Supply chain data management method and system based on data analysis

Abstract

The invention discloses a supply chain data management method and system based on data analysis, and relates to the technical field of supply chain data management, and the method comprises the steps: carrying out the feature analysis of the information of each node in a supply chain, and obtaining the information of non-repeated nodes; according to the method, firstly, the information of each node in the supply chain is screened, the repeated node information is removed, the subsequent data processing time is shortened, secondly, the non-repeated node information is subjected to data analysis, the core nodes of the supply chain are determined, the subsequent data calling is more convenient, then the core nodes of the supply chain are compared, and the data processing efficiency is improved. According to the method, the central node of the supply chain is determined, and finally, the supply chain data management rule is generated according to the central node of the supply chain and the supply chain data is managed and monitored, so that unified management of the supply chain data is realized, multiple times of data calling of databases of different enterprises during data calling are avoided, the complexity of data calling is reduced, and the data calling efficiency is improved. And the product prediction time is indirectly shortened.

Description

Technical Field

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

[0002] The supply chain refers to the entire process from producing parts, making intermediate products and final products, and finally delivering them to consumers. The suppliers, manufacturers, distributors, and consumers involved are connected through upstream and downstream members.

[0003] Existing supply chain data is stored in each company's own database. When it is necessary to call the production data and sales data of a certain product to predict the product's sales prospects, it is necessary to call data from the databases of raw material companies, production companies and product sales companies, which wastes a lot of time and prolongs the product prediction time. Summary of the invention

[0004] In order 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 mentioned in the above background technology that the existing supply chain data are stored in each enterprise's own database. When it is necessary to call the production data and sales data of a certain product to predict the sales prospects of the product, it is necessary to call data 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.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A supply chain data management method based on data analysis, comprising: Perform feature analysis on each node information in the supply chain to obtain non-repetitive node information; Based on the data management terminal, data analysis and processing of non-repetitive node information is performed to determine the core nodes of the supply chain; Based on the data management terminal, the core nodes of the supply chain are classified and processed to determine the central nodes of the supply chain; Based on the data management terminal, supply chain data management rules are set according to the supply chain central node.

[0006] Preferably, the step of performing feature analysis on each node information in the supply chain to obtain non-repetitive node information specifically comprises the following steps: Determine the location along each link in the supply chain and obtain the node location; Based on the node location, information is extracted from the enterprise to which the node location belongs to determine the information of each node in the supply chain; Perform duplication analysis on each node information in the supply chain to obtain non-duplicate node information.

[0007] Preferably, the performing duplication analysis on each node information in the supply chain to obtain non-duplicate node information specifically comprises the following steps: Based on the data management terminal, feature extraction is performed on each node information in the supply chain to obtain the features of each node, wherein the node features include the enterprise name and the enterprise business direction; Based on the data management terminal, the characteristics of each node are compared with each other; If the enterprise names are different, output non-duplicate node information; If the enterprise names are the same and the business directions are the same, the two compared node features are the same enterprise, and any node information is removed to output non-duplicate node information; If the enterprise names are the same but the business directions are different, the two compared node features are not for the same enterprise, and non-duplicate node information is output.

[0008] Preferably, the data management terminal is used to analyze and process the non-repetitive node information to determine the core node of the supply chain, which specifically includes the following steps: Based on the data management terminal, data reading and processing are performed on the non-repeated node information to obtain the non-repeated node data; Based on the data cleaning algorithm, data preprocessing is performed on the non-duplicate node data, and the data preprocessing includes data deduplication processing, data missing value supplement processing and data format standardization processing; Based on the data management terminal, the non-repetitive node data are matched with each other to obtain the associated node pairs; Based on the data management terminal, feature analysis is performed on the associated node pairs to determine the supply chain core nodes, where the number of the supply chain core nodes is at least one.

[0009] Preferably, the step of performing mutual matching processing on non-repetitive node data based on the data management terminal to obtain associated node pairs specifically includes the following steps: Based on the random number algorithm, a non-repeated node data is randomly selected as the reference node data; Based on the data management terminal, the reference node data is used as a feature to match the remaining non-repetitive node data; If the remaining non-repeated node data contain data that is completely identical or partially identical to the reference node data, the reference node and the node that contains data that is completely identical or partially identical to the reference node data are set as an associated node pair; If there is no data in the remaining non-duplicate node data that is completely identical or partially identical to the reference node data, the reference node data is deleted.

