CN113905400B - Network optimization processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
According to the network optimization processing method, the network optimization processing device, the electronic equipment and the storage medium, the networking use records of the pre-selected application programs on the user terminal are obtained; determining perception indexes corresponding to each pre-selected application program at different times and different base stations according to the time information and the access base station information in the networking use record; determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score; optimizing a processing network according to the network optimizing instruction; the method and the device determine the influence of the operator network on the user perception by acquiring the networking use record of the application program, improve the accuracy of the user perception evaluation and realize the effective optimization of the network.
Description
Technical Field
The present application relates to the internet technologies, and in particular, to a network optimization processing method, a device, an electronic device, and a storage medium.
Background
With the development of the internet and mobile terminals, the variety, number and functions of mobile phone Application programs (App) are explosive growth, and capacity impact is brought to an operator network, so how to guarantee the perception of using App on the operator network under high pressure becomes an important means for the operation Shang Wei to save the user.
In the prior art, the fluency of a mobile phone App is generally collected and analyzed according to an evaluation system provided by a developer of the mobile phone App, and then the mobile phone App is determined to be optimized according to the fluency of the mobile phone App.
However, in the method, the perception situation of the mobile phone App is counted from the perspective of a developer of the mobile phone App, the end-to-end influence is not evaluated, the method is not combined with an operator network, so that the perception evaluation of the user is inaccurate, the network cannot be effectively optimized, and further the user loss is caused.
Disclosure of Invention
The application provides a network optimization processing method, a network optimization processing device, electronic equipment and a storage medium.
In a first aspect, the present application provides a network optimization processing method, including: acquiring a networking use record of a preselected application program on a user terminal; determining perception indexes corresponding to each pre-selected application program at different times and different base stations according to the time information and the access base station information in the networking use record; determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score; and optimizing and processing the network according to the network optimizing instruction.
In other optional embodiments, before the acquiring the networking usage record of the pre-selected application program on the user terminal, the method further includes: acquiring the types of all application programs on a user terminal; clustering all application programs according to the types to obtain clustering clusters; and determining the application program with the highest use frequency in each cluster as the pre-selected application program.
In other optional embodiments, before determining, according to the perception index and the preset weight, a single perception score corresponding to each pre-selected application program at different times and different base stations, the method further includes: acquiring historical networking data of a preselected application program on a user terminal; and processing the historical networking data by adopting a multiple regression algorithm to acquire the preset weight.
In other optional embodiments, before determining, according to the perception index and the preset weight, a single perception score corresponding to each pre-selected application program at different times and different base stations, the method further includes: normalizing the perception indexes; the determining, according to the perception index and the preset weight, a single perception score corresponding to each pre-selected application program at different times and different base stations includes: and determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the normalized perception indexes and preset weights.
In other alternative embodiments, the method further comprises: and determining an application program perception score according to the single perception score so that an application program developer optimizes the application program according to the application program perception score.
In other alternative embodiments, the method further comprises: and determining a base station perception score according to the single perception score so that an operator optimizes the base station according to the base station perception score.
In other optional embodiments, the perceptual indicator comprises at least one of: network rate, delay, and number of jams.
In a second aspect, the present application provides a network optimization processing device, including: the data acquisition module is used for acquiring networking use records of the pre-selected application programs on the user terminal; the first processing module is used for determining the corresponding perception index of each pre-selected application program at different time and different base stations according to the time information and the access base station information in the networking use record; the second processing module is used for determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; the third processing module is used for determining a user perception score according to the single perception score and outputting a corresponding network optimization instruction according to the user perception score; and the fourth processing module is used for optimizing and processing the network according to the network optimization instruction.
In a third aspect, the present application provides an electronic device comprising: at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of any one of the preceding claims.
In a fourth aspect, the present application provides a readable storage medium having stored therein computer-executable instructions which, when executed by a processor, implement a method as claimed in any preceding claim.
According to the network optimization processing method, the network optimization processing device, the electronic equipment and the storage medium, the networking use records of the pre-selected application programs on the user terminal are obtained; determining perception indexes corresponding to each pre-selected application program at different times and different base stations according to the time information and the access base station information in the networking use record; determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score; optimizing a processing network according to the network optimizing instruction; the method and the device determine the influence of the operator network on the user perception by acquiring the networking use record of the application program, improve the accuracy of the user perception evaluation and realize the effective optimization of the network.
