CN109960650A - Application assessment method, apparatus, medium and electronic equipment based on big data - Google Patents
Application assessment method, apparatus, medium and electronic equipment based on big data Download PDFInfo
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- 238000011156 evaluation Methods 0.000 claims abstract description 65
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- 238000004590 computer program Methods 0.000 claims description 6
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- 238000012549 training Methods 0.000 claims description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
- G06F11/3608—Software analysis for verifying properties of programs using formal methods, e.g. model checking, abstract interpretation
Abstract
The invention belongs to big data technical fields, are related to a kind of application assessment method and device, electronic equipment and storage medium based on big data.This method comprises: obtaining the functional module to be assessed that the application program includes；Multiple evaluation indexes of the functional module to be assessed are determined according to the feature of the functional module to be assessed；It calculates and determines the corresponding each index parameter value of the multiple evaluation index；The score value of the functional module to be assessed is determined according to each index parameter value.The accuracy of application assessment can be improved in the technical solution of the embodiment of the present invention.
Description
Technical field
The present invention relates to big data technical fields, in particular to a kind of application assessment side based on big data
Method, device, medium and electronic equipment.
Background technique
With the rapid development of computer software technology, the type and quantity of computer applied algorithm are also exponentially increased.
In general, different functions may be implemented in different application programs, and be able to achieve the application program of same type function
Type is but very more.For example, mobile phone user is when selecting application program, the application program that same function can filter out is very
More, in the application shop of mobile phone, user can select according to the scoring of each application program, but application program is commented
Point is often determined by the subjective assessment of used user, and body when terminal device different can also cause user to use
It tests difference, and existing the assessment for function is lacked to application program scoring.Therefore, the evaluation system of existing application
Objectivity is lacked for the assessment of each application program, leads to scoring inaccuracy, reference value is not high.
It should be noted that information is only used for reinforcing the reason to background of the invention disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The embodiment of the present invention is designed to provide a kind of application assessment method based on big data, and then at least exists
The assessment inaccuracy to application program is overcome the problems, such as to a certain extent.
Other characteristics and advantages of the invention will be apparent from by the following detailed description, or partially by the present invention
Practice and acquistion.
According to a first aspect of the embodiments of the present invention, a kind of application assessment method based on big data is provided, is wrapped
It includes:
Obtain the functional module to be assessed that the application program includes；
Multiple evaluation indexes of the functional module to be assessed are determined according to the feature of the functional module to be assessed；
It calculates and determines the corresponding each index parameter value of the multiple evaluation index；
The score value of the functional module to be assessed is determined according to each index parameter value.
In a kind of example embodiment of the invention, it is described according to the feature of the functional module to be assessed determine it is described to
Multiple evaluation indexes of evaluation function module include:
The feature of the functional module to be assessed is inputted into machine learning model, according to the output of the machine learning model
Determine multiple evaluation indexes of the functional module to be assessed.
In a kind of example embodiment of the invention, the machine learning model includes:
The sample data of preset quantity is obtained, includes the multiple features and each feature of functional module in the sample data
Corresponding evaluation index；
Based on the sample data training machine learning model.
In a kind of example embodiment of the invention, the feature of the functional module to be assessed includes:
One or more of participation, acceptance, retention degree；
In a kind of example embodiment of the invention, it is described according to the feature of the functional module to be assessed determine it is described to
Multiple evaluation indexes of evaluation function module include:
Determine that the evaluation index is liveness contribution rate according to the participation；
Determine that the evaluation index is attainment rate according to the acceptance；
Determine that the evaluation index is to enliven growth rate according to the retention degree.
It is described that the function to be assessed is determined according to each index parameter value in a kind of example embodiment of the invention
The score value of module includes:
Obtain the output parameter of the functional module to be assessed；
The output parameter is calculated at a distance from the feature of the functional module to be assessed；
The weight that the evaluation index is determined according to the distance, to the corresponding index parameter value of each evaluation index into
Row weighted sum, to obtain the score value of the module to be assessed.
It is described that the function to be assessed is determined according to each index parameter value in a kind of example embodiment of the invention
The score value of module includes:
Logarithmic calculation is carried out to each index parameter value, each index parameter value after Logarithmic calculation is added and work
For the score value of the functional module to be assessed.
