CN112418659A - Cross-border commodity tracing method, system and device based on block chain system - Google Patents
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Abstract
The invention discloses a cross-border commodity tracing method based on a block chain system, which comprises the following steps: acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked; if the verification is passed, calling a block chain intelligent contract enterprise credit rating evaluation model to evaluate the credit rating of the enterprise, and performing cross validation on the enterprise related to the product; if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked; if the risk level is in the allowed range, allowing the chain to be linked on the products to be linked, and recording related information of the related enterprises into the block chain node; and receiving a source tracing request of the cross-border commodity sent by the client, and feeding back a query result to the client by the block chain network. Can thoroughly solve whether the product to be linked is a certified product or a qualified product from the source.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a cross-border commodity tracing method, system and device based on a block chain.
Background
The block chain technology is a technical scheme for collectively maintaining a reliable database in a decentralized and distrust-free mode. Each block is "linked" to the next block using a cryptographic signature, shared and collaborated among anyone with sufficient authority, and collaboratively maintains the authenticity of the ledger through a consensus algorithm. The main flow of the block chain comprises five processes of transaction generation, transaction propagation, consensus, full node verification and block chain recording.
In the cross-border trade field, a plurality of application tests in the fields of data supervision and risk monitoring by using a block chain technology exist, based on the current concept of the block chain traceability technology, if commodities around people have own identity IDs, the commodities can be traced, the food safety problem caused by counterfeit and shoddy products can be greatly reduced, and the cross-border trade method is greatly beneficial to consumers, manufacturers and supervision layers.
But tracing is a difficult one in many application scenarios of the blockchain technology in actual operation. The anti-counterfeiting tracing based on the block chain technology has the following defects: if the source or some manufacturer or node on the block chain uploads the fake information, the source tracing is meaningless, so that the problem needs to be solved from the source, how to find out that the commodity is a fake product from the source is solved, and the application is provided for the fake product.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a cross-border commodity tracing method, system and device based on a block chain.
In order to solve the technical problem, the invention is solved by the following technical scheme:
a cross-border commodity tracing method based on a blockchain system deploys a blockchain network and deploys a corresponding intelligent contract model on the blockchain network, and the method comprises the following steps:
acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked;
if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and receiving a tracing request of the cross-border commodities sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
As an implementable embodiment, the related information of the product and the related information of the associated enterprise specifically include:
taking a raw material purchasing of a processing plant as a starting point, detecting reports of the raw material, a qualification certificate of a merchant, factory qualification information of raw material purchasing and processing in each step, processing information in each step, and spot inspection information and gateway information of the customs entry and exit of imported food;
the system also comprises production main body enterprise information and environment information thereof, associated main body enterprise information and environment information thereof, and related information related to enterprises in a production link, a storage link, a transportation link and a customs declaration link.
As one possible implementation, the block chain intelligent contract chain product detection model comprises a product identification model and a product composition correction model;
performing near infrared spectrum scanning on a product to be chain-wound, importing the obtained spectrum data into a target chain-wound product identification model of the same type, and judging whether the product to be chain-wound is of the type;
if so, introducing the spectral data into the correction model of the same type of target uplink product to obtain the content of various components of the same type of target uplink product; and obtaining the component information of the product to be uplink, comparing the component information with the actual components of the products of the same category, and if the component information and the actual components are within the error range, allowing the product information to be uplink.
As an implementation manner, the method for constructing the product identification model and the product composition correction model specifically comprises the following steps:
near infrared spectrum scanning is carried out on the same type of target chain loading products, and spectrum data of the same type of target chain loading products are collected;
preprocessing the acquired spectral data and selecting wave bands to obtain a spectral array sequence of characteristic wave bands;
clustering all elements in the spectrum array sequence to obtain a clustering center, distinguishing the class of each node according to the attribution degree, using the clustering center as a sample set for training the same type of target chain loading product identification model, and training and testing the same type of target chain loading product identification model through the sample set to obtain the same type of target chain loading product identification model meeting the requirements;
and performing regression fitting calculation on the spectral array sequence of the characteristic wave band and the real content values of various corresponding components in the same type of target uplink products by adopting a partial least square method, and establishing a component correction model of the same type of target uplink products.
