponding to the target problem type.

[0066] In a first implementation manner, when the corresponding relationship includes a target problem type, a solution corresponding to the target problem type may be determined directly according to the corresponding relationship.

[0067] In a second implementation, when the corresponding relationship does not include the target problem type, similar problem types to the target problem type may be determined from the problem types included in the corresponding relationship, and solutions corresponding to the similar problem types may be determined as solutions corresponding to the target problem type.

[0068] After the target solution is determined, the target solution can be sent to the client and displayed in the corresponding user operation interface of the client, so that the staff can repair the network problem in the target network area based on the target solution.

[0069] As can be seen from the above, when the solution generation scheme provided by this embodiment is applied, after receiving the solution generation request sent by the client, the server analyzes the network data obtained by network testing the target network area, determines the target problem type of the network problem in the target network area based on the analysis result, and then determines the target solution for solving the above network problem based on the target problem type. Compared with the prior art in which the staff generates solutions based on experience, the automation of solution generation is realized, which does not rely on manual generation of solutions, and significantly improves the efficiency of solution generation.

[0070] Furthermore, the server determines the target solution corresponding to the target problem type of the network problem in the target network area based on the correspondence between the solution and the problem type. When the solution has a correspondence with the problem type, it means that the solution can effectively repair the network problem of the problem type corresponding to the solution. Therefore, it is determined that the obtained target problem solution can perform network repair on the network problem of the target problem type. Therefore, the target solution is generated by adopting this embodiment, which effectively guarantees the accuracy of the generated solution.

[0071] In addition to generating the target solution mentioned in step S103 above, a customized network data report can also be generated. Based on this, in one embodiment of the present invention, see Figure 2 , Figure 2 The present invention provides a flowchart of a second solution generation method according to an embodiment of the present invention. The method includes the following steps S201-S204.

[0072] Step S201: receiving a solution generation request carrying network data of a target network area sent by a client, parsing the solution generation request, and obtaining the network data of the target network area.

[0073] The above network data is: data obtained by the client performing a network test on the target network area.

[0074] Step S202: Analyze the network data, and determine the target problem type of the network problem in the target network area based on the analysis result.

[0075] Step S203: Based on the correspondence between the solution and the problem type, determine a solution corresponding to the target problem type as a target solution for solving the network problem.

[0076] Specifically, the above steps S201-S203 are respectively Figure 1 In the illustrated embodiment, steps S101 - S103 are the same and will not be described in detail herein.

[0077] Step S204: Based on the values ​​of the preset parameter items in the network data, a network data report table representing parameter information of the preset parameter items is generated.

[0078] The above-mentioned preset parameter items can be pre-set by the staff based on actual needs. For example, the staff needs to obtain a network data report that represents the parameter information of the uplink and downlink rates and network signal transmission delay of the target network area. Then, the preset parameter items are: uplink and downlink rates, network signal transmission delay.

[0079] Specifically, the network data report can be generated according to the following two implementation methods.

[0080] In the first embodiment, the values ​​of the preset parameter items in the network data can be statistically analyzed to generate a report containing the statistical values ​​of the preset parameter items as a network data report representing the parameter information of the preset parameter items. The above statistical analysis method can include calculating the average value, variance, standard deviation, etc.

[0081] In the second implementation manner, a report including the values ​​of the preset parameter items in the network data may be generated as a network data report representing the parameter information of the preset parameter items.

[0082] As can be seen from the above, since the generated network data report can represent the parameter information of the preset parameter items, the generated network data is highly targeted, and can enable users to more intuitively and clearly know the parameter information of the preset parameter items based on the above network data report. In addition, in the scenario where the user needs to know the parameter information of a specific parameter item, a network data report representing the parameter information of the above specific parameter item can be generated, realizing a customized network data report.

[0083] In the above Figure 1 In step S102 of the embodiment shown, in addition to using the statistical analysis method mentioned above to determine the target problem type, the following method can also be used: Figure 3 The implementation method of determining the target question type mentioned in the illustrated embodiment determines the target question type. In one embodiment of the present invention, see Figure 3 , Figure 3 A schematic diagram of a flow chart of a third solution generation method provided in an embodiment of the present invention, wherein the Figure 1 Step S102 of the embodiment shown in the figure may be performed according to Figure 3 Step S302 is shown as being implemented.

[0084] Specifically, Figure 3 The illustrated embodiment includes the following steps S301 - S303 .

[0085] Step S301: receiving a solution generation request carrying network data of a target network area sent by a client, parsing the solution generation request, and obtaining the network data of the target network area.

[0086] The above network data is: data obtained by the client performing a network test on the target network area.

[0087] In one embodiment of the present invention, the above network data may include: test data obtained by performing network testing on target sampling points included in each network cell in the target network area.

[0088] The above step S301 is the same as the above Figure 1 Step S101 in the illustrated embodiment is the same and will not be described in detail here.

[0089] A network area usually includes multiple network cells. Specifically, network signals transmitted by different base stations can cover different network cells in the network area, and network signals transmitted by different antennas of the same base station can also cover different network cells in the network area. In other words, different network cells in the network area can correspond to different base stations or to different antennas of the same base station.

[0090] Each network cell may include multiple target sampling points, and the client may perform network testing at each target sampling point to obtain test data.

[0091] The test data corresponding to the target sampling point may include: sampling point information of the target sampling point, network information of the network cell to which it belongs, network information of neighboring cells of the network cell to which it belongs, base station information of the corresponding base station, and the like.

[0092] Step S302: Analyze the test data corresponding to the target sampling points included in each network cell in the target network area, and determine the problem type corresponding to the target cell with network problems in each network cell based on the analysis result as the target problem type of the network problem in the target network area.

[0093] The implementation method for determining the target problem type corresponding to the target network area can be found in the subsequent embodiments and will not be described in detail here.

[0094] Since the test data corresponding to the target sampling points included in each network cell are analyzed, the obtained analysis results can reflect the network information of each network cell. Therefore, based on the analysis results, the problem type corresponding to the target cell with network problems can be determined more accurately, thereby obtaining a more accurate target problem type corresponding to the target network area.

[0095] Step S303: Based on the correspondence between the solution and the problem type, determine a solution corresponding to the target problem type as a target solution for solving the network problem.

[0096] The above step S303 is the same as the above Figure 1Step S103 in the illustrated embodiment is the same and will not be described in detail here.

[0097] The foregoing Figure 3 In step S302 of the illustrated embodiment, determining the target problem type corresponding to the target network area may be implemented according to the following four implementation methods.

[0098] In a first implementation manner, the test data corresponding to the target sampling point includes a target level value of the network cell where the target sampling point is located. The above step S302 analyzes the test data corresponding to the target sampling point included in each network cell in the target network area, which can be implemented according to the following steps A1 to A3.

[0099] Step A1: Determine a first sampling point in which a target level value included in the test data among target sampling points is less than a preset level value.

[0100] The preset level value may be set by a staff member, for example, the preset level value may be -100dBm, -90dBm, etc.

[0101] In one implementation, when determining the first sampling point, it may be determined whether a target level value in the test data corresponding to the target sampling point is less than a preset level value. If so, the target sampling point is determined to be the first sampling point.

[0102] For example, the level values ​​corresponding to the target sampling points included in the network cell are shown in Table 1-1 below.

[0103] Table 1-1

[0104] Target sampling point PC11 -120dBm Target sampling point PC12 -110dBm Target sampling point PC13 -95dBm Target sampling point PC14 -90dBm Target sampling point PC15 -50dBm

[0105] Assume that the preset level value is -90dBm. In the above Table 1-1, since -120dBm, -110dBm, and -95dBm are all less than -90dBm, the target sampling points PC11, PC12, and PC13 are the first sampling points.

[0106] Step A2: Calculate a first quantity ratio between the quantity of the first sampling points and the total quantity of the target sampling points.

[0107] The first quantity ratio reflects the proportion of the first sampling point in all target sampling points included in the network cell.

[0108] Using the example corresponding to Table 1-1 above, the total number of target sampling points included in the network cell is 5, and the number of first sampling points is 3, so the first number ratio is: 3 / 5*100%=60%.

[0109] Step A3: Perform statistical analysis on the target level value corresponding to the target sampling point to obtain a first statistical value.

[0110] The above statistical analysis may include finding the mean, variance, standard deviation, etc.

[0111] Using the example corresponding to Table 1-1 above, the average value of the target level values ​​corresponding to the target sampling points in Table 1 is calculated, and we can get ((-120dBm)+(-110dBm)+(-95dBm)+(-90dBm)+(-50dBm)) / 5=-93dBm, that is, the first statistical value is -93dBm.

[0112] On the basis of the above steps A1-A3, the above step S302 determines the problem type corresponding to the target cell having the network problem in the target network area based on the analysis result, which can be implemented according to the following step A4.

[0113] Step A4: Based on the first quantity ratio corresponding to each network cell, the total number of target sampling points included and the corresponding first statistical value, determine the target cell in each network cell that meets the preset network weak coverage condition, and determine the problem type corresponding to the determined target cell as: weak coverage type.

[0114] The above preset network weak coverage condition may be preset by the staff. When the network cell meets the above network weak coverage condition, it means that the network cell has a network problem, and the problem type of the network problem is: weak coverage type.

[0115] The above-mentioned network weak coverage conditions may be: a first quantity ratio corresponding to the network cell is greater than a first preset quantity ratio, the total number of target sampling points included in the network cell is greater than the first preset total number, and a first statistical value corresponding to the network cell is less than the first preset statistical value. When the network cell meets the above conditions, it can be determined that the network cell is a target cell with network problems, and the problem type of the network problem of the network cell is: weak coverage type, and the network cell can be called a weak coverage cell.

