CN108876213B - Block chain-based product management method, device, medium and electronic equipment - Google Patents
Block chain-based product management method, device, medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a product management method, a device, a medium and electronic equipment based on a block chain, wherein the product management method based on the block chain comprises the following steps: storing a plurality of product-supply-chain information over a blockchain network; determining attribute information of various types of products based on a plurality of product supply chain information stored in the blockchain network; and if detecting that new product supply chain information is input into the block chain network, determining whether the new product supply chain information is abnormal or not according to the attribute information of each type of product and the new product supply chain information. The technical scheme of the embodiment of the invention avoids the problems of information omission, recording error, illegal data tampering and the like of the product supply chain information caused by manual recording of the product supply chain information, can effectively improve the analysis efficiency of the product supply chain information, saves the labor cost, and promotes the effective popularization of the block chain technology in the product supply chain information management scheme.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a block chain-based product management method, device, medium and electronic equipment.
Background
At present, the management of product supply chain information is mainly performed in a manual recording and analysis or centralized system recording and analysis mode, which is not only low in efficiency, but also occupies more labor cost. Although the centralized system entry and analysis improves the efficiency, the traceability and the security of the supply chain information are lacked, so that the problems of information omission, recording errors, illegal data tampering, incapability of accurately identifying the supply chain source and the like are inevitably caused.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a medium, and an electronic device for product management based on a block chain, so as to overcome, at least to a certain extent, problems of difficult information management of a product supply chain, easy information omission, recording errors, and illegal data tampering.
Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to a first aspect of the embodiments of the present invention, there is provided a block chain-based product management method, including: storing a plurality of product-supply-chain information over a blockchain network; determining attribute information of various types of products based on a plurality of product supply chain information stored in the blockchain network; and if detecting that new product supply chain information is input into the block chain network, determining whether the new product supply chain information is abnormal or not according to the attribute information of each type of product and the new product supply chain information.
In some embodiments of the present invention, based on the foregoing solution, determining attribute information of each type of product based on a plurality of product-supply-chain information stored in the blockchain network includes: extracting attribute features of each product supply chain information based on a plurality of product supply chain information stored in the blockchain network; and training a machine learning model according to the attribute characteristics of the product supply chain information to determine the attribute information of the various products.
In some embodiments of the present invention, based on the foregoing solution, training a machine learning model according to the attribute features of the individual product-supply-chain information includes: weighting the attribute features of each product supply chain information to obtain a training sample corresponding to the attribute features of each product supply chain information; training the machine learning model through the training samples.
In some embodiments of the present invention, based on the foregoing solution, the machine learning model includes a clustering model, and the attribute information of the products includes clustering centers of the products.
In some embodiments of the present invention, based on the foregoing solution, determining whether there is an abnormality in the new product-supply-chain information according to the attribute information of the products and the new product-supply-chain information includes: extracting attribute features of the new product supply chain information; inputting the attribute characteristics of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute characteristics of the new product supply chain information and a clustering center of the target product type; and determining whether the new product supply chain information has abnormality or not according to the target product type and the target distance.
In some embodiments of the present invention, based on the foregoing solution, determining whether there is an abnormality in the new product-supply-chain information according to the target product type and the target distance includes: and if the product type identified by the new product supply chain information is the same as the target product type and the target distance is less than or equal to a distance threshold, determining that the new product supply chain information has no abnormality.
In some embodiments of the present invention, based on the foregoing solution, the method for block chain-based product management further includes: and if the new product supply chain information is determined to be abnormal, adjusting the determined attribute information of the various products through the new product supply chain information.
According to a second aspect of the embodiments of the present invention, there is provided a block chain-based product management apparatus, including: a storage unit for storing a plurality of product-supply-chain information through a blockchain network; a determining unit, configured to determine attribute information of each type of product based on a plurality of product supply chain information stored in the blockchain network; and the processing unit is used for determining whether the new product supply chain information is abnormal or not according to the attribute information of each type of product and the new product supply chain information when detecting that the new product supply chain information is input into the block chain network.
