CN111475536B - Data analysis method and device based on search engine - Google Patents
Data analysis method and device based on search engine Download PDFInfo
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- CN111475536B CN111475536B CN201910063092.0A CN201910063092A CN111475536B CN 111475536 B CN111475536 B CN 111475536B CN 201910063092 A CN201910063092 A CN 201910063092A CN 111475536 B CN111475536 B CN 111475536B
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application provides a data analysis method and device based on a search engine, wherein the method comprises the following steps: the method comprises the steps of obtaining keywords input by a user, generating a plurality of related keywords by word expansion processing of the keywords, generating a keyword data packet by aggregation processing of the related keywords, inquiring in a preset search database according to the keyword data packet, obtaining search data corresponding to the keyword data packet, and finally analyzing the keywords according to the search data. Therefore, the mining of the real demands of the users is improved, accurate prediction can be carried out aiming at target matters, and the user experience is improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a data analysis method and apparatus based on a search engine.
Background
With the development of internet technology, users can search through a search engine to obtain corresponding results according to requirements. That is, the search data may reflect the real demands of the user in daily life, however, the existing search data can only analyze the current and historical data, greatly reducing the mining of the real demands of the user and resulting in hysteresis for most things.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, the application provides a data analysis method and device based on a search engine, which are used for solving the technical problems that the existing search data in the prior art can only analyze the current and historical data, so that the mining of the real demands of users is greatly reduced, and the hysteresis of most situations is caused.
To achieve the above object, an embodiment of a first aspect of the present application provides a data analysis method based on a search engine, including:
obtaining keywords input by a user, and performing word expansion processing on the keywords to generate a plurality of related keywords;
performing aggregation processing on the related keywords to generate a keyword data packet;
inquiring in a preset search database according to the keyword data packet to obtain search data corresponding to the keyword data packet;
and analyzing the keywords according to the search data.
According to the data analysis method based on the search engine, keywords input by a user are obtained, word expansion processing is conducted on the keywords to generate a plurality of related keywords, aggregation processing is conducted on the related keywords to generate a keyword data packet, query is conducted in a preset search database according to the keyword data packet, search data corresponding to the keyword data packet are obtained, and finally the keywords are analyzed according to the search data. Therefore, the mining of the real demands of the users is improved, accurate prediction can be carried out aiming at target matters, and the user experience is improved.
To achieve the above object, an embodiment of a second aspect of the present application provides a data analysis device based on a search engine, including:
the first acquisition module is used for acquiring the current submission time interval and the current submission times of the user account to be intercepted;
the first acquisition module is used for acquiring keywords input by a user, and performing word expansion processing on the keywords to generate a plurality of related keywords;
the aggregation module is used for carrying out aggregation processing on the plurality of related keywords to generate a keyword data packet;
the query module is used for querying in a preset search database according to the keyword data packet to obtain search data corresponding to the keyword data packet;
and the analysis module is used for analyzing the keywords according to the search data.
According to the data analysis device based on the search engine, keywords input by a user are obtained, word expansion processing is conducted on the keywords to generate a plurality of related keywords, aggregation processing is conducted on the related keywords to generate a keyword data packet, query is conducted in a preset search database according to the keyword data packet, search data corresponding to the keyword data packet are obtained, and finally the keywords are analyzed according to the search data. Therefore, the mining of the real demands of the users is improved, accurate prediction can be carried out aiming at target matters, and the user experience is improved.
To achieve the above object, an embodiment of a third aspect of the present application provides a computer apparatus, including: a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the search engine based data analysis method as described in the embodiment of the first aspect.
To achieve the above object, an embodiment of a fourth aspect of the present application proposes a non-transitory computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a search engine-based data analysis method according to the embodiment of the first aspect.
To achieve the above object, an embodiment of a fifth aspect of the present application proposes a computer program product implementing a search engine based data analysis method according to the embodiment of the first aspect, when instructions in the computer program product are executed by a processor.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a data analysis method based on a search engine according to an embodiment of the present application;
FIG. 2 is a schematic diagram of generating a preset search database according to an embodiment of the present application;
FIGS. 3 a-3 b are schematic diagrams illustrating keyword analysis according to search data according to embodiments of the present application;
fig. 4 is a schematic structural diagram of a data analysis device based on a search engine according to an embodiment of the present application;
FIG. 5 is a schematic diagram of another data analysis device based on a search engine according to an embodiment of the present application; and
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The following describes a data analysis method and device based on a search engine according to an embodiment of the present application with reference to the accompanying drawings.
Fig. 1 is a flow chart of a data analysis method based on a search engine according to an embodiment of the present application.