[0010] Preferably, the method of performing feature analysis on 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, the associated node pairs are counted to obtain the number of occurrences of the nodes in the associated node pairs; Based on the data management terminal, the number of node occurrences in the associated node pair is compared and judged with a set number of occurrence thresholds; If the node number of the associated node pair is greater than or equal to the set occurrence threshold, the node is set as the supply chain core node; If the number of node occurrences of the associated node pair is less than the set occurrence threshold, the node is not a core node of the supply chain.

[0011] Preferably, the classification of the supply chain core nodes based on the data management terminal to determine the supply chain central node specifically includes the following steps: Based on the random number algorithm, two supply chain core nodes are randomly selected to form the first core node pair; Based on the first core node pair, matching processing is performed on the associated node pair; If there is a first core node pair in the associated node pair, the first core node pair is set as a node pair to be verified; If the first core node pair does not exist in the associated node pair, any core node in the first core node pair is set as a fixed node, and the remaining supply chain core nodes are randomly selected to form a second core node pair through a random number algorithm, and the remaining supply chain core nodes do not include the nodes in the first core node pair; Based on the second core node pair, matching processing is performed on the associated node pair to obtain the node pair to be verified; When all the supply chain core nodes are selected, the matching process with the associated node pairs ends; Based on the data management terminal, all pairs of nodes to be verified are counted to obtain the number of occurrences of the nodes to be verified; Based on the bubble sort method, the maximum value of the number of occurrences of the node to be verified is screened to determine the maximum value of the number of occurrences of the node to be verified, and the core node of the supply chain corresponding to the maximum value of the number of occurrences of the node to be verified is set as the central node of the supply chain.

[0012] Preferably, the step of setting supply chain data management rules based on the data management terminal and the supply chain central node specifically includes the following steps: Based on the data management terminal, a parent folder named with the supply chain central node is set in the root directory of the data storage device; Based on the data management terminal, constructing several groups of sub-folders in the parent folder, the number of the sub-folders being one less than the number of the supply chain core nodes; Based on the data management terminal, several groups of subfolders are named after the core nodes of the supply chain; Based on the core nodes of the supply chain, the associated node pairs are screened and processed to obtain the associated child nodes; Based on the data management terminal, a corresponding number of folders are set inside the subfolder according to the number of associated child nodes, and the data of the associated child nodes are stored in the folders; Based on the data management terminal, the subfolders and the nodes of the supply chain corresponding to the subfolders are marked.

[0013] Preferably, the marking process of the subfolder and the supply chain node corresponding to the subfolder based on the data management terminal specifically includes the following steps: Based on the data management terminal, a pair of unique tags is generated according to the subfolder and the node of the supply chain corresponding to the subfolder; Based on the data management terminal, a pair of unique tags are respectively embedded into the subfolder and the node of the supply chain corresponding to the subfolder; Monitor the node information of the supply chain based on the data management terminal; If the node information of the supply chain changes, the data management terminal will modify, replace or delete the data in the subfolder through the unique tag.

[0014] Furthermore, a supply chain data management system based on data analysis is proposed, which is used to implement the supply chain data management method based on data analysis as described above, including: A data management terminal, which is used to perform repetitive analysis, node pairing, and supply chain core node determination on each node information in the supply chain, and generate supply chain data management rules; A data storage device, the data storage device being used to store supply chain data; A node screening module, which is used to perform duplication analysis on each node information in the supply chain to determine non-duplicate node information; A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on 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 node of the supply chain; A data management rule generation module generates data management rules according to the supply chain central node and stores and monitors the supply chain data.

[0015] Furthermore, a storage medium is proposed, on which a computer program is stored, and when the computer program is called and run, it executes a supply chain data management method based on data analysis as described above.

