A supply chain data management method and system based on data analysis

Abstract

The present invention discloses a supply chain data management method and system based on data analysis, which relates to the technical field of supply chain data management, and includes performing feature analysis on each node information in the supply chain to obtain non-repeated node information. First, the present invention screens each node information in the supply chain to remove duplicate node information, which shortens the subsequent data processing time. Secondly, data analysis is performed on the non-repeated node information to determine the core nodes of the supply chain, making it more convenient to call data subsequently. Then, the core nodes of the supply chain are compared to determine the central node of the supply chain. Finally, supply chain data management rules are generated based on the central node of the supply chain and the supply chain data is managed and monitored, realizing the unified management of supply chain data, avoiding the need to repeatedly retrieve data from the databases of different enterprises when calling data, reducing the complexity of data retrieval, and indirectly shortening the product prediction time.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain data management, and specifically relates to a supply chain data management method and system based on data analysis. Background Art

[0002] A supply chain refers to a network structure formed by the connection of upstream and downstream members including suppliers, manufacturers, distributors, and consumers involved in the entire process from production of parts, manufacture of intermediate products and final products, and finally delivery to consumers.

[0003] Existing supply chain data is stored in the databases of each enterprise. When it is necessary to call the production data and sales data of a certain product to predict the sales prospect of the product, data needs to be called from the databases of raw material enterprises, production enterprises, and product sales enterprises, which wastes a lot of time and prolongs the product prediction time. Summary of the Invention

[0004] To solve the above technical problems, a supply chain data management method and system based on data analysis are provided. This technical solution solves the problem that existing supply chain data is stored in the databases of each enterprise, and when it is necessary to call the production data and sales data of a certain product to predict the sales prospect of the product, data needs to be called from the databases of raw material enterprises, production enterprises, and product sales enterprises, which wastes a lot of time and prolongs the product prediction time as proposed in the above background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A supply chain data management method based on data analysis, including:

[0007] Performing feature analysis on each node information in the supply chain to obtain non-repetitive node information;

[0008] Based on a data management terminal, performing data analysis and processing on the non-repetitive node information to determine the core nodes of the supply chain;

[0009] Based on a data management terminal, classifying the core nodes of the supply chain to determine the central nodes of the supply chain;

[0010] Based on a data management terminal, setting supply chain data management rules according to the central nodes of the supply chain.

[0011] Preferably, the performing feature analysis on each node information in the supply chain to obtain non-repetitive node information specifically includes the following steps:

[0012] Determining the position along each link in the supply chain to obtain the node position;

[0013] Based on the node location, perform information extraction processing on the enterprise to which the node location belongs, and determine the information of each node in the supply chain;

[0014] Perform a duplication analysis on the information of each node in the supply chain to obtain non-duplicated node information.

[0015] Preferably, the performing a duplication analysis on the information of each node in the supply chain to obtain non-duplicated node information specifically includes the following steps:

[0016] Based on the data management terminal, perform feature extraction processing on the information of each node in the supply chain to obtain each node feature, and the node feature includes the enterprise name and the enterprise business direction;

[0017] Based on the data management terminal, perform a mutual comparison process on each node feature;

[0018] If the enterprise names are different, output non-duplicated node information;

[0019] If the enterprise names are the same and the enterprise business directions are the same, the two node features being compared are of the same enterprise, remove any one of the node information, and output non-duplicated node information;

[0020] If the enterprise names are the same and the enterprise business directions are different, the two node features being compared are not of the same enterprise, output non-duplicated node information.

[0021] Preferably, the based on the data management terminal, performing data analysis processing on the non-duplicated node information to determine the core nodes of the supply chain specifically includes the following steps:

[0022] Based on the data management terminal, perform data reading processing on the non-duplicated node information to obtain non-duplicated node data;

[0023] Based on the data cleaning algorithm, perform data preprocessing on the non-duplicated node data, and the data preprocessing includes data deduplication processing, data missing value supplementation processing, and data format standardization processing;

[0024] Based on the data management terminal, perform a mutual matching process on the non-duplicated node data to obtain associated node pairs;

[0025] Based on the data management terminal, perform feature analysis on the associated node pairs to determine the core nodes of the supply chain, and the number of the core nodes of the supply chain is at least one.