According to a second aspect of the embodiments of the present invention, a kind of application assessment device based on big data is provided, is wrapped
It includes:
Modular unit to be assessed is obtained, for obtaining functional module to be assessed；
Evaluation index unit is determined, for determining the function mould to be assessed according to the feature of the functional module to be assessed
Multiple evaluation indexes of block；
Computing unit determines the corresponding each index parameter value of the multiple evaluation index for calculating；
Determining sub-unit, for determining the score value of the functional module to be assessed according to each index parameter value.
According to a third aspect of the embodiments of the present invention, a kind of computer-readable medium is provided, computer is stored thereon with
Program realizes the application journey based on big data as described in first aspect in above-described embodiment when described program is executed by processor
Sequence appraisal procedure.
According to a fourth aspect of the embodiments of the present invention, a kind of electronic equipment is provided, comprising: one or more processors；
Storage device, for storing one or more programs, when one or more of programs are held by one or more of processors
When row, so that one or more of processors realize the application based on big data as described in first aspect in above-described embodiment
Program appraisal procedure.
Technical solution provided in an embodiment of the present invention can include the following benefits:
In the technical solution provided by some embodiments of the present invention, by obtaining the application program function to be assessed that includes
Energy module, multiple evaluation indexes of functional module to be assessed are determined according to the feature of functional module to be assessed, and it is multiple to calculate determination
The corresponding each index parameter value of evaluation index, the score value of functional module to be assessed is determined according to each index parameter value, can be with needle
To assessing each function in application program for property, to improve the accuracy of application program scoring and the property of can refer to；
At the same time it can also objectively be assessed, the reliability of application assessment is improved.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
It can the limitation present invention.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.It should be evident that the accompanying drawings in the following description is only the present invention
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.In the accompanying drawings:
Fig. 1 diagrammatically illustrates the process of the application assessment method based on big data of embodiment according to the present invention
Figure；
Fig. 2 diagrammatically illustrates the application assessment method based on big data according to another embodiment of the present invention
Flow chart；
Fig. 3 diagrammatically illustrates the frame of the application assessment device based on big data of embodiment according to the present invention
Figure；
Fig. 4 shows the structural schematic diagram for being suitable for the computer system for the electronic equipment for being used to realize the embodiment of the present invention.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the present invention will more
Fully and completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner
In example.In the following description, many details are provided to provide and fully understand to the embodiment of the present invention.However,
It will be appreciated by persons skilled in the art that technical solution of the present invention can be practiced without one or more in specific detail,
Or it can be using other methods, constituent element, device, step etc..In other cases, it is not shown in detail or describes known side
Method, device, realization or operation are to avoid fuzzy each aspect of the present invention.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or realized in one or more hardware modules or integrated circuit
These functional entitys, or these functional entitys are realized in heterogeneous networks and/or processor device and/or microcontroller device.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all content and operation/step,
It is not required to execute by described sequence.For example, some operation/steps can also decompose, and some operation/steps can close
And or part merge, therefore the sequence actually executed is possible to change according to the actual situation.
The embodiment of the present invention provides a kind of application assessment method based on big data first.As shown in Figure 1, this method
It may include step S110, S120, S130, S140.Wherein:
Step S110 obtains the functional module to be assessed that the application program includes；
Step S120 determines multiple assessments of the functional module to be assessed according to the feature of the functional module to be assessed
Index；
Step S130 is calculated and is determined the corresponding each index parameter value of the multiple evaluation index；
Step S140 determines the score value of the functional module to be assessed according to each index parameter value.
According to the application assessment method based on big data in this example embodiment, include by obtaining application program
Functional module to be assessed, multiple evaluation indexes of functional module to be assessed are determined according to the feature of functional module to be assessed, count
It calculates and determines the corresponding each index parameter value of multiple evaluation indexes, the score of functional module to be assessed is determined according to each index parameter value
Value, targetedly each function in application program can be assessed, thus improve application program scoring accuracy and
The property of can refer to；At the same time it can also objectively be assessed, the reliability of application assessment is improved.
In the following, Fig. 1 will be combined to each step of the application assessment method based on big data in this example embodiment
Suddenly it is described in more details.
Step S110 obtains the functional module to be assessed that the application program includes.
In this exemplary embodiment, application program can be executable file or computer program.Application program can be with
It runs on an operating system, interacts to realize certain function with user, can also have visual interface.For example,
Text editor, multimedia player, management system etc..This example embodiment does not do particular determination to this.Application program can be with
It is made of multiple functional modules.For example, wechat can be by functional modules such as transmission message function, reception message functions.It is to be assessed
Functional module can be the functional module assessed, for example, display function, phonetic function etc..
