CN114817265B - Financial information acquisition method by utilizing big data server - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2452—Query translation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24578—Query processing with adaptation to user needs using ranking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
- G06F16/278—Data partitioning, e.g. horizontal or vertical partitioning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/906—Clustering; Classification
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a financial information acquisition method by utilizing a big data server, and relates to the technical field of big data information acquisition; for better acquisition of data information; the server includes: the big database is used for storing big data; and the keyword generation module is used for generating keywords for acquiring the corresponding information. According to the invention, by arranging the keyword generation module, the classification module, the sub-database and the like, corresponding keywords can be generated based on user requirements, so that corresponding data information can be acquired more accurately, and due to the characteristic of big data, very effective information can not be acquired directly by less searches.
Description
Technical Field
The invention relates to the technical field of big data information acquisition, in particular to a financial information acquisition method by utilizing a big data server.
Background
Big data, or referred to as huge amount of data, refers to information that the related data amount is so large that the data cannot be retrieved, managed, processed and organized through the current mainstream software tool in a reasonable time to be more positive for helping business operation decision; big data has five characteristics of large quantity, high speed, diversity, low value density and authenticity; therefore, the big data can be effectively utilized to better carry out predictive analysis, user behavior analysis or the use of some other advanced data analysis method; at present, certain difficulty exists in obtaining corresponding types of data in big data, and how to sort and sort effective data in the big data is a problem to be considered.
Through retrieval, the patent with the Chinese patent application number of CN202010638459.X discloses an information collection and analysis system based on big data, which comprises a data acquisition unit, a data importing unit, a data collection unit, a data rating unit, a data display unit and a data storage unit, wherein the front end of the information collection and analysis system acquires sales data through the data acquisition unit, the data correspondingly acquired through the data importing unit is classified and classified, the classified and classified data corresponds to product information one by one, and the information collection and analysis system stores the classified and classified data in the data collection unit to correspondingly generate collection data. The information collecting and analyzing system in the above patent has the following disadvantages: although the utility model can meet certain use requirements, the utility model can not realize self-perfected functions, so the utility model needs to be improved.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a financial information acquisition method using a big data server.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a financial information acquisition method using a big data server, the server comprising:
the big database is used for storing big data;
the keyword generation module is used for generating keywords for acquiring corresponding information;
the classification module is used for classifying the acquired information based on different keywords;
the searching module searches corresponding information in the large database according to different keywords;
the sub-databases are multiple in number and are used for storing different types of data information.
Preferably: the acquisition method comprises the following steps:
s1: a user generates corresponding keywords according to requirements through a keyword generation module;
s2: searching through a searching module by utilizing the generated keywords;
s3: the searching module searches the large database to obtain corresponding data information;
s4: the classification module judges whether to classify the searched data information according to the keywords, the classification is carried out in the step S5, and the classification is carried out in the step S6;
s5: the corresponding data information is divided into matched sub-databases, and if the matched sub-databases do not exist, the corresponding data information is divided into new sub-databases;
s6: and (5) ending.
Further: the keywords are classified into category keywords and other keywords, and the keyword generation module comprises:
the information interaction unit is used for writing in requirements of users so as to facilitate the generation of keywords;
the keyword generation unit is used for generating specific keywords;
the keyword library is used for storing generated keywords;
the keyword class dividing unit adds class codes at the tail end of the keyword information and is used for dividing the keyword classes, and the class codes are identified by the classification module; the category keywords provide a classification basis for the classification module, and other keywords are only used as data information corresponding to the search module.
Further preferred is: the keyword generation method of the keyword generation module comprises the following steps:
s11: the user inputs the requirement through the information interaction unit;
s12: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s13: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s14: dividing the generated keywords into category keywords or other keywords according to the judging result;
s15: the keyword category dividing unit adds category codes to the end of the classified category keywords or other keywords;
s16: and after the key words are generated, putting the key words into use and storing the key words into a key word stock.
As a preferred embodiment of the present invention: the search module comprises:
the accurate searching unit is used for accurately searching according to the generated keyword content;
and the rough searching unit is used for searching by calling the keywords with similar or related meanings from the keyword library according to the generated keywords.