[0116] For example: Assuming that the first preset quantity ratio is: 45%, the first preset total quantity is: 90, the first preset statistical value is: -90dBm, the first quantity ratio corresponding to each network cell, the total number of target sampling points included and the corresponding first statistical value are shown in Table 1-2 below.

[0117] Table 1-2

[0118] Network Community First quantity ratio Total quantity First statistic Network Community NC11 60％ 100 -110dBm Network Community NC12 50％ 90 -120dBm Network Community NC13 40％ 95 -90dBm

[0119] Since 60%>45%, 100>90, and -110dBm<-90dBm, the network cell NC11 meets the preset network weak coverage condition, the network cell NC11 has a network problem, and the problem type of the network problem is: weak coverage type.

[0120] As can be seen from the above, since the first quantity ratio corresponding to the network cell reflects the proportion of the number of first sampling points in the network cell to the total number of all sampling points in the network cell, the target level value obtained by the test of the first sampling point is less than the preset level value, that is, the target level value obtained by the test of the first sampling point is relatively small, so the first quantity ratio reflects the proportion of sampling points with relatively small target level values ​​obtained by the test in the network cell; the first statistical value reflects the overall situation of the target level values ​​obtained by the test of each target sampling point in the network cell. Therefore, when determining the problem type corresponding to the network cell based on the first quantity ratio corresponding to the network cell, the total number of target sampling points and the first statistical value, the proportion of sampling points with relatively small target level values ​​obtained by the test in the network cell and the number of all sampling points are considered, and the overall level value of the network cell is also considered. In addition, when the network cell is a weak coverage cell, its overall level value is relatively poor, and the proportion of relatively small level values ​​measured in the weak coverage cell is high. Therefore, based on the first quantity ratio corresponding to the network cell, the total number of target sampling points and the first statistical value, the target cell that meets the preset network weak coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0121] In the second implementation manner, the test data corresponding to the target sampling point includes the target position of the target sampling point, the first identifier of the target base station corresponding to the network cell where the target sampling point is located, and the second identifier of the adjacent base station of the above target base station. The above step S302 analyzes the test data corresponding to the target sampling point included in each network cell in the target network area, which can be implemented according to the following steps B1 to B3.

[0122] Step B1: Determine the second position of the target base station and the third position of the adjacent base station of the target base station according to the first identifier and the second identifier in the test data corresponding to each target sampling point, and calculate the first distance between the first position and the second position and the second distance between the first position and the third position of each target sampling point.

[0123] The area covered by the network signal transmitted by the target base station is called a network cell. When a network cell corresponds to a base station, it means that the network signal covered by the network cell is the network signal transmitted by the corresponding base station.

[0124] When the client performs a network test at a sampling point, in addition to collecting base station information of a target base station corresponding to the network cell where the sampling point is located, base station information of adjacent base stations of the target base station can also be collected. An adjacent base station refers to a base station whose distance from the target base station is less than a first preset distance.

[0125] The server can store the location of each base station locally. Based on this, when obtaining the second position and the third position, the position corresponding to the first identifier can be obtained from the locally stored base station positions as the second position of the target base station, and the position corresponding to the second identifier can be obtained as the third position of the adjacent base station of the target base station.

[0126] The first distance reflects the distance between the target sampling point and the target base station corresponding to the network cell where the target sampling point is located, and the second distance reflects the distance between the target sampling point and a base station adjacent to the target base station.

[0127] Step B2: Calculate the distance ratio between the first distance and the second distance corresponding to each target sampling point, and determine the second sampling point whose distance ratio is greater than the preset distance ratio among the target sampling points.

[0128] The preset distance ratio may be preset by a staff member, and the preset distance ratio may be 1.2, 1.5, etc.

[0129] In one implementation, when determining the second sampling point, for each target sampling point included in the network cell, it can be determined whether the distance ratio corresponding to the target sampling point is greater than a preset distance ratio. If so, the target sampling point is determined to be the second sampling point.

[0130] The following describes the process of determining the second sampling point in conjunction with Table 2-1.

[0131] Table 2-1 shows the first distance, the second distance, and the calculated distance ratio corresponding to each target sampling point included in the network cell.

[0132] Table 2-1

[0133] Target sampling point First distance Second distance Distance Scale Target sampling point PC21 100m 200m 0.5 Target sampling point PC22 300m 100m 3 Target sampling point PC23 200m 50m 4

[0134] Assume that the preset distance ratio is 1.2. Since 3 and 4 are both greater than 1.2, the target sampling point PC22 and the target sampling point PC23 are the second sampling points.

[0135] Step B3: Calculate a second quantity ratio between the quantity of the second sampling points and the total quantity of the target sampling points.

[0136] The second quantity ratio reflects the proportion of the second sampling point in all target sampling points included in the network cell.

[0137] Using the above Table 2-1, the number of the second sampling points is 2, and the total number of target sampling points included in the network cell is 3. It is calculated that 2 / 3*100%=66.67%, that is, the second quantity ratio is: 66.67%.

[0138] On the basis of the above steps B1-B3, the above step S302 determines the problem type corresponding to the target cell having the network problem in the target network area based on the analysis result, which can be implemented according to the following step B4.

[0139] Step B4: Based on the second quantity ratio corresponding to each network cell and the total number of target sampling points included, determine the target cell in each network cell that meets the preset network over-coverage condition, and determine the problem type corresponding to the determined target cell as: over-coverage type.

[0140] The preset network over-coverage condition may be preset by a staff member. When a network cell meets the network over-coverage condition, it indicates that the network cell has a network problem, and the problem type of the network problem is: over-coverage type.

[0141] The above-mentioned network over-coverage condition may be: the second quantity ratio corresponding to the network cell is greater than the second preset quantity ratio, and the total number of target sampling points included in the network cell is greater than the second preset total number. When the network cell meets the above-mentioned conditions, it can be determined that the network cell is a target cell with network problems, and the problem type of the network problem of the network cell is: over-coverage type, and the network cell can be called an over-coverage cell.

[0142] For example, assuming that the second preset quantity ratio is 10% and the second preset total quantity is 20, the second quantity ratio corresponding to each network cell and the total number of target sampling points included are shown in Table 2-2 below.

[0143] Table 2-2

[0144] Network Community The second quantity ratio Total quantity Network Community NC21 30％ 40 Network Cell NC22 20％ 10 Network Cell NC23 5％ 30

[0145] Since 30%>10% and 40>20, the network cell NC21 meets the preset network over-coverage condition, the network cell NC21 has a network problem, and the problem type of the network problem is: over-coverage type.

[0146] From the above, it can be seen that since the second quantity ratio corresponding to the network cell reflects the proportion of the second sampling point in the network cell to all target sampling points included in the network cell, the second sampling point refers to a sampling point whose distance ratio is greater than the preset distance ratio, and the distance ratio reflects the size relationship between the first distance between the sampling point and the target base station and the second distance between the sampling point and the adjacent base station of the target base station. The larger the distance ratio, the greater the first distance is greater than the second distance, and the smaller the distance ratio, the smaller the first distance is less than the second distance. Since the distance ratio corresponding to the second sampling point is greater than the preset distance ratio, that is, the distance ratio corresponding to the second sampling point is relatively large, the distance between the second sampling point and the target base station is relative to the adjacent base station. The distance between them is large, so the second quantity ratio reflects the proportion of sampling points in the network cell that are relatively far away from the target base station; therefore, when determining the problem type corresponding to the network cell based on the second quantity ratio corresponding to the network cell and the total number of target sampling points, the proportion of sampling points in the network cell that are relatively far away from the target base station is considered. Since when the network cell is an over-coverage cell, the distance between the network cell and the target base station corresponding to the network cell is relatively far, therefore, based on the second quantity ratio corresponding to the network cell and the total number of target sampling points, the target cell that meets the preset network over-coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0147] In a third implementation manner, the test data of the target sampling point includes a target level value of the network cell where the target sampling point is located, a first RSRP value of the network cell where the target sampling point is located, and a second RSRP value of a co-frequency adjacent network cell of the network cell where the target sampling point is located. In the above step S302, the test data corresponding to the target sampling point included in each network cell in the target network area is analyzed, which can be implemented according to the following steps C1 to C3.

[0148] Step C1: Based on the first RSRP value and the second RSRP value in the test data corresponding to the target sampling point, determine a sampling point that meets a first preset condition as the third sampling point.

[0149] The co-frequency adjacent network cell refers to a network cell whose network signal frequency is the same as the frequency of the network signal of the network cell where the target sampling point is located, and whose distance from the network cell where the target sampling point is located is less than a second preset distance. When the client performs a network test at the sampling point, in addition to collecting the RSRP value of the network cell where the sampling point is located, the RSRP value of the co-frequency adjacent network cell of the above network cell can also be collected.

[0150] The first preset condition is that the first RSRP value is greater than or equal to the first preset RSRP value, and there are at least a first preset number of target RSRP values ​​among the second RSRP values ​​included in the target sampling point, and the target RSRP value is a second RSRP value whose difference with the first RSRP value is greater than the first preset RSRP difference.

[0151] The first preset RSRP value, the first preset number, and the first preset RSRP difference may all be preset by the staff. For example, the first preset RSRP level value may be -95dBm, etc., the first preset number may be 3, 4, 5, etc., and the first preset RSRP difference may be -6dBm.

[0152] In one implementation, when determining the third sampling point, a difference between each second RSRP value corresponding to the target sampling point and the first RSRP value may be calculated. If there are at least a first preset number of differences greater than the first preset RSRP difference among the calculated differences, and the first RSRP value corresponding to the target sampling point is greater than or equal to the first preset RSRP value, the target sampling point is determined as the third sampling point.

