CN110851488A - Multi-source-based multi-modal data fusion analysis processing method and platform - Google Patents
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Abstract
The invention discloses a multi-source multi-modal data fusion analysis processing method, which is characterized in that data acquisition is carried out in the modes of interface service acquisition, sensing and reading of the Internet of things, database synchronization, file synchronization, data crawling and the like, and various data cleaning models are established for processing multi-modal data aiming at the problems of data loss, abnormal space-time data, inconsistent data and the like existing in the acquired massive data such as structured database data, semi-structured network data, unstructured text, video and the like; the multi-source multi-modal data fusion analysis module constructs an algorithm library of common data fusion algorithms, and can support text data analysis and image data analysis, and fusion analysis of structured data and unstructured data from multiple sources.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a multi-source multi-modal data fusion analysis processing method and a multi-source multi-modal data fusion analysis processing platform.
Background
In the multi-source heterogeneous data in the fields of traffic, judicial and the like, the data are various in types, wide in sources and inconsistent in protocol diversification, and the problems of disordered data, low quality, disordered data architecture, inconsistent storage and the like exist.
Since the original data contains a large amount of error and redundant data, and the quality of the data directly affects the reliability of the analysis result of the upper-layer application and the real realization of the application target, the data quality of the original data needs to be evaluated so as to provide richer data information for the upper-layer application. Based on the modeling of the quality of heterogeneous multi-source multi-modal data, a new fusion analysis processing method needs to be designed for solving the problems of data loss, abnormal space-time data, inconsistent data and the like in the multi-source multi-modal data, and the multi-modal data is processed through a plurality of data cleaning models.
Disclosure of Invention
In view of the above, the present invention provides a multi-source-based multi-modal data fusion analysis processing method, and further provides a multi-source-based multi-modal data fusion analysis processing platform, which constructs an algorithm library for cluster analysis, association analysis, classification prediction, and the like, according to different application requirements and data structures, so as to implement fusion analysis on structured data and unstructured data from multiple sources, and solve the problems of data loss, temporal-spatial data abnormality, data inconsistency, and the like in the existing multi-source multi-modal data.
One of the purposes of the invention is realized by the following technical scheme:
the multi-source multi-modal data fusion analysis processing method comprises the following steps:
step S1, data acquisition, including system fusion acquisition, Internet of things data acquisition and Internet data acquisition; the system fusion acquisition acquires data of the open data interface and the accessed database service through a fusion acquisition system; the data of the Internet of things is subjected to real-time data acquisition by adopting a distributed coordination service Zookeeper and a message middleware Kafka in a distributed environment; the internet data acquisition is realized by crawling data through a built distributed crawler system;
step S2: the method comprises the steps of performing fusion analysis on collected data, performing feature extraction and attribute fusion on the data according to different characteristics of the data, and constructing a clustering analysis, association analysis and classification prediction algorithm library according to different application requirements and data structures to realize fusion analysis on structured data and unstructured data from multiple sources.
In particular, in the step S1, in the process of system fusion acquisition and data acquisition of the internet of things, the temporary storage Redis, the permanent storage MySQL, and the distributed storage HDFS are performed on the acquired data according to the situation, and the problems of data redundancy, missing, abnormality, inconsistency, and the like existing in the data are removed, complemented, or modified through a data quality detection model on the structured data, so as to improve the quality of the structured data.
In particular, in the step S1, in the internet data acquisition process, the HDFS is distributively stored for the acquired data according to the situation, the chinese named entity recognition is performed for the massive text data by using Lattice LSTM, the entity extraction is performed, and the relationship mining is performed by using the iterative iteration of bootstrapping.
The second purpose of the invention is realized by the following technical scheme:
the multi-source based multi-modal data fusion analysis processing platform comprises
The data acquisition module comprises a system fusion acquisition unit, an internet data acquisition unit and an internet of things data acquisition unit;
the infrastructure of the hardware is such that,
the virtual facility is used for realizing server virtualization, storage virtualization and network virtualization;
the scheduling center is used for realizing task scheduling, resource scheduling, availability management and load balancing;
the data preprocessing module is used for realizing preprocessing and data extraction of unstructured data, semi-structured data and structured data;
the data mining module is used for realizing the feature extraction, the association analysis, the classification prediction and the clustering analysis of the preprocessed data;
the central module is applied to the application of the central module,
an open center module.
Particularly, the system fusion acquisition unit acquires data of the open data interface and the accessed database service through a fusion acquisition system; the Internet of things data acquisition unit acquires real-time data by adopting a distributed coordination service Zookeeper and a message middleware Kafka in a distributed environment; the internet data acquisition unit crawls data through the built distributed crawler system.