Step S120 determines multiple assessments of the functional module to be assessed according to the feature of the functional module to be assessed
Index.
The feature of functional module to be assessed can be used to indicate user to the service condition of the functional module to be assessed.Citing
For, the feature of functional module to be assessed can be participation, acceptance, retention degree etc..In this example embodiment, feature can be with
It is above-mentioned participation, acceptance, one or more in retention degree.Participation can indicate that user's registration uses this function
Degree, acceptance can be user to the program that receives of this function, and retention degree can be indicated in the process using the application program
Middle user still retains or participates in using this function.Certainly, according to the actual situation, feature with evaluation function module can also be with
It is other features, for example, liveness, conversion degree etc..
The evaluation index of functional module to be assessed can be determined according to the feature of functional module to be assessed.Each feature can be with
It determines an evaluation index, can also determine that an evaluation index or a feature determine multiple evaluation indexes with multiple features.
In this exemplary embodiment, it can determine that the evaluation index of participation is liveness contribution rate according to features described above, acceptance
Evaluation index is attainment rate, and the evaluation index of degree of retention is to enliven growth rate.Certainly, in a practical situation, evaluation index
It can also be other indexs, for example, probability of transaction, conversion ratio etc..
In this exemplary embodiment, evaluation index can also be determined using machine learning model, so that assessment refers to
Target determination is more intelligent.It, can be according to machine learning after the feature of functional module to be assessed is inputted machine learning model
The output result of model determines evaluation index.For example, inputting machine learning model, machine learning model for conversion degree as feature
Probability of transaction etc. can be exported.Further, machine learning model can be obtained by the training method of machine learning.Pass through acquisition
The sample data of preset quantity can use sample data training machine learning model.Sample data can be multiple groups feature with
The corresponding data of evaluation index.Each sample can be a feature and an evaluation index.The quantity of sample can basis
Actual conditions determine, for example, 1000,100 etc..
Step S130 is calculated and is determined the corresponding each index parameter value of the multiple evaluation index.
Index parameter value can be determined according to the calculating between feature.For example, for some application program, such as
Fruit participation character representation uses the number of users of the functional module, then the evaluation index of this feature is attainment rate, it should
The index parameter value of attainment rate can be number of users using the functional module divided by the download of the application program
Value.Index parameter value can also be according to the weight calculation of each evaluation index, for example, index parameter value × mesh of attainment rate
Mark the weight etc. of delivery rate.In addition, index parameter value can also be calculated with to counting method, the logarithmic formula used be may is thatWherein, xiIndex parameter value, max (x) are the corresponding Maximum Index parameter value of the evaluation index.When
So, index parameter value can also be determined by other methods, for example, carrying out operation between each feature, enliven the finger of growth rate
Mark parameter value can be acceptance divided by the value etc. of participation.
Step S140 determines the score value of the functional module to be assessed according to each index parameter value.
The score value of functional module to be assessed can be used to assess functional module to be assessed.Score value is higher can to indicate this
The functional module to be assessed of program to be applied is better.The score value of functional module to be assessed can pass through each index parameter value phase
Calais determines.It can certainly determine by other means, for example, each index parameter value is weighted and averaged, to each index
Parameter value is averaged.Further, each index parameter value can also be balanced with the method for Logarithmic calculation, is avoided
Gap between each index parameter value is excessive and influences score value.The formula of Logarithmic calculation may is that log2(1+a/max
(A)), wherein A is all index parameter values, one of them in all index parameter values of a.Or it can be by each
The method that index parameter value is weighted and averaged determines, the weight of available each evaluation index, to each evaluation index
Corresponding index parameter value multiplied by the evaluation index weight, then using the sum of product addition as functional module to be assessed
Score value.Therefore, step S210, S220, S230 are further comprised in this example embodiment.As shown in Figure 2, in which:
Step S210 obtains the output parameter of the functional module to be assessed.
Output parameter can be the output result of functional module to be assessed.The output result of functional module to be assessed can wrap
Feature is included, for example, retention degree, conclusion of the business degree etc., turnover etc., also may include the corresponding numerical value of each feature, for example, to be evaluated
Estimate the output parameter of functional module are as follows: " retention degree is 0.5 ".