In some embodiments of the present invention, based on the foregoing scheme, the determining unit includes: a feature extraction unit, configured to extract attribute features of each product-supply-chain information based on a plurality of product-supply-chain information stored in the blockchain network; and the training unit is used for training a machine learning model according to the attribute characteristics of the supply chain information of each product so as to determine the attribute information of each product.
In some embodiments of the invention, based on the foregoing solution, the training unit is configured to: weighting the attribute features of each product supply chain information to obtain a training sample corresponding to the attribute features of each product supply chain information; training the machine learning model through the training samples.
In some embodiments of the present invention, based on the foregoing scheme, the machine learning model includes a clustering model, and the attribute information of each product category includes a clustering center of each product category.
In some embodiments of the present invention, based on the foregoing solution, the processing unit is configured to: extracting attribute features of the new product supply chain information; inputting the attribute characteristics of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute characteristics of the new product supply chain information and a clustering center of the target product type; and determining whether the new product supply chain information has abnormality or not according to the target product type and the target distance.
In some embodiments of the present invention, based on the foregoing solution, the processing unit is configured to: and if the product type identified by the new product supply chain information is the same as the target product type and the target distance is less than or equal to a distance threshold, determining that the new product supply chain information has no abnormality.
In some embodiments of the present invention, based on the foregoing solution, the device for block chain-based product management further includes: and the adjusting unit is used for adjusting the determined attribute information of the various products through the new product supply chain information when the new product supply chain information is determined to be abnormal.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the blockchain-based product management method according to the first aspect of the embodiments.
According to a fourth aspect of embodiments of the present invention, there is provided an electronic apparatus, including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method for block chain based product management as described in the first aspect of the embodiments.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the technical solutions provided by some embodiments of the present invention, a blockchain network stores a plurality of product supply chain information, so that the blockchain network can ensure that the product supply chain information is not tampered with, and traceability of the product supply chain information can be achieved, thereby avoiding problems of missing information, recording errors, illegal data tampering, and the like of the product supply chain information caused by manual recording or centralized system management of the product supply chain information. The attribute information of various products is determined based on the plurality of product supply chain information stored in the block chain network, and whether the new product supply chain information is abnormal or not is determined according to the attribute information of various products and the new product supply chain information, so that the analysis of the new product supply chain information can be realized based on reliable data stored in the block chain network, the analysis efficiency of the product supply chain information is effectively improved, the labor cost is saved, and the effective popularization of the block chain technology in the product supply chain information management scheme is promoted.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 schematically shows a flow chart of a blockchain based product management method according to a first embodiment of the present invention;
FIG. 2 schematically illustrates a flow diagram for determining attribute information for various types of products based on a plurality of product-supply-chain information stored in a blockchain network, according to one embodiment of the invention;
FIG. 3 schematically illustrates a flow diagram for determining whether new product-supply-chain information is anomalous, according to one embodiment of the invention;
fig. 4 schematically shows a flow chart of a blockchain based product management method according to a second embodiment of the present invention;
FIG. 5 schematically illustrates a block diagram of a system for implementing product-supply-chain information management in a blockchain network, according to an embodiment of the present invention;
FIG. 6 schematically shows a block diagram of a blockchain-based product management apparatus according to an embodiment of the present invention;
FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 schematically shows a flowchart of a blockchain-based product management method according to a first embodiment of the present invention, and an execution subject of the product management method may be a server or a terminal device, etc.
As shown in fig. 1, the block chain based product management method according to the first embodiment of the present invention includes the following steps S110, S120, and S130, which are described in detail below:
in step S110, a plurality of product-supply-chain information is stored through the blockchain network.
In one embodiment of the invention, the product-supply-chain information may include: product supply source, product supply price, product supplier's credit rating, product supply time, product supply duration, product supply size, product quality of supply (as may be represented by a return rate), customer feedback issues with respect to the product, and the like.