Particularly, in the internet data acquisition process, distributed storage HDFS is carried out on the acquired data according to the situation, Chinese naming entity identification is carried out on massive text data by adopting Lattice LSTM, entity extraction is carried out, and relationship mining is carried out by adopting repeat iteration of bootstrapping.
The invention has the beneficial effects that:
the invention provides a multi-source multi-modal data fusion analysis processing method, which is characterized in that data acquisition is carried out in the modes of interface service acquisition, sensing and reading of the Internet of things, database synchronization, file synchronization, data crawling and the like, and various data cleaning models are established for processing multi-modal data aiming at the problems of data loss, abnormal space-time data, inconsistent data and the like existing in the acquired massive data such as structured database data, semi-structured network data, unstructured text, video and the like; the multi-source multi-modal data fusion analysis module constructs an algorithm library of common data fusion algorithms, and can support text data analysis and image data analysis, and fusion analysis of structured data and unstructured data from multiple sources.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a platform architecture diagram according to the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
As shown in FIG. 1, the multi-source multi-modal data fusion analysis processing method based on the invention comprises the following steps:
step S1, data acquisition, including system fusion acquisition, Internet of things data acquisition and Internet data acquisition; the system fusion acquisition acquires data of the open data interface and the accessed database service through a fusion acquisition system; the method comprises the following steps that data of the Internet of things are subjected to real-time data acquisition in a distributed environment by adopting a distributed coordination service Zookeeper and a message middleware Kafka; the internet data acquisition is realized by crawling data through a built distributed crawler system, so that the data can be efficiently crawled, and meanwhile, an anti-crawler mechanism of a social network is prevented from being triggered; in the process of system fusion acquisition and data acquisition of the Internet of things, temporary storage Redis, permanent storage MySQL and distributed storage HDFS are carried out on the acquired data according to conditions, and data elimination, completion or modification is carried out on the problems of data redundancy, deficiency, abnormity, inconsistency and the like existing in the data through a data quality detection model aiming at the structured data, so that the quality of the structured data is improved.
In the internet data acquisition process, distributed storage HDFS is carried out on the acquired data according to conditions, Chinese named entity identification is carried out on massive text data by adopting Lattice LSTM, entity extraction is carried out, and relationship mining is carried out by adopting repeat iteration of bootstrapping.
Step S2: the method comprises the steps of performing fusion analysis on collected data, performing feature extraction and attribute fusion on the data according to different characteristics of the data, and constructing a clustering analysis, association analysis and classification prediction algorithm library according to different application requirements and data structures to realize fusion analysis on structured data and unstructured data from multiple sources.
Based on the design idea of the method, the invention also provides a multi-source multi-modal data fusion analysis and processing platform, which comprises the following components:
(1) the data acquisition module comprises a system fusion acquisition unit, an internet data acquisition unit and an internet of things data acquisition unit; the system fusion acquisition unit acquires data of the open data interface and the accessed database service through the fusion acquisition system; the Internet of things data acquisition unit acquires real-time data by adopting a distributed coordination service Zookeeper and a message middleware Kafka in a distributed environment; the Internet data acquisition unit crawls data through a built distributed crawler system;
in the internet data acquisition process, distributed storage HDFS is carried out on the acquired data according to conditions, Chinese named entity identification is carried out on massive text data by adopting Lattice LSTM, entity extraction is carried out, and relationship mining is carried out by adopting repeat iteration of bootstrapping.
(2) An infrastructure hardware facility; including the infrastructure for implementing the various functions; the system comprises server resources, network resources and storage resources;
(3) virtual facilities: the system is used for realizing server virtualization, storage virtualization and network virtualization;
(4) the scheduling center is used for realizing task scheduling, resource scheduling, availability management and load balancing;
(5) the data preprocessing module is used for realizing preprocessing and data extraction of unstructured data, semi-structured data and structured data;
(5) the data mining module is used for realizing the feature extraction, the association analysis, the classification prediction and the clustering analysis of the preprocessed data;
(6) an application center module: for implementing various functional applications;
(7) an open center module: including software development kit SDK, application programming interface API, and other interfaces and kit software for use by developers.
The big data fusion analysis infrastructure platform can provide mobile terminal APP, city management decision support, public service support and the like.
In the implementation process, the multi-source multi-modal data fusion analysis needs to perform cross-network and cross-modal association analysis on the multi-source multi-modal data. Meanwhile, an algorithm library containing common data fusion algorithms needs to be constructed, so that the platform is convenient for fusion analysis of data. The multi-source multi-modal data analysis platform can support text data analysis and image data analysis, and also faces the need of fusion analysis of text data and image data from multiple sources in many application scenes. Massive text data are analyzed, and the text data analysis can be divided into word segmentation, feature extraction, training models and application. The multi-source multi-modal data analysis platform provides a text analysis algorithm library in order to enable a user to quickly use the platform to analyze text data, and the text analysis algorithm comprises a common word segmentation method, a feature extraction method and a common model.