[0153] For example, the first RSRP value and the second RSRP value corresponding to the target sampling points included in the network cell are shown in Table 3-1 below.

[0154] Table 3-1

[0155]

[0156]

[0157] Assume that the first preset RSRP value is -95dBm, the first preset number is 3, and the first preset RSRP difference is -6dBm. In the above Table 3-1, the first RSRP values ​​corresponding to the target sampling points PC32, PC33, PC34, and PC35 are all greater than -95dBm.

[0158] Furthermore, there are four target RSRP values ​​(-80dBm, -70dBm, -60dBm, -75dBm) in the second RSRP value corresponding to the target sampling point PC32;

[0159] There are three target RSRP values ​​(-66dBm, -65dBm, -70dBm) in the second RSRP value corresponding to the target sampling point PC33;

[0160] There is a target RSRP value (-40dBm) in the second RSRP value corresponding to the target sampling point PC34;

[0161] There are three target RSRP values ​​(-20dBm, -10dBm, -15dBm) in the second RSRP value corresponding to the target sampling point PC35;

[0162] Therefore, the target sampling points PC32, PC33, PC35 meet the first preset condition, that is, the target sampling points PC32, PC33, PC35 are the third sampling points.

[0163] Step C2: Calculating a third quantity ratio between the quantity of the third sampling points and the total quantity of the target sampling points.

[0164] The third quantity ratio reflects the proportion of the third sampling point in all target sampling points included in the network cell.

[0165] Using the example corresponding to Table 3-1 above, the total number of target sampling points included in the network cell is 5, and the number of third sampling points is 3, so the third number ratio is: 3 / 5*100%=60%.

[0166] Step C3: Perform statistical analysis on the level value corresponding to the third sampling point to obtain a second statistical value.

[0167] The above statistical analysis may include finding the mean, variance, standard deviation, etc.

[0168] Using the example corresponding to Table 3-1 above, the average value of the level values ​​corresponding to the third sampling points in Table 3-1 is calculated, and we can get ((-90dBm)+(-80dBm)+(-30dBm)) / 3=-66.67dBm, that is, the second statistical value is -66.67dBm.

[0169] On the basis of the above steps C1-C3, the above step S302 determines the problem type corresponding to the target cell having the network problem in the target network area based on the analysis result, which can be implemented according to the following step C4.

[0170] Step C4: Based on the third quantity ratio corresponding to each network cell, the total number of target sampling points included, and the corresponding second statistical value, determine the target cell in each network cell that meets the preset network overlapping coverage condition, and determine the problem type corresponding to the determined target cell as: overlapping coverage type.

[0171] The preset network overlapping coverage condition may be preset by a staff. When a network cell meets the network overlapping coverage condition, it indicates that the network cell has a network problem, and the problem type of the network problem is: overlapping coverage type.

[0172] The above-mentioned network overlapping coverage conditions may be: the third quantity ratio corresponding to the network cell is greater than the third preset quantity ratio, and the total number of target sampling points included in the network cell is greater than the third preset total number, and the second statistical value corresponding to the network cell is greater than or equal to the second preset statistical value. When the network cell meets the above conditions, it can be determined that the network cell is a target cell with network problems, and the problem type of the network problem of the network cell is: overlapping coverage type, and the network cell can be called an overlapping coverage cell.

[0173] For example: Assuming that the third preset quantity ratio is: 20%, the third preset total quantity is: 90, the second preset statistical value is: -85dBm, the third quantity ratio corresponding to each network cell, the total number of target sampling points included and the corresponding second statistical value are shown in Table 3-2 below.

[0174] Table 3-2

[0175]

[0176]

[0177] Since 40%>20%, 100>90, and -110dBm<-85dBm, the network cell NC31 meets the preset network overlapping coverage condition, the network cell NC31 has a network problem, and the problem type of the network problem is: overlapping coverage type.

[0178] From the above, it can be seen that since the third quantity ratio corresponding to the network cell reflects the proportion of the third sampling point in the network cell to all target sampling points included in the network cell, and the third sampling point is determined based on the first RSRP value and the second RSRP value obtained by the test, the third sampling point can reflect the RSRP information of the network cell where the sampling point is located and the RSRP information of the network cell with the same frequency and adjacent to the network cell where the sampling point is located. Therefore, the third quantity ratio reflects the RSRP information of the network cell and the RSRP information of the adjacent network cells with the same frequency, and the second statistical value reflects the overall situation of the target level value obtained by the test of the third sampling point. Therefore, based on the third quantity ratio corresponding to the network cell , the total number of target sampling points and the second statistical value, when determining the problem type corresponding to the network cell, the RSRP information of the network cell, the RSRP information of the co-frequency adjacent network cells, and the overall situation of the target level value in the network cell are considered. In addition, when the network cell is an overlapping coverage cell, the RSRP value of the overlapping coverage cell and the network cell, the RSRP value of the adjacent co-frequency network cells, and the overall situation of the level value of the network cell are related. Therefore, based on the third quantity ratio corresponding to the network cell, the total number of target sampling points and the second statistical value, the target cell that meets the preset network overlapping coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0179] In a fourth implementation manner, the test data corresponding to the target sampling point includes: the test data of the target sampling point includes a first PCI (Physical Cell Identifier) ​​value and a first RSRP value of a network cell where the target sampling point is located, and a second PCI value and a second RSRP value of a co-frequency adjacent network cell of the network cell where the target sampling point is located. In the above step S302, the test data corresponding to the target sampling point included in each network cell in the target network area is analyzed, which can be implemented according to the following steps D1-D2.

[0180] Step D1: Based on the first PCI value, the first RSRP value, the second PCI value, and the second RSRP value in the test data corresponding to the target sampling point, determine a sampling point that meets the second preset condition as the fourth sampling point.

[0181] Among them, the above-mentioned second preset condition is: the first RSRP value is greater than the second preset RSRP value, and there are at least a second preset number of target PCI values ​​in the included second PCI value, and the difference between the second RSRP value of the same-frequency adjacent network cell corresponding to the target PCI value and the first RSRP value is greater than the second preset RSRP value.

[0182] The target PCI value is a second PCI value whose modulo 3 value is equal to the modulo 3 value of the first PCI value.

[0183] In one implementation, when determining the fourth sampling point, the modulo 3 value of each second PCI value corresponding to the target sampling point can be calculated, and a second PCI value in which the modulo 3 value is equal to the modulo 3 value of the first PCI value is determined as the target PCI value, and the difference between the second RSRP value and the first RSRP value of the co-frequency adjacent network cell corresponding to each target PCI value is calculated. If the number of target PCI values ​​corresponding to the target sampling point is greater than the second preset number, and the first RSRP value is greater than the second preset RSRP value, and the calculated difference is greater than the second preset RSRP difference, the target sampling point is determined as the fourth sampling point.

[0184] For example, the first RSRP value, the second RSRP value, the first PCI value, and the second PCI value corresponding to the target sampling point included in the network cell are shown in Table 4-1 below.

[0185] Table 4-1

[0186]

[0187]

[0188] Assume that the second preset RSRP value is -95dBm, the second preset number is 1, and the second preset RSRP difference is -3dBm. In the above Table 4-1, there are two target PCI values ​​in the second PCI values ​​included in the target sampling point PC41, there are two target PCI values ​​in the second PCI values ​​included in the target sampling point PC42, and there are three target PCI values ​​in the second PCI values ​​included in the target sampling point PC45.

[0189] Moreover, among the target sampling points PC41, PC42 and PC45, the first RSRP values ​​of the target sampling points PC42 and PC45 are both greater than the second preset RSRP value;

[0190] At the target sampling point PC42, the second RSRP values ​​of the same-frequency adjacent network cells corresponding to the target PCI value are: -95dBm, -80dBm, respectively. The differences between the above two second RSRP values ​​and the first RSRP value are: 0, 15, respectively, both of which are greater than the second preset RSRP difference of -3dBm. Therefore, the target sampling point PC42 meets the above second preset condition;

[0191] At the target sampling point PC45, the second RSRP values ​​of the same-frequency adjacent network cells corresponding to the target PCI values ​​are: -25dBm, -38dBm, and -28dBm, respectively. The differences between the above three second RSRP values ​​and the first RSRP value are: 5, -8, and 2, respectively, which are not all greater than the second preset RSRP difference of -3dBm. Therefore, the target sampling point PC45 does not meet the above second preset condition.

[0192] Step D2: Calculating a fourth quantity ratio between the quantity of the fourth sampling points and the total quantity of the target sampling points.

[0193] The fourth quantity ratio reflects the proportion of the fourth sampling point in all target sampling points included in the network cell.

[0194] Using the example corresponding to Table 4-1 above, the total number of target sampling points included in the network cell is 5, and the number of the fourth sampling point is 1, so the fourth number ratio is: 1 / 5*100%=20%.

[0195] On the basis of the above steps D1-D2, the above step S302 determines the problem type corresponding to the target cell having the network problem in the target network area based on the analysis result, which can be implemented according to the following step D3.

[0196] Step D3: Based on the fourth quantity ratio corresponding to each network cell and the total number of target sampling points included, determine the target cell in each network cell that meets the preset MOD3 (modulo 3) interference condition, and determine the problem type corresponding to the determined target cell as: MOD3 interference type.

[0197] For each network cell, determine whether the fourth quantity ratio corresponding to the network cell is greater than the fourth preset quantity ratio, and whether the total number of target sampling points included in the network cell is greater than the fourth preset number. If so, determine the network cell as a problem cell with network problems, and determine the problem type of the problem cell as: network signal MOD3 interference type.

