CN111008518B - Dynamic friction industry identification analysis data updating method and system - Google Patents
Dynamic friction industry identification analysis data updating method and system Download PDFInfo
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- CN111008518B CN111008518B CN201911259377.8A CN201911259377A CN111008518B CN 111008518 B CN111008518 B CN 111008518B CN 201911259377 A CN201911259377 A CN 201911259377A CN 111008518 B CN111008518 B CN 111008518B
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Abstract
The invention discloses a dynamic friction industry identification analysis data updating method, which comprises the following steps: acquiring an identification code; extracting the coding version number of the identification code; when the coding version number is not consistent with the current version number, acquiring a coding rule of the coding version number and the current version number; restoring the identification code into original data based on the code version number; converting the original data into a new identification code based on the current version number. The invention judges whether the identification code is the old code rule adopted or not through the comparison of the version numbers, and can automatically convert the identification code adopting the old code rule into the code rule adopting the new identification code, thereby realizing the update of the old identification code when the code rule changes.
Description
Technical Field
The invention relates to the technical field of identification analysis, in particular to a method and a system for updating identification analysis data in the dynamic friction industry.
Background
In the Internet age, an analysis system becomes a central nervous system of the Internet, and the ecology of the whole Internet is prospered; entering the Internet of things era of the Internet of everything, the construction of an analysis system of layout identification is required to be considered in advance in strategy, and the ecology of the Internet of things of the Internet of everything is constructed.
In the prior art, information of various materials in the dynamic friction (motorcycle and power equipment) industry can be formed into corresponding codes to be stored through an identification analysis technology. However, due to rapid development and progress of technology, particularly for industries in which such products are composed of a large number of components in the motor-driven friction industry, the structure of the products changes with time as the technology progresses. In addition, because the structure of the dynamic friction industry is complex, the relation among different links is also changed at any time. These changes may cause the coding rules to change, and in order to adapt to the change of the product structure, the coding rules may also change correspondingly, however, this may cause the original coding to be inconsistent with the new coding rules, so that the analysis of the old data may be wrong.
In summary, how to update the old identification code when the coding rule changes is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention actually solves the problems that: how to update the old identification code when the coding rule changes.
The invention adopts the following technical scheme:
a dynamic friction industry identification analysis data updating method comprises the following steps:
acquiring an identification code;
extracting the coding version number of the identification code;
when the coding version number is not consistent with the current version number, acquiring a coding rule of the coding version number and the current version number;
restoring the identification code into original data based on the code version number;
converting the original data into a new identification code based on the current version number.
Preferably, the identification code includes a version number field, and extracting the code version number of the identification code includes extracting a version number field; and when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number.
Preferably, the version number field includes version identifiers not included in other fields, the extracted version number field includes version identifiers in the identification code, and the field including the version identifiers is judged as the version number field for extraction.
Preferably, the extracting the encoded version number of the identification code includes:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
Preferably, the method for training the version classification neural network comprises the following steps:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
The utility model provides a move trade identification analysis data updating system, includes data acquisition module, draws module, contrast module and conversion module, wherein:
the data acquisition module is used for acquiring the identification code;
the extraction module is used for extracting the coding version number of the identification code;
the comparison module is used for comparing whether the coding version number is consistent with the current version number;
when the coding version number is not consistent with the current version number, the data acquisition module is also used for acquiring coding rules of the coding version number and the current version number;
the conversion module is used for restoring the identification code into original data based on the coding version number;
the conversion module is also used for converting the original data into a new identification code based on the current version number.
Preferably, the identification code includes a version number field, and extracting the code version number of the identification code includes extracting a version number field; and when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number.
Preferably, the version number field includes version identifiers not included in other fields, the extracted version number field includes version identifiers in the identification code, and the field including the version identifiers is judged as the version number field for extraction.
Preferably, the extracting the encoded version number of the identification code includes:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
Preferably, the method for training the version classification neural network comprises the following steps:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
In summary, the invention discloses a method for updating identification analysis data of the motor-driven friction industry, which comprises the following steps: acquiring an identification code; extracting the coding version number of the identification code; when the coding version number is not consistent with the current version number, acquiring a coding rule of the coding version number and the current version number; restoring the identification code into original data based on the code version number; converting the original data into a new identification code based on the current version number. The invention judges whether the identification code is the old code rule adopted or not through the comparison of the version numbers, and can automatically convert the identification code adopting the old code rule into the code rule adopting the new identification code, thereby realizing the update of the old identification code when the code rule changes.
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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a dynamic friction industry identification analysis data updating method disclosed by the invention;
fig. 2 is a schematic structural diagram of a dynamic friction industry identification analysis data updating system disclosed by the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a dynamic friction industry identification analysis data updating method, which comprises the following steps:
acquiring an identification code;
extracting the coding version number of the identification code;
when the coding version number is not consistent with the current version number, acquiring a coding rule of the coding version number and the current version number;
restoring the identification code into original data based on the code version number;
converting the original data into a new identification code based on the current version number.
In case the coding rules are known, the mutual conversion between coding and data is prior art and will not be described in detail here.
