CN111885485B - Network quality assessment method and device - Google Patents
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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
Technical Field
The embodiment of the application relates to the technical field of communication, in particular to a method and a device for evaluating network quality.
Background
The evaluation result of the network quality can guide the work of network planning, network optimization and the like. Currently, a commonly used network quality evaluation method is Measurement Report (MR) evaluation, and the principle is that a terminal acquires network data and reports the acquired network data to a server. Then, the server carries out network quality evaluation according to the network data to generate a network quality evaluation result.
It can be seen that, when the network quality is evaluated by using the MR evaluation method, the network quality is evaluated only by aiming at the acquired network data, and only the network data actively reported by the terminal is limited, so that when the network quality of the scene where a small number of terminals are located is evaluated according to the network data acquired by a small number of terminals, the evaluation result is inaccurate.
Disclosure of Invention
The application provides a method and a device for evaluating network quality, which solve the problem of inaccurate evaluation of the network quality.
In a first aspect, the present application provides a method for evaluating network quality, which is applied to a device for evaluating network quality, and includes: the evaluation device of the network quality firstly acquires MR data and map data, and marks the longitude and latitude information of a target in the MR data in the map data to obtain first map data. And then, the evaluation device of the network quality inputs the first map data into a preset scene recognition model to obtain a scene recognition result, and performs preliminary evaluation on the network according to the scene recognition result and the MR data to obtain a preliminary evaluation result. And if the data volume of the MR data of the target scene in the scene recognition result is smaller than the preset threshold value, the evaluation device of the network quality formulates a drive test scheme according to the target scene and issues the drive test scheme so that relevant personnel can carry out drive test according to the drive test scheme. And finally, the evaluation device of the network quality acquires the drive test data, comprehensively evaluates the network according to the drive test data and the preliminary evaluation result, and generates an evaluation report.
In the scheme, the evaluation device of the network quality firstly marks the target longitude and latitude information of the acquired MR data into the map data to obtain the first map data. And then determining a scene corresponding to the MR data according to the first map data and a preset scene recognition model, when determining that the data volume of the MR data corresponding to a certain scene is smaller than a preset threshold value, formulating a drive test scheme according to the scene and issuing the drive test scheme so that relevant personnel can carry out drive test according to the drive test scheme. And meanwhile, performing primary evaluation on the network according to the scene recognition result and the MR data to obtain a primary evaluation result. And finally, comprehensively evaluating the network according to the preliminary evaluation result and the drive test data to obtain an evaluation report. Therefore, when the MR data of a certain key scene is determined to be insufficient, the data of the key scene is supplemented by using the drive test, and the evaluation of the network quality can be more targeted by the scheme of combining scene identification and network evaluation, so that the evaluation result of the network quality is more accurate.
In a second aspect, the present application provides an evaluation apparatus for network quality, the evaluation apparatus comprising:
and the acquisition module is used for acquiring the MR data and the map data. And the generating module is used for marking the target longitude and latitude information in the MR data in the map data to obtain first map data. And the recognition module is used for inputting the first map data into a preset scene recognition model to obtain a scene recognition result. And the processing module is used for carrying out preliminary evaluation on the network according to the scene recognition result and the MR data to obtain a preliminary evaluation result. And 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 in the scene recognition result is smaller than the preset threshold value, so that related personnel can carry out drive test according to the drive test scheme. The acquisition module is further used for acquiring the drive test data. And the processing module is also used for comprehensively evaluating the network according to the drive test data and the preliminary evaluation result to generate an evaluation report.
In a third aspect, the present application provides an apparatus for evaluating network quality, including a processor, where when the apparatus for evaluating network quality operates, the processor executes computer-executable instructions to cause the apparatus for evaluating network quality to execute the method for evaluating network quality as described above.
In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method for evaluating network quality as described above.
In a fifth aspect, the present application provides a computer program product comprising instruction codes for performing the method for evaluating network quality as described above.
It is understood that any one of the above-mentioned evaluation apparatuses for network quality, computer-readable storage media or computer program products is used to execute the above-mentioned methods, and therefore, the beneficial effects achieved by the above-mentioned methods and the beneficial effects of the corresponding schemes in the following embodiments are referred to and will not be described herein again.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic hardware structure diagram of an apparatus for evaluating network quality according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for evaluating network quality according to an embodiment of the present application;
fig. 3 is a first schematic diagram of a geographic scene provided in an embodiment of the present application;
fig. 4 is a schematic diagram of a geographic scene provided in 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 Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, 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 mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.
