CN111885485B - A method and device for evaluating network quality - Google Patents

Abstract

The application provides a method and a device for evaluating network quality, relates to the technical field of communication, and solves the problem of inaccurate evaluation of network quality. The method comprises the following steps: acquiring MR data and map data, and marking the longitude and latitude information of a target in the MR data in the map data to obtain first map data; inputting the first map data into a preset scene recognition model to obtain a scene recognition result, and performing primary evaluation on the network according to the scene recognition result and the MR data to obtain a primary evaluation result; if the data volume of the MR data of the target scene is smaller than a preset threshold value in the scene recognition result, a drive test scheme is formulated according to the target scene and issued so that relevant personnel can carry out drive test according to the drive test scheme; and acquiring the drive test data, and comprehensively evaluating the network according to the drive test data and the preliminary evaluation result to generate an evaluation report. The embodiment of the application is applied to evaluating the network quality.

Description

A method and device for evaluating network quality

technical field

Embodiments of the present application relate to the field of communications technologies, and in particular, to a method and apparatus for evaluating network quality.

Background technique

The evaluation results of network quality can guide network planning and network optimization. At present, a commonly used network quality evaluation method is measurement report (measurement report, MR) evaluation, the principle of which is that the terminal collects network data and reports the collected network data to the server. Then, the server performs network quality evaluation according to the network data, and generates a network quality evaluation result.

It can be seen that when using the MR evaluation method to evaluate the network quality, the network quality is only evaluated based on the collected network data, and only the network data actively reported by the terminal is limited. When evaluating the network quality of the scenario where the few terminals are located, the evaluation result appears to be inaccurate.

SUMMARY OF THE INVENTION

The present application provides a method and device for evaluating network quality, which solves the problem of inaccurate network quality evaluation.

In a first aspect, the present application provides a method for evaluating network quality, which is applied to a device for evaluating network quality. The method includes: the device for evaluating network quality first acquires MR data and map data, and stores the target longitude and latitude information in the MR data in the MR data. Mark the map data to obtain the first map data. After that, the network quality evaluation device inputs the first map data into the preset scene recognition model to obtain the scene recognition result, and performs a preliminary evaluation on the network according to the scene recognition result and the MR data to obtain the preliminary evaluation result. If it is determined that the amount of MR data of the target scene in the scene recognition result is less than the preset threshold, the network quality evaluation device formulates a drive test plan according to the target scene, and delivers it, so that relevant personnel can conduct drive tests according to the drive test plan. Finally, the network quality evaluation device obtains the drive test data, and comprehensively evaluates the network according to the drive test data and the preliminary evaluation result, and generates an evaluation report.

In the above solution, the device for evaluating network quality first marks the target latitude and longitude information of the acquired MR data into the map data to obtain the first map data. Then, the scene corresponding to the MR data is determined according to the first map data and the preset scene recognition model. When it is determined that the data amount of the MR data corresponding to a certain scene is less than the preset threshold, a drive test plan is formulated according to the scene and delivered. In order to facilitate the relevant personnel to carry out the drive test according to the drive test plan. At the same time, the network is preliminarily evaluated according to the scene recognition results and MR data, and the preliminary evaluation results are obtained. Finally, the network is comprehensively evaluated according to the preliminary evaluation results and the drive test data, and an evaluation report is obtained. In this way, when it is determined that the MR data of a key scene is insufficient, the drive test is used to supplement the data of the key scene, and the scheme of combining scene recognition and network evaluation can make the evaluation of network quality more targeted, thereby making the network quality more targeted. The results of network quality assessment are more accurate.

In a second aspect, the present application provides an evaluation device for network quality, the evaluation device comprising:

The acquisition module is used to acquire MR data and map data. The generating module is used for marking the target latitude and longitude information in the MR data in the map data to obtain the first map data. The recognition module is used for inputting the first map data into a preset scene recognition model to obtain a scene recognition result. The processing module is used to perform preliminary evaluation on the network according to the scene recognition result and the MR data, and obtain the preliminary evaluation result. The processing module is further configured to, if it is determined that the data volume of the MR data of the target scene in the scene recognition result is less than the preset threshold, the network quality evaluation device formulates a drive test plan according to the target scene, and sends it out, so that the relevant personnel can make a drive test plan according to the road test plan. Test plan for road test. The acquisition module is also used to acquire drive test data. The processing module is also used for comprehensively evaluating the network according to the drive test data and preliminary evaluation results, and generating an evaluation report.