[0198] The preset MOD3 interference condition may be preset by a staff member. When a network cell meets the MOD3 interference condition, it indicates that the network cell has a network problem, and the problem type of the network problem is: MOD3 interference type.

[0199] The above-mentioned MOD3 interference condition may be: the fourth quantity ratio corresponding to the network cell is greater than the fourth preset quantity ratio, and the total number of target sampling points included in the network cell is greater than the fourth preset total number. When the network cell meets the above-mentioned conditions, it can be determined that the network cell is a target cell with a network problem, and the problem type of the network problem of the network cell is: MOD3 interference type, and the network cell can be called a MOD3 interference cell.

[0200] For example, assuming that the fourth preset quantity ratio is 20%, and the fourth preset total quantity is 90, the fourth quantity ratio corresponding to each network cell and the total number of target sampling points included are shown in Table 4-2 below.

[0201] Table 4-2

[0202] Network Community The third quantity ratio Total quantity Network Community NC41 40％ 100 Network cell NC42 30％ 90 Network Community NC43 20％ 95

[0203] Since 40%>20% and 100>90, the network cell NC31 meets the preset MOD3 interference condition, the network cell NC41 has a network problem, and the problem type of the network problem is: MOD interference type.

[0204] From the above, it can be seen that since the fourth quantity ratio corresponding to the network cell reflects the proportion of the fourth sampling point in the network cell to all target sampling points included in the network cell, and the fourth sampling point reflects the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value, the third quantity ratio reflects the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value, therefore, when determining the problem type corresponding to the network cell based on the third quantity ratio corresponding to the network cell and the total number of target sampling points, , taking into account the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value of the network cell, and because when the network cell is a MOD3 interference cell, the MOD3 interference cell is related to the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value of the network cell, therefore, based on the fourth number ratio corresponding to the network cell and the total number of target sampling points, the target cell that meets the preset MOD3 interference condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0205] In one embodiment of the present invention, when the problem type corresponding to the network cell is: weak coverage type, the corresponding solution may be: enhancing the reference signal power of the network cell, replacing a higher gain antenna, etc. to optimize the coverage;

[0206] When the problem type corresponding to the network cell is: over-coverage type, the corresponding solution may be: reducing the power of the network signal of the network cell;

[0207] When the problem type corresponding to the network cell is: overlapping coverage type, the corresponding solution may be: enhancing the reference signal power of the network cell and weakening the reference signal power of the adjacent network cell;

[0208] When the problem type corresponding to the network cell is: MOD3 interference type, the corresponding solution may be: adjusting the PCI value of the network signal of the network cell and reducing the transmission power of the adjacent network cell with the same frequency.

[0209] Corresponding to the above-mentioned solution generation method applied to the server, an embodiment of the present invention provides a network testing method applied to the client.

[0210] Before describing the network testing method, first, the execution subject of the embodiment of the present invention is described.

[0211] The execution subject of the embodiment of the present invention is a client.

[0212] In one implementation, the client may run on a drone device, or may run on a terminal device bound to the drone device. The terminal device may be a smart phone. Specifically, before the network test, the terminal device may be fixed to the drone body within a reasonable load of the drone device. The device combining the drone device and the terminal device may be called a test device.

[0213] The current drone industry is developing rapidly, showing a development trend of close integration with cellular mobile communication technology, forming "networked drones". Through cellular network networking, it can initially meet the needs of safe drone flight, realize convenient real-name registration of drones, trusted location verification, and real-time and reliable data transmission. At the same time, the rapid development and popularization of 5G networks can provide drones with high-performance networks with large bandwidth and low latency, realizing real-time image transmission, highly reliable networked safe flight management, remote control and business applications, etc. Therefore, the client is combined with the drone equipment, and network testing is performed through the client combined with the drone equipment. Compared with the existing technology, it does not need to rely on staff to perform network testing, which significantly improves the efficiency and cost of network testing and reduces the difficulty of testing.

[0214] The network testing method provided by the embodiment of the present invention is described below.

[0215] See also Figure 4 , Figure 4This is a flow chart of a first network testing method provided by an embodiment of the present invention. The method includes the following steps S401-S403.

[0216] Step S401: Obtain a test route and a test script for a target network area to be tested.

[0217] The test route may include the test path when the client performs network testing, the overall direction of the test, turning angles, straight-line distance, key points passed, etc.

[0218] The test script may include test code, test script identification, etc.

[0219] In one implementation, a staff member may input a test route and a test script through a user operation interface provided by a client, and the client may thereby obtain the test route and the test script.

[0220] Specifically, the client can provide a variety of alternative scripts for test services in the user operation interface. The alternative scripts for the above test services can be applicable to GSM (Global System for Mobile Communications) network, WCDMA (Wide band Code Division Multiple Access) network, TD-SCDMA (Time Division-Synchronous Code Division Multiple Access) network, CDMA (Code Division Multiple Access) network, LTE (Long Term Evolution) network and NR (New Ratio) network, so that the selected script can be applied to various networks.

[0221] The above test services may include: a drive test service and a call quality dialing test service, wherein the alternative script for the drive test service is a DT (Drive Test) script, and the alternative script for the call quality dialing test service is a CQT (Call Quality Test) script.

[0222] The test service content of the above DT script includes DT voice short call service test, DT voice long call service test and DT data service test. The DT voice short call service test does not include MOS test, that is, it does not include subjective test. The DT voice long call service test includes subjective test. The DT data service test includes FTP (File Transfer Protocol) upload / download test, Ping (Packet Internet Groper) test and HTTP (Hyper Text Transfer Protocol) login test.

[0223] The test service content of the above CQT script includes CQT voice short call service test and CQT data service test. The CQT voice short call service test includes subjective test, and the CQT data service test includes FTP upload / download test, Ping test and HTTP login test.

[0224] When the target network area is an area where a preset subway tunnel is located, the above-mentioned test route may be: a route determined from a 3D model of the test route tunnel.

[0225] Step S402: Perform a network test on the target network area using a test script according to the test route to obtain network data of the target network area.

[0226] During the network test, the entire network test process can be recorded based on the video acquisition device connected to the client, the acquired video can be obtained, and the acquired video can be sent to the server to facilitate subsequent playback and problem screening based on the acquired video.

[0227] The specific implementation method of network testing can be found in the following Figure 5a The corresponding embodiments are not described in detail here.

[0228] In one embodiment of the present invention, before performing a network test, a pre-test may be performed on the client, the terminal device run by the client, and the target network area to determine whether the functions of the client and the terminal device run by the client are complete, whether the network signal of the target network area is available, etc. If the pre-test passes, a test script is used to perform a network test.

[0229] Step S403: Generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

[0230] As can be seen from the above, the solution provided in this embodiment is used to perform network testing. Since the client performs network testing on the target network area according to the test route and uses a test script to obtain the network data of the target network area, and generates a request for the solution containing the network data to the server, compared with the prior art, there is no need to rely on staff to manually perform network testing and manually import test data into the server, which improves the efficiency of network testing.

[0231] In the aforementioned Figure 4 In step S402 of the corresponding embodiment, the following method may be used: Figure 5a In the illustrated embodiment, steps S502-S504 perform a network test on the target network area and obtain network data of the target network area. Figure 5a , Figure 5a A schematic diagram of a flow chart of a second network testing method provided by an embodiment of the present invention, wherein the Figure 4 The step S402 of the corresponding embodiment can be implemented according to steps S502-S504.

[0232] Specifically, Figure 5a The corresponding embodiment includes the following steps S501-S505.

[0233] Step S501: Obtain a test route and a test script for a target network area to be tested.

[0234] The above step S501 is similar to the above step S502. Figure 4 Step S401 in the illustrated embodiment is the same and will not be described in detail here.

[0235] Step S502: Determine a target sampling point in a target network area according to the test route, and perform a network test using a test script at the determined target sampling point to obtain test data.

[0236] In one embodiment, when determining the target sampling point, starting from the starting point of the test route, the route points at each interval of a preset length can be determined as the target sampling point, or the route points reached at each interval of a preset time length can be determined as the target sampling point.

[0237] After each target sampling point is determined, a network test may be performed at the determined target sampling point, or after all target sampling points are determined, a network test may be performed at each target sampling point at the same time.

[0238] When the client runs on a drone device, while the drone device is driving along a test route, factors such as poor network quality or complex geographical environment may interfere with the normal driving of the drone device. The drone device can detect the driving process. When a deviation from the test route is detected, the deviation direction and distance can be determined, and the drone can be driven to the test route along the determined deviation direction.

[0239] Specifically, the vertical distance between the flight point of the current flight and the test route can be detected. If the detected vertical distance is greater than the preset distance, it indicates that a deviation from the test route has occurred. In this case, the vertical distance can be determined as the deviation distance, and the offset direction of the flight direction of the current flight relative to the direction of the test route can be determined as the deviation direction. The drone travels along the determined deviation direction along the route of the above deviation distance, so that the drone approaches the test route and finally returns to the test route.

[0240] Step S503: for each target sampling point, obtain the base station position of the target base station corresponding to the network cell where the target sampling point is located, and determine the position of the target sampling point based on the base station position and the network signal strength in the test data corresponding to the target sampling point.

[0241] In one implementation, when obtaining the above-mentioned base station position, the client may pre-store the position of each base station, and the test data obtained by performing a network test at the target sampling point includes an identifier of the target base station corresponding to the network cell where the target sampling point is located. From the stored positions of each base station, the position corresponding to the identifier of the target base station is determined as the base station position of the target base station.