The rapid development of mobile internet services and the commercial deployment of intelligent pipelines provide higher requirements for the refined operation of the mobile internet services, and operators face new challenges for the operation management of service levels. The network quality can be better evaluated, network planning and optimization work can be better carried out, good network conditions can be further created for management of operators, and meanwhile user experience can be improved.
Currently, commonly used network quality evaluation methods include MR evaluation, drive test evaluation, Key Performance Indicator (KPI) evaluation, and the like. The MR evaluation principle is that a terminal acquires network data and reports the acquired network data to a server. Then, the server carries out network quality evaluation according to the network data to generate a network quality evaluation result. The process of the drive test evaluation is that a tester takes a relevant vehicle and tests the whole road section by using a professional test instrument to generate a network quality evaluation result. The KPI evaluation principle is to collect key indexes, evaluate the network quality and generate a network quality evaluation result.
It can be seen that, when the network quality evaluation is performed by using the MR evaluation method, the network quality is evaluated only by aiming at the acquired network data, and only the network data actively reported by the terminal is limited, so that when the network quality of a scene where a small number of terminals are located is evaluated according to the network data acquired by the small number of terminals, the evaluation result is inaccurate. When the network quality evaluation is performed by using a drive test evaluation mode, a large amount of manpower and material resources are consumed, and the efficiency is low. When the KPI evaluation method is used to evaluate the network quality, the evaluation result is not accurate as in the MR evaluation method.
In view of the above problems, the present application provides a method and an apparatus for evaluating network quality, where the method includes: the evaluation device of the network quality marks the target longitude and latitude information in the MR data to the map data to obtain first map data, and identifies a scene corresponding to the MR data in a preset scene identification model according to the first map data. And when the data volume of the MR data corresponding to a certain identified scene is determined to be smaller than a preset threshold value, a drive test scheme is formulated for the scene, and meanwhile, the network is preliminarily evaluated according to the scene identification result and the MR data to obtain a preliminary evaluation result. And finally, an evaluation report is generated according to the drive test data and the preliminary evaluation result, so that the result of the network quality evaluation can be more accurate.
In a specific implementation, the network quality assessment device has the components shown in fig. 1. Fig. 1 is a device for evaluating network quality according to an embodiment of the present application, and the device may include a processor 102, where the processor 102 is configured to execute application program codes, so as to implement a method for evaluating network quality according to 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 ics for controlling the execution of programs in accordance with the present disclosure.
As shown in fig. 1, the network quality evaluation apparatus may further include a memory 103. The memory 103 is used for storing application program codes for executing the scheme of the application, and the processor 102 controls the execution.
The memory 103 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, 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 disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 103 may be a separate device and is connected to the processor 102 via a bus. Memory 103 may also be integrated with processor 102.
As shown in fig. 1, the network quality assessment apparatus 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 interaction with other devices, for example, information interaction between an evaluation apparatus supporting network quality and other devices.
It is noted that the device structure shown in fig. 1 does not constitute a limitation of the network quality evaluation means, which may comprise more or less components than those shown in fig. 1, or a combination of some components, or a different arrangement of components, in addition to those shown in fig. 1.
The following describes a method for evaluating network quality provided by an embodiment of the present application with reference to fig. 2 in conjunction with an evaluation apparatus for network quality shown in fig. 1.
Fig. 2 is a schematic flowchart of a method for evaluating network quality according to an embodiment of the present disclosure. Referring to fig. 2, the method for evaluating the network quality includes the following steps.
201. The network quality evaluation device acquires measurement report MR data and map data.
The MR data comprises target longitude and latitude information and network quality information, the map data comprises longitude and latitude information, and the target longitude and latitude information belongs to one part of the longitude and latitude information.
Specifically, if it is determined that network quality evaluation needs to be performed on a certain area, the MR data reported by the terminal in the area is acquired, and a map is called to acquire map data. Alternatively, the map may be a satellite map.