In one embodiment of the invention, product-supply-chain information may be stored in a blockchain network as transaction information in the blockchain network.
In step S120, attribute information of each type of product is determined based on a plurality of product-supply-chain information stored in the blockchain network.
In an embodiment of the present invention, the attribute information of each product can embody the characteristics of each product, that is, the attribute information of each product can be used to identify each product. As shown in fig. 2, the process of determining attribute information of each type of product based on a plurality of product-supply-chain information stored in a block-chain network according to an embodiment of the present invention may specifically include:
step S210, extracting attribute features of each product supply chain information based on a plurality of product supply chain information stored in the block chain network.
In one embodiment of the present invention, the attribute characteristic of the product-supply-chain information may be a specific value of the product-supply-chain information, such as a specific numerical value of information such as a supply source of the product, a product supply price, a credit rating of a product supplier, a product supply time, a product supply duration, a product supply scale, a product quality of supply, and the like.
Step S220, training the machine learning model according to the attribute characteristics of each product supply chain information to determine the attribute information of each product.
In one embodiment of the present invention, the dimension of the attribute feature of different product supply chain information may be the same or different, for example, the attribute feature of the type a product supply chain information may be { supply source, supply price, supplier credit rating, supply time, supply duration, supply component quality }; the attribute characteristics of the class b product supply chain information may be { supply source, supply price, supplier credit rating, supply component quality, market price change, customer feedback questions }, etc.
In an embodiment of the present invention, since the ratio of each attribute feature of the product supply chain information may be different, before the machine learning model is trained according to the attribute feature of each product supply chain information, the attribute feature of each product supply chain information may be weighted, so as to obtain a training sample corresponding to the attribute feature of each product supply chain information, and then the machine learning model is trained through the training sample.
In an embodiment of the present invention, the machine learning model may be a clustering model, such as a K-Nearest Neighbor (KNN) clustering algorithm, a K-means clustering algorithm, or the like. The process of training the clustering model by the training samples is the process of clustering the training samples by the corresponding clustering algorithm, and the training is stopped after the clustering algorithm converges (i.e. after the clustering center is determined). For the clustering model, the attribute information of each product may be the clustering center of each product.
In other embodiments of the present invention, the machine learning model may also be other machine learning models, such as a Convolutional Neural Network (CNN) model, a Recurrent Neural Network (RNN) model, and other Neural Network models. For the neural network model, the attribute information of each product may be feature mapping information learned by the neural network model.
With reference to fig. 1, in step S130, if it is detected that new product supply chain information is entered into the block chain network, it is determined whether there is an abnormality in the new product supply chain information according to the attribute information of each product type and the new product supply chain information.
In an embodiment of the present invention, if the attribute information of each type of product is determined according to the clustering model, as shown in fig. 3, the process of determining whether the new product supply chain information has an abnormality according to the attribute information of each type of product and the new product supply chain information in step S130 may specifically include the following steps:
step S310, extracting the attribute characteristics of the new product supply chain information.
In one embodiment of the present invention, the attribute characteristics of the new product-supply-chain information are consistent with the attribute characteristics of the product-supply-chain information stored in the blockchain network, such as specific numerical values of the supply source of the product, the product supply price, the credit rating of the product supplier, the product supply time, the product supply duration, the product supply scale, the product quality of the supply, and the like.
Step S320, inputting the attribute feature of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute feature of the new product supply chain information and a clustering center of the target product type.
In one embodiment of the present invention, since the machine learning model is trained according to the attribute features of the product supply chain information stored in the block chain network, after the attribute features of the new product supply chain information are input into the trained clustering model, the target product type corresponding to the new product supply chain information and the target distance between the attribute features of the new product supply chain information and the clustering center of the target product type can be obtained.