In a third aspect, the present application provides an apparatus for evaluating network quality, including a processor, and when the apparatus for evaluating network quality runs, the processor executes computer execution instructions, so that the apparatus for evaluating network quality performs the above-mentioned network quality evaluation method.

In a fourth aspect, the present application provides a computer-readable storage medium, including instructions, which, when the instructions are executed on a computer, cause the computer to execute the above-mentioned method for evaluating network quality.

In a fifth aspect, the present application provides a computer program product, the computer program product includes instruction code, and the instruction code is used to execute the above-mentioned method for evaluating network quality.

It can be understood that any network quality evaluation device, computer-readable storage medium or computer program product provided above are all used to execute the method provided above. Therefore, the beneficial effects that can be achieved can be referred to above. The method and the beneficial effects of the corresponding solutions in the following specific implementation manner will not be repeated here.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present application more clearly, the following briefly introduces the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some of the drawings in the present application. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a schematic diagram of a hardware structure of a network quality evaluation apparatus provided by an embodiment of the present application;

2 is a schematic flowchart of a method for evaluating network quality provided by an embodiment of the present application;

3 is a schematic diagram 1 of a geographic scene provided by an embodiment of the present application;

FIG. 4 is a second schematic diagram of a geographic scene provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an apparatus for evaluating network quality according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or illustrations. Any embodiments or designs described in the embodiments of the present application as "exemplary" or "such as" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present the related concepts in a specific manner.

Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may expressly or implicitly include one or more of that feature. In the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

The rapid development of mobile Internet services and the commercial deployment of smart pipes have put forward higher demands on the refined operation of mobile Internet services, and operators are also facing new challenges in business-level operation management. Evaluating network quality can better carry out network planning and optimization, further create favorable network conditions for operator management, and at the same time, improve user experience.

Currently, commonly used network quality evaluation methods include MR evaluation, drive test evaluation, key performance indicator (key performance indication, KPI) evaluation and other methods. The principle of MR evaluation is that the terminal collects network data and reports the collected network data to the server. Then, the server performs network quality evaluation according to the network data, and generates a network quality evaluation result. The process of road test evaluation is that testers ride on relevant vehicles and use professional test instruments to test the entire road section to generate network quality evaluation results. The KPI evaluation principle is to collect key indicators, evaluate network quality, and generate network quality evaluation results.

It can be seen that when using the MR evaluation method to evaluate the network quality, the network quality is only evaluated based on the collected network data, and only the network data actively reported by the terminal is limited. When evaluating the network quality of the scenario where the few terminals are located, the evaluation result appears to be inaccurate. When using the drive test evaluation method to evaluate the network quality, a lot of manpower and material resources are required, and the efficiency is low. When using the KPI evaluation method to evaluate the network quality, it is the same as the MR evaluation method, and the evaluation results are also inaccurate.

In view of the above problems, the present application provides a network quality evaluation method and device, the evaluation method includes: the network quality evaluation device marks the target latitude and longitude information in the MR data into the map data to obtain the first map data, and according to The first map data identifies the scene corresponding to the MR data in the preset scene identification model. When it is determined that the amount of MR data corresponding to an identified scene is less than the preset threshold, a drive test plan is formulated for the scene, and at the same time, the network is preliminarily evaluated according to the scene identification result and the MR data, and a preliminary evaluation result is obtained. Finally, an evaluation report is generated according to the drive test data and the preliminary evaluation result, which can make the result of the network quality evaluation more accurate.

In a specific implementation, the device for evaluating network quality has the components shown in FIG. 1 . FIG. 1 is an apparatus for evaluating network quality provided by an embodiment of the present application, which may include a processor 102, where the processor 102 is configured to execute application code, thereby implementing the method for evaluating network quality in the present application.

The processor 102 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the programs of the present application. circuit.

As shown in FIG. 1 , the apparatus for evaluating network quality may further include a memory 103 . Wherein, the memory 103 is used for storing the application program code for executing the solution of the present application, and the execution is controlled by the processor 102 .

The memory 103 may be read-only memory (ROM) or other type of static storage device that can store static information and instructions, random access memory (RAM) or other type of static storage device that can store information and instructions It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disk storage, optical disk storage ( including compact discs, laser discs, compact discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or capable of carrying or storing desired program code in the form of instructions or data structures and capable of being stored by a computer any other medium taken, but not limited to this. The memory 103 may exist independently and be connected to the processor 102 through a bus. The memory 103 may also be integrated with the processor 102 .

As shown in FIG. 1 , the apparatus for evaluating network quality may further include a communication interface 101 , wherein the communication interface 101 , the processor 102 , and the memory 103 may be coupled to each other, for example, via a bus 104 . The communication interface 101 is used for information exchange with other devices, for example, information exchange between an evaluation apparatus supporting network quality and other devices.

