CN106126592B - Processing method and device for search data - Google Patents
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- CN106126592B CN106126592B CN201610446858.XA CN201610446858A CN106126592B CN 106126592 B CN106126592 B CN 106126592B CN 201610446858 A CN201610446858 A CN 201610446858A CN 106126592 B CN106126592 B CN 106126592B
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Abstract
The disclosure relates to a processing method and a device for search data, wherein the method comprises the following steps: uploading first search data in a search engine to a cloud data platform; screening out second search data with the frequency weight more than or equal to a preset weight in the first search data; acquiring the second search data and identification information of the second search data; and obtaining the credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine. The beneficial effect of this disclosure does: the credit weight of the user is obtained by calculating the first search data of the user through the cloud data platform, so that the analysis of the financial behavior of the user is completed, the evaluation and judgment of the financial behavior of the user are well realized through the first search data, and the important role is played in describing the financial image of the user.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a method and a device for processing search data.
Background
In the process of the traditional financial loan service, banks need to carry out credit investigation and consultation on various information (such as identity information, deposit and loan, work information, monthly running water, real property and the like) of customers, some of the information can be obtained through network data platforms which are interconnected by various banks, and some of the information needs to be prechecked by business personnel related to the banks and each data information of the customers to be checked on the spot, so that the cost cycle is long, and the efficiency of the traditional financial loan is low.
In the era of mobile internet, the credit of the user is evaluated through the behavior of the mobile terminal user, namely the credit of the user is evaluated through a behavior finance mode, so that the loan service of the terminal user is realized.
The user portrait of the user has important scene factors, the data is used for describing and knowing the client, people are animals with abnormal complexity, the information latitude is also very complex, people are very low-end people only by external data to portray people, the credit information and the population attribute are mainly used, strong relevant information is obtained, weak relevant information is ignored, portrayal data with more weight values can be used as portrait of the user, a plurality of latitudes such as population attribute, credit latitude, consumption characteristic, interest, social information and the like during portrayal of the user are organized and concentrated to find relevant data in the same service scene, classifying and labeling the data, judging whether external data needs to be introduced according to business needs, such as bank credit bureaus, social software, etc., the present disclosure provides a method by which a credit rating of a corresponding user may be derived through processing of first search data of an individual.
How to convert the representation of the user behavior in the terminal into exact data so as to make the data better judge the credit level of the user and further serve financial risk control is a problem to be solved urgently at present.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
The present disclosure provides a method and an apparatus for processing search data, so that a corresponding behavior and a credit evaluation thereof are obtained through processing of first search data in a user terminal, thereby better implementing judgment of the behavior of a user.
In a first aspect, an embodiment of the present disclosure provides a processing method for searching data, where the method includes:
uploading first search data of a plurality of users in a search engine to a cloud data platform;
screening out second search data with the frequency weight more than or equal to a preset weight in the first search data;
acquiring the second search data and identification information of the second search data;
and obtaining the credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the cloud data platform is used for screening the first search data to obtain second search data with larger weight and identification information corresponding to each second search data, and the credit weight is calculated according to the identification information and the proportion of the first search data appearing in the search engine of the second search data, so that the financial behavior of the user is analyzed, the actual financial situation of the user is not needed to be inspected by spending a large amount of manpower and material resources, the financial behavior of the user is well evaluated and judged through the first search data, and the important role is played in describing the financial image of the user.
With reference to the other aspect, in a possible implementation manner of the other aspect, before uploading the first search data in the search engine to the cloud data platform, the method includes:
acquiring browsing data sets in each search engine;
acquiring the use frequency of each item of browsing data in the browsing data set;
comparing the use frequency with a preset use frequency, and selecting browsing data with the use frequency more than or equal to the preset use frequency as first search data;
the first search data is a browsing data set from which browsing data with a frequency lower than a preset use frequency is deleted.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: before the first search data is uploaded to the cloud data platform, browsing data appearing in a search engine can be further screened to obtain the first search data to be uploaded, and discarding operation is performed on non-functional browsing data with the use frequency lower than the preset use frequency, so that the quality of the first search data is guaranteed on the source of the first search data.
With reference to the other aspect, in a possible implementation manner of the other aspect, when obtaining the second search data and the identification information of the second search data, the method includes:
acquiring the occurrence frequency of each second search data;
taking the second search data with the largest occurrence times as clustering data;
and acquiring identification information of the clustering data in the cloud data platform.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the second search data with the largest occurrence frequency in the second search data is used as the clustering data, and the identification information of the clustering data on the cloud data platform is obtained, wherein the identification information is a label made for the clustering result of the second search data, and the label can reflect the financial behavior most frequently performed by the user, so that the credit evaluation corresponding to the financial behavior of the user can be reflected.