[0026] Preferably, the based on the data management terminal, performing a mutual matching process on the non-duplicated node data to obtain associated node pairs specifically includes the following steps:

[0027] Based on the random number algorithm, randomly select a non-duplicated node data as the reference node data;

[0028] Based on the data management terminal, perform data matching on the remaining non-repeating node data with the reference node data as the feature;

[0029] If there is data that is exactly the same as the reference node data or partially the same data in the remaining non-repeating node data, set the reference node and the node with data that is exactly the same as the reference node data or partially the same data as the associated node pair;

[0030] If there is no data that is exactly the same as the reference node data or partially the same data in the remaining non-repeating node data, delete the reference node data.

[0031] Preferably, the method for determining the specific core nodes of the supply chain by performing feature analysis on the associated node pairs based on the data management terminal specifically includes the following steps:

[0032] Based on the data management terminal, perform a counting process on the associated node pairs to obtain the number of times the nodes in the associated node pairs appear;

[0033] Based on the data management terminal, compare and judge the number of times the nodes in the associated node pairs appear with the set number-of-occurrences threshold;

[0034] If the number of times the nodes in the associated node pair is greater than or equal to the set number-of-occurrences threshold, set this node as the core node of the supply chain;

[0035] If the number of times the nodes in the associated node pair appears less than the set number-of-occurrences threshold, this node is not the core node of the supply chain.

[0036] Preferably, the method for determining the specific central nodes of the supply chain by classifying the core nodes of the supply chain based on the data management terminal specifically includes the following steps:

[0037] Based on the random number algorithm, randomly select two core nodes of the supply chain to form the first core node pair;

[0038] Based on the first core node pair, perform a matching process on the associated node pairs;

[0039] If the first core node pair exists in the associated node pair, set the first core node pair as the node pair to be verified;

[0040] If the first core node pair does not exist in the associated node pair, set any one of the core nodes in the first core node pair as the fixed node, and through the random number algorithm, randomly select the remaining core nodes of the supply chain to form the second core node pair, where the remaining core nodes of the supply chain do not include the nodes in the first core node pair;

[0041] Based on the second core node pair, perform a matching process on the associated node pairs to obtain the node pair to be verified;

[0042] When all the core nodes of the supply chain are selected, end the matching process for associated node pairs;

[0043] Based on the data management terminal, perform a counting process on all pairs of nodes to be verified, and obtain the occurrence times of the nodes to be verified;

[0044] Based on the bubble sort method, perform a maximum value screening on the occurrence times of the nodes to be verified, determine the maximum value of the occurrence times of the nodes to be verified, and set the core supply chain node corresponding to the maximum value of the occurrence times of the nodes to be verified as the central supply chain node.

[0045] Preferably, the setting of the supply chain data management rules according to the central supply chain node based on the data management terminal specifically includes the following steps:

[0046] Based on the data management terminal, set a parent folder named after the central supply chain node under the root directory of the data storage device;

[0047] Based on the data management terminal, construct several groups of sub-folders in the parent folder, and the number of the sub-folders is one less than the number of the core supply chain nodes;

[0048] Based on the data management terminal, name several groups of sub-folders after the core supply chain nodes;

[0049] Based on the core supply chain nodes, perform a screening process on the associated node pairs to obtain associated sub-nodes;

[0050] Based on the data management terminal, set a corresponding number of folders inside the sub-folders according to the number of the associated sub-nodes, and store the data of the associated sub-nodes in the folders;

[0051] Based on the data management terminal, perform a marking process on the sub-folders and the nodes of the supply chain corresponding to the sub-folders.

[0052] Preferably, the marking process on the sub-folders and the nodes of the supply chain corresponding to the sub-folders based on the data management terminal specifically includes the following steps:

[0053] Based on the data management terminal, generate a pair of unique labels according to the sub-folders and the nodes of the supply chain corresponding to the sub-folders;

[0054] Based on the data management terminal, embed a pair of unique labels into the sub-folders and the nodes of the supply chain corresponding to the sub-folders respectively;

[0055] Based on the data management terminal, monitor the node information of the supply chain;

[0056] If the node information of the supply chain changes, the data management terminal modifies, replaces, or deletes the data in the subfolder through the unique label.

