CN118396249B - Cream production information processing method based on supply chain - Google Patents
Cream production information processing method based on supply chain Download PDFInfo
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- CN118396249B CN118396249B CN202410855336.XA CN202410855336A CN118396249B CN 118396249 B CN118396249 B CN 118396249B CN 202410855336 A CN202410855336 A CN 202410855336A CN 118396249 B CN118396249 B CN 118396249B
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Abstract
The invention discloses a cream production information processing method based on a supply chain, which belongs to the technical field of information processing and comprises the following steps: s1, acquiring supply chain information of a cream production process, and generating a global traversal model for the supply chain information; s2, generating a supply chain encryption function based on the global traversal model; s3, encrypting the supply chain information by using a supply chain encryption function. According to the invention, text analysis is carried out on the supply chain data of the raw materials involved in the cream production process, a global traversal model is generated, the global traversal model is combined with a hash encryption algorithm, so that final supply chain information encryption is finished, the safety of the supply chain information in the whole cream production process is ensured, and the confidentiality of a cream production formula is improved.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a cream production information processing method based on a supply chain.
Background
For food processing enterprises, raw materials to be purchased from outside are often of a plurality of types, storage conditions required by different raw materials are often different, commodity data in a supply chain are transmitted through information streams, and the data comprise important information of enterprises, suppliers, sales terminals and the like. In order to ensure that the raw material formulation for cream production and processing is not leaked and that the specific formulation of food processing enterprises is maintained, many enterprises need to analyze and encrypt the supply chain data on line.
Disclosure of Invention
The invention provides a cream production information processing method based on a supply chain in order to solve the problems.
The technical scheme of the invention is as follows: a cream production information processing method based on a supply chain comprises the following steps:
S1, acquiring supply chain information of a cream production process, and generating a global traversal model for the supply chain information;
S2, generating a supply chain encryption function based on the global traversal model;
s3, encrypting the supply chain information by using a supply chain encryption function.
Further, S1 comprises the following sub-steps:
S11, acquiring supply chain data of each supply commodity in the cream production process as supply chain information;
s12, constructing data multi-core weights for each supply commodity according to the supply chain data of each supply commodity;
s13, constructing data multi-core weights by using an evaluation function and each supply commodity, and selecting a plurality of supply chain data from the supply chain information;
s14, determining a global traversal model by using the selected plurality of supply chain data.
The beneficial effects of the above-mentioned further scheme are: in the invention, a plurality of raw materials are matched in cream production, such as light cream, low-gluten powder, eggs, milk and the like, each raw material is used as a supply commodity and has corresponding supply chain data.
Further, S12 comprises the following sub-steps:
S121, eliminating stop words of supply chain data of the supply commodity to obtain standard supply chain data of the supply commodity;
S122, performing single-heat coding on each word in standard supply chain data of the supplied commodity, and generating a specific vector for each word;
s123, calculating the multi-core weight of the data of the supplied commodity according to the specific vector of each word in the standard supply chain data.
The single-hot coding is to map the values of words into a high-dimensional space, and each word corresponds to a point of the high-dimensional space.
Further, in S123, the calculation formula of the data multi-core weight q of the supplied commodity is: ; where X represents a specific vector of a word of the highest word frequency in the standard supply chain data, ε represents a Gaussian kernel parameter, |·| 1 represents an L1 norm operation, X 1 represents a specific vector of a1 st word in the standard supply chain data, d 1 represents a dimension of a1 st word in the standard supply chain data, X 2 represents a specific vector of a2 nd word in the standard supply chain data, d 2 represents a dimension of a specific vector of a2 nd word in the standard supply chain data, X N-1 represents a specific vector of an N-1 st word in the standard supply chain data, d N-1 represents a dimension of a specific vector of an N-1 st word in the standard supply chain data, and N represents a number of words of the standard supply chain data.
Further, S13 comprises the following sub-steps:
s131, constructing an evaluation function;
S132, inputting the multi-core weight of the data of each supplied commodity into an evaluation function to obtain an evaluation value of each supplied commodity;
S133, selecting the top ranking of the evaluation values And (c) supply chain data for the supply item, wherein,Representing an upward rounding function, and N represents the number of supplied goods.