Further preferred as the present invention is: the classification module comprises:
the identification unit is used for identifying the category codes added by the keyword category dividing unit;
the classification unit transfers the searched corresponding data information into a corresponding sub-database based on the category codes;
and the marking unit can add marking codes to the searched data information according to the requirements to mark.
As still further aspects of the invention: the marking method of the marking unit specifically comprises the following steps:
s21: the user inputs requirements through the information interaction unit, wherein the requirements comprise search content requirements, search mode requirements and mark requirements;
s22: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s23: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s24: dividing the generated keywords into category keywords or other keywords according to the judging result;
s25: the keyword category dividing unit adds category codes to the end of the classified category keywords or other keywords;
s26: after the key words are generated, putting the key words into use and storing the key words into a key word stock;
s26: selecting a corresponding accurate search unit or a corresponding rough search unit to search according to the search mode requirement of a user by utilizing the generated keywords;
s27: providing the searched data information for the user;
s28: the marking unit adds marking codes to the searched data information according to marking requirements of users;
s29: the classifying unit transfers the searched corresponding data information into the corresponding sub-database based on the category codes.
Based on the scheme: setting the keywords as X, setting the category codes as A0 and A1 … … AN (wherein A0 is regarded as no category), and setting the keywords added with the category codes by the keyword category dividing unit as XA0 and XA1 … … XAN;
the serial numbers of the sub databases are K1 and K2 … … KN, and the serial numbers of the sub databases correspond to the category codes;
setting the data information as Y, marking codes as B1 and B2 … … BN, marking once as YB1, marking twice as YB2, and so on, and marking N times as YBN; the priority displayed during searching is YBN > YBN-1 > … … > YB1.
Based on the scheme: the accurate searching unit of the searching module searches the data information in the large database in real time through the keyword library, the classifying unit divides the searched data information into matched sub-databases in real time, and the searching module searches the sub-databases in the searching process of the user.
Preferred on the basis of the foregoing scheme: the sub-database is provided with a data self-cleaning function, and the data information which is not searched in the past 1 year and has the marking code is cleaned according to the warehousing sequence, except for the data information which is not searched in the past 1 year.
The beneficial effects of the invention are as follows:
1. according to the invention, by arranging the keyword generation module, the classification module, the sub-database and the like, corresponding keywords can be generated based on user requirements, so that corresponding data information can be acquired more accurately, and due to the characteristic of big data, very effective information can not be acquired directly by less searches.
2. According to the invention, by setting the keyword category dividing unit, the keyword library and the like, different types of keywords can be generated according to the requirements of users, so that the data can be classified selectively later, and the practicability and the flexibility are improved.
3. According to the invention, by arranging the accurate searching unit and the rough searching unit, different searching results can be provided according to actual requirements, so that the requirements of users are better met, and the practicability is improved.
4. According to the invention, the marking unit is arranged, so that the searched data information can be marked by adding the marking code according to the requirement, the data information can be searched again, and the practicability is further improved.
Drawings
Fig. 1 is a flowchart of a financial information obtaining method using a big data server according to embodiment 1 of the present invention.
Detailed Description
The technical scheme of the patent is further described in detail below with reference to the specific embodiments.
Example 1:
a financial information acquisition method using a big data server, the server comprising:
the big database is used for storing big data;
the keyword generation module is used for generating keywords for acquiring corresponding information;
the classification module is used for classifying the acquired information based on different keywords;
the searching module searches corresponding information in the large database according to different keywords;
the sub-databases are multiple in number and are used for storing different types of data information.
By arranging the keyword generation module, the classification module, the sub-database and the like, corresponding keywords can be generated based on user requirements so as to obtain corresponding data information more accurately, and due to the characteristic of big data, less searches can not directly obtain very effective information.
To facilitate information acquisition; the acquisition method comprises the following steps:
s1: a user generates corresponding keywords according to requirements through a keyword generation module;
s2: searching through a searching module by utilizing the generated keywords;
s3: the searching module searches the large database to obtain corresponding data information;
s4: the classification module judges whether to classify the searched data information according to the keywords, the classification is carried out in the step S5, and the classification is carried out in the step S6;
s5: the corresponding data information is divided into matched sub-databases, and if the matched sub-databases do not exist, the corresponding data information is divided into new sub-databases;
s6: and (5) ending.