[0057] Furthermore, a supply chain data management system based on data analysis is proposed to implement a supply chain data management method based on data analysis as described above, including:

[0058] A data management terminal, which is used to perform repetitive analysis, node pairing, and determination of the core nodes of the supply chain on the information of each node in the supply chain, and generate supply chain data management rules;

[0059] A data storage device, which is used to store supply chain data;

[0060] A node screening module, which is used to perform a duplication analysis on the information of each node in the supply chain and determine the non-duplicate node information;

[0061] A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on the non-duplicate node information to determine the core nodes of the supply chain;

[0062] A central node determination module, which is used to perform node pairing and node quantity sorting on the core nodes of the supply chain to determine the central nodes of the supply chain;

[0063] A data management rule generation module, which generates data management rules according to the central nodes of the supply chain and stores and monitors the supply chain data.

[0064] Still further, a storage medium is proposed, on which a computer program is stored. When the computer program is called and run, it executes a supply chain data management method based on data analysis as described above.

[0065] Compared with the prior art, the present invention provides a supply chain data management method and system based on data analysis, which have the following beneficial effects:

[0066] First, the present invention screens the information of each node in the supply chain, removes duplicate node information, and shortens the subsequent data processing time. Second, it then performs data analysis on the non-duplicate node information to determine the core nodes of the supply chain, making it more convenient to call data subsequently. Then, it compares the core nodes of the supply chain to determine the central nodes of the supply chain. Finally, it generates supply chain data management rules according to the central nodes of the supply chain and manages and monitors the supply chain data, realizing the unified management of supply chain data, avoiding the need to retrieve data from the databases of different enterprises multiple times when calling data, reducing the complexity of data retrieval, and indirectly shortening the product prediction time. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flow chart of steps S100 - S400 in a supply chain data management method based on data analysis proposed by the present invention;

[0068] Figure 2 It is a structural block diagram of a supply chain data management system based on data analysis proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0069] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0070] Referring to Figure 1 as shown, a supply chain data management method based on data analysis includes:

[0071] S100. Conduct feature analysis on each node information in the supply chain to obtain non - repeating node information;

[0072] It can be understood that a product may require multiple raw material suppliers. Therefore, a manufacturing enterprise may be associated with multiple raw material enterprises, and the supply chain links of these raw material suppliers will point to a manufacturing enterprise. Therefore, there may be duplicate nodes;

[0073] S200. Based on the data management terminal, conduct data analysis and processing on the non - repeating node information to determine the core nodes of the supply chain;

[0074] It can be understood that there may be multiple core nodes in the supply chain. For example, a product requires multiple manufacturing enterprises to jointly produce, and these manufacturing enterprises are also associated with different raw material enterprises, and these manufacturing enterprises are the core nodes of the supply chain;

[0075] S300. Based on the data management terminal, classify the core nodes of the supply chain to determine the central node of the supply chain;

[0076] It can be understood that there may be multiple core nodes in a supply chain, but there is only one central node of the supply chain. Because the central node of the supply chain is responsible for the allocation of product production and sales, and the unified management of supply chain data should also be carried out according to the central node of the supply chain;

[0077] S400. Based on the data management terminal, set supply chain data management rules according to the central node of the supply chain;

[0078] Those skilled in the art can understand that the supply chain is composed of a series of enterprises such as raw material enterprises, product manufacturing enterprises, and product sales enterprises, similar to a spider-web structure. Each enterprise will record various data of the product. If the various data of the product are stored separately in the corresponding enterprises, when it is necessary to call the product data for predictive analysis of product sales, it may be necessary to call the data multiple times, and the data called comes from different enterprises. For example, the raw material inventory data needs to be read from the enterprise database of the raw material enterprise, while the product inventory needs to be read from the enterprise databases of the manufacturer and the distributor. The network links of the databases of different enterprises may be different. Therefore, it is necessary to adjust the network link multiple times to achieve data reading, which indirectly prolongs the product prediction time. Therefore, it is necessary to uniformly manage the data of these enterprises regarding the product to avoid adjusting the network link multiple times for data reading.