As shown in fig. 1, the search engine-based data analysis method may include the steps of:
step 101, obtaining keywords input by a user, and performing word expansion processing on the keywords to generate a plurality of related keywords.
In practical application, the user can input corresponding search sentences according to practical application needs, such as how to the mobile phone, how to the price of the house, and the like, so that the search sentences input by the user can be directly used as keywords input by the user, such as the price of the house, as keywords input by the user, keywords extracted by word segmentation processing of the search sentences can be used as keywords input by the user, such as how to the mobile phone, and how to the mobile phone.
After the keywords input by the user are acquired, word development processing is performed on the keywords to generate a plurality of related keywords, for example, the keywords are cold, word development processing is performed on the cold to obtain cold with good and fast eating, pregnant woman cold with good and fast eating, cold with cough with good and fast eating, cold with cold, cold with cough, cold with cold, wind-heat with cold, cold with cold symptoms, lactation cold with cold, and the like, and then related keywords are extracted from the sentences after word development processing, for example, cold with cold, cold with cough and cold with cold.
Step 102, performing aggregation processing on a plurality of related keywords to generate a keyword data packet.
Step 103, inquiring in a preset search database according to the keyword data packet to obtain search data corresponding to the keyword data packet.
After generating a plurality of related keywords, the related keywords may be aggregated to generate a keyword data packet, that is, the related keywords are stored in a data packet, so that the keyword data packet is queried in a preset search database to obtain search data corresponding to the keyword data packet.
It will be appreciated that, as one possible implementation, with the search database to be pre-generated, as shown in fig. 2, it includes:
step 201, obtaining search keywords input by a user in different time periods.
Step 202, analyzing the plurality of search keywords, storing the search keywords in a preset mode, and generating a preset search database.
Specifically, the search keywords input by a plurality of users in the time period from 1996 to 2018 can be obtained, the search time, the search mode and the attribution of the search user account corresponding to the search keywords are recorded, the search keywords such as 'cold' are obtained through analyzing the search time and the attribution of the search user account, the corresponding times of searching in months each year are relatively high, the times of searching in attributions are relatively high, and the like, so that the search database is generated by storing the keywords such as the search time, the keyword search region, the keyword search type and the like in a classified manner.
The search data may be one or more of search time data, search zone data, and search type data.
And 104, analyzing the keywords according to the search data.
It will be appreciated that the results of the analysis of keywords by different search data are different, for example as follows:
the first example, the search data includes: searching time data and searching region data, determining the prediction time corresponding to the keywords according to the searching time data, determining the prediction region corresponding to the keywords according to the searching region data, and displaying the prediction time and the prediction region to a user.
For example, the keyword "cold" is generated, a plurality of related keywords "cold", "cold cough" and "wind-cold" are generated, and an aggregation process is performed to generate a keyword data packet, which is "cold+cold cough+wind-cold", and the keyword data packet is queried in a preset search database, so as to obtain search data corresponding to the keyword data packet, such as search time data shown in fig. 3a and search region data shown in fig. 3 b. Therefore, the prediction time corresponding to the cold can be determined to be 12 months and 1 month each year according to the search time data, the prediction region corresponding to the cold is determined to be the south China, the first year of 12 months and 1 month of China can be predicted, and the high-incidence period of the cold can be prevented and prepared in advance.
The second example, the search data includes: the method comprises the steps of searching type data and searching region data, determining a prediction type corresponding to a keyword according to the searching type data, determining a prediction region corresponding to the keyword according to the searching region data, and displaying the prediction type and the prediction region to a user.
For example, the keyword "car" is generated, a plurality of related keywords "car", "new energy car" and "electric car" are generated, and an aggregate process is performed to generate a keyword data packet, which is "car+new energy car+electric car", and search data corresponding to the keyword data packet is obtained by querying in a preset search database, so that the prediction type corresponding to the "car" can be determined as the electric car according to the search type data, the prediction region corresponding to the "car" can be determined as the big cities such as Beijing, shanghai according to the search region data, the sales quantity of electric cars predicting big cities such as Beijing, shanghai in the next year can be increased, and research and production of electric cars in these cities can be increased.
The third example, the search data includes: the method comprises the steps of searching prompt data and searching region data, determining prompt information corresponding to keywords according to the search prompt data, determining predicted regions corresponding to the keywords according to the searching region data, and displaying the prompt information and the predicted regions to a user.
