CN104243501A - Filtering and intercepting method for junk mail - Google Patents
Filtering and intercepting method for junk mail Download PDFInfo
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- CN104243501A CN104243501A CN201410543038.3A CN201410543038A CN104243501A CN 104243501 A CN104243501 A CN 104243501A CN 201410543038 A CN201410543038 A CN 201410543038A CN 104243501 A CN104243501 A CN 104243501A
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Abstract
The invention relates to a filtering and intercepting method for junk mail. The method filters and recognizes the mail for multiple layers: obtaining the classification attribute of mail for the user according to the expression and/or phonetic feature data of the user for receiving and sending the mail; for the mail, which cannot be confirmed on category, orderly inquiring the black/white list of the user node local and friend node, the friend node is the network node having higher mail interaction frequency with the current user node; reminding the user for judging the category of the mail. By filtering and recognizing the mail for multiple layers, the problem of the prior art for recognizing the junk mail with larger cost and low efficiency can be solved.
Description
Technical field
The present invention relates to network communication field, particularly relate to a kind of multi-level filtration hold-up interception method of spam.
Background technology
Along with the development of internet, applications, Email is widely used, and become one of service the most basic on Internet, user can carry out economy, convenience and information interchange efficiently by Email and long-distance user.But, just while Email becomes a kind of indispensable important information media of communication gradually, also becoming a kind of commercial advertisement means.User is while receiving useful information, plenty of time and the how various mail of energy also must be spent to carry out Classification and Identification, to filter " rubbish " mail, and existing classification of mail recognition methods or adopt more single classifying identification method and cause result inaccurate, or use too complicated RM and improve time cost.Therefore, the accuracy rate and the efficiency that how to improve classification of mail identification are the hot issues studied at present.
Classification of mail recognition methods conventional at present has a variety of.Such as, based on SVM, decision tree, black and white lists, bayesian algorithm, fuzzy theory, intelligent computation, neural net, inference technology, based on the filtration of keyword Sum fanction, taxonomy database, sole user's discovery learning etc.
Although each own respective advantage of these methods, each own different shortcoming, classification accuracy is the highest about 80%, can't meet the requirement of actual use.Because the cost of unit Spam filtering is comparatively large, and accuracy rate is not high usually, and Spam filtering task is all given server, will obviously increase the resource overhead of server end again.
Prior art is fruitful in process unit Spam filtering.Nowadays, best Spam filtering accumulation mistake is far below 1%.This seems to mean that people achieve triumph in the campaign of antagonism spam.But in addition on the one hand, the survey report that well-known release mechanism and China Internet association anti-rubbish mail center are issued over the years from the world, observes from user perspective, although the amount of spam is in minimizing on the whole, panesthesia remains incessant after repeated prohibition, cannot effect a radical cure.Meanwhile, user's being discontented with for existing anti-spam functionality at wire-speed weakness, occupies the first place that all restriction users use network mailbox factor.
By long-term research, prior art is fruitful in process unit Spam filtering.But carrying out in a deep going way of complex network and the research of community network aspect in recent years, people generally guess that real world network all has uncalibrated visual servo and microcosmic characteristic, such as computer network, nervous system, transportation network, electric power networks, mail network, social relation network etc.
Why between existing present situation and Consumer's Experience, there is there so big drop? this Spam filtering that should ascribe in the past considers the control of spam mostly from personal user's angle, thus have ignored to be actually between user that one influences each other, co-operating relation and being bound up, and then show some similar characteristic.
There is many isolated nodes in network, if and if there is mutual (receiving and dispatching mail) frequency of larger mail between two network user node, then mean there is higher homogeney between these two user nodes.Mail receipts/sender never contacted before the spam overwhelming majority that user receives comes from; On the other hand, along with being familiar with or the increase of trusting degree of mail receipts/sender, mail is that the probability of spam will reduce rapidly.
Owing to carrying out the huger and continuous dynamic growth of the mutual network ip address quantity of mail with user node, multianalysis proprietary mail interactive relation to be impossible, also there is no need.
Summary of the invention
Main purpose of the present invention is the filtration hold-up interception method providing a kind of spam, and the method is carried out many levels to mail and filtered identification: the categorical attribute first obtaining user's receiving and dispatching mail according to expression during user's receiving and dispatching mail and/or voice feature data; For the mail that cannot confirm classification, the black/white list list of inquiring user node this locality and friend's node thereof successively, described friend's node refers to the network node between active user's node with higher mail frequency of interaction; Finally, user can be pointed out to judge the classification of this mail.By above-mentioned filtration interception at many levels, identification cost comparatively large, the efficiency lower problem of prior art for spam can be solved.