As an implementation manner, the preprocessing method includes one or more methods of convolution smoothing processing, first order convolution derivation processing, second order convolution derivation processing, multiple scattering correction processing, standard normal variable transformation processing, multiple scattering correction processing, and normalization processing.
As an implementable manner, the following method is also included:
receiving a preset authority management rule;
and performing authentication on the signature certificate in the service request according to the authority management rule to generate an authentication result, wherein the service request comprises: the method comprises the steps of carrying out uplink product detection request, enterprise credit evaluation request and risk level evaluation request;
calling a block chain intelligent contract model according to the authentication result; wherein the blockchain intelligent contract model comprises: the system comprises a block chain intelligent contract uplink product detection model, a block chain intelligent contract enterprise credit degree model and a block chain intelligent contract risk level evaluation model.
A cross-border commodity traceability system based on a block chain system comprises an acquisition verification module, a cross verification module, a risk level evaluation module, an entry node module and a result feedback module;
the acquisition and verification module is used for acquiring information of the products to be linked and calling the block chain intelligent contract chain product detection model to verify the products to be linked;
the cross-validation module configured to: if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
the risk level assessment module configured to: if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
the logging node module configured to: if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and the result feedback module is used for receiving a source tracing request of the cross-border commodity sent by the client, connecting the block chain network for inquiry, and feeding the inquiry result back to the client by the block chain network.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of:
acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked;
if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and receiving a tracing request of the cross-border commodities sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
A cross-border commodity tracing apparatus based on a blockchain system, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the following method steps:
acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked;
if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and receiving a tracing request of the cross-border commodities sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
by the method, aiming at the risk assessment problem of the entry and exit products, the entry and exit environment with equal main bodies and autonomous data is oriented, the accuracy rate of the risk assessment of the entry and exit products can be improved as a target, the problems of incomplete data, non-objective evaluation, no feedback effect and the like caused by the dependence of the entry and exit products and enterprise intermediaries are analyzed by combining the specific requirements of a typical entry and exit inspection scene, and whether the products to be linked are genuine products or qualified products or not can be thoroughly solved from the source through the method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic overall flow diagram of the process of the present invention;
fig. 2 is a schematic diagram of the overall structure of the system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples, which are illustrative of the present invention and are not to be construed as being limited thereto.
In the prior art, the block chain technology is more extensive, and the anti-counterfeiting traceability based on the block chain technology has a defect that: if the source or some manufacturer or node on the block chain uploads the fake information, the source tracing is meaningless, so that the problem needs to be solved from the source, how to find out that the commodity is a fake product from the source is solved, and the technical problem is solved.
Example 1:
a cross-border tracing method for commodities based on a blockchain system, as shown in fig. 1, deploys a blockchain network, and deploys a corresponding intelligent contract model on the blockchain network, the method comprising the following steps:
s100, acquiring information of a product to be chain-linked, and calling a block chain intelligent contract chain-linking product detection model to check the product to be chain-linked;
s200, if the verification is passed, acquiring related information of an upper chain product related enterprise, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprise, and performing cross validation on the product related enterprise based on the related information of the product and the related information of the related enterprise;
s300, if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
s400, if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into a block chain node;
s500, receiving a source tracing request of the cross-border commodity sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
In the prior art, a traceability technology based on a block chain cannot solve the quality problem of imported goods from the source, for example, when imported customs records, a product to be recorded is verified by a block chain intelligent contract uplink product detection model, after the verification is passed, a block chain intelligent contract enterprise credit rating evaluation model is called to evaluate the credit of an enterprise, the enterprise related to the product is cross-verified based on related information of the product and related information of the related enterprise, the product is evaluated by a block chain intelligent contract risk rating evaluation model after the verification, the risk level of the product to be uplink is obtained, if the risk level is in an allowable range, uplink of the product to be uplink is allowed, and the related information of the product is checked, And the related information of the product-related enterprise is input into the block chain node, and whether the product is qualified or not is thoroughly solved from the source.
