CN104618216B - Information management method, equipment and system - Google Patents
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- CN104618216B CN104618216B CN201310544507.9A CN201310544507A CN104618216B CN 104618216 B CN104618216 B CN 104618216B CN 201310544507 A CN201310544507 A CN 201310544507A CN 104618216 B CN104618216 B CN 104618216B
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Abstract
The invention discloses information management method, equipment and systems, which comprises character corresponding to Twitter message based on the received determines corresponding fisrt feature mark；By in identified fisrt feature mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as default fisrt feature mark.In the embodiment of the present invention, the default fisrt feature mark has the Twitter message template of same body content corresponding with what client uploaded, to, it is identified based on the default fisrt feature, the Twitter message that can be uploaded to client the management such as be clustered, be filtered and be operated, it saves client timeline and the space with the Twitter message of same body content is presented, so as to avoid client user from omitting important Twitter message, the user experience is improved.
Description
Technical field
The present invention relates to Internet technology more particularly to information management methods, equipment and system.
Background technique
With the development of internet technology and network it is universal, more and more network users are not only satisfied with simply
Obtained information by internet, the platform that microblogging is shared and propagated as a kind of information has been deep into the daily life of people
In work, become a kind of indispensable mode that people obtain information.Since microblogging spreads news, speed is fast, threshold is low, makes micro-
The rich preferred platform to spread news as numerous third parties, for example, participating in movable microblogging when third party initiates an activity and using
Family can upload Twitter message relevant to this activity, be full of in the time shaft (timeline) that peak activity will lead to client
Twitter message relevant to activity is made troubles so that more importantly Twitter message can be missed to user, and user's body is influenced
It tests.
In conclusion client shows more microblogging relevant to this activity when the relevant technologies are to third party's promotional activities
Message, there is no effective solution at the problem of causing microblog users to miss important Twitter message.
Summary of the invention
In view of this, the main purpose of the embodiment of the present invention is to provide a kind of information management method, equipment and system, with
It at least solves to be full of Twitter message relevant to activity due to the timeline of client, to can not be in timeline homepage
The problem of showing other Twitter messages, user caused to miss more importantly Twitter message, with user-friendly, promotion user's body
It tests.
In order to achieve the above objectives, the technical solution of the embodiment of the present invention is achieved in that
The embodiment of the present invention provides a kind of information management method, which comprises
Server character corresponding to Twitter message based on the received, determines corresponding fisrt feature mark；
By in identified fisrt feature mark, the frequency of occurrences of preset quantity reaches the fisrt feature mark of the first desired value
Knowledge is determined as default fisrt feature mark.
The embodiment of the present invention also provides a kind of information management method, which comprises
Client uploads Twitter message, and the Twitter message returned after processing is presented；Wherein,
The Twitter message of the upload is used for according to corresponding character, determines corresponding fisrt feature mark, and by institute
In determining fisrt feature mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as presetting
Fisrt feature mark.
The embodiment of the present invention also provides a kind of server, and the server includes:
First determination unit determines corresponding fisrt feature mark for character corresponding to Twitter message based on the received
Know；
Second determination unit, for by identified fisrt feature mark, the frequency of occurrences of preset quantity to reach first
The fisrt feature mark of desired value is determined as default fisrt feature mark.
The embodiment of the present invention also provides a kind of client, and the client includes:
Communication unit for uploading Twitter message, and receives the Twitter message returned after processing；
Display unit, for rendering Twitter message returned after processing；Wherein,
The Twitter message of the upload is used for according to corresponding character, determines corresponding fisrt feature mark, and by institute
In determining fisrt feature mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as presetting
Fisrt feature mark.
The embodiment of the present invention also provides a kind of message management system, and the message management system includes above-described service
Device and client.
Information management method, equipment provided by the embodiment of the present invention and system, the Twitter message uploaded according to client
Corresponding character determines corresponding fisrt feature mark, by identified fisrt feature mark, the frequency of occurrences of preset quantity
The fisrt feature mark for reaching the first desired value is determined as default fisrt feature mark, and the default fisrt feature identifies and occurs
The Twitter message with same body content that frequency reaches the first desired value is corresponding, thus, it is based on the default fisrt feature
Mark, the Twitter message that can be uploaded to client the management such as be clustered, be filtered and be operated, and saving client timeline is in
The now space of the Twitter message with same body content, so as to avoid client user from omitting important Twitter message,
The user experience is improved.
Detailed description of the invention
Fig. 1 is the flow chart one of information management method in the embodiment of the present invention；
Fig. 2 is the flowchart 2 of information management method in the embodiment of the present invention；
Fig. 3 is the presentation schematic diagram of Twitter message in the related technology；
Fig. 4 is that schematic diagram is presented in Twitter message polymerization in the embodiment of the present invention；
Fig. 5 is the flow chart of message management in the embodiment of the present invention；
Fig. 6 is the structural schematic diagram of server in the embodiment of the present invention；
Fig. 7 is the structural schematic diagram of client in the embodiment of the present invention
Fig. 8 is the structural schematic diagram of message management system in the embodiment of the present invention.
Specific embodiment
The present invention is further elaborated in the following with reference to the drawings and specific embodiments.
The embodiment of the present invention records a kind of information management method, and the method can be by the server that is connected with microblog
To execute；Fig. 1 is the flow chart one of information management method in the embodiment of the present invention, as shown in Figure 1, comprising the following steps:
Step 101, server character corresponding to Twitter message based on the received, determines corresponding fisrt feature mark.