To achieve these goals, according to an aspect of the present invention, provide a kind of filtration hold-up interception method of spam, comprise the following steps:
Step 1, expression during acquisition user's receiving and dispatching mail and/or voice feature data; And the categorical attribute of the mail of user's transmitting-receiving is obtained according to described expression and/or voice feature data, described categorical attribute comprises: normal email, spam and cannot confirm;
If the categorical attribute obtained is normal email or spam, then terminate classification, otherwise perform step 2;
Step 2, the blacklist list of inquiring user node this locality and white list list, to determine the type of current mail.
Further, if in the blacklist list of the address of this mail of user's transmitting-receiving not in user node this locality and white list list, then perform following steps:
Step 3, user node sends an inquiry request to all friend's nodes, and described inquiry request comprises the address information of current mail;
Step 4, friend's node is according to the blacklist list of described inquiry request search oneself and white list list, if find hit blacklist list or white list list, then return Query Result to this user node, described Query Result represents that the type of this mail is spam or normal email;
If receive the Query Result that friend's node returns, and email type represented by all Query Results is identical, then perform step 5; Otherwise, perform step 6;
Step 5, user node upgrades local blacklist list or white list list according to this Query Result;
Step 6, prompting user judges the classification of this mail;
Wherein, described friend's node is selected from the network node having mail mutual between active user's node;
Wherein, described friend's node refers to the network node between active user's node with higher mail frequency of interaction.
Further, in network, this locality of each node stores friend's node listing, and this list comprises N number of friend address of node and degree of association score value, and the account form of described degree of association score value is:
Degree of association score value=(returning the number of times of blacklist or white list Query Result in the mail interaction times+B* cycle T in A* cycle T)/T;
Wherein, the quantity N of coefficient A, B, cycle T and friend's node both can be constant, also can by default and according to actual needs dynamic conditioning.
Further, the initialization procedure of described friend's node listing is:
The network node having mail mutual with this locality is sorted to low from height according to mail interaction times, the top n node in selected and sorted result as friend's node, to set up initial friend's node listing; Wherein, the initial value of described degree of association score value is all 0.
Further, the renewal process of described friend's node listing is:
At interval of fixing cycle T, calculate the degree of association score value of each network node having mail mutual in current cycle T with this locality, sort to low from height according to degree of association score value, the top n node in selected and sorted result as friend's node, thus upgrades friend's node listing.
Further, the quantity N of described coefficient A, B, cycle T and friend's node can be: A=10, B=20, T=24 (hour), N=50.
Further, described expressive features data comprise: eye position information, eye shape information, eyebrow positional information, eyebrow shape information, face positional information and face shape information;
Described voice feature data comprises: tone information, word speed information and filterability keyword.
Further, the categorical attribute obtaining the mail of user's transmitting-receiving according to described expression and/or voice feature data in described step 1 comprises:
The default expression matched from default expression and/or voice feature data library lookup and described expression and/or voice feature data and/or voice feature data;
When find out described expression and/or voice feature data and first preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the first expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is the first kind, wherein, described first to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, the corresponding relation of expression and/or voice feature data and email type is also stored in described default expression and/or voice feature data storehouse, and
When find out described expression and/or voice feature data and second preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the second expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is Second Type, wherein, described second to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, and described second presets expression and/or voice feature data default to express one's feelings and/or voice feature data is different expression and/or voice feature data with described first.
Further, after determining that the type of the mail that described user receives and dispatches is Second Type, also comprise:
The priority of more described first expression and/or speech data and described second expression and/or speech data;
When the priority comparing described first expression and/or speech data is higher than described second expression and/or the priority of speech data, before the mail controlling the described first kind is arranged in the mail of described Second Type; And
When the priority comparing described first expression and/or speech data is lower than described second expression and/or the priority of speech data, after the mail controlling the described first kind is arranged in the mail of described Second Type.
Further, more described first expression and/or speech data and described second expression and/or speech data priority before, also comprise:
Receive the setting instruction of described user; And
The priority of described first expression and/or speech data and described second expression and/or speech data is determined according to described setting instruction.