Drawings
FIG. 1 is a schematic diagram of a network architecture on which the present application is based;
fig. 2 is a schematic flow chart of a network optimization processing method provided by the application;
FIG. 3 is a schematic flow chart of another network optimization method according to the present application;
FIG. 4 is a schematic diagram of a clustering process according to the present application;
FIG. 5 is an exploded view of a user perception score provided by the present application;
fig. 6 is a schematic structural diagram of a network optimization processing device provided by the present application;
fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application examples.
With the development of the internet and the mobile terminal, the variety, the number and the functions of the mobile phone App are explosive growth, and capacity impact is brought to an operator network, so that how to ensure the perception of using the App on the operator network by a user under high pressure becomes an important means for the operation Shang Wei to save the user.
In the prior art, the fluency of the mobile phone App is generally collected and analyzed according to an evaluation system provided by a developer of the mobile phone App, and then how to optimize the mobile phone App is determined according to the fluency of the mobile phone App, that is, the prior art counts the perception situation of the mobile phone App from the perspective of an App developer, and has no end-to-end influence of evaluation, and is not combined with an operator network, so that the perception evaluation of a user is inaccurate, the network cannot be effectively optimized, and further the user loss is caused.
Aiming at the problems, the technical concept of the application is that the network utilization record of the mobile phone App when the mobile phone App is accessed to the network base station of the operator is obtained, the perception index of the mobile phone App is determined according to the network utilization record, further the perception score of the user is determined, and the network optimization instruction is output according to the perception score of the user, so that the network of the operator is effectively optimized.
Fig. 1 is a schematic diagram of a network architecture according to the present application, as shown in fig. 1, where one network architecture provided by the present application includes a plurality of mobile terminals 1 and a server 2, where a plurality of application programs are installed on the mobile terminals 1, and the server 2 obtains a network usage record of the application program on the mobile terminals 1, so as to execute a network optimization processing method described in each of the following embodiments.
In a first aspect, an embodiment of the present application provides a network optimization processing method, and fig. 2 is a schematic flow chart of the network optimization processing method provided by the present application.
As shown in fig. 2, the network optimization processing method includes:
step 101, acquiring a networking usage record of a pre-selected application program on a user terminal.
Specifically, a plurality of apps are installed on a mobile terminal held by a user, and in order to improve the optimization efficiency, apps with higher frequency of use can be selected as pre-selected application programs. Optionally, the networking usage record includes: user identification information, app identification information, base station information, time information and the like, wherein the user identification information can be a user identity card number or a user terminal identification and the like; the App identification information may be a platform identification corresponding to App, etc.; the base station information is the identification of the base station accessed by the user terminal in the application process.
In this embodiment, user ticket data, user plane data, network management data, and the like may be obtained from operator network side devices, such as a big data platform and a user perceived quality (Service & Experience Quality, abbreviated as SEQ) platform, and networking usage records corresponding to users and corresponding apps may be screened out.
Step 102, determining the corresponding perception index of each pre-selected application program at different time and different base stations according to the time information and the access base station information in the networking use record.
Specifically, the networking usage records include App usage time information and access base station information, and user perception indexes can be determined according to the networking usage records screened by each platform. Optionally, the perceptual indicator includes at least one of: network rate, time delay and number of jams, in general, the faster the network rate, the shorter the time delay, the fewer the number of jams, the better the mobile App perception.
Step 103, determining a single perception score corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and the preset weights.
Specifically, when different time periods and access base stations are different, the influence weights of the sensing indexes (network rate, time delay and cartoon times) on the mobile phone App sensing are different, the influence weights can be set according to experience of a person skilled in the art, and then the sum of products of the sensing indexes and the corresponding influence weights can be determined as a single sensing score, that is, the single sensing score can be used for representing the sensing score of a certain user in a certain base station using a certain App in a certain time period.
As an alternative embodiment, before step 103, the method further includes: acquiring historical networking data of a preselected application program on a user terminal; and processing the historical networking data by adopting a multiple regression algorithm to acquire the preset weight.
Specifically, the preset weight may be set according to experience of a person skilled in the art, and preferably, the preset weight may be determined according to historical networking data of the user, that is, by acquiring historical networking data of each application program, where the historical networking data may include data such as user identification information, app identification information, each sensing index (network rate, time delay, katon), and historical sensing score; and then, adopting a multiple regression algorithm to process the historical networking data, and determining the functional relation between the historical perception score and each perception index, namely determining the preset weight of each perception index.
As an alternative embodiment, after step 102, further includes: normalizing the perception indexes; step 103 comprises: and determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the normalized perception indexes and preset weights.
