CN110889036A - Multi-dimensional information processing method and device and terminal equipment - Google Patents
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Abstract
The application is applicable to the technical field of computers, and provides a method and a device for processing multi-dimensional information and terminal equipment, wherein the method comprises the following steps: acquiring registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account; extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set; reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set; and pushing the second type information set to the user account. Through the data pushing method and device, the multi-dimensional data can be classified, the demand information can be more conveniently issued, the demand information can be more accurately pushed, and the effectiveness of data pushing is guaranteed.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a processing method and device of multi-dimensional information and terminal equipment.
Background
With the development of computer internet technology, user account demand relationships established based on social electronic commerce products, transactions, data management and the like are more and more complex, and correspondingly generated information dimensions are more and more abundant.
At present, when information is queried through the internet, keywords representing the queried information need to be summarized first, and required information content needs to be searched by inputting the keywords or sentence information containing the keywords. Based on the traditional information query and search mode, the user can check the existing webpage information or can issue the required information in a questioning mode; however, with the increasingly rich and complex information content and dimension, the demand information sent among multiple user accounts is relatively independent and scattered, and the demand information randomly generated among users cannot be better solved based on the current information management processing mode.
Disclosure of Invention
The embodiment of the application provides a processing method and device for multi-dimensional information and terminal equipment, and can solve the problems that the demand information sent among multiple user accounts is relatively independent and scattered at present and the demand information randomly generated among users cannot be better solved based on the current information management processing mode.
In a first aspect, an embodiment of the present application provides a method for processing multidimensional information, including:
acquiring registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account;
extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set;
reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set;
and pushing the second type information set to the user account.
In a possible implementation manner of the first aspect, after acquiring the registration information and the requirement information uploaded by the user, the method includes:
and respectively coding the registration information and the demand information, and acquiring a user account code uniquely corresponding to the registration information and a demand information code corresponding to the demand information.
In a possible implementation manner of the first aspect, extracting a keyword in the requirement information includes:
segmenting the target text of the demand information to obtain word segmentation in the target text;
performing part-of-speech tagging on the participles according to a preset specified part-of-speech;
taking the marked participles as nodes, and respectively establishing links with other participles in the target text;
setting an initial weight value for each participle according to the link quantity established by each node, wherein the initial weight value corresponds to each participle;
performing iterative operation on the weight value of a certain participle according to the initial weight values of all participles connected with the certain participle to obtain the iterative operation result of the weight values of all participles;
and acquiring the participles with weight values meeting a preset threshold value as the keywords according to the iterative operation result.
In a possible implementation manner of the first aspect, the preset keyword dataset is a demand information dataset set according to an intention category;
comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set, wherein the first type information set comprises the following steps:
calculating the similarity of the extracted keywords and a preset keyword data set;
and determining the intention category to which the keyword belongs according to the similarity, classifying the demand information corresponding to the keyword according to the belonging intention category, and acquiring the first type information set.
In a possible implementation manner of the first aspect, the attribute identifier includes a basic information identifier, a feature information identifier, a preference information identifier, and a relationship information identifier;
reading the attribute identification in the registration information corresponding to the first type information set, grouping the first type information set according to the attribute identification, and acquiring a second type information set, wherein the method comprises the following steps:
grouping the first type information set according to one or more attribute identifications in the basic information identification, the characteristic information identification, the preference information identification or the relationship information to form a data dictionary dynamic model based on the attribute identifications so as to obtain the second type information set; the data dictionary dynamic model is a keyword set model of the demand information according to dynamic change and is used for matching the demand information with an information set and classifying the demand information.
In a possible implementation manner of the first aspect, pushing the second type information set to the user account includes:
acquiring release address information corresponding to the second type information set and acquiring a preset address range in which the release address is positioned;
and if the positioning information of the current user account is within the preset address range, pushing the second information set to a map display interface of the current user account, and displaying the release address information of the second information set.
In a possible implementation manner of the first aspect, the demand information is subjected to cluster analysis to obtain corresponding multi-class demand data;
performing correlation analysis on user information corresponding to the multiple types of demand data, and reducing preset dimension information of the demand data;
calculating a similarity value and a difference value of the demand data after the preset dimension information is eliminated;
and classifying the demand data according to the similarity value and the difference value to obtain the second type information set.