In combination with another aspect, in a possible implementation manner of another aspect, when uploading first search data of a plurality of users in a search engine to a cloud data platform, the uploading includes:
acquiring browsing data in a search engine;
dividing each browsing data according to data attributes to obtain type weights;
and uploading browsing data with the type weight larger than a preset threshold value to a cloud data platform as first search data.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: according to another screening mode of the browsing data appearing in the search engine of the user, the browsing data are divided through data attributes, so that the browsing data with the type weight larger than a preset threshold value are obtained and uploaded, and the identification information of the user is accurately obtained, so that credit evaluation is accurately made on the financial behaviors of the user.
With reference to the other aspect, in a possible implementation manner of the other aspect, obtaining the credit weight according to the identification information of the second search data and the number of second search data included in the first search data appearing in the search engine includes:
judging to obtain an application scene according to the first search data;
calculating the relevance ratio of each second search data according to the data relevance strength corresponding to each application scene and the type weight of the first search data;
and obtaining a corresponding credit weight according to the association proportion and the identification information.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the data association strength can be used as a constant K for credit evaluation, and all user stations can use the same unified evaluation standard when obtaining the credit weight through the constant K, so that the credit weight of the user is more objective and closer to the credit evaluation of the real user.
With reference to the other aspect, in a possible implementation manner of the other aspect, when uploading the first search data in the search engine to the cloud data platform, the method includes:
acquiring application scenes of the first search data;
and performing coarse-grained analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse-grained analysis.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: the coarse grain analysis of the application scene of each first search data is also used as a screening mode for the first search data, the second search data can be obtained through a certain screening rule, and the first search data can obtain a more accurate classification result through the coarse grain analysis mode.
In a second aspect, the present disclosure further provides a processing apparatus for searching data, and a specific technical solution includes the following:
the uploading module is configured for uploading the first search data in the search engine to the cloud data platform;
the screening module is configured to screen out second search data of which the frequency weight is greater than or equal to a preset weight in the first search data;
an obtaining module configured to obtain the second search data and identification information of the second search data;
and the weight calculation module is configured to obtain the credit weight according to the identification information of the second search data and the quantity of the second search data included in the first search data appearing in the search engine.
With reference to the other aspect, in a possible implementation manner of the other aspect, the apparatus further includes:
the browsing acquisition module is configured to acquire the browsing data sets in the search engines;
the frequency acquisition module is configured to acquire the use frequency of each item of browsing data in the browsing data set;
the comparison module is configured to compare the use frequency with a preset use frequency, select browsing data with the use frequency greater than or equal to the preset use frequency as first search data, and discard the browsing data with the use frequency less than the preset use frequency;
the first search data in the uploading module is a browsing data set after browsing data lower than a preset using frequency are deleted.
With reference to the other aspect, in a possible implementation manner of the other aspect, the weight calculating module further includes:
the number obtaining module is configured to obtain the occurrence number of each second search data;
the clustering data module is configured to take the second search data with the largest occurrence frequency as clustering data;
the identification acquisition module is configured to acquire identification information of the clustered data in the cloud data platform.
With reference to the other aspect, in a possible implementation manner of the other aspect, the uploading module further includes:
a browse data module configured to obtain browse data in a search engine;
the type dividing module is configured to divide the browsing data according to data attributes to obtain type weights;
the data uploading module is configured to upload browsing data with the type weight larger than a preset threshold value to the cloud data platform as first search data.
With reference to the other aspect, in a possible implementation manner of the other aspect, the weight calculating module further includes:
the judging module is configured for judging to obtain an application scene according to the first search data;
the association proportion calculation module is configured to calculate the association proportion of each second search data according to the data association strength corresponding to each application scene and the type weight of the first search data;
and the second weight calculation module is used for obtaining a corresponding credit weight according to the association proportion and the identification information.
With reference to the other aspect, in a possible implementation manner of the other aspect, the uploading module further includes:
a scene acquisition module configured to acquire an application scene of each of the first search data;
the granularity analysis module is configured to perform coarse granularity analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse granularity analysis.