In one embodiment of the present invention, before the attribute features of the new product supply chain information are input into the trained clustering model, the attribute features of the new product supply chain information may be weighted, and then the weighted attribute features may be input into the trained clustering model.
Step S330, determining whether the new product supply chain information is abnormal or not according to the target product type and the target distance.
In an embodiment of the present invention, the process of determining whether there is an abnormality in the new product supply chain information according to the target product type and the target distance in step S330 may specifically include: and if the product type identified by the new product supply chain information is the same as the target product type and the target distance is less than or equal to the distance threshold, determining that the new product supply chain information has no abnormality.
In an embodiment of the present invention, since the product type is identified in the product-supply-chain information, if the product type identified by the new product-supply-chain information is the same as the target product type identified by the clustering model, and the target distance between the attribute feature of the new product-supply-chain information and the clustering center of the target product type is less than or equal to the distance threshold, it may be determined that there is no abnormality in the new product-supply-chain information. Otherwise, it may be determined that the new product-supply-chain information is abnormal, such as determining that the new product-supply-chain is illegal or fraudulent.
The technical solution of the embodiment shown in fig. 3 can determine whether there is an abnormality in the new product supply chain information through the processing of the clustering model. In other embodiments of the present invention, the processing may also be performed by other machine learning models, such as a neural network model, and specifically, the attribute features of the new product supply chain information may be input into a trained neural network model, so as to determine a product type corresponding to the new product supply chain information through the trained neural network model, and then determine whether the new product supply chain information is abnormal based on the product type determined by the trained neural network model and the product type identified in the new product supply chain information, for example, if the product type determined based on the trained neural network model is the same as the product type identified in the new product supply chain information, determine that the new product supply chain information is not abnormal; otherwise, it can be determined that the new product supply chain information is abnormal.
Based on the foregoing solution, in an embodiment of the present invention, if it is determined that there is no abnormality in the new product supply chain information, the attribute information of each determined product may be adjusted according to the new product supply chain information, so as to optimize system parameters, so as to improve accuracy of identifying whether there is an abnormality in the new product supply chain information.
In an embodiment of the present invention, when the determined attribute information of each type of product is adjusted by using the new product supply chain information, the machine learning model may be trained again by using the attribute characteristics of the new product supply chain information, so as to optimize the parameters of the machine learning model, thereby improving the output accuracy of the machine learning model.
The technical scheme of the embodiment of the invention avoids the problems of information omission, recording errors, illegal data tampering and the like of the product supply chain information caused by manual recording or centralized system management of the product supply chain information. And the analysis of new product supply chain information can be realized based on the reliable data stored in the blockchain network, the analysis efficiency of the product supply chain information is effectively improved, the labor cost is saved, and the effective popularization of the blockchain technology in the product supply chain information management scheme is promoted.
The implementation details of the embodiment of the present invention are described in detail below with reference to fig. 4 to 5:
as shown in fig. 4, a block chain-based product management method according to a second embodiment of the present invention includes the steps of:
step S410, building a blockchain node and a blockchain network.
In one embodiment of the invention, after the blockchain node is selected, a blockchain network may be constructed based on the selected blockchain node. For example, a blockchain network may be constructed with insurance company base business as a minimum node and based on the participation of one or more insurance groups/companies.
Step S420, store the product supply chain information based on the data structure and the storage method in the embodiment of the present invention.
In one embodiment of the invention, the product-supply-chain information may be stored in the blockchain network in the form of product-supply-chain transaction information, the input of which may be a supply-time information record and a supplier's public key and signature, etc.; the output of the product supply chain transaction information may be a deposit link for supply time material, a public key (account address) of the relevant information visitor, etc. Alternatively, product-supply-chain transaction information may be stored by a data structure as shown in table 1 to ensure high efficiency of information storage and information processing:
TABLE 1
In the data structure shown in table 1, since the supply-related information material and other materials usually include some information with a relatively large data size, such as images and documents, in order to improve storage efficiency and solve the problem of excessive tile information, in an embodiment of the present invention, the relatively large material, such as an image, may be stored in a tile in a linked form, where the linked value is a hash value obtained by encrypting the material by a hash function, such as SHA1, and the way of obtaining pointer links by the hash function can ensure that the content is not tampered. The actual materials can be stored in local storage equipment of the block chain nodes and can also be stored in a cloud storage mode. Meanwhile, in order to ensure high reliability of material storage, the material may be stored by using a redundant coding method, such as RS coding (Reed-Solomon codes, which is a forward error correction channel coding that is effective for a polynomial generated by correcting oversampled data) or LDPC (Low Density Parity Check Code) coding.