[0079] Example 1: Conduct feature analysis on each node information in the supply chain to obtain non-duplicate node information, which specifically includes the following steps:

[0080] S101: Determine the position along each link in the supply chain to obtain the node position;

[0081] S102: Based on the node position, perform information extraction processing on the enterprise to which the node position belongs to determine each node information in the supply chain;

[0082] S103: Conduct duplicate analysis on each node information in the supply chain to obtain non-duplicate node information;

[0083] In this embodiment, the supply chain is a network structure. A node may be connected to multiple nodes. For example, a manufacturing enterprise may cooperate with multiple raw material suppliers, and the end points of the links where these raw material suppliers are located will all point to one position, that is, the position of the manufacturing enterprise. Therefore, for more convenient subsequent data management, these node information are screened, and the duplicate nodes are screened out, only one is retained, that is, the non-duplicate node information in this application.

[0084] Example 2: Conduct duplicate analysis on each node information in the supply chain to obtain non-duplicate node information, which specifically includes the following steps:

[0085] S1031: Based on the data management terminal, perform feature extraction processing on each node information in the supply chain to obtain each node feature, and the node feature includes the enterprise name and the enterprise business direction;

[0086] S1032: Based on the data management terminal, perform mutual comparison processing on each node feature;

[0087] S1033: If the enterprise names are different, output non-duplicate node information;

[0088] S1034. If the enterprise names are the same and the enterprise business directions are the same, the two compared node features belong to the same enterprise. Remove any one of the node information and output the non-duplicate node information;

[0089] S1035. If the enterprise names are the same and the enterprise business directions are different, the two compared node features do not belong to the same enterprise. Output the non-duplicate node information;

[0090] In this embodiment, there may be enterprises with the same name in a supply chain, but these enterprises may or may not be the same. For example, there is a company name in the raw material enterprises that is the same as a company name in the product sales enterprises, but the two are not the same company. To avoid data loss during subsequent data management, compare the enterprise business directions to determine whether they are the same enterprise. If they are the same enterprise, they are duplicate nodes and one needs to be deleted and one retained. If they are not the same enterprise, they are not duplicate nodes and both need to be retained.

[0091] Embodiment 3. Based on the data management terminal, perform data analysis and processing on the non-duplicate node information to determine the specific steps of the core nodes of the supply chain as follows:

[0092] S201. Based on the data management terminal, perform data reading processing on the non-duplicate node information to obtain non-duplicate node data;

[0093] S202. Based on the data cleaning algorithm, perform data preprocessing on the non-duplicate node data. The data preprocessing includes data deduplication processing, data missing value filling processing, and data format standardization processing;

[0094] It can be understood that different enterprises have different ways of storing and managing data. Therefore, to achieve unified management of supply chain data, perform preprocessing on it, remove duplicate data, fill in missing data, and unify the data format;

[0095] The data cleaning algorithm includes the deletion method, the mode filling method, and the format conversion algorithm. The deletion method is used to delete duplicate data, the mode filling method is used to fill in missing data, and the format conversion algorithm is used to unify the data format;

[0096] S203. Based on the data management terminal, perform mutual matching processing on the non-duplicate node data to obtain associated node pairs;

[0097] S204. Based on the data management terminal, perform feature analysis on the associated node pairs to determine the core nodes of the supply chain. The number of the core nodes of the supply chain is at least one;

[0098] In this embodiment, there is more than one core node in the supply chain. For example, there are multiple enterprises selling the same product, and then these enterprises distribute these products to other enterprises, which are the core nodes of the supply chain. Similarly, the same is true for manufacturing enterprises. They split the products into multiple parts and hand them over to different enterprises for production. The purpose of determining the core nodes of the supply chain is to make the data orderly and more convenient and efficient to call during subsequent data management. For example, if there is an abnormality in a certain component of a product, one only needs to retrieve the enterprise that produced the component in the folder corresponding to the core node of the supply chain to quickly obtain the data during the production of the component.