In a third aspect, the present disclosure further provides a processing apparatus for searching data, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
uploading first search data in a search engine to a cloud data platform;
screening out second search data with the frequency weight more than or equal to a preset weight in the first search data;
acquiring the second search data and identification information of the second search data;
and obtaining the credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Fig. 1 is a schematic flowchart of a processing method for searching data according to an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating browsing data screening according to an exemplary embodiment of the present disclosure;
FIG. 3 is a label diagram during clustering according to an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic illustration of type weights for an exemplary embodiment of the present disclosure;
FIG. 5 is a method diagram of an application scenario of an exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram of a processing device for searching data according to an exemplary embodiment of the present disclosure;
fig. 7 is a block diagram illustrating an apparatus 800 for a method of processing first search data according to an example embodiment.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not limiting of the disclosure. It should be further noted that, for the convenience of description, only some of the structures relevant to the present disclosure are shown in the drawings, not all of them.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the steps are depicted in the flowchart as a sequential process, many of the steps can be performed in parallel, concurrently, or simultaneously. Further, the order of the steps may be rearranged, the process may be terminated when its operations are completed, and other steps not included in the drawings may be included. The processing may correspond to methods, functions, procedures, subroutines, and the like.
The utility model relates to a processing method and device of search data, it mainly applies to in the scene of carrying on credit evaluation through the terminal in order to realize financial risk control, carry out screening, cluster analysis, calculation of credit weight etc. to the search data that appear in the terminal search engine through the cloud data platform, make the data analysis through the cloud data platform can obtain the user's credit weight, can evaluate the financial behavior of user better through this credit weight, thus serve financial risk control better, have avoided offering the financial money to the user that the credit is bad.
The present embodiment may be applied to a case where a terminal with a search engine performs credit evaluation, and the method may be performed by a control device of the terminal. The device may be implemented by software and/or hardware, and may be generally integrated in a terminal or controlled by a service management center of a cloud data platform.
The user searches data through a search engine in a terminal (such as a mobile phone, a pad, and other terminals with a search function) or a search engine of an app installed in the terminal to obtain information. These search terms may be used to side-write the financial capabilities of the user. During implementation, information such as an account number and a mobile terminal identifier can be used for identifying which search data belong to the same user.
For example, a mobile phone number registered by the user, a unique terminal identifier bound to the mobile phone number, account numbers of various shopping websites and the like. After receiving first search data a uploaded by a terminal from a terminal browser, first search data b from a shopping website and first search data c searched by an operating system, the cloud data platform can determine that the first search data a and c belong to a user A according to terminal identifiers of the first search data a and c uploaded, and determine that the first search data b also belongs to the user A according to an account number of the shopping website.
As shown in fig. 1, a method for processing search data according to an exemplary embodiment of the present disclosure may be applied to a terminal, and specifically includes the following steps:
in step 110, uploading first search data in a search engine to a cloud data platform;
the search engine of the intelligent terminal may include a stand-alone search engine capable of being used for network connection, such as Baidu, Google, Bi, etc., or may be first search data formed when searching for items in a website in a search engine of an application at a client, for example, some shopping applications.
The first search data may be first search data obtained by filtering historical browsing data when searching is performed in a corresponding search engine.
The cloud data platform provides an effective data interface, receives raw data (first search data) of each mobile terminal and provides services for relevant data statistics, such as data integration, analysis, fusion, distribution, aggregation and the like.
The first search data appearing in the search engine can be uploaded to the cloud data platform through a wired or wireless data interface in the terminal.
In step 120, screening out second search data with the frequency weight greater than or equal to a preset weight from the first search data;
the aggregation analysis of the first search data is performed in the cloud data platform, when the uploaded first search data is large, the uploaded first search data can be analyzed in a keyword monitoring mode, when the uploaded first search data is analyzed through keywords, each keyword can be one or more first search data possibly appearing in the first search data, the first search data included in the keyword can better reflect cash fusion, for example, the keyword is "credit", the related first search data can include "cheat credit", "loan", "high interest credit", and the like, the first search data related to the keyword is used as second search data, and each second search data has identification information corresponding to the second search data, and the identification information corresponds to a credit risk value of each first search data related to the keyword.
In another way that can be taken, the way of keyword monitoring can also be varied, for example:
counting the frequency weight in the first search data in the cloud data platform;
taking each first search data with the counted frequency weight more than or equal to the preset weight as second search data;
when the frequency weight is obtained by taking the first search data uploaded by all terminals in the cloud data platform as a calculation base number, the frequency weight is obtained by calculating the proportion of the same first search data in the calculation base number of each first search data uploaded by a certain terminal, namely the frequency weight is the frequency of each first search data in the cloud data platform, and the preset weight can be preset through the cloud data platform.
In step 130, acquiring second search data and identification information of the second search data;
after the filtering of the frequency weight in step 120, the corresponding second search data can be obtained, and the identification information of each second search data can be obtained.
The identification information is a tag for each second search data, the tag being a conventional credit weight for each second search data. The conventional credit weighting of such financial activity of the terminal is typically determined upon the occurrence of the second search data.