In one embodiment, the related information of the product and the related information of the associated enterprise specifically include:
taking a raw material purchasing of a processing plant as a starting point, detecting reports of the raw material, a qualification certificate of a merchant, factory qualification information of raw material purchasing and processing in each step, processing information in each step, and spot inspection information and gateway information of the customs entry and exit of imported food;
the system also comprises production main body enterprise information and environment information thereof, associated main body enterprise information and environment information thereof, and related information related to enterprises in a production link, a storage link, a transportation link and a customs declaration link.
In order to make imported food or other products safer, if the product is allowed to be linked, all the related information of the linked product needs to be linked, including not only the information of the related manufacturer but also all the information related to the storage link, the production link, the transportation link, and the like.
In one embodiment, the blockchain intelligent contract link product detection model comprises a product identification model and a product composition correction model;
performing near infrared spectrum scanning on a product to be chain-wound, importing the obtained spectrum data into a target chain-wound product identification model of the same type, and judging whether the product to be chain-wound is of the type;
if so, introducing the spectral data into the correction model of the same type of target uplink product to obtain the content of various components of the same type of target uplink product; and obtaining the component information of the product to be uplink, comparing the component information with the actual components of the products of the same category, and if the component information and the actual components are within the error range, allowing the product information to be uplink.
The near infrared analysis technology is widely applied to the field of analyzing food, medicines, skin care products and cosmetics, and can accurately know the components and the component quantity of the products, so that before the products are linked, in order to check whether the products belong to 'certified products', the products to be linked can be checked by combining the near infrared technology, but before the check, the content of the 'certified products' components must be known, and thus, whether the products to be linked meet the initial requirements of the linking can be checked by an identification model and a product component correction model.
In one embodiment, the specific steps for constructing the product identification model and the product composition correction model are detailed and comprise:
near infrared spectrum scanning is carried out on the same type of target chain loading products, and spectrum data of the same type of target chain loading products are collected;
preprocessing the acquired spectral data and selecting wave bands to obtain a spectral array sequence of characteristic wave bands;
clustering all elements in the spectrum array sequence to obtain a clustering center, distinguishing the class of each node according to the attribution degree, using the clustering center as a sample set for training the same type of target chain loading product identification model, and training and testing the same type of target chain loading product identification model through the sample set to obtain the same type of target chain loading product identification model meeting the requirements;
and performing regression fitting calculation on the spectral array sequence of the characteristic wave band and the real content values of various corresponding components in the same type of target uplink products by adopting a partial least square method, and establishing a component correction model of the same type of target uplink products.
In one embodiment, the following technical solution may be adopted to implement preprocessing on the characteristic wave band to obtain a spectrum array sequence, obtain a clustering center, distinguish the category to which each node belongs according to the degree of attribution, and use the obtained cluster as a sample set of a training product identification model, and the specific steps include:
calculating Euclidean distance between every two data in the data set, taking a negative value of the Euclidean distance as similarity, and further forming a similarity matrix;
selecting the minimum value except 0 in the similarity matrix, replacing all the zero points on the main diagonal in the similarity matrix with the minimum value to form a new similarity matrix, and recording the new similarity matrix as the similarity matrix;
calculating an attraction degree matrix and an attribution degree matrix of the similarity matrix, summing the attraction degree and the attribution degree of the sample points to obtain the sum of the attraction degree and the attribution degree, and taking the sum as a clustering center;
repeating the calculating and summing steps until the clustering center is unchanged or the iteration is finished when the specified iteration times are reached, taking the node with the positive sum of the attraction degree and the attribution degree as the clustering center, and distinguishing the category of each node according to the attribution degree to form a plurality of category arrays;
labeling the arrays clustered into a plurality of classes according to the classes to which the arrays belong to form a sample set;
and dividing the array of each category in the sample set into two parts, wherein one part is a training set, and the other part is a testing set.
