CN113283604A - Multi-element situation self-adaptive reasoning method and application - Google Patents

Multi-element situation self-adaptive reasoning method and application Download PDF

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CN113283604A
CN113283604A CN202110572122.8A CN202110572122A CN113283604A CN 113283604 A CN113283604 A CN 113283604A CN 202110572122 A CN202110572122 A CN 202110572122A CN 113283604 A CN113283604 A CN 113283604A
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李攀硕
万力衡
鲁仁全
周琪
李鸿一
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Guangdong University of Technology
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Abstract

In order to solve the defects of the prior art, the invention provides a multi-element situation self-adaptive inference method and application, comprising the following steps: associating knowledge entities in the knowledge map with knowledge to obtain a knowledge iterative reasoning process representing a learning mixing rule; based on the knowledge iterative reasoning process of the expression learning mixing rule, searching elements which are effectively and strongly associated with different industrial chain situations, and establishing a category matching rule of the complex industrial chain situation and the different elements; acquiring weight of multi-element association of a complex industrial chain situation based on an entropy analysis method; analyzing elements corresponding to different situations through the category matching rules; and combining the weight associated with the multiple elements of the complex industry chain situation to obtain the self-adaptive reasoning method of the multiple element situation. The method can determine the category matching rule of the multi-element situation, and determine the influence weight of different production elements on the industrial chain decision through an entropy analysis method, thereby realizing the self-adaptive reasoning of various time-varying complex industrial chain situations.

Description

Multi-element situation self-adaptive reasoning method and application
Technical Field
The invention relates to the technical field of industrial chain intelligent decision, in particular to a multi-element situation self-adaptive inference method applied to an industrial chain.
Background
The new generation artificial intelligence technology continuously attacks and customs around big data intelligence, group intelligence, industrial autonomous intelligent systems and other directions, and constructs the ecological environment of knowledge groups, technology groups and product groups from the aspects of basic theory, support systems, key technologies, innovation application and the like.
There are a number of links in the industrial chain system that require recommendations and decisions, all of which rely on an accurate sense of the industrial chain context. The traditional industry chain situation awareness technology has some applications, however, most of the technology is oriented to a single industry field, and research and application of the reasoning technology aiming at the multi-element situation of the complex industry chain are insufficient.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a multi-element situation self-adaptive inference method and application, which aims to construct a multi-element situation self-adaptive inference technology covering the whole industrial chain in the industrial field by means of industrial big data, artificial intelligence and other technologies.
In order to achieve the purpose, the invention adopts the specific scheme that:
a multi-element situation self-adaptive reasoning method comprises the following steps:
associating knowledge entities in the knowledge map with knowledge to obtain a knowledge iterative reasoning process representing a learning mixing rule;
based on the knowledge iterative reasoning process of the expression learning mixing rule, searching elements which are effectively and strongly associated with different industrial chain situations, and establishing a category matching rule of the complex industrial chain situation and the different elements;
acquiring weight of multi-element association of a complex industrial chain situation based on an entropy analysis method;
analyzing elements corresponding to different situations through the category matching rules; and combining the weight associated with the multiple elements of the complex industry chain situation to obtain the self-adaptive reasoning method of the multiple element situation.
The method for obtaining the knowledge iterative inference process expressing the learning mixing rule by associating the knowledge entity with the knowledge in the knowledge graph comprises the following steps:
associating the existing knowledge entities in the knowledge map with knowledge, and learning by utilizing representation to obtain new knowledge entities;
analyzing the support degree, the confidence degree and the promotion degree of the correlation by a correlation analysis method, and extracting an effective strong correlation set;
and performing expression learning through a strong association rule to obtain the expression learning mixed rule knowledge iterative reasoning process.
