Traditional Chinese medicine supply chain collaborative management system and method based on industrial internet

Abstract

The invention provides a traditional Chinese medicine supply chain collaborative management system and method based on an industrial internet, and solves the problems of traditional Chinese medicine supply chain collaborative management and the like, and the method comprises the following steps: S1, supply chain data collection and preprocessing; s2, selecting a data analysis algorithm; s3, carrying out model training; s4, detecting the prediction performance of the model, and evaluating the risk of the supply chain; and S5, supply chain collaborative management is carried out. The method has the advantages of good management effect, high safety and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of traditional Chinese medicine production supply chain, and specifically relates to a collaborative management system and method for a traditional Chinese medicine supply chain based on the industrial Internet. Background Art

[0002] With the gradual popularization of industrial Internet technology, various industries have applied it to production and management processes. As one of China's traditional industries, the Chinese medicine industry is facing many challenges such as quality control, efficiency improvement and supply chain complexity. The existing Chinese medicine supply chain management system has limited ability to process real-time data and respond quickly to market changes. Traditional ERP and SCM systems are more based on regular data updates and batch processing, and cannot process large amounts of data from planting, processing, warehousing, logistics and other links in real time, resulting in slow response when responding to sudden demand or supply chain anomalies. In the Chinese medicine supply chain, especially in the quality traceability of medicinal materials, data security and credibility are key. However, due to the lack of advanced encryption and anti-tampering technology, traditional systems are prone to data leakage and tampering risks, and it is difficult to ensure the integrity and authenticity of data. This brings challenges to the compliance management and quality control of Chinese medicinal materials. Existing supply chain management systems usually focus on local optimization, such as improving production efficiency and optimizing inventory management in a single link, but lack a global optimization perspective of the entire supply chain. Due to the isolated operations of each link, it is difficult to maximize the overall benefits of the supply chain. For example, in the harvesting, processing, and transportation of Chinese medicinal materials, it is difficult to effectively coordinate to avoid waste of resources and increased costs.

[0003] In order to solve the shortcomings of the existing technology, people have conducted long-term exploration and proposed various solutions. For example, a Chinese patent document discloses a supply chain management system and management method for Chinese herbal medicine slices [201910930990.1], which includes a drug supply chain, a pharmacy end set on the drug supply chain, and the pharmacy end is used to initiate a procurement plan; an operation platform end connected to the pharmacy end, and the operation platform end is used to purchase Chinese herbal medicine slices according to the procurement plan; a supplier end connected to the operation platform end, and the supplier end is used to supply Chinese herbal medicine slices to the operation platform end; the drug supply chain is used to store and share data on the pharmacy end, the operation platform end, and the supplier end.

[0004] The above solution has solved the problem of supply chain supervision and management to a certain extent, but the solution still has many shortcomings, such as the lack of corresponding coordination and management methods for the entire supply chain. Summary of the invention

[0005] The purpose of the present invention is to provide a collaborative management method for the Chinese medicine supply chain based on the industrial Internet that achieves overall optimization of the supply chain and high security in response to the above-mentioned problems.

[0006] Another object of the present invention is to address the above-mentioned problems and provide a collaborative management system for the supply chain of traditional Chinese medicine based on the industrial Internet that reduces the overall supply cost.

[0007] To achieve the above object, the present invention adopts the following technical solution: a collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, comprising the following steps:

[0008] S1: Supply chain data collection and preprocessing;

[0009] S2: Data analysis algorithm selection;

[0010] S3: Perform model training;

[0011] S4: Test the model prediction performance and assess supply chain risks;

[0012] S5: Conduct collaborative supply chain management.

[0013] In the above-mentioned collaborative management method of the traditional Chinese medicine supply chain based on the industrial Internet, the supply chain in step S1 includes the traditional Chinese medicine planting and production link, the primary processing link at the place of origin, the warehousing management link, the logistics management link, the traceability link, the inspection link and the trade link. Step S1 uses the traceability system to collect data and pre-process the data based on the cloud platform.

[0014] In the above-mentioned collaborative management method of the Chinese medicine supply chain based on the industrial Internet, the traceability system in step S1 is processed using blockchain, which includes the following steps:

[0015] S11: After data processing, each supply chain link is uploaded to the data collection node, which processes the data through smart contracts and uploads it to the blockchain network;

[0016] S12: The blockchain network performs hash calculations on the data in each link and extracts the summary, and uses digital signatures for verification;

[0017] S13: Encrypt the summary using the private key and store it in a local database;

[0018] S14: The local database receives the public key and decrypts the data, and compares the decrypted data with the hashed data for verification;

[0019] S15: If the data verification is passed, the PoR algorithm is used to upload the data to the chain and generate a new block.