[0242] In one implementation, determining the position of each target sampling point of the target may be achieved according to the following steps E1-E2.

[0243] Step E1: Calculate the target distance between the target sampling point and the target base station based on the network signal strength in the test data corresponding to the target sampling point and the input power of the network signal transmitted by the target base station.

[0244] The longer the distance between the sampling point and the base station, the weaker the network signal strength collected at the sampling point; when the distance between the sampling point and the base station is shorter, the stronger the network signal strength collected at the sampling point. Based on this relationship, the corresponding relationship between the network signal strength and the distance can be determined in advance.

[0245] by Figure 5b For example, Figure 5b A schematic diagram of a subway tunnel leaky cable coverage model is shown. Figure 5bThe rectangular box in the lower middle represents the leaky cable. The signal source is injected from one end of the leaky cable. The signal is attenuated after a certain distance of transmission and gradually weakens. Different signal strengths correspond to different coverage distances of the signal source. The above coverage distance reflects the distance between the sampling point and the base station. It can be seen that the network signal enhancement is inversely proportional to the interval distance.

[0246] Based on this, in one implementation, when calculating the target distance, the target interval distance corresponding to the network signal strength corresponding to the target sampling point can be determined according to the preset correspondence between the network signal strength and the interval distance, as the target distance between the target sampling point and the target base station.

[0247] In one embodiment of the present invention, when the target network area is an area where a preset subway tunnel is located, in one implementation, the target distance H between the target sampling point and the target base station can be calculated according to the following expression:

[0248] H=[Pin-(P0+L1+L2+L3+L4+L5)] / S

[0249] Among them, Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body attenuation value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the leaky cable transmission loss per meter.

[0250] Step E2: Calculate the position of the target sampling point based on the target distance and the base station position of the target base station.

[0251] In one implementation, when calculating the position of the target sampling point, a circular curve with the position of the target base station as the center and the target distance as the radius may be determined, and the positions of the points on the circular curve may be determined based on the position of the target base station. A target direction of the target network area relative to the target base station is obtained, and the intersection between a straight line starting from the target base station and the circular curve along the target direction is determined, and the position of the determined intersection is determined as the position of the target sampling point.

[0252] Since the network signal strength is related to the interval distance between the sampling point and the base station, the target distance between the target sampling point and the target base station calculated based on the network signal strength is highly accurate, thereby making the position of the determined target sampling point highly accurate. In summary, this embodiment uses network signal strength for auxiliary positioning, and even in a network area where the GPS (Global Positioning System) positioning signal is difficult to reach, the position of the target sampling point is obtained with high accuracy, thereby achieving precise positioning.

[0253] Step S504: Determine the test data corresponding to each target sampling point and the position of each target sampling point as the network data of the target network area.

[0254] Step S505: Generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

[0255] The above step S505 is the same as the above Figure 4 Step S403 in the illustrated embodiment is the same and will not be described in detail here.

[0256] It can be seen from the above that since the network signal strength in the test data corresponding to the target sampling point can reflect the distance between the target sampling point and the target base station, based on the above network signal strength and the position of the target base station, a relatively accurate position of the target sampling point can be obtained. Compared with the prior art, there is no need to rely on the GPS positioning signal to determine the position of the target sampling point. The solution provided in this embodiment is used to perform auxiliary positioning based on the network signal strength. Even in a network area where the GPS positioning signal is difficult to reach, the position of the target sampling point obtained has high accuracy, thereby achieving precise positioning.

[0257] The following combination Figure 6 The network testing and solution generation method provided in the embodiment of the present invention is fully described.

[0258] Figure 6 A complete flowchart of a solution generation method provided in an embodiment of the present invention. Figure 6 Including S601-S608.

[0259] Figure 6 The scenario targeted is: network testing and solution generation for the network area of ​​a subway tunnel.

[0260] S601: Preliminary preparation for testing.

[0261] The preliminary preparations mainly include: checking the availability of intelligent test terminals and drones, wireless base station parameter settings, determining the wireless network test area environment, test time points, routes, wireless base station locations, and importing 3D tunnel models.

[0262] S602: Combine the test terminal and the drone to obtain a test device.

[0263] The test terminal is fixed to the drone body within the reasonable load range of the drone to form a test automatic optimization device.

[0264] S603: Connect the test device to the wireless network where the base station is located.

[0265] In this step, you can test whether the terminal functions normally and whether the surrounding wireless networks are available.

[0266] S604: The testing device obtains the selected test script for testing.

[0267] S605: The test equipment performs network testing and signal-assisted positioning according to a predetermined test route.

[0268] S606: The testing device saves the wireless network test result and sends it to the server for data processing.

[0269] S607: The server processes the test data and automatically determines the type of network problem.

[0270] S608: The server performs analysis and outputs customized test reports and automatic optimization solutions.

[0271] Corresponding to the above-mentioned solution generation method applied to the server, an embodiment of the present invention further provides a solution generation device.

[0272] See also Figure 7 , Figure 7 This is a schematic diagram of the structure of a first solution generation device provided in an embodiment of the present invention. The device includes the following modules 701-703.

[0273] The data acquisition module 701 is used to receive a solution generation request carrying network data of a target network area sent by a client, parse the solution generation request, and obtain the network data of the target network area, wherein the network data is data obtained by the client performing a network test on the target network area;

[0274] A type determination module 702 is used to analyze the network data and determine a target problem type of the network problem in the target network area based on the analysis result;

[0275] The solution generation module 703 is used to determine the solution corresponding to the target problem type based on the correspondence between the solution and the problem type as the target solution for solving the network problem.

[0276] As can be seen from the above, when the solution generation scheme provided by this embodiment is applied, after receiving the solution generation request sent by the client, the server analyzes the network data obtained by network testing the target network area, determines the target problem type of the network problem in the target network area based on the analysis result, and then determines the target solution for solving the above network problem based on the target problem type. Compared with the prior art in which the staff generates solutions based on experience, the automation of solution generation is realized, which does not rely on manual generation of solutions, and significantly improves the efficiency of solution generation.

[0277] Furthermore, the server determines the target solution corresponding to the target problem type of the network problem in the target network area based on the correspondence between the solution and the problem type. When the solution has a correspondence with the problem type, it means that the solution can effectively repair the network problem of the problem type corresponding to the solution. Therefore, it is determined that the obtained target problem solution can perform network repair on the network problem of the target problem type. Therefore, the target solution is generated by adopting this embodiment, which effectively guarantees the accuracy of the generated solution.

[0278] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a second solution generation device provided in an embodiment of the present invention. The device includes the following modules 801-804.

[0279] The data acquisition module 801 is used to receive a solution generation request carrying network data of a target network area sent by a client, parse the solution generation request, and obtain the network data of the target network area, wherein the network data is data obtained by the client performing a network test on the target network area;

[0280] A type determination module 802 is used to analyze the network data and determine a target problem type of the network problem in the target network area based on the analysis result;

[0281] The solution generation module 803 is used to determine the solution corresponding to the target problem type based on the correspondence between the solution and the problem type as the target solution for solving the network problem.

[0282] The report generation module 804 is used to generate a network data report representing parameter information of the preset parameter items based on the values ​​of the preset parameter items in the network data.

[0283] As can be seen from the above, since the generated network data report can represent the parameter information of the preset parameter items, the generated network data is highly targeted, and can enable users to more intuitively and clearly know the parameter information of the preset parameter items based on the above network data report. In addition, in the scenario where the user needs to know the parameter information of a specific parameter item, a network data report representing the parameter information of the above specific parameter item can be generated, realizing a customized network data report.

[0284] In one embodiment of the present invention, the above-mentioned network data includes: test data obtained by performing network testing on target sampling points included in each network cell in the target network area; the type determination module 802 is specifically used to analyze the test data corresponding to the target sampling points included in each network cell in the target network area, and determine the problem type corresponding to the target cell with network problems in each network cell based on the analysis result as the target problem type of the network problem in the target network area.

[0285] Since the test data corresponding to the target sampling points included in each network cell are analyzed, the obtained analysis results can reflect the network information of each network cell. Therefore, based on the analysis results, the problem type corresponding to the target cell with network problems can be determined more accurately, thereby obtaining a more accurate target problem type corresponding to the target network area.

[0286] In one embodiment of the present invention, the test data corresponding to the target sampling point includes a target level value of the network cell where the target sampling point is located. The type determination module 802 is specifically used to determine a first sampling point in which the target level value included in the test data of the target sampling point is less than a preset level value; calculate a first quantity ratio between the number of the first sampling points and the total number of target sampling points; perform statistical analysis on the target level values ​​corresponding to the target sampling points to obtain a first statistical value; based on the first quantity ratio corresponding to each network cell, the total number of target sampling points included, and the corresponding first statistical value, determine the target cell in each network cell that meets the preset network weak coverage condition, and determine the problem type corresponding to the determined target cell as: weak coverage type.

