CN113779223A - Artificial intelligence service method, system and equipment based on deep learning - Google Patents
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Abstract
The application provides an artificial intelligence service method, system and equipment based on deep learning, and the method, system and equipment obtain question and answer data related to law; preprocessing the question and answer data to obtain a question sample and an answer sample; training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, and obtaining a question-answer model; obtaining a question to be consulted; searching a plurality of corresponding answers in a database according to the question to be consulted; and inputting the questions to be consulted and a plurality of corresponding answers into the question-answer model, and taking the answer with the highest matching rate as an output result. The application greatly improves the accuracy of the consultation response.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to an artificial intelligence service method, system and device based on deep learning.
Background
Along with social development, the legal consciousness of citizens is continuously enhanced, people pay more and more high attention to the law, and the requirements for obtaining legal help and solving legal problems are continuously increased.
However, at present, the public can only search some legal problems through a search engine such as a hundred-degree search engine, the obtained answers are not high in accuracy and are relatively messy, and the public problems cannot be effectively solved.
Therefore, the application provides an artificial intelligence service method, system and device based on deep learning.
Disclosure of Invention
The embodiment of the application aims to provide an artificial intelligence service method, an artificial intelligence service system and artificial intelligence service equipment based on deep learning so as to solve the problem that answer accuracy is not high. The specific technical scheme is as follows:
in a first aspect, an artificial intelligence service method based on deep learning is provided, the method including:
acquiring question and answer data related to law;
preprocessing the question and answer data to obtain a question sample and an answer sample;
training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, and obtaining a question-answer model;
obtaining a question to be consulted;
searching a plurality of corresponding answers in a database according to the question to be consulted;
and inputting the questions to be consulted and a plurality of corresponding answers into the question-answer model, and taking the answer with the highest matching rate as an output result.
Optionally, the obtaining legally relevant question and answer data includes:
and acquiring the legally related question and answer data in the preset website by using the web crawler.
Optionally, the preprocessing the question and answer data to obtain a question sample and an answer sample includes:
filtering data which is not related to law in the question and answer data;
filtering out data violating laws and social posts in the question and answer data;
extracting question semantic feature words and answer semantic feature words from the filtered data;
and performing associated sequencing according to the logic sequence of the question semantic feature words and the answer semantic feature words to obtain a question sample and an answer sample.
Optionally, the searching for a plurality of corresponding answers in the database according to the question to be consulted includes:
extracting key words in the questions to be consulted;
and searching a plurality of answers related to the key words in a database according to the key words.
Optionally, the inputting the question to be consulted and the corresponding multiple answers into the question-answer model, and the taking the answer with the highest matching rate as an output result includes:
assigning the question to be consulted and a plurality of answers as a number of question-answer pairs equal to the number of answers;
preprocessing the question-answer pairs to obtain a plurality of question-answer samples;
and inputting the plurality of question-answer samples into the question-answer model to output the matching probability of the questions and the answers, and taking the answer with the highest matching rate as an output result.
Optionally, before the preprocessing the question-answer data to obtain a question sample and an answer sample, the method further includes:
and performing data expansion preprocessing on the question and answer data.
In a second aspect, an artificial intelligence service system based on deep learning is provided, the system comprising:
a first acquisition unit for acquiring legally-related question and answer data;
the preprocessing unit is used for preprocessing the question and answer data to obtain a question sample and an answer sample;
the training unit is used for training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, so that a question-answer model is obtained;
the second acquisition unit is used for acquiring the problem to be consulted;
the searching unit is used for searching a plurality of corresponding answers in the database according to the questions to be consulted;
and the output unit is used for inputting the questions to be consulted and the corresponding answers into the question-answer model and taking the answer with the highest matching rate as an output result.
In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of the first aspect when executing a program stored in the memory.
In a fourth aspect, a computer-readable storage medium is provided, having stored thereon a computer program which, when being executed by a processor, carries out the method steps of any of the first aspects.
In a fifth aspect, there is provided a computer program product containing instructions which, when run on a computer, cause the computer to perform any of the above-described artificial intelligence service methods based on deep learning.
