CN111538887A - Big data image-text recognition system and method based on artificial intelligence - Google Patents
Big data image-text recognition system and method based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the technical field of proofreading, and discloses a big data image-text recognition system and method based on artificial intelligence, which comprises the following steps: the method comprises the steps of acquiring network public resources of a designated public whole website by using an available big data management platform, capturing network information by using big data, performing distributed capture, performing intelligent capture after accidental disconnection, performing anti-capture, performing intelligent judgment time, performing intelligent anti-heavy, performing periodic capture, performing continuous capture and the like, accurately and completely acquiring the network information, and finally performing image-text identification and proofreading on captured data, so as to achieve a healthy and civilized big data platform.
Description
Technical Field
The invention belongs to the technical field of big data, and discloses a big data image-text recognition system and method based on artificial intelligence.
Background
With the advent of the information age, cloud computing technology, digital technology, internet technology, and the like have been further developed and applied, and the competitiveness of the information industry is continuously increased, and for large enterprises, big data has been raised partly because computing power can be obtained at lower cost, and various systems can now perform multitasking. 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, so that the server cluster is simpler and simpler, the server cluster has potential value and can bring great profit to businesses, but complex processing data information is needed, and a lot of image-text information can be used for solving bad image-text information through the technical field, so that the system and the method for identifying the large-data image-text manually and intelligently provided by the application can fully utilize the data information for analysis and utilization.
Disclosure of Invention
Aiming at the defects of the traditional management platform, the invention aims to provide a big data image-text recognition system and method based on artificial intelligence, which comprises a big data management platform, a big data capturing method and a big data image-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 capture is used for capturing public data in the whole network, and the public data comprises, but is not limited to, Baidu, dog search, 360, microblog, WeChat and other websites for capture;
furthermore, the big data image-text recognition system is used for filtering unhealthy image-text information of big data capturing image-text information to achieve a healthy and civilized big data platform;
the invention provides a big data capturing method, which comprises the following steps:
distributed grabbing: building a distributed method by using a distributed principle to perform distributed grabbing;
secondly, grabbing after accidental disconnection: the system is accidentally disconnected due to special reasons, and when the system is reconnected, the system can effectively continue to capture the remaining information according to the data captured last time, so that the loss caused by special conditions is prevented;
thirdly, anti-grabbing: the system has the capability of self-management and learning progress, can quickly learn the existing knowledge and perform subsequent improvement to prevent others from grabbing;
judging the time: the contents captured every day are different, the current data can be effectively captured through time judgment, and data before yesterday is filtered out;
prevent repeated snatching: the data of each public website and each page are possibly identical, so that in order to avoid the occurrence of repeated data, the data titles and the content need to be analyzed and then captured, the repeated capture is avoided, and the resource consumption is reduced;
grabbing keywords: data capture is carried out through keywords, and network public data can be accurately and effectively captured;
and (c) regularly and continuously grabbing: the regular grabbing is to grab data within a certain time, and the data grabbing is not carried out any more after the time, and the data grabbing is kept all the time by continuous grabbing;
memory collection points: the artificial intelligence memorization method can intelligently identify and accurately collect the required data just like human memory as long as the collected public websites are disclosed, intelligently filters useless data, only retains image-text information, can effectively memorize the collection progress when the collection process stops working due to accidents, and can finish unfinished work when the collection process is restarted;
ninthly, automatically analyzing and classifying: automatically analyzing and filtering useless information such as advertisements and the like, and storing required image-text information; automatically analyzing production acquisition rules, and intelligently capturing image-text information of each public whole website; automatic analysis and correction can be realized, the content of manual error correction can be intelligently learned, and the accuracy is more and more accurate;
the invention provides a big data image-text identification method, which comprises the following steps:
capturing image-text information data through big data, and performing image-text identification through a tess4j technology;
obtaining character and picture information in the picture and then checking;
thirdly, filtering bad information such as a plurality of dirty words by using the full text retrieval image-text information, and directly filtering the whole image-text information once the word is found;
fourthly, searching the image and text information in full text by self-defining dirty words;
a keyword library: the artificial intelligence learns a keyword library similar to a Xinhua dictionary to analyze whether the image-text information is positive or not;
sixthly, intelligently analyzing the graphics and text: intelligent analysis of graphics context content through keyword library
And (c) intelligently judging an analysis result: the analysis result is intelligently judged through the comparison between the intelligent analysis image-text and the keyword library, and the judgment result is as follows: positive information, neutral information, and negative information;
eighthly, intelligently adding new words: when a new word appears, the new word can be intelligently learned, the new word is automatically added into the keyword library, and manual addition of the new word is also provided;
ninthly, intelligently analyzing 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 of red: automatically analyzing and filtering the information of the pictures and the texts, classifying and summarizing the picture and text information, and giving a corresponding analysis result;
compared with the prior art, the invention has the obvious advantages and effects that: the invention belongs to the technical field of big data, and discloses a big data image-text recognition system and method based on artificial intelligence, which comprises the following steps: the method comprises the steps of obtaining network public resources of a designated public whole website by using an available big data management platform, capturing network information by using big data, obtaining network information in a distributed manner, intelligently capturing after accidental disconnection, carrying out anti-capturing, intelligently judging time, intelligently preventing heavy, capturing regularly and continuously, and the like, obtaining the network information accurately and completely, and finally storing the captured data in a hbase, a MongoDB and an elastic search in a distributed manner so as to solve the problem of tens of millions of data processing, thereby greatly improving the big data acquisition efficiency and reducing the workload of technical personnel in the big data acquisition process.