The filtration hold-up interception method of spam of the present invention can realize following beneficial effect:
The first, by expression during acquisition user's receiving and dispatching mail and/or voice feature data; And according to expression and/or voice feature data, the mail that user receives and dispatches is classified.
Generally speaking, when user handles postal matter, mood often changes because of Mail Contents, or itself be in a kind of mood, different mood can make the expressive features data of user different, by obtaining expressive features data during user's receiving and dispatching mail, then based on the expressive features data got, mail is classified, because user is relatively more deep to emotional memory when handling postal matter to oneself, thus can by the expressive features data corresponding with mood quickly to mail preliminary classification.
Simultaneously, for some spams (such as advertisement), or often comprise the voice that some are strange, or there are the voice of a lot of business marketing term, sensitive word or other set forms, or due to format recording, there is more stable word speed and intonation, and these are easier to classification identification often.
By expression and/or speech recognition, the Classification and Identification time can be shortened, to realize the preliminary classification identification of mail.
Second, due to based on the representational friend's node more frequently that communicates with local node, often also can receive similar spam and/or the characteristic of normal email simultaneously, by means of black, the white list list of these friend's nodes of concurrent inquiry in a network, spam and/or normal email can be filtered out rapidly, greatly can simplify local Analysis and Screening work.
3rd, the degree of association score value between friend's node and user's local node, can embody communication frequency between the two in certain period, can embody again hit probability that is black, white list list.By comprehensive above two factors, friend's node listing of dynamic determination degree of association the best.
4th, for the mail being finally beyond recognition classification, prompting local user distinguish, can prevent the erroneous judgement of the undetected of spam or normal email like this.
Accompanying drawing explanation
The accompanying drawing forming a application's part is used to provide a further understanding of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart of the filtration hold-up interception method of spam according to the embodiment of the present invention.
Fig. 2 is the structure of the friend's node listing according to the embodiment of the present invention.
Embodiment
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the present invention in detail in conjunction with the embodiments.
The embodiment provides a kind of filtration hold-up interception method of spam, below the filtration hold-up interception method of the spam that the embodiment of the present invention provides be specifically introduced:
Fig. 1 is the flow chart of the filtration hold-up interception method of spam according to the embodiment of the present invention.As shown in Figure 1, the method comprises following step:
Step 1, expression during acquisition user's receiving and dispatching mail and/or voice feature data; And the categorical attribute of the mail of user's transmitting-receiving is obtained according to described expression and/or voice feature data, described categorical attribute comprises: normal email, spam and cannot confirm;
If the categorical attribute obtained is normal email or spam, then terminate classification, otherwise perform step 2;
Step 2, the blacklist list of inquiring user node this locality and white list list, to determine the type of current mail.
If the blacklist list of miss user node this locality or white list list, then perform following steps 3-5:
Step 3, user node sends an inquiry request to all friend's nodes, and described inquiry request comprises the address information of current mail;
Step 4, friend's node is according to the blacklist list of described inquiry request search oneself and white list list, if find hit blacklist list or white list list, then return Query Result to this user node, described Query Result represents that the type of this mail is spam or normal email;
If receive the Query Result that friend's node returns, and email type represented by all Query Results is identical, then perform step 5; Otherwise, perform step 6;
Step 5, user node upgrades local blacklist list or white list list according to this Query Result;
Step 6, prompting user judges the classification of this mail;
Wherein, described friend's node is selected from the network node having mail mutual between active user's node;
Wherein, described friend's node refers to the network node between active user's node with higher mail frequency of interaction.
Because the cost of unit Spam filtering is comparatively large, and accuracy rate is not high usually, and Spam filtering task is all given server, will obviously increase the expense of server end again.Therefore, the Spam filtering hold-up interception method of the application carries out the analysis of concurrent type frog collaborative filtering by utilizing in network with other user nodes of local interaction.
There is many isolated nodes in network, if and if there is mutual (receiving and dispatching mail) frequency of larger mail between two network user node, then mean there is higher homogeney between these two user nodes.In this application, the network node between user node with higher mail frequency of interaction is called " friend's node " (such as, multiple user nodes of same company).Owing to may there is roughly the same statistical property between friend's node, the so obvious Network Synchronization concurrent type frog Cooperative Analysis based on mail frequency of interaction can be advised in the classification comparatively fast and relatively easily provided about local mail, because this Cooperative Analysis is network concurrent collaboration type, therefore efficiency is higher, and can not increase the computational burden of local node.