As a preferred embodiment, character corresponding to the Twitter message, to be filtered in the Twitter message
Fall corresponding character after Arabic numerals, English alphabet and punctuate；In practical application, the Arabic numerals, English words
Female and punctuate is corresponding with ascii character, correspondingly, when Twitter message uses Unicode format transformation -8(UTF-8) character
When form, ascii character can be filtered out in the corresponding UTF-8 character of Twitter message, according to the UTF-8 word obtained after filtering
Symbol determines corresponding fisrt feature mark.
The fisrt feature mark uses preset algorithm, carries out calculating determination, institute to the corresponding character of the Twitter message
Stating algorithm includes: Message Digest Algorithm 5 (MD5, Message Digest Algorithm5), secure hash algorithm
(SHA, Secure Hash Algorithm).
Step 102, by identified fisrt feature mark, the frequency of occurrences of preset quantity reaches the of the first desired value
One signature identification is determined as default fisrt feature mark.
Inventors have found that when the content of Twitter message relevant to activity changes, including following several scenes:
1) Twitter message that all microblog users participation activities issue is identical.
2) body matter of Twitter message is identical, but the object spread is different, correspondingly, the microblogging in Twitter message after@
Account is different.
3) numerical character in Twitter message changes.
That is the main part of Twitter message is identical, and changed is only numerical character.
4) the movable network address information of participation described in Twitter message changes.
The main part of Twitter message is identical, but uniform resource locator (URL, Uniform Resource Locator)
Address is different.
5) there is part noun inconsistent in the body matter of Twitter message.
For scene 1)~4) described in URL, numerical character and account information it is corresponding with ascii character, will be micro-
UTF-8 character is obtained after the rich corresponding UTF-8 character filtering ascii character of message, obtains the based on filtered UTF-8 character
One signature identification will obtain in the fisrt feature mark for different Twitter messages, and the frequency of occurrences of preset quantity reaches the
The fisrt feature of one desired value identifies, and is determined as default fisrt feature mark, and the default fisrt feature mark is and frequency occurs
The Twitter message template with same body content that rate reaches the first desired value is corresponding.
For scene 5), according to, with the matched keyword of default dictionary, determining corresponding the in the received Twitter message of institute
Two signature identifications；Wherein, pass through multimode matching algorithm, such as AC(Aho-Corasick with the matched keyword of default dictionary
Automaton) algorithm determines；The second feature mark uses preset algorithm, calculates the keyword being matched to
It determines, the algorithm includes: MD5, SHA；Wherein, the frequency of occurrences of the second feature mark reflects the Twitter message hair
Therefore the second feature mark that the frequency of occurrences of preset quantity reaches the second desired value is determined as default second by the frequency of table
When signature identification, the default second feature mark reach with the frequency of occurrences the second desired value with same body content
Twitter message template is corresponding.
As a preferred embodiment, described by identified fisrt feature mark, the appearance of preset quantity is frequently
The fisrt feature mark that rate reaches the first desired value is determined as default fisrt feature mark, comprising: in m × t time, with it is each when
Between granularity t be unit, fisrt feature corresponding to the received Twitter message of institute is identified, it is several with preset first Hash (Hash)
Matched according to the fisrt feature mark in library, if be matched to, will be matched in the first Hash database the
One signature identification is corresponding, and count is incremented, if be not matched to, identified fisrt feature mark is stored to described first
In Hash database, and corresponding count of fisrt feature mark stored into the first Hash database is assigned a value of 1, directly
Fisrt feature mark matching to the received Twitter message of institute in m × t time finishes, wherein m is the positive integer not less than 1；
The counting of preset quantity in the first Hash database is reached to the fisrt feature mark of first desired value
It is determined as the default fisrt feature mark, wherein the counting is corresponding with any n time granularity t, and n is just no more than m
Integer.
For example, m value is relatively when it needs to be determined that the corresponding default fisrt feature of Twitter message identifies in the long period
Greatly, when it needs to be determined that the corresponding default fisrt feature of Twitter message identifies in the short period, m value is relatively small.Below with m
Value is illustrated for 3:
In first time granularity t, by received Twitter message fisrt feature identify ai(i is just not less than 1
Integer) it is matched one by one with the fisrt feature mark in the first Hash database, it, will be described first if be matched to
The fisrt feature mark a being matched in Hash databaseiIt is corresponding to count taiAdd 1, if be not matched to, by identified
One signature identification aiIt stores into the first Hash database, and the first spy into the first Hash database will be stored
Sign mark is corresponding to count taiIt is assigned a value of 1；
In second time granularity t, by received Twitter message fisrt feature identify bi(i is just not less than 1
Integer) it is matched one by one with the fisrt feature mark in the first Hash database, if be matched to, by the first Hash
Fisrt feature in database identifies biIt is corresponding to count tbiAdd 1, if be not matched to, identified fisrt feature is identified
biIt stores into the first Hash database, and identify b for the fisrt feature into the first Hash database is storediPhase
The counting t answeredbiAssignment 1；
In third time granularity t, by received Twitter message fisrt feature identify ci(i is just not less than 1
Integer) it is matched one by one with the fisrt feature mark in the first Hash database, if be matched to, by the first Hash
Fisrt feature in database identifies ciIt is corresponding to count tciAdd 1, if be not matched to, identified fisrt feature is identified
ciIt stores into the first Hash database, and identify c for the fisrt feature into the first Hash database is storediPhase
The counting t answeredciAssignment 1；
The counting of preset quantity in 1 time granularity t any in the first Hash database is reached into the first desired value
Fisrt feature mark, be determined as the default fisrt feature mark；Or,
The counting of preset quantity in 2 time granularity t any in the first Hash database is reached into the first desired value
Fisrt feature mark, be determined as the default fisrt feature mark, to choose first time granularity t and second time grain
It spends for t, the t that fisrt feature each in the first Hash database is identifiedaiAnd tbiIt is added, by the t of preset quantityaiWith
tbiThe fisrt feature mark that adduction reaches the first desired value is determined as the default fisrt feature mark；Or,
By the counting of preset quantity reaches the of the first desired value in 3 time granularity t in the first Hash database
One signature identification is determined as the default fisrt feature mark, i.e., by each fisrt feature mark in the first Hash database
The t of knowledgeai、tbiAnd tciIt is added, by the t of preset quantityai、tbiAnd tciThe fisrt feature mark that adduction reaches the first desired value determines
It is identified for the default fisrt feature.