[0020] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, step S2 adopts decision tree algorithm to perform data analysis, which includes the following steps:

[0021] S21: Divide the data into a training set and a test set, use multiple interpolation to complete the data in the training set, and then discretize the data;

[0022] S22: Select decision attributes from the data, including facility attributes, storage attributes, logistics attributes, information attributes, procurement attributes, and pricing attributes;

[0023] S23: The training set uses a recursive method to construct a decision tree, calculates the data, obtains the information gain and information gain rate of the decision attribute, and uses the information gain rate as the branch point;

[0024] S24: When the expected error rate of a subtree exceeds the expected error rate of a single leaf, replace the subtree with the leaf;

[0025] S25: Use cross-validation to verify the accuracy of the decision tree;

[0026] S26: Use decision tree model to analyze the data.

[0027] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, step S3 includes the following steps:

[0028] S31: System initialization, each node of the Industrial Internet calculates the node weight according to its own activity, data contribution value and historical behavior;

[0029] S32: Select the node with the highest weight from all node weights as the proxy node;

[0030] S33: The proxy node performs data transmission, data verification, data caching, and data recording;

[0031] S34: Each node in the industrial Internet updates its own weight according to the performance of the proxy node. If the data is accurate, the weight is increased, otherwise the weight is reduced;

[0032] S35: Return to step S32, reselect a new proxy node according to the updated weight and replace the proxy node.

[0033] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, step S33 includes the following steps:

[0034] S331: The client sends a request to the proxy node;

[0035] S332: The master node receives the request, generates a pre-preparation message and forwards it to all proxy nodes;

[0036] S323: The proxy node receives the pre-prepared message from the master node, verifies whether the message is consistent with the local calculation, and if not, updates the local weight and executes the consistency protocol between nodes, retrieves the data stored in the database for comparison and completes the consensus;

[0037] S324: The master node receives feedback messages from all proxy nodes, confirms the messages and forwards them to all nodes.

[0038] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, step S4 adopts standard classification algorithm to perform prediction performance detection, and its prediction performance includes precision P, recall rate R, F1 metric value and sensitivity S, wherein precision P is as follows:

[0039]

[0040] Among them, TP is the true positive prediction, FP is the false positive prediction, and the recall rate R is as follows:

[0041]

[0042] Among them, FN is the false negative prediction, and the F1 metric value is as follows:

[0043]

[0044] Among them, the sensitivity S is as follows:

[0045]

[0046] Among them, i is the target variable, u(i n |e) is the post-validation distribution.

[0047] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, the collaborative management of supply chain in step S5 includes strategic collaboration, information collaboration, operational collaboration and decision-making collaboration.

[0048] In the above-mentioned collaborative management method of traditional Chinese medicine supply chain based on industrial Internet, the strategic collaboration in step S5 includes determining supply chain collaboration goals, strategic matching coordination, and collaborative evaluation improvement; the information system includes communication technology collaboration, workflow technology collaboration, and information integration technology collaboration; operational collaboration includes production collaboration, procurement collaboration, transportation collaboration, warehousing collaboration, and sales collaboration; and decision-making collaboration adopts a supply chain contract system.

[0049] A collaborative management system for the supply chain of traditional Chinese medicine based on the industrial Internet adopts the above-mentioned collaborative management method for the supply chain of traditional Chinese medicine based on the industrial Internet.

[0050] Compared with the existing technology, the advantages of the present invention are: through the industrial Internet technology, the whole process monitoring and traceability of Chinese medicinal materials from planting, harvesting to processing and circulation can be realized, the transparency of the supply chain can be improved, and the quality and safety of Chinese medicinal materials can be ensured; various data in the supply chain can be analyzed and predicted to help enterprises formulate production plans and inventory strategies more accurately, reduce inventory backlogs and logistics costs; the industrial Internet can realize real-time data exchange and instant communication in all links of the supply chain, improve the response speed of the supply chain to market changes, and realize more flexible supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the method of the present invention;

[0052] Figure 2 It is a data collection and preprocessing flow chart of the present invention;

[0053] Figure 3 is a data analysis flow chart of the present invention;

[0054] Figure 4 is a model training flow chart of the present invention;

