CN110472885A - A kind of website assessment system and its working method - Google Patents
A kind of website assessment system and its working method Download PDFInfo
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Abstract
The application provides a kind of website assessment system and its working method, is related to website assessment technology field, the working method of the website assessment system is the following steps are included: step S1, obtains the web site features data of website to be assessed；Preset evaluation index is put into the building tree-shaped map of index system in tree-shaped diagram generator by step S2；Wherein, every layer of the tree-shaped map of index system represents the rank of evaluation index；Step S3 establishes Rating Model and weight for each node of the tree-shaped map of index system；Web site features data are sent into the tree-shaped map of index system by step S4, obtain the appraisal result of tree-shaped each node of map of index system；Step S5, according to the overall score of the appraisal result of tree-shaped each node of map of index system and weight calculation website.The application can carry out automatic scoring to web site features data, participate in, score high-efficient without artificial, and scoring accuracy is high.
Description
Technical field
This application involves website assessment technology field more particularly to a kind of website assessment systems and its working method.
Background technique
Currently, with the fast development of internet big data, more and more websites are built to, in order to help setting for website
Meter person or manager understand the status of website operation, then need to assess website, by analyzing assessment result design
Person optimizes and improves to website, the final development for promoting website itself, therefore, the accuracy and high efficiency pair of website assessment
Website designer and manager are highly important.
It is existing that scoring is carried out by the way of manually scoring to website, it is counted by acquiring a large amount of artificial score data
The overall score of website is calculated, which takes time and effort, and score inefficiency, and the effect that scores is poor, there is artificial malice and scores
Hidden danger, lead to final appraisal result inaccuracy.
Summary of the invention
The application's is designed to provide a kind of website assessment system and its working method, can carry out to web site features data
Automatic scoring is participated in without artificial, is scored high-efficient, and scoring accuracy is high.
In order to achieve the above objectives, the application provides a kind of working method of website assessment system, comprising the following steps: step
S1 obtains the web site features data of website to be assessed；Preset evaluation index is put into tree-shaped diagram generator and constructs by step S2
The tree-shaped map of index system；Wherein, every layer of the tree-shaped map of index system represents the rank of evaluation index；Step S3 is index
Each node of the tree-shaped map of system establishes Rating Model and weight；Web site features data are sent into index system by step S4
In tree-shaped map, the appraisal result of tree-shaped each node of map of index system is obtained；Step S5, according to index system dendrogram
Compose the appraisal result of each node and the overall score of weight calculation website.
As above, wherein step S3 includes: step S310, is each section in each layer of the tree-shaped map of index system
One node weights Q of point buildingTn；Step S320 constructs index system dendrogram according to website spy's vector matrix and node weights
Compose the primary Rating Model of interior joint；Step S330 optimizes primary Rating Model using loss function.
As above, wherein step S310 includes following sub-step:
Step S311: Assessment of Important number of the acquisition multiple groups to each node in all layers of the tree-shaped map of index system
According to；
Step S312: the corresponding node importance value a of each node in each layer of the tree-shaped map of parameter systemTn, meter
It is as follows to calculate formula:
Wherein, aTnIndicate the node importance value of tree-shaped T layers of map of n-th of the node of index system, mnIndicate index body
It is the unessential data volume of n-th of node of T layers of tree-shaped map, mtIndicate tree-shaped T layers of map of all sections of index system
The unessential data volume summation of point, hnIndicate the important data volume of tree-shaped T layers of map of n-th of the node of index system, htIt indicates
The important data volume summation of all nodes of T layers of index system tree-shaped map；
Step S313, to node importance value aTnFurther weighted sum, each node of the tree-shaped map of parameter system
Corresponding node weights QTn, eliminate number differences bring error in each rank；Calculation formula is as follows:
Wherein, QTnIndicate the node weights of n-th of node in tree-shaped T layers of map of index system.
As above, wherein primary Rating Model is as follows:
Wherein, XijIndicate web site features vector matrix；N indicates the group number of all input web site features data；B indicates deviation
Amount；f(x)
Indicate that primary Rating Model, act indicate grade form.
As above, wherein optimize the Rating Model after primary Rating Model is optimized, calculation formula using loss function
It is as follows:
Wherein, F (x) is the Rating Model after optimization, QTn-1For the node weights of (n-1)th node in T layers；θ is represented
Study rate parameter；F (x) indicates the primary Rating Model optimized without loss function；It indicates loss function L (x)
Local derviation is asked to the node weights of (n-1)th node in T layers.
