CN111538887B - Big data graph and text recognition system and method based on artificial intelligence - Google Patents
Big data graph and text recognition system and method based on artificial intelligence Download PDFInfo
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Abstract
The application belongs to the technical field of proofreading, and discloses a big data graph and text recognition system and method based on artificial intelligence, wherein the method comprises the following steps: the method comprises the steps of acquiring network public resources of a specified public full website by utilizing an available big data management platform, acquiring network information by utilizing big data, and acquiring the network information by utilizing the methods of distributed acquisition, intelligent acquisition after accidental disconnection, reverse acquisition, intelligent judgment time, intelligent weight prevention, periodic acquisition, continuous acquisition and the like, accurately and completely acquiring the network information, and finally performing image-text recognition and correction on the acquired data, so that a healthy and civilized big data platform is achieved.
Description
Technical Field
The application belongs to the technical field of big data, and discloses a big data graph and text recognition system and method based on artificial intelligence.
Background
With the advent of the information age, cloud computing technology, digital technology, internet technology, etc. have further evolved and applied, and the competitiveness of the information industry has been continually increasing, in part, because of the availability of computing power at lower costs for large enterprises, and the ability of various types of systems to perform multitasking today. Secondly, the cost of the memory is also reduced in a straight line, enterprises can process more data in the memory than before, and the computers are aggregated into a server cluster more and more simply, so that the server cluster has potential value and can bring huge profits to businesses, but data information which is subjected to complex processing is needed, and a plurality of image-text information is needed to be processed through the technical field to solve bad image-text information, therefore, the application provides the artificial intelligent large data image-text recognition system and method, and the data information is fully utilized for analysis and utilization.
Disclosure of Invention
Aiming at the defects of the traditional management platform, the application aims to provide a big data graph-text recognition system and method based on artificial intelligence, wherein the system and method comprise a big data management platform, a big data capturing method and a big data graph-text recognition method.
The big data management platform performs data management and method management on big data capture and big data image-text recognition;
the big data grabbing is used for grabbing public whole network stations, and grabbing is performed through hundreds of degrees, dog searching, 360 degrees, microblogs, weChat and other public data of public whole websites;
further, the big data image-text recognition system is used for filtering unhealthy image-text information of the big data capturing image-text information to achieve a big data platform of healthy civilization;
the application provides a big data grabbing method, which comprises the following steps:
(1) distributed grabbing: constructing a distributed method by using a distributed principle to perform distributed grabbing;
(2) the accidental disconnection is followed by grabbing: the system is accidentally disconnected due to special reasons, and after reconnection, the last captured data can be effectively continued to capture the rest information, so that the loss caused by special conditions is prevented;
(3) can reversely grasp: the self-management and learning progress capability is provided, so that the existing knowledge can be quickly learned and the follow-up improvement can be performed to prevent other people from grabbing;
(4) and (3) time judgment: the contents grabbed every day are different, the current data can be effectively grabbed through time judgment, and the data before yesterday are filtered;
(5) repeated grabbing is prevented: the data of each public full website and each page are possibly identical, so that the data titles and the contents are required to be analyzed and then captured in order to avoid the occurrence of repeated data, the repeated capture is avoided, and the resource consumption is reduced;
(6) keyword grabbing: the network public data can be accurately and effectively captured by capturing the data through the keywords;
(7) periodic and continuous grabbing: the regular grabbing is to grab data in a certain time, and the grabbing is not carried out after the time, so that the continuous grabbing always keeps the grabbing of the data;
(8) memory acquisition points: the artificial intelligent memory method only needs to collect the public whole website, can intelligently identify and accurately collect the required data just like the memory of people, intelligently filters useless data, only retains image-text information, can effectively memorize the collection progress when stopping working due to accidents in the collection process, and can then finish unfinished work when re-working.
(9) Automatic analysis and classification: automatically analyzing and filtering unused information such as advertisements and the like, and storing needed image-text information; automatically analyzing production collection rules, and intelligently capturing image-text information of each public full website; automatic analysis and correction can be performed, and the content of manual error correction can be intelligently learned, so that the accuracy is more and more accurate.
The application provides a method for identifying big data images and texts, which comprises the following steps:
(1) capturing graphic and text information data through big data, and carrying out graphic and text recognition through a test 4j technology;
(2) acquiring text and picture information in the picture, and then checking;
(3) the text retrieval text information filters bad information such as multi-dirty words, and once the words are found, the text information is directly filtered;
(4) full text retrieval of the textual information by custom dirty words;
(5) keyword library: artificial intelligence learns a keyword library similar to a Xinhua dictionary to analyze whether graphic information is positive;
(6) intelligent analysis of graphics context: intelligent analysis of graphic content through keyword library
(7) And (3) intelligently judging analysis results: and comparing the intelligent analysis results with the keyword library through intelligent analysis of the images and texts, and judging the results: results of positive information, neutral information, negative information, and the like;
(8) intelligent addition of new words: when new words appear, the method can intelligently learn the new words, automatically add the new words into the keyword library, and manually add the new words;
(9) intelligent analysis of picture characters: when the picture has characters, the information result of the characters can be intelligently analyzed, and the result is given;
automatic analysis and classification: automatically analyzing and filtering the information of the picture characters, classifying and summarizing the picture-text information, and giving out corresponding analysis results.