[0055] Figure 5 It is a schematic diagram of supply chain collaborative management of the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] like Figure 1-5 As shown in the figure, a collaborative management method for the supply chain of traditional Chinese medicine based on the industrial Internet is increasingly widely used in the supply chain of traditional Chinese medicine. Through the industrial Internet, supply chain managers can monitor the supply chain more accurately, optimize production plans, and reduce inventory backlogs and waste. At the same time, artificial intelligence is introduced to optimize supply chain scheduling, predict potential risks, and improve decision-making efficiency. For example, through machine learning algorithms, the system can identify bottlenecks in the supply chain and make improvement suggestions, which specifically include the following steps:

[0058] S1: Supply chain data collection and preprocessing, which introduces blockchain technology to improve data security;

[0059] S2: Data analysis algorithm selection, preferably decision tree algorithm for data analysis;

[0060] S3: Perform model training to obtain a Chinese medicine supply chain information management model;

[0061] S4: Test the model prediction performance and assess supply chain risks;

[0062] S5: Combine upstream and downstream enterprises to carry out collaborative supply chain management.

[0063] Specifically, the supply chain in step S1 includes the Chinese medicine planting and production link, the primary processing link at the place of origin, the warehousing management link, the logistics management link, the traceability link, the inspection link and the trade link. Step S1 uses the traceability system to collect data and pre-process data based on the cloud platform. The traceability system used assigns a unique traceability code to each batch of Chinese medicinal materials, which records the full process information of the planting, harvesting, processing, warehousing, transportation and sales of the medicinal materials. By scanning the traceability code, consumers and regulatory authorities can query the full life cycle information of the product. Using QR codes or RFID tags on the packaging of Chinese medicinal materials facilitates rapid information collection and tracing in each link, and improves traceability efficiency and accuracy.

[0064] In order to ensure the authenticity and immutability of traceability information, the system can introduce blockchain technology. The data of each link is encrypted and recorded on the blockchain, so as to achieve transparent management of data and prevent information from being tampered with. Therefore, the traceability system in step S1 uses blockchain for processing, which includes the following steps:

[0065] S11: After data processing, each supply chain link is uploaded to the data collection node, which processes the data through smart contracts and uploads it to the blockchain network;

[0066] S12: The blockchain network performs hash calculations on the data in each link and extracts the summary, and uses digital signatures for verification;

[0067] S13: Encrypt the summary using the private key and store it in a local database;

[0068] S14: The local database receives the public key and decrypts the data, and compares the decrypted data with the hashed data for verification;

[0069] S15: If the data verification is passed, the PoR algorithm is used to upload the data to the chain and generate a new block. In addition, the block information needs to be uploaded to the database for backup storage.

[0070] Furthermore, step S2 uses a decision tree algorithm to perform data analysis, which includes the following steps:

[0071] S21: Divide the data into a training set and a test set, use multiple interpolation to complete the data in the training set, and then discretize the data;

[0072] S22: Select decision attributes from the data, including facility attributes, storage attributes, logistics attributes, information attributes, procurement attributes and pricing attributes; among them, facility attributes determine the efficiency of Chinese medicine planting and processing, including production capacity, equipment utilization rate, equipment working cost and other attributes; storage attributes include warehousing cost, goods turnover rate and goods accumulation rate and other attributes; information attributes include supply and demand volatility of the supply chain and other attributes; logistics attributes include freight cost, freight method, etc.; procurement attributes include purchase price fluctuations and supply quality, transaction volume and other attributes; pricing attributes include order quantity and sales price and other attributes.

[0073] S23: The training set uses a recursive method to construct a decision tree. The data is calculated to obtain the information gain and information gain rate of the decision attribute. The information gain rate is used as a branch point. A branch is established for each decision attribute and the training set is divided. The information gain rate is used as the only measurement standard to obtain a relatively simplified decision tree.

[0074] S24: When the expected error rate of a subtree exceeds the expected error rate of a single leaf, the leaf is replaced by the subtree. This step is mainly to avoid overfitting in subsequent verification;

[0075] S25: Use cross-validation to verify the accuracy of the decision tree;

[0076] S26: Use decision tree model to analyze the data.