202. The network quality evaluation device marks the longitude and latitude information of the target in the map data to generate first map data.
Specifically, the network quality evaluation device queries and marks points corresponding to the target longitude and latitude information in the map data to generate first map data. For example, at the t-th time, if 6 terminals in the target area report MR data, the MR data correspond to 6 pieces of target longitude and latitude information, and at this time, the network quality evaluation device queries points corresponding to the 6 pieces of target longitude and latitude information in the map data and makes a mark.
203. And the evaluation device of the network quality inputs the first map data into a preset scene recognition model to obtain a scene recognition result.
Specifically, before the first map data is input into the preset scene recognition model, the preset scene recognition model needs to be established first.
The process of establishing the preset scene recognition model comprises the following steps: the network quality evaluation device acquires second map data. The second map data comprises marking data, and the marking data represent different scenes. And then, the network quality evaluation device generates a preset scene recognition model according to the second map data.
More specifically, the network quality evaluation device acquires map data from the internet, wherein the map data includes latitude and longitude information. And then, preprocessing such as cutting map data according to the geographic position. And the manager manually marks the preprocessed map data, marks key scenes needing to be identified, such as scenes of buildings, water areas, parks, scenic spots and the like, and generates second map data. And then, 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, water 330, and scenic spots 340. Where the road 300 is closest in distance to the building 320, the road 301 is closest in distance to the water 330, and the road 302 is closest in distance to the scenic spot 340. Thus, the road 300 and building 320 are divided into scene a, labeled building; dividing the road 301 and the water area 330 into a scene B, and marking as a water area; the road 302 and the scenic spot 340 are divided into scenes C, labeled scenic spots. Thus, second map data is obtained.
For another example, referring to FIG. 4, another schematic diagram of a geographic scene is provided. The geographic scene is a building scene corresponding to the scene a in fig. 3, and includes a road 300, an end user 310, 313 and a building 320, where the longitude range and the latitude range of the scene a are (a1, a6) and (b1, b 6). When the evaluation device of the network quality learns the second map data, it can learn that the second map data includes the scene a, the name of the scene a is a building, the corresponding longitude range is (a1, a6), and the corresponding latitude range is (b1, b 6).
Thus, the network quality evaluation device can obtain the scene recognition result after inputting the first map data into the preset scene recognition model. For example, at time t, in scenario a shown in fig. 4, the end user 310 reports MR data 313. At this time, the target longitude and latitude in the MR data reported by the end user 310 is (a2, b 4); the target longitude and latitude in the MR data reported by the end user 311 is (a3, b 2); the target longitude and latitude in the MR data reported by the end user 312 are (a5, b3), and the target longitude and latitude in the MR data reported by the end user 313 are (a4, b 5). A2, a3, a5 and a4 all belong to the range of (a1 and a 6); b4, b2, b3 and b5 all belong to the range of (b1 and b 6). Thus, it can be identified that the end users 310 and 313 are all located in the building.
204. And the evaluation device of the network quality performs preliminary evaluation on the network according to the MR data and the scene recognition result to obtain a preliminary evaluation result.
Specifically, the evaluation device of the network quality performs preprocessing on the MR data, for example, deletes abnormal data, invalid data, and the like in the MR data. And then, performing preliminary evaluation on the network where the MR data is located according to the network quality information in the MR data to obtain a preliminary evaluation result.
205. And if the data volume of the MR data of the target scene is smaller than a preset threshold value, making a drive test scheme according to the target scene and issuing the drive test scheme.
And the target scene is any scene in the scene recognition result. The drive test scheme includes a drive test scenario, a drive test route, and a data volume of the drive test data.
For example, scenario a in fig. 3 includes end user 310 and 313, for a total of 4 end users; scenario B includes 3 end users including end user 314 and 316; scenario C includes 2 end users including end user 317 and 318. And if the size of the preset threshold is 3 terminal users and the size of the data volume of the reported MR data, determining the scene C as a target scene.
And then, making a drive test scheme according to the target scene. For example, a drive test scheme is formulated according to scenario C in fig. 3, which indicates that the drive test scenario is a scenic spot 340, the drive test route is a road 302, and the data volume of the drive test data is MR data measured by 10 terminals.