[0099] Embodiment 4. Based on the data management terminal, the non-repetitive node data is mutually matched to obtain the associated node pairs, which specifically includes the following steps:

[0100] S2031. Based on the random number algorithm, arbitrarily select a non-repetitive node data as the reference node data;

[0101] It can be understood that a non-repetitive node is randomly selected, and then the remaining nodes are matched through the selected node to determine whether there is an association between the nodes, that is, whether there is a link between the nodes;

[0102] S2032. Based on the data management terminal, use the reference node data as a feature to perform data matching on the remaining non-repetitive node data;

[0103] S2033. If there is data that is exactly the same as or partially the same as the reference node data among the remaining non-repetitive node data, set the reference node and the node with data that is exactly the same as or partially the same as the reference node data as the associated node pair;

[0104] It can be understood that there is an association between the manufacturing enterprise and the raw material enterprise, between the manufacturing enterprise and the sales enterprise, and between the upper-level sales enterprise and the lower-level sales enterprise. Therefore, multiple matches are required until all non-repetitive node data has been matched;

[0105] S2034. If there is no data that is exactly the same as or partially the same as the reference node data among the remaining non-repetitive node data, delete the reference node data;

[0106] In this embodiment, during the production process of a product, adjustments may be made. After the product is adjusted, a certain raw material may no longer be used, and the supplier of this raw material will not have a link with the manufacturer, and there will be no identical or partially identical data. To improve the orderliness of the supply chain, this node is deleted. In addition, to make the supply chain data management more orderly, the related nodes are screened out to form associated node pairs, and corresponding folders are set through the associated node pairs to achieve orderly data management.

[0107] Embodiment 5. Based on the data management terminal, performing feature analysis on the associated node pairs to determine the core nodes of the supply chain specifically includes the following steps:

[0108] S2041. Based on the data management terminal, perform a counting process on the associated node pairs to obtain the number of times the nodes in the associated node pairs appear;

[0109] S2042. Based on the data management terminal, compare and judge the number of times the nodes in the associated node pairs appear with the set appearance frequency threshold;

[0110] S2043. If the number of times the nodes of the associated node pair is greater than or equal to the set appearance frequency threshold, set this node as the core node of the supply chain;

[0111] S2044. If the number of times the nodes of the associated node pair appears less than the set appearance frequency threshold, this node is not the core node of the supply chain;

[0112] In this embodiment, there is at least one core node of the supply chain, but the number of times the core nodes of the supply chain appear is very large. For example, a sales enterprise distributes all products to multiple secondary sales enterprises, and this sales enterprise will appear multiple times among these secondary sales enterprises. Similarly, for a production enterprise, a product is split into multiple components and handed over to different secondary production enterprises for production, and this production enterprise is one of the core nodes of the supply chain. Therefore, by judging the number of times the nodes in the associated node pairs appear, the core nodes of the supply chain are determined.

[0113] Embodiment 6. Based on the data management terminal, performing classification processing on the core nodes of the supply chain to determine the central node of the supply chain specifically includes the following steps:

[0114] S301. Based on the random number algorithm, arbitrarily select two core nodes of the supply chain to form the first core node pair;

[0115] S302. Based on the first core node pair, perform a matching process on the associated node pairs;

[0116] S303. If the first core node pair exists in the associated node pair, set the first core node pair as the node pair to be verified;

[0117] S304. If there is no first core node pair in the associated node pairs, set any one of the core nodes in the first core node pair as a fixed node, and randomly select the remaining supply chain core nodes through a random number algorithm to form a second core node pair. The remaining supply chain core nodes do not include the nodes in the first core node pair;

[0118] S305. Based on the second core node pair, perform matching processing on the associated node pairs to obtain the node pairs to be verified;

[0119] S306. When all the supply chain core nodes are selected, end the matching processing with the associated node pairs;

[0120] S307. Based on the data management terminal, count all the node pairs to be verified to obtain the occurrence times of the nodes to be verified;

[0121] S308. Based on the bubble sort method, screen the maximum value of the occurrence times of the nodes to be verified, determine the maximum value of the occurrence times of the nodes to be verified, and set the supply chain core node corresponding to the maximum value of the occurrence times of the nodes to be verified as the supply chain central node;

[0122] In this embodiment, there may be multiple supply chain core nodes, but there is only one supply chain central node, and the supply chain central node exists in the supply chain core nodes. Therefore, only by screening the supply chain core nodes can the supply chain central node be determined, and the supply chain central node is the node with the most occurrences in the supply chain core nodes. Because the supply chain is a structure similar to a spider web, and the supply chain central node is the center of the spider web, while the supply chain core nodes are the nodes closest to the center of the spider web. Therefore, by judging the occurrence times of the supply chain core nodes, the supply chain central node is determined. Finally, according to the supply chain central node, the supply chain data management rules are determined.