In a second aspect, an embodiment of the present application provides a processing apparatus for multidimensional information, including:
the acquisition module is used for acquiring registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account;
the first processing module is used for extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set;
the second processing module is used for reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set;
and the pushing module is used for pushing the second type information set to the user account.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method.
In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a terminal device, causes the terminal device to execute the method for processing multidimensional information according to any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: according to the embodiment, the registration information and the demand information uploaded by a user are obtained, wherein the registration information comprises the attribute identification of the user account; extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set; reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set; and pushing the second type information set to the user account. The problem that the demand information sent among multiple user accounts is relatively independent and scattered at present and the demand information randomly generated among the users cannot be solved based on the current information management processing mode is solved; the realization is to the categorised processing of multidimension degree data for demand information's processing is more convenient, and demand information's propelling movement is more accurate, has guaranteed data push's validity, has stronger ease for use and practicality.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a system provided by an embodiment of the present application;
fig. 2 is a schematic structural diagram of a mobile terminal to which a method for processing multidimensional information provided in an embodiment of the present application is applied;
fig. 3 is a schematic flowchart of a processing method of multi-dimensional information according to an embodiment of the present application;
FIG. 4 is a diagram illustrating an example of an application scenario for multi-dimensional information processing according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a processing apparatus for providing multi-dimensional information according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
Fig. 1 is a schematic system diagram of a processing method for multidimensional information according to an embodiment of the present application. As shown in the figure, the processing method of the multi-dimensional information provided in the embodiment of the present application may be applied to a mobile terminal or a fixed device, for example: the method includes the steps that the intelligent mobile terminal 101, the notebook computer 102, the desktop computer 103 and the like are adopted, the specific type of the terminal equipment is not limited at all, the terminal equipment interacts data with the server 104 in a wired or wireless mode, and multi-dimensional information processing is achieved through the terminal equipment; the wireless method includes internet, WiFi network or mobile network, wherein the mobile network may include existing 2G (such as Global System for mobile communication (GSM)), 3G (such as Universal Mobile Telecommunications System (UMTS)), 4G (such as FDD LTE, TDD LTE) and 4.5G, 5G.
The terminal device of the embodiment of the present application is described by taking a mobile terminal as an example. Fig. 2 is a block diagram illustrating a partial structure of a mobile terminal implementing a multi-dimensional information processing method according to an embodiment of the present application. Referring to fig. 2, the mobile terminal includes: a Radio Frequency (RF) circuit 210, a memory 220, an input unit 230, a display unit 240, a wireless fidelity (WiFi) module 250, a processor 260, and a power supply 270. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 2 is not intended to be limiting of mobile terminals and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 2:
the RF circuit 210 may be used for receiving and transmitting signals during information transmission and reception or during a call, and in particular, receives downlink information of a base station and then processes the received downlink information to the processor 260; in addition, the data for designing uplink is transmitted to the base station. Typically, the RF circuitry includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 210 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.
The input unit 230 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the input unit 230 may include a touch panel 231 and other input devices 232. The touch panel 231, also referred to as a touch screen, may collect touch operations of a user (e.g., operations of the user on or near the touch panel 231 using any suitable object or accessory such as a finger, a stylus, etc.) thereon or nearby, and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 231 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 260, and can receive and execute commands sent by the processor 260. In addition, the touch panel 231 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 230 may include other input devices 232 in addition to the touch panel 231. In particular, other input devices 232 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 240 may be used to display information input by a user or information provided to the user and various menus of the mobile terminal. The Display unit 240 may include a Display panel 241, and optionally, the Display panel 241 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 231 may cover the display panel 241, and when the touch panel 231 detects a touch operation thereon or nearby, the touch panel is transmitted to the processor 260 to determine the type of the touch event, and then the processor 260 provides a corresponding visual output on the display panel 241 according to the type of the touch event. Although the touch panel 231 and the display panel 241 are shown as two separate components in fig. 2 to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 231 and the display panel 241 may be integrated to implement the input and output functions of the mobile terminal.