The credit weight is a credit evaluation value and is a determined value capable of reflecting the financial behavior risk of the user.
In step 140, a credit weight is obtained based on the identification information of the second search data and the number of second search data included in the first search data that appears in the search engine.
The identification information of each second search datum is a conventional credit weight M, and the proportion of the second search datum in the corresponding first search datum is a constant N, then the credit weight P is mxn, and the credit weight P is used for evaluating the financial behavior of the user related to the financial transaction.
In another implementation scenario of the exemplary embodiment of the present disclosure, as shown in fig. 2, before step 110, a step of screening browsing data and then uploading the browsing data to a cloud data platform is further included, so that preliminary screening is performed on browsing data appearing in a search engine, where the step includes the following steps:
in step 210, a browsing data set in each search engine is acquired;
it is possible to include useful first search data, such as "loan", "risk", "finance industry", etc., in the browsing data set presented in the search engine, as well as meaningless browsing data, such as some numbers and shuffled letters, "2 ad"/"lkdo", etc.
In step 220, obtaining the frequency of use of each item of browsing data in the browsing data set;
each item of browsing data appearing in the search engine has different use frequency due to factors such as use habits and whether the search engine is a common search engine, and the use frequency of the browsing data is counted.
In step 230, comparing the usage frequency with a preset usage frequency, and selecting browsing data with the usage frequency greater than or equal to the preset usage frequency as first search data; (ii) a
For browsing data sets, when the useful browsing data is used more frequently than the preset using frequency, the civil browsing data is used as the first search data to be uploaded, and for some meaningless browsing data with the using frequency lower than the preset using frequency, a discarding operation can be performed, or other forms of processing can be performed.
Therefore, the first search data in the search engine when the first search data arrives at the cloud data platform is browsing data with a frequency of use greater than the preset frequency of use in the browsing data set after the meaningless browsing data lower than the preset frequency of use is deleted.
In an implementation manner of the exemplary embodiment of the present disclosure, as shown in fig. 3, when performing step 120, a step of performing cluster analysis on the second search data to obtain identification information thereof is included, so that the analysis of the data can obtain the identification information thereof more intelligently from the perspective of big data analysis, and specific steps thereof include the following steps:
in step 310, the number of occurrences of each second search data is obtained.
The second search data may be a result of secondary screening analysis on the most original browsing data set, and the second search data at this time can reflect a real data situation related to the financial aspect, and the occurrence frequency of the second search data is counted, where the occurrence frequency is the total occurrence frequency of each second search data in all the first search data.
In step 320, the second search data with the largest number of occurrences is used as cluster data.
In general, only one second search data having the largest number of occurrences among the second search data is used as the cluster data, and in some cases, the second search data having the largest number of occurrences may be the same, for example, if the "loan" and the "cash-out" in the second search data have the same number of occurrences as a (a is greater than or equal to 2), the analysis is performed according to the situation:
for the case where the frequency of use of the second search data can be calculated:
the use frequencies B and C of "loan" and "cash-out" in the second search data are acquired, respectively, and at this time, either one of the cluster data can be selected, but the number of occurrences of the cluster data a1 is a × (1+ (1-B) × (1-C)), that is, the number of occurrences of the cluster data is raised in this case accordingly.
For the case where the frequency of use of the second search data cannot be calculated:
directly making the occurrence number A1 of the cluster data equal to K1X A, wherein K1A constant greater than 1, which can be set by statistics of the data.
In step 330, identification information of the clustered data in the cloud data platform is obtained.
For the obtained cluster data, it correspondingly has an identification information in the cloud data platform, where the identification information is a label of the cluster data, and is usually an evaluation coefficient capable of reflecting credit evaluation, for example, when the cluster data is "high interest loan", the identification information may be lower than 0.6, that is, no loan is suggested to the user.
In another implementation scenario of the exemplary embodiment of the present disclosure, as shown in fig. 4, when performing step 110, a manner may also be performed to perform type division on browsing data in a search engine, so as to perform another manner of filtering on browsing data in the search engine before uploading the first search data, where the specific operation steps of the process include the following steps:
in step 410, browsing data in a search engine is acquired;
as with step 210, the set of browsing data present in the search engine in the mobile terminal may include useful first search data, such as "loan", "risk", "finance industry", "high interest loan", and "cash-out", etc., as well as useless or nonsensical browsing data, such as some numbers and shuffled letters, "2 ad"/"lkdo", etc.
In step 420, dividing each browsing data according to data attributes to obtain a type weight;
the data attribute can be a type characteristic attribute of browsing data, browsing data with different types of characteristics can be obtained when the browsing data is divided, and each type of browsing data correspondingly has a type weight.