It should be pointed out that the device structure shown in FIG. 1 does not constitute a limitation of the network quality evaluation device. In addition to the components shown in FIG. 1 , the network quality evaluation device may include more or more components than those shown in the figure. Fewer components, or a combination of certain components, or a different arrangement of components.

The following describes the network quality evaluation method provided by the embodiment of the present application with reference to the network quality evaluation apparatus shown in FIG. 1 and FIG. 2 .

FIG. 2 is a schematic flowchart of a method for evaluating network quality according to an embodiment of the present application. Referring to Figure 2, the network quality evaluation method includes the following steps.

201. The network quality evaluation apparatus acquires the measurement report MR data and the map data.

The MR data includes target longitude and latitude information and network quality information, the map data includes longitude and latitude information, and the target longitude and latitude information is a part of the longitude and latitude information.

Specifically, if it is determined that the network quality assessment needs to be performed on a certain area, the MR data reported by the terminal in the area is obtained, and the map is called to obtain the map data. Optionally, the map can be a satellite map.

202. The device for evaluating network quality marks the latitude and longitude information of the target in the map data to generate first map data.

Specifically, the network quality evaluation device queries and marks the point corresponding to the target latitude and longitude information in the map data, and generates the first map data. For example, at the t-th moment, there are 6 terminals in the target area that have reported MR data, which correspond to the 6 target latitude and longitude information. At this time, the network quality evaluation device searches the map data for the points corresponding to the 6 target latitude and longitude information. and make a mark.

203. The device for evaluating network quality inputs the first map data into a preset scene recognition model to obtain a scene recognition result.

Specifically, before inputting the first map data into the preset scene recognition model, a preset scene recognition model needs to be established first.

The process of establishing the preset scene recognition model includes: obtaining second map data by the network quality evaluation device. Wherein, the second map data includes label data, and the label data represents different scenes. After that, the device for evaluating the network quality generates a preset scene recognition model according to the second map data.

More specifically, the device for evaluating network quality obtains map data from the Internet, wherein the map data includes longitude and latitude information. After that, the map data is preprocessed by cutting according to the geographic location. Managers manually annotate the preprocessed map data, and mark the key scenes that need to be identified, such as buildings, waters, parks, scenic spots, etc., to generate second map data. After that, the network quality evaluation device learns the second map data to generate a preset scene recognition model.

For example, referring to FIG. 3 , the present application provides a schematic diagram of a geographic scene. The geographic scene includes roads 300 - 302 , end users 310 - 318 , buildings 320 , waters 330 , and scenic spots 340 . The road 300 is closest to the building 320 in distance, the road 301 is closest to the water area 330 in distance, and the road 302 is closest to the scenic spot 340 in distance. Therefore, road 300 and building 320 are divided into scene A and marked as building; road 301 and water area 330 are divided into scene B and marked as water area; road 302 and scenic spot 340 are divided into scene C and marked as scenic spot. In this way, the second map data is obtained.

For another example, referring to FIG. 4 , the present application provides another schematic diagram of a geographic scene. The geographic scene is a building scene corresponding to scene A in FIG. 3, including road 300, end users 310-313, and building 320, wherein the longitude range corresponding to scene A is (a1, a6), and the latitude range is (b1, b6). Then, when the network quality evaluation device learns the second map data, it can learn that the second map data includes scene A, the name of scene A is a building, the corresponding longitude range is (a1, a6), and the latitude range is (b1). , b6).

In this way, after the apparatus for evaluating network quality inputs the first map data into the preset scene recognition model, the scene recognition result can be obtained. For example, at time t, in scenario A shown in FIG. 4 , end users 310 - 313 report MR data. At this time, the target longitude and latitude in the MR data reported by the end user 310 is (a2, b4); the target longitude and latitude in the MR data reported by the end user 311 is (a3, b2); the target longitude and latitude in the MR data reported by the end user 312 is (a5, b3), and the target latitude and longitude in the MR data reported by the end user 313 is (a4, b5). And a2, a3, a5, a4 all belong to (a1, a6) range; b4, b2, b3, b5 all belong to (b1, b6) range. In this way, it can be recognized that the scenes where the end users 310-313 are located are all buildings.

204. The network quality evaluation device performs a preliminary evaluation on the network according to the MR data and the scene identification result, and obtains a preliminary evaluation result.

