Agent probe acquisition method for manufacturing industry supply chain data

Abstract

The invention relates to the technical field of information technology and intelligent manufacturing, in particular to a manufacturing industry supply chain data AGENT probe acquisition method. Comprising the following steps: deploying an Agent probe, and collecting original data of a supply chain; processing the original data through a data standardization module to generate standardized data; configuring a standardized data interface, and outputting standardized data; and deploying a security gateway, encrypting transmission and safely filtering the data to ensure that the data is safely transmitted to the client. According to the method, the Agent probe is deployed in a supplier enterprise, standardized and interface acquisition of supply chain data is realized, meanwhile, the data acquisition problem caused by internal and external network isolation is effectively solved, and the efficiency, accuracy and safety of data acquisition are improved.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and intelligent manufacturing technology, and particularly to a method for collecting manufacturing supply chain data by an AGENT probe. Background Art

[0002] In today's globalized business environment, the importance of supply chain management has become increasingly prominent. Enterprises need to obtain data of each link in the supply chain in real time and accurately to achieve efficient operation decision-making, inventory management, risk warning, etc. However, there are many challenges in current supply chain data collection.

[0003] On the one hand, the internal data formats and interfaces of supplier enterprises vary widely, lacking unified data standards and interface specifications. This results in customer enterprises having to invest a large amount of manpower and material resources in data format conversion and interface adaptation work when obtaining supplier data. For example, Supplier A stores inventory data in a custom data format, while Supplier B uses a slightly different format that is common in the industry. When customer enterprises integrate the data of these two suppliers, they often need to write specialized data parsing programs for different formats, greatly increasing the complexity and cost of data collection.

[0004] On the other hand, there are security requirements for internal and external network isolation in the supply chain. Many supplier enterprises isolate their internal networks from external networks for data security considerations, making it difficult for external customers to directly obtain their internal data. Traditional data collection methods, such as manually collecting data regularly and manually entering it into the system, are inefficient and error-prone and cannot meet the real-time requirements; while some data collection methods that attempt to break through internal and external network isolation have relatively large security risks and may lead to data leakage of supplier enterprises. For example, some unauthorized external programs attempt to obtain internal network data through network vulnerabilities, bringing serious security hazards to enterprises.

[0005] Therefore, developing a data collection method that can effectively solve data standardization, interface unification, and security for internal and external network isolation is of great significance for improving the efficiency and security of supply chain management. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for collecting manufacturing supply chain data by an AGENT probe, which realizes standardized and interfaced collection of supply chain data by deploying Agent probes inside supplier enterprises, and at the same time effectively solves the problem of data acquisition caused by internal and external network isolation, improving the efficiency, accuracy, and security of data collection.

[0007] The technical solution adopted by the present invention is as follows: A method for collecting manufacturing supply chain data by an AGENT probe includes the following steps:

[0008] Step 1: Deploy an Agent probe with autonomous operation ability within the internal network of the supplier enterprise. The Agent probe docks with the supplier's business system through preset rules and permissions to collect original supply chain data;

[0009] Step 2: Through the data standardization module integrated in the Agent probe, perform format conversion and semantic standardization processing on the collected original data according to the preset unified data specification to generate standardized data;

[0010] Step 3: Configure a standardized data interface in the Agent probe. The interface defines the data transmission protocol, format, and invocation method, and outputs the standardized data through the interface;

[0011] Step 4: Deploy a security gateway at the network boundary of the supplier, establish an encrypted communication channel between the Agent probe and the security gateway. After the security gateway performs security verification and filtering on the output data, it transmits the data that complies with the security policy to the external network client.

[0012] As a further improvement of the present invention, in Step 1, the Agent probe is deployed on at least one of the ERP system server, warehouse management system server, and production management system server of the supplier, and collects real-time business data through API interfaces and direct database connection methods.

[0013] As a further improvement of the present invention, the data standardization module consists of a semantic mapping unit, a format conversion unit, and a data verification unit.