[0016] Compared with the prior art, the present invention provides a supply chain data management method and system based on data analysis, which has the following beneficial effects: The present invention firstly screens the information of each node in the supply chain and removes duplicate node information, thereby shortening the subsequent data processing time. Secondly, data analysis is performed on the non-duplicate node information to determine the core nodes of the supply chain, making it more convenient to call the data later. 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 according to the central node of the supply chain and the supply chain data is managed and monitored, thereby realizing unified management of supply chain data and avoiding the need to retrieve data from databases of different enterprises multiple times when calling data, thereby reducing the complexity of data retrieval and indirectly shortening the product prediction time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of steps S100-S400 in a supply chain data management method based on data analysis proposed by the present invention; Figure 2 This is a structural block diagram of a supply chain data management system based on data analysis proposed by the present invention. DETAILED DESCRIPTION

[0018] 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 described below are only examples, and those skilled in the art may think of other obvious variations.

[0019] Reference Figure 1 As shown, a supply chain data management method based on data analysis includes: S100, perform feature analysis on each node information in the supply chain to obtain non-repetitive node information; It is understandable that a product may require multiple raw material suppliers, so a manufacturing company may be associated with multiple raw material companies, and the supply chain links of these raw material suppliers will point to one manufacturing company, so there may be duplicate nodes; S200, based on the data management terminal, performing data analysis and processing on the non-repetitive node information to determine the core nodes of the supply chain; It is understandable that there may be multiple supply chain core nodes. For example, a product requires the joint production of multiple manufacturers, and these manufacturers are related to different raw material companies, and these manufacturers are the supply chain core nodes; S300, based on the data management terminal, classify the core nodes of the supply chain and determine the central node of the supply chain; It is understandable that there may be multiple supply chain core nodes in a supply chain, but there is only one supply chain central node, because the supply chain central node is responsible for the production and sales of products, and the unified management of supply chain data should also be managed according to the supply chain central node; S400, based on the data management terminal, setting supply chain data management rules according to the supply chain central node; Those skilled in the art can understand that the supply chain is a spider web-like structure consisting of a series of enterprises such as raw material enterprises, product manufacturing enterprises, and product sales enterprises. Each enterprise will record various data of the product. If the various data of the product are stored in the corresponding enterprises respectively, when it is necessary to call the product data to perform predictive analysis on product sales, it may be necessary to call the data multiple times, and the called data comes from different enterprises. For example, the raw material inventory data needs to be read from the raw material enterprise database, while the product inventory needs to be read from the manufacturer and seller enterprise databases. The network links of the databases of different enterprises may be different. Therefore, it is necessary to adjust the network links multiple times to realize data reading, which indirectly prolongs the product prediction time. Therefore, it is necessary to uniformly manage the data of these enterprises about the product to avoid adjusting the network links multiple times for data reading.

[0020] In Example 1, feature analysis is performed on each node information in the supply chain to obtain non-repetitive node information, specifically including the following steps: S101, determine the position along each link in the supply chain and obtain the node position; S102, based on the node location, extract information from the enterprise to which the node location belongs, and determine the information of each node in the supply chain; S103, performing duplication analysis on each node information in the supply chain to obtain non-duplicate node information; In this embodiment, the supply chain is a mesh structure, and one node may be connected to multiple nodes. For example, a manufacturing company may cooperate with multiple raw material suppliers, and the end points of the links where these raw material suppliers are located will point to one location, namely the location of the manufacturing company. Therefore, in order to facilitate subsequent data management, these node information are screened, and duplicate nodes are screened out, leaving only one, namely the non-duplicate node information in this application.

[0021] In Example 2, performing duplication analysis on each node information in the supply chain to obtain non-duplicate node information specifically includes the following steps: S1031. Based on the data management terminal, feature extraction is performed on each node information in the supply chain to obtain features of each node, where the node features include the enterprise name and the enterprise business direction; S1032, based on the data management terminal, performing a comparison process on each node feature; S1033. If the enterprise names are not the same, output non-duplicate node information; S1034. If the enterprise names are the same and the business directions are the same, and the two compared node features are the same enterprise, remove any one of the node information and output non-duplicate node information; S1035. If the enterprise names are the same and the business directions of the enterprises are different, the two compared node features are not the same enterprise, and non-duplicate node information is output; In this embodiment, there may be companies with the same name in a supply chain, but these companies may or may not be the same company. For example, there is a company name in the raw material company that is the same as a company name in the product sales company, but the two are not the same company. In order to avoid data missing in subsequent data management, the business directions of the companies are compared to determine whether the two are the same company. If they are the same company, they are duplicate nodes, and one needs to be deleted and the other needs to be retained. If they are not the same company, they are not duplicate nodes and both need to be retained.

