CN108764484B - Deployment method of machine learning and artificial intelligence application all-in-one machine - Google Patents
Deployment method of machine learning and artificial intelligence application all-in-one machine Download PDFInfo
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Abstract
The invention discloses a deployment method of a machine learning and artificial intelligence application all-in-one machine, which specifically comprises the following steps: s1, determining the interior of machine learning according to the actual use condition, and preparing and archiving the interior of machine learning; s2, learning the machine learning content prepared in S1 by an interpretation learning method to form an internal system framework; s3, after learning is finished, the contents learned in S2 are transmitted through the data sending service unit and are transmitted to an artificial intelligence system to form an intelligent operation frame; and S4, the artificial intelligence system operates the machine in real time according to the learned function. The deployment method of the machine learning and artificial intelligence application all-in-one machine greatly improves the efficiency of overall application, relatively reduces the resource occupancy rate, thereby promoting the actual value of data mining, better meeting the social needs of increasing development, promoting the development of scientific technology and further improving the propagation of learning knowledge.
Description
Technical Field
The invention relates to the technical field of machine learning and artificial intelligence application, in particular to a machine learning and artificial intelligence application all-in-one deployment method.
Background
Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence, a field of research that includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others. Since the birth of artificial intelligence, theories and technologies become mature day by day, and application fields are expanded continuously, so that science and technology products brought by the artificial intelligence in the future can be assumed to be 'containers' of human intelligence. The artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but can think like a human, and can also exceed human intelligence.
Machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer.
Artificial intelligence is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. The development of artificial intelligence has important significance for the progress of modern society, the key research in the method is machine learning in the artificial intelligence, the machine learning is an important research field in the artificial intelligence, the machine learning is always concerned by the artificial intelligence and a cognitive psychologist, the research of the machine learning is promoted, and the method has an immeasurable effect on the development of the artificial intelligence.
The latest stage of machine learning begins in 1986, the origin of artificial intelligence is earlier, machine learning and artificial intelligence are fully developed after decades of development, however, the development of artificial intelligence based on machine learning has great defects, the integrated application of machine learning and artificial intelligence is not widely known, a wider space is provided for exploration, the overall application efficiency is very low, the resource occupancy rate is high, and the social needs of gradual development are not met.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a machine learning and artificial intelligence application integrated deployment method, which solves the problems that the machine learning and artificial intelligence integrated application is not widely known, has a wider space for exploration, has very low overall application efficiency and higher resource occupancy rate, and cannot meet the social requirement of developing increasingly.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a deployment method of a machine learning and artificial intelligence application all-in-one machine specifically comprises the following steps:
s1, determining the interior of machine learning according to the actual use condition, and preparing and archiving the interior of machine learning;
s2, learning the machine learning content prepared in S1 by an interpretation learning method to form an internal system framework;
s3, after learning is finished, the contents learned in S2 are transmitted through the data sending service unit and are transmitted to an artificial intelligence system to form an intelligent operation frame;
and S4, the artificial intelligence system operates the machine in real time according to the learned function.
The invention also discloses an interpretation learning method, which specifically comprises the following steps:
step one, generating an explanation structure by solving an example: after a user inputs an example, the system firstly solves the problem;
step two, generalizing the interpretation structure to obtain a general control rule: the resulting interpreted structure and events are generalized.
Preferably, in S3, the data transmission service unit includes a data extraction module, a data management module and a data transmission module, and an output terminal of the data extraction module is connected to an input terminal of the data management module, and an output terminal of the data management module is connected to an input terminal of the data transmission module.
Preferably, in S4, the artificial intelligence system includes a management unit and an execution unit, and the management unit and the execution unit are connected in two ways.
(III) advantageous effects
The invention provides a machine learning and artificial intelligence application all-in-one deployment method. The method has the following beneficial effects: the machine learning and artificial intelligence application integrated machine deployment method is characterized in that the internal part of the machine learning is determined according to the actual use condition through S1, and the internal part of the machine learning is prepared and filed; s2, learning the machine learning content prepared in S1 by an interpretation learning method to form an internal system framework; s3, after learning is finished, the contents learned in S2 are transmitted through the data sending service unit and are transmitted to an artificial intelligence system to form an intelligent operation frame; s4, the artificial intelligence system carries out a series of deployment steps of real-time operation on the machine by the learned function, the defects of the current artificial intelligence development based on machine learning are overcome, the wide cognition of people on the machine learning and artificial intelligence integrated application is promoted, the exploration on the machine learning and artificial intelligence integrated application is completed, the overall application efficiency is greatly improved, and the resource occupancy rate is relatively reduced, so that the actual value of data mining is promoted, the increasingly developed social requirements are better met, a better basis is provided for the development of the machine learning and artificial intelligence integrated application, the development of scientific and technological knowledge is promoted, and the propagation of learning knowledge is better improved.
Drawings
FIG. 1 is a flow chart of an application deployment method of the present invention;
FIG. 2 is a schematic diagram of a data transmission service unit according to the present invention;
FIG. 3 is a schematic diagram of an artificial intelligence system according to the present invention;
in the figure, 1 data transmission service unit, 101 data extraction module, 102 data management module, 103 data transmission module, 2 artificial intelligence system, 201 management unit, 202 execution unit.