Specifically, the device for evaluating network quality first preprocesses the MR data, for example, deletes abnormal data, invalid data, and the like in the MR data. After that, a preliminary evaluation is performed on the network where the MR data is located according to the network quality information in the MR data, and a preliminary evaluation result is obtained.

205. If it is determined that the data amount of the MR data of the target scene is less than the preset threshold, formulate a drive test plan according to the target scene, and deliver it.

The target scene is any scene in the scene recognition result. The drive test solution includes the drive test scenario, the drive test route, and the data volume of the drive test data.

For example, scenario A in FIG. 3 includes terminal users 310-313, a total of 4 terminal users; scenario B includes terminal users 314-316, a total of 3 terminal users; scenario C includes terminal users 317-318, a total of 2 terminals user. If the size of the preset threshold is the size of 3 end users and the amount of MR data reported, it is determined that scene C is the target scene.

After that, a drive test plan is developed according to the target scene. For example, a drive test plan is formulated according to scenario C in FIG. 3, indicating that the drive test scene is the scenic spot 340, the drive test route is the road 302, and the data volume of the drive test data is MR data measured by 10 terminals.

206. The device for evaluating network quality acquires drive test data.

The drive test data is network data measured according to the drive test scheme.

Specifically, the network quality evaluation device will issue the developed drive test plan, instruct the relevant personnel to carry out the drive test according to the drive test plan, and upload the drive test data.

207. The network quality evaluation device comprehensively evaluates the network according to the drive test data and the preliminary evaluation result, and generates an evaluation report.

In the above solution, the device for evaluating network quality first marks the target latitude and longitude information of the acquired MR data into the map data to obtain the first map data. Then, the scene corresponding to the MR data is determined according to the first map data and the preset scene recognition model. When it is determined that the data amount of the MR data corresponding to a certain scene is less than the preset threshold, a drive test plan is formulated according to the scene and delivered. In order to facilitate the relevant personnel to carry out the drive test according to the drive test plan. At the same time, the network is preliminarily evaluated according to the scene recognition results and MR data, and the preliminary evaluation results are obtained. Finally, the network is comprehensively evaluated according to the preliminary evaluation results and the drive test data, and an evaluation report is obtained. In this way, when it is determined that the MR data of a key scene is insufficient, the drive test is used to supplement the data of the key scene, and the scheme of combining scene recognition and network evaluation can make the evaluation of network quality more targeted, thereby making the network quality more targeted. The results of network quality assessment are more accurate.

Further, in this application, only real-time drive tests are performed on key scenarios that require drive tests, so as to implement supplementary measurement for network quality assessment, which saves manpower and material resources while improving network quality assessment efficiency.

In this embodiment of the present application, the network quality evaluation apparatus may be divided into functional modules according to the foregoing method embodiments. For example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. middle. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that, the division of modules in the embodiments of the present application is schematic, and is only a logical function division, and there may be other division manners in actual implementation.

The present application provides an apparatus for evaluating network quality, and the apparatus for evaluating network quality is used to execute the steps in the method shown in FIG. 2 above. The apparatus for evaluating network quality provided in this embodiment of the present application may include modules corresponding to corresponding steps.

In the case where each functional module is divided according to each function, FIG. 5 shows a possible schematic structural diagram of the apparatus for evaluating network quality. As shown in FIG. 5 , the apparatus for evaluating network quality includes an acquisition module 51 , a generation module 52 , an identification module 53 , and a processing module 54 .

The acquiring module 51 is used for acquiring the measurement report MR data and the map data. The MR data includes target longitude and latitude information. For example, referring to FIG. 2 , the obtaining module 51 is configured to execute step 201 . The generating module 52 is configured to mark the latitude and longitude information of the target in the map data obtained by the obtaining module 51 to generate the first map data. For example, referring to FIG. 2 , the generating module 52 is configured to perform step 202 . The identification module 53 is configured to input the first map data generated by the generation module 52 into a preset scene identification model to obtain a scene identification result. For example, referring to FIG. 2 , the identification module 53 is configured to perform step 203 . The processing module 54 is configured to perform a preliminary evaluation on the network according to the MR data obtained by the obtaining module 51 and the scene recognition result obtained by the identification module 53 to obtain a preliminary evaluation result. For example, referring to FIG. 2 , the processing module 54 is configured to perform step 204 . The processing module 54 is further configured to formulate a drive test plan according to the target scene and deliver it if it is determined that the data amount of the MR data of the target scene is less than the preset threshold. The target scene is any scene in the scene recognition result. For example, referring to FIG. 2 , the processing module 54 is further configured to execute step 205 . The obtaining module 51 is further configured to obtain drive test data. The drive test data is the network data measured according to the drive test scheme. For example, referring to FIG. 2 , the obtaining module 51 is further configured to execute step 206 . The processing module 54 is further configured to comprehensively evaluate the network according to the drive test data obtained by the obtaining module 51 and the preliminary evaluation result, and generate an evaluation report. For example, referring to FIG. 2 , the processing module 54 is further configured to execute step 207 .