WiFi belongs to a short-distance wireless transmission technology, and the mobile terminal can help a user to send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 250, and provides wireless broadband internet access for the user. Although fig. 2 shows the WiFi module 250, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 260 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 220 and calling data stored in the memory 220, thereby integrally monitoring the mobile terminal. Alternatively, processor 260 may include one or more processing units; preferably, the processor 260 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 260.
The mobile terminal also includes a power supply 270 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 260 via a power management system that may be configured to manage charging, discharging, and power consumption.
In addition, although not shown, the mobile terminal may further include a bluetooth module, etc., which will not be described herein.
Fig. 3 shows a schematic flow chart of the multidimensional data processing method provided by the present application, which can be applied to the mobile terminal described above by way of example and not limitation.
Step S301, obtaining registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account.
In a possible implementation manner, the multidimensional data processing method may be implemented based on an application program of a terminal device, for example, an application program APP for a social e-commerce platform; when a user logs in an application program through an account, registration information is required to be uploaded to register a user account, wherein the registration information comprises attribute identification of the user account, and the attribute identification comprises multi-dimensional information associated with the user account, such as user basic information, feature information, preference information, relationship information with other user accounts and the like.
Specifically, the basic information of the user comprises information such as a mobile phone number, address book information, gender, birth date, address and the like of the user; the characteristic information comprises characteristic information of occupation, work post, work unit, academic calendar, academic position, specialty, graduation colleges, professional qualifications and the like of the user; the preference information comprises short-term task-oriented preference information such as interests and demand preferences of the user, such as house buying, car buying, decoration, volunteering and the like, and can also be long-term habit and preference information such as sports (sports information such as football, basketball, table tennis, swimming and the like can be further selected), music (music information such as classical music, popular music, concert and the like can be further selected), and movies (movie and preference information such as comedy, science fiction, horror and the like can be further selected).
Optionally, the terminal device sets a keyword of the preference information, and determines the preference information of the user account according to the keyword selected and input by the user; the terminal equipment can also acquire preference information directly and manually added and input by a user.
Specifically, the relationship information with other users includes various relationship information such as friend relationship information, friends of friends, and strangers. The relation user can be added by scanning the two-dimensional code of the user account and the mobile phone number of the user account, and the user account can be searched and added by one or more kinds of information in the attribute information of the user account.
Optionally, the terminal device obtains the demand information input by the user in one or more of text input, voice input, picture input, video input, keyword input, and the like.
In one possible implementation manner, after acquiring the registration information and the requirement information uploaded by the user, the method includes:
and respectively coding the registration information and the demand information, and acquiring a user account code uniquely corresponding to the registration information and a demand information code corresponding to the demand information.
In this embodiment, after the user registration information is obtained, the registration information is compressed and encoded to generate unique encoded information corresponding to the registration information, where the encoded information of the user includes registration time, serial number, user explicit code, feature code, and the like. After the demand information is acquired, compressing and encoding the demand to generate unique specific encoding information corresponding to the demand information, combining the unique specific encoding information with the content of the demand information, and packaging the unique specific encoding information into complete demand encoding information, wherein the complete demand encoding information comprises: user codes, required information entry time, serial numbers, feature codes and the like.
Step S302, extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set.
In one possible implementation manner, the preset keyword dataset is a keyword dataset dynamically generated according to demand information issued by a user, that is, after the demand information is acquired each time, keywords corresponding to the demand information are collected and summarized to generate a corresponding keyword dataset; specifically, the keyword information in the keyword data set can be classified, the preset keyword data set is divided into a plurality of categories of keyword information, after the new keyword of the demand information is acquired, the keyword information can be compared with all keywords of each category, and the preliminary classification of the demand information is more accurate.
Optionally, after acquiring new demand information, performing comparative analysis on the acquired new demand information and the summarized keywords in the preset keyword dataset, and performing corresponding category division on the currently acquired demand information according to the similarity between the currently acquired demand information and each keyword to acquire a first type information set based on the content of the demand information.