The method for obtaining the new knowledge entity by means of representation learning through the association of the existing knowledge entity and knowledge in the knowledge graph comprises the following steps:
based on the existing knowledge entities and association rules in the industry chain knowledge graph, embedding the associated knowledge entities and the triples of the corresponding association rules into a low-dimensional continuous vector space for processing;
carrying out normalization processing on the initialized embedded vectors of the knowledge entities and the association rules;
through the TranSparse model, the knowledge entities according to different heads
Figure BDA0003082946710000021
And relation of
Figure BDA0003082946710000022
Obtaining new knowledge entities
Figure BDA0003082946710000023
The knowledge entity
Figure BDA0003082946710000024
The knowledge graph of the industry chain is supplemented, and the process of the knowledge graph is satisfied
Figure BDA0003082946710000025
Evaluation was performed by a scoring function:
Figure BDA0003082946710000026
wherein the content of the first and second substances,
Figure BDA0003082946710000031
θminis a hyper-parameter related to sparsity, satisfies 0 ≦ θmin≤1，NrIs the number of pairs of entities to which the relationship r connects,
Figure BDA0003082946710000032
is the most numerous relationship of connected entities.
In addition, to solve the problem of unbalanced association relationship, the scoring function employed in the TranSparse model can be further expressed as:
Figure BDA0003082946710000033
wherein embh，embtIs that different projection matrixes for solving the unbalanced incidence relation in the TranSparse model correspond to
Figure BDA0003082946710000034
Namely different sparsity of the head and tail knowledge entities, the following sparsity is satisfied:
Figure BDA0003082946710000035
the "analyzing the support degree, the confidence degree and the promotion degree of the association by an association analysis method to extract an effective strong association set" includes:
selecting a rule set with high confidence by combining a correlation analysis method with a knowledge graph, wherein the method comprises the following steps:
wherein, the analysis of the support degree:
Figure BDA0003082946710000036
the above formula represents
Figure BDA0003082946710000037
Wherein I represents the total set of the industry chain knowledge-graphs, and num () represents the number of times of occurrence in a certain set;
wherein, the confidence coefficient analysis:
Figure BDA0003082946710000041
the above formula represents the knowledge entity from the head
Figure BDA0003082946710000042
Deducing new tail knowledge entity by incidence relation r
Figure BDA0003082946710000043
The probability of (d);
wherein, the analysis of the lifting degree:
Figure BDA0003082946710000044
the above formula shows that a part of the knowledge graph contains
Figure BDA0003082946710000045
Under the conditions of (A) and (B) at the same time contain
Figure BDA0003082946710000046
Is a probability of not containing
Figure BDA0003082946710000047
Under the condition of containing
Figure BDA0003082946710000048
The ratio of the probabilities of (a);
distinguishing strong association rules from invalid strong association rules by:
Figure BDA0003082946710000049
the "learning mixing rule knowledge iterative inference process based on the expression, searching for elements having effective and strong association with different industrial chain situations, and establishing a category matching rule of a complex industrial chain situation and different elements" includes:
under different types of industrial chain situations, mining elements relevant to the situations according to different situations through the expression learning mixing rule knowledge iterative reasoning process based on the industrial chain knowledge map;
introducing a membership function, analyzing the association degree of the mined elements and the industrial chain situation, and establishing a category matching rule of the complex industrial chain situation and different elements:
A(x)∈[0，1]
the above formula characterizes how high or low the element x belongs to the context a.