In network, this locality of all nodes (comprising local node, all friend's nodes) all stores a blacklist list and a white list list.Local node can inquire about its blacklist list and white list list; All friend's nodes of local node can ask local node to inquire about blacklist list and the white list list of this local node, and return Query Result.Wherein, described blacklist list comprises the address of spam, and white list list comprises the address of normal email.Initial value that is black, white list list can be obtained by cloud server, and can real-time update.
Meanwhile, in network, this locality of all nodes (comprising local node, all friend's nodes) stores friend's node listing.
In reality, due to based on the representational friend's node more frequently that communicates with local node, often also can receive similar spam and/or the characteristic of normal email simultaneously, therefore by means of black, the white list list of these friend's nodes of concurrent inquiry in a network, spam and/or normal email can be filtered out rapidly, greatly can simplify local Analysis and Screening work.
Fig. 2 is the structure of the friend's node listing according to the embodiment of the present invention.As shown in Figure 2, described friend's node listing comprises N number of friend address of node and degree of association score value.
During initialization, the value of all degree of association score values is all 0, and this friend's node listing upgrades once at interval of cycle T.
Initialization and the renewal process of friend's node listing are as follows:
A. initialization: have the network node of mail mutual (sending and receiving mail) to sort to low from height according to mail interaction times by with this locality, the top n node in selected and sorted result as friend's node, to set up initial friend's node listing.
Wherein, the quantity N of cycle T and friend's node both can be constant, also can by default and dynamic conditioning according to actual needs.
B. upgrade: at interval of fixing cycle T, calculate the degree of association score value of each network node having mail mutual (sending and receiving mail) in current cycle T with this locality, sort to low from height according to degree of association score value, top n node in selected and sorted result as friend's node, thus upgrades friend's node listing; The account form of described degree of association score value is as follows:
Degree of association score value=(returning the number of times of blacklist or white list Query Result in the mail interaction times+B* cycle T in A* cycle T)/T;
Wherein, the quantity N of coefficient A, B, cycle T and friend's node both can be constant; Also by default and dynamic conditioning according to actual needs, such as, can be able to select:
A=10, B=20, T=24 (hour), N=50.
In a preferred embodiment of the invention, by expression during acquisition user's receiving and dispatching mail and/or voice feature data; And according to expression and/or voice feature data, preliminary classification is carried out to the mail that user receives and dispatches.
Generally speaking, when user handles postal matter, mood often changes because of Mail Contents, or itself be in a kind of mood, different mood can make expression and/or the voice feature data difference of user, by obtaining expression during user's receiving and dispatching mail and/or voice feature data, then based on the expression got and/or voice feature data, mail is classified, because user is more deep to emotional memory when handling postal matter to oneself, thus can by the expression corresponding with mood and/or voice feature data quickly to mail preliminary classification.
Meanwhile, for some spams (such as advertisement), or often comprise some strange voice, or there are the voice of a lot of business marketing term, sensitive word or other set forms, and these are easier to classification identification often.
By expression and/or speech recognition, the Classification and Identification time can be shortened.
In a preferred embodiment of the invention, described expressive features data can comprise: eye position information, eye shape information, eyebrow positional information, eyebrow shape information, face positional information and face shape information etc. compare the expressive features data being easy to recognize;
Described voice feature data can comprise: tone information, word speed information, filterability keyword etc.
Wherein, the categorical attribute obtaining the mail of user's transmitting-receiving according to described expression and/or voice feature data comprises:
After the expression getting user and/or voice feature data, the default expression matched from default expression and/or voice feature data library lookup and described expression and/or voice feature data and/or voice feature data; Wherein, store and the type information of expressing one's feelings and/or voice feature data is corresponding in described default expression and/or voice feature data storehouse;
When find out described expression and/or voice feature data and first preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the first expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is the first kind, wherein, described first to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, the corresponding relation of expression and/or voice feature data and email type is also stored in described default expression and/or voice feature data storehouse, and
When find out described expression and/or voice feature data and second preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the second expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is Second Type, wherein, described second to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, and described second presets expression and/or voice feature data default to express one's feelings and/or voice feature data is different expression and/or voice feature data with described first.