Compared with the prior art, the application has the obvious advantages and effects that: the application belongs to the technical field of big data, and discloses a big data graph text recognition system and method based on artificial intelligence, wherein the method comprises the following steps: the network public resources of the appointed public full website are acquired by utilizing the big data management platform, the network information is acquired by utilizing the big data, the method comprises the steps of distributed acquisition, intelligent acquisition after accidental disconnection, reverse acquisition, intelligent judgment time, intelligent weight prevention, periodic acquisition, continuous acquisition and the like, the network information is accurately and completely acquired, and finally the acquired data are distributed and stored in hbase, mongoDB, elasticsearch to solve the problem of tens of millions of data processing, so that the big data acquisition efficiency is greatly improved, and the workload of technicians in the big data acquisition process is reduced.
Drawings
The application is described in further detail below with reference to the drawings and the specific embodiments.
FIG. 1 is a diagram of an artificial intelligence enabled big data collection and storage system of the present application;
wherein, the reference numerals are as follows: the system comprises a big data management platform module 1, a big data grabbing module 2 and a big data image-text recognition module 3;
fig. 2 is a flow chart.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, the technical scheme for realizing the application is as follows: the large data graph and text recognition system and method based on artificial intelligence comprises a large data management platform, a large data capturing and method and a large data storage and method;
the big data management platform performs data management and method management on big data capture and big data book identification;
the big data grabbing is used for grabbing public whole network stations, and grabbing is performed through hundreds of degrees, dog searching, 360 degrees, microblogs, weChat and other public data of public whole websites;
further, the big data image-text recognition system is used for filtering unhealthy image-text information of the big data capturing image-text information to achieve a big data platform of healthy civilization;
the application provides a big data grabbing method, which comprises the following steps:
(1) distributed grabbing: constructing a distributed method by using a distributed principle to perform distributed grabbing;
(2) the accidental disconnection is followed by grabbing: the system is accidentally disconnected due to special reasons, and after reconnection, the last captured data can be effectively continued to capture the rest information, so that the loss caused by special conditions is prevented;
(3) can reversely grasp: the self-management and learning progress capability is provided, so that the existing knowledge can be quickly learned and the follow-up improvement can be performed to prevent other people from grabbing;
(4) and (3) time judgment: the contents grabbed every day are different, the current data can be effectively grabbed through time judgment, and the data before yesterday are filtered;
(5) repeated grabbing is prevented: the data of each public full website and each page are possibly identical, so that the data titles and the contents are required to be analyzed and then captured in order to avoid the occurrence of repeated data, the repeated capture is avoided, and the resource consumption is reduced;
(6) keyword grabbing: the network public data can be accurately and effectively captured by capturing the data through the keywords;
(7) periodic and continuous grabbing: the regular grabbing is to grab data in a certain time, and the grabbing is not carried out after the time, so that the continuous grabbing always keeps the grabbing of the data;
(8) memory acquisition points: the artificial intelligent memory method only needs to collect the public whole website, can intelligently identify and accurately collect the required data just like the memory of people, intelligently filters the useless data, only retains the image-text information, can effectively memorize the collection progress when stopping working due to accidents in the collection process, and can then finish unfinished work when restarting;
(9) automatic analysis and classification: automatically analyzing and filtering unused information such as advertisements and the like, and storing needed image-text information; automatically analyzing production collection rules, and intelligently capturing image-text information of each public full website; automatic analysis and correction can be performed, and the content of manual error correction can be intelligently learned, so that the accuracy is more and more accurate.
The application provides a method for identifying big data images and texts, which comprises the following steps:
(1) capturing graphic and text information data through big data, and carrying out graphic and text recognition through a test 4j technology;
(2) acquiring text and picture information in the picture, and then checking;
(3) the text retrieval text information filters bad information such as multi-dirty words, and once the words are found, the text information is directly filtered;
(4) full text retrieval of the textual information by custom dirty words;
(5) keyword library: artificial intelligence learns a keyword library similar to a Xinhua dictionary to analyze whether graphic information is positive;
(6) intelligent analysis of graphics context: intelligent analysis of image-text content through a keyword library;
(7) and (3) intelligently judging analysis results: and comparing the intelligent analysis results with the keyword library through intelligent analysis of the images and texts, and judging the results: results of positive information, neutral information, negative information, and the like;
(8) intelligent addition of new words: when new words appear, the method can intelligently learn the new words, automatically add the new words into the keyword library, and manually add the new words;
(9) intelligent analysis of picture characters: when the picture has characters, the information result of the characters can be intelligently analyzed, and the result is given;
automatic analysis and classification: automatically analyzing and filtering the information of the picture characters, classifying and summarizing the picture-text information, and giving out a corresponding analysis result;
compared with the prior art, the application has the obvious advantages and effects that: the application belongs to the technical field of proofreading, and discloses an artificial intelligence big data image-text recognition system and method, wherein the system comprises the following steps: the method comprises the steps of acquiring network public resources of a specified public full website by utilizing an available big data management platform, acquiring network information by utilizing big data, and acquiring the network information by utilizing the methods of distributed acquisition, intelligent acquisition after accidental disconnection, reverse acquisition, intelligent judgment time, intelligent weight prevention, periodic acquisition, continuous acquisition and the like, accurately and completely acquiring the network information, and finally performing image-text recognition and correction on the acquired data, thereby achieving the available big data platform with healthy civilization.