For example, the keyword "protection animals" is generated, a plurality of related keywords "protection animals", "risk animals" and "extinct animals" are generated, and are subjected to aggregation treatment to generate a keyword data packet, wherein the keyword data packet is "protection animals+risk animals+extinct animals" and is searched in a preset search database, so that search data corresponding to the keyword data packet is obtained, prompt information corresponding to the "protection animals" can be determined as enhanced protection according to the search prompt data, prediction regions corresponding to the "protection animals" are determined as south China regions according to the search region data, the enhanced protection of the protection animals in the south China for predicting the next year can be made, possible damages are avoided, and biological protection is facilitated.
According to the data analysis method based on the search engine, keywords input by a user are obtained, word expansion processing is conducted on the keywords to generate a plurality of related keywords, aggregation processing is conducted on the related keywords to generate keyword data packages, query is conducted in a preset search database according to the keyword data packages, search data corresponding to the keyword data packages are obtained, and finally the keywords are analyzed according to the search data. Therefore, the mining of the real demands of the users is improved, accurate prediction can be carried out aiming at target matters, and the user experience is improved.
In order to realize the embodiment, the application also provides a data analysis device based on the search engine.
Fig. 4 is a schematic structural diagram of a data analysis device based on a search engine according to an embodiment of the present application.
As shown in fig. 4, the search engine-based data analysis apparatus may include: a first acquisition module 410, an aggregation module 420, a query module 430, and an analysis module 440. Wherein, the liquid crystal display device comprises a liquid crystal display device,
the first obtaining module 410 is configured to obtain a keyword input by a user, and perform word expansion processing on the keyword to generate a plurality of related keywords.
The aggregation module 420 is configured to aggregate the plurality of related keywords to generate a keyword data packet.
And the query module 430 is configured to query in a preset search database according to the keyword data packet, and obtain search data corresponding to the keyword data packet.
And an analysis module 440, configured to analyze the keywords according to the search data.
In a possible implementation manner of the embodiment of the present application, as shown in fig. 5, the method further includes, on the basis of fig. 4: a second acquisition module 450 and a generation module 460.
And a second obtaining module 450, configured to obtain the search keyword input by the user in different time periods.
The generating module 460 is configured to analyze the plurality of search keywords, store the search keywords according to a preset manner, and generate a preset search database.
In one possible implementation manner of the embodiment of the present application, searching data includes: search time data and search region data; the analysis module 440 is specifically configured to: determining the prediction time corresponding to the keyword according to the search time data; determining a predicted region corresponding to the keyword according to the search region data; and displaying the predicted time and the predicted region to the user.
In one possible implementation manner of the embodiment of the present application, searching data includes: search type data and search region data; the analysis module 440 is specifically configured to: determining a prediction type corresponding to the keyword according to the search type data; determining a predicted region corresponding to the keyword according to the search region data; the prediction type and the prediction region are displayed to the user.
In one possible implementation manner of the embodiment of the present application, searching data includes: search prompt data and search region data; the analysis module 440 is specifically configured to: determining prompt information corresponding to the keywords according to the search prompt data; determining a predicted region corresponding to the keyword according to the search region data; and displaying prompt information and predicted regions to the user.
It should be noted that the foregoing explanation of the embodiment of the data analysis method based on the search engine is also applicable to the data analysis device based on the search engine of this embodiment, and the implementation principle is similar, and will not be repeated here.
According to the data analysis device based on the search engine, keywords input by a user are obtained, word expansion processing is conducted on the keywords to generate a plurality of related keywords, aggregation processing is conducted on the related keywords to generate a keyword data packet, query is conducted in a preset search database according to the keyword data packet, search data corresponding to the keyword data packet are obtained, and finally the keywords are analyzed according to the search data. Therefore, the mining of the real demands of the users is improved, accurate prediction can be carried out aiming at target matters, and the user experience is improved.
By way of implementing the above-described embodiments, the present application also proposes a computer device comprising: a processor and a memory. Wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the search engine-based data analysis method as described in the foregoing embodiment.
Fig. 6 is a schematic structural diagram of a computer device provided by an embodiment of the present application, showing a block diagram of an exemplary computer device 90 suitable for use in implementing an embodiment of the present application. The computer device 90 shown in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 6, the computer device 90 is in the form of a general purpose computer device. Components of computer device 90 may include, but are not limited to: one or more processors or processing units 906, a system memory 910, and a bus 908 that connects the various system components, including the system memory 910 and the processing units 906.
Bus 908 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Computer device 90 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 90 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 910 may include computer-system-readable media in the form of volatile memory such as random access memory (Random Access Memory; hereinafter: RAM) 911 and/or cache memory 912. The computer device 90 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 913 may be used to read from or write to a non-removable, nonvolatile magnetic medium (not shown in FIG. 6, commonly referred to as a "hard disk drive"). Although not shown in fig. 6, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 908 via one or more data media interfaces. The system memory 910 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the application.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
Programs/utilities 914 having a set (at least one) of program modules 9140 can be stored in, for example, system memory 910, such program modules 9140 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 9140 generally perform the functions and/or methods in the described embodiments of the application.