Specifically, considering that each sensing index (such as network rate, delay, blocking, etc.) has different units and has a large magnitude difference, normalization processing is required to be performed on each sensing index to eliminate the difference of units and magnitudes between the sensing indexes. Optionally, normalization processing is performed on each perception index in a Log function conversion mode, and then a single perception score is determined by using the normalized perception index and preset weights.
Step 104, determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score.
And 105, optimizing the processing network according to the network optimizing instruction.
Specifically, the user perception score is a user's perception of all pre-selected applications, i.e., a single perception score for all pre-selected applications on a user terminal. When the user perception score is higher, the user perception is higher, and the network optimization is not needed by the operator network; when the user perception score is lower, the fact that the user perception is poor at the moment is indicated, a network optimization instruction can be output at the moment, and the network is optimized according to the network optimization instruction, such as expanding capacity of a base station accessed by the user.
As an alternative embodiment, the method further comprises: and determining an application program perception score according to the single perception score so that a user optimizes the application program according to the application program perception score.
Specifically, the application perception score is the sum of single perception scores of all base stations accessed by a certain application in a preset time period, and according to the application perception score, a developer of the application can determine whether to optimize the application, how to optimize the application, and the like.
As an alternative embodiment, the method further comprises: and determining a base station perception score according to the single perception score so that a user optimizes the base station according to the base station perception score.
Specifically, the base station perception score is the sum of single perception scores of apps of a certain base station accessed to the base station in a preset time period, and according to the base station perception score, operators can perform capacity expansion and other processing on the base station according to the base station perception score.
In summary, the embodiment obtains the user perception comprehensively and accurately by obtaining the single perception score, and then determining the perception scores of three dimensions of the user perception, the application perception and the base station perception according to the single perception score, thereby realizing the effective optimization of the network.
According to the network optimization processing method provided by the embodiment of the application, the networking use record of the preselected application program on the user terminal is obtained; determining perception indexes corresponding to each pre-selected application program at different times and different base stations according to the time information and the access base station information in the networking use record; determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score; optimizing a processing network according to the network optimizing instruction; in the embodiment of the application, the influence of the operator network on the user perception is determined by acquiring the networking use record of the application program, so that the accuracy of the user perception evaluation is improved, and the effective optimization of the network is realized.
With reference to the foregoing embodiments, fig. 3 is a schematic flow chart of another network optimization processing method provided by the present application, and as shown in fig. 3, the network optimization processing method includes:
step 201, the types of all application programs on the user terminal are obtained.
And 202, clustering all application programs according to the types to obtain clusters.
Step 203, determining the application program with the highest use frequency in each cluster as the pre-selected application program.
Step 204, obtaining a networking usage record of the pre-selected application program on the user terminal.
Step 205, determining the corresponding perception index of each pre-selected application program at different time and different base stations according to the time information and the access base station information in the networking usage record.
And 206, determining a single perception score corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and the preset weights.
Step 207, determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score.
Step 208, optimizing the processing network according to the network optimizing instruction.
The implementation manners of step 204, step 205, step 206, step 207 and step 208 in this embodiment are similar to those of step 101, step 102, step 103, step 104 and step 105 in the foregoing embodiments, respectively, and are not described herein.
Different from the foregoing embodiment, in this embodiment, in order to improve the optimization efficiency, the types of all the applications on the user terminal are acquired; clustering all application programs according to the types to obtain clustering clusters; and determining the application program with the highest use frequency in each cluster as the pre-selected application program.
In particular, there are various types of applications, such as sports, social, video, live, etc. In this embodiment, the type of the application program may be determined by acquiring the characteristic label of the application program, then clustering all application programs by using a clustering algorithm, such as a k-means clustering algorithm (k-means), to obtain a plurality of clusters, selecting the application program with the highest frequency in each cluster to determine the application program as the preselected application program, and then performing the processing of step 204-step 207, which is not described herein again.
The method shown in the above embodiment will be described in detail by way of specific examples with reference to fig. 4 and 5.
Firstly, the types and networking data records of all application programs on a user terminal are acquired, and then clustering processing is carried out to determine the pre-selected application programs. FIG. 4 is a schematic diagram of a clustering process according to the present application, and as shown in FIG. 4, it is assumed that A is installed on a mobile phone held by a user 1 -A n N Apps in total can obtain C after the k-means clustering algorithm 1 -C K K clusters are used, and then, according to the networking data record, the App with the highest frequency in each cluster is determined as a preselected application program, namely, K preselected application programs, which can be called as K observation objects, can be obtained.
