CN110825462A - Intelligent logic analysis system - Google Patents
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- CN110825462A CN110825462A CN201910899100.5A CN201910899100A CN110825462A CN 110825462 A CN110825462 A CN 110825462A CN 201910899100 A CN201910899100 A CN 201910899100A CN 110825462 A CN110825462 A CN 110825462A
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Abstract
The invention discloses an intelligent logic analysis system. The invention relates to an intelligent logic analysis system, which comprises: a set of causal definitions, a set of causal analysis algorithms, and a set of data, wherein: the cause and effect definition set is a cause and effect classification set with determined meanings, wherein any classification can be a cause, an effect or both, and each classification comprises an individual object; the cause and effect analysis algorithm set is an algorithm set for analyzing effect objects from cause objects in the cause and effect set; the data sets are the different data required by the causal analysis algorithm. Has the advantages that: the invention obtains the algorithm three-dimensional construction of different results by implementing different analysis algorithms on various data, so that each algorithm corresponding to the data can interact in a three-dimensional space.
Description
Technical Field
The invention particularly relates to an intelligent logic analysis system.
Background
When large data is used for a wide variety of data inference analyses, often the output(s) may result in a myriad of analysis and inference paths, some of which may lead to the desired conclusion. Most analysis and inference paths can not draw useful conclusions, so that a large amount of computing resources are wasted.
Disclosure of Invention
The invention aims to provide an intelligent logic analysis system.
In order to solve the above technical problem, the present invention provides an intelligent logic analysis system, including: a set of causal definitions, a set of causal analysis algorithms, and a set of data, wherein:
the cause and effect definition set is a cause and effect classification set with determined meanings, wherein any classification can be a cause, an effect or both, and each classification comprises an individual object; the cause and effect analysis algorithm set is an algorithm set for analyzing effect objects from cause objects in the cause and effect set; the data sets are the different data required by the causal analysis algorithm.
In one embodiment, the causal analysis algorithm set is organized into a three-dimensional corresponding inference structure; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all possible causes in the cause and effect set; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and one axial direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect, and the cause-effect analysis algorithms of the cause-effect relationship corresponding to the axial direction can be used for analyzing the analyzed effect object by the corresponding cause object.
In one embodiment, the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
In one embodiment, the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to all possible causes in the cause and effect set; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
In one embodiment, the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the depth direction Z axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
In one embodiment, the transverse X-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
In one embodiment, the factor object or the effect object may be single or multiple.
In one embodiment, the causal analysis algorithm in the three-dimensional corresponding reasoning structure in each reasoning process is described by using an order to form a reasoning graph.
In one embodiment, each causal analysis algorithm may be another causal analysis algorithm in the intelligent logic analysis system described in any one of the preceding paragraphs.
In one embodiment, the system further comprises a machine learning system, the inputs of which are any one of the inputs of the intelligent logic analysis system and the filtered corresponding correct outputs, and corresponding three-dimensional corresponding inference structures; the intelligent logic analysis system can determine the three-dimensional reasoning structure according to the input of the intelligent logic analysis system and form an output result by using the three-dimensional reasoning structure by using the model.
The invention has the beneficial effects that:
the invention obtains the algorithm three-dimensional construction of different results by implementing different analysis algorithms on various data, so that the algorithms of each corresponding data can be interacted in a three-dimensional space, and each analysis and inference path can be described by the same serial analysis algorithms, thereby laying the foundation of analysis and inference quantification. Based on the basic analysis reasoning method, the method can be used as an object to perform clustering analysis and deep learning, and the self-learning intelligent reasoning of the machine learning machine is realized.
Drawings
FIG. 1 is a schematic diagram of an intelligent logic analysis system according to the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1, an intelligent logic analysis system includes: a set of causal definitions, a set of causal analysis algorithms, and a set of data, wherein:
the cause and effect definition set is a cause and effect classification set with determined meanings, wherein any classification can be a cause, an effect or both, and each classification comprises an individual object; the cause and effect analysis algorithm set is an algorithm set for analyzing effect objects from cause objects in the cause and effect set; the data sets are the different data required by the causal analysis algorithm.
In one embodiment, the causal analysis algorithm set is organized into a three-dimensional corresponding inference structure; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all possible cause classifications in the cause and effect set; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all effect classifications in the cause and effect set; and one axial direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect, and the cause-effect analysis algorithms of the cause-effect relationship corresponding to the axial direction can be used for analyzing the analyzed effect object by the corresponding cause object.
In one embodiment, the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
In one embodiment, the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to all possible causes in the cause and effect set; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
In one embodiment, the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the depth direction Z axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
In one embodiment, the transverse X-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
In one embodiment, the factor object or the effect object may be single or multiple.
In one embodiment, the causal analysis algorithm in the three-dimensional corresponding reasoning structure in each reasoning process is described by using an order to form a reasoning graph.
In one embodiment, each causal analysis algorithm may be another causal analysis algorithm in the intelligent logic analysis system described in any one of the preceding paragraphs.