[0022] Embodiment 3, based on the data management terminal, data analysis and processing are performed on the non-repetitive node information to determine the core node of the supply chain, specifically including the following steps: S201, based on the data management terminal, performing data reading and processing on the non-repeated node information to obtain the non-repeated node data; S202, performing data preprocessing on the non-duplicate node data based on a data cleaning algorithm, wherein the data preprocessing includes data deduplication processing, data missing value supplementation processing, and data format standardization processing; It is understandable that different companies have different ways of storing and managing data. Therefore, in order to achieve unified management of supply chain data, it is necessary to pre-process it, remove duplicate data, supplement missing data, and unify the data format; Data cleaning algorithms include deletion method, majority filling method, and format conversion algorithm. The deletion method is used to delete duplicate data, the majority filling method is used to supplement missing data, and the format conversion algorithm is used to unify the data format. S203, based on the data management terminal, matching processing is performed on non-repetitive node data to obtain associated node pairs; S204, based on the data management terminal, performing feature analysis on the associated node pairs to determine the supply chain core node, where the number of the supply chain core node is at least one; In this embodiment, there is more than one core node in the supply chain. For example, there are multiple companies selling the same product, and then these companies distribute these products to other companies. These companies are the core nodes of the supply chain. Similarly, the same is true for manufacturing companies. The products are divided into multiple parts and handed over to different companies for production. The core node of the supply chain is determined to make the data orderly during subsequent data management and more convenient and quick to call. For example, if a component of a product is abnormal, you only need to search for the company that produces the component in the folder corresponding to the core node of the supply chain to quickly obtain the data when the component was produced.

[0023] Embodiment 4, based on the data management terminal, matching processing is performed on non-repetitive node data to obtain associated node pairs, specifically including the following steps: S2031. Based on a random number algorithm, arbitrarily select a non-repetitive node data as reference node data; It can be understood that a non-repeated node is randomly selected, and then the remaining nodes are matched by the selected node to determine whether there is a correlation between the nodes, that is, whether there is a link between the nodes; S2032, based on the data management terminal, performing data matching on the remaining non-duplicate node data with reference node data as a feature; S2033. If the remaining non-duplicate node data contains data that is completely identical or partially identical to the reference node data, the reference node and the node that contains data that is completely identical or partially identical to the reference node data are set as an associated node pair; It is understandable that the production enterprise is associated with the raw material enterprise, the production enterprise is associated with the sales enterprise, and the upper-level sales enterprise is associated with the lower-level sales enterprise, so multiple matching is required until all non-repeated node data are matched; S2034. If there is no data in the remaining non-duplicate node data that is completely identical or partially identical to the reference node data, delete the reference node data; In this embodiment, a product may be adjusted during the production process. After the product is adjusted, a certain raw material may not be used, and the supplier of the raw material will not be linked with the manufacturer, so there will be no identical data or partially identical data. In order to improve the orderliness of the supply chain, the node is deleted. In addition, in order to make the supply chain data management more orderly, the associated nodes are screened out to form associated node pairs. The corresponding folders are set by the associated node pairs to achieve orderly management of data.

[0024] Example 5, based on the data management terminal, feature analysis is performed on the associated node pairs to determine the core nodes of the supply chain, specifically including the following steps: S2041. Based on the data management terminal, count the associated node pairs to obtain the number of occurrences of the nodes in the associated node pairs; S2042: Based on the data management terminal, compare and judge the number of node occurrences in the associated node pair and the set number of occurrence thresholds; S2043. If the number of nodes of the associated node pair is greater than or equal to the set occurrence threshold, the node is set as a supply chain core node; S2044. If the number of node appearances of the associated node pair is less than the set number of appearances threshold, the node is not a core node of the supply chain; In this embodiment, there is at least one supply chain core node, but the number of times the supply chain core node appears is very large. For example, a sales company distributes all products to multiple second-level sales companies, and the sales company will appear multiple times in these second-level sales companies. Similarly, the same is true for a production company, which splits a product into multiple parts and gives them to different second-level production companies for production. The production company is one of the supply chain core nodes. Therefore, the supply chain core node is determined by judging the number of times the node appears in the associated node pair.