Wherein, after determining that the type of the mail that described user receives and dispatches is Second Type, also comprise:
The priority of more described first expression and/or speech data and described second expression and/or speech data;
When the priority comparing described first expression and/or speech data is higher than described second expression and/or the priority of speech data, before the mail controlling the described first kind is arranged in the mail of described Second Type; And
When the priority comparing described first expression and/or speech data is lower than described second expression and/or the priority of speech data, after the mail controlling the described first kind is arranged in the mail of described Second Type.
Wherein, more described first expression and/or speech data and described second expression and/or speech data priority before, also comprise:
Receive the setting instruction of described user; And
The priority of described first expression and/or speech data and described second expression and/or speech data is determined according to described setting instruction.
In a preferred embodiment of the invention, the expressive features data obtaining user are mated mainly through existing face recognition technology (such as regional characteristics analysis algorithm), built skin detection and the user got characteristic of expressing one's feelings is utilized to carry out signature analysis, result according to analyzing provides a similar value, can be determined whether as certain expression user-defined by this value.
In a preferred embodiment of the invention, the voice feature data obtaining user mates mainly through existing speech recognition technology, built phonetic feature template is utilized to carry out signature analysis with the user vocal feature data got, result according to analyzing provides a similar value, can determine whether as certain voice user-defined by this value; In addition, if comprise some common filtration sensitive words, commercial advertisement publicity vocabulary in mail, and some other User Defined filterability term and vocabulary, can Classification and Identification be spam.
In a preferred embodiment of the invention, because the definition of mood respective between different user and identification have a lot of complexity and otherness, different people may be expressed one's feelings and/or be had very big-difference between the performance of voice and actual mood.In the preferred embodiment of the present invention, user can extract the characteristic information of the current expression of user and/or voice by camera/microphone when self-defined expression and/or voice feature data, and the expression and/or phonetic feature mail that these are expressed one's feelings and/or voice are corresponding is set simultaneously, realize self-defined setting fast and easily and express one's feelings and/or voice feature data.When guiding user oneself definition expression and/or voice feature data, user can be guided to be that different expressions and/or voice feature data distribute a unique ID, such as the expression that shows respectively under the various mood such as happy, sad, excited, detest, doubt and/or voice feature data is corresponding arranges a unique ID.
In a preferred embodiment of the invention, allow user can be arranged by User Defined in advance expression and/or arranging of voice feature data, also can arrange in following process: when user's receiving and dispatching mail, Real-time Obtaining is carried out to user's expression now and/or voice feature data, and the default expression of inquiry and/or voice feature data storehouse are to obtain the default expression corresponding with the expression got and/or voice feature data and/or voice feature data, and then determine the type of the type of the mail that user is now received and dispatched corresponding to the default expression that finds and/or voice feature data.
But, when not finding the default expression corresponding with the current expression that gets and/or voice feature data and/or the words of voice feature data in default expression and/or voice feature data storehouse, user is then described also not to this expression and/or voice feature data define at present, categorical attribute now in step 1 is for confirming, namely expression and/or phonetic feature Classification and Identification step after, if can not determine that the categorical attribute of mail is normal email or spam, then need to proceed Classification and Identification by building grader to the mail that these cannot confirm.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a filtration hold-up interception method for spam, it is characterized in that, the method comprises the following steps:
Step 1, expression during acquisition user's receiving and dispatching mail and/or voice feature data; And the categorical attribute of the mail of user's transmitting-receiving is obtained according to described expression and/or voice feature data, described categorical attribute comprises: normal email, spam and cannot confirm;
If the categorical attribute obtained is normal email or spam, then terminate classification, otherwise perform step 2;
Step 2, the blacklist list of inquiring user node this locality and white list list, to determine the type of current mail.
2. method according to claim 1, is characterized in that, if in the blacklist list of the address of this mail of user's transmitting-receiving not in user node this locality and white list list, then perform following steps:
Step 3, user node sends an inquiry request to all friend's nodes, and described inquiry request comprises the address information of current mail;
Step 4, friend's node is according to the blacklist list of described inquiry request search oneself and white list list, if find hit blacklist list or white list list, then return Query Result to this user node, described Query Result represents that the type of this mail is spam or normal email;
If receive the Query Result that friend's node returns, and email type represented by all Query Results is identical, then perform step 5; Otherwise, perform step 6;
Step 5, user node upgrades local blacklist list or white list list according to this Query Result;
Step 6, prompting user judges the classification of this mail;
Wherein, described friend's node refers to the network node between active user's node with higher mail frequency of interaction.