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The invention is described in further detail below with reference to the figures and specific embodiments.
FIG. 1 is a block diagram of a manually intelligent big data acquisition and storage system capable of carrying out the invention;
wherein the reference numerals are: the big data management platform module 1, the big data capture module 2 and the big data image-text identification module 3;
fig. 2 is a flowchart.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the technical solution of the present invention is realized as follows: a big data image-text recognition system and method based on artificial intelligence comprises a big data management platform, a big data capturing method and a big data storage method;
the big data management platform performs data management and method management on big data capture and big data book identification;
the big data capture is used for capturing public whole websites and respectively captures the public data of hundred-degree websites, dog searches, 360-degree websites, microblogs, WeChats and other public whole websites;
furthermore, the big data image-text recognition system is used for filtering unhealthy image-text information of big data capturing image-text information to achieve a healthy and civilized big data platform;
the invention provides a big data capturing method, which comprises the following steps:
distributed grabbing: building a distributed method by using a distributed principle to perform distributed grabbing;
secondly, grabbing after accidental disconnection: the system is accidentally disconnected due to special reasons, and when the system is reconnected, the system can effectively continue to capture the remaining information according to the data captured last time, so that the loss caused by special conditions is prevented;
thirdly, anti-grabbing: the system has the capability of self-management and learning progress, can quickly learn the existing knowledge and perform subsequent improvement to prevent others from grabbing;
judging the time: the contents captured every day are different, the current data can be effectively captured through time judgment, and data before yesterday is filtered out;
prevent repeated snatching: the data of each public whole website and each page are possibly identical, so that in order to avoid the occurrence of repeated data, the data titles and the content need to be analyzed and then captured, the repeated capture is avoided, and the resource consumption is reduced;
grabbing keywords: data capture is carried out through keywords, and network public data can be accurately and effectively captured;
and (c) regularly and continuously grabbing: the regular grabbing is to grab data within a certain time, and the data grabbing is not carried out any more after the time, and the data grabbing is kept all the time by continuous grabbing;
memory collection points: the artificial intelligence memorization method can intelligently identify and accurately collect the required data just like human memory as long as the collected public whole website, intelligently filters useless data, only retains image-text information, can effectively remember the collection progress when the collection process stops working due to accidents in the collection process, and can finish unfinished work when the collection process is restarted;
ninthly, automatically analyzing and classifying: automatically analyzing and filtering useless information such as advertisements and the like, and storing required image-text information; automatically analyzing production acquisition rules, and intelligently capturing image-text information of each public whole website; automatic analysis and correction can intelligently learn the content of manual error correction, so that the accuracy is more and more accurate.
The invention provides a big data image-text identification method, which comprises the following steps:
capturing image-text information data through big data, and performing image-text identification through a tess4j technology;
obtaining character and picture information in the picture and then checking;
thirdly, filtering bad information such as a plurality of dirty words by using the full text retrieval image-text information, and directly filtering the whole image-text information once the word is found;
fourthly, searching the image and text information in full text by self-defining dirty words;
a keyword library: the artificial intelligence learns a keyword library similar to a Xinhua dictionary to analyze whether the image-text information is positive or not;
sixthly, intelligently analyzing the graphics and text: intelligently analyzing the image-text content through a keyword library;
and (c) intelligently judging an analysis result: the analysis result is intelligently judged through the comparison between the intelligent analysis image-text and the keyword library, and the judgment result is as follows: positive information, neutral information, and negative information;
eighthly, intelligently adding new words: when a new word appears, the new word can be intelligently learned, the new word is automatically added into the keyword library, and manual addition of the new word is also provided;
ninthly, intelligently analyzing 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 of red: automatically analyzing and filtering the information of the pictures and the texts, classifying and summarizing the picture and text information, and giving a corresponding analysis result;
compared with the prior art, the invention has the obvious advantages and effects that: the invention belongs to the technical field of proofreading, and discloses a system and a method for manually and intelligently identifying big data pictures and texts, which comprises the following steps: the method comprises the steps of acquiring network public resources of a designated public whole website by using an available big data management platform, capturing network information by using big data, performing distributed capture, performing intelligent capture after accidental disconnection, performing anti-capture, performing intelligent judgment time, performing intelligent anti-heavy, performing periodic capture, performing continuous capture and the like, accurately and completely acquiring the network information, and finally performing image-text identification and proofreading on captured data, so as to achieve the available big data platform of a healthy civilization.