[0025] Example 6: Based on the data management terminal, the supply chain core nodes are classified and processed, and the supply chain central nodes are determined, which specifically includes the following steps: S301. Based on a random number algorithm, arbitrarily select two supply chain core nodes to form a first core node pair; S302, performing matching processing on the associated node pair based on the first core node pair; S303: If there is a first core node pair in the associated node pairs, set the first core node pair as a node pair to be verified; S304. If the first core node pair does not exist in the associated node pair, any core node in the first core node pair is set as a fixed node, and the remaining supply chain core nodes are randomly selected to form a second core node pair through a random number algorithm, and the remaining supply chain core nodes do not include the nodes in the first core node pair; S305: Based on the second core node pair, matching processing is performed on the associated node pair to obtain the node pair to be verified; S306, when all the supply chain core nodes are selected, the matching process with the associated nodes is terminated; S307: Based on the data management terminal, count all pairs of nodes to be verified to obtain the number of occurrences of the nodes to be verified; S308, based on the bubble sort method, the maximum value of the number of occurrences of the node to be verified is screened, the maximum value of the number of occurrences of the node to be verified is determined, and the supply chain core node corresponding to the maximum value of the number of occurrences of the node to be verified is set as the supply chain central node; 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, the supply chain central node can be determined by simply screening the supply chain core nodes, and the supply chain central node is the node that appears most frequently in the supply chain core nodes, because the supply chain is a spider web-like structure, and the supply chain central node is the center of the spider web, and the supply chain core node is the node closest to the center of the spider web. Therefore, the supply chain central node is determined by judging the number of occurrences of the supply chain core node, and finally, the supply chain data management rules are implemented according to the supply chain central node.

[0026] Example 7, based on the data management terminal, setting supply chain data management rules according to the supply chain central node specifically includes the following steps: S401, based on the data management terminal, setting a parent folder named with the supply chain central node in the root directory of the data storage device; S402. Based on the data management terminal, construct a plurality of groups of sub-folders in the parent folder, wherein the number of the sub-folders is one less than the number of the supply chain core nodes; S403, based on the data management terminal, naming several groups of subfolders with the supply chain core nodes as names; S404, based on the supply chain core node, filter the associated node pairs to obtain associated child nodes; S405, based on the data management terminal, setting a corresponding number of folders within the subfolder according to the number of associated child nodes, and storing the data of the associated child nodes in the folders; S406: Based on the data management terminal, mark the subfolder and the supply chain node corresponding to the subfolder; In this embodiment, in order to achieve orderly management and simple retrieval of supply chain data, a corresponding parent folder is set in the root directory of the data storage device according to the central node of the supply chain, and then a corresponding subfolder is set in the parent folder according to the core node of the supply chain. It is worth noting that the data stored here are all data related to the supply chain products, and data unrelated to the products will not be stored. In addition, in order to achieve the timeliness of the data, each node of the supply chain is monitored, that is, each enterprise in the supply chain is monitored; The supply chain data management rule is to generate corresponding folders according to the correlation and number of occurrences between the supply chain central node, the supply chain core node and the remaining nodes through the Apriori algorithm, and then store the corresponding data in the corresponding folders.

[0027] Embodiment 8, based on the data management terminal, marking the subfolder and the supply chain node corresponding to the subfolder specifically includes the following steps: S4061. Based on the data management terminal, a pair of unique tags is generated according to the subfolder and the node of the supply chain corresponding to the subfolder; S4062. Based on the data management terminal, a pair of unique tags are respectively embedded into the subfolder and the node of the supply chain corresponding to the subfolder; S4063. Monitor the node information of the supply chain based on the data management terminal; 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 tag; In this embodiment, the data of the product may change, for example, the adjustment of the production process, the replacement of raw materials, etc. Therefore, in order to achieve the timeliness of the data, a pair of unique tags are generated according to the sub-folder and the node of the supply chain corresponding to the sub-folder. When the data of the product changes, the data in the sub-folder is updated through the unique tag, and the unique tag communicates and connects the sub-folder with the database system of the corresponding enterprise through a network link to achieve real-time data update. In addition, when it is necessary to call certain data of the product, for example, the production data of the product, it is only necessary to select the corresponding sub-folder under the parent folder to realize the reading of the product production data, avoiding the establishment of a connection with the database of the production enterprise and shortening the data reading time.