In step 430, the browsing data with the type weight larger than the preset threshold value is uploaded to the cloud data platform as first search data.
And comparing the type weight with a preset threshold value, uploading the browsing data of which the type weight is greater than the preset threshold value, and discarding the browsing data of which the type weight is less than the preset threshold value.
The browsing data are screened through the type characteristics and then serve as the first search data to be uploaded to the cloud data platform, and during further clustering analysis, clustering analysis can be performed according to the first search data with the same type characteristics, and the first search data do not need to be clustered again through a new mode, so that the follow-up method can be conveniently executed.
As shown in fig. 5, a credit weight calculation process provided by an exemplary embodiment of the present disclosure comprehensively obtains constants that affect a credit weight through an application scenario of first search data and an association strength thereof, and further more accurately obtains a credit evaluation, where the steps of implementing the process include the following steps:
in step 510, judging an application scene according to the first search data;
the application scenario may be an application program, that is, a source of the first search data, for example, a search engine such as a microblog, a hundred, a browser, a facebook, a google mobile, or the like, or an application scenario reflected in a propagation path of the social network in the terminal.
In step 520, calculating the relevance ratio of each second search data according to the data relevance strength corresponding to each application scene and the type weight of the first search data;
the first search data obtained from different application scenarios has correspondingly different data association strengths, such as the data association strength of the first search data obtained from a microblog search engine, which is generally greater than the data association strength of the first search data obtained from google mobile, the data association strength can be divided into a strong association and a weak association, the strength value for the strongly associated data can be set to be greater than 1, the strength value for the weakly associated data can be set to be less than 1, and the relevance ratio of the first search data is obtained comprehensively based on the type weight obtained by the method in step 420, which can be used as another constant K of the first search data, since the K is also different due to the difference of the association strengths, the credit weights of different users can be obtained by applying the manner of scenario judgment, i.e. credit rating of the user.
In step 530, a corresponding credit weight is obtained according to the association weight and the identification information.
And according to the association proportion obtained by the data association strength and the type weight and the identification information of the first search data after screening, synthesizing to obtain the corresponding credit weight.
In another possible implementation manner of the exemplary embodiment of the disclosure, when the first search data is classified, the first search data may be analyzed by a coarse-grained analysis method to obtain a clustering result, and a specific implementation process of the method includes the following steps:
acquiring application scenes of the first search data;
the application scenario may be a specific application of the source of the first search data, synchronizing the application scenario in step 510.
And performing coarse-grained analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse-grained analysis.
The application scene of the first search data is used as a basic element to perform coarse-grained analysis, that is, the first search data obtained under the same application scene can be used as a large class, so that the cluster analysis of the first search data from the same source is facilitated.
The implementation work of the method of the present disclosure may include:
(1) acquiring uploaded first search data;
first search data of each user in the near future, namely the first search data of the browsing data of the user after the keyword/word screening, is stored in the cloud data platform. The first search data belonging to a user can be identified through information such as an account number, a mobile terminal identification and the like.
(2) Selecting data of n users for analysis;
first search data of n users in a cloud data platform are obtained, wherein n is a constant which accords with the idea of big data.
(3) Performing cluster analysis on keyword/word information, for example, 100000 pieces of data contain 680000 more meaningful words, selecting 10000 words with the occurrence frequency more than 100 as feature words according to the occurrence frequency of the words in a document, constructing feature vectors by using a TFIDF (term frequency-inverse document frequency) method, and then clustering by using a Kmeans algorithm.
(4) Obtaining a clustering analysis result, and labeling the different clustering results with labels for identifying information, such as the following two clustering results:
category number, size, number of users (category number, label) included in category
kinds:0
size:284
The max K attr lottery ticket is a double-color ball, The lottery ticket is a browser, The lotto is luck, The color is: Hongkong, The trend is luck, The color is: a color ball is live, The star color is: sports, The horse party is started, The universal is predicted, The quick playing is performed, The key is arranged, The scene is on, The star color is today, The color is closed, The body color is red rice, The assistant is on, The news is forecasted, The star is ringtone, The Fucai is double, The running is recorded, The air conditioner is on, The test machine is illegal, The time is super, The period is train ticket is happy, The Dongfeng:
#################################################################
kinds:1
size:121
the max K attr indicates that The pregnant woman is pregnant for a long time, The symptoms are that The belly is early, The woman is painful, The driver is a grandmother, The fetus is a same room, The baby is measured, The menstruation is measured, The dreams influence The period of The period, three pains are produced, The girl is four, The daughter is several, The pains are dropped, The father and The father react, The days are later, The child is a male, The pregnancy is a multi-month, The attention is paid to The normality, The driving is taken, The mother suffers from abortion, The woman wants to calculate, The notes are:
#################################################################
the identification information of kinds 0 may be a lottery ticket and the identification information of kinds 1 may be pregnant.