In a possible implementation manner, extracting a keyword in the requirement information includes:
a1, segmenting the target text of the demand information to obtain word segmentation in the target text;
in this embodiment, the requirement information is preprocessed to screen unnecessary data and obtain a corresponding target text, for example, a target text of "XX cell-tenant" is obtained after preprocessing "whether" XX cell can be rented in XX cell "or not. And segmenting the target text to acquire word segmentation in the target text, such as 'XX cell' and 'rent room'.
A2, performing part-of-speech tagging on the participles according to preset appointed part-of-speech;
in this embodiment, the part-of-speech of each obtained participle is labeled, for example, "XX cell" is labeled as a noun, and "rent room" is labeled as a verb of action.
A3, taking the marked participles as nodes, and respectively establishing links with other participles in the target text; setting an initial weight value for each participle according to the link quantity established by each node, wherein the initial weight value corresponds to each participle; performing iterative operation on the weight value of a certain participle according to the initial weight values of all participles connected with the certain participle to obtain the iterative operation result of the weight values of all participles; and acquiring the participles with weight values meeting a preset threshold value as the keywords according to the iterative operation result.
In this embodiment, when a plurality of participles exist in the target text, each participle in the target text is used as a node to establish a link with other participles, for example, a plurality of participles such as "XX cell", "rent house", "several rooms and several halls" exist in the target text, a link between "XX cell" and "rent house" is established, and a link between "rent house" and "several rooms and several halls" is established; setting a weight value of each participle according to the number of links, for example, setting a link between an "XX cell" and a link between several rooms and several halls ", setting two links for renting a house, setting initial weights corresponding to the" XX cell ", the" rent house ", and the" several rooms and several halls "as 0.2, 0.6, and 0.2, performing fourier iteration operation on the initial weights to obtain target weights meeting a preset threshold, taking the participles corresponding to the target weights as keywords or keywords, for example, finally, determining that the two participles are the keywords of the demand information if the target weights corresponding to the" XX cell "and the" several rooms and several halls "meet the preset threshold.
Optionally, the preset keyword dataset is a demand information dataset set according to intention categories;
comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set, wherein the first type information set comprises the following steps:
b1, calculating the similarity between the extracted keywords and a preset keyword data set;
in this embodiment, matching is performed on the acquired keyword and a preset keyword dataset, and the similarity after matching is acquired.
And B2, determining the intention category to which the keyword belongs according to the similarity, classifying the demand information corresponding to the keyword according to the belonging intention category, and acquiring the first-class information set.
In this embodiment, according to the similarity, an intention category corresponding to the demand information is determined, the demand information is classified according to the intention category to which the demand information belongs, and a first type information set based on the content of the demand information is acquired.
Step S303, reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set.
In this embodiment, the attribute identifier includes multi-dimensional information associated with the user account, for example, the attribute identifier includes a basic information identifier, a feature information identifier, a preference information identifier, and a relationship information identifier.
Optionally, performing dimension grouping on the first type information set, and acquiring the second type information set includes:
grouping the first type information set according to one or more attribute identifications in the basic information identification, the characteristic information identification, the preference information identification or the relationship information to form a data dictionary dynamic model based on the attribute identifications so as to obtain the second type information set; the data dictionary dynamic model is a keyword set model of the demand information according to dynamic change and is used for matching the demand information with an information set and classifying the demand information.
For example, the corresponding demand issued in a certain user account is further classified according to the sex or address information in the basic information identifier, or the demand information corresponding to the user account is further classified according to the specialty and academic degree of the user, so that the demand information can be classified according to different information dimensions as required, and a demand information set corresponding to the category is obtained. Specifically, a user (a) who originally releases one demand information (a) may initiate an application for combining (or conditionally combining) demand information (B) originally released by another user (B), and if the user B agrees, the demand B is merged into the user a. The merge condition may be an administrative authority in the group of requirements (a + b) participating in the merge. The user requirement information can be automatically analyzed and compared, the system initiates a combined application to the requirement original issuing user (A, B, C, D, E, F …), and after the user agrees (such as A, B, C), the related requirements (a, b and c) are combined (to be a + b + c).
Step S304, pushing the second type information set to the user account.