Then, according to the networking use record, determining the perception index of each observation object in the K observation objects in the operator network, wherein the perception index comprises a network rate v, a time delay tau and a katon s; and then respectively carrying out normalization processing on the network rate v, the time delay tau and the katon s according to formulas (1), (2) and (3), wherein the normalization processing is as follows:
wherein v is * 、τ * 、s * Respectively representing the network rate, time delay and blocking after normalization processing.
And substituting the normalized perception indexes into a perception score model to obtain a single perception score, wherein the perception score model can be determined by performing multiple regression calculation on the historical networking data of the application program, and can be shown by a reference formula (4):
wherein p is i Indicating that a user in a base station uses the perception score of the ith App for a certain time, b i 、c i 、d i Respectively represent corresponding preset weights, a 0 Is a constant deviation, determined by a historical networking data multiple regression algorithm.
Then, a user perceived score, an application perceived score, and a base station perceived score are determined from the single perceived score. Fig. 5 is a schematic diagram illustrating the decomposition of user perception scores according to the present application, as shown in fig. 5, the user perception may be decomposed to a specific time period of each base station corresponding to each App used by the user for statistics, and the following formula (5) may be referred to:
wherein B represents a base station, j represents all base stations accessed by a user U; a represents an application program, and l represents the number of all apps corresponding to the user terminal; t represents the using time period of the App, epsilon represents the number of time periods of using various Apps on each access base station; t (T) t Representing the duration of different time periods, T U Representing the total duration of all time periods.
Similar to the user awareness algorithm, application A will be I Perception scoreThe statistics of the specific time period from the solution to each base station corresponding to each user using the App can be referred to as formula (6), as follows:
where w represents the total number of users using the App.
Similar to the user perception algorithm, base station B Y The statistics are performed after the perception decomposition is performed on the specific time period corresponding to each App used by each user, and the following formula (7) can be referred to:
and finally, optimizing the network according to the user perception score, the application program perception score and the base station perception score.
Based on the foregoing embodiment, the types of all the applications on the user terminal are obtained; clustering all application programs according to the types to obtain clustering clusters; and determining the application program with the highest use frequency in each cluster as the pre-selected application program, thereby realizing the improvement of the optimization efficiency.
In a second aspect, an example of the present application provides a network optimization processing device, and fig. 6 is a schematic structural diagram of the network optimization processing device provided by the present application, as shown in fig. 6, where the network optimization processing device includes:
a data acquisition module 10, configured to acquire a networking usage record of a pre-selected application program on a user terminal; the first processing module 20 is configured to determine, according to the time information and the access base station information in the network usage record, a perception index corresponding to each pre-selected application program at different times and different base stations; a second processing module 30, configured to determine a single perception score corresponding to each pre-selected application program at different times and different base stations according to the perception index and a preset weight; a third processing module 40, configured to determine a user perception score according to the single perception score, and output a corresponding network optimization instruction according to the user perception score; a fourth processing module 50, configured to optimize a processing network according to the network optimization instruction.
In other alternative embodiments, the data acquisition module 10 is further configured to: acquiring the types of all application programs on a user terminal; clustering all application programs according to the types to obtain clustering clusters; and determining the application program with the highest use frequency in each cluster as the pre-selected application program.
In other alternative embodiments, the data acquisition module 10 is further configured to: acquiring historical networking data of a preselected application program on a user terminal; and processing the historical networking data by adopting a multiple regression algorithm to acquire the preset weight.
In other alternative embodiments, the second processing module 30 is further configured to: normalizing the perception indexes; and determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the normalized perception indexes and preset weights.
In other alternative embodiments, the third processing module 40 is further configured to: and determining an application program perception score according to the single perception score so that an application program developer optimizes the application program according to the application program perception score.
In other alternative embodiments, the third processing module 40 is further configured to: and determining a base station perception score according to the single perception score so that an operator optimizes the base station according to the base station perception score.
In other optional embodiments, the perceptual indicator comprises at least one of: network rate, delay, and number of jams.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and corresponding beneficial effects of the network optimization processing device described above may refer to the corresponding process in the foregoing method example, which is not described herein again.
