CN115081457A - Information processing method and system based on artificial intelligence technology - Google Patents
Information processing method and system based on artificial intelligence technology Download PDFInfo
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- CN115081457A CN115081457A CN202210664462.8A CN202210664462A CN115081457A CN 115081457 A CN115081457 A CN 115081457A CN 202210664462 A CN202210664462 A CN 202210664462A CN 115081457 A CN115081457 A CN 115081457A
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Abstract
The invention discloses an information processing method and system based on an artificial intelligence technology. The method comprises the following steps: receiving a multimodal information file input; reading and understanding an input multi-modal information file as a whole, performing semantic vector modeling and representation on the multi-modal information file, performing relational mapping on a semantic vector on a natural language semantic structure through natural language processing, and mapping the semantic vector into a knowledge graph of a specific target of the data set; and interfaces for classified browsing, navigation, searching, calculation and the like are provided, and the interfaces are directly called to output required processing results. By adopting the technical scheme of the invention, the development complexity of the information system software engineering can be greatly reduced, the processing efficiency and quality of large-scale file information are improved, the application threshold of the artificial intelligence technology is reduced, the complexity and threshold of changing data into production elements by enterprises are reduced, the possibility of creating value of data information is improved, and the popularity of the information processing application of the artificial intelligence technology is enabled to be possible.
Description
Technical Field
The invention relates to the field of data processing, in particular to an information processing method and system based on an artificial intelligence technology.
Background
In the current mainstream information system, file analysis is adopted from an entrance on the aspect of file types, different modes are used for coding and decoding different types of files, data types are processed separately, and data display data application and forward processing are separated. For example, a market file management system and a knowledge management system can distinguish document types of information contents, such as a webpage, a text, a word document, a pdf document, a video, a dynamic information graph and the like, which respectively need different entries, and the difference of display styles is large, so that most of systems can only classify and display the lacking information contents in the word and the document according to file names and manually specified classification directories. However, the presentation of these information has no definite paradigm for the knowledge extraction of the importance and content of different web pages and documents. The mainstream method is to perform word segmentation on contents, then establish an inverted index of the relation between words and documents, then calculate the relevance based on the word frequency relation, and recall and sort to a reasonable position during searching.
Disclosure of Invention
The invention provides an information processing method based on an artificial intelligence technology, which comprises the following steps:
receiving multi-modal information file input through a file object recognition interface, and recognizing the received file object as a whole;
performing information understanding on input multi-modal information, constructing a domain language model, converting and fusing information semantics, and expressing the information semantics as human cognizable and understandable information semantic codes to obtain domain semantic vector data set expression;
the method comprises the steps of training a language semantic model to carry out natural language relational mapping on a field semantic vector data set on a natural language semantic structure, mapping a plurality of groups of specific relations in the field semantic vector data set into a knowledge graph of a specific target of the data set, combining an accessed business logic management system with the specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework, and realizing automatic mapping of language vector representation, business logic and human natural expression.
The information processing method based on artificial intelligence technology as described above, wherein the multimodal information file includes a web page, a doc format file, a pdf format file, a txt format file, an audio file, and a video file.
In the information processing method based on the artificial intelligence technology, the input multi-mode information is understood, specifically, the input multi-mode information is understood by using a robot simulation mode, and the acquisition of the file content information is realized by using the robot simulation mode of browsing, reading, watching video and listening to audio.
In the information processing method based on the artificial intelligence technology, during the process of translating the information of the document, the computing framework starts to perform semantic vector modeling and representation on the text semantic information and the context information of the document, and the semantic vector representation of the document in the range of the data set is established through the semantic structure framework.
The information processing method based on the artificial intelligence technology is characterized in that the semantic content is represented on a specific word, sentence, paragraph and context structure by performing relational mapping on the semantic vector on the natural language semantic structure through the language semantic model.
An information processing method based on artificial intelligence technology as described above, wherein the language semantic model is a basic language model trained based on a specific data set.
The information processing method based on the artificial intelligence technology further comprises the following steps: and reserving a business logic mapping interface, accessing a management system of the business logic mapping of the company through the business logic mapping interface, realizing the combination of a specific data set and the business of the company, and finally realizing the service of the algorithm and the model system for a business target in the operation of the system.
The invention also provides an information processing system based on the artificial intelligence technology, which comprises: the system comprises a file object identification interface, an information processing unit and a system output interface;
a document object recognition interface for receiving multimodal information document input;
the information processing unit is used for reading and understanding the input multi-modal information file as a whole, performing semantic vector modeling and representation on the multi-modal information file, performing relation mapping on a semantic vector on a natural language semantic structure through natural language processing, and mapping the semantic vector into a knowledge graph of a specific target of the data set;
and the system output interface is used for providing interfaces for classified browsing, navigation, searching, calculation and the like, and can output a required processing result by directly calling the information.