As above, wherein the calculation method of loss function is as follows:
By feature PiWith feature PjIt is respectively fed in primary Rating Model, primary Rating Model is to feature PiWith feature PjPoint
Assessment score R is not calculatediAnd Rj, feature PiWith feature PjTrue score be respectively SiAnd Sj；
The calculation formula of loss function is as follows:
L (x)=L1+L2；
Wherein, L (x) is total loss function, L1For the value of first-loss function；L2For the value of the second loss function；
Work as Si< Sj, and Ri>Rj；Or Si>Sj, and Ri<RjWhen；
As above, wherein further include step S340, is one layer weight of each layer building of the tree-shaped map of index system
wT。
As above, wherein step S340 includes following sub-step:
Step S341 calculates the corresponding layer importance value q of each layerT。
Wherein, qTIndicate tree-shaped T layers of map of the layer importance value of index system, bTIndicate tree-shaped T layers of map of index system
Unessential data volume, btIndicate unessential data volume summation in all layers of the tree-shaped map of index system, gTIndicate index system
T layers of important data volume of tree-shaped map, gtIndicate data volume summation important in all layers of the tree-shaped map of index system.
Step S342, to layer importance value qTFurther weighted sum calculates the corresponding layer weight w of each layerT, eliminate at different levels
Not middle number differences bring error；Calculation formula is as follows:
Wherein, wTIndicate tree-shaped T layers of map of the layer weight of index system.
As above, wherein include following sub-step in step S5:
Step S501 calculates the total score of each layer of all nodes, and calculation formula is as follows:
Wherein, yTIndicate the total score of the tree-shaped T layers of all nodes of map of index system；N indicates each
The number of node layer；α is parameter, value be 1 into n any one number；QαIndicate the weight of the α node；XαIndicate α
The scoring of node；
Step S502 calculates all layers of overall score, and calculation formula is as follows:
Wherein, F indicates the overall score of all layers of the tree-shaped map of index system；T indicates that index system is tree-shaped
The number of plies of map；β is parameter, β value be 1 into T any one number；wβIndicate β layers of weight；yβIndicate that β layers comment
Point.
The application also provides a kind of website assessment system, comprising: handling module, the website for obtaining website to be assessed are special
Levy data；Tree-shaped diagram generator constructs the tree-shaped map of index system according to preset evaluation index；Weight generation module, to refer to
Each node of the tree-shaped map of mark system establishes node weights；Every layer for the tree-shaped map of index system is established layer weight；
Rating Model generation module establishes Rating Model for each node of the tree-shaped map of index system；Index system tree
Shape map includes multilayer, and every layer includes multiple nodes；Scoring obtains module, for obtaining the appraisal result of each node, according to
The appraisal result of each node obtains the overall score of website；Data memory module, for storing web site features data, assessment refers to
The data such as mark, Rating Model, weighted data and appraisal result.
What the application realized has the beneficial effect that:
(1) the application effectively identifies website performance situation, improves website and assesses efficiency.
(2) the application calls different evaluation index standards for different types of website, improves the accuracy of scoring.
(3) then the application is commented using the weight calculation primary Rating Model after optimization using loss function optimization primary
Sub-model optimized after Rating Model, improve the accuracy of scoring.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The some embodiments recorded in application can also be obtained according to these attached drawings other attached for those skilled in the art
Figure.
Fig. 1 is a kind of flow chart of the working method of website assessment system of the present invention.
Fig. 2 is the sub-step flow chart that the present invention establishes Rating Model.
Fig. 3 is a kind of schematic diagram of website assessment system of the present invention.
Specific embodiment
Below with reference to the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Ground description, it is clear that described embodiment is some embodiments of the present application, instead of all the embodiments.Based on the application
In embodiment, those skilled in the art's every other embodiment obtained without making creative work, all
Belong to the range of the application protection.
Embodiment one
A kind of working method of website assessment system, comprising the following steps:
Step S1 obtains the web site features data of website to be assessed；
The contents such as picture, icon or the text information of webpage are obtained, stores into text variable, filters out inessential letter
Breath obtains web site features data, carries out dimension-reduction treatment to web site features data.
The Web page image obtained is inputted, gray level image is converted to Web page image；Using s igmoid activation primitive to defeated
The image entered is normalized, and normalizes to [0,1].
The type feature of website is obtained to distinguish the type of website, to determine the type of assessment object, and then is selected different
Index system score different types of website.The type of website includes: that government, education, business etc. are different types of
Website, obtains the type feature of website to be assessed, and the type feature of website includes that web site name, domain name, the ICP number of putting on record, ICP recognize
Card, electronic mark number and database I D etc..