The computer device 90 may also communicate with one or more external devices 10 (e.g., keyboard, pointing device, display 100, etc.), one or more devices that enable a user to interact with the terminal device 90, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 90 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 902. Moreover, computer device 90 may also communicate with one or more networks such as a local area network (Local Area Network; hereinafter LAN), a wide area network (Wide Area Network; hereinafter WAN) and/or a public network such as the Internet via network adapter 900. As shown in fig. 6, network adapter 900 communicates with other modules of computer device 90 over bus 908. It should be appreciated that although not shown in fig. 6, other hardware and/or software modules may be used in connection with computer device 90, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 906 executes various functional applications and search engine-based data analysis by running programs stored in the system memory 910, for example, implementing the search engine-based data analysis method mentioned in the foregoing embodiment.
In order to implement the above-described embodiments, the present application also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a search engine-based data analysis method as described in the above-described embodiments.
To achieve the above embodiments, the present application also proposes a computer program product, which when executed by a processor, implements a search engine based data analysis method as described in the previous embodiments.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A search engine-based data analysis method, comprising the steps of:
obtaining keywords input by a user, and performing word expansion processing on the keywords to generate a plurality of related keywords;
performing aggregation processing on the related keywords to generate a keyword data packet;
inquiring in a preset search database according to the keyword data packet to obtain search data corresponding to the keyword data packet;
analyzing the keywords according to the search data;
before the query is performed in the preset search database according to the keyword data packet, the method further comprises the following steps:
acquiring search keywords input by a user in different time periods;
analyzing the plurality of search keywords, storing the search keywords in a preset mode, and generating a preset search database;
the storing according to the preset mode comprises the following steps:
and storing the keywords in a classified manner according to the keyword-searching time, the keyword-searching region and the keyword-searching type.
2. The method of claim 1, wherein the search data comprises: search time data and search region data;
the analyzing the keywords according to the search data comprises the following steps:
determining the predicted time corresponding to the keyword according to the search time data;
determining a predicted region corresponding to the keyword according to the search region data;
and displaying the predicted time and the predicted region to the user.
3. The method of claim 1, wherein the search data comprises: search type data and search region data;
the analyzing the keywords according to the search data comprises the following steps:
determining a prediction type corresponding to the keyword according to the search type data;
determining a predicted region corresponding to the keyword according to the search region data;
the prediction type and the prediction territory are presented to the user.
4. The method of claim 1, wherein the search data comprises: search prompt data and search region data;
the analyzing the keywords according to the search data comprises the following steps:
determining prompt information corresponding to the keywords according to the search prompt data;
determining a predicted region corresponding to the keyword according to the search region data;
and displaying the prompt information and the predicted region to the user.
5. A search engine-based data analysis device, comprising:
the first acquisition module is used for acquiring keywords input by a user, and performing word expansion processing on the keywords to generate a plurality of related keywords;
the aggregation module is used for carrying out aggregation processing on the plurality of related keywords to generate a keyword data packet;
the query module is used for querying in a preset search database according to the keyword data packet to obtain search data corresponding to the keyword data packet;
the analysis module is used for analyzing the keywords according to the search data;
the second acquisition module is used for acquiring search keywords input by a user in different time periods;
the generation module is used for analyzing the plurality of search keywords, storing the search keywords in a preset mode and generating a preset search database;
the storing according to the preset mode comprises the following steps:
and storing the keywords in a classified manner according to the keyword-searching time, the keyword-searching region and the keyword-searching type.
6. The apparatus of claim 5, wherein the search data comprises: search time data and search region data;
the analysis module is specifically configured to:
determining the predicted time corresponding to the keyword according to the search time data;
determining a predicted region corresponding to the keyword according to the search region data;
and displaying the predicted time and the predicted region to the user.
7. The apparatus of claim 5, wherein the search data comprises: search type data and search region data;
the analysis module is specifically configured to:
determining a prediction type corresponding to the keyword according to the search type data;
determining a predicted region corresponding to the keyword according to the search region data;
the prediction type and the prediction territory are presented to the user.
8. The apparatus of claim 5, wherein the search data comprises: search prompt data and search region data;
the analysis module is specifically configured to:
determining prompt information corresponding to the keywords according to the search prompt data;
determining a predicted region corresponding to the keyword according to the search region data;
and displaying the prompt information and the predicted region to the user.
9. A computer device comprising a processor and a memory;
wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the search engine based data analysis method according to any one of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a search engine based data analysis method according to any of claims 1-4.
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