In one embodiment of the present invention, each product-supply-chain information may be uploaded and stored into the blockchain network via the format of table 1 above. Wherein the product supply chain information may include: product supply source, product supply price, product supplier's credit rating, product supply time, product supply duration, product supply size, product quality of supply (as may be represented by a return rate), customer feedback issues for the product, and the like.
For example, the attribute characteristics of a product supply chain information may be represented as { supply source value 1; supply price is 2000; vendor credit rating v 1; supply time 2016.01; the supply time is 1 year; the return rate is 8% }, the input of the supply chain transaction information may be a supply time information record and the public key and signature of the supplier, and the output of the supply chain transaction information may be a storage link of supply time material, i.e. attribute values, the nature of the transaction event (compliance/violation/fraud, etc.), the public key (account address) of the visitor of the information related to the transaction event, etc., wherein one supply chain transaction information may constitute one block.
Step S430, performing product supply chain management according to the information stored in the blockchain network.
In one embodiment of the invention, multidimensional discrete data can be constructed based on product supply chain information, such as multidimensional discrete data determined by product supply sources, product supply prices, credit ratings of product suppliers, product supply time, product supply duration, product supply scale, supplied product quality (return rate), market price changes, customer feedback problems, emergencies (forbidden, trade warfare, natural disasters), and the like, and then a training sample is constructed according to the multidimensional discrete data corresponding to each piece of product supply chain information stored in the block chain network, and the K-nearest neighbor cluster model is trained through the training sample to search and identify possible violations and fraud behaviors based on the trained model.
For example, the blockchain network includes n types of products, and the attribute characteristics of the supply chain information of different types of products may be the same or different according to the product characteristics, for example, the attribute characteristics of the supply chain information of the a-th type of product may be { supply source, supply price, supplier credit level, supply time, supply duration, supply component quality }; the attribute characteristics of the type b product supply chain information may be { supply source, supply price, supplier credit rating, supply component quality, market price change, customer feedback problem }. According to supply chain information (containing no violation information) of n types of products stored in a block chain network, weighting the attribute characteristics of the product supply chain information according to different occupation ratios of the attribute characteristics, and then taking the weighted attribute characteristics as input to train a KNN model, wherein each type of product corresponds to a clustering center, and with the increase of the product supply chain information stored in the block chain network, parameter adjustment and model optimization are continuously carried out on the KNN model so as to ensure that the correct classification of the KNN model and the distance from the attribute characteristics of each product supply chain information to the corresponding clustering center are smaller than a set threshold value T.
When a new product-supply-chain transaction message is generated in the blockchain network (assuming that the product category is identified as a product of type a in the product-supply-chain transaction message), extracting an attribute feature of the product-supply-chain transaction message, such as { supply source value 1; the supply price is 2000; vendor credit rating v 1; supply time 2017.01; the supply time is 1 year; and (4) when the return rate is 10%, inputting the extracted attribute features into a trained KNN model, wherein the output result of the trained KNN model is that the product is a type a product and the distance between the product and the clustering center of the type a product is less than T, and further judging that the product supply chain information does not have illegal behaviors. On the contrary, if the result output by the trained KNN model indicates that the product is a b-type product or does not belong to any known product, the product supply chain information can be basically determined to have violation or fraud behaviors, and further the responsibility investigation can be carried out on the product supply event.