Further, in S131, the expression of the evaluation function J is: ; where q represents the data multi-core weight of the offered good, Representing an upward rounding function, and N represents the number of supplied goods.
Further, in S14, the expression of the global traversal model R is: ; where y i,j represents a word vector of a J-th word in the supply chain data of the selected I-th supply item, I represents the number of selected supply items, J represents the number of words of the supply chain data of the selected supply items, E i+1_i represents a Dice coefficient between the supply chain data of the selected I-th supply item and the supply chain data of the i+1th supply item, and E represents an index.
Further, in S2, the expression of the supply chain encryption function G is: ; where g (-) represents a hash encryption operation, length (-) represents a text length calculation function, text represents supply chain information of a cream production process, and max (-) represents a maximum value taking operation.
The beneficial effects of the above-mentioned further scheme are: in the invention, the encryption operation is completed by utilizing the global traversal model value and the text length value of the whole supply chain data and combining the hash encryption function to generate the encryption function.
The beneficial effects of the invention are as follows: according to the invention, various raw materials are considered in the cream production process, and the supply chain data of the raw materials relate to the quality and the quality of the cream production, so that the invention carries out text analysis on the supply chain data of the raw materials in the cream production process, generates a global traversal model, combines the global traversal model with a hash encryption algorithm, completes final supply chain information encryption, ensures the safety of the supply chain information in the whole cream production process, and improves the confidentiality of a cream production formula.
Drawings
Fig. 1 is a flowchart of a cream production information processing method based on a supply chain.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a cream production information processing method based on a supply chain, comprising the steps of:
S1, acquiring supply chain information of a cream production process, and generating a global traversal model for the supply chain information;
S2, generating a supply chain encryption function based on the global traversal model;
s3, encrypting the supply chain information by using a supply chain encryption function.
In an embodiment of the present invention, S1 comprises the following sub-steps:
S11, acquiring supply chain data of each supply commodity in the cream production process as supply chain information;
s12, constructing data multi-core weights for each supply commodity according to the supply chain data of each supply commodity;
s13, constructing data multi-core weights by using an evaluation function and each supply commodity, and selecting a plurality of supply chain data from the supply chain information;
s14, determining a global traversal model by using the selected plurality of supply chain data.
In the invention, a plurality of raw materials are matched in cream production, such as light cream, low-gluten powder, eggs, milk and the like, each raw material is used as a supply commodity and has corresponding supply chain data.
In an embodiment of the present invention, S12 includes the following sub-steps:
S121, eliminating stop words of supply chain data of the supply commodity to obtain standard supply chain data of the supply commodity;
S122, performing single-heat coding on each word in standard supply chain data of the supplied commodity, and generating a specific vector for each word;
s123, calculating the multi-core weight of the data of the supplied commodity according to the specific vector of each word in the standard supply chain data.
The single-hot coding is to map the values of words into a high-dimensional space, and each word corresponds to a point of the high-dimensional space.
In the embodiment of the present invention, in S123, the calculation formula of the data multi-core weight q of the supplied commodity is: ; where X represents a specific vector of a word of the highest word frequency in the standard supply chain data, ε represents a Gaussian kernel parameter, |·| 1 represents an L1 norm operation, X 1 represents a specific vector of a1 st word in the standard supply chain data, d 1 represents a dimension of a1 st word in the standard supply chain data, X 2 represents a specific vector of a2 nd word in the standard supply chain data, d 2 represents a dimension of a specific vector of a2 nd word in the standard supply chain data, X N-1 represents a specific vector of an N-1 st word in the standard supply chain data, d N-1 represents a dimension of a specific vector of an N-1 st word in the standard supply chain data, and N represents a number of words of the standard supply chain data.
In an embodiment of the present invention, S13 includes the following sub-steps:
s131, constructing an evaluation function;
S132, inputting the multi-core weight of the data of each supplied commodity into an evaluation function to obtain an evaluation value of each supplied commodity;
S133, selecting the top ranking of the evaluation values And (c) supply chain data for the supply item, wherein,Representing an upward rounding function, and N represents the number of supplied goods.