[0123] Embodiment 7. Based on the data management terminal, setting the supply chain data management rules according to the supply chain central node specifically includes the following steps:

[0124] S401. Based on the data management terminal, set a parent folder named after the supply chain central node in the root directory of the data storage device;

[0125] S402. Based on the data management terminal, construct several groups of sub-folders in the parent folder. The number of sub-folders is one less than the number of supply chain core nodes;

[0126] S403. Based on the data management terminal, name several groups of sub-folders with the supply chain core nodes;

[0127] S404. Screen and process the associated node pairs based on the core nodes of the supply chain to obtain associated child nodes;

[0128] S405. Based on the data management terminal, set the corresponding number of folders inside the subfolder according to the number of associated child nodes, and store the data of the associated child nodes in the folders;

[0129] S406. Based on the data management terminal, mark the subfolder and the nodes of the supply chain corresponding to the subfolder;

[0130] In this embodiment, in order to achieve the orderly management and simple retrieval of supply chain data, according to the central node of the supply chain, a corresponding parent folder is set under the root directory of the data storage device. Then, according to the core nodes of the supply chain, corresponding subfolders are set in the parent folder. It should be noted that the data stored here are all data related to supply chain products, and no data unrelated to products will be stored. In addition, in order to achieve the timeliness of data, each node of the supply chain is monitored, that is, each enterprise in the supply chain is monitored;

[0131] The supply chain data management rule is to generate corresponding folders through the Apriori algorithm according to the relevance and occurrence times among the central node of the supply chain, the core nodes of the supply chain, and the remaining nodes. Then, the corresponding data are stored in the corresponding folders.

[0132] Embodiment 8. Based on the data management terminal, the specific steps for marking the subfolder and the nodes of the supply chain corresponding to the subfolder are as follows:

[0133] S4061. Based on the data management terminal, generate a pair of unique labels according to the subfolder and the nodes of the supply chain corresponding to the subfolder;

[0134] S4062. Based on the data management terminal, embed a pair of unique labels into the subfolder and the nodes of the supply chain corresponding to the subfolder respectively;

[0135] S4063. Based on the data management terminal, monitor the node information of the supply chain;

[0136] S4064. If the node information of the supply chain changes, the data management terminal modifies, replaces, or deletes the data in the subfolder through the unique label;

[0137] In this embodiment, the data of the product may change. For example, the production process is adjusted, the raw materials are replaced, etc. Therefore, in order to achieve the timeliness of the data, a pair of unique tags are generated according to the subfolder and the nodes of the supply chain corresponding to the subfolder. After the data of the product changes, the data in the subfolder is updated through the unique tags. The unique tags are used to communicate and connect the subfolder with the database system of its corresponding enterprise through a network link to achieve real-time data update. In addition, when some data of the product needs to be called, for example, the production data of the product, only the corresponding subfolder needs to be selected under the parent folder to read the production data of the product, avoiding establishing a connection with the database of the production enterprise and shortening the data reading time.

[0138] Refer to Figure 2 As shown, a supply chain data management system based on data analysis is used to implement a supply chain data management method based on data analysis as described above, including:

[0139] A data management terminal, which is used to perform repetitive analysis, node pairing, and determination of the core nodes of the supply chain on the information of each node in the supply chain, and generate supply chain data management rules;

[0140] A data storage device, which is used to store supply chain data;

[0141] A node screening module, which is used to perform a repeatability analysis on the information of each node in the supply chain to determine non-repetitive node information;

[0142] The node screening module compares the information of each node in the supply chain with each other through the sorting comparison method. If there are identical elements in the comparison result, they are duplicate nodes. The duplicate nodes are deleted, and only one is retained. The retained node is the non-repetitive node;

[0143] A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on the non-repetitive node information to determine the core nodes of the supply chain;

[0144] The core node determination module first selects reference node data through a random number algorithm, and then performs data matching on the reference node data and the remaining non-repetitive node data through the Jaccard similarity algorithm to determine whether there are nodes in the remaining non-repetitive node data that are exactly the same as the reference node data or partially the same as the reference node data;