In other embodiments, the product identification model is constructed based on the Softmax algorithm, and may be constructed in other manners, where the specific process of constructing based on the Softmax algorithm is as follows:
constructing a Softmax regression model:
wherein, P (y)(i)＝j|x(i)(ii) a θ) represents the input x(i)Probability of belonging to class j, j representing the class of belonging, x(i)Representing an input matrix needing to be classified, and representing model parameters by theta;
defining the cost function of the Softmax regression model:
wherein, 1{ y(i)J represents when y(i)J, 1{ y }(i)J ═ 1, when y(i)1{ y when not equal to j(i)＝j}＝0，
For an attenuation term, λ>0 is an attenuation factor, m represents the number of samples in the training set;
solving the minimum value by adopting a gradient descent method based on the training set data to obtain a Softmax regression model parameter;
and testing the trained Softmax regression model by adopting a test set, wherein the trained Softmax regression model is the product identification model when the accuracy of the test result meets the expected requirement, and if the accuracy of the test result does not meet the requirement, returning to the clustering step for re-clustering until the Softmax regression model meets the requirement.
In one embodiment, the preprocessing method includes one or more of convolution smoothing, first order convolution derivation, second order convolution derivation, multiple scattering correction, standard normal-to-variable transformation, multiple scattering correction, and normalization. The step is to process the obtained spectral data, eliminate inconsistent wave bands or data, obtain data required by later modeling, obtain a more accurate established model and further enable the final result to be more accurate.
In one embodiment, the spectrum data may be normalized to obtain a spectrum array sequence, and the specific operation manner is as follows:
zk＝(Dk-Dk,min)/(Dk,max-Dk,min)
wherein z iskThe normalized data is obtained; dkThe measured data before normalization; dk,minIs the minimum of the parameters, Dk,maxIs the maximum value among the parameters.
In one embodiment, the method further comprises:
receiving a preset authority management rule;
and performing authentication on the signature certificate in the service request according to the authority management rule to generate an authentication result, wherein the service request comprises: the method comprises the steps of carrying out uplink product detection request, enterprise credit evaluation request and risk level evaluation request;
calling a block chain intelligent contract model according to the authentication result; wherein the blockchain intelligent contract model comprises: the system comprises a block chain intelligent contract uplink product detection model, a block chain intelligent contract enterprise credit degree model and a block chain intelligent contract risk level evaluation model.
In one embodiment, the enterprise credit information and the product risk level can be implemented by using the prior art, and are not described in detail herein.
In one embodiment, based on the product batch information, the related information of the product and the related information of the enterprise uploaded by different enterprises in each link are obtained, and the enterprise related to the product is verified to obtain a verification result. The step is a cross validation process, which actually needs to evaluate the credit degree of the enterprise by means of enterprise data given by government or related institutions, and if the credit degree of the enterprise is not good enough, the cross validation cannot be passed.
After the associated enterprise passes the verification, the product needs to be further evaluated, that is, the risk level of the product to be linked is given, and if the risk level is lower, the linking is not recommended. If the risk level is within the allowable range, the product is allowed to uplink, and the associated enterprise information needs to be uplink together.
By the method, whether the product to be linked is a certified product or a qualified product can be thoroughly solved from the source.
Example 2:
a cross-border product tracing system based on a blockchain system, as shown in fig. 2, includes an acquisition verification module 100, a cross validation module 200, a risk level evaluation module 300, an entry node module 400, and a result feedback module 500;
the obtaining and verifying module 100 is configured to obtain information of a product to be linked, and invoke a block chain intelligent contract link product detection model to verify the product to be linked;
the cross-validation module 200 is configured to: if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
the risk level assessment module 300 is configured to: if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
the logging node module 400 is configured to: if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
the result feedback module 500 is configured to receive a tracing request of a cross-border commodity sent by a client, connect the blockchain network to perform query, and feed back a query result to the client by the blockchain network.