The information processing system based on the artificial intelligence technology, as described above, wherein the information processing unit specifically includes: the system comprises a semantic vector modeling module, a natural language mapping module, a knowledge map mapping module and a business logic mapping module;
the semantic vector modeling module is used for dynamically carrying out semantic vector modeling and representation on the multi-modal information files according to industries and fields;
the natural language mapping module is used for carrying out relational mapping on the semantic vector on a natural language semantic structure;
the knowledge graph mapping module is used for mapping the n groups of specific relations into a knowledge graph of a specific target of the data set;
and the business logic mapping module is used for combining the accessed business logic management system with a specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework and realizing automatic mapping of the calculation language vector representation and the business logic and human natural expression.
The invention has the following beneficial effects: by adopting the technical scheme of the invention, the development complexity of the information system software engineering can be greatly reduced, the processing efficiency and quality of large-scale file information are improved, the application threshold of the artificial intelligence technology is reduced, the complexity and threshold of changing data into production elements by enterprises are reduced, the possibility of creating value of data information is improved, and the popularity of the information processing application of the artificial intelligence technology is enabled to be possible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flowchart of an information processing method based on artificial intelligence technology according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an information processing method;
fig. 3 is a schematic diagram of an information processing system based on artificial intelligence technology according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are 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 some, but not all, embodiments of the present invention. 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.
Example one
As shown in fig. 1 and 2, an embodiment of the present invention provides an information processing method based on an artificial intelligence technique, including:
the invention receives multimodal information file input through a file object recognition interface, wherein the multimodal information file input comprises but is not limited to a webpage, a doc format file, a pdf format file, a txt format file, an audio file, a video file and the like, and the file objects are recognized as a whole.
after the file object identification interface receives file input, the acquisition of file content information is realized by simulating modes of people for browsing, reading, watching video and listening to audio through a robot; in the process of information translation of the document, the computing framework starts to perform semantic vector modeling and representation on text semantic information and context information of the document, and semantic vector representation of the document in a data set range is established through the semantic structure framework. And automatically constructing a domain language model to express language semantic features such as documents, texts, information, data, dynamic information graphs and the like.
And 130, performing natural language relation mapping on the field semantic vector data set on a natural language semantic structure through language semantic model training, mapping a plurality of groups of specific relations in the field semantic vector data set into a knowledge graph of a specific target of the data set, combining an accessed business logic management system with the specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework, and realizing automatic mapping of language vector representation, business logic and human natural expression.
In the embodiment of the application, the semantic vectors are subjected to relational mapping on a natural language semantic structure through a language semantic model, and semantic content is represented on specific words, sentences, paragraphs and context structures. Wherein the language semantic model in the mapping is a base language model (foundation model) trained on a particular dataset. In the information processing process, the n groups of specific relations are mapped into a knowledge graph of a specific target of the domain semantic vector data set, and the knowledge graph is output of an algorithm and is a constraint and scheduling condition for the operation of the algorithm. In the software system, a business logic mapping interface is reserved at the position, a program can be accessed to a management system of company business logic mapping through the business logic mapping interface, combination of a specific data set and company business is realized, and finally, the algorithm and the model system serve as business targets in system operation.
Example two
As shown in fig. 3, a second embodiment of the present invention provides an information processing system 3 based on artificial intelligence technology, including: a file object recognition interface 31, an information processing unit 32, and a system output interface 33. Wherein:
a document object recognition interface 31 for receiving multimodal information document input including, but not limited to, web pages, doc formatted documents, pdf formatted documents, txt formatted documents, audio documents, video documents, etc., and recognizing these document objects as a whole.
The information processing unit 32 is used for reading and understanding the input multi-modal information files as a whole, performing semantic vector modeling and representation on the multi-modal information files, performing relation mapping on semantic vectors on a natural language semantic structure through natural language processing, and mapping the semantic vectors into a knowledge graph of a specific target of the data set;
specifically, the information processing unit 32 specifically includes: a semantic vector modeling module 321, a natural language mapping module 322, a knowledge graph mapping module 323 and a business logic mapping module 324; wherein:
and the semantic vector modeling module 321 is used for performing semantic vector modeling and representation on the multi-modal information files dynamically according to industries and fields. Specifically, in the process of information translation of a file, a computing framework starts to perform semantic vector modeling and expression on text semantic information and context information of the file, and semantic vector expression of the file in a data set range is established through a semantic structure framework;
a natural language mapping module 322, configured to perform relational mapping on the semantic vector on a natural language semantic structure;
a knowledge graph mapping module 323, configured to map the n groups of specific relationships into a knowledge graph of a specific target of the data set, where the knowledge graph is not only an output of the algorithm, but also a constraint and a scheduling condition for operation of the algorithm;
and the business logic mapping module 324 is used for combining the accessed business logic management system with a specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework, and realizing automatic mapping of the calculation language vector representation and the business logic and human natural expression. In the information system of the application, a business logic mapping interface is reserved, a management system of company business logic mapping is accessed through the interface, combination of a specific data set and company business is realized, and finally the algorithm and the model system serve as business targets in system operation.