Preset evaluation index is put into the building tree-shaped map of index system in tree-shaped diagram generator, wherein refer to by step S2
Every layer of the tree-shaped map of mark system represents the rank of evaluation index；Every layer of the tree-shaped map of index system includes multiple nodes,
Each specific evaluation content of node on behalf.
It is trained using web site features data, to obtain the tree-shaped map of index system.
The level of the tree-shaped map of index system include information disclose, online service, public participation, user satisfaction and the world
The indexs such as change.
1st grade of information disclose including nodal information have: active public information, open, Policy Interpretation and data according to application
It is open etc.；The nodal information that 2nd grade of online service includes has: convenience service, hommization etc.；The section that 3rd level public participation includes
Point information has: online interview, mailbox channel and will of the people collection etc.；The nodal information that 4th grade of user satisfaction includes includes self-service
Service, intelligent retrieval, search engine etc., the 5th grade of nodal information for including that internationalizes have trade information, foreign affairs service and interaction body
It tests.
Web site features data are classified, web site features data are sent into the tree-shaped map of index system, web site features
Data are respectively corresponded according to the difference of rank to score in T layers that are sent into the tree-shaped map of index system, and T indicates index system
The number of plies of tree-shaped map.
Step S3 establishes Rating Model and weight for each node of the tree-shaped map of index system；
Multiple groups web site features data are input in the tree-shaped map of index system and are trained, Rating Model is constructed.
Constructing Rating Model includes following sub-step:
Web site features data are converted to website spy's vector matrix X by step S300ij。
Step S310 is that each node constructs a node weights Q in each layer of the tree-shaped map of index systemTn。
Specifically, step S310 includes:
Step S311: Assessment of Important number of the acquisition multiple groups to each node in all layers of the tree-shaped map of index system
According to.
Step S312: the corresponding node importance value a of each node in each layer of the tree-shaped map of parameter systemTn, meter
It is as follows to calculate formula:
Wherein, aTnIndicate the node importance value of tree-shaped T layers of map of n-th of the node of index system, mnIndicate index body
It is the unessential data volume of n-th of node of T layers of tree-shaped map, mtIndicate tree-shaped T layers of map of all sections of index system
The unessential data volume summation of point, hnIndicate the important data volume of tree-shaped T layers of map of n-th of the node of index system, htIt indicates
The important data volume summation of all nodes of T layers of index system tree-shaped map.
Step S313, to node importance value aTnFurther weighted sum, each node of the tree-shaped map of parameter system
Corresponding node weights QTn, eliminate number differences bring error in each rank；Calculation formula is as follows:
Wherein, QTnIndicate n-th node in tree-shaped T layers of map of index system
Node weights.
Step S320, according to the primary of website spy's vector matrix and the node weights building tree-shaped map interior joint of index system
Rating Model:
Wherein, XijIndicate web site features vector matrix；QTnIndicate n-th node in tree-shaped T layers of map of index system
Node weights, N indicate the group number of all input web site features data；B indicates departure；F (x) indicates primary Rating Model, act
Indicate grade form.
Step S330 optimizes primary Rating Model using loss function.
Step S330 includes following sub-step:
Step S331: loss function is calculated.
Each data in first group are assessed by Rating Model, assessment score are obtained, for the number in first group
According to, if assessment score result it is different from actual result, generate first-loss function.
By feature PiWith feature PjIt is respectively fed in primary Rating Model, primary Rating Model is to feature PiWith feature PjPoint
Assessment score R is not calculatediAnd Rj, feature PiWith feature PjTrue score be respectively SiAnd Sj。
The calculation formula of loss function is as follows:
L (x)=L1+L2；
Wherein, L (x) is total loss function, L1For the value of first-loss function；L2For the value of the second loss function.
Work as Si< Sj, and Ri>Rj；Or Si>Sj, and Ri<RjWhen；
Step S332: optimize the Rating Model after primary Rating Model is optimized using loss function.
Calculation formula is as follows:
Wherein, F (x) is the Rating Model after optimization, QTn-1For (n-1)th node in T layers of index system tree-shaped map
Node weights；θ represents Study rate parameter；F (x) indicates the primary Rating Model optimized without loss function；
Indicate that loss function seeks local derviation to the node weights of (n-1)th node in T layers.
By the Rating Model that the Rating Model F (x) after loss function optimizes is final as each node.
Between step S310 and step S320 further include:
Step S340 is one layer weight w of each layer building of the tree-shaped map of index systemT。
Step S340 includes following sub-step:
Step S341, the corresponding layer importance value q of each layer of the tree-shaped map of parameter systemT。
Wherein, qTIndicate tree-shaped T layers of map of the layer importance value of index system, bTIndicate tree-shaped T layers of map of index system
Unessential data volume, btIndicate unessential data volume summation in all layers of the tree-shaped map of index system, gTIndicate index system
T layers of important data volume of tree-shaped map, gtIndicate data volume summation important in all layers of the tree-shaped map of index system.