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 invention provides a deployment method of a machine learning and artificial intelligence application all-in-one machine, which specifically comprises the following steps as shown in FIGS. 1-3:
s1, determining the interior of machine learning according to the actual use condition, and preparing and archiving the interior of machine learning;
s2, learning the machine learning content prepared in S1 by an interpretation learning method to form an internal system framework;
s3, after learning is finished, the data transmission service unit 1 is used for transmitting the content learned in S2 and transmitting the content to the artificial intelligence system 2 to form an intelligent operation frame;
and S4, the artificial intelligence system 2 operates the machine in real time according to the learned function.
The invention also discloses an interpretation learning method, which specifically comprises the following steps:
step one, generating an explanation structure by solving an example: after a user inputs an example, the system firstly solves the problem; if the target guides reverse reasoning, relevant rules are searched from the domain knowledge base, and the subsequent pieces are matched with the target; after such a rule is found, the target is used as a back piece, the rule is used as a front piece, and the cause-effect relationship is recorded; then, taking the front piece of the rule as a sub-target, and further decomposing and reasoning; repeating the steps along the causal chain until the solution is finished; once the solution is obtained, it is proved that the objectives of the example can be satisfied, and a proven causal explanation structure is obtained; the structure of the interpretation typically has two modes: firstly, operators used by each inference for solving the problem are collected to form an action sequence as an explanation structure; the other is a traversal from top to bottom of the certification tree structure; the former is more general, and some description of facts about examples is omitted; the latter is more detailed, each fact appearing in the certification tree; the construction of the interpretation may be performed simultaneously with the problem solving, or may be performed along the solution path after the problem solving is completed.
Step two, generalizing the interpretation structure to obtain a general control rule: generalizing the obtained interpretation structure and events; at this step, the method usually adopted is to convert constants into variables, namely, some data in the example is converted into variables, some unimportant information is omitted, only those key information necessary for solving are reserved, and the key information is combined in a certain way to form a production formula rule, so that general control knowledge is obtained; a target concept is to be learned.
In S3, the data transmission service unit 1 includes a data extraction module 101, a data management module 102, and a data transmission module 103, where the data extraction module 101, the data management module 102, and the data transmission module 103 establish a network connection therebetween, an output end of the data extraction module 101 is connected to an input end of the data management module 102, an output end of the data management module 102 is connected to an input end of the data transmission module 103, the data extraction module 101 is configured to respond to a request and extract required data, and the data management module 102 is configured to obtain data from the data extraction module 101, package the data, and transmit the data to the artificial intelligence system 2 through the data transmission module 103.
In S4, the artificial intelligence system 2 according to the present invention includes a management unit 201 and an execution unit 202, and the management unit 201 and the execution unit 202 are connected in a bidirectional manner, the management unit 201 is responsible for allocating a work instruction to the corresponding execution unit 202, the execution unit 202 is only responsible for receiving the work instruction allocated by the management unit 201, and executing a corresponding work task, and after the work task is completed or an abnormality is encountered during the process of executing the work task, the work task may be fed back to the management unit 201, or may be submitted to the management unit 201 to related system performance information.
It is 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.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. A machine learning and artificial intelligence application all-in-one deployment method is characterized by comprising the following steps:
s1, determining the interior of machine learning according to the actual use condition, and preparing and archiving the interior of machine learning;
s2, explaining and learning the machine learning content prepared in S1 to form an internal system framework;
s3, after learning is finished, the contents learned in S2 are transmitted through the data sending service unit (1) and are transmitted to the artificial intelligence system (2), and an intelligent operation frame is formed;
s4, the artificial intelligence system (2) operates the machine in real time according to the learned function;
in the step S2, the explanation of the learning method specifically includes the following steps;
step one, generating an explanation structure by solving an example: after a user inputs an example, the system firstly solves the problem;
step two, generalizing the interpretation structure to obtain a general control rule: the resulting interpreted structure and events are generalized.
2. The machine learning and artificial intelligence application ensemble deployment method of claim 1, wherein: in S3, the data transmission service unit (1) concerned includes a data extraction module (101), a data management module (102), and a data transmission module (103), and an output terminal of the data extraction module (101) is connected to an input terminal of the data management module (102), and an output terminal of the data management module (102) is connected to an input terminal of the data transmission module (103).
3. The machine learning and artificial intelligence application ensemble deployment method of claim 1, wherein: in S4, the artificial intelligence system (2) includes a management unit (201) and an execution unit (202), and the management unit (201) and the execution unit (202) are connected in a bidirectional manner.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101536020A (en) * | 2005-07-18 | 2009-09-16 | 微软公司 | Training a learning system with arbitrary cost functions |
| CN104951425A (en) * | 2015-07-20 | 2015-09-30 | 东北大学 | Cloud service performance adaptive action type selection method based on deep learning |
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| KR20150091777A (en) * | 2014-02-04 | 2015-08-12 | 한국전자통신연구원 | Discussion learning system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101536020A (en) * | 2005-07-18 | 2009-09-16 | 微软公司 | Training a learning system with arbitrary cost functions |
| CN104951425A (en) * | 2015-07-20 | 2015-09-30 | 东北大学 | Cloud service performance adaptive action type selection method based on deep learning |
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