Step S220 calculates the output parameter at a distance from the feature of the functional module to be assessed.
Output parameter can use distance computation techniques at a distance from the feature of functional module to be assessed to calculate.For example,
Euclidean distance, mahalanobis distance etc..It can be using output parameter as feature, to calculate output ginseng using Euclidean distance calculation method
Several similarities with the feature of functional module to be assessed, as output parameter is at a distance from feature.Can also by output parameter with
Feature is converted into vector, using the result of multiplication of vectors as output parameter at a distance from feature.It is of course also possible to its other party
Method, for example, output parameter are as follows: retention degree, the feature of functional module to be assessed are as follows: participation, then can use formula:Distance is calculated, wherein x is output parameter retention degree, and y is participation.
Step S230 determines the weight of the evaluation index according to the distance.
The feature of functional module to be assessed can be used as function to be assessed at a distance from the output parameter of functional module to be assessed
The weight of the corresponding evaluation index of feature of energy module.In step S220, each functional module to be assessed can be calculated
Feature is at a distance from above-mentioned output parameter, so that it is determined that the weight of each evaluation index out.
The device of the invention embodiment introduced below can be used for executing the above-mentioned application journey based on big data of the present invention
Sequence appraisal procedure.As shown in figure 3, the application assessment device 300 based on big data includes:
Modular unit 310 to be assessed is obtained, for obtaining functional module to be assessed；
Evaluation index unit 320 is determined, for determining the function to be assessed according to the feature of the functional module to be assessed
Multiple evaluation indexes of energy module；
Computing unit 330 determines the corresponding each index parameter value of the multiple evaluation index for calculating；
Determining sub-unit 340, for determining the score of the functional module to be assessed according to each index parameter value
Value.
Due to example embodiments of the present invention the application assessment device based on big data each functional module with
The step of example embodiment of the above-mentioned application assessment method based on big data, is corresponding, therefore apparatus of the present invention are implemented
Undisclosed details in example please refers to the embodiment of the above-mentioned application assessment method based on big data of the present invention.
Below with reference to Fig. 4, it illustrates the computer systems 400 for the electronic equipment for being suitable for being used to realize the embodiment of the present invention
Structural schematic diagram.The computer system 400 of electronic equipment shown in Fig. 4 is only an example, should not be to the embodiment of the present invention
Function and use scope bring any restrictions.
As shown in figure 4, computer system 400 includes central processing unit (CPU) 401, it can be read-only according to being stored in
Program in memory (ROM) 402 or be loaded into the program in random access storage device (RAM) 403 from storage section 408 and
Execute various movements appropriate and processing.In RAM 403, it is also stored with various programs and data needed for system operatio.CPU
401, ROM 402 and RAM 403 is connected with each other by bus 404.Input/output (I/O) interface 405 is also connected to bus
404。
I/O interface 405 is connected to lower component: the importation 406 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 407 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 408 including hard disk etc.；
And the communications portion 409 of the network interface card including LAN card, modem etc..Communications portion 409 via such as because
The network of spy's net executes communication process.Driver 410 is also connected to I/O interface 405 as needed.Detachable media 411, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 410, in order to read from thereon
Computer program be mounted into storage section 408 as needed.
Particularly, according to an embodiment of the invention, may be implemented as computer above with reference to the process of flow chart description
Software program.For example, the embodiment of the present invention includes a kind of computer program product comprising be carried on computer-readable medium
On computer program, which includes the program code for method shown in execution flow chart.In such reality
It applies in example, which can be downloaded and installed from network by communications portion 409, and/or from detachable media
411 are mounted.When the computer program is executed by central processing unit (CPU) 401, executes and limited in the system of the application
Above-mentioned function.
It should be noted that computer-readable medium shown in the present invention can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the present invention, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In invention, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc. are above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Being described in unit involved in the embodiment of the present invention can be realized by way of software, can also be by hard
The mode of part realizes that described unit also can be set in the processor.Wherein, the title of these units is in certain situation
Under do not constitute restriction to the unit itself.
As on the other hand, present invention also provides a kind of computer-readable medium, which be can be
Included in electronic equipment described in above-described embodiment；It is also possible to individualism, and without in the supplying electronic equipment.
Above-mentioned computer-readable medium carries one or more program, when the electronics is set by one for said one or multiple programs
When standby execution, so that the electronic equipment realizes such as the above-mentioned application assessment method as described in the examples based on big data.