In order to guarantee the timeliness of fisrt feature mark in the first Hash data, that is, guarantee the first Hash data
Fisrt feature mark in library is always corresponding with the nearest body matter template of received Twitter message, or, for guarantee described the
One Hash database is not take up excessive capacity, in one preferred embodiment, also deletes in the first Hash database
The smallest fisrt feature mark is counted in nearest s × t time, wherein s is the positive integer no more than m.
As a preferred embodiment, described by identified second feature mark, the appearance of preset quantity is frequently
The second feature mark that rate reaches the second desired value is determined as default second feature mark, comprising:
In m × t time, as unit of each time granularity t, by second feature mark corresponding to received Twitter message
Know, is matched with the second feature mark in preset 2nd Hash database, it, will be described second if be matched to
The second feature mark being matched in Hash database is corresponding, and count is incremented, special by identified second if be not matched to
Sign mark is stored into the 2nd Hash database, and to store the mark of the second feature into the 2nd Hash database
It is corresponding to count assignment 1, until the second feature mark of the received Twitter message of institute is matched and is finished in m × t time, wherein m is
Positive integer not less than 1；
The second feature mark that the counting of preset quantity in the 2nd Hash database reaches the second desired value is determined
It is identified for the default second feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
For example, m value is relatively when it needs to be determined that the corresponding default second feature of Twitter message identifies in the long period
Greatly, when it needs to be determined that the corresponding default second feature of Twitter message identifies in the short period, m value is relatively small.Below with m
Value is illustrated for 3:
In first time granularity t, by received Twitter message second feature identify ai(i is just not less than 1
Integer) it is matched one by one with the second feature mark in the 2nd Hash database, it, will be described second if be matched to
The second feature mark a being matched in Hash databaseiIt is corresponding to count taiAdd 1, if be not matched to, by identified
Two signature identification aiIt stores into the 2nd Hash database, and the second spy into the 2nd Hash database will be stored
Sign mark is corresponding to count taiIt is assigned a value of 1；
In second time granularity t, by received Twitter message second feature identify bi(i is just not less than 1
Integer) it is matched one by one with the second feature mark in the 2nd Hash database, if be matched to, by the 2nd Hash
Second feature in database identifies biIt is corresponding to count tbiAdd 1, if be not matched to, identified second feature is identified
biIt stores into the 2nd Hash database, and identify b for the second feature into the 2nd Hash database is storediPhase
The counting t answeredbiAssignment 1；
In third time granularity t, by received Twitter message second feature identify ci(i is just not less than 1
Integer) it is matched one by one with the second feature mark in the 2nd Hash database, if be matched to, by the 2nd Hash
Second feature in database identifies ciIt is corresponding to count tciAdd 1, if be not matched to, identified second feature is identified
ciIt stores into the 2nd Hash database, and identify c for the second feature into the 2nd Hash database is storediPhase
The counting t answeredciAssignment 1；
The counting of preset quantity in 1 time granularity t any in the 2nd Hash database is reached into the second desired value
Second feature mark, be determined as the default second feature mark；Or,
The counting of preset quantity in 2 time granularity t any in the 2nd Hash database is reached into the second desired value
Second feature mark, be determined as the default second feature mark, to choose first time granularity t and second time grain
It spends for t, the t that second feature each in the 2nd Hash database is identifiedaiAnd tbiIt is added, by the t of preset quantityaiWith
tbiThe second feature mark that adduction reaches the second desired value is determined as the default second feature mark；Or,
By the counting of preset quantity reaches the of the second desired value in 3 time granularity t in the 2nd Hash database
Two signature identifications are determined as the default second feature mark, i.e., by each second feature mark in the 2nd Hash database
The t of knowledgeai、tbiAnd tciIt is added, by the t of preset quantityai、tbiAnd tciThe second feature mark that adduction reaches the second desired value determines
It is identified for the default second feature.
In order to guarantee the timeliness of second feature mark in the 2nd Hash data, that is, guarantee the 2nd Hash data
In library second feature mark always with it is nearest institute the body matter template of received Twitter message it is corresponding, or, be guarantee described in
2nd Hash database is not take up excessive capacity, in one preferred embodiment, also deletes the 2nd Hash database
In the smallest second feature mark is counted in s × t time recently, wherein s is the positive integer no more than m.
As a preferred embodiment, the method also includes:
Server by the fisrt feature mark of received Twitter message identify with the default fisrt feature and carry out one by one
Matching is identified according to the default fisrt feature being matched to, the received Twitter message of mark institute.
Wherein, the fisrt feature mark of received Twitter message when being mismatched with the default fisrt feature mark, will
The mark of second feature corresponding to received Twitter message identify with the default second feature and matched one by one, according to institute
The default second feature mark being matched to, the received Twitter message of mark institute, i.e., carry out clustering processing for Twitter message.