Step S342, to layer importance value qTFurther weighted sum, each layer of the tree-shaped map of parameter system are corresponding
Layer weight wT, eliminate number differences bring error in each rank；Calculation formula is as follows:
Wherein, wTIndicate T layers of layer weight.
Web site features data are sent into the tree-shaped map of index system by step S4, and it is each to obtain the tree-shaped map of index system
The appraisal result of a node；
Web site features data are sent in the tree-shaped map of index system, index system tree is obtained according to Rating Model F (x)
The scoring of shape map T n-th of node of layer.
Step S5, according to the general comment of the appraisal result of tree-shaped each node of map of index system and weight calculation website
Point.
Include following sub-step in step S5:
Step S501, the total score of all nodes of each layer of the tree-shaped map of parameter system, calculation formula are as follows:
Wherein, yTIndicate the total score of the tree-shaped T layers of all nodes of map of index system；N indicates index
The number of each node layer of the tree-shaped map of system；α is parameter, value be 1 into n any one number；QαIndicate the α node
Weight；XαIndicate the scoring of the α node.
Step S502, the overall score of all layers of the tree-shaped map of parameter system, calculation formula are as follows:
Wherein, F indicates the overall score of all layers of the tree-shaped map of index system；T indicates that index system is tree-shaped
The number of plies of map；β is parameter, β value be 1 into T any one number；qβIndicate β layers of weight；yβIndicate that β layers comment
Point.
Embodiment two
A kind of website assessment system 100, comprising:
Handling module 10, for obtaining the web site features data of website to be assessed；
Tree-shaped diagram generator 20 constructs the tree-shaped map of index system according to preset evaluation index；
Weight generation module 30 establishes node weights for each node of the tree-shaped map of index system；For index system tree
Every layer of shape map establishes layer weight；
Rating Model generation module 40 establishes Rating Model for each node of the tree-shaped map of index system；Index system
Tree-shaped map includes multilayer, and every layer includes multiple nodes；
Scoring obtains module 50 and is obtained for obtaining the appraisal result of each node according to the appraisal result of each node
Take the overall score of website；
Data memory module 60, for storing web site features data, evaluation index, Rating Model, weighted data and scoring
The data such as a result；
Handling module 10 is mainly used for replicating the characteristic element of website, acquires picture, icon, the website name of website
The data such as title, domain name, the ICP number of putting on record, electronic mark number and database ID.
Website assessment system further include:
Judgment module 70 judges the Type of website according to web site features data；
Evaluation index transfers module 80, for calling different evaluation indexes according to the different Type of website.
Website assessment system further include:
Data processing unit handles web site features data, and web site features data are converted to website spy's moment of a vector
Battle array Xij。
What the application realized has the beneficial effect that:
(1) the application effectively identifies website performance situation, improves website and assesses efficiency.
(2) the application calls different evaluation index standards for different types of website, improves the accuracy of scoring.
(3) then the application is commented using the weight calculation primary Rating Model after optimization using loss function optimization primary
Sub-model optimized after Rating Model, improve the accuracy of scoring.
One embodiment of the present invention has been described in detail above, but the content is only preferable implementation of the invention
Example, should not be considered as limiting the scope of the invention.It is all according to all the changes and improvements made by the present patent application range
Deng should all fall within the scope of the patent of the present invention.
Claims (10)
1. a kind of working method of website assessment system, which comprises the following steps:
Step S1 obtains the web site features data of website to be assessed；
Preset evaluation index is put into the building tree-shaped map of index system in tree-shaped diagram generator by step S2；Wherein, index body
It is every layer of tree-shaped map and represents the rank of evaluation index；
Step S3 establishes Rating Model and weight for each node of the tree-shaped map of index system；
Web site features data are sent into the tree-shaped map of index system by step S4, obtain each section of the tree-shaped map of index system
The appraisal result of point；
Step S5, according to the overall score of the appraisal result of tree-shaped each node of map of index system and weight calculation website.
2. working method according to claim 1, which is characterized in that step S3 includes:
Step S310 is that each node constructs a node weights Q in each layer of the tree-shaped map of index systemTn；
Step S320, according to primary the scoring of website spy's vector matrix and the node weights building tree-shaped map interior joint of index system
Model；
Step S330 optimizes primary Rating Model using loss function.