The cross-validation module 200 is configured to: taking a raw material purchasing of a processing plant as a starting point, detecting reports of the raw material, a qualification certificate of a merchant, factory qualification information of raw material purchasing and processing in each step, processing information in each step, and spot inspection information and gateway information of the customs entry and exit of imported food;
the system also comprises production main body enterprise information and environment information thereof, associated main body enterprise information and environment information thereof, and related information related to enterprises in a production link, a storage link, a transportation link and a customs declaration link.
The acquisition verification module 100 is configured to: the block chain intelligent contract chain product detection model comprises a product identification model and a product component correction model;
performing near infrared spectrum scanning on a product to be chain-wound, importing the obtained spectrum data into a target chain-wound product identification model of the same type, and judging whether the product to be chain-wound is of the type;
if so, introducing the spectral data into the correction model of the same type of target uplink product to obtain the content of various components of the same type of target uplink product; and obtaining the component information of the product to be uplink, comparing the component information with the actual components of the products of the same category, and if the component information and the actual components are within the error range, allowing the product information to be uplink.
The acquisition verification module 100 is configured to: and constructing the product identification model and the product component correction model, and the specific steps comprise:
near infrared spectrum scanning is carried out on the same type of target chain loading products, and spectrum data of the same type of target chain loading products are collected;
preprocessing the acquired spectral data and selecting wave bands to obtain a spectral array sequence of characteristic wave bands;
clustering all elements in the spectrum array sequence to obtain a clustering center, distinguishing the class of each node according to the attribution degree, using the clustering center as a sample set for training the same type of target chain loading product identification model, and training and testing the same type of target chain loading product identification model through the sample set to obtain the same type of target chain loading product identification model meeting the requirements;
and performing regression fitting calculation on the spectral array sequence of the characteristic wave band and the real content values of various corresponding components in the same type of target uplink products by adopting a partial least square method, and establishing a component correction model of the same type of target uplink products.
The acquisition verification module 100 is configured to: the preprocessing method comprises one or a combination of convolution smoothing processing, first-order convolution derivation processing, second-order convolution derivation processing, multivariate scattering correction processing, standard normal variable transformation processing, multivariate scattering correction processing and normalization processing.
The system is further configured to:
the system receives a preset authority management rule;
and performing authentication on the signature certificate in the service request according to the authority management rule to generate an authentication result, wherein the service request comprises: the method comprises the steps of carrying out uplink product detection request, enterprise credit evaluation request and risk level evaluation request;
calling a block chain intelligent contract model according to the authentication result; wherein the blockchain intelligent contract model comprises: the system comprises a block chain intelligent contract uplink product detection model, a block chain intelligent contract enterprise credit degree model and a block chain intelligent contract risk level evaluation model.
Example 3:
a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of:
acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked;
if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and receiving a tracing request of the cross-border commodities sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
Example 4:
in one embodiment, a cross-border product tracing device based on a blockchain system is provided, and the cross-border product tracing device based on the blockchain system can be a server or a mobile terminal. The cross-border commodity tracing device based on the block chain system comprises a processor, a memory, a network interface and a database which are connected through a system bus. The processor of the cross-border product tracing device based on the blockchain system is used for providing computing and controlling capability. The memory of the cross-border product tracing device based on the block chain system comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database stores all data of the cross-border commodity tracing device based on the block chain system. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for cross-border tracing of commodities based on a blockchain system.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that:
reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
In addition, it should be noted that the specific embodiments described in the present specification may differ in the shape of the components, the names of the components, and the like. All equivalent or simple changes of the structure, the characteristics and the principle of the invention which are described in the patent conception of the invention are included in the protection scope of the patent of the invention. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (9)
1. A cross-border commodity tracing method based on a blockchain system deploys a blockchain network and deploys a corresponding intelligent contract model on the blockchain network, and is characterized by comprising the following steps:
acquiring information of a product to be linked, and calling a block chain intelligent contract link product detection model to check the product to be linked;
if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and receiving a tracing request of the cross-border commodities sent by the client, connecting the blockchain network for inquiry, and feeding the inquiry result back to the client by the blockchain network.