3. method according to claim 2, is characterized in that, in network, this locality of each node stores friend's node listing, and this list comprises N number of friend address of node and degree of association score value, and the account form of described degree of association score value is:
Degree of association score value=(returning the number of times of blacklist or white list Query Result in the mail interaction times+B* cycle T in A* cycle T)/T;
Wherein, the quantity N of coefficient A, B, cycle T and friend's node both can be constant, also can by default and according to actual needs dynamic conditioning.
4. method according to claim 3, is characterized in that, the initialization procedure of described friend's node listing is:
The network node having mail mutual with this locality is sorted to low from height according to mail interaction times, the top n node in selected and sorted result as friend's node, to set up initial friend's node listing; Wherein, the initial value of described degree of association score value is all 0.
5. method according to claim 4, is characterized in that, the renewal process of described friend's node listing is:
At interval of fixing cycle T, calculate the degree of association score value of each network node having mail mutual in current cycle T with this locality, sort to low from height according to degree of association score value, the top n node in selected and sorted result as friend's node, thus upgrades friend's node listing.
6. method according to claim 5, is characterized in that, the quantity N of described coefficient A, B, cycle T and friend's node can be:
A＝10，B＝20，T＝24，N＝50。
7. the method according to any one of claim 1-6, is characterized in that,
Described expressive features data comprise: eye position information, eye shape information, eyebrow positional information, eyebrow shape information, face positional information and face shape information;
Described voice feature data comprises: tone information, word speed information and filterability keyword.
8. method according to claim 7, is characterized in that, the categorical attribute obtaining the mail of user's transmitting-receiving in described step 1 according to described expression and/or voice feature data comprises:
The default expression matched from default expression and/or voice feature data library lookup and described expression and/or voice feature data and/or voice feature data;
When find out described expression and/or voice feature data and first preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the first expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is the first kind, wherein, described first to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, the corresponding relation of expression and/or voice feature data and email type is also stored in described default expression and/or voice feature data storehouse, and
When find out described expression and/or voice feature data and second preset express one's feelings and/or voice feature data matches time, determine that described expression and/or expression corresponding to voice feature data and/or speech data are the second expression and/or speech data, and determine that the type of the mail that described user receives and dispatches is Second Type, wherein, described second to preset expression and/or voice feature data be arbitrary expression in described default expression and/or voice feature data storehouse and/or voice feature data, and described second presets expression and/or voice feature data default to express one's feelings and/or voice feature data is different expression and/or voice feature data with described first.
9. method according to claim 8, is characterized in that,
After determining that the type of the mail that described user receives and dispatches is Second Type, also comprise:
The priority of more described first expression and/or speech data and described second expression and/or speech data;
When the priority comparing described first expression and/or speech data is higher than described second expression and/or the priority of speech data, before the mail controlling the described first kind is arranged in the mail of described Second Type; And
When the priority comparing described first expression and/or speech data is lower than described second expression and/or the priority of speech data, after the mail controlling the described first kind is arranged in the mail of described Second Type.
10. method according to claim 9, is characterized in that,
More described first expression and/or speech data and described second expression and/or speech data priority before, also comprise:
Receive the setting instruction of described user; And
The priority of described first expression and/or speech data and described second expression and/or speech data is determined according to described setting instruction.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108710606A (en) * | 2018-04-09 | 2018-10-26 | 平安科技（深圳）有限公司 | A kind of Task Progress monitoring method, computer readable storage medium and terminal device |
| CN108766416A (en) * | 2018-04-26 | 2018-11-06 | Oppo广东移动通信有限公司 | Audio recognition method and Related product |
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| CN108710606B (en) * | 2018-04-09 | 2021-10-26 | 平安科技（深圳）有限公司 | Task progress monitoring method, computer readable storage medium and terminal equipment |
| CN108766416A (en) * | 2018-04-26 | 2018-11-06 | Oppo广东移动通信有限公司 | Audio recognition method and Related product |
| CN108766416B (en) * | 2018-04-26 | 2021-06-25 | Oppo广东移动通信有限公司 | Speech recognition method and related product |
| CN109523241A (en) * | 2018-12-13 | 2019-03-26 | 杭州安恒信息技术股份有限公司 | A kind of E-mail communication method for limiting and system |
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