For convenience of description, the above devices are described as being divided into various units and modules by functions, respectively. Of course, the functions of the units and modules may be implemented in one or more software and/or hardware when the present application is implemented. From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented 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., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.
The above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
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-type 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 herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer 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.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. A big data image-text recognition system and method based on artificial intelligence comprises a big data management platform, a big data capturing method and a big data image-text recognition method;
the big data management platform performs data management and method management on big data capture and big data storage;
the big data capture is used for capturing public data in the whole network, and the public data comprises, but is not limited to, Baidu, dog search, 360, microblog, WeChat and other websites for capture;
furthermore, the big data image-text recognition system is used for filtering unhealthy image-text information of big data capturing image-text information to achieve a healthy and civilized big data platform;
the invention provides a big data capturing method, which comprises the following steps:
distributed grabbing: building a distributed method by using a distributed principle to perform distributed grabbing;
secondly, grabbing after accidental disconnection: the system is accidentally disconnected due to special reasons, and when the system is reconnected, the system can effectively continue to capture the remaining information according to the data captured last time, so that the loss caused by special conditions is prevented;
thirdly, anti-grabbing: the system has the capability of self-management and learning progress, can quickly learn the existing knowledge and perform subsequent improvement to prevent others from grabbing;
judging the time: the contents captured every day are different, the current data can be effectively captured through time judgment, and data before yesterday is filtered out;
prevent repeated snatching: the data of each public whole website and each page are possibly identical, so that in order to avoid the occurrence of repeated data, the data titles and the content need to be analyzed and then captured, the repeated capture is avoided, and the resource consumption is reduced;
grabbing keywords: data capture is carried out through keywords, and network public data can be accurately and effectively captured;
and (c) regularly and continuously grabbing: the regular grabbing is to grab data within a certain time, and the data grabbing is not carried out any more after the time, and the data grabbing is kept all the time by continuous grabbing;
memory collection points: the artificial intelligence memorization method can intelligently identify and accurately collect the required data just like human memory as long as the collected public whole website, intelligently filters useless data, only retains image-text information, can effectively remember the collection progress when the collection process stops working due to accidents in the collection process, and can finish unfinished work when the collection process is restarted;
ninthly, automatically analyzing and classifying: automatically analyzing and filtering useless information such as advertisements and the like, and storing required image-text information; automatically analyzing production acquisition rules, and intelligently capturing image-text information of each public whole website; automatic analysis and correction can be realized, the content of manual error correction can be intelligently learned, and the accuracy is more and more accurate;
the invention provides a big data image-text identification method, which comprises the following steps:
capturing image-text information data through big data, and performing image-text identification through a tess4j technology;
obtaining character and picture information in the picture and then checking;
thirdly, filtering bad information such as a plurality of dirty words by using the full text retrieval image-text information, and directly filtering the whole image-text information once the word is found;
fourthly, searching the image and text information in full text by self-defining dirty words;
a keyword library: the artificial intelligence learns a keyword library similar to a Xinhua dictionary to analyze whether the image-text information is positive or not;
sixthly, intelligently analyzing the graphics and text: intelligent analysis of graphics context content through keyword library
And (c) intelligently judging an analysis result: the analysis result is intelligently judged through the comparison between the intelligent analysis image-text and the keyword library, and the judgment result is as follows: positive information, neutral information, and negative information;
eighthly, intelligently adding new words: when a new word appears, the new word can be intelligently learned, the new word is automatically added into the keyword library, and manual addition of the new word is also provided;
ninthly, intelligently analyzing 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 of red: automatically analyzing and filtering the information of the pictures and the texts, classifying and summarizing the picture and text information, and giving a corresponding analysis result;
compared with the prior art, the invention has the obvious advantages and effects that: the invention belongs to the technical field of proofreading, and discloses a big data image-text recognition system and method based on artificial intelligence, which comprises the following steps: the method comprises the steps of acquiring network public resources of a designated public whole website by using an available big data management platform, capturing network information by using big data, performing distributed capture, performing intelligent capture after accidental disconnection, performing anti-capture, performing intelligent judgment time, performing intelligent anti-heavy, performing periodic capture, performing continuous capture and the like, accurately and completely acquiring the network information, and finally performing image-text identification and proofreading on captured data, so as to achieve the available big data platform of a healthy civilization.
2. The system and the method for manually and intelligently acquiring and storing the big data as claimed in claim 1, wherein: the big data management module is used for judging abnormal behaviors in the user operation management process so as to identify abnormal users and perform safety control on the account numbers of the abnormal users.
3. The system and the method for manually and intelligently acquiring and storing the big data as claimed in claim 2, wherein: and judging the abnormality occurring in the concurrency in the big data capturing process so as to identify the abnormal data and perform safety control on the abnormal data.
4. The system and the method for big data image-text recognition based on artificial intelligence according to claim 3 are characterized in that: the big data picture identification module is used for identifying the image-text information captured by the big data, identifying the image-text information and filtering unhealthy image-text information.
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