Correspondingly, in the request for receiving Twitter message, by requested Twitter message, carry and described default the
The matched mark of one signature identification, or the Twitter message for identifying matched mark with the default second feature return, so as to connect
Receive that the client of Twitter message carries according to received Twitter message with the matched mark of default fisrt feature mark or with
Default second feature identifies matched mark, and the received Twitter message of institute is presented in polymerization, to avoid microblogging relevant to activity
Message is full of the timeline of client, other Twitter messages can not be presented in timeline first page, lead to client user
Omit important Twitter message；Alternatively,
In the request for receiving Twitter message, in requested Twitter message, will not carry and default first spy
Sign mark and the Twitter message for identifying matched mark with the default second feature are sent to client, so that the visitor
Requested Twitter message is presented in family end, i.e., will be filtered out in the Twitter message of client request with default fisrt feature mark,
And default second feature identifies matched Twitter message, and is sent to client, which is adapted to need to ask client
The scene that the Twitter message asked is filtered correspondingly can achieve the effect that purify client timeline, so as to make
Client is presented other Twitter messages in timeline first page, and client user is avoided to omit important Twitter message.
In actual treatment, due to the processing of the UTF-8 encoding filter ASCII to Twitter message and determining fisrt feature mark
Efficiency is higher, and most scene, i.e. scene 1)~4) in Twitter message can be based on the mark determination of corresponding fisrt feature
Default fisrt feature mark, i.e., with the Twitter message template of same body content；Therefore, in the present embodiment, first by microblogging
The fisrt feature mark of message is identified with the default fisrt feature to be matched, with the currently processed Twitter message of determination whether
It is corresponding with the Twitter message template obtained according to filtering ascii character, that is, it determines and is identified in the fisrt feature of current Twitter message
Whether matched with default fisrt feature mark, in such manner, it is possible to guarantee the treatment effeciency to Twitter message；Also, in current microblogging
When the fisrt feature mark of message is mismatched with default fisrt feature mark, then by the second feature mark of currently processed Twitter message
Know to identify with default second feature and be matched, with determination currently processed Twitter message whether with obtained according to Keywords matching
Twitter message template is corresponding, to guarantee to scene 5) corresponding to Twitter message handle.
The embodiment of the present invention also records a kind of information management method, and the method can be executed by client；Fig. 2 is this
The flowchart 2 of information management method in inventive embodiments, as shown in Figure 2, comprising:
Step 201, client uploads Twitter message.
Step 202, the Twitter message returned after processing is presented.
Wherein, the Twitter message of the upload is for making server word according to corresponding to the Twitter message of the upload
Symbol, determines corresponding fisrt feature mark, and by identified fisrt feature mark, and the frequency of occurrences of preset quantity reaches the
The fisrt feature mark of one desired value is determined as default fisrt feature mark.
As a preferred embodiment, character corresponding to the Twitter message, to be filtered in the Twitter message
Fall corresponding character after Arabic numerals, English alphabet and punctuate.
As a preferred embodiment, the method also includes:
The client request Twitter message, according to the requested Twitter message of the return received carry with it is described
Default fisrt feature identifies matched mark, or identifies matched mark with the default second feature, and polymerization is presented and received
Return requested Twitter message.
As a preferred embodiment, the method also includes:
The client request Twitter message, receives and in the requested Twitter message that now returns to, and described receives
Return requested Twitter message do not carry with the default fisrt feature mark and with the default second feature mark
Know matched mark.
Twitter message is presented to client conglomerate below with reference to Fig. 3 and Fig. 4 to be illustrated, Fig. 3 is in the related technology
The presentation schematic diagram of Twitter message, as shown in figure 3, user A listens to user B, C, D, E in client, when user B, C, D, E are equal
When delivering with a certain movable related Twitter message, show what user B, C, D, E were delivered in the client timeline of user A
The list of Twitter message, when message is more, the current page and continued page of timeline can be accounted for by the relevant Twitter message of activity
According to causing user A to omit relatively important Twitter message；
It is micro- due to also being carried in requested Twitter message when the client of user A obtains requested Twitter message
Rich message and default fisrt feature identify or default second feature identifies matched mark, for example, delivered as user B, C, D, E
When Twitter message is related with same campaign, correspondingly, the Twitter message delivered can be carried and same default fisrt feature mark
Know or same default second feature identifies matched mark, in this way, the client of user A can determine requested Twitter message
Belong to same activity, i.e., it is corresponding with same Twitter message template, therefore, requested Twitter message is polymerize and is shown, Fig. 4 is this
Schematic diagram is presented in Twitter message polymerization in inventive embodiments, as shown in figure 4, only showing in the timeline of the client of user A
The Twitter message that user B is delivered, and user B, C, D, E is prompted also to deliver similar Twitter message, opposite Fig. 3 is saved
The space of timenline avoids user A from omitting opposite thus, it is possible to which other Twitter messages are presented in timeline current page
Important Twitter message.
The embodiment of the present invention also records a kind of computer storage medium, and the computer storage medium is stored with computer journey
Sequence, the computer program are used to execute the information management method of the embodiment of the present invention.
Information management method in the embodiment of the present invention is described in further detail again below, Fig. 5 is the embodiment of the present invention
The flow chart of middle message management, as shown in Figure 5, comprising the following steps:
Step 501, the corresponding fisrt feature mark of Twitter message that client uploads is determined.
The fisrt feature is identified as using preset algorithm, UTF-8 character filtering ASCII corresponding to the Twitter message
After character, calculating determination is carried out according to filtered UTF-8 character, the algorithm includes: MD5, SHA.
Step 502, fisrt feature mark determined by judging can be matched to default fisrt feature mark, if matching
It arrives, thens follow the steps 503；Otherwise, step 504 is executed.
The default fisrt feature is identified as before step 501, the corresponding to the Twitter message uploaded according to client
In one signature identification, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined.