To facilitate the division of keywords; the keywords are classified into category keywords and other keywords, and the keyword generation module comprises:
the information interaction unit is used for writing in requirements of users so as to facilitate the generation of keywords;
the keyword generation unit is used for generating specific keywords;
the keyword library is used for storing generated keywords;
the keyword class dividing unit adds class codes at the tail end of the keyword information and is used for dividing the keyword classes, and the class codes are identified by the classification module; the category keywords provide a classification basis for the classification module, and other keywords are only used as data information corresponding to the search module.
By setting the keyword category dividing unit, the keyword library and the like, different types of keywords can be generated according to the requirements of users, so that the data can be classified selectively, and the practicability and the flexibility are improved.
To facilitate the generation of keywords; the keyword generation method of the keyword generation module comprises the following steps:
s11: the user inputs the requirement through the information interaction unit;
s12: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s13: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s14: dividing the generated keywords into category keywords or other keywords according to the judging result;
s15: the keyword category dividing unit adds category codes to the end of the classified category keywords or other keywords;
s16: and after the key words are generated, putting the key words into use and storing the key words into a key word stock.
Example 2:
a financial information acquisition method using big data server is provided, in order to promote searching effect; the present embodiment makes the following modifications on the basis of embodiment 1, and the search module includes:
the accurate searching unit is used for accurately searching according to the generated keyword content;
and the rough searching unit is used for searching by calling the keywords with similar or related meanings from the keyword library according to the generated keywords.
Through setting up accurate search unit, rough search unit, can provide different search results according to actual demand to better satisfy user's demand, promoted the practicality.
In order to facilitate the marking of the required data information; the classification module comprises:
the identification unit is used for identifying the category codes added by the keyword category dividing unit;
the classification unit transfers the searched corresponding data information into a corresponding sub-database based on the category codes;
and the marking unit can add marking codes to the searched data information according to the requirements to mark.
Through setting up the mark unit, can add the mark code to the data information that searches for according to the demand and mark to search for this piece of data information again, further promoted the practicality.
In order to improve the information processing effect; the marking method of the marking unit specifically comprises the following steps:
s21: the user inputs requirements through the information interaction unit, wherein the requirements comprise search content requirements, search mode requirements and mark requirements;
s22: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s23: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s24: dividing the generated keywords into category keywords or other keywords according to the judging result;
s25: the keyword category dividing unit adds category codes to the end of the classified category keywords or other keywords;
s26: after the key words are generated, putting the key words into use and storing the key words into a key word stock;
s26: selecting a corresponding accurate search unit or a corresponding rough search unit to search according to the search mode requirement of a user by utilizing the generated keywords;
s27: providing the searched data information for the user;
s28: the marking unit adds marking codes to the searched data information according to marking requirements of users;
s29: the classifying unit transfers the searched corresponding data information into the corresponding sub-database based on the category codes.
Specifically, the keywords are set as X, the category codes are A0 and A1 … … AN (wherein A0 is regarded as no category), and the keywords added with the category codes through the keyword category dividing unit are XA0 and XA1 … … XAN; the serial numbers of the sub databases are K1 and K2 … … KN, and the serial numbers of the sub databases correspond to the category codes; setting the data information as Y, marking codes as B1 and B2 … … BN, marking once as YB1, marking twice as YB2, and so on, and marking N times as YBN; the priority displayed during searching is YBN > YBN-1 > … … > YB1.
In order to improve the searching efficiency; the accurate searching unit of the searching module searches the data information in the large database in real time through the keyword library, the classifying unit divides the searched data information into matched sub-databases in real time, and the searching module searches the sub-databases in the searching process of the user.