The method for obtaining the weight of the multi-element association of the complex industry chain situation based on the entropy analysis method comprises the following steps:
selecting n scenarios containing the same m elements, then xijThe value of the j element of the i-th scenario (i-1, 2 …, n; j-1, 2 …, m);
normalizing the index to let xij＝|xij|：
The forward direction index is as follows:
Figure BDA0003082946710000051
negative direction index:
Figure BDA0003082946710000052
x'ijThe value of the j element of the i contexts (i ═ 1, 2 …, n; j ═ 1, 2 …, m);
x'ijIs marked as xijAcquiring the proportion of the jth element under the ith situation:
Figure BDA0003082946710000053
obtaining the entropy value of the jth element:
Figure BDA0003082946710000054
wherein k is 1/ln (n) > 0, and satisfies ej＞0；
Obtaining information entropy redundancy: dj＝1-ej；
Obtaining the weight of each element:
Figure BDA0003082946710000055
acquiring a comprehensive score of each situation:
Figure BDA0003082946710000056
has the advantages that: the knowledge graph reasoning method adopts the representation learning method to carry out knowledge graph reasoning, effectively solves the problems of heterogeneity and imbalance existing in the association relation between knowledge entities in the knowledge graph of the industrial chain, and simultaneously adopts the association analysis method to extract the strong association rule set, thereby further improving the knowledge reasoning efficiency and realizing the knowledge iterative reasoning of the representation learning mixed rule; meanwhile, due to the fact that the actual production environment has complex conditions of multiple links, long flow, complex dynamic constraint, time-varying industrial situation and the like, the method determines the category matching rule of the multi-element situation by knowledge reasoning and membership function analysis; and determining the influence weight of different production elements on the industrial chain decision aiming at the same elements under different industrial chain situations through an entropy analysis method, and adjusting the weight ratio of different production elements in real time according to the analysis result and the actual production environment to finally realize the self-adaptive reasoning of various time-varying complex industrial chain situations.
Aiming at the problems of heterogeneity and unbalance existing in the association relation between knowledge entities in the knowledge graph of the industrial chain, the knowledge iterative inference expressing the learning mixing rule is realized by adopting a combination of expression learning and an association analysis method; the method can determine the category matching rule of the multi-element situation by combining the membership function analysis through a knowledge reasoning method according to the complex situation in the actual production environment; moreover, aiming at the problem that the same elements exist in different industrial chain situations and the different situations are difficult to further distinguish, the influence weight of different production elements on industrial chain decision is determined through an entropy analysis method, and the adaptive reasoning of various time-varying complex industrial chain situations is realized.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1, when the multi-element situation adaptive inference method of the present invention is applied to an industrial industry chain, the method specifically includes the following steps:
step 1: establishing a knowledge iterative inference method based on expression learning mixing rules:
step 1.1: knowledge inference method based on representation learning
Based on the existing knowledge entities and association rules in the industry chain knowledge graph, embedding (embedding) the associated knowledge entities and the triples of the corresponding association rules into a low-dimensional continuous vector space, for example:
Figure BDA0003082946710000071
wherein, the emb () represents the initial embedding method of the knowledge entity and the association rule,
Figure BDA0003082946710000072
representing the head (head) knowledge entity, association rules, tail (tail) knowledge entity, and their initialization embedding vectors, respectively.
Then, the initialized embedded vector of the knowledge entity and the association rule is normalized, such as:
Figure BDA0003082946710000073
wherein the content of the first and second substances,
Figure BDA0003082946710000074
euclidean distances representing knowledge entities and their initialized embedded vectors.
There are many representations of learning models used to learn the representation of entities and relationships in the knowledge graph, including TransE, TransH, TransR, TransSparse, and FT models, among which the TransSparse model can deal well with the heterogeneity and imbalance in the knowledge graph of the industry chain, where heterogeneity refers to the difference in the number of entities connected by relationships in the knowledge graph, some relationships may be connected by a large number of entities, and some relationships may be connected by only a small number of entities. Imbalance means that the types and numbers of head and tail entities connected by relationships are different and may be very different.
Through the TranSparse model, the knowledge entities according to different heads
Figure BDA0003082946710000075
And relation of
Figure BDA0003082946710000076
Obtaining new knowledge entities
Figure BDA0003082946710000077
And the knowledge graph of the industry chain is supplemented, and the process of the knowledge graph of the industry chain is satisfied
Figure BDA0003082946710000078
The evaluation can be done by a scoring function:
Figure BDA0003082946710000081
wherein the content of the first and second substances,
Figure BDA0003082946710000082
θminis a hyper-parameter related to sparsity, satisfies 0 ≦ θmin≤1，NrIs the number of pairs of entities to which the relationship r connects,
Figure BDA0003082946710000083
is the most numerous relationship of connected entities.