206. The evaluation device of the network quality acquires drive test data.
And the drive test data is network data measured according to the drive test scheme.
Specifically, the network quality evaluation device issues the formulated drive test scheme, instructs related personnel to carry out drive test according to the drive test scheme, and uploads drive test data.
207. And the network quality evaluation device comprehensively evaluates the network according to the drive test data and the preliminary evaluation result to generate an evaluation report.
In the above scheme, the network quality evaluation device first labels the target longitude and latitude information of the acquired MR data to the map data to obtain the first map data. And then determining a scene corresponding to the MR data according to the first map data and a preset scene recognition model, when determining that the data volume of the MR data corresponding to a certain scene is smaller than a preset threshold value, formulating a drive test scheme according to the scene and issuing the drive test scheme so that relevant personnel can carry out drive test according to the drive test scheme. And simultaneously, performing primary evaluation on the network according to the scene recognition result and the MR data to obtain a primary evaluation result. And finally, comprehensively evaluating the network according to the preliminary evaluation result and the drive test data to obtain an evaluation report. Therefore, when the MR data of a certain key scene is determined to be insufficient, the data of the key scene is supplemented by using the drive test, and the evaluation of the network quality can be more targeted by the scheme of combining scene identification and network evaluation, so that the evaluation result of the network quality is more accurate.
Furthermore, only the key scenes needing the drive test are subjected to real-time drive test in the method, so that the supplementary measurement of network quality evaluation is realized, and the manpower and material resources are saved while the evaluation efficiency of the network quality is improved.
In the embodiment of the present application, the functional modules of the network quality evaluation apparatus may be divided according to the above 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. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
The present application provides an apparatus for evaluating network quality, which is used for executing the steps in the method shown in fig. 2. The device for evaluating network quality provided by the embodiment of the application can comprise modules corresponding to the corresponding steps.
Fig. 5 shows a schematic diagram of a possible structure of the network quality evaluation device in the case of dividing the functional modules according to the functions. As shown in fig. 5, the network quality evaluation device includes an acquisition module 51, a generation module 52, an identification module 53, and a processing module 54.
An obtaining module 51, configured to obtain measurement report MR data and map data. The MR data comprises longitude and latitude information of the target. 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 target longitude and latitude information in the map data acquired by the acquiring module 51, and generate first map data. For example, referring to FIG. 2, the generation module 52 is configured to perform step 202. And the recognition module 53 is configured to input the first map data generated by the generation module 52 into a preset scene recognition model, so as to obtain a scene recognition result. For example, referring to FIG. 2, the identification module 53 is configured to perform step 203. And the processing module 54 is configured to perform a preliminary evaluation on the network according to the MR data acquired by the acquisition module 51 and the scene recognition result acquired by the recognition module 53, so as 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, if it is determined that the data amount of the MR data of the target scene is smaller than the preset threshold, formulate a drive test scheme according to the target scene, and issue the drive test scheme. 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 perform step 205. The obtaining module 51 is further configured to obtain drive test data. The drive test data is network data measured according to a 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 perform comprehensive evaluation on the network according to the drive test data and the preliminary evaluation result acquired by the acquisition module 51, and generate an evaluation report. For example, referring to FIG. 2, the processing module 54 is further configured to perform step 207.
Optionally, the obtaining module 51 is further configured to obtain the second map data. The second map data includes label data. The annotation data represents different scenes. 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 scheme includes a drive test scenario, a drive test route, and a data volume of the drive test data.
Another embodiment of the present application further provides a computer-readable storage medium, in which instructions are stored, and when the instructions are executed on an evaluation apparatus of network quality, the evaluation apparatus of network quality executes the steps in the evaluation method of network quality according to the embodiment shown in fig. 2.
In another embodiment of the present application, there is also provided a computer program product comprising computer executable instructions stored in a computer readable storage medium; the processor of the network quality assessment apparatus may read the computer-executable instructions from the computer-readable storage medium, and the processor executes the computer-executable instructions to cause the network quality assessment apparatus to execute the steps in the network quality assessment method according to the embodiment shown in fig. 2.
All relevant contents of the steps related to the above method embodiments may be referred to the functional description of the corresponding functional module, and the functions thereof are not described herein again.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art would appreciate that the various illustrative modules, elements, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall 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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