To facilitate cleaning up the database; the sub-database is provided with a data self-cleaning function, and the data information which is not searched in the past 1 year and has the marking code is cleaned according to the warehousing sequence, except for the data information which is not searched in the past 1 year.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (1)
1. A financial information acquisition method using a big data server, the server comprising:
the big database is used for storing big data;
the keyword generation module is used for generating keywords for acquiring corresponding information;
the classification module is used for classifying the acquired information based on different keywords;
the searching module searches corresponding information in the large database according to different keywords;
the number of the sub-databases is multiple, and the sub-databases are used for storing different types of data information;
the acquisition method comprises the following steps:
s1: a user generates corresponding keywords according to requirements through a keyword generation module;
s2: searching through a searching module by utilizing the generated keywords;
s3: the searching module searches the large database to obtain corresponding data information;
s4: the classification module judges whether to classify the searched data information according to the keywords, the classification is carried out in the step S5, and the classification is carried out in the step S6;
s5: the corresponding data information is divided into matched sub-databases, and if the matched sub-databases do not exist, the corresponding data information is divided into new sub-databases;
s6: ending;
the keywords are classified into category keywords and other keywords, and the keyword generation module comprises:
the information interaction unit is used for writing in requirements of users so as to facilitate the generation of keywords;
the keyword generation unit is used for generating specific keywords;
the keyword library is used for storing generated keywords;
the keyword class dividing unit adds class codes at the tail end of the keyword information and is used for dividing the keyword classes, and the class codes are identified by the classification module; the category keywords provide a classification basis for the classification module, and other keywords are only used as data information corresponding to the search module;
the keyword generation method of the keyword generation module comprises the following steps:
s11: the user inputs the requirement through the information interaction unit;
s12: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s13: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s14: dividing the generated keywords into category keywords or other keywords according to the judging result;
s15: the keyword category dividing unit adds category codes at the tail ends of the classified category keywords or other keywords;
s16: after the key words are generated, putting the key words into use and storing the key words into a key word stock;
the search module comprises:
the accurate searching unit is used for accurately searching according to the generated keyword content;
the rough searching unit is used for searching by calling the keywords with similar or related meanings from the keyword library according to the generated keywords;
the classification module comprises:
the identification unit is used for identifying the category codes added by the keyword category dividing unit;
the classification unit transfers the searched corresponding data information into a corresponding sub-database based on the category codes;
the marking unit can add marking codes to the searched data information to mark according to requirements;
the marking method of the marking unit specifically comprises the following steps:
s21: the user inputs requirements through the information interaction unit, wherein the requirements comprise search content requirements, search mode requirements and mark requirements;
s22: the keyword generation unit generates one or more corresponding keywords according to the requirements of the user;
s23: the keyword class dividing unit judges the generated keywords in combination with the requirement input by the user;
s24: dividing the generated keywords into category keywords or other keywords according to the judging result;
s25: the keyword category dividing unit adds category codes at the tail ends of the classified category keywords or other keywords;
s26: after the key words are generated, putting the key words into use and storing the key words into a key word stock;
s26: selecting a corresponding accurate search unit or a corresponding rough search unit to search according to the search mode requirement of a user by utilizing the generated keywords;
s27: providing the searched data information for the user;
s28: the marking unit adds marking codes to the searched data information according to marking requirements of users;
s29: the classifying unit transfers the searched corresponding data information into a corresponding sub-database based on the category codes;
let the keyword be X and the category code be A 0 、A 1 ……A N Wherein A is 0 The keywords with the category codes added through the keyword category dividing unit are regarded as no category and are XA 0 、XA 1 ……XA N ；
The number of the sub database is K 1 、K 2 ……K N The number of the sub database corresponds to the category code;
let the data information be Y and the mark code be B 1 、B 2 ……B N Marked as YB once 1 Marked twice as YB 2 And so on, marked N times as YB N The method comprises the steps of carrying out a first treatment on the surface of the Display at search timePriority of YB N ＞YB N-1 ＞……＞YB 1 ；
The accurate searching unit of the searching module searches the data information in the large database in real time through the keyword library, the classifying unit divides the searched data information into matched sub-databases in real time, and the searching module searches the sub-databases in the searching process of the user;
the sub-database is provided with a data self-cleaning function, and the data information which is not searched in the past 1 year and has the marking code is cleaned according to the warehousing sequence, except for the data information which is not searched in the past 1 year.
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