Step S440, updating and optimizing system parameters based on the system performance.
In an embodiment of the invention, timeliness and effectiveness of product supply chain management can be evaluated, and system parameters can be continuously adjusted and optimized according to identification accuracy of abnormal product supply chain information, so that tracking management of a product supply chain is effectively realized in a block chain network, and effective popularization of a block chain technology in the aspect of product supply chain information management is powerfully promoted.
Embodiments of the apparatus of the present invention are described below with reference to the accompanying drawings.
Fig. 5 schematically shows a block diagram of a system implementing product-supply-chain information management in a blockchain network according to an embodiment of the present invention.
Referring to fig. 5, a system for implementing product supply chain information management in a blockchain network according to an embodiment of the present invention includes: a blockchain network building subsystem 510, a data format definition subsystem 520, a product supply chain information storage subsystem 530, a supply chain management subsystem 540, and a system performance evaluation subsystem 550.
The blockchain network building subsystem 510 is responsible for building, updating, and maintaining the blockchain nodes and the blockchain network. For example, a blockchain network may be constructed with insurance company base business as a minimum node and based on the participation of one or more insurance groups/companies.
Data format definition subsystem 520 may store product-supply-chain transaction information according to the data structure shown in table 1 above to ensure high efficiency in information storage and information processing. The input of the product supply chain transaction information can be a supply time information record, a public key and signature of a supplier and the like; the output of the product supply chain transaction information may be a deposit link for supply time material, a public key (account address) of the relevant information visitor, etc.
The product-supply-chain information storage subsystem 530 is used to store product-supply-chain information. Specifically, each product-supply-chain information may be uploaded into the blockchain network via the format of table 1 above for storage by the product-supply-chain information storage subsystem 530. Wherein the product supply chain information may include: product supply source, product supply price, product supplier's credit rating, product supply time, product supply duration, product supply size, product quality of supply (as may be represented by a return rate), customer feedback issues for the product, and the like.
For example, the attribute characteristics of a product supply chain information may be represented as { supply source value 1; the supply price is 2000; vendor credit rating v 1; supply time 2016.01; the supply time is 1 year; the return rate is 8% }, the input of the supply chain transaction information may be a supply time information record and the public key and signature of the supplier, and the output of the supply chain transaction information may be a storage link of supply time material, i.e. attribute values, the nature of the transaction event (compliance/violation/fraud, etc.), the public key (account address) of the visitor of the information related to the transaction event, etc., wherein one supply chain transaction information may constitute one block.
The supply chain management subsystem 540 is used to perform product supply chain management based on information stored in the blockchain network.
In one embodiment of the invention, multidimensional discrete data can be constructed based on product supply chain information, such as multidimensional discrete data determined by product supply sources, product supply prices, credit ratings of product suppliers, product supply time, product supply duration, product supply scale, supplied product quality (return rate), market price changes, customer feedback problems, emergencies (forbidden, trade warfare, natural disasters), and the like, and then a training sample is constructed according to the multidimensional discrete data corresponding to each piece of product supply chain information stored in the block chain network, and the K-nearest neighbor cluster model is trained through the training sample to search and identify possible violations and fraud behaviors based on the trained model.
For example, the blockchain network includes n types of products, and the attribute characteristics of the supply chain information of different types of products may be the same or different according to the product characteristics, for example, the attribute characteristics of the supply chain information of the a-th type of product may be { supply source, supply price, supplier credit level, supply time, supply duration, supply component quality }; the attribute characteristics of the type b product supply chain information may be { supply source, supply price, supplier credit rating, supply component quality, market price change, customer feedback problem }. According to supply chain information (containing no violation information) of n types of products stored in a block chain network, weighting the attribute characteristics of the product supply chain information according to different occupation ratios of the attribute characteristics, and then taking the weighted attribute characteristics as input to train a KNN model, wherein each type of product corresponds to a clustering center, and with the increase of the product supply chain information stored in the block chain network, parameter adjustment and model optimization are continuously carried out on the KNN model so as to ensure that the correct classification of the KNN model and the distance from the attribute characteristics of each product supply chain information to the corresponding clustering center are smaller than a set threshold value T.