In this embodiment, the demand information is classified according to different dimensions, and demand information sets of different categories are obtained; according to the attribute identification of the user, the user account can be selectively pushed, for example, according to the occupation information of the user, the requirement information about the related occupation is pushed for the user, so as to realize the sharing and reference of the requirement information.
Optionally, pushing the second type information set to the user account includes:
acquiring release address information corresponding to the second type information set and acquiring a preset address range in which the release address is positioned;
and if the positioning information of the current user account is within the preset address range, pushing the second information set to a map display interface of the current user account, and displaying the release address information of the second information set.
In this embodiment, the user account may issue the demand information on the map, issue the demand for the online user in the preset address range, and set the time period for the demand issue. Such as: user a may select on the map that at 7 months, 30 am 8: 00-10: 00 the counseling need to "whether XXX anticancer drugs are effective" was published in the area of the affiliated tumor hospital of zhongshan university in guangzhou. When the demand release time point is reached, the system automatically triggers that the area is provided with a user which is allowed to receive strangers to release the demand for broadcasting, and the user in the area can be seen in the nearby demand.
By way of example and not limitation, the processing method of the multi-dimensional information includes:
c1, carrying out cluster analysis on the demand information to obtain corresponding multi-class demand data; performing correlation analysis on user information corresponding to the multiple types of demand data, and reducing preset dimension information of the demand data;
in this embodiment, the unknown category of demand information is subjected to cluster analysis, and demand data of a new category or a known category is obtained. The method comprises the steps of performing correlation analysis on user information corresponding to demand data of unknown categories or known categories, for example, selecting certain directional dimension information of a user account according to the demand information sent by a user, screening out attribute information of other dimensions, counting the demand information issued by the user according to the directional dimension information, reducing the redundancy of data processing, and improving the processing efficiency of multi-dimensional information.
C2, calculating a similarity value and a difference value of the demand data after the preset dimension information is eliminated; and classifying the demand data according to the similarity value and the difference value to obtain the second type information set.
In this embodiment, after a large amount of demand information is acquired, similarity values and difference values between multiple demand believes after reduction of the dimension information are calculated, the demand information is further classified according to the similarity values of the attribute information, and a second type information set corresponding to the similarity determination based on the multi-dimension attribute information is obtained, so that the information processing and statistics are more reliable, and the actual demands of the service are better met.
By the embodiment, the problems that the traditional information issuing mode is inconvenient for counting and processing information, so that a lot of inconvenience exists in information transmission and popularization and the service requirements of users cannot be met are solved; the release of the demand information is more convenient, the pushing of the demand information is more accurate, and the effectiveness of data pushing is guaranteed.
Referring to fig. 4, which is an exemplary diagram of an application scenario for processing multidimensional information according to an embodiment of the present application, in the method, requirement information issued by a user account is combined with registration information of the user account, and the requirement information and the registration information are decomposed, compared and aggregated to form a requirement information set, and further multidimensional attribute information of each user in the set is read, and a dynamic information classification table is formed according to the multidimensional attribute information, so that the requirement set is classified and grouped, and further, coupling information is matched or pushed to a target user account.
Specifically, the method comprises the steps of obtaining demand solution information or demand combination solution information by decomposing, comparing and converging demand information of scattered user accounts, so that the accuracy and pertinence of the classification of the multi-dimensional demand information are improved, and the processing of redundant data is reduced; in addition, the method and the device realize that the information at the next moment is combined with the information at the previous moment, scattered and unknown demand information at the next moment is combined with the demand information set at the previous moment, and the combined demand information is further subjected to multi-dimensional analysis and extraction to obtain different types of required information sets.
Fig. 4 is a diagram of an exemplary application scenario of multi-dimensional information processing, further illustrating a specific processing procedure and flow of multi-dimensional information; as shown in the figure, after obtaining the multidimensional data, preprocessing the obtained multidimensional data mainly includes: the multidimensional data are screened and denoised, errors such as abnormal or illegal and unnecessary values are filtered when the multidimensional demand data are acquired, redundant attributes can be eliminated through relevant analysis by a dimensionality reduction method, and therefore the accuracy is improved for later-stage pushing.