It should be understood by those skilled in the art that the timing sequence of the method steps provided in the above embodiments may be adaptively adjusted according to actual situations, or may be concurrently performed according to actual situations.
All or part of the steps in the methods according to the above embodiments may be implemented by a program instructing related hardware, where the program may be stored in a storage medium readable by a computer device and used to execute all or part of the steps in the methods according to the above embodiments. The computer device, for example: personal computer, server, network equipment, intelligent mobile terminal, intelligent home equipment, wearable intelligent equipment, vehicle-mounted intelligent equipment and the like; the storage medium, for example: RAM, ROM, magnetic disk, magnetic tape, optical disk, flash memory, U disk, removable hard disk, memory card, memory stick, network server storage, network cloud storage, etc.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. The multi-source-based multi-modal data fusion analysis processing method is characterized by comprising the following steps of: the method comprises the following steps:
step S1, data acquisition, including system fusion acquisition, Internet of things data acquisition and Internet data acquisition; the system fusion acquisition acquires data of the open data interface and the accessed database service through a fusion acquisition system; the data of the Internet of things is subjected to real-time data acquisition by adopting a distributed coordination service Zookeeper and a message middleware Kafaka in a distributed environment; the internet data acquisition is realized by crawling data through a built distributed crawler system;
step S2: the method comprises the steps of performing fusion analysis on collected data, performing feature extraction and attribute fusion on the data according to different characteristics of the data, and constructing a clustering analysis, association analysis and classification prediction algorithm library according to different application requirements and data structures to realize fusion analysis on structured data and unstructured data from multiple sources.
2. The multi-source multi-modal data fusion analysis processing method based on claim 1, wherein: in the step S1, in the process of system fusion acquisition and data acquisition of the internet of things, the temporary storage Redis, the permanent storage MySQL and the distributed storage HDFS are performed on the acquired data according to the situation, and data elimination, completion or modification is performed on the problems of data redundancy, deficiency, abnormality, inconsistency and the like existing in the data through a data quality detection model on the structured data, so as to improve the quality of the structured data.
3. The multi-source multi-modal data fusion analysis processing method based on claim 1, wherein: in the step S1, in the internet data collection process, the HDFS is distributively stored for the collected data according to the situation, the lattic LSTM is used to identify the named entity in chinese for the mass text data, the entity extraction is performed, and the relationship mining is performed by using the iterative iteration of bootstrapping.
4. Based on multisource multimode data fusion analysis processing platform, its characterized in that: the platform comprises
The data acquisition module comprises a system fusion acquisition unit, an internet data acquisition unit and an internet of things data acquisition unit;
the infrastructure of the hardware is such that,
the virtual facility is used for realizing server virtualization, storage virtualization and network virtualization;
the scheduling center is used for realizing task scheduling, resource scheduling, availability management and load balancing;
the data preprocessing module is used for realizing preprocessing and data extraction of unstructured data, semi-structured data and structured data;
the data mining module is used for realizing the feature extraction, the association analysis, the classification prediction and the clustering analysis of the preprocessed data;
the central module is applied to the application of the central module,
an open center module.
5. The multi-source multi-modal data fusion analysis processing platform of claim 4, wherein: the system fusion acquisition unit acquires data of the open data interface and the accessed database service through a fusion acquisition system; the Internet of things data acquisition unit acquires real-time data by adopting a distributed coordination service Zookeeper and a message middleware Kafka in a distributed environment; the internet data acquisition unit crawls data through the built distributed crawler system.
6. The multi-source multi-modal data fusion analysis processing platform of claim 4, wherein: in the internet data acquisition process, distributed storage HDFS is carried out on the acquired data according to conditions, Chinese named entity identification is carried out on massive text data by adopting Lattice LSTM, entity extraction is carried out, and relationship mining is carried out by adopting repeat iteration of bootstrapping.
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| CN112214531A (en) * | 2020-10-12 | 2021-01-12 | 海南大学 | Cross-data, information and knowledge multi-modal feature mining method and component |
| CN112214531B (en) * | 2020-10-12 | 2021-11-05 | 海南大学 | Cross-data, information and knowledge multi-modal feature mining method and component |
| WO2022078142A1 (en) * | 2020-10-12 | 2022-04-21 | 海南大学 | Multi-modal feature mining method and component across data, information and knowledge |
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| CN116881335B (en) * | 2023-07-24 | 2024-06-04 | 郑州华商科技有限公司 | Multi-mode data intelligent analysis system and method |
| CN118012850A (en) * | 2024-04-08 | 2024-05-10 | 北京市农林科学院智能装备技术研究中心 | Intelligent irrigation multisource information-oriented database construction system, method and equipment |
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