In one embodiment, the system further comprises a machine learning system, the inputs of which are any one of the inputs of the intelligent logic analysis system and the filtered corresponding correct outputs, and corresponding three-dimensional corresponding inference structures; the intelligent logic analysis system can determine the three-dimensional reasoning structure according to the input of the intelligent logic analysis system and form an output result by using the three-dimensional reasoning structure by using the model.
The invention has the beneficial effects that:
the invention obtains the algorithm three-dimensional construction of different results by implementing different analysis algorithms on various data, so that the algorithms of each corresponding data can be interacted in a three-dimensional space, and each analysis and inference path can be described by the same serial analysis algorithms, thereby laying the foundation of analysis and inference quantification. Based on the basic analysis reasoning method, the method can be used as an object to perform clustering analysis and deep learning, and the self-learning intelligent reasoning of the machine learning machine is realized.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. An intelligent logic analysis system, comprising: a set of causal definitions, a set of causal analysis algorithms, and a set of data, wherein:
the cause and effect definition set is a cause and effect classification set with definite meanings, any classification can be a cause, an effect or both, and each classification comprises an individual object. The cause and effect analysis algorithm set is an algorithm set for analyzing effect objects from cause objects in the cause and effect set; the data sets are the different data required by the causal analysis algorithm.
2. The intelligent logic analysis system of claim 1, wherein the causal analysis algorithm set constitutes a three-dimensional corresponding inference structure; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all possible causes in the cause and effect set; one axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and one axial direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect, and the cause-effect analysis algorithms of the cause-effect relationship corresponding to the axial direction can be used for analyzing the analyzed effect object by the corresponding cause object.
3. The intelligent logic analysis system of claim 2, wherein the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to all possible causes in the causal set; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
4. The intelligent logic analysis system of claim 2, wherein the depth in the three-dimensional corresponding inference structure corresponds to a cause in all possible causal sets in the Z-axis direction; the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
5. The intelligent logic analysis system of claim 2, wherein the longitudinal Y-axis direction in the three-dimensional corresponding inference structure corresponds to all possible causes in the causal set; the depth direction Z axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the transverse X-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of the paired cause-effect.
6. The intelligent logic analysis system of claim 2, wherein the lateral X-axis direction in the three-dimensional corresponding inference structure corresponds to the causes in all possible causal sets; the longitudinal Y-axis direction in the three-dimensional corresponding reasoning structure corresponds to all the effects in the cause and effect set; and the depth direction Z-axis direction in the three-dimensional corresponding reasoning structure corresponds to a plurality of cause-effect analysis algorithms of paired cause-effect.
7. The intelligent logic analysis system of any one of claims 2 to 6, wherein the cause object or effect object may be single or multiple.
8. The intelligent logic analysis system of any one of claims 2 to 7, wherein the causal analysis algorithms in the three-dimensional corresponding inference structure in each inference process are described using an order to form an inference graph.
9. The intelligent logic analysis system of any one of claims 2-8, wherein each causal analysis algorithm may be a causal analysis algorithm in another intelligent logic analysis system of any one of claims 2-8.
10. The intelligent logic analysis system according to any one of claims 2 to 9, further comprising a machine learning system having inputs of the intelligent logic analysis system according to any one of claims 2 to 9 and the respective filtered correct outputs, and the respective three-dimensional corresponding inference structures; the intelligent logic analysis system can determine the three-dimensional reasoning structure according to the input of the intelligent logic analysis system and form an output result by using the three-dimensional reasoning structure by using the model.
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| CN201910899100.5A CN110825462A (en) | 2019-09-23 | 2019-09-23 | Intelligent logic analysis system |
| PCT/CN2019/117549 WO2021056733A1 (en) | 2019-09-23 | 2019-11-12 | Intelligent logic analysis system |
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| FR2962823B1 (en) * | 2010-07-13 | 2012-08-17 | Ensuite Inf | SITUATION ANALYSIS PROCESSOR |
| US8892484B2 (en) * | 2012-09-28 | 2014-11-18 | Sphere Of Influence, Inc. | System and method for predicting events |
| US10685331B2 (en) * | 2015-12-08 | 2020-06-16 | TCL Research America Inc. | Personalized FUNC sequence scheduling method and system |
| CN107662617B (en) * | 2017-09-25 | 2019-11-05 | 重庆邮电大学 | Vehicle-mounted interactive controlling algorithm based on deep learning |
| CN109598347A (en) * | 2017-09-30 | 2019-04-09 | 日本电气株式会社 | For determining causal method, system and computer program product |
| JP6730340B2 (en) * | 2018-02-19 | 2020-07-29 | 日本電信電話株式会社 | Causal estimation device, causal estimation method, and program |
| CN109947898B (en) * | 2018-11-09 | 2021-03-05 | 中国电子科技集团公司第二十八研究所 | Equipment fault testing method based on intellectualization |
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| CN109948678A (en) * | 2019-03-08 | 2019-06-28 | 国网浙江省电力有限公司 | A kind of long-term electricity demand forecasting method based on Fuzzy Bayesian Theory |
| CN110110043B (en) * | 2019-04-11 | 2023-04-11 | 中山大学 | Multi-hop visual problem reasoning model and reasoning method thereof |
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