The invention judges whether the identification code is the old code rule adopted or not through the comparison of the version numbers, and can automatically convert the identification code adopting the old code rule into the code rule adopting the new identification code, thereby realizing the update of the old identification code when the code rule changes.
In specific implementation, the identification code includes a version number field, and extracting the code version number of the identification code includes extracting the version number field; and when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number.
The identification code may comprise a plurality of different fields, each field being used to represent a different meaning. Thus, a fixed version number field may be set at a fixed location of each segment of the identification code, e.g., the beginning or end of the identification code is set as the version number field. Thus, the version number can be rapidly extracted.
In a specific implementation, the version number field includes version identifiers that are not included in other fields, the extracted version number field includes version identifiers in the identification code, and the field including the version identifiers is determined as the version number field to be extracted.
When there is no position of the fixed version number field, special characters, which are not included in other fields, may be set in the version number field as version identifiers, for example "×", "&" characters, etc. In this way, even if there is no provision for the location of the determined version number field, the version number can be quickly looked up by identifying the version identifier.
In a specific implementation, the extracting the code version number of the identification code includes:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
Besides the mode of setting a special version number field, because the coded identification codes of the coding rules of different versions have different characteristics, the version classification neural network can be obtained by training the neural network, and the coding version number can be obtained by classifying the representation codes through the neural network.
In specific implementation, the method for training the version classification neural network comprises the following steps:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
The present invention is not limited to the use of a particular neural network. For the parameter setting of the neural network, the setting can be performed according to specific conditions.
As shown in fig. 2, the invention also discloses a system for updating the dynamic friction industry identification analysis data, which comprises a data acquisition module, an extraction module, a comparison module and a conversion module, wherein:
the data acquisition module is used for acquiring the identification code;
the extraction module is used for extracting the coding version number of the identification code;
the comparison module is used for comparing whether the coding version number is consistent with the current version number;
when the coding version number is not consistent with the current version number, the data acquisition module is also used for acquiring coding rules of the coding version number and the current version number;
the conversion module is used for restoring the identification code into original data based on the coding version number;
the conversion module is also used for converting the original data into a new identification code based on the current version number.
In specific implementation, the identification code includes a version number field, and extracting the code version number of the identification code includes extracting the version number field; and when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number.
In a specific implementation, the version number field includes version identifiers that are not included in other fields, the extracted version number field includes version identifiers in the identification code, and the field including the version identifiers is determined as the version number field to be extracted.
In a specific implementation, the extracting the code version number of the identification code includes:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
In specific implementation, the method for training the version classification neural network comprises the following steps:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be understood that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The method for updating the dynamic friction industry identification analysis data is characterized by comprising the following steps:
acquiring an identification code;
extracting the coding version number of the identification code; the identification code comprises a version number field, and the extracting of the code version number of the identification code comprises extracting the version number field; the version number field comprises version identifiers which are not included in other fields, the extracted version number field comprises version identifiers in the identification code, and the field comprising the version identifiers is judged to be the version number field for extraction; when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number;
when the coding version number is not consistent with the current version number, acquiring a coding rule of the coding version number and the current version number;
restoring the identification code into original data based on the code version number;
converting the original data into a new identification code based on the current version number.
2. The method for updating analysis data of a motor-driven friction industry as described in claim 1, wherein said extracting the code version number of the identification code comprises:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
3. The method for updating motor and motorcycle industry identification resolution data according to claim 2, wherein the method for training the version classification neural network comprises:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
4. The utility model provides a move trade identification analysis data updating system which characterized in that includes data acquisition module, draws module, contrast module and conversion module, wherein:
the data acquisition module is used for acquiring the identification code;
the extraction module is used for extracting the coding version number of the identification code; the identification code comprises a version number field, and the extracting of the code version number of the identification code comprises extracting the version number field; the version number field comprises version identifiers which are not included in other fields, the extracted version number field comprises version identifiers in the identification code, and the field comprising the version identifiers is judged to be the version number field for extraction; when the version number field is different from the version number field of the current version, judging that the coding version number is different from the current version number;
the comparison module is used for comparing whether the coding version number is consistent with the current version number;
when the coding version number is not consistent with the current version number, the data acquisition module is also used for acquiring coding rules of the coding version number and the current version number;
the conversion module is used for restoring the identification code into original data based on the coding version number;
the conversion module is also used for converting the original data into a new identification code based on the current version number.
5. The motor-driven-friction-industry-identification-resolution-data updating system according to claim 4, wherein the extracting the coded version number of the identification code comprises:
and inputting the identification codes into a version classification neural network, and outputting the identification codes to obtain the code version numbers.
6. The motor drive industry identification resolution data updating system according to claim 5, wherein the method of training the version classification neural network comprises:
s1, acquiring different coding version numbers and corresponding identification coding sets;
s2, dividing the identification coding set of each coding version number into a training set and a testing set;
s3, training the version classification neural network to be trained by adopting different coding version numbers and corresponding training sets;
s4, testing the trained version classification neural network by adopting different coding version numbers and corresponding test sets, and finishing training when the classification accuracy is greater than the preset accuracy, otherwise, returning to the step S3.
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