[0287] As can be seen from the above, since the first quantity ratio corresponding to the network cell reflects the proportion of the number of first sampling points in the network cell to the total number of all sampling points in the network cell, the target level value obtained by the test of the first sampling point is less than the preset level value, that is, the target level value obtained by the test of the first sampling point is relatively small, so the first quantity ratio reflects the proportion of sampling points with relatively small target level values ​​obtained by the test in the network cell; the first statistical value reflects the overall situation of the target level values ​​obtained by the test of each target sampling point in the network cell. Therefore, when determining the problem type corresponding to the network cell based on the first quantity ratio corresponding to the network cell, the total number of target sampling points and the first statistical value, the proportion of sampling points with relatively small target level values ​​obtained by the test in the network cell and the number of all sampling points are considered, and the overall level value of the network cell is also considered. In addition, when the network cell is a weak coverage cell, its overall level value is relatively poor, and the proportion of relatively small level values ​​measured in the weak coverage cell is high. Therefore, based on the first quantity ratio corresponding to the network cell, the total number of target sampling points and the first statistical value, the target cell that meets the preset network weak coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0288] In one embodiment of the present invention, the test data corresponding to the target sampling point includes a first position of the target sampling point, a first identifier of a target base station corresponding to a network cell where the target sampling point is located, and the type determination module 802 is specifically configured to determine, according to the first identifier and the second identifier in the test data corresponding to each target sampling point, the second position of the target base station and the third position of an adjacent base station of the target base station, calculate a first distance between the first position of each target sampling point and the second position, and a second distance between the first position and the third position; calculate a distance ratio between the first distance and the second distance corresponding to each target sampling point, and determine a second sampling point whose distance ratio is greater than a preset distance ratio among each target sampling point; calculate a second quantity ratio between the number of the second sampling points and the total number of target sampling points; determine a target cell in each network cell that meets a preset network over-coverage condition based on the second quantity ratio corresponding to each network cell and the total number of target sampling points included, and determine the problem type corresponding to the determined target cell as: over-coverage type.

[0289] From the above, it can be seen that since the second quantity ratio corresponding to the network cell reflects the proportion of the second sampling point in the network cell to all target sampling points included in the network cell, the second sampling point refers to a sampling point whose distance ratio is greater than the preset distance ratio, and the distance ratio reflects the size relationship between the first distance between the sampling point and the target base station and the second distance between the sampling point and the adjacent base station of the target base station. The larger the distance ratio, the greater the first distance is greater than the second distance, and the smaller the distance ratio, the smaller the first distance is less than the second distance. Since the distance ratio corresponding to the second sampling point is greater than the preset distance ratio, that is, the distance ratio corresponding to the second sampling point is relatively large, the distance between the second sampling point and the target base station is relative to the adjacent base station. The distance between them is large, so the second quantity ratio reflects the proportion of sampling points in the network cell that are relatively far away from the target base station; therefore, when determining the problem type corresponding to the network cell based on the second quantity ratio corresponding to the network cell and the total number of target sampling points, the proportion of sampling points in the network cell that are relatively far away from the target base station is considered. Since when the network cell is an over-coverage cell, the distance between the network cell and the target base station corresponding to the network cell is relatively far, therefore, based on the second quantity ratio corresponding to the network cell and the total number of target sampling points, the target cell that meets the preset network over-coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0290] In one embodiment of the present invention, the test data of the target sampling point includes a target level value of the network cell where the target sampling point is located, a first RSRP value of the network cell where the target sampling point is located, and a second RSRP value of a network cell with the same frequency and adjacent to the network cell where the target sampling point is located. The type determination module 802 is specifically used to determine a sampling point that meets a first preset condition as a third sampling point based on the first RSRP value and the second RSRP value in the test data corresponding to the target sampling point, wherein the first preset condition is: the first RSRP value is greater than or equal to the first preset RSRP value, and there are at least a first preset number of first target RSRP values ​​in the second RSRP value, and the first target RSRP value is: a second RSRP value whose difference with the first RSRP value is greater than the first preset RSRP difference; calculate a third quantity ratio between the number of the third sampling points and the total number of the target sampling points; perform statistical analysis on the level value corresponding to the third sampling point to obtain a second statistical value; determine a target cell that meets a preset network overlapping coverage condition in each network cell based on the third quantity ratio corresponding to each network cell, the total number of target sampling points included, and the corresponding second statistical value, and determine the problem type corresponding to the determined target cell as: overlapping coverage type.

[0291] From the above, it can be seen that since the third quantity ratio corresponding to the network cell reflects the proportion of the third sampling point in the network cell to all target sampling points included in the network cell, and the third sampling point is determined based on the first RSRP value and the second RSRP value obtained by the test, the third sampling point can reflect the RSRP information of the network cell where the sampling point is located and the RSRP information of the network cell with the same frequency and adjacent to the network cell where the sampling point is located. Therefore, the third quantity ratio reflects the RSRP information of the network cell and the RSRP information of the adjacent network cells with the same frequency, and the second statistical value reflects the overall situation of the target level value obtained by the test of the third sampling point. Therefore, based on the third quantity ratio corresponding to the network cell , the total number of target sampling points and the second statistical value, when determining the problem type corresponding to the network cell, the RSRP information of the network cell, the RSRP information of the co-frequency adjacent network cells, and the overall situation of the target level value in the network cell are considered. In addition, when the network cell is an overlapping coverage cell, the RSRP value of the overlapping coverage cell and the network cell, the RSRP value of the adjacent co-frequency network cells, and the overall situation of the level value of the network cell are related. Therefore, based on the third quantity ratio corresponding to the network cell, the total number of target sampling points and the second statistical value, the target cell that meets the preset network overlapping coverage condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0292] In one embodiment of the present invention, the test data corresponding to the target sampling point includes a first PCI value and a first RSRP value of a network cell where the target sampling point is located, and a second PCI value and a second RSRP value of a network cell having the same frequency as the network cell where the target sampling point is located. The type determination module 802 is specifically configured to determine a sampling point that meets a second preset condition as a fourth sampling point based on the first PCI value, the first RSRP value, the second PCI value, and the second RSRP value in the test data corresponding to the target sampling point, wherein the second preset condition is that the first RSRP value is greater than the second preset RSRP value, and the second PCI value included is There are at least a second preset number of target PCI values, and the difference between the second RSRP value of the co-frequency adjacent network cell corresponding to the target PCI value and the first RSRP value is greater than the second preset RSRP difference, and the target PCI value is: a second PCI value whose modulo 3 value is equal to the modulo 3 value of the first PCI value; calculate the fourth quantity ratio between the number of the fourth sampling points and the total number of target sampling points; based on the fourth quantity ratio corresponding to each network cell and the total number of target sampling points included, determine the target cell in each network cell that meets the preset MOD3 interference condition, and determine the problem type corresponding to the determined target cell as: MOD3 interference type.

[0293] From the above, it can be seen that since the fourth quantity ratio corresponding to the network cell reflects the proportion of the fourth sampling point in the network cell to all target sampling points included in the network cell, and the fourth sampling point reflects the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value, the third quantity ratio reflects the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value, therefore, when determining the problem type corresponding to the network cell based on the third quantity ratio corresponding to the network cell and the total number of target sampling points, , taking into account the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value of the network cell, and because when the network cell is a MOD3 interference cell, the MOD3 interference cell is related to the RSRP value of the network cell, the modulo 3 value of the PCI value, the RSRP value of the co-frequency adjacent network cell, and the modulo 3 value of the PCI value of the network cell, therefore, based on the fourth number ratio corresponding to the network cell and the total number of target sampling points, the target cell that meets the preset MOD3 interference condition can be determined more accurately, thereby improving the accuracy of the obtained problem type.

[0294] Corresponding to the above-mentioned network testing method applied to the client, an embodiment of the present invention further provides a network testing device.

[0295] See also Fig. 9 , Fig. 9 The first network testing device provided in the embodiment of the present invention is a schematic diagram of the structure, which is applied to the client. The device includes the following modules 901-903:

[0296] The script acquisition module 901 is used to obtain the test route and test script of the target network area to be tested;

[0297] A network testing module 902 is used to perform a network test on the target network area according to the test route and using the test script to obtain network data of the target network area;

[0298] The request sending module 903 is used to generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

[0299] As can be seen from the above, the solution provided in this embodiment is used to perform network testing. Since the client performs network testing on the target network area according to the test route and uses a test script to obtain the network data of the target network area, and generates a request for the solution containing the network data to the server, compared with the prior art, there is no need to rely on staff to manually perform network testing and manually import test data into the server, which improves the efficiency of network testing.

[0300] See also Fig.10 , Fig.10 The schematic diagram of the structure of the second network testing device provided by the embodiment of the present invention is applied to the client. The device includes the following modules 1001-1005:

[0301] The script acquisition module 1001 is used to obtain the test route and test script of the target network area to be tested;

[0302] The network test submodule 1002 is used to determine the target sampling point in the target network area according to the test route, and perform network testing using the test script at the determined target sampling point to obtain test data;

[0303] The position determination submodule 1003 is used to obtain, for each target sampling point, the base station position of the target base station corresponding to the network cell where the target sampling point is located, and determine the position of the target sampling point based on the base station position and the network signal strength in the test data corresponding to the target sampling point;

[0304] The data determination submodule 1004 is used to determine the test data corresponding to each target sampling point and the position of each target sampling point as the network data of the target network area.

[0305] The request sending module 1005 is used to generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

[0306] From the above, it can be seen that, based on the network signal strength in the test data corresponding to the target sampling point, the target distance between the target sampling point and the target base station is calculated, and then based on the target distance and the base station position of the target base station, the position of the target sampling point is determined. Compared with the prior art, there is no need to rely on the GPS positioning signal to determine the position of the target sampling point. In a network area where the GPS positioning signal is difficult to reach, the solution provided in this embodiment can still determine the position of the target sampling point.