[0145] The Jaccard similarity algorithm performs an intersection process on the data to determine whether there is exactly the same data or partially the same data between the data;

[0146] Central node determination module, which is used to pair nodes and sort the number of nodes of the core nodes in the supply chain to determine the central node of the supply chain;

[0147] The central node determination module first selects two core nodes in the supply chain through a random number algorithm to form the first core node pair, and then compares the first core node pair and the associated node pair with each other through a sorting comparison method. If the first core node pair does not exist in the associated node pair, a second core node pair is re-formed according to the random number algorithm and compared again to determine the node pair to be verified. Then, the simple counting method is used to count all the node pairs to be verified. Finally, the number of occurrences of the node pairs to be verified is screened through the bubble sorting method to determine the central node of the supply chain;

[0148] Data management rule generation module, which generates data management rules according to the central node of the supply chain and stores and monitors the supply chain data.

[0149] The data management rule generation module generates corresponding folders according to the relevance and number of occurrences among the central node of the supply chain, the core nodes of the supply chain, and the remaining nodes through the Apriori algorithm. That is, the node with the most occurrences (the central node of the supply chain) is set as the parent folder through the Apriori algorithm. Then, the node with the second most occurrences (the core nodes of the supply chain) is set as the sub-folder. Finally, according to the relevance between the remaining nodes and the core nodes of the supply chain, they are set in the corresponding sub-folders;

[0150] Furthermore, a storage medium is proposed, on which a computer program is stored. When the computer program is called and run, it executes a supply chain data management method based on data analysis as described above. Among them, the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD), etc.

[0151] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A supply chain data management method based on data analysis, characterized in that Including: Conduct feature analysis on the information of each node in the supply chain to obtain non-duplicate node information; Based on the data management terminal, conduct data analysis and processing on the non-duplicate node information to determine the core nodes of the supply chain, specifically including the following steps: Based on the data management terminal, conduct data reading and processing on the non-duplicate node information to obtain non-duplicate node data; Based on the data cleaning algorithm, conduct data preprocessing on the non-duplicate node data; Based on the data management terminal, conduct mutual matching processing on the non-duplicate node data to obtain associated node pairs; Based on the data management terminal, conduct feature analysis on the associated node pairs to determine the core nodes of the supply chain, and the number of the core nodes of the supply chain is at least one; The step of conducting feature analysis on the associated node pairs based on the data management terminal to determine the core nodes of the supply chain specifically includes the following steps: Based on the data management terminal, conduct counting processing on the associated node pairs to obtain the occurrence times of the nodes in the associated node pairs; Based on the data management terminal, compare and judge the occurrence times of the nodes in the associated node pairs with the set occurrence times threshold; If the node times of the associated node pair are greater than or equal to the set occurrence times threshold, set the node as the core node of the supply chain; If the occurrence times of the nodes in the associated node pair are less than the set occurrence times threshold, the node is not the core node of the supply chain; Based on the data management terminal, conduct classification processing on the core nodes of the supply chain to determine the central nodes of the supply chain, specifically including the following steps: Based on the random number algorithm, randomly select two core nodes of the supply chain to form the first core node pair; Based on the first core node pair, conduct matching processing on the associated node pairs; If the first core node pair exists in the associated node pair, set the first core node pair as the node pair to be verified; If the first core node pair does not exist in the associated node pair, set any one of the core nodes in the first core node pair as the fixed node, and through the random number algorithm, randomly select the remaining core nodes of the supply chain to form the second core node pair, and the remaining core nodes of the supply chain do not include the nodes in the first core node pair; Based on the second core node pair, conduct matching processing on the associated node pairs to obtain the node pairs to be verified; When all the core nodes of the supply chain are selected, end the matching processing with the associated node pairs; Based on the data management terminal, conduct counting processing on all the node pairs to be verified to obtain the occurrence times of the nodes to be verified; Based on the bubble sort method, conduct maximum value screening on the occurrence times of the nodes to be verified to determine the maximum value of the occurrence times of the nodes to be verified, and set the core node of the supply chain corresponding to the maximum value of the occurrence times of the nodes to be verified as the central node of the supply chain; Based on the data management terminal, set the supply chain data management rules according to the central node of the supply chain.