[0014] As a further improvement of the present invention, the semantic mapping unit is used to map heterogeneous data fields of different suppliers to a unified field name; the format conversion unit is used to convert date, numerical, and text formats into a preset standard format; the data verification unit is used to detect data integrity and trigger an exception alarm.

[0015] As a further improvement of the present invention, the Agent probe performs multi-level verification after data standardization. Among them, the first-level verification: perform logical rationality check on the data based on a preset threshold; the second-level verification: perform consensus verification on key data through blockchain nodes and generate an immutable record.

[0016] As a further improvement of the present invention, the standardized data interface in Step 3 supports JSON, XML, and Protobuf formats, and performs encrypted transmission through the HTTPS protocol and the MQTT protocol.

[0017] As a further improvement of the present invention, the security gateway adopts a dynamic firewall policy, only allowing Agent probes authenticated by digital certificates to communicate, and performing content-level filtering on the transmitted data to block data packets containing sensitive fields and abnormal formats.

[0018] As a further improvement of the present invention, a zero-trust architecture is adopted between the security gateway and the client. Through two-way authentication and micro-segmentation technologies, the data transmission path and access rights are restricted to achieve fine-grained security control.

[0019] As a further improvement of the present invention, the Agent probe is built with a dynamic rule engine, which automatically adjusts the data collection frequency, field range, and standardization rules according to customer requirements and changes in the supplier's business.

[0020] Advantages of the present invention: (1) Improve data collection efficiency: Through the automated deployment of Agent probes, the present invention eliminates the need for manual data collection and entry, greatly reducing the time cost of data collection. For example, originally, it took several days per month to manually collect supplier data. After adopting this method, data can be collected automatically in real-time or at set intervals, shortening the data collection time to a few hours or even less.

[0021] (2) Enhance data accuracy: The application of the data standardization module of the present invention avoids data errors and chaos caused by inconsistent data formats. The standardized data is more reliable in subsequent analysis and applications, reducing the risk of decision-making errors caused by data quality problems. For example, in inventory data analysis, due to the unified data format, the statistical results are more accurate, enabling enterprises to manage inventory more precisely and reducing the risk of inventory backlog or stockouts.

[0022] (3) Ensure data security: The unique internal and external network isolation design of the present invention effectively prevents threats to the internal data of supplier enterprises from external illegal network access. Through the strict filtering and inspection of the security gateway, it is ensured that only authorized data can be transmitted to the external network, greatly improving data security BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flowchart of a method for collecting manufacturing supply chain data by an AGENT probe of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to make the technical problems, technical solutions, and beneficial effects to be solved by the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] The present invention provides a method for collecting data of a manufacturing supply chain data AGENT probe, comprising the following steps:

[0026] Step 1: Deploy an Agent probe with autonomous operation ability in the internal network of the supplier enterprise. The Agent probe docks with the supplier's business system through preset rules and permissions to collect original supply chain data. The Agent probe is deployed on at least one of the supplier's ERP system server, warehouse management system server, and production management system server, and collects real-time business data through API interfaces and database direct connection methods;

[0027] Step 2: Through the data standardization module integrated by the Agent probe, perform format conversion and semantic standardization processing on the collected original data according to a preset unified data specification to generate standardized data. The Agent probe performs multi-level verification after data standardization. Among them, the first-level verification: check the logical rationality of the data based on a preset threshold; the second-level verification: perform consensus verification on key data through blockchain nodes and generate an immutable record;

[0028] Step 3: Configure a standardized data interface in the Agent probe. The interface defines the data transmission protocol, format, and call method, and outputs the standardized data through the interface. The standardized data interface supports JSON, XML, and Protobuf formats, and performs encrypted transmission through the HTTPS protocol and MQTT protocol;

[0029] Step 4: Deploy a security gateway at the network boundary of the supplier, establish an encrypted communication channel between the Agent probe and the security gateway. After the security gateway performs security verification and filtering on the output data, it transmits the data that complies with the security policy to the external network client. The security gateway adopts a dynamic firewall policy, only allowing Agent probes authenticated by digital certificates to communicate, and performs content-level filtering on the transmitted data, shielding data packets containing sensitive fields and abnormal formats. A zero-trust architecture is adopted between the security gateway and the client, and through two-way identity authentication and micro-segmentation technology, the data transmission path and access rights are restricted to achieve fine-grained security control.