By acquiring the risk value of the identification information of kinds in the cloud data platform, the influence factors related to the identification information in the method, such as the type weight of the first search data, the data association strength, the use frequency, the clustering result and the like, are comprehensively calculated to obtain the corresponding credit weight so as to evaluate the credit level.
The method can acquire the new credit weight of the identification information again at preset time intervals, and when the value of the credit weight is greater than a certain risk value, the network financial behavior of the user can be limited by reporting to the financial data center.
Fig. 6 is a block diagram of a processing apparatus for searching data according to the present disclosure, and as shown in fig. 6, the processing apparatus for searching data according to the present disclosure mainly includes an uploading module 610, a screening module 620, an obtaining module 630 and a weight calculating module 640, where two of the modules may be in communication connection with each other and may be in communication connection with a central control unit of a user terminal.
The uploading module 610 is configured to upload first search data in a search engine to a cloud data platform;
a screening module 620 configured to screen out second search data with a frequency weight greater than or equal to a preset weight from the first search data;
an obtaining module 630, configured to obtain the second search data and identification information of the second search data;
the weight calculation module 640 is configured to obtain the credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine of each user.
In another implementation scenario of an exemplary embodiment of the present disclosure, the apparatus further includes:
the browsing acquisition module is configured for acquiring browsing data sets in the search engines;
the frequency acquisition module is configured to acquire the use frequency of each item of browsing data in the browsing data set;
the comparison module is configured to compare the use frequency with a preset use frequency, select browsing data with the use frequency greater than or equal to the preset use frequency as first search data, and discard the browsing data with the use frequency less than the preset use frequency;
the first search data in the uploading module is a browsing data set after browsing data lower than a preset using frequency are deleted.
In another implementation scenario of the exemplary embodiment of the present disclosure, the weight calculating module 630 further includes:
the number obtaining module is configured to obtain the occurrence number of each second search data;
the clustering data module is configured to take the second search data with the largest occurrence frequency as clustering data;
the identification acquisition module is configured to acquire identification information of the clustered data in the cloud data platform.
In another implementation scenario of the exemplary embodiment of the present disclosure, the uploading module 610 further includes:
a browse data module configured to obtain browse data in a search engine;
the type dividing module is configured to divide the browsing data according to data attributes to obtain type weights;
the data uploading module is configured to upload browsing data with the type weight larger than a preset threshold value to the cloud data platform as first search data.
In another implementation scenario of the exemplary embodiment of the present disclosure, the weight calculating module 630 further includes:
the judging module is configured for judging to obtain an application scene according to the first search data;
the association proportion calculation module is configured to calculate the association proportion of each second search data according to the data association strength corresponding to each application scene and the type weight of the first search data;
and the second weight calculation module is used for obtaining a corresponding credit weight according to the association proportion and the identification information.
In another implementation scenario of the exemplary embodiment of the present disclosure, the uploading module 610 further includes:
a scene acquisition module configured to acquire an application scene of each of the first search data;
the granularity analysis module is configured to perform coarse granularity analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse granularity analysis.
The processing device for search data provided in the above embodiments may execute the processing method for search data provided in any embodiment of the present disclosure, and have corresponding functional modules and advantageous effects for executing the method.
The present disclosure also provides a processing apparatus for searching data, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
uploading first search data in a search engine to a cloud data platform;
screening out second search data with the frequency weight more than or equal to a preset weight in the first search data;
acquiring the second search data and identification information of the second search data;
and obtaining the credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine.
The processor is also configured to perform the above processing method of searching data.
It will be appreciated that the disclosure also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the disclosure into practice. The program may be in the form of source code, object code, a code intermediate source and object code such as partially compiled form, or in any other form suitable for use in the implementation of the method according to the disclosure. It will also be noted that such programs may have many different architectural designs. For example, program code implementing the functionality of a method or system according to the present disclosure may be subdivided into one or more subroutines.
Many different ways to distribute the functionality among these subroutines will be apparent to the skilled person. The subroutines may be stored together in one executable file, forming a self-contained program. Such an executable file may include computer-executable instructions, such as processor instructions and/or interpreter instructions (e.g., Java interpreter instructions). Alternatively, one or more or all of the subroutines may be stored in at least one external library file and linked with the main program either statically or dynamically (e.g., at run time). The main program contains at least one call to at least one of the subroutines. Subroutines may also include function calls to each other. Embodiments directed to a computer program product comprising computer executable instructions for performing each of the process steps of at least one of the set forth methods. These instructions may be subdivided into subroutines and/or stored in one or more files, which may be statically or dynamically linked.