In m × t time, as unit of each time granularity t, corresponding to Twitter message that the client is uploaded
One signature identification is matched with the fisrt feature mark in preset first Hash database, will be in institute if be matched to
State the fisrt feature mark being matched in the first Hash database it is corresponding count is incremented, if be not matched to, will determined by
Fisrt feature mark is stored into the first Hash database, and to store the first spy into the first Hash database
Corresponding count of sign mark is assigned a value of 1, until the fisrt feature mark for the Twitter message that the client uploads in m × t time
Matching finishes, wherein m is the positive integer not less than 1；
The fisrt feature mark that the counting of preset quantity in the first Hash database reaches the first desired value is determined
It is identified for the default fisrt feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
Step 503, by fisrt feature identified in the first Hash database mark, corresponding count is incremented.
Illustrate first when identified fisrt feature mark is matched to default fisrt feature mark referring to step 502
The fisrt feature mark of the determination is stored in Hash database, correspondingly, by what is be matched in the first Hash database
Count is incremented for default fisrt feature mark, thus, it, can be with according to the counting situation of different time granularity t in the Hash data
Determine the quantity for identifying matched Twitter message in corresponding time granularity with the default fisrt feature, correspondingly, it is subsequent can be right
The counting of default fisrt feature mark is preset fisrt feature mark to this and is handled, for example, in n nearest time granularity t,
A certain default fisrt feature mark is counted as 1, identifies the publisher that the Twitter message does not have other same content, can should
Default fisrt feature mark is deleted.
Step 504, identified fisrt feature mark is stored into the first Hash database, and will be identified
The corresponding counting of fisrt feature mark is assigned a value of 1.
Before step 504, identified fisrt feature mark is searched also in the first Hash database, if searched
Arrive, then by the fisrt feature found mark it is corresponding count is incremented, if do not found, then follow the steps 504, by really
Fixed fisrt feature mark is stored to the first Hash database, and identified fisrt feature is identified corresponding counting and is assigned
Value is 1.It is subsequent can according to default fisrt feature identify counting to this preset fisrt feature mark handle, for example, most
In n close time granularity t, a certain default fisrt feature mark is counted as 1, and identifying the Twitter message does not have identical content
This can be preset fisrt feature mark and deleted by publisher, and the fisrt feature mark to guarantee the first Hash database purchase is total
Be with deliver frequency it is high have the Twitter message of same body content it is corresponding.
Step 505, the second feature mark for the Twitter message that client uploads is determined.
The second feature mark uses preset algorithm, carries out to the Twitter message and the matched keyword of default dictionary
It calculates and determines, the algorithm includes: MD5, SHA.
Step 506, fisrt feature mark determined by judging can be matched to default second feature mark, if matching
It arrives, thens follow the steps 507；Otherwise, step 508 is executed.
The default second feature is identified as before step 505, the corresponding to the Twitter message uploaded according to client
In two signature identifications, the second feature mark that the frequency of occurrences of preset quantity reaches the second desired value is determined.
In m × t time, as unit of each time granularity t, corresponding to Twitter message that the client is uploaded
Two signature identifications are matched with the second feature mark in preset 2nd Hash database, will be in institute if be matched to
State the second feature mark being matched in the 2nd Hash database it is corresponding count is incremented, if be not matched to, will determined by
Second feature mark is stored into the 2nd Hash database, and to store the second spy into the 2nd Hash database
Corresponding count of sign mark is assigned a value of 1, until the second feature mark for the Twitter message that the client uploads in m × t time
Matching finishes, wherein m is the positive integer not less than 1；
The second feature mark that the counting of preset quantity in the 2nd Hash database reaches the second desired value is determined
It is identified for the default second feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
Step 507, by second feature identified in the 2nd Hash database mark, corresponding count is incremented.
Illustrate second when identified second feature mark is matched to default second feature mark referring to step 506
It is stored with the second feature mark of the determination in Hash database, correspondingly, the 2nd Hash database is matched to pre-
If count is incremented for second feature mark, thus, it, can be true according to the counting situation of different time granularity t in the Hash data
The quantity of matched Twitter message is identified in the fixed time granularity with the default second feature, correspondingly, it is subsequent can be to default
The counting of second feature mark is preset second feature mark to this and is handled, for example, in n nearest time granularity t, it is a certain
Default second feature mark is counted as 1, identifies the publisher that the Twitter message does not have identical content, this can be preset second
Signature identification is deleted.
Step 508, identified second feature mark is stored into the 2nd Hash database, and will be identified
The corresponding counting of second feature mark is assigned a value of 1.
Before step 508, identified second feature mark is searched also in the 2nd Hash database, if searched
Arrive, then by the second feature found mark it is corresponding count is incremented, if do not found, then follow the steps 508, by really
Fixed second feature mark is stored to the 2nd Hash database, and identified second feature is identified corresponding counting and is assigned
Value is 1.The subsequent counting that can be identified to default second feature is preset second feature mark to this and is handled, for example, recently
N time granularity t in, a certain default second feature mark is counted as 1, identifies the hair that the Twitter message does not have identical content
This can be preset second feature mark and deleted by cloth person, to guarantee the second feature mark of the 2nd Hash database purchase always
With deliver frequency it is high have the Twitter message of same body content it is corresponding.
The embodiment of the present invention, by client upload Twitter message fisrt feature mark with default fisrt feature identify into
Row matching, the default fisrt feature mark is corresponding with the Twitter message template that the frequency of occurrences is high, therefore, can determine and delivers frequency
Rate is high and the identical Twitter message of body matter, so that the polymerization that client carries out Twitter message is presented, or to default first
Signature identification matched Twitter message such as is filtered at the operation；Also, default fisrt feature identify upload with client it is micro-
When the fisrt feature mark of rich message mismatches, the Twitter message for also uploading client and the mark progress of default second feature
Match, while ensure that treatment effeciency, the Twitter message in different scenes described above can be handled by also assuring.