Secondly, after data compression is applied to obtain user basic data and dimension data, data blocks are reduced in the multi-dimension demand data analysis process, clustering or parameter models are used for replacing original data, and therefore the purposes of reducing the scale of the mined data and removing redundant data are achieved, or the reduction of the scale of the multi-dimension demand data can be achieved by generalization based on a concept tree; then, carrying out rule processing, rule classification and data analysis on the multi-dimensional demand data; specifically, the rule processing and rule classification includes: the data conversion is mainly applied to the design of a database architecture, the data are subjected to escaping and splitting according to a designed data dictionary, the data are subjected to combined and linked table query according to actual scene services, a data cube can be constructed by a data aggregation method, and a group of data is divided into a plurality of categories according to similarity and difference. Further, the data analysis may include, after the trend analysis is based on the underlying behavioral data, long-term tracking of core indicators, such as: click rate, GMV, active user. Typically, a simple trend graph of the data is generated, and it is necessary to observe future trend changes in the data, whether periodicity exists, whether inflection points exist, and to analyze the underlying cause, whether internal or external. The best output of the trend analysis is the ratio. Medium, year and basal rate; cross-contrasts will be analyzed from the horizontal and vertical dimensions, with contrasts being compared to themselves. The most common data indicator is that a comparison to a target value is required to answer whether we have completed the target; and (3) vertical comparison: the key to the a/B test is to ensure that there is only one variable in both groups, as compared to the other races, and that the other conditions are consistent. The release effect of different channels needs to be checked, you need to ensure that the products are the same, the release investment is the same, the online time is kept unchanged, and the tested data are meaningful. Quadrant analysis each comparison topic is divided into four quadrants based on different data. The quality and number of traffic sources can be divided into four quadrants and fixed time points are selected to compare the traffic cost for each channel. Quality can be preserved by the total amount of this dimension. As a standard. The maintenance of high quality and large volume channels continues, the number of introductions is increased for high quality and low capacity channels, low quality small volume transfers, low quality and large volume attempts are put into the strategy and requirements, and this quadrant analysis enables us to compare and analyze to obtain very intuitive and fast results.
And finally, performing similarity matching, dimension verification and pushing on the data according to a required format according to an associated rule and conditions of basis, such as effectiveness and the like, and building a server cluster through distributed computation.
According to the embodiment, the registration information and the demand information uploaded by a user are obtained, wherein the registration information comprises the attribute identification of the user account; extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set; reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set; and pushing the second type information set to the user account. The multi-dimensional data are classified, so that the demand information is more conveniently processed and more accurately pushed, and the effectiveness of pushing the demand information is ensured; the information required by the user and the information required by other users are dynamically classified and combined, so that the sharing and dynamic association of the information among the multiple user accounts of the client are realized, and the demand information randomly generated among the users is better solved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 5 shows a block diagram of a processing apparatus for processing multidimensional information provided in the embodiment of the present application, corresponding to the method for processing multidimensional information described in the above embodiment, and only the relevant parts of the embodiment of the present application are shown for convenience of description.
Referring to fig. 5, the apparatus includes:
the acquiring module 51 is configured to acquire registration information and demand information uploaded by a user, where the registration information includes an attribute identifier of a user account;
the first processing module 52 is configured to extract keywords in the demand information, compare the keywords with a preset keyword data set, classify the demand information according to a comparison result, and obtain a first type information set;
the second processing module 53 is configured to read the attribute identifier in the registration information corresponding to the first-class information set, and perform dimension grouping on the first-class information set according to the attribute identifier to obtain a second-class information set;
a pushing module 54, configured to push the second type information set to the user account.
By the embodiment, the problems that the traditional information publishing mode is inconvenient for counting and processing information, so that the information is inconvenient to propagate and popularize and cannot meet the service requirements of users are solved; the release of the demand information is more convenient; the information required by the user and the requirement information of other users are dynamically combined and classified, so that the sharing and dynamic association of the requirement information among the user accounts of the client are realized, the requirement information randomly generated among the users is better solved, the requirement information is more accurately pushed, and the effectiveness of data pushing is ensured.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 6, the terminal device 6 of this embodiment includes: at least one processor 60 (only one shown in fig. 6), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 implements the steps of any of the various multi-dimensional information processing method embodiments described above when executing the computer program 62.