[0307] Furthermore, since the network signal strength is related to the interval distance between the sampling point and the base station, the target distance between the target sampling point and the target base station calculated based on the network signal strength is highly accurate, thereby making the position of the determined target sampling point highly accurate. In summary, the present embodiment uses the network signal strength for auxiliary positioning, and even in the network area where the GPS positioning signal is difficult to reach, the position of the target sampling point obtained is highly accurate, and accurate positioning is achieved. As can be seen from the above, since the network signal strength in the test data corresponding to the target sampling point can reflect the distance between the target sampling point and the target base station, based on the above network signal strength and the position of the target base station, the position of the target sampling point can be obtained more accurately. Compared with the prior art, there is no need to rely on the GPS positioning signal to determine the position of the target sampling point. The solution provided by the present embodiment is used to perform auxiliary positioning based on the network signal strength. Even in the network area where the GPS positioning signal is difficult to reach, the position of the target sampling point obtained is highly accurate, and accurate positioning is achieved.

[0308] In one embodiment of the present invention, the position determination submodule 1003 includes:

[0309] A distance calculation unit, used to calculate a target distance between a target sampling point and a target base station based on a network signal strength in test data corresponding to the target sampling point;

[0310] The position calculation unit is used to calculate the position of the target sampling point based on the target distance and the base station position of the target base station.

[0311] Since the network signal strength is related to the interval distance between the sampling point and the base station, the target distance between the target sampling point and the target base station calculated based on the network signal strength is highly accurate, thereby making the position of the determined target sampling point highly accurate. In summary, this embodiment uses network signal strength for auxiliary positioning, and even in a network area where the GPS positioning signal is difficult to reach, the position of the target sampling point obtained is highly accurate, thereby achieving precise positioning.

[0312] In one embodiment of the present invention, the distance calculation unit is specifically used to calculate the target distance H between the target sampling point and the target base station according to the following expression:

[0313] H=[Pin-(P0+L1+L2+L3+L4+L5)] / S

[0314] Among them, Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body attenuation value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the leaky cable transmission loss per meter.

[0315] In one embodiment of the present invention, the client is run on a drone device, or a terminal device bound to the drone device.

[0316] The embodiment of the present invention also provides a server, such as Fig.11 As shown, it includes a processor 1101, a communication interface 1102, a memory 1103 and a communication bus 1104, wherein the processor 1101, the communication interface 1102, and the memory 1103 communicate with each other through the communication bus 1104.

[0317] Memory 1103, used for storing computer programs;

[0318] The processor 1101 is used to implement the above-mentioned solution generation method steps when executing the program stored in the memory 1103.

[0319] The communication bus mentioned in the above server can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0320] The communication interface is used for communication between the above server and other devices.

[0321] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0322] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0323] The embodiment of the present invention also provides a client, such as Fig.12 As shown, it includes a processor 1201, a communication interface 1202, a memory 1203 and a communication bus 1204, wherein the processor 1201, the communication interface 1202, and the memory 1203 communicate with each other through the communication bus 1204.

[0324] Memory 1203, used for storing computer programs;

[0325] The processor 1201 is used to implement the above-mentioned network testing method steps when executing the program stored in the memory 1203.

[0326] The communication bus mentioned by the client above may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0327] The communication interface is used for communication between the above-mentioned client and other devices.

[0328] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0329] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0330] In another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned solution generation or network testing method steps are implemented.

[0331] In another embodiment of the present invention, a computer program product including instructions is provided, which, when executed on a computer, enables the computer to execute the solution generation or network testing method in the above embodiment.

[0332] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0333] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0334] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0335] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A solution generation method, characterized in that: Applied to a server, the method comprises: Receive a solution generation request sent by a client and carrying network data of a target network area, parse the solution generation request, and obtain the network data of the target network area, wherein the network data is: data obtained by the client performing a network test on the target network area, the target network area includes: a network area where a subway tunnel to be detected for a network problem is located, and when the target network area is the network area where the subway tunnel to be detected for a network problem is located, the network data includes: a location of a target sampling point in the target network area, wherein the location of the target sampling point It is determined according to the target distance between the target sampling point and the target base station and the base station position of the target base station. The target distance H is calculated according to the following expression: H = [Pin-(P0+L1+L2+L3+L4+L5)] / S, wherein Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body fading value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the leaky cable transmission loss per meter; Analyze the network data, and determine the target problem type of the network problem in the target network area based on the analysis result; Based on the correspondence between the solution and the problem type, a solution corresponding to the target problem type is determined as a target solution for solving the network problem.

2. The method according to claim 1, characterized in that The method further comprises: Based on the values ​​of the preset parameter items in the network data, a network data report table representing parameter information of the preset parameter items is generated.

3. The method according to claim 1 or 2, characterized in that: The network data includes: test data obtained by performing network testing on target sampling points included in each network cell in the target network area; The analyzing the network data and determining the target problem type of the network problem in the target network area based on the analysis result includes: The test data corresponding to the target sampling points included in each network cell in the target network area are analyzed, and based on the analysis results, the problem type corresponding to the target cell with network problems in each network cell is determined as the target problem type of the network problem in the target network area.

4. The method according to claim 3, characterized in that The test data corresponding to the target sampling point includes a target level value of a network cell where the target sampling point is located, and the analyzing the test data corresponding to the target sampling point included in each network cell in the target network area includes: The test data corresponding to the target sampling points included in each network cell are analyzed in the following manner: Determine a first sampling point at which a target level value included in the test data of the target sampling point is less than a preset level value; Calculating a first quantity ratio between the quantity of the first sampling points and the total quantity of target sampling points; Performing statistical analysis on the target level values ​​corresponding to the target sampling points to obtain a first statistical value; The determining, based on the analysis result, a problem type corresponding to the target cell having a network problem in the target network area includes: Based on the first quantity ratio corresponding to each network cell, the total number of target sampling points included and the corresponding first statistical value, the target cell that meets the preset network weak coverage condition in each network cell is determined, and the problem type corresponding to the determined target cell is determined as: weak coverage type.

5. The method according to claim 3, characterized in that: The test data corresponding to the target sampling point includes a first position of the target sampling point, a first identifier of a target base station corresponding to a network cell where the target sampling point is located, and a second identifier of a base station adjacent to the target base station. The analyzing the test data corresponding to the target sampling point included in each network cell in the target network area includes: The test data corresponding to the target sampling points included in each network cell are analyzed in the following manner: Determine the second position of the target base station and the third position of a base station adjacent to the target base station according to the first identifier and the second identifier in the test data corresponding to each target sampling point, and calculate a first distance between the first position of each target sampling point and the second position, and a second distance between the first position and the third position; Calculate the distance ratio between the first distance and the second distance corresponding to each target sampling point, and determine the second sampling point whose distance ratio is greater than the preset distance ratio among the target sampling points; calculating a second quantity ratio between the quantity of the second sampling points and the total quantity of the target sampling points; The determining, based on the analysis result, a problem type corresponding to the target cell having a network problem in the target network area includes: Based on the second quantity ratio corresponding to each network cell and the total number of target sampling points included, a target cell in each network cell that meets a preset network over-coverage condition is determined, and the problem type corresponding to the determined target cell is determined as: over-coverage type.

6. The method according to claim 3, characterized in that The test data of the target sampling point includes a target level value of the network cell where the target sampling point is located, a first RSRP value of the network cell where the target sampling point is located, and a second RSRP value of a network cell having the same frequency as the network cell where the target sampling point is located, and the analyzing the test data corresponding to the target sampling point included in each network cell in the target network area includes: The test data corresponding to the target sampling points included in each network cell are analyzed in the following manner: Based on the first RSRP value and the second RSRP value in the test data corresponding to the target sampling point, determine a sampling point that meets the first preset condition as the third sampling point, wherein the first preset condition is: the first RSRP value is greater than or equal to the first preset RSRP value, and there are at least a first preset number of first target RSRP values ​​in the second RSRP value, and the first target RSRP value is: a second RSRP value whose difference with the first RSRP value is greater than the first preset RSRP difference; Calculating a third quantity ratio between the quantity of the third sampling points and the total quantity of the target sampling points; Performing statistical analysis on the level value corresponding to the third sampling point to obtain a second statistical value; The determining, based on the analysis result, a problem type corresponding to the target cell having a network problem in the target network area includes: Based on the third quantity ratio corresponding to each network cell, the total number of target sampling points included, and the corresponding second statistical value, the target cell that meets the preset network overlapping coverage condition in each network cell is determined, and the problem type corresponding to the determined target cell is determined as: overlapping coverage type.

7. The method according to claim 3, characterized in that The test data corresponding to the target sampling point includes a first PCI value and a first RSRP value of a network cell where the target sampling point is located, and a second PCI value and a second RSRP value of a network cell having the same frequency as the network cell where the target sampling point is located. The analyzing the test data corresponding to the target sampling point included in each network cell in the target network area includes: The test data corresponding to the target sampling points included in each network cell are analyzed in the following manner: Based on the first PCI value, the first RSRP value, the second PCI value and the second RSRP value in the test data corresponding to the target sampling point, a sampling point satisfying the second preset condition is determined as the fourth sampling point, wherein the second preset condition is: the first RSRP value is greater than the second preset RSRP value, and there are at least a second preset number of target PCI values ​​in the included second PCI values, and the difference between the second RSRP value of the co-frequency adjacent network cell corresponding to the target PCI value and the first RSRP value is greater than the second preset RSRP difference, and the target PCI value is: a second PCI value whose modulo 3 value is equal to the modulo 3 value of the first PCI value; Calculating a fourth quantity ratio between the quantity of the fourth sampling points and the total quantity of the target sampling points; The determining, based on the analysis result, a problem type corresponding to the target cell having a network problem in the target network area includes: Based on the fourth quantity ratio corresponding to each network cell and the total number of target sampling points included, a target cell in each network cell that meets the preset MOD3 interference condition is determined, and the problem type corresponding to the determined target cell is determined as: MOD3 interference type.