The embodiment of the present invention also records a kind of server, and Fig. 6 is the structural schematic diagram of server in the embodiment of the present invention, such as
Shown in Fig. 6, the server includes:
First determination unit 61 determines corresponding fisrt feature for character corresponding to Twitter message based on the received
Mark；
Second determination unit 62, for by identified fisrt feature mark, the frequency of occurrences of preset quantity to reach the
The fisrt feature mark of one desired value is determined as default fisrt feature mark.
Wherein, first determination unit is also used to the received Twitter message of institute filtering out Arabic numerals, English words
Female and punctuate determines corresponding fisrt feature mark according to character corresponding to the Twitter message obtained after filtering.
Wherein, the server further include: third determination unit 63, for according in the received Twitter message of institute and default
The matched keyword of dictionary determines corresponding second feature mark；
4th determination unit 64, for by identified second feature mark, the frequency of occurrences of preset quantity to reach the
The second feature mark of two desired values is determined as default second feature mark.
Wherein, the server further include: first identifier unit 65, for by received Twitter message fisrt feature
Mark is matched one by one with the default fisrt feature mark, is identified according to the default fisrt feature being matched to, is identified institute
Received Twitter message.
Wherein, the server further include: second identifier unit 66, for received Twitter message fisrt feature
When mark is mismatched with the default fisrt feature mark, the second feature mark of the received Twitter message of institute is preset with described
Second feature mark is matched one by one, is identified according to the default second feature being matched to, the received Twitter message of mark institute.
Wherein, the server further include: the first Transmit-Receive Unit 67 after the request for receiving Twitter message, returns
Requested Twitter message, the Twitter message of the return, which is carried, identifies matched mark with the default fisrt feature, or takes
Band identifies matched mark with the default second feature.
Wherein, the server further include: the second Transmit-Receive Unit 68 after the request for receiving Twitter message, returns
Requested Twitter message, and the Twitter message of the return do not carry with the default fisrt feature mark and with it is described
Default second feature identifies matched mark.
Wherein, second determination unit 62, was also used within m × t time, as unit of each time granularity t, by institute
The mark of fisrt feature corresponding to received Twitter message, identifies with the fisrt feature in preset first Hash database and carries out
Matching, if be matched to, by the fisrt feature being matched in the first Hash database mark, count is incremented accordingly,
If be not matched to, identified fisrt feature mark is stored into the first Hash database, and to store to institute
Corresponding count of fisrt feature mark stated in the first Hash database is assigned a value of 1, until the received microblogging of institute in m × t time
The fisrt feature mark matching of message finishes, wherein m is the positive integer not less than 1；
The counting of preset quantity in the first Hash database is reached to the fisrt feature mark of first desired value
It is determined as the default fisrt feature mark, wherein the counting is corresponding with any n time granularity t, and n is just no more than m
Integer.
Wherein, the 4th determination unit 64, was also used within m × t time, as unit of each time granularity t, by institute
State second feature corresponding to received Twitter message mark, with the second feature in preset 2nd Hash database identify into
The second feature being matched in the 2nd Hash database is identified corresponding count and added by matching of going if be matched to
1, if be not matched to, by identified second feature mark store into the 2nd Hash database, and for store to
Corresponding count of second feature mark in the 2nd Hash database is assigned a value of 1, until institute is received micro- in m × t time
The second feature mark matching of rich message finishes, wherein m is the positive integer not less than 1；
The counting of preset quantity in the 2nd Hash database is reached to the second feature mark of second desired value
It is determined as the default second feature mark, wherein the counting is corresponding with any n time granularity t, and n is just no more than m
Integer.
The embodiment of the present invention also records a kind of client, and Fig. 7 is the structural schematic diagram of client in the embodiment of the present invention, such as
Shown in Fig. 7, the client includes:
Communication unit 71 for uploading Twitter message, and receives the Twitter message returned after processing；
Display unit 72, for rendering Twitter message returned after processing；Wherein,
The Twitter message of the upload is used for according to corresponding character, determines corresponding fisrt feature mark, and by institute
In determining fisrt feature mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as presetting
Fisrt feature mark.
Wherein, character corresponding to the Twitter message, to filter out Arabic numerals, English words in the Twitter message
Corresponding character after female and punctuate.
Wherein, the Twitter message that the communication unit 71 is uploaded is also used to according to the Twitter message uploaded and presets
The matched keyword of dictionary, determines corresponding second feature mark, and by identified second feature mark, preset quantity
The second feature mark that the frequency of occurrences reaches the second desired value is determined as default second feature mark.
Wherein, the communication unit 71, is also used to request Twitter message；
The display unit 72, be also used to entrained by the Twitter message according to received by the communication unit 71 with institute
It states default fisrt feature and identifies matched mark, or identify matched mark with the default second feature, polymerize described in presenting
Twitter message received by communication unit 71.
Wherein, the communication unit 71, is also used to request Twitter message；
The display unit 72 is also used to present the Twitter message that the communication unit 71 receives, and the communication unit
71 Twitter messages that receive of member do not carry with the default fisrt feature mark and with the default second feature mark
The mark matched.
The embodiment of the present invention also records a kind of message management system, and Fig. 8 is message management system in the embodiment of the present invention
Structural schematic diagram, as shown in figure 8, the message management system includes server 81 and client 82,81 He of server
The composed structure of the client 82 and the function of each unit are identical as the above, and which is not described herein again.
Correspondingly, the embodiment of the present invention also records a kind of computer storage medium, wherein it is stored with computer program, the meter
Calculation machine program is used to execute the above-mentioned information management method of the embodiment of the present invention.