Optionally, the acquiring module 51 is further configured to acquire second map data. The second map data includes label data. Annotated data represent different scenarios. The generating module 52 is further configured to generate a preset scene recognition model according to the second map data acquired by the acquiring module 51 .

Optionally, the drive test solution includes a drive test scenario, a drive test route, and a data volume of drive test data.

Another embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium. When the instructions are executed on the network quality evaluation device, the network quality evaluation device performs as shown in FIG. 2 Steps in the network quality assessment method of the illustrated embodiment.

In another embodiment of the present application, a computer program product is also provided. The computer program product includes computer-executable instructions, and the computer-executable instructions are stored in a computer-readable storage medium; The computer-readable storage medium reads the computer-executable instructions, and the processor executes the computer-executable instructions to cause the apparatus for evaluating network quality to perform the steps in the method for evaluating network quality in the embodiment shown in FIG. 2 .

Wherein, all relevant contents of the steps involved in the above method embodiments can be cited in the functional descriptions of the corresponding functional modules, and the functions thereof will not be repeated here.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the modules, units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods may be implemented in other manners. For example, the device embodiments described above are merely illustrative, eg, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: read-only memory, English abbreviation: ROM), random access memory (full name in English: random access memory, English abbreviation: RAM), magnetic Various media that can store program codes, such as a disc or an optical disc.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (8)

1. A method for evaluating network quality, comprising:

acquiring measurement report MR data and map data; the MR data comprises target longitude and latitude information;

marking the longitude and latitude information of the target in the map data to generate first map data;

inputting the first map data into a preset scene recognition model to obtain a scene recognition result;

performing primary evaluation on the network according to the MR data and the scene recognition result to obtain a primary evaluation result;

if the data volume of the MR data of the target scene is smaller than a preset threshold value, a drive test scheme is formulated according to the target scene and issued; the target scene is any scene in the scene recognition result;

acquiring drive test data; the drive test data is network data measured according to the drive test scheme;

and comprehensively evaluating the network according to the drive test data and the preliminary evaluation result to generate an evaluation report.

2. The evaluation method according to claim 1, wherein before the first map data is input into a preset scene recognition model to obtain a scene recognition result, the evaluation method further comprises:

acquiring second map data; the second map data comprises marking data; the annotation data represents different scenes;

And generating the preset scene recognition model according to the second map data.

3. The evaluation method according to claim 1,

the drive test scheme includes a drive test scenario, a drive test route, and a data volume of drive test data.

4. An apparatus for evaluating network quality, comprising:

the acquisition module is used for acquiring measurement report MR data and map data; the MR data comprises target longitude and latitude information;

the generating module is used for marking the target longitude and latitude information in the map data acquired by the acquiring module to generate first map data;

the recognition module is used for inputting the first map data generated by the generation module into a preset scene recognition model to obtain a scene recognition result;

the processing module is used for carrying out preliminary evaluation on the network according to the MR data acquired by the acquisition module and the scene identification result acquired by the identification module to acquire a preliminary evaluation result;

the processing module is further used for making a drive test scheme according to the target scene and issuing the drive test scheme if the data volume of the MR data of the target scene is smaller than a preset threshold value; the target scene is any scene in the scene recognition result;

The acquisition module is also used for acquiring drive test data; the drive test data is network data measured according to the drive test scheme;

the processing module is further configured to perform comprehensive evaluation on the network according to the drive test data and the preliminary evaluation result obtained by the obtaining module, and generate an evaluation report.

5. The evaluation device of claim 4,

the acquisition module is further used for acquiring second map data; the second map data comprises marking data; the annotation data represents different scenes;

the generating module is further configured to generate the preset scene recognition model according to the second map data acquired by the acquiring module.

6. The evaluation device of claim 4,

the drive test scheme includes a drive test scenario, a drive test route, and a data volume of drive test data.

7. An evaluation apparatus of network quality, comprising a processor which executes computer-executable instructions to cause the evaluation apparatus of network quality to perform the evaluation method of network quality according to any one of claims 1 to 3 when the evaluation apparatus of network quality is operated.

8. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of assessing network quality of any one of claims 1-3.

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