In addition, to solve the problem of unbalanced association relationship, the scoring function employed in the TranSparse model can be further expressed as:
Figure BDA0003082946710000084
wherein embh，embtIs that different projection matrixes for solving the unbalanced incidence relation in the TranSparse model correspond to
Figure BDA0003082946710000085
Namely different sparsity of the head and tail knowledge entities, the following sparsity is satisfied:
Figure BDA0003082946710000086
step 1.2: association rule set extraction based on association analysis method
In order to improve the efficiency of knowledge inference and knowledge graph supplementation, on the basis of the step 1.1, association rules need to be mined, and association rules capable of learning and inferring new knowledge with higher efficiency are extracted, and in order to achieve the purpose, an association analysis method is adopted to select a rule set with high confidence degree in combination with a knowledge graph, and the method comprises the following steps:
support (Support) analysis:
Figure BDA0003082946710000087
the above formula represents
Figure BDA0003082946710000088
Wherein I represents the total set of industry chain knowledge-graphs, num () represents the number of occurrences in a certain set,
confidence (Confidence) analysis:
Figure BDA0003082946710000091
the above formula represents the knowledge entity from the head
Figure BDA0003082946710000092
Deducing new tail knowledge entity by incidence relation r
Figure BDA0003082946710000093
The probability of (c).
Lift (Lift) analysis:
Figure BDA0003082946710000094
the above formula shows that a part of the knowledge graph contains
Figure BDA0003082946710000095
Under the conditions of (A) and (B) at the same time contain
Figure BDA0003082946710000096
Is a probability of not containing
Figure BDA0003082946710000097
Under the condition of containing
Figure BDA0003082946710000098
The ratio of the probabilities of (a) to (b).
The rule which satisfies the minimum support degree and the minimum confidence coefficient is called as a strong association rule through analysis based on the method. However, in the strong association rule, the valid strong association rule and the invalid strong association rule are also distinguished by the following method:
Figure BDA0003082946710000099
in summary, the step aims to obtain a new knowledge entity through representation learning by associating the existing knowledge entity with knowledge in the knowledge map, and meanwhile, in order to improve the efficiency of knowledge representation learning and avoid excessive calculation power on irrelevant association rules, the support degree, confidence degree and promotion degree of association are analyzed through an association analysis method, so that an effective strong association set is extracted, and then representation learning is performed through strong association rules, so that the knowledge iterative inference method based on the representation learning mixing rules of the knowledge map is realized.
Step 2: knowledge iterative reasoning-based industrial chain situation multi-element category matching rule:
and (3) based on the expression learning mixed rule knowledge iterative reasoning in the step (1), searching different elements which are effectively and strongly associated with different industrial chain situations, and establishing a category matching rule of the complex industrial chain situation and the different elements. The method comprises the following specific steps:
under different types of industrial chain situations, based on the industrial chain knowledge graph, by the knowledge iterative reasoning method in the step 1, elements relevant to the situations, such as places, time, people, products, production data and the like, are fully mined aiming at different situations.
Then, introducing a membership function, and analyzing the association degree of the mined elements and the industrial chain situation:
A(x)∈[0，1]
the above formula characterizes how high or low the element x belongs to the context a.
Through the membership function, elements with high correlation degree in different industrial chain situations can be distinguished, and therefore the multi-element class matching rule for repeating the complex industrial chain situations is established.
And step 3: establishing multi-element association weight of a complex industrial chain situation based on an entropy analysis method:
according to task requirements, firstly, in a complex industry chain situation, different situations may match the same category element, and in the case of the matching of the same element, an entropy analysis method is introduced, and under the condition that the matched elements are the same, the self-adaptive association weight of the industry chain situation element is further established according to different functions of the same element in different situations.
First, n scenarios containing the same m elements are selected, and then x is obtainedijIs the value of the j-th element of the first context (i-1, 2 …, n; j-1, 2 …, m).