If unit described in the embodiment of the present invention is realized in the form of software function module and is sold as independent product
Or it in use, also can store in a computer readable storage medium.Based on this understanding, technology in the art
Personnel are it should be appreciated that the embodiment of the present invention can provide as method, system or computer program product.Therefore, the present invention can be used
The form of full hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects.Moreover, the present invention can
Using the calculating implemented in the computer-usable storage medium that one or more includes wherein computer usable program code
The form of machine program product, the storage medium include but is not limited to USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic disk storage, CD-ROM, optics
Memory etc..
The present invention be according to the method for the embodiment of the present invention, the flow chart of equipment (system) and computer program product and/
Or block diagram describes.It should be understood that each process that can be realized by computer program instructions in flowchart and/or the block diagram and/
Or the combination of the process and/or box in box and flowchart and/or the block diagram.It can provide these computer program instructions
To general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices processor to generate one
A machine so that by the instruction that the processor of computer or other programmable data processing devices executes generate for realizing
The device for the function of being specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications can be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Claims (25)
1. a kind of information management method, which is characterized in that the described method includes:
Server character corresponding to Twitter message based on the received, determines corresponding fisrt feature mark；
By in identified fisrt feature mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is true
It is set to default fisrt feature mark；
The fisrt feature mark of the received Twitter message is identified progress one by one with the default fisrt feature by server
Match, is identified according to the default fisrt feature being matched to, identify the Twitter message；
After server obtains the request of Twitter message, requested Twitter message is returned to, and the Twitter message of the return carries
Matched mark is identified with the default fisrt feature.
2. information management method according to claim 1, which is characterized in that character corresponding to the Twitter message, for institute
State character corresponding after filtering out Arabic numerals, English alphabet and punctuate in Twitter message.
3. information management method according to claim 1, which is characterized in that the method also includes:
Server determines corresponding second feature mark based on the received in the Twitter message with the matched keyword of default dictionary
Know；
By in identified second feature mark, the second feature mark that the frequency of occurrences of preset quantity reaches the second desired value is true
It is set to default second feature mark.
4. information management method according to claim 3, which is characterized in that the fisrt feature of the Twitter message identifies and institute
When stating default fisrt feature mark mismatch, the method also includes:
Server matches the second feature mark of the received Twitter message with default second feature mark one by one, root
According to the default second feature mark being matched to, the Twitter message is identified.
5. according to the information management method of claim 3 or 4, which is characterized in that the method also includes:
After server obtains the request of Twitter message, requested Twitter message is returned to, and the Twitter message of the return carries
Matched mark is identified with the default second feature.
6. according to the information management method of claim 3 or 4, which is characterized in that the method also includes:
After server obtains the request of Twitter message, requested Twitter message is returned to, and the Twitter message of the return is not taken
Band identifies matched mark with the default fisrt feature mark and with the default second feature.
7. according to claim 1 to any one of 4 information management methods, which is characterized in that described special by identified first
In sign mark, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as default fisrt feature mark
Know, comprising:
In m × t time, as unit of each time granularity t, by fisrt feature corresponding to received Twitter message identify,
It is matched with the fisrt feature mark in preset first Hash Hash database, it, will be described first if be matched to
The fisrt feature mark being matched in Hash database is corresponding, and count is incremented, special by identified first if be not matched to
Sign mark is stored into the first Hash database, and is identified the fisrt feature into the first Hash database is stored
Corresponding count is assigned a value of 1, until the fisrt feature mark of the received Twitter message of institute is matched and finished in m × t time, wherein m
For the positive integer not less than 1；
The fisrt feature mark that the counting of preset quantity in the first Hash database reaches first desired value is determined
It is identified for the default fisrt feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
8. information management method according to claim 3, which is characterized in that it is described by identified second feature mark in,
The second feature mark that the frequency of occurrences of preset quantity reaches the second desired value is determined as default second feature mark, comprising:
In m × t time, as unit of each time granularity t, by second feature corresponding to received Twitter message identify,
It is matched with the second feature mark in preset 2nd Hash database, it, will be in the 2nd Hash if be matched to
The second feature mark being matched in database is corresponding, and count is incremented, if be not matched to, by identified second feature mark
Knowledge is stored into the 2nd Hash database, and the second feature that will be stored into the 2nd Hash database identifies accordingly
Counting be assigned a value of 1, until the second feature mark matching of the received Twitter message of institute finishes in m × t time, wherein m is not
Positive integer less than 1；
The second feature mark that the counting of preset quantity in the 2nd Hash database reaches second desired value is determined
For default second feature mark, wherein the counting is corresponding with any n time granularity t, and n is the positive integer no more than m.
9. a kind of information management method, which is characterized in that the described method includes:
Client uploads Twitter message, and the Twitter message returned after processing is presented；Wherein,
The Twitter message of the upload is used to determine corresponding fisrt feature mark, and will determine according to corresponding character
Fisrt feature mark in, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as default first
Signature identification；
The Twitter message of the return, which is carried, identifies matched mark with the default fisrt feature；
It is described to identify the matched fisrt feature mark for being identified as server for the Twitter message with the default fisrt feature
It is matched one by one with the default fisrt feature mark, is identified, identified described micro- according to the default fisrt feature being matched to
Rich message；
The client request Twitter message, what is carried according to the requested Twitter message of the return received presets with described
Fisrt feature identifies matched mark, the requested Twitter message of the return received described in polymerization presentation.
10. information management method according to claim 9, which is characterized in that
Character corresponding to the Twitter message, to filter out Arabic numerals, English alphabet and mark in the Twitter message
Corresponding character after point.
11. information management method according to claim 9, which is characterized in that the method also includes:
The client request Twitter message, carried according to the requested Twitter message of the return received with default second
The matched mark of signature identification polymerize the requested Twitter message of the return received described in presenting.