The network optimization processing device provided by the embodiment of the application is used for acquiring the networking use record of the preselected application program on the user terminal through the data acquisition module; the first processing module is used for determining the corresponding perception index of each pre-selected application program at different time and different base stations according to the time information and the access base station information in the networking use record; the second processing module is used for determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights; the third processing module is used for determining a user perception score according to the single perception score and outputting a corresponding network optimization instruction according to the user perception score; the fourth processing module is used for optimizing and processing the network according to the network optimization instruction; in the embodiment of the application, the influence of the operator network on the user perception is determined by acquiring the networking use record of the application program, so that the accuracy of the user perception evaluation is improved, and the effective optimization of the network is realized.
In a third aspect, an example of the present application provides an electronic device, and fig. 7 is a schematic hardware structure of the electronic device provided by the present application, as shown in fig. 7, including:
at least one processor 701 and a memory 702.
In a specific implementation process, at least one processor 701 executes computer-executable instructions stored in the memory 702, so that the at least one processor 701 executes the network optimization processing method as described above, where the processor 701 and the memory 702 are connected through the bus 703.
The specific implementation process of the processor 701 can be referred to the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
In the embodiment shown in fig. 7, it should be understood that the processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, digital signal processors (english: digital Signal Processor, abbreviated as DSP), application specific integrated circuits (english: application Specific Integrated Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise high speed RAM memory or may further comprise non-volatile storage NVM, such as at least one disk memory.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.
In a fourth aspect, the present application also provides a readable storage medium, where computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the above network optimization processing method is implemented.
The above-described readable storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may reside as discrete components in a device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (6)
1. A network optimization processing method, comprising:
acquiring a networking usage record of a preselected application program on a user terminal, wherein the networking usage record comprises usage time information and access base station information of the preselected application program;
determining the corresponding perception index of each pre-selected application program at different times and different base stations according to the using time information and the access base station information of the pre-selected application program;
determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights;
determining a user perception score according to the single perception score, and outputting a corresponding network optimization instruction according to the user perception score;
optimizing a processing network according to the network optimizing instruction;
before the network usage record of the pre-selected application program on the user terminal is obtained, the method further comprises:
acquiring the types of all application programs on a user terminal;
clustering all application programs according to the types to obtain clustering clusters;
determining the application program with highest use frequency in each cluster as the pre-selected application program;
before determining the single perception score corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and the preset weights, the method further comprises:
acquiring historical networking data of a preselected application program on a user terminal, wherein the historical networking data comprises all perception indexes and historical perception scores;
processing the historical networking data by adopting a multiple regression algorithm to acquire the preset weight;
before determining the single perception score corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and the preset weights, the method further comprises:
normalizing the perception indexes;
the determining, according to the perception index and the preset weight, a single perception score corresponding to each pre-selected application program at different times and different base stations includes:
determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the normalized perception indexes and preset weights;
the method further comprises the steps of: and determining a base station perception score according to the single perception score so that an operator optimizes the base station according to the base station perception score.
2. The method according to claim 1, wherein the method further comprises: and determining an application program perception score according to the single perception score so that an application program developer optimizes the application program according to the application program perception score.
3. The method of claim 1, wherein the perceptual indicator comprises at least one of: network rate, delay, and number of jams.
4. A network optimization processing device, comprising:
the system comprises a data acquisition module, a network access module and a network access module, wherein the data acquisition module is used for acquiring a networking usage record of a preselected application program on a user terminal, and the networking usage record comprises usage time information and access base station information of the preselected application program;
the first processing module is used for determining the corresponding perception index of each pre-selected application program at different time and different base stations according to the using time information and the access base station information of the pre-selected application program;
the second processing module is used for determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the perception indexes and preset weights;
the third processing module is used for determining a user perception score according to the single perception score and outputting a corresponding network optimization instruction according to the user perception score;
the fourth processing module is used for optimizing and processing the network according to the network optimization instruction;
the data acquisition module is further configured to: acquiring the types of all application programs on a user terminal; clustering all application programs according to the types to obtain clustering clusters; determining the application program with highest use frequency in each cluster as the pre-selected application program;
the data acquisition module is further configured to: acquiring historical networking data of a preselected application program on a user terminal, wherein the historical networking data comprises all perception indexes and historical perception scores; processing the historical networking data by adopting a multiple regression algorithm to acquire the preset weight;
the second processing module is further configured to: normalizing the perception indexes; determining single perception scores corresponding to each pre-selected application program at different times and different base stations according to the normalized perception indexes and preset weights;
the third processing module is further configured to: and determining a base station perception score according to the single perception score so that an operator optimizes the base station according to the base station perception score.
5. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the method of any one of claims 1 to 3.
6. A readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the method of any one of claims 1 to 3.
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