Then normalizing the indexes, i.e. homogenizing heterogeneous indexes, because the measurement units of all indexes are not uniform, before calculating the comprehensive index by using the indexes, normalizing the indexes, i.e. converting the absolute value of the index into a relative value and making x be a relative valueij＝|xijTherefore, the homogenization problem of various heterogeneous index values is solved. Moreover, since the positive index and the negative index have different meanings (the higher the positive index value is, the better the negative index value is), the data normalization processing is performed on the high and low indexes by using different algorithms. The specific method comprises the following steps:
the forward direction index is as follows:
Figure BDA0003082946710000111
negative direction index:
Figure BDA0003082946710000112
x'ijIs the value of the j-th element of the i contexts (i ═ 1, 2 …, n; j ═ 1, 2 …, m). For convenience, x will remainijIs marked as xij。
Then, the specific gravity of the j element in the i context is calculated:
Figure BDA0003082946710000113
calculating the entropy value of the jth element:
Figure BDA0003082946710000114
wherein k 1/ln (n) > 0' satisfies ej＞0。
Calculating the information entropy redundancy: dj＝1-ej。
Calculating the weight of each element:
Figure BDA0003082946710000115
calculating a composite score for each context:
Figure BDA0003082946710000116
therefore, by the method of entropy analysis, the adaptive association weight of the context elements of the industry chain can be further established according to different functions of the same element under different contexts.
And 4, step 4: the self-adaptive reasoning method of the multi-element situation comprises the following steps:
the step 2 and the step 3 are combined, elements corresponding to different situations are analyzed through the situation category matching rules established in the step 2, then the multi-element association weight obtained through entropy analysis is combined, the self-adaptive reasoning method of the multi-element situation is further obtained, and accurate reasoning of the complex industrial chain situation with various elements and time varying is achieved.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily change or replace the present invention within the technical scope of the present invention. Therefore, the protection scope of the present invention is subject to the protection scope of the claims.

Claims (8)

1. A multi-element situation self-adaptive reasoning method is characterized by comprising the following steps:
associating knowledge entities in the knowledge map with knowledge to obtain a knowledge iterative reasoning process representing a learning mixing rule;
based on the knowledge iterative reasoning process of the expression learning mixing rule, searching elements which are effectively and strongly associated with different industrial chain situations, and establishing a category matching rule of the complex industrial chain situation and the different elements;
acquiring weight of multi-element association of a complex industrial chain situation based on an entropy analysis method;
analyzing elements corresponding to different situations through the category matching rules; and combining the weight associated with the multiple elements of the complex industry chain situation to obtain the self-adaptive reasoning method of the multiple element situation.
2. The multi-element context adaptive inference method according to claim 1, wherein said "associating knowledge entities in a knowledge graph with knowledge to obtain a knowledge iterative inference process representing a learning mixing rule", comprises:
associating the existing knowledge entities in the knowledge map with knowledge, and learning by utilizing representation to obtain new knowledge entities;
analyzing the support degree, the confidence degree and the promotion degree of the correlation by a correlation analysis method, and extracting an effective strong correlation set;
and performing expression learning through a strong association rule to obtain the expression learning mixed rule knowledge iterative reasoning process.
3. The method of claim 2, wherein the "learning to obtain new knowledge entity by representation through association of knowledge entity with knowledge existing in knowledge map" comprises:
based on the existing knowledge entities and association rules in the industry chain knowledge graph, embedding the associated knowledge entities and the triples of the corresponding association rules into a low-dimensional continuous vector space for processing;
carrying out normalization processing on the initialized embedded vectors of the knowledge entities and the association rules;
through the TranSparse model, the knowledge entities according to different heads
Figure FDA0003082946700000021
And relation of
Figure FDA0003082946700000022
Obtaining new knowledge entities
Figure FDA0003082946700000023
4. The multi-element context adaptive inference method according to claim 3, characterized by:
the knowledge entity
Figure FDA0003082946700000024
The knowledge graph of the industry chain is supplemented, and the process of the knowledge graph is satisfied