Another embodiment related to a computer program product comprises computer executable instructions for each of the means corresponding to at least one of the systems and/or products set forth. These instructions may be subdivided into subroutines and/or stored in one or more files, which may be statically or dynamically linked.
The carrier of the computer program may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium such as a (ROM, e.g. a cd ROM or a semiconductor ROM) or a magnetic recording medium, e.g. a floppy disk or hard disk. Further, the carrier may be a transmissible carrier such as an electrical or optical signal, which may be conveyed via electrical or optical cable or by radio or other means. When the program is embodied in such a signal, the carrier may be constituted by such cable or device. Alternatively, the carrier may be an integrated circuit in which the program is embedded, the integrated circuit being adapted for performing, or for use in the performance of, the relevant method.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb "comprise" and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
If desired, the different functions discussed herein may be performed in a different order and/or concurrently with each other. Further, if desired, one or more of the functions described above may be optional or may be combined.
The steps discussed above are not limited to the order of execution in the embodiments, and different steps may be executed in different orders and/or concurrently with each other, if desired. Further, in other embodiments, one or more of the steps described above may be optional or may be combined.
Although various aspects of the disclosure are set out in the independent claims, other aspects of the disclosure comprise combinations of features from the described embodiments and/or the dependent claims with the features of the independent claims, and not solely the combinations explicitly set out in the claims.
It is noted herein that while the above describes example embodiments of the disclosure, these descriptions should not be viewed in a limiting sense. Rather, several variations and modifications may be made without departing from the scope of the present disclosure as defined in the appended claims.
It should be understood by those skilled in the art that the modules in the apparatus of the embodiment of the present disclosure may be implemented by a general-purpose computing apparatus, and the modules may be integrated into a single computing apparatus or a network group of computing apparatuses, and the apparatus in the embodiment of the present disclosure corresponds to the method in the foregoing embodiment, and may be implemented by executable program code, or by a combination of integrated circuits, so that the present disclosure is not limited to specific hardware or software, and combinations thereof.
It should be understood by those skilled in the art that the modules in the apparatus of the embodiment of the present disclosure may be implemented by a general-purpose mobile terminal, and the modules may be integrated in a single mobile terminal or a combination of devices composed of mobile terminals, and the apparatus in the embodiment of the present disclosure corresponds to the method in the foregoing embodiment, and may be implemented by editing executable program code or by a combination of integrated circuits, so that the present disclosure is not limited to specific hardware or software or a combination thereof.
Fig. 7 is a block diagram illustrating an apparatus 800 for a method of processing first search data according to an example embodiment. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 7, the apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the apparatus 800 and other apparatuses. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present disclosure and the technical principles employed. Those skilled in the art will appreciate that the present disclosure is not limited to the particular embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the present disclosure. Therefore, although the present disclosure has been described in greater detail with reference to the above embodiments, the present disclosure is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present disclosure, the scope of which is determined by the scope of the appended claims.
Claims (12)
1. A method of processing search data, the method comprising:
uploading first search data in a search engine to a cloud data platform;
screening out second search data with the frequency weight more than or equal to a preset weight in the first search data;
acquiring the second search data and identification information of the second search data, wherein the identification information is a conventional credit weight of each second search data;
obtaining credit weights according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine, namely the proportion of the second search data to the corresponding first search data, wherein the credit weight of each second search data is the conventional credit weight of each second search data multiplied by the proportion of the second search data to the corresponding first search data;
when the second search data and the identification information of the second search data are acquired, the method includes:
acquiring the occurrence frequency of each second search data;
taking the second search data with the largest occurrence times as clustering data;
and acquiring identification information of the clustering data in the cloud data platform.
2. The method of claim 1, wherein prior to uploading the first search data in the search engine to the cloud data platform, comprising:
acquiring browsing data sets in each search engine;
acquiring the use frequency of each item of browsing data in the browsing data set;
comparing the use frequency with a preset use frequency, and selecting browsing data with the use frequency more than or equal to the preset use frequency as first search data;
the first search data is a browsing data set from which browsing data with a frequency lower than a preset use frequency is deleted.
3. The method of claim 1, wherein uploading the first search data in the search engine to the cloud data platform comprises:
acquiring browsing data in a search engine;
dividing each browsing data according to data attributes to obtain type weights;
and uploading browsing data with the type weight larger than a preset threshold value to a cloud data platform as first search data.