[0028] Reference Figure 2 As shown, a supply chain data management system based on data analysis is used to implement the supply chain data management method based on data analysis as described above, including: A data management terminal, which is used to perform repetitive analysis, node pairing, and supply chain core node determination on each node information in the supply chain, and generate supply chain data management rules; A data storage device, the data storage device being used to store supply chain data; A node screening module, which is used to perform duplication analysis on each node information in the supply chain to determine non-duplicate node information; The node screening module compares the information of each node in the supply chain through the sorting comparison method. If there are identical elements in the comparison results, it is a duplicate node. The duplicate node is deleted and only one is retained. The retained node is the non-duplicate node. A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on non-repetitive node information to determine the core nodes of the supply chain; The core node determination module first selects reference node data through a random number algorithm, and then matches the reference node data with the remaining non-repeated node data through the Jaccard similarity algorithm to determine whether there are nodes in the remaining non-repeated node data that have data that is completely identical or partially identical to the reference node data; The Jaccard similarity algorithm performs intersection processing on data to determine whether there is exactly the same data or partially the same data between the data; 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 node of the supply chain; The central node determination module first selects two supply chain core nodes 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 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 a random number algorithm, and then compared again to determine the node pair to be verified, and then counts all the node pairs to be verified through a simple counting method. Finally, the number of occurrences of the nodes to be verified is screened through a bubble sort method to determine the central node of the supply chain; A data management rule generation module generates data management rules according to the supply chain central node and stores and monitors the supply chain data.

[0029] The data management rule generation module generates corresponding folders according to the correlation and occurrence times between the supply chain central node, the supply chain core node and the remaining nodes through the Apriori algorithm, that is, the node with the most occurrence times (supply chain central node) is set as the parent folder through the Apriori algorithm, and then the node with the second most occurrence times (supply chain core node) is set as a subfolder again. Finally, according to the correlation between the remaining nodes and the supply chain core node, they are set in the corresponding subfolders; 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, wherein the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state drive (SSD).

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A supply chain data management method based on data analysis, characterized in that: include: Perform feature analysis on each node information in the supply chain to obtain non-repetitive node information; Based on the data management terminal, data analysis and processing of non-repetitive node information is performed to determine the core nodes of the supply chain; Based on the data management terminal, the core nodes of the supply chain are classified and processed to determine the central nodes of the supply chain; Based on the data management terminal, supply chain data management rules are set according to the supply chain central node.

2. A supply chain data management method based on data analysis according to claim 1, characterized in that: The characteristic analysis of each node information in the supply chain to obtain non-repetitive node information specifically includes the following steps: Determine the location along each link in the supply chain and obtain the node location; Based on the node location, information is extracted from the enterprise to which the node location belongs to determine the information of each node in the supply chain; Perform duplication analysis on each node information 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: The duplication analysis of each node information in the supply chain to obtain non-duplicate node information specifically includes the following steps: Based on the data management terminal, feature extraction is performed on each node information in the supply chain to obtain the features of each node, wherein the node features include the enterprise name and the enterprise business direction; Based on the data management terminal, the characteristics of each node are compared with each other; If the enterprise names are different, output non-duplicate node information; If the enterprise names are the same and the business directions are the same, the two compared node features are the same enterprise, and any node information is removed to output non-duplicate node information; If the enterprise names are the same but the business directions are different, the two compared node features are not for the same enterprise, and non-duplicate node information is output.

4. The supply chain data management method based on data analysis according to claim 1 is characterized in that: The data management terminal is used to analyze and process the non-repetitive node information to determine the core nodes of the supply chain, which specifically includes the following steps: Based on the data management terminal, data reading and processing are performed on the non-repeated node information to obtain the non-repeated node data; Based on the data cleaning algorithm, data preprocessing is performed on the non-duplicate node data, and the data preprocessing includes data deduplication processing, data missing value supplement processing and data format standardization processing; Based on the data management terminal, the non-repetitive node data are matched with each other to obtain the associated node pairs; Based on the data management terminal, feature analysis is performed on the associated node pairs to determine the supply chain core nodes, where the number of the supply chain core nodes is at least one.