2. The method of claim 1, wherein the product-related information and the enterprise-related information are specifically:
taking a raw material purchasing of a processing plant as a starting point, detecting reports of the raw material, a qualification certificate of a merchant, factory qualification information of raw material purchasing and processing in each step, processing information in each step, and spot inspection information and gateway information of the customs entry and exit of imported food;
the system also comprises production main body enterprise information and environment information thereof, associated main body enterprise information and environment information thereof, and related information related to enterprises in a production link, a storage link, a transportation link and a customs declaration link.
3. The method of claim 1, wherein the chain product detection model comprises a product identification model and a product composition correction model;
performing near infrared spectrum scanning on a product to be chain-wound, importing the obtained spectrum data into a target chain-wound product identification model of the same type, and judging whether the product to be chain-wound is of the type;
if so, introducing the spectral data into the correction model of the same type of target uplink product to obtain the content of various components of the same type of target uplink product; and obtaining the component information of the product to be uplink, comparing the component information with the actual components of the products of the same category, and if the component information and the actual components are within the error range, allowing the product information to be uplink.
4. The method as claimed in claim 3, wherein the steps of constructing the product identification model and the product component correction model include:
near infrared spectrum scanning is carried out on the same type of target chain loading products, and spectrum data of the same type of target chain loading products are collected;
preprocessing the acquired spectral data and selecting wave bands to obtain a spectral array sequence of characteristic wave bands;
clustering all elements in the spectrum array sequence to obtain a clustering center, distinguishing the class of each node according to the attribution degree, using the clustering center as a sample set for training the same type of target chain loading product identification model, and training and testing the same type of target chain loading product identification model through the sample set to obtain the same type of target chain loading product identification model meeting the requirements;
and performing regression fitting calculation on the spectral array sequence of the characteristic wave band and the real content values of various corresponding components in the same type of target uplink products by adopting a partial least square method, and establishing a component correction model of the same type of target uplink products.
5. The cross-border product tracing method based on the block chain system as claimed in claim 1, wherein the preprocessing method comprises one or more of convolution smoothing processing, first-order convolution derivation processing, second-order convolution derivation processing, multivariate scattering correction processing, standard normal-to-variable transformation processing, multivariate scattering correction processing and normalization processing.
6. The method of cross-border product tracing based on blockchain system as claimed in claim 1, further comprising the steps of:
receiving a preset authority management rule;
and performing authentication on the signature certificate in the service request according to the authority management rule to generate an authentication result, wherein the service request comprises: the method comprises the steps of carrying out uplink product detection request, enterprise credit evaluation request and risk level evaluation request;
calling a block chain intelligent contract model according to the authentication result; wherein the blockchain intelligent contract model comprises: the system comprises a block chain intelligent contract uplink product detection model, a block chain intelligent contract enterprise credit degree model and a block chain intelligent contract risk level evaluation model.
7. A cross-border commodity traceability system based on a block chain system is characterized by comprising an acquisition verification module, a cross verification module, a risk level evaluation module, an entry node module and a result feedback module;
the acquisition and verification module is used for acquiring information of the products to be linked and calling the block chain intelligent contract chain product detection model to verify the products to be linked;
the cross-validation module configured to: if the verification is passed, acquiring related information of the enterprises associated with the uplink products, calling a block chain intelligent contract enterprise credit evaluation model to evaluate the credit of the enterprises, and performing cross validation on the enterprises associated with the products based on the related information of the products and the related information of the associated enterprises;
the risk level assessment module configured to: if the cross verification is passed, calling a block chain intelligent contract risk level evaluation model to carry out risk level evaluation on the product based on the related information of the product to obtain the risk level of the product to be linked;
the logging node module configured to: if the risk level is in the allowed range, allowing the chain to be linked on the product to be linked, and recording the related information of the product and the related information of the product-related enterprise into the block chain node;
and the result feedback module is used for receiving a source tracing request of the cross-border commodity sent by the client, connecting the block chain network for inquiry, and feeding the inquiry result back to the client by the block chain network.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of one of claims 1 to 6.
9. A trans-border tracing apparatus based on a blockchain system, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the method steps according to any one of claims 1 to 6 when executing the computer program.
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