[0077] Furthermore, step S3 includes the following steps:

[0078] S31: System initialization, each node of the industrial Internet calculates the node weight according to its own activity, data contribution value and historical behavior; the own activity is the interval length of the node in a fixed time and the number of data interactions; the data contribution value is the total data volume and accuracy in the traditional Chinese medicine supply chain; the historical behavior calculation node weight is determined by step S33;

[0079] S32: Select the node with the highest weight from all the node weights as the proxy node. The specific weights are as follows:

[0080] Wi=α*Wa(i)+β*Wd(i)+γ*Wh(i);

[0081] Among them, Wa(i), Wd(i) and Wh(i) are the node weights calculated by the node according to its own activity, data contribution value and historical behavior; when the proxy node is selected, other nodes are temporarily excluded;

[0082] S33: The proxy node performs data transmission, data verification, data caching, and data recording, and each node updates the proxy node according to step S35;

[0083] S34: Each node in the industrial Internet updates its own weight according to the performance of the proxy node. If the data is accurate, the weight is increased, otherwise the weight is reduced;

[0084] S35: Return to step S32, reselect a new proxy node according to the updated weight and replace the proxy node.

[0085] In addition, step S33 includes the following steps:

[0086] S331: The client sends a request to the proxy node;

[0087] S332: The master node receives the request, generates a pre-preparation message and forwards it to all proxy nodes;

[0088] S323: The proxy node receives the pre-prepared message from the master node, verifies whether the message is consistent with the local calculation, and if not, updates the local weight and executes the consistency protocol between nodes, retrieves the data stored in the database for comparison and completes the consensus;

[0089] S324: The master node receives feedback messages from all proxy nodes, confirms the messages and forwards them to all nodes.

[0090] At the same time, step S4 uses a standard classification algorithm to perform prediction performance detection, and its prediction performance includes precision P, recall rate R, F1 metric value and sensitivity S, where precision P is as follows:

[0091]

[0092] Among them, TP is the true positive prediction, FP is the false positive prediction, and the recall rate R is as follows:

[0093]

[0094] Among them, FN is the false negative prediction, and the F1 metric value is as follows:

[0095]

[0096] Among them, the sensitivity S is as follows:

[0097]

[0098] Among them, i is the target variable, u(i n |e) is the post-validation distribution. The sensitivity analysis used here obtains the inverse relationship between variables, and the reaction function is fitted through Taylor expansion, thereby simplifying the reaction function and solving the supply chain planning problem.

[0099] It can be seen that supply chain collaboration is the interaction and coordination between the node enterprises in the supply chain in order to improve the overall competitiveness of the supply chain. In step S5, supply chain collaborative management includes strategic collaboration, information collaboration, operational collaboration, and decision-making collaboration. Through the industrial Internet platform, information sharing between upstream and downstream enterprises in the supply chain is realized. The production, inventory, and logistics information of each link can be transmitted in real time between supply chain enterprises to enhance the synergy effect and reduce information delays and errors. Monitor market demand changes in real time and dynamically adjust the supply chain plan according to demand changes. For example, when market demand suddenly increases, the system can automatically adjust the production and inventory plans to ensure the timeliness of market supply.

[0100] Obviously, the strategic collaboration in step S5 includes determining supply chain collaboration goals, strategic matching coordination, and collaborative evaluation and improvement; the information system includes communication technology collaboration, workflow technology collaboration, and information integration technology collaboration; operational collaboration includes production collaboration, procurement collaboration, transportation collaboration, warehousing collaboration, and sales collaboration; and decision-making collaboration adopts the supply chain contract system.

[0101] A collaborative management system for the supply chain of traditional Chinese medicine based on the industrial Internet adopts the collaborative management method for the supply chain of traditional Chinese medicine based on the industrial Internet. The system adopts a modular design and includes a planting management module, a processing management module, a warehousing management module, a quality management module, a sales management module, etc. Each module is responsible for managing a specific link in the production and processing of traditional Chinese medicines, and realizes seamless integration between modules through data interfaces.

[0102] By establishing a unified data management platform, the centralized management and sharing of data in all links can be achieved through the platform. All production, processing, warehousing and sales data are aggregated into the same database to ensure data consistency and integrity. Using the Internet of Things technology, the system can monitor the key parameters of the production and processing of Chinese herbal medicines in real time (such as temperature, humidity, light, processing time, etc.), and automatically issue alarms and suggestions when abnormalities occur, helping enterprises to adjust the production and processing process in a timely manner.

[0103] To sum up, the principle of this embodiment is: to collect supply chain data based on the industrial Internet, use blockchain technology to encrypt the data, then use the decision tree algorithm to analyze the data and train the corresponding data transmission model, test the model prediction performance and conduct risk assessment, and use the above model to perform supply chain collaborative management.