3. working method according to claim 2, which is characterized in that step S310 includes following sub-step:
Step S311: Assessment of Important data of the acquisition multiple groups to each node in all layers of the tree-shaped map of index system；
Step S312: the corresponding node importance value a of each node in each layer of the tree-shaped map of parameter systemTn, calculate public
Formula is as follows:
Wherein, aTnIndicate the node importance value of tree-shaped T layers of map of n-th of the node of index system, mnIndicate index system tree
The unessential data volume of n-th of node of T layers of shape map, mtIndicate tree-shaped T layers of map of all nodes of index system not
Important data volume summation, hnIndicate the important data volume of tree-shaped T layers of map of n-th of the node of index system, htIndicate index
The important data volume summation of all nodes of T layers of system tree-shaped map；
Step S313, to node importance value aTnFurther weighted sum, each node of the tree-shaped map of parameter system are corresponding
Node weights QTn, eliminate number differences bring error in each rank；Calculation formula is as follows:
Wherein, QTnIndicate the node weights of n-th of node in tree-shaped T layers of map of index system.
4. working method according to claim 3, which is characterized in that primary Rating Model is as follows:
Wherein, XijIndicate web site features vector matrix；N indicates the group number of all input web site features data；B indicates departure；f
(x)
Indicate that primary Rating Model, act indicate grade form.
5. working method according to claim 4, which is characterized in that optimize primary Rating Model using loss function and obtain
Rating Model after optimization, calculation formula are as follows:
Wherein, F (x) is the Rating Model after optimization, QTn-1For the node weights of (n-1)th node in T layers；θ represents learning rate
Parameter；F (x) indicates the primary Rating Model optimized without loss function；Indicate loss function L (x) to T
The node weights of (n-1)th node seek local derviation in layer.
6. working method according to claim 5, which is characterized in that the calculation method of loss function is as follows:
By feature PiWith feature PjIt is respectively fed in primary Rating Model, primary Rating Model is to feature PiWith feature PjIt calculates separately
Score R is assessed outiAnd Rj, feature PiWith feature PjTrue score be respectively SiAnd Sj；
The calculation formula of loss function is as follows:
L (x)=L1+L2；
Wherein, L (x) is total loss function, L1For the value of first-loss function；L2For the value of the second loss function；
Work as Si< Sj, and Ri>Rj；Or Si>Sj, and Ri<RjWhen；
7. working method according to claim 6, which is characterized in that further include step S340, be index system dendrogram
One layer weight w of each layer building of spectrumT。
8. working method according to claim 7, which is characterized in that step S340 includes following sub-step:
Step S341 calculates the corresponding layer importance value q of each layerT；
Wherein, qTIndicate tree-shaped T layers of map of the layer importance value of index system, bTTree-shaped T layers of map of index system are indicated not weigh
The data volume wanted, btIndicate unessential data volume summation in all layers of the tree-shaped map of index system, gTIndicate that index system is tree-shaped
T layers of important data volume of map, gtIndicate data volume summation important in all layers of the tree-shaped map of index system；
Step S342, to layer importance value qTFurther weighted sum calculates the corresponding layer weight w of each layerT, eliminate in each rank
Number differences bring error；Calculation formula is as follows:
Wherein, wTIndicate tree-shaped T layers of map of the layer weight of index system.
9. working method according to claim 8, which is characterized in that include following sub-step in step S5:
Step S501 calculates the total score of each layer of all nodes, and calculation formula is as follows:
Wherein, yTIndicate the total score of the tree-shaped T layers of all nodes of map of index system；N indicates each layer of section
The number of point；α is parameter, value be 1 into n any one number；QαIndicate the weight of the α node；XαIndicate the α node
Scoring；
Step S502 calculates all layers of overall score, and calculation formula is as follows:
Wherein, F indicates the overall score of all layers of the tree-shaped map of index system；T indicates the tree-shaped map of index system
The number of plies；β is parameter, β value be 1 into T any one number；wβIndicate β layers of weight；yβIndicate β layers of scoring.
10. a kind of website assessment system characterized by comprising
Handling module, for obtaining the web site features data of website to be assessed；
Tree-shaped diagram generator constructs the tree-shaped map of index system according to preset evaluation index；
Weight generation module establishes node weights for each node of the tree-shaped map of index system；For the tree-shaped map of index system
Every layer establish layer weight；
Rating Model generation module establishes Rating Model for each node of the tree-shaped map of index system；Index system dendrogram
Spectrum includes multilayer, and every layer includes multiple nodes；
Scoring obtains module, for obtaining the appraisal result of each node, obtains website according to the appraisal result of each node
Overall score；
Data memory module, for storing web site features data, evaluation index, Rating Model, weighted data and appraisal result etc.
Data.
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Application publication date: 20191119 |