The embodiment of the application has the following beneficial effects:
the embodiment of the application provides an artificial intelligence service method, system and device based on deep learning, wherein question and answer data related to law are obtained; preprocessing the question and answer data to obtain a question sample and an answer sample; training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, and obtaining a question-answer model; obtaining a question to be consulted; searching a plurality of corresponding answers in a database according to the question to be consulted; and inputting the questions to be consulted and a plurality of corresponding answers into the question-answer model, and taking the answer with the highest matching rate as an output result. The application greatly improves the accuracy of the consultation response.
Of course, not all advantages described above need to be achieved at the same time in the practice of any one product or method of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an artificial intelligence service method based on deep learning according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an artificial intelligence service system based on deep learning according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
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.
The embodiment of the present application provides an artificial intelligence service method based on deep learning, which is described in detail below with reference to specific embodiments, and as shown in fig. 1, the artificial intelligence service method based on deep learning provided by the embodiment of the present application includes the following specific steps:
step S101: legal-related question and answer data is obtained.
Step S102: and preprocessing the question and answer data to obtain a question sample and an answer sample.
Step S103: and training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, so as to obtain a question-answer model.
Step S104: and acquiring the problem to be consulted.
Step S105: and searching a plurality of corresponding answers in the database according to the questions to be consulted.
Step S106: and inputting the questions to be consulted and a plurality of corresponding answers into the question-answer model, and taking the answer with the highest matching rate as an output result.
Optionally, the obtaining legally relevant question and answer data includes:
and acquiring the legally related question and answer data in the preset website by using the web crawler.
In one example, the preset web address may be a Baidu Bar, Hotan, Baidu, or the like.
Optionally, the preprocessing the question and answer data to obtain a question sample and an answer sample includes:
filtering data which is not related to law in the question and answer data;
filtering out data violating laws and social posts in the question and answer data;
extracting question semantic feature words and answer semantic feature words from the filtered data;
and performing associated sequencing according to the logic sequence of the question semantic feature words and the answer semantic feature words to obtain a question sample and an answer sample.
Optionally, the searching for a plurality of corresponding answers in the database according to the question to be consulted includes:
extracting key words in the questions to be consulted;
and searching a plurality of answers related to the key words in a database according to the key words.
Optionally, the inputting the question to be consulted and the corresponding multiple answers into the question-answer model, and the taking the answer with the highest matching rate as an output result includes:
assigning the question to be consulted and a plurality of answers as a number of question-answer pairs equal to the number of answers;
preprocessing the question-answer pairs to obtain a plurality of question-answer samples;
and inputting the plurality of question-answer samples into the question-answer model to output the matching probability of the questions and the answers, and taking the answer with the highest matching rate as an output result.
Optionally, before the preprocessing the question-answer data to obtain a question sample and an answer sample, the method further includes:
and performing data expansion preprocessing on the question and answer data.
Because the question-answer data obtained from the network is less, and a large amount of data is needed for model training, in order to meet the training requirement, data Luochong preprocessing is needed, specifically, various associated or approximate data can be generated according to the existing question-answer data, for example, "do you can get back after leaving a wedding" can generate the problems of "do you can get back after leaving a wedding", "do you get to a wedding after leaving a wedding", and the like.
In a second aspect, based on the same inventive concept, there is provided an artificial intelligence service system based on deep learning, as shown in fig. 2, the system comprising:
a first acquisition unit 201 for acquiring legally relevant question-answer data;
the preprocessing unit 202 is configured to preprocess the question and answer data to obtain a question sample and an answer sample;
the training unit 203 is used for training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, so as to obtain a question-answer model;
a second obtaining unit 204, configured to obtain a question to be consulted;
the searching unit 206 is configured to search a plurality of corresponding answers in the database according to the question to be consulted;
the output unit 206 is configured to input the question to be consulted and the corresponding multiple answers into the question-answer model, and take the answer with the highest matching rate as an output result.
Based on the same technical concept, the embodiment of the present invention further provides an electronic device, as shown in fig. 3, including a processor 301, a communication interface 302, a memory 303, and a communication bus 304, where the processor 301, the communication interface 302, and the memory 303 complete mutual communication through the communication bus 304,
a memory 303 for storing a computer program;
the processor 301 is configured to implement the artificial intelligence service method based on deep learning when executing the program stored in the memory 303, and includes:
the communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In yet another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the above-mentioned artificial intelligence service method based on deep learning.