5. A supply chain data management method based on data analysis according to claim 4, characterized in that: The method of performing mutual matching processing on non-repetitive node data based on the data management terminal to obtain associated node pairs specifically includes the following steps: Based on the random number algorithm, a non-repeated node data is randomly selected as the reference node data; Based on the data management terminal, the reference node data is used as a feature to match the remaining non-repetitive node data; If the remaining non-repeated node data contain data that is completely identical or partially identical to the reference node data, the reference node and the node that contains data that is completely identical or partially identical to the reference node data are set as an associated node pair; If there is no data in the remaining non-duplicate node data that is completely identical or partially identical to the reference node data, the reference node data is deleted.

6. A supply chain data management method based on data analysis according to claim 4, characterized in that: The method of performing 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, the associated node pairs are counted to obtain the number of occurrences of the nodes in the associated node pairs; Based on the data management terminal, the number of node occurrences in the associated node pair is compared and judged with a set number of occurrence thresholds; If the node number of the associated node pair is greater than or equal to the set occurrence threshold, the node is set as the supply chain core node; If the number of node occurrences of the associated node pair is less than the set occurrence threshold, the node is not a core node of the supply chain.

7. A supply chain data management method based on data analysis according to claim 1, characterized in that: The process of classifying the supply chain core nodes based on the data management terminal and determining the supply chain central nodes specifically includes the following steps: Based on the random number algorithm, two supply chain core nodes are randomly selected to form the first core node pair; Based on the first core node pair, matching processing is performed on the associated node pair; If there is a first core node pair in the associated node pair, the first core node pair is set as a node pair to be verified; If the first core node pair does not exist in the associated node pair, any core node in the first core node pair is set as a fixed node, and the remaining supply chain core nodes are randomly selected to form a second core node pair through a random number algorithm, and the remaining supply chain core nodes do not include the nodes in the first core node pair; Based on the second core node pair, matching processing is performed on the associated node pair to obtain the node pair to be verified; When all the supply chain core nodes are selected, the matching process with the associated node pairs ends; Based on the data management terminal, all pairs of nodes to be verified are counted to obtain the number of occurrences of the nodes to be verified; Based on the bubble sort method, the maximum value of the number of occurrences of the node to be verified is screened to determine the maximum value of the number of occurrences of the node to be verified, and the core node of the supply chain corresponding to the maximum value of the number of occurrences of the node to be verified is set as the central node of the supply chain.

8. The supply chain data management method based on data analysis according to claim 1, characterized in that: The step of setting supply chain data management rules based on the data management terminal and the supply chain central node specifically includes the following steps: Based on the data management terminal, a parent folder named with the supply chain central node is set in the root directory of the data storage device; Based on the data management terminal, constructing several groups of sub-folders in the parent folder, the number of the sub-folders being one less than the number of the supply chain core nodes; Based on the data management terminal, several groups of subfolders are named after the core nodes of the supply chain; Based on the core nodes of the supply chain, the associated node pairs are screened and processed to obtain the associated child nodes; Based on the data management terminal, a corresponding number of folders are set inside the subfolder according to the number of associated child nodes, and the data of the associated child nodes are stored in the folders; Based on the data management terminal, the subfolders and the nodes of the supply chain corresponding to the subfolders are marked.

9. A supply chain data management method based on data analysis according to claim 8, characterized in that: The marking process of the subfolder and the supply chain node corresponding to the subfolder based on the data management terminal specifically includes the following steps: Based on the data management terminal, a pair of unique tags is generated according to the subfolder and the node of the supply chain corresponding to the subfolder; Based on the data management terminal, a pair of unique tags are respectively embedded into the subfolder and the node of the supply chain corresponding to the subfolder; Monitor the node information of the supply chain based on the data management terminal; If the node information of the supply chain changes, the data management terminal will modify, replace or delete the data in the subfolder through the unique tag.

10. A supply chain data management system based on data analysis, used to implement a supply chain data management method based on data analysis as claimed in any one of claims 1 to 9, characterized in that: include: A data management terminal, which is used to perform repetitive analysis, node pairing, and supply chain core node determination on each node information in the supply chain, and generate supply chain data management rules; A data storage device, the data storage device being used to store supply chain data; A node screening module, which is used to perform duplication analysis on each node information in the supply chain to determine non-duplicate node information; A core node determination module, which is used to perform node pairing processing and node pair feature analysis processing on 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 node of the supply chain; A data management rule generation module generates data management rules according to the supply chain central node and stores and monitors the supply chain data.

Images