For example, the electronic equipment may be implemented as shown in Figure 1: step S110 obtains the application package
The functional module to be assessed contained；Step S120 determines the function mould to be assessed according to the feature of the functional module to be assessed
Multiple evaluation indexes of block；Step S130 is calculated and is determined the corresponding each index parameter value of the multiple evaluation index；Step
S140 determines the score value of the functional module to be assessed according to each index parameter value.
For another example, each step as shown in Figure 2 may be implemented in the electronic equipment.
It should be noted that although being referred to several modules or list for acting the equipment executed in the above detailed description
Member, but this division is not enforceable.In fact, embodiment according to the present invention, it is above-described two or more
Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould
The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the present invention
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, touch control terminal or network equipment etc.) executes embodiment according to the present invention
Method.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to of the invention its
Its embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
Person's adaptive change follows general principle of the invention and including the undocumented common knowledge in the art of the present invention
Or conventional techniques.The description and examples are only to be considered as illustrative, and true scope and spirit of the invention are by following
Claim is pointed out.
It should be understood that the present invention is not limited to the precise structure already described above and shown in the accompanying drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present invention is limited only by the attached claims.
Claims (10)
1. a kind of application assessment method based on big data characterized by comprising
Obtain the functional module to be assessed that the application program includes；
Multiple evaluation indexes of the functional module to be assessed are determined according to the feature of the functional module to be assessed；
It calculates and determines the corresponding each index parameter value of the multiple evaluation index；
The score value of the functional module to be assessed is determined according to each index parameter value.
2. the application assessment method according to claim 1 based on big data, which is characterized in that described according to
The feature of functional module to be assessed determines that multiple evaluation indexes of the functional module to be assessed include:
The feature of the functional module to be assessed is inputted into machine learning model, is determined according to the output of the machine learning model
Multiple evaluation indexes of the functional module to be assessed.
3. the application assessment method according to claim 2 based on big data, which is characterized in that the machine learning
Model includes:
The sample data of preset quantity is obtained, multiple features comprising functional module and each feature are corresponding in the sample data
Evaluation index；
Based on the sample data training machine learning model.
4. the application assessment method according to claim 1 based on big data, which is characterized in that the function to be assessed
Can the feature of module include:
One or more of participation, acceptance, retention degree.
5. the application assessment method according to claim 4 based on big data, which is characterized in that described according to
The feature of functional module to be assessed determines that multiple evaluation indexes of the functional module to be assessed include:
Determine that the evaluation index is liveness contribution rate according to the participation；
Determine that the evaluation index is attainment rate according to the acceptance；
Determine that the evaluation index is to enliven growth rate according to the retention degree.
6. the application assessment method according to claim 1 based on big data, which is characterized in that described according to
Each index parameter value determines that the score value of the functional module to be assessed includes:
Obtain the output parameter of the functional module to be assessed；
The output parameter is calculated at a distance from the feature of the functional module to be assessed；
The weight that the evaluation index is determined according to the distance adds the corresponding index parameter value of each evaluation index
Power summation, to obtain the score value of the module to be assessed.
7. the application assessment method according to claim 1 based on big data, which is characterized in that described according to
Each index parameter value determines that the score value of the functional module to be assessed includes:
Logarithmic calculation is carried out to each index parameter value, the sum that each index parameter value after Logarithmic calculation is added is as institute
State the score value of functional module to be assessed.
8. a kind of application assessment device based on big data characterized by comprising
Modular unit to be assessed is obtained, for obtaining functional module to be assessed；
Evaluation index unit is determined, for determining the functional module to be assessed according to the feature of the functional module to be assessed
Multiple evaluation indexes；
Computing unit determines the corresponding each index parameter value of the multiple evaluation index for calculating；
Determining sub-unit, for determining the score value of the functional module to be assessed according to each index parameter value.
9. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is executed by processor
Application assessment method based on big data of the Shi Shixian as described in any one of claims 1 to 7.
10. a kind of electronic equipment characterized by comprising
One or more processors；
Storage device, for storing one or more programs, when one or more of programs are by one or more of processing
Device execute when so that one or more of processors realize as described in any one of claims 1 to 7 based on big data
Application assessment method.
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| CN113807717A (en) * | 2021-09-23 | 2021-12-17 | 深圳市易平方网络科技有限公司 | Application program function evaluation method and device, terminal equipment and storage medium |
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