The terminal device 6 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is only an example of the terminal device 6, and does not constitute a limitation to the terminal device 6, and may include more or less components than those shown, or combine some components, or different components, such as an input/output device, a network access device, and the like.
The Processor 60 may be a Central Processing Unit (CPU), and the Processor 60 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may in some embodiments be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may also be an external storage device of the terminal device 6 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the terminal device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), random-access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method for processing multidimensional information is characterized by comprising the following steps:
acquiring registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account;
extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set;
reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set;
and pushing the second type information set to the user account.
2. The method for processing multidimensional information as recited in claim 1, after obtaining the registration information and the requirement information uploaded by the user, comprising:
and respectively coding the registration information and the demand information, and acquiring a user account code uniquely corresponding to the registration information and a demand information code corresponding to the demand information.
3. The method for processing multidimensional information as recited in claim 1, wherein extracting keywords in the demand information comprises:
segmenting the target text of the demand information to obtain word segmentation in the target text;
performing part-of-speech tagging on the participles according to a preset specified part-of-speech;
taking the marked participles as nodes, and respectively establishing links with other participles in the target text;
setting an initial weight value for each participle according to the link quantity established by each node, wherein the initial weight value corresponds to each participle;
performing iterative operation on the weight value of a certain participle according to the initial weight values of all participles connected with the certain participle to obtain the iterative operation result of the weight values of all participles;
and acquiring the participles with weight values meeting a preset threshold value as the keywords according to the iterative operation result.
4. The method for processing multidimensional information as recited in claim 3, wherein the preset keyword dataset is a demand information dataset set according to intention categories;
comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set, wherein the first type information set comprises the following steps:
calculating the similarity of the extracted keywords and a preset keyword data set;
and determining the intention category to which the keyword belongs according to the similarity, classifying the demand information corresponding to the keyword according to the belonging intention category, and acquiring the first type information set.
5. The method for processing multidimensional information as recited in claim 1, wherein the attribute identifier comprises a basic information identifier, a characteristic information identifier, a preference information identifier and a relationship information identifier;
reading the attribute identification in the registration information corresponding to the first type information set, grouping the first type information set according to the attribute identification, and acquiring a second type information set, wherein the method comprises the following steps:
grouping the first type information set according to one or more attribute identifications in the basic information identification, the characteristic information identification, the preference information identification or the relationship information to form a data dictionary dynamic model based on the attribute identifications so as to obtain the second type information set; the data dictionary dynamic model is a keyword set model of the demand information according to dynamic change and is used for matching the demand information with an information set and classifying the demand information.
6. The method for processing multidimensional information as recited in claim 1, wherein pushing the second type of information set to the user account comprises:
acquiring release address information corresponding to the second type information set and acquiring a preset address range in which the release address is positioned;
and if the positioning information of the current user account is within the preset address range, pushing the second information set to a map display interface of the current user account, and displaying the release address information of the second information set.
7. The method for processing multidimensional information as recited in claim 1, comprising:
performing cluster analysis on the demand information to obtain corresponding multi-class demand data;
performing correlation analysis on user information corresponding to the multiple types of demand data, and reducing preset dimension information of the demand data;
calculating a similarity value and a difference value of the demand data after the preset dimension information is eliminated;
and classifying the demand data according to the similarity value and the difference value to obtain the second type information set.
8. An apparatus for processing multidimensional information, comprising:
the acquisition module is used for acquiring registration information and demand information uploaded by a user, wherein the registration information comprises an attribute identifier of a user account;
the first processing module is used for extracting keywords in the demand information, comparing the keywords with a preset keyword data set, classifying the demand information according to a comparison result, and acquiring a first type information set;
the second processing module is used for reading the attribute identification in the registration information corresponding to the first type information set, and performing dimension grouping on the first type information set according to the attribute identification to obtain a second type information set;
and the pushing module is used for pushing the second type information set to the user account.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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