12. any one of 1 information management method according to claim 1, which is characterized in that the method also includes:
The client request Twitter message, receives and in the requested Twitter message that now returns to, and it is described receive return
The requested Twitter message returned do not carry with the default fisrt feature mark and with the default second feature mark
The mark matched.
13. a kind of server, which is characterized in that the server includes:
First determination unit determines corresponding fisrt feature mark for character corresponding to Twitter message based on the received；
Second determination unit, for by identified fisrt feature mark, the frequency of occurrences of preset quantity to reach the first expectation
The fisrt feature mark of value is determined as default fisrt feature mark；
The server further include:
First identifier unit, for by received Twitter message fisrt feature mark and the default fisrt feature mark by
It is a to be matched, it is identified according to the default fisrt feature being matched to, the received Twitter message of mark institute；
First Transmit-Receive Unit after the request for receiving Twitter message, returns to requested Twitter message, the return it is micro-
Rich message, which is carried, identifies matched mark with the default fisrt feature.
14. 3 server according to claim 1, which is characterized in that
First determination unit is also used to the received Twitter message of institute filtering out Arabic numerals, English alphabet and mark
Point determines corresponding fisrt feature mark according to character corresponding to the Twitter message obtained after filtering.
15. 3 server according to claim 1, which is characterized in that the server further include:
Third determination unit, for according to, with the matched keyword of default dictionary, being determined corresponding in the received Twitter message of institute
Second feature mark；
4th determination unit, for by identified second feature mark, the frequency of occurrences of preset quantity to reach the second expectation
The second feature mark of value is determined as default second feature mark.
16. server according to claim 15, which is characterized in that the server further include:
Second identifier unit, for received Twitter message fisrt feature mark with the default fisrt feature mark not
When matching, by received Twitter message second feature mark with default second feature identify match one by one, according to institute
The default second feature mark being matched to, the received Twitter message of mark institute.
17. 6 server according to claim 1, which is characterized in that
First Transmit-Receive Unit after the request for receiving Twitter message, returns to requested Twitter message, the return
Twitter message carry and identify matched mark with the default second feature.
18. 6 server according to claim 1, which is characterized in that the server further include:
Second Transmit-Receive Unit after the request for receiving Twitter message, returns to requested Twitter message, and the return
Twitter message does not carry with the default fisrt feature mark and identifies matched mark with the default second feature.
19. any one of 3 to 18 server according to claim 1, which is characterized in that
Second determination unit, was also used within m × t time, as unit of each time granularity t, by the received microblogging of institute
The mark of fisrt feature corresponding to message is matched with the fisrt feature mark in preset first Hash Hash database,
If be matched to, by the fisrt feature being matched in the first Hash database mark, count is incremented accordingly, if not
It is matched to, then stores identified fisrt feature mark into the first Hash database, and to store to described first
Corresponding count of fisrt feature mark in Hash database be assigned a value of 1, until in m × t time received Twitter message
Fisrt feature mark matching finishes, wherein m is the positive integer not less than 1；
The fisrt feature mark that the counting of preset quantity in the first Hash database reaches first desired value is determined
It is identified for the default fisrt feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
20. server according to claim 15, which is characterized in that
4th determination unit, was also used within m × t time, will be described received micro- as unit of each time granularity t
Second feature mark corresponding to rich message is matched, such as with the second feature mark in preset 2nd Hash database
Fruit is matched to, then by the second feature being matched in the 2nd Hash database mark it is corresponding count is incremented, if not
It is fitted on, then stores identified second feature mark into the 2nd Hash database, and to store to described second
Corresponding count of second feature mark in Hash database be assigned a value of 1, until in m × t time received Twitter message
Second feature mark matching finishes, wherein m is the positive integer not less than 1；
The second feature mark that the counting of preset quantity in the 2nd Hash database reaches second desired value is determined
It is identified for the default second feature, wherein the counting is corresponding with any n time granularity t, and n is just whole no more than m
Number.
21. a kind of client, which is characterized in that the client includes:
Communication unit for uploading Twitter message, and receives the Twitter message returned after processing；
Display unit, for rendering Twitter message returned after processing；Wherein,
The Twitter message of the upload is used to determine corresponding fisrt feature mark, and will determine according to corresponding character
Fisrt feature mark in, the fisrt feature mark that the frequency of occurrences of preset quantity reaches the first desired value is determined as default first
Signature identification；
The Twitter message of the return, which is carried, identifies matched mark with the default fisrt feature；
It is described to identify the matched fisrt feature mark for being identified as server for the Twitter message with the default fisrt feature
It is matched one by one with the default fisrt feature mark, is identified, identified described micro- according to the default fisrt feature being matched to
Rich message；
The display unit is also used to entrained by the Twitter message according to received by the communication unit with described default the
Twitter message received by the communication unit is presented in the matched mark of one signature identification, polymerization.
22. the client according to claim 21, which is characterized in that
The Twitter message that the communication unit is uploaded is also used to according to the Twitter message and the matched pass of default dictionary uploaded
Keyword determines corresponding second feature mark, and by identified second feature mark, the frequency of occurrences of preset quantity reaches
The second feature mark of second desired value is determined as default second feature mark.
23. the client according to claim 22, which is characterized in that
The communication unit is also used to request Twitter message；
The display unit is also used to special with default second entrained by the Twitter message according to received by the communication unit
Sign identifies matched mark, and Twitter message received by the communication unit is presented in polymerization.
24. according to any one of claim 22 to 23 client, which is characterized in that
The communication unit is also used to request Twitter message；
The display unit is also used to present the Twitter message that the communication unit receives, and the communication unit receives
Twitter message do not carry with the default fisrt feature mark and identify matched mark with default second feature.
25. a kind of message management system, which is characterized in that the message management system includes claim 13 to 18,20 any
Any one of the item server and claim 21 to 23 client.
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