[0030] In the present invention, the data standardization module consists of a semantic mapping unit, a format conversion unit, and a data verification unit. The semantic mapping unit is used to map heterogeneous data fields of different suppliers to a unified field name; the format conversion unit is used to convert date, numerical, and text formats into a preset standard format; the data verification unit is used to detect data integrity and trigger an exception alarm.

[0031] In the present invention, the Agent probe is built with a dynamic rule engine, which automatically adjusts the data collection frequency, field range, and standardization rules according to customer requirements and supplier business changes.

[0032] Example:

[0033] An automobile manufacturing enterprise needs to obtain the inventory, production progress, and logistics data of its component suppliers distributed in multiple regions in real time. Since the management systems (such as ERP and warehousing systems) used by each supplier have strong heterogeneity, large differences in data formats, and the supplier's intranet is isolated from the extranet, the traditional manual data collection method is inefficient and has security risks. After adopting the method of the present invention, the enterprise has realized the automated, standardized, and secure collection of supply chain data.

[0034] (I) Deployment and data collection of Agent probes

[0035] Selection of deployment location: Agent probes are respectively deployed in the ERP system server of Supplier A, the warehousing management system server of Supplier B, and the production management system server of Supplier C. The probe directly interfaces with the ERP database through the API interface (such as RESTful API) opened by the supplier system, or directly connects to the MySQL database of the production management system through the JDBC protocol to collect real-time business data.

[0036] Permission and rule configuration: Set data collection permissions according to the agreement between the supplier and the manufacturing enterprise. For example, only fields such as inventory quantity, production order number, and estimated delivery date are allowed to be collected. The dynamic rule engine automatically adjusts the data collection cycle from once a day to once an hour according to the needs of the manufacturing enterprise (such as increasing the collection frequency during peak seasons).

[0037] (II) Data standardization and multi-level verification

[0038] Standardization processing: (1) Semantic mapping: The "Product_ID" field in the ERP system of Supplier A is mapped to the standard field "SKU_Code"; the "Stock_Qty" field of Supplier B is mapped to "Inventory_Quantity". (2) Format conversion: The date format "DD / MM / YYYY" of Supplier C is uniformly converted to "YYYY-MM-DD"; the inventory value is uniformly retained to two decimal places. (3) Data verification: The verification unit detects a negative value (logical exception) in the "Inventory_Quantity" field of a certain batch of data, triggers an alarm, and notifies Supplier B for verification.

[0039] Multi-level Verification: (1) First-level Verification (Logical Rationality): Preset threshold rules, such as "The production progress completion rate shall not exceed 100%". If a certain supplier reports data of 120%, it will be marked as abnormal data and the transmission will be suspended. (2) Second-level Verification (Blockchain Consensus): For key data (such as order delivery time), the hash value is uploaded to a private blockchain network and jointly verified by the manufacturing enterprise, logistics company and third-party audit node to generate an immutable evidence record.

[0040] (III) Standardized Interfaces and Data Transmission

[0041] Interface Configuration: Supplier A chooses to transmit JSON format data through the HTTPS protocol, including fields: {"SKU_Code": "A001", "Inventory_Quantity": 1500, "Next_Delivery_Date": "2024-10-05"}. Supplier B uses the MQTT protocol to transmit Protobuf format data to achieve low-latency transmission.

[0042] Edge Computing Optimization: The Agent probe conducts preliminary analysis on the collected inventory data locally, calculates indicators such as inventory turnover rate and out-of-stock risk index, and only transmits the analysis results to the client to reduce bandwidth occupancy.

[0043] (IV) Security Gateway and Zero-Trust Architecture

[0044] Realization of Secure Communication: A security gateway is deployed at the network boundary of the supplier and communicates with the Agent probe through a TLS1.3 encrypted channel. The dynamic firewall policy only allows Agent probes holding digital certificates issued by the manufacturing enterprise to access and intercepts communication requests from unauthenticated probes.