In yet another embodiment, a computer program product containing instructions is provided, which when run on a computer causes the computer to perform any one of the above-mentioned deep learning based artificial intelligence service methods.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, in this document, 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 identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. An artificial intelligence service method based on deep learning, which is characterized by comprising the following steps:
acquiring question and answer data related to law;
preprocessing the question and answer data to obtain a question sample and an answer sample;
training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, and obtaining a question-answer model;
obtaining a question to be consulted;
searching a plurality of corresponding answers in a database according to the question to be consulted;
and inputting the questions to be consulted and a plurality of corresponding answers into the question-answer model, and taking the answer with the highest matching rate as an output result.
2. The artificial intelligence service method based on deep learning of claim 1, wherein the obtaining of legally relevant question and answer data comprises:
and acquiring the legally related question and answer data in the preset website by using the web crawler.
3. The artificial intelligence service method based on deep learning of claim 1, wherein the preprocessing the question and answer data to obtain question samples and answer samples comprises:
filtering data which is not related to law in the question and answer data;
filtering out data violating laws and social posts in the question and answer data;
extracting question semantic feature words and answer semantic feature words from the filtered data;
and performing associated sequencing according to the logic sequence of the question semantic feature words and the answer semantic feature words to obtain a question sample and an answer sample.
4. The artificial intelligence service method based on deep learning of claim 1, wherein the searching of the corresponding plurality of answers in the database according to the question to be consulted comprises:
extracting key words in the questions to be consulted;
and searching a plurality of answers related to the key words in a database according to the key words.
5. The artificial intelligence service method based on deep learning of claim 1, wherein the question to be consulted and the corresponding plurality of answers are input into the question-answer model, and the step of taking the answer with the highest matching rate as an output result comprises:
assigning the question to be consulted and a plurality of answers as a number of question-answer pairs equal to the number of answers;
preprocessing the question-answer pairs to obtain a plurality of question-answer samples;
and inputting the plurality of question-answer samples into the question-answer model to output the matching probability of the questions and the answers, and taking the answer with the highest matching rate as an output result.
6. The artificial intelligence service method based on deep learning of claim 1, wherein before the preprocessing the question and answer data to obtain question samples and answer samples, the method further comprises:
and performing data expansion preprocessing on the question and answer data.
7. An artificial intelligence service system based on deep learning, the system comprising:
a first acquisition unit for acquiring legally-related question and answer data;
the preprocessing unit is used for preprocessing the question and answer data to obtain a question sample and an answer sample;
the training unit is used for training the question samples and the answer samples based on a deep learning mode until the matching probability of the output questions and answers is maximized, so that a question-answer model is obtained;
the second acquisition unit is used for acquiring the problem to be consulted;
the searching unit is used for searching a plurality of corresponding answers in the database according to the questions to be consulted;
and the output unit is used for inputting the questions to be consulted and the corresponding answers into the question-answer model and taking the answer with the highest matching rate as an output result.
8. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-6 when executing a program stored in the memory.
9. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 6.
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| CN107305578A (en) * | 2016-04-25 | 2017-10-31 | 北京京东尚科信息技术有限公司 | Human-machine intelligence's answering method and device |
| CN110321414A (en) * | 2019-04-19 | 2019-10-11 | 四川政资汇智能科技有限公司 | A kind of artificial intelligence counseling services method and system based on deep learning |
| CN110765257A (en) * | 2019-12-30 | 2020-02-07 | 杭州识度科技有限公司 | Intelligent consulting system of law of knowledge map driving type |
| CN111414457A (en) * | 2020-03-20 | 2020-07-14 | 深圳前海微众银行股份有限公司 | Intelligent question-answering method, device, equipment and storage medium based on federal learning |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305578A (en) * | 2016-04-25 | 2017-10-31 | 北京京东尚科信息技术有限公司 | Human-machine intelligence's answering method and device |
| CN110321414A (en) * | 2019-04-19 | 2019-10-11 | 四川政资汇智能科技有限公司 | A kind of artificial intelligence counseling services method and system based on deep learning |
| CN110765257A (en) * | 2019-12-30 | 2020-02-07 | 杭州识度科技有限公司 | Intelligent consulting system of law of knowledge map driving type |
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