Figure FDA0003082946700000025
Evaluation was performed by a scoring function:
Figure FDA0003082946700000026
wherein the content of the first and second substances,
Figure FDA00030829467000000211
θminis a hyper-parameter related to sparsity, satisfies 0 ≦ θmin≤1，NrIs the number of pairs of entities to which the relationship r connects,
Figure FDA0003082946700000027
is the most numerous relationship of connected entities;
in addition, the scoring function employed in the TranSparse model can be further expressed as:
Figure FDA0003082946700000028
wherein embh，embtIs the TranSparse modelIn order to solve the problem of unbalanced different projection matrixes
Figure FDA0003082946700000029
Namely different sparsity of the head and tail knowledge entities, the following sparsity is satisfied:
Figure FDA00030829467000000210
5. the multi-element context adaptive inference method according to claim 2, wherein said "analyzing support, confidence and promotion of said association by association analysis method to extract effective strong association set" comprises:
selecting a rule set with high confidence by combining a correlation analysis method with a knowledge graph, wherein the method comprises the following steps:
wherein, the analysis of the support degree:
Figure FDA0003082946700000031
the above formula represents
Figure FDA0003082946700000032
Wherein I represents the total set of the industry chain knowledge-graphs, and num () represents the number of times of occurrence in a certain set;
wherein, the confidence coefficient analysis:
Figure FDA0003082946700000033
the above formula represents the knowledge entity from the head
Figure FDA0003082946700000034
Deducing new tail knowledge entity by incidence relation r
Figure FDA0003082946700000035
The probability of (d);
wherein, the analysis of the lifting degree:
Figure FDA0003082946700000036
the above formula shows that a part of the knowledge graph contains
Figure FDA0003082946700000037
Under the conditions of (A) and (B) at the same time contain
Figure FDA0003082946700000038
Is a probability of not containing
Figure FDA0003082946700000039
Under the condition of containing
Figure FDA00030829467000000310
The ratio of the probabilities of (a);
distinguishing strong association rules from invalid strong association rules by:
Figure FDA00030829467000000311
6. the method according to claim 1, wherein said "finding elements that are strongly and effectively associated with different industry chain contexts based on said representation learning blending rule knowledge iterative inference process, and establishing a category matching rule between a complex industry chain context and different elements" comprises:
under different types of industrial chain situations, mining elements relevant to the situations according to different situations through the expression learning mixing rule knowledge iterative reasoning process based on the industrial chain knowledge map;
introducing a membership function, analyzing the association degree of the mined elements and the industrial chain situation, and establishing a category matching rule of the complex industrial chain situation and different elements:
A(x)∈[0，1]
the above formula characterizes how high or low the element x belongs to the context a.
7. The multi-element situation adaptive inference method according to claim 1, wherein said "obtaining weights of multi-element associations of complex industry chain scenarios based on entropy analysis" comprises:
selecting n scenarios containing the same m elements, then xijThe value of the j element of the i-th situation (i 1, 2., n; j 1, 2., m);
normalizing the index to let xij＝|xij|：
The forward direction index is as follows:
Figure FDA0003082946700000041
negative direction index:
Figure FDA0003082946700000042
x'ijThe value of the j element of the i contexts (i 1, 2.., n; j 1, 2.., m);
x'ijIs marked as xijAcquiring the proportion of the jth element under the ith situation:
Figure FDA0003082946700000043
obtaining the entropy value of the jth element:
Figure FDA0003082946700000044
wherein k is 1/ln (n) > 0, and satisfies ej＞0；
Obtaining information entropy redundancy: dj＝1-ej；
Obtaining the weight of each element:
Figure FDA0003082946700000051
acquiring a comprehensive score of each situation:
Figure FDA0003082946700000052
8. an application of the multi-element situation adaptive inference method according to any one of claims 1-7 in the direction of industrial industry chain.
CN202110572122.8A 2021-05-25 2021-05-25 Multi-element situation self-adaptive reasoning method and application Pending CN113283604A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116029604A (en) * 2023-02-03 2023-04-28 华南农业大学 Cage-raised meat duck breeding environment regulation and control method based on health comfort level

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116029604A (en) * 2023-02-03 2023-04-28 华南农业大学 Cage-raised meat duck breeding environment regulation and control method based on health comfort level
CN116029604B (en) * 2023-02-03 2024-01-23 华南农业大学 Cage-raised meat duck breeding environment regulation and control method based on health comfort level

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