8. A network testing method, characterized in that: Applied to a client, the method comprises: Obtaining a test route and a test script of a target network area to be tested, wherein the network area includes: a network area where a subway tunnel to be tested for network problems is located; According to the test route, the test script is used to perform a network test on the target network area to obtain network data of the target network area, wherein, in the case where the target network area is the network area where the subway tunnel to be detected for network problems is located, the network data includes: the position of the target sampling point in the target network area, wherein the position of the target sampling point is determined according to the target distance between the target sampling point and the target base station and the base station position of the target base station, and the target distance H is calculated according to the following expression: H=[Pin-(P0+L1+L2+L3+L4+L5)] / S, wherein Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body fading value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the transmission loss per meter of the leaky cable; A solution generation request including the network data of the target network area is generated, and the solution generation request is sent to a server.

9. The method according to claim 8, characterized in that The step of performing a network test on the target network area according to the test route and using the test script to obtain network data of the target network area includes: Determine the target sampling point in the target network area according to the test route, and perform network testing using the test script at the determined target sampling point to obtain test data; For each target sampling point, obtain a base station position of a target base station corresponding to a network cell where the target sampling point is located, and determine the position of the target sampling point based on the base station position and a network signal strength in the test data corresponding to the target sampling point; The test data corresponding to each target sampling point and the position of each target sampling point are determined as the network data of the target network area.

10. The method according to claim 9, characterized in that The location of each target sampling point is determined as follows: Based on the network signal strength in the test data corresponding to the target sampling point, the target distance between the target sampling point and the target base station is calculated; Based on the target distance and the base station position of the target base station, the position of the target sampling point is calculated.

11. The method according to claim 8, characterized in that The client runs on the drone device, or a terminal device bound to the drone device.

12. A solution generating device, characterized in that: Applied to a server, the device comprises: A data acquisition module is used to receive a solution generation request sent by a client and carrying network data of a target network area, parse the solution generation request, and obtain the network data of the target network area, wherein the network data is: data obtained by the client performing a network test on the target network area, the target network area includes: a network area where a subway tunnel to be detected for network problems is located, and in the case where the target network area is the network area where the subway tunnel to be detected for network problems is located, the network data includes: a location of a target sampling point in the target network area, wherein the target sampling point The location of the sampling point is determined according to the target distance between the target sampling point and the target base station and the base station location of the target base station. The target distance H is calculated according to the following expression: H = [Pin-(P0+L1+L2+L3+L4+L5)] / S, wherein Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body fading value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the leaky cable transmission loss per meter; A type determination module, configured to analyze the network data and determine a target problem type of the network problem in the target network area based on the analysis result; The solution generation module is used to determine the solution corresponding to the target problem type based on the corresponding relationship between the solution and the problem type, as the target solution for solving the network problem.

13. The device according to claim 12, characterized in that The device further comprises: a report generation module, which is used to generate a network data report representing parameter information of the preset parameter items based on the values ​​of the preset parameter items in the network data.

14. The device according to claim 12 or 13, characterized in that The network data includes: test data obtained by performing network testing on target sampling points included in each network cell in the target network area; the type determination module is specifically used to analyze the test data corresponding to the target sampling points included in each network cell in the target network area, and determine the problem type corresponding to the target cell with network problems in each network cell based on the analysis result as the target problem type of the network problem in the target network area.

15. The device according to claim 14, characterized in that The test data corresponding to the target sampling point includes a target level value of the network cell where the target sampling point is located, and the type determination module is specifically used to determine a first sampling point in the target sampling point where the target level value included in the test data is less than a preset level value; Calculating a first quantity ratio between the quantity of the first sampling points and the total quantity of target sampling points; Performing statistical analysis on the target level values ​​corresponding to the target sampling points to obtain a first statistical value; Based on the first quantity ratio corresponding to each network cell, the total number of target sampling points included and the corresponding first statistical value, the target cell that meets the preset network weak coverage condition in each network cell is determined, and the problem type corresponding to the determined target cell is determined as: weak coverage type.

16. The device according to claim 14, characterized in that The test data corresponding to the target sampling point includes a first position of the target sampling point, a first identifier of a target base station corresponding to a network cell where the target sampling point is located, and the type determination module is specifically configured to determine, according to the first identifier and the second identifier in the test data corresponding to each target sampling point, a second position of the target base station and a third position of a base station adjacent to the target base station, and calculate a first distance between the first position of each target sampling point and the second position, and a second distance between the first position and the third position; Calculate the distance ratio between the first distance and the second distance corresponding to each target sampling point, and determine the second sampling point whose distance ratio is greater than the preset distance ratio among the target sampling points; Calculate the second quantity ratio between the number of the second sampling points and the total number of target sampling points; determine the target cell in each network cell that meets the preset network over-coverage condition based on the second quantity ratio corresponding to each network cell and the total number of target sampling points included, and determine the problem type corresponding to the determined target cell as: over-coverage type.

17. The device according to claim 14, characterized in that The test data of the target sampling point includes a target level value of the network cell where the target sampling point is located, a first RSRP value of the network cell where the target sampling point is located, and a second RSRP value of a network cell with the same frequency as the network cell where the target sampling point is located. The type determination module is specifically used to determine a sampling point that meets a first preset condition as a third sampling point based on the first RSRP value and the second RSRP value in the test data corresponding to the target sampling point, wherein the first preset condition is: the first RSRP value is greater than or equal to the first preset RSRP value, and there are at least a first preset number of first target RSRP values ​​in the second RSRP value, and the first target RSRP value is: a second RSRP value whose difference with the first RSRP value is greater than the first preset RSRP difference; calculate a third quantity ratio between the number of the third sampling points and the total number of the target sampling points; perform statistical analysis on the level value corresponding to the third sampling point to obtain a second statistical value; determine a target cell that meets a preset network overlapping coverage condition in each network cell based on the third quantity ratio corresponding to each network cell, the total number of the included target sampling points, and the corresponding second statistical value, and determine the problem type corresponding to the determined target cell as: overlapping coverage type.

18. The device according to claim 14, characterized in that The test data corresponding to the target sampling point includes a first PCI value and a first RSRP value of a network cell where the target sampling point is located, and a second PCI value and a second RSRP value of a network cell having the same frequency as the network cell where the target sampling point is located. The type determination module is specifically configured to determine a sampling point that meets a second preset condition as a fourth sampling point based on the first PCI value, the first RSRP value, the second PCI value, and the second RSRP value in the test data corresponding to the target sampling point, wherein the second preset condition is that the first RSRP value is greater than the second preset RSRP value, and at least one of the included second PCI values ​​meets the second preset condition. Assume that there are a number of target PCI values, and the difference between the second RSRP value and the first RSRP value of the adjacent network cell with the same frequency corresponding to the target PCI value is greater than the second preset RSRP difference, and the target PCI value is: a second PCI value whose modulo 3 value is equal to the modulo 3 value of the first PCI value; calculate the fourth quantity ratio between the number of the fourth sampling points and the total number of target sampling points; based on the fourth quantity ratio corresponding to each network cell and the total number of target sampling points included, determine the target cell in each network cell that meets the preset MOD3 interference condition, and determine the problem type corresponding to the determined target cell as: MOD3 interference type.

19. A network testing device, characterized in that: Applied to a client, the device comprises: A script acquisition module, used to obtain a test route and a test script of a target network area to be tested, wherein the network area includes: a network area where a subway tunnel to be tested for network problems is located; A network testing module, configured to perform a network test on the target network area according to the test route and using the test script to obtain network data of the target network area; The network test module includes: a position determination submodule, which is used to obtain, for each target sampling point, a base station position of a target base station corresponding to the network cell where the target sampling point is located, and determine the position of the target sampling point based on the base station position and the network signal strength in the test data corresponding to the target sampling point; The position determination submodule includes: a distance calculation unit, which is used to calculate the target distance between the target sampling point and the target base station based on the network signal strength in the test data corresponding to the target sampling point, wherein, when the target network area is the network area where the subway tunnel to be detected for network problems is located, the distance calculation unit is specifically used to calculate the target distance H between the target sampling point and the target base station according to the following expression: H = [Pin-(P0+L1+L2+L3+L4+L5)] / S wherein Pin is the injection power of the input leaky cable corresponding to the target base station in the subway tunnel, P0 is the network signal strength in the test data corresponding to the target sampling point, L1 is the preset leaky cable coupling loss, L2 is the preset human body fading value, L3 is the preset width factor, L4 is the preset attenuation margin, L5 is the preset vehicle body loss, and S is the leaky cable transmission loss per meter; The request sending module is used to generate a solution generation request including the network data of the target network area, and send the solution generation request to the server.

20. The device according to claim 19, characterized in that The network testing module comprises: A network test submodule, configured to determine a target sampling point in the target network area according to the test route, and perform a network test using the test script at the determined target sampling point to obtain test data; The data determination submodule is used to determine the test data corresponding to each target sampling point and the position of each target sampling point as the network data of the target network area.

21. The device according to claim 20, characterized in that The position determination submodule comprises: The position calculation unit is used to calculate the position of the target sampling point based on the target distance and the base station position of the target base station.

22. The device according to claim 19, characterized in that The client runs on the drone device, or a terminal device bound to the drone device.

23. A server, characterized in that: The invention comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory is used to store computer programs; and the processor is used to implement the steps of any method described in claims 1-7 when executing the program stored in the memory.

24. A client, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor is used to implement the steps of any method described in claims 8-11 when executing the program stored in the memory.

25. 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 a processor, the steps of the method described in any one of claims 1-7 or 8-11 are implemented.

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