In the embodiment of the present invention, in S131, the expression of the evaluation function J is: ; where q represents the data multi-core weight of the offered good, Representing an upward rounding function, and N represents the number of supplied goods.
In the embodiment of the present invention, in S14, the expression of the global traversal model R is: ; where y i,j represents a word vector of a J-th word in the supply chain data of the selected I-th supply item, I represents the number of selected supply items, J represents the number of words of the supply chain data of the selected supply items, E i+1_i represents a Dice coefficient between the supply chain data of the selected I-th supply item and the supply chain data of the i+1th supply item, and E represents an index.
In the embodiment of the present invention, in S2, the expression of the supply chain encryption function G is: ; where g (-) represents a hash encryption operation, length (-) represents a text length calculation function, text represents supply chain information of a cream production process, and max (-) represents a maximum value taking operation.
In the invention, the encryption operation is completed by utilizing the global traversal model value and the text length value of the whole supply chain data and combining the hash encryption function to generate the encryption function.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (1)
1. A cream production information processing method based on a supply chain, characterized by comprising the steps of:
S1, acquiring supply chain information of a cream production process, and generating a global traversal model for the supply chain information;
S2, generating a supply chain encryption function based on the global traversal model;
S3, encrypting the supply chain information by using a supply chain encryption function;
The step S1 comprises the following substeps:
S11, acquiring supply chain data of each supply commodity in the cream production process as supply chain information;
s12, constructing data multi-core weights for each supply commodity according to the supply chain data of each supply commodity;
s13, constructing data multi-core weights by using an evaluation function and each supply commodity, and selecting a plurality of supply chain data from the supply chain information;
s14, determining a global traversal model by using the selected plurality of supply chain data;
the step S12 comprises the following substeps:
S121, eliminating stop words of supply chain data of the supply commodity to obtain standard supply chain data of the supply commodity;
S122, performing single-heat coding on each word in standard supply chain data of the supplied commodity, and generating a specific vector for each word;
s123, calculating the data multi-core weight of the supplied commodity according to the specific vector of each word in the standard supply chain data;
in S123, the calculation formula of the data multi-core weight q of the supplied commodity is: ; wherein X represents a specific vector of a word of the highest word frequency in the standard supply chain data, epsilon represents a gaussian kernel parameter, |·| 1 represents an L1 norm operation, X 1 represents a specific vector of a1 st word in the standard supply chain data, d 1 represents a dimension of a1 st word in the standard supply chain data, X 2 represents a specific vector of a2 nd word in the standard supply chain data, d 2 represents a dimension of a specific vector of a2 nd word in the standard supply chain data, X N-1 represents a specific vector of an N-1 st word in the standard supply chain data, d N-1 represents a dimension of a specific vector of an N-1 st word in the standard supply chain data, and N represents the number of words of the standard supply chain data;
The step S13 includes the sub-steps of:
s131, constructing an evaluation function;
S132, inputting the multi-core weight of the data of each supplied commodity into an evaluation function to obtain an evaluation value of each supplied commodity;
S133, selecting the top ranking of the evaluation values And (c) supply chain data for the supply item, wherein,Representing an upward rounding function, N representing the number of supplied goods;
in S131, the expression of the evaluation function J is: ; where q represents the data multi-core weight of the offered good, Representing an upward rounding function, N representing the number of supplied goods;
in S14, the expression of the global traversal model R is: ; wherein y i,j represents a word vector of a J-th word in the supply chain data of the selected I-th supply commodity, I represents the number of selected supply commodities, J represents the number of words of the supply chain data of the selected supply commodity, E i+1_i represents a Dice coefficient between the supply chain data of the selected I-th supply commodity and the supply chain data of the i+1th supply commodity, and E represents an index;
in S2, the expression of the supply chain encryption function G is: ; where g (-) represents a hash encryption operation, length (-) represents a text length calculation function, text represents supply chain information of a cream production process, and max (-) represents a maximum value taking operation.
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