2. The supply chain data management method based on data analysis according to claim 1, characterized in that, The step of conducting feature analysis on the information of each node in the supply chain to obtain non-duplicate node information specifically includes the following steps: Determine the position along each link in the supply chain to obtain the node position; Based on the node position, conduct information extraction processing on the enterprise to which the node position belongs to determine the information of each node in the supply chain; Conduct duplicate degree analysis on the information of each node in the supply chain to obtain non-duplicate node information.

3. A supply chain data management method based on data analysis according to claim 2, characterized in that, Performing duplicate analysis on the information of each node in the supply chain to obtain non-duplicate node information specifically includes the following steps: Based on the data management terminal, perform feature extraction processing on the information of each node in the supply chain to obtain each node feature, where the node feature includes the enterprise name and the enterprise business direction; Based on the data management terminal, perform mutual comparison processing on each node feature; If the enterprise names are different, output non-duplicate node information; If the enterprise names are the same and the enterprise business directions are the same, the two compared node features are of the same enterprise, remove any one of the node information, and output non-duplicate node information; If the enterprise names are the same and the enterprise business directions are different, the two compared node features are not of the same enterprise, output non-duplicate node information.

4. A supply chain data management method based on data analysis according to claim 1, characterized in that, The data preprocessing includes data deduplication processing, data missing value supplementation processing, and data format standardization processing.

5. A supply chain data management method based on data analysis according to claim 1, characterized in that, The step of performing mutual matching processing on the non-duplicate node data based on the data management terminal to obtain associated node pairs specifically includes the following steps: Based on the random number algorithm, randomly select one non-duplicate node data as the reference node data; Based on the data management terminal, perform data matching on the remaining non-duplicate node data using the reference node data as the feature; If there is data that is exactly the same or partially the same as the reference node data among the remaining non-duplicate node data, set the reference node and the node with data that is exactly the same or partially the same as the reference node data as the associated node pair; If there is no data that is exactly the same or partially the same as the reference node data among the remaining non-duplicate node data, delete the reference node data.

6. A supply chain data management method based on data analysis according to claim 1, characterized in that, The step of setting the supply chain data management rules based on the central node of the supply chain based on the data management terminal specifically includes the following steps: Based on the data management terminal, set a parent folder named after the central node of the supply chain in the root directory of the data storage device; Based on the data management terminal, construct several groups of sub-folders in the parent folder, where the number of sub-folders is one less than the number of core nodes of the supply chain; Based on the data management terminal, name several groups of sub-folders after the core nodes of the supply chain; Based on the core nodes of the supply chain, perform screening processing on the associated node pairs to obtain associated sub-nodes; Based on the data management terminal, set the corresponding number of folders inside the sub-folders according to the number of associated sub-nodes, and store the data of the associated sub-nodes in the folders; Based on the data management terminal, perform marking processing on the sub-folders and the nodes of the supply chain corresponding to the sub-folders.

7. A supply chain data management method based on data analysis according to claim 6, characterized in that, The step of performing marking processing on the sub-folders and the nodes of the supply chain corresponding to the sub-folders based on the data management terminal specifically includes the following steps: Based on the data management terminal, generate a pair of unique labels according to the sub-folders and the nodes of the supply chain corresponding to the sub-folders; Based on the data management terminal, embed a pair of unique labels into the sub-folders and the nodes of the supply chain corresponding to the sub-folders respectively; Based on the data management terminal, monitor the node information of the supply chain; If the node information of the supply chain changes, the data management terminal modifies, replaces, or deletes the data in the sub-folders through the unique labels.

8. A supply chain data management system based on data analysis, which is used to implement a supply chain data management method based on data analysis as described in any one of claims 1-7, characterized in that Including: A data management terminal, which is used to perform repetitive analysis, node pairing, and determination of the core nodes of the supply chain on the information of each node in the supply chain, and generate supply chain data management rules; A data storage device, which is used to store supply chain data; A node screening module, which is used to analyze the repetition degree of the information of each node in the supply chain and determine the non-repetitive node information; A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on the non-repetitive node information to determine the core nodes of the supply chain; A central node determination module, which is used to perform node pairing and node quantity sorting on the core nodes of the supply chain to determine the central nodes of the supply chain; A data management rule generation module, which generates data management rules according to the central nodes of the supply chain and stores and monitors the supply chain data.

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