When a new product-supply-chain transaction message is generated in the blockchain network (assuming that the product category is identified as a product of type a in the product-supply-chain transaction message), extracting an attribute feature of the product-supply-chain transaction message, such as { supply source value 1; the supply price is 2000; vendor credit rating v 1; supply time 2017.01; the supply time is 1 year; and (4) the goods return rate is 10%, then the extracted attribute features are input into a trained KNN model, and the output result of the trained KNN model is that the product is a type a product and the distance between the product and the clustering center of the type a product is less than T, so that the condition that no illegal behavior exists in the product supply chain information can be judged. On the contrary, if the result output by the trained KNN model indicates that the product is a b-class product or does not belong to any class of known products, the product supply chain information can be basically determined to have violation or fraud behaviors, and further responsibility investigation can be carried out on the product supply event.
The system performance evaluation subsystem 550 can evaluate timeliness and effectiveness of product supply chain management, and continuously adjust and optimize system parameters according to identification accuracy of abnormal product supply chain information, so as to effectively realize tracking management of a product supply chain in a block chain network, thereby powerfully promoting effective popularization of a block chain technology in the aspect of product supply chain information management.
Fig. 6 schematically shows a block diagram of a blockchain-based product management apparatus according to an embodiment of the present invention.
Referring to fig. 6, a block chain-based product management apparatus 600 according to an embodiment of the present invention includes: a storage unit 602, a determination unit 604 and a processing unit 606.
The storage unit 602 is configured to store a plurality of product-supply-chain information via a blockchain network; the determining unit 604 is configured to determine attribute information of each type of product based on a plurality of product-supply-chain information stored in the blockchain network; the processing unit 606 is configured to, when it is detected that new product supply chain information is entered in the block chain network, determine whether the new product supply chain information is abnormal according to the attribute information of each product and the new product supply chain information.
In one embodiment of the present invention, the determining unit 604 comprises: a feature extraction unit, configured to extract attribute features of each product-supply-chain information based on a plurality of product-supply-chain information stored in the blockchain network; and the training unit is used for training a machine learning model according to the attribute characteristics of the supply chain information of each product so as to determine the attribute information of each product.
In one embodiment of the invention, the training unit is configured to: weighting the attribute features of each product supply chain information to obtain a training sample corresponding to the attribute features of each product supply chain information; training the machine learning model through the training samples.
In one embodiment of the present invention, the machine learning model includes a clustering model, and the attribute information of the products of the category includes a clustering center of the products of the category.
In one embodiment of the invention, the processing unit 606 is configured to: extracting attribute features of the new product supply chain information; inputting the attribute characteristics of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute characteristics of the new product supply chain information and a clustering center of the target product type; and determining whether the new product supply chain information has abnormality or not according to the target product type and the target distance.
In one embodiment of the invention, the processing unit 606 is configured to: and if the product type identified by the new product supply chain information is the same as the target product type and the target distance is less than or equal to a distance threshold, determining that the new product supply chain information has no abnormality.
In an embodiment of the present invention, the block chain based product management apparatus 600 further includes: and the adjusting unit is used for adjusting the determined attribute information of the various products through the new product supply chain information when the new product supply chain information is determined to be abnormal.
For details that are not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the block chain based product management method of the present invention for the details that are not disclosed in the embodiments of the apparatus of the present invention.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with the electronic device implementing an embodiment of the present invention. The computer system 700 of the electronic device shown in fig. 7 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program executes the above-described functions defined in the system of the present application when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the block chain based product management method as described in the above embodiments.