4. The method of claim 3, wherein obtaining the credit weight based on the identification information of the second search data and a number of second search data included in the first search data that appears in the search engine comprises:
judging to obtain an application scene according to the first search data;
calculating the relevance ratio of each second search data according to the data relevance strength corresponding to each application scene and the type weight of the first search data;
and obtaining a corresponding credit weight according to the association proportion and the identification information.
5. The method of claim 1, wherein uploading the first search data in the search engine to the cloud data platform comprises:
acquiring application scenes of the first search data;
and performing coarse-grained analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse-grained analysis.
6. A processing apparatus for searching data, the apparatus comprising:
the uploading module is configured for uploading the first search data in the search engine to the cloud data platform;
the screening module is configured to screen out second search data of which the frequency weight is greater than or equal to a preset weight in the first search data;
an obtaining module, configured to obtain the second search data and identification information of the second search data, where the identification information is a conventional credit weight of each second search data;
the weight calculation module is configured to obtain a credit weight according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine, namely the proportion of the second search data to the corresponding first search data, wherein the credit weight of each second search data is the conventional credit weight of each second search data multiplied by the proportion of the second search data to the corresponding first search data;
the weight calculation module further comprises:
the number obtaining module is configured to obtain the occurrence number of each second search data;
the clustering data module is configured to take the second search data with the largest occurrence frequency as clustering data;
the identification acquisition module is configured to acquire identification information of the clustered data in the cloud data platform.
7. The processing apparatus according to claim 6, wherein the apparatus further comprises:
the browsing acquisition module is configured for acquiring browsing data sets in the search engines;
the frequency acquisition module is configured to acquire the use frequency of each item of browsing data in the browsing data set;
the comparison module is configured to compare the use frequency with a preset use frequency, select browsing data with the use frequency greater than or equal to the preset use frequency as first search data, and discard the browsing data with the use frequency less than the preset use frequency;
the first search data in the uploading module is a browsing data set after browsing data lower than a preset using frequency are deleted.
8. The processing apparatus of claim 6, wherein the upload module further comprises:
a browse data module configured to obtain browse data in a search engine;
the type dividing module is configured to divide the browsing data according to data attributes to obtain type weights;
the data uploading module is configured to upload browsing data with the type weight larger than a preset threshold value to the cloud data platform as first search data.
9. The processing apparatus as defined in claim 8, wherein the weight calculation module further comprises:
the judging module is configured for judging to obtain an application scene according to the first search data;
the association proportion calculation module is configured to calculate the association proportion of each second search data according to the data association strength corresponding to each application scene and the type weight of the first search data;
and the second weight calculation module is used for obtaining a corresponding credit weight according to the association proportion and the identification information.
10. The processing apparatus of claim 6, wherein the upload module further comprises:
a scene acquisition module configured to acquire an application scene of each of the first search data;
the granularity analysis module is configured to perform coarse granularity analysis on the application scene to obtain first search data included in an analysis rule embodied by the coarse granularity analysis.
11. A processing apparatus for searching data, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
uploading first search data in a search engine to a cloud data platform;
screening out second search data with the frequency weight more than or equal to a preset weight in the first search data;
acquiring the second search data and identification information of the second search data, wherein the identification information is a conventional credit weight of each second search data;
obtaining credit weights according to the identification information of the second search data and the number of the second search data included in the first search data appearing in the search engine, namely the proportion of the second search data to the corresponding first search data, wherein the credit weight of each second search data is the conventional credit weight of each second search data multiplied by the proportion of the second search data to the corresponding first search data;
when the second search data and the identification information of the second search data are acquired, the method includes:
acquiring the occurrence frequency of each second search data;
taking the second search data with the largest occurrence times as clustering data;
and acquiring identification information of the clustering data in the cloud data platform.
12. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any of claims 1-5.
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| CN106658216B (en) * | 2016-12-20 | 2020-04-21 | 天脉聚源(北京)传媒科技有限公司 | Method and device for obtaining information together |
| CN106874435B (en) * | 2017-01-25 | 2020-02-14 | 北京航空航天大学 | User portrait construction method and device |
| CN108304426B (en) * | 2017-04-27 | 2021-12-17 | 腾讯科技（深圳）有限公司 | Identification obtaining method and device |
| CN108764141B (en) * | 2018-05-25 | 2021-07-02 | 广州虎牙信息科技有限公司 | Game scene description method, device, equipment and storage medium thereof |
| CN112328595A (en) * | 2020-10-30 | 2021-02-05 | 上海钐昆网络科技有限公司 | Data searching method, device, equipment and storage medium |
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