For convenience of description, the above devices are described as being functionally divided into various units and modules. Of course, the functions of the units, modules may be implemented in the same piece or pieces of software and/or hardware when implementing the application. From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. In the description of the present specification, reference to the terms "one embodiment," "example," "specific example," and the like, 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 application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the structures of this application and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the application or from the scope of the application as defined in the accompanying claims.
Claims (4)
1. An artificial intelligence based big data graph text recognition system comprises a big data management platform,
the big data management platform performs data management and method management on big data capture and big data storage;
the big data grabbing is used for grabbing public data in the whole network,
further, the big data image-text recognition system is used for filtering unhealthy image-text information of the big data capturing image-text information to achieve a healthy civilized big data platform;
the big data grabbing comprises the following steps:
(1) distributed grabbing: constructing a distributed method by using a distributed principle to perform distributed grabbing;
(2) the accidental disconnection is followed by grabbing: the system is accidentally disconnected due to special reasons, and after reconnection, the last captured data can be effectively continued to capture the rest information, so that the loss caused by special conditions is prevented;
(3) can reversely grasp: the self-management and learning progress capability is provided, so that the existing knowledge can be quickly learned and the follow-up improvement can be performed to prevent other people from grabbing;
(4) and (3) time judgment: the contents grabbed every day are different, the current data can be effectively grabbed through time judgment, and the data before yesterday are filtered;
(5) repeated grabbing is prevented: the data of each public full website and each page are possibly identical, so that the data titles and the contents are required to be analyzed and then captured in order to avoid the occurrence of repeated data, the repeated capture is avoided, and the resource consumption is reduced;
(6) keyword grabbing: the network public data can be accurately and effectively captured by capturing the data through the keywords;
(7) periodic and continuous grabbing: the regular grabbing is to grab data in a certain time, and the grabbing is not carried out after the time, so that the continuous grabbing always keeps the grabbing of the data;
(8) memory acquisition points: the artificial intelligent memory method only needs to collect the public whole website, can intelligently identify and accurately collect the required data just like the memory of people, intelligently filters the useless data, only retains the image-text information, can effectively memorize the collection progress when stopping working due to accidents in the collection process, and can then finish unfinished work when restarting;
(9) automatic analysis and classification: automatically analyzing and filtering unused information of advertisements and storing needed image-text information; automatically analyzing production collection rules, and intelligently capturing image-text information of each public full website; the automatic analysis and correction can intelligently learn the content of manual error correction, so that the accuracy is more and more accurate;
wherein the image-text recognition comprises the following steps:
(1) capturing graphic and text information data through big data, and carrying out graphic and text recognition through a test 4j technology;
(2) acquiring text and picture information in the picture, and then checking;
(3) the text retrieval image-text information filters bad information of multiple dirty words, and once the words are found, the text information is directly filtered;
(4) full text retrieval of the textual information by custom dirty words;
(5) keyword library: the artificial intelligence learns the keyword library of the Xinhua dictionary to analyze whether the graphic information is positive information or not;
(6) intelligent analysis of graphics context: intelligent analysis of graphic content through keyword library
(7) And (3) intelligently judging analysis results: and comparing the intelligent analysis results with the keyword library through intelligent analysis of the images and texts, and judging the results: positive information, neutral information, and negative information results;
(8) intelligent addition of new words: when new words appear, the method can intelligently learn the new words, automatically add the new words into the keyword library, and manually add the new words;
(9) intelligent analysis of picture characters: when the picture has characters, the information result of the characters can be intelligently analyzed, and the result is given;
automatic analysis and classification: automatically analyzing and filtering the information of the picture characters, classifying and summarizing the picture-text information, and giving out corresponding analysis results.
2. The artificial intelligence based big data graph text recognition system of claim 1, wherein: the big data management platform is used for judging abnormal behaviors in the user operation management process so as to identify abnormal users and safely control accounts of the abnormal users.
3. An artificial intelligence based big data graph text recognition system according to claim 2, wherein: and judging the abnormality occurring in concurrency in the large data grabbing process to identify abnormal data and safely controlling the abnormal data.
4. An artificial intelligence based big data graph text recognition system according to claim 3, wherein: the big data image-text recognition system is used for recognizing image-text information captured by big data, recognizing the image-text information and filtering unhealthy image-text information.
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