And the system output interface 33 is used for providing interfaces for classified browsing, navigation, searching, calculation and the like, and can output a required processing result by directly calling the information.
By adopting the technical scheme of the invention, millions of web pages, doc, pdf, txt, audio and video files are tested, the recall rate is over 91% in semantic representation, the accuracy rate is over 80, and the acquisition of information of various information input source files in software engineering is greatly reduced.
Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
the processor is used for executing one or more program instructions to execute an information processing method based on artificial intelligence technology.
The disclosed embodiments of the present invention provide a computer-readable storage medium, in which computer program instructions are stored, and when the computer program instructions are run on a computer, the computer is caused to execute the above-mentioned information processing method based on artificial intelligence technology.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic device, discrete hardware component. The method specifically adopts a heterogeneous computation mode of a CPU + GPU acceleration mode, and the GPU uses a heterogeneous acceleration computation programmable logic device to perform matrix operation and convolution computation.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will recognize that the functionality described in this disclosure may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. An information processing method based on artificial intelligence technology is characterized by comprising the following steps:
receiving multi-modal information file input through a file object recognition interface, and recognizing the received file object as a whole;
performing information understanding on input multi-modal information, constructing a domain language model, converting and fusing information semantics, and expressing the information semantics as human cognizable and understandable information semantic codes to obtain domain semantic vector data set expression;
the method comprises the steps of training a language semantic model to carry out natural language relational mapping on a field semantic vector data set on a natural language semantic structure, mapping a plurality of groups of specific relations in the field semantic vector data set into a knowledge graph of a specific target of the data set, combining an accessed business logic management system with the specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework, and realizing automatic mapping of language vector representation, business logic and human natural expression.
2. The information processing method based on artificial intelligence technique as claimed in claim 1, wherein the multimodal information file includes a web page, a doc formatted file, a pdf formatted file, a txt formatted file, an audio file, a video file.
3. The information processing method based on artificial intelligence technology as claimed in claim 1, wherein the information understanding is performed on the inputted multi-modal information, specifically, the information understanding is performed on the inputted multi-modal information by using a robot simulation mode, and the acquisition of the document content information is realized by using a robot simulation mode of browsing, reading, watching video and listening to audio.
4. The information processing method based on artificial intelligence technology as claimed in claim 1, wherein in the process of information translation of the document, the computing framework starts semantic vector modeling and representation of text semantic information and context information, and semantic vector representation of the document in the range of the data set is established through the semantic structure framework.
5. The information processing method based on artificial intelligence technology as claimed in claim 1, wherein the semantic content is represented on specific words, sentences, paragraphs, and context structures by relational mapping of semantic vectors on natural language semantic structures through a language semantic model.
6. An information processing method based on artificial intelligence technology as claimed in claim 5, characterized in that said language semantic model is a basic language model trained on a specific data set.
7. The information processing method based on artificial intelligence technology as claimed in claim 1, further comprising: and reserving a business logic mapping interface, accessing a management system of the business logic mapping of the company through the business logic mapping interface, realizing the combination of a specific data set and the business of the company, and finally realizing the service of the algorithm and the model system for a business target in the operation of the system.
8. An information processing system based on artificial intelligence technology, comprising: the system comprises a file object identification interface, an information processing unit and a system output interface;
a document object recognition interface for receiving multimodal information document input;
the information processing unit is used for reading and understanding the input multi-modal information file as a whole, performing semantic vector modeling and representation on the multi-modal information file, performing relation mapping on a semantic vector on a natural language semantic structure through natural language processing, and mapping the semantic vector into a knowledge graph of a specific target of the data set;
and the system output interface is used for providing classified browsing, navigation, searching and computing interfaces and calling the interfaces to output a required processing result.
9. The information processing system based on artificial intelligence technique of claim 8, wherein the information processing unit specifically includes: the system comprises a semantic vector modeling module, a natural language mapping module, a knowledge map mapping module and a business logic mapping module;
the semantic vector modeling module is used for dynamically carrying out semantic vector modeling and representation on the multi-modal information files according to industries and fields;
the natural language mapping module is used for carrying out relational mapping on the semantic vector on a natural language semantic structure;
the knowledge graph mapping module is used for mapping the n groups of specific relations into a knowledge graph of a specific target of the data set;
and the business logic mapping module is used for combining the accessed business logic management system with a specific data set, dynamically constructing semantic representation according to the field industry, providing a business logic semantic representation framework and realizing automatic mapping of the calculation language vector representation and the business logic and human natural expression.
10. A computer-readable storage medium, comprising: at least one memory and at least one processor;
the memory is used for storing one or more program instructions;
a processor for executing one or more program instructions to perform an artificial intelligence technology based information processing method according to any one of claims 1 to 7.
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