For example, the electronic device may implement the following as shown in fig. 1: step S110, storing a plurality of product supply chain information through a block chain network; step S120, determining attribute information of various products based on a plurality of product supply chain information stored in the block chain network; step S130, if it is detected that new product supply chain information is entered into the block chain network, determining whether the new product supply chain information is abnormal according to the attribute information of each product and the new product supply chain information.
As another example, the electronic device may implement the steps shown in fig. 2 to 4.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit according to an embodiment of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (7)
1. A block chain-based product management method is characterized by comprising the following steps:
storing a plurality of product-supply-chain information over a blockchain network; the product supply chain information comprises product supply sources, product supply prices, credit ratings of product suppliers, product supply time, product supply duration, product supply scale, product quality of supply, and problems of customers for product feedback;
determining attribute information of various types of products based on a plurality of product supply chain information stored in the blockchain network;
determining attribute information of various types of products based on a plurality of product supply chain information stored in the blockchain network, including: extracting attribute features of each product supply chain information based on a plurality of product supply chain information stored in the blockchain network; training a machine learning model according to the attribute characteristics of the product supply chain information to determine the attribute information of the various products; the machine learning model comprises a clustering model, and the attribute information of each product comprises a clustering center of each product;
if detecting that new product supply chain information is input into the block chain network, extracting attribute characteristics of the new product supply chain information;
inputting the attribute characteristics of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute characteristics of the new product supply chain information and a clustering center of the target product type;
and determining whether the new product supply chain information has abnormality or not according to the target product type and the target distance.
2. The blockchain-based product management method according to claim 1, wherein training a machine learning model according to the attribute feature of each product supply chain information includes:
weighting the attribute features of each product supply chain information to obtain a training sample corresponding to the attribute features of each product supply chain information;
training the machine learning model through the training samples.
3. The blockchain-based product management method according to claim 1, wherein determining whether the new product supply chain information is abnormal according to the target product type and the target distance includes:
and if the product type identified by the new product supply chain information is the same as the target product type and the target distance is less than or equal to a distance threshold, determining that the new product supply chain information has no abnormality.
4. The blockchain-based product management method according to any one of claims 1 to 3, further comprising:
and if the new product supply chain information is determined to be abnormal, adjusting the determined attribute information of the various products through the new product supply chain information.
5. A blockchain-based product management apparatus, comprising:
a storage unit for storing a plurality of product-supply-chain information through a blockchain network; the product supply chain information comprises product supply sources, product supply prices, credit ratings of product suppliers, product supply time, product supply duration, product supply scale, product quality of supply, and problems of customers for product feedback;
a determining unit, configured to extract attribute features of each product-supply-chain information based on a plurality of product-supply-chain information stored in the blockchain network; training a machine learning model according to the attribute characteristics of the supply chain information of each product to determine the attribute information of each product; the machine learning model comprises a clustering model, and the attribute information of each product comprises a clustering center of each product;
the processing unit is used for extracting the attribute characteristics of new product supply chain information when detecting that the new product supply chain information is input into the block chain network; inputting the attribute features of the new product supply chain information into the trained clustering model to determine a target product type corresponding to the new product supply chain information and a target distance between the attribute features of the new product supply chain information and a clustering center of the target product type; and determining whether the new product supply chain information has abnormality or not according to the target product type and the target distance.
6. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for blockchain-based product management according to any one of claims 1 to 4.
7. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the blockchain based product management method of any one of claims 1 to 4.
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| CN109472518B (en) * | 2018-12-10 | 2021-06-29 | 泰康保险集团股份有限公司 | Block chain-based sales behavior evaluation method and device, medium and electronic equipment |
| CN109359901A (en) * | 2018-12-13 | 2019-02-19 | 泰康保险集团股份有限公司 | Method and device, medium and electronic equipment are determined based on the business risk of block chain |
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| CN109857611A (en) * | 2019-01-31 | 2019-06-07 | 泰康保险集团股份有限公司 | Test method for hardware and device, storage medium and electronic equipment based on block chain |
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