[0045] Content-level Filtering and Microsegmentation: The security gateway conducts in-depth detection on the transmitted data packets and shields data containing sensitive fields such as "customer privacy information". The client adopts a zero-trust architecture, through two-way mTLS authentication, only allows access from the specified security gateway IP, and restricts the data transmission path to a specific business system (such as the SCM system of the manufacturing enterprise) through microsegmentation technology.

[0046] As can be seen from the above embodiments, (1) Efficiency improvement: The data collection cycle is shortened from 3 days of manual processing to 15 minutes, and the real-time performance is improved by 96%. (2) Enhanced accuracy: The data error rate after standardization is reduced from 12% to 0.5%, and the inventory analysis accuracy rate is increased to 99.8%. (3) Security guarantee: The security gateway intercepts 98% of abnormal access requests, and the blockchain evidence achieves 100% anti-tampering of key data.

[0047] In summary, the method for collecting manufacturing supply chain data by the AGENT probe of the present invention realizes the comprehensive, efficient, and secure collection of supply chain data, providing strong support for the supply chain management and decision-making of manufacturing enterprises. Through the Agent probes deployed inside the supplier enterprises, the present invention not only solves the problem of data acquisition caused by the isolation between the internal and external networks, but also ensures the accuracy and reliability of the data through data standardization and multi-level verification mechanisms. At the same time, the application of the security gateway and the zero-trust architecture provides all-round security protection for data transmission, effectively preventing the risks of data leakage and illegal access. Therefore, the present invention has broad application prospects and important practical value in the field of manufacturing supply chain data collection.

[0048] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collecting data of an AGENT probe in a manufacturing supply chain, characterized in that, It includes the following steps: Step 1: Deploy an Agent probe with autonomous operation ability in the internal network of the supplier enterprise. The Agent probe docks with the supplier's business system through preset rules and permissions to collect original supply chain data; Step 2: Through the data standardization module integrated by the Agent probe, perform format conversion and semantic standardization processing on the collected original data according to the preset unified data specification to generate standardized data; Step 3: Configure a standardized data interface in the Agent probe. The interface defines the data transmission protocol, format, and call method, and outputs the standardized data through the interface; Step 4: Deploy a security gateway at the supplier network boundary, establish an encrypted communication channel between the Agent probe and the security gateway. After the security gateway performs security verification and filtering on the output data, it transmits the data that complies with the security policy to the external network client.

2. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that In Step 1, the Agent probe is deployed on at least one of the supplier's ERP system server, warehouse management system server, and production management system server, and collects real-time business data through the API interface and database direct connection method.

3. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that The data standardization module consists of a semantic mapping unit, a format conversion unit, and a data verification unit.

4. A method for collecting manufacturing supply chain data AGENT probes according to claim 3, characterized in that, The semantic mapping unit is used to map heterogeneous data fields of different suppliers to a unified field name; the format conversion unit is used to convert the date, numerical value, and text format into a preset standard format; the data verification unit is used to detect data integrity and trigger an exception alarm.

5. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that, The Agent probe performs multi-level verification after data standardization. Among them, the first-level verification: perform logical rationality check on the data based on a preset threshold; the second-level verification: perform consensus verification on key data through blockchain nodes and generate an immutable record.

6. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that In Step 3, the standardized data interface supports JSON, XML, and Protobuf formats, and performs encrypted transmission through the HTTPS protocol and MQTT protocol.

7. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that, The security gateway adopts a dynamic firewall policy, only allowing Agent probes authenticated by digital certificates to communicate, and performs content-level filtering on the transmitted data, shielding data packets containing sensitive fields and abnormal formats.

8. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that, A zero-trust architecture is adopted between the security gateway and the client. Through two-way identity authentication and micro-segmentation technology, it restricts the data transmission path and access rights to achieve fine-grained security control.

9. A method for collecting manufacturing supply chain data AGENT probes according to claim 1, characterized in that, The Agent probe is built with a dynamic rule engine, which automatically adjusts the data collection frequency, field range, and standardization rules according to customer requirements and supplier business changes.

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