[0104] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0105] Although this article uses more terms such as supply chain, blockchain, and cloud platform, it does not exclude the possibility of using other terms. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

Claims

1. A collaborative management method for traditional Chinese medicine supply chain based on industrial Internet, characterized in that: The steps include: S1: Supply chain data collection and preprocessing; S2: Data analysis algorithm selection; S3: Perform model training; S4: Test the model prediction performance and assess supply chain risks; S5: Conduct collaborative supply chain management.

2. According to a method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 1, it is characterized in that: The supply chain in step S1 includes the Chinese medicine planting and production link, the primary processing link at the place of origin, the warehousing management link, the logistics management link, the traceability link, the inspection link and the trade link. Step S1 uses the traceability system to collect data and pre-processes data based on the cloud platform.

3. According to a method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 2, it is characterized in that: In step S1, the traceability system uses blockchain for processing, which includes the following steps: S11: After data processing, each supply chain link is uploaded to the data collection node, which processes the data through smart contracts and uploads it to the blockchain network; S12: The blockchain network performs hash calculations on the data in each link and extracts the summary, and uses digital signatures for verification; S13: Encrypt the summary using the private key and store it in a local database; S14: The local database receives the public key and decrypts the data, and compares the decrypted data with the hashed data for verification; S15: If the data verification is passed, the PoR algorithm is used to upload the data to the chain and generate a new block.

4. According to a method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 1, it is characterized in that: The step S2 uses a decision tree algorithm to perform data analysis, which includes the following steps: S21: Divide the data into a training set and a test set, use multiple interpolation to complete the data in the training set, and then discretize the data; S22: Select decision attributes from the data, including facility attributes, storage attributes, logistics attributes, information attributes, procurement attributes, and pricing attributes; S23: The training set uses a recursive method to construct a decision tree, calculates the data, obtains the information gain and information gain rate of the decision attribute, and uses the information gain rate as the branch point; S24: When the expected error rate of a subtree exceeds the expected error rate of a single leaf, replace the subtree with the leaf; S25: Use cross-validation to verify the accuracy of the decision tree; S26: Use decision tree model to analyze the data.

5. According to the method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 4, it is characterized in that: The step S3 comprises the following steps: S31: System initialization, each node of the Industrial Internet calculates the node weight according to its own activity, data contribution value and historical behavior; S32: Select the node with the highest weight from all node weights as the proxy node; S33: The proxy node performs data transmission, data verification, data caching, and data recording; S34: Each node in the industrial Internet updates its own weight according to the performance of the proxy node. If the data is accurate, the weight is increased, otherwise the weight is reduced; S35: Return to step S32, reselect a new proxy node according to the updated weight and replace the proxy node.

6. The method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 5 is characterized in that: The step S33 comprises the following steps: S331: The client sends a request to the proxy node; S332: The master node receives the request, generates a pre-preparation message and forwards it to all proxy nodes; S323: The proxy node receives the pre-prepared message from the master node, verifies whether the message is consistent with the local calculation, and if not, updates the local weight and executes the consistency protocol between nodes, retrieves the data stored in the database for comparison and completes the consensus; S324: The master node receives feedback messages from all proxy nodes, confirms the messages and forwards them to all nodes.

7. The method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 5 is characterized in that: The step S4 uses a standard classification algorithm to perform prediction performance detection, and its prediction performance includes precision P, recall rate R, F1 metric value and sensitivity S, where the precision P is as follows: Among them, TP is the true positive prediction, FP is the false positive prediction, and the recall rate R is as follows: Among them, FN is the false negative prediction, and the F1 metric value is as follows: Among them, the sensitivity S is as follows: Among them, i is the target variable, u(i n |e) is the post-validation distribution.

8. The method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 1 is characterized in that: The supply chain collaborative management in step S5 includes strategic collaboration, information collaboration, operational collaboration and decision-making collaboration.

9. The method for collaborative management of a Chinese medicine supply chain based on the industrial Internet according to claim 8 is characterized in that: In step S5, the strategic collaboration includes determining supply chain collaboration goals, strategic matching coordination, and collaborative evaluation improvement; the information system includes communication technology collaboration, workflow technology collaboration, and information integration technology collaboration; operational collaboration includes production collaboration, procurement collaboration, transportation collaboration, warehousing collaboration, and sales collaboration; and decision-making collaboration adopts a supply chain contract system.

10. A collaborative management system for the supply chain of traditional Chinese medicine based on the industrial Internet, characterized in that: The collaborative management method of the traditional Chinese medicine supply chain based on the industrial Internet as described in any one of claims 1 to 9 above is adopted.

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