Industrial production data entity identification method based on Merkley-tree
An identification method and data entity technology, applied in the field of entity identification, can solve the problem of increased misjudgment rate of identification results, and achieve the effect of improving entity identification accuracy and ensuring identification efficiency.
Pending Publication Date: 2019-05-21
LIAONING UNIVERSITY
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AI Technical Summary
Problems solved by technology
For industrial production data, the data is floating. If the traditional entity recognition technology is used, the misjudgment rate of the recognition result will be greatly increased.
Method used
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Experimental program
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Embodiment 1
[0057] 1), experimental data set
[0058] Due to the confidentiality of the industrial production process, the data set used in this experiment is a simulated data set generated by using the generation tool datafactory according to the rules in the "Steelmaking Common Diagram Data Manual". And the IDEM algorithm proposed in this paper, the traditional threshold-based entity recognition method (Part), and the more general entity recognition algorithm based on similarity graph clustering (Clustering) are compared and analyzed. Table 1 gives some properties of the datasets used in the experiments.
[0059] Table 1 Datasets used in experiments
[0060]
[0061]
[0062] 2), Experimental results and analysis
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The invention provides an industrial production data entity identification method based on Merkley-tree. The method comprises the following steps of: 1) for the floatability of industrial production data, performing corresponding standardized processing on the data by utilizing the properties of a matrix and a vector to ensure that numerical attribute values of the same entity are the same; 2) calculating the information entropy of each attribute column, obtaining attribute sensitivity information, removing attributes with low sensitivity, and sequencing the rest attributes in a descending order according to the sensitivity; 3) proposing a chain structure called St-Chain; performing progressive Hash coding based on the St-Chain , and dividing the entities with the same Hash value into thesame block; and 4) for the structure obtained in the step 3), continuing to calculate the hash value of the subsequent attribute in each tuple, and repeatedly dividing the tuples into blocks accordingto the difference of the hash values to finally obtain an entity identification result, thereby providing an entity identification method with moderate algorithm operation efficiency and high identification precision.
Description
technical field [0001] The invention relates to an entity recognition method, especially a Merkle-tree-based industrial production data entity recognition method. Background technique [0002] With the development of information technology and Internet of Things technology, modern industry has accumulated a large amount of data over time. However, due to the fact that some variables are not easy to control in the production process, and the variables affect each other and are related to each other, these data are floating. . Industrial big data contains great value, and how to improve the availability of industrial big data has become a research hotspot. [0003] As an important method to improve data availability, entity recognition can divide a data set into several entity sets, thus solving the problem of entity identity in the data set. In the existing methods, before operating the data with identity problem, it first divides the data into blocks, then calculates the s...
Claims
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Login to View More IPC IPC(8): G06F16/901
CPCY02P90/30
Inventor 王妍曾辉杨冰清李玉诺
Owner LIAONING UNIVERSITY
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