Multi-field intelligent supply chain decision-making system based on AI decision-making and method thereof

Abstract

The invention provides a multi-field intelligent supply chain decision system and method based on AI decision, and the system comprises an identification analysis module which is used for carrying out the identification analysis of an identification carried by a material when the material flows in different supply chain links, and obtaining the identification analysis data; and the purchase management module, the logistics management module, the inventory management module and the production plan management module are used for performing purchase management, logistics management, inventory management and production plan management on the supply chain based on the AI decision and according to the identification analysis data. According to the invention, based on the AI decision, the data is analyzed according to the identifier, and intelligent decision and optimization management are realized, so that the efficiency and transparency of the whole supply chain are improved, and the production takt of the supply chain is optimized.

Description

Technical Field

[0001] The present invention relates to the field of supply chain AI decision technology, and in particular to a multi-domain intelligent supply chain decision system and method based on AI decision. Background Art

[0002] Modern supply chains generate a large amount of data, including data from suppliers, production lines, logistics, market demand and other fields. These data are of various types, such as sensor data, order records, market feedback, etc. Traditional supply chain decision-making methods often have difficulty in effectively processing these massive and diverse data, resulting in the decision-making process may not be accurate or timely. The dynamic changes in the global market are accelerating, and consumer demand changes rapidly and is difficult to predict. Traditional supply chain management methods usually rely on historical data and forecasting models. These methods often cannot make timely adjustments when faced with sudden market trends or demand fluctuations, resulting in excess or shortage of inventory, affecting business flexibility and customer satisfaction. In the digital age, enterprises have an increasing demand for real-time decision-making. Traditional supply chain management methods often rely on periodic reports and analysis, and cannot provide real-time insights and responsiveness, which limits the competitiveness of enterprises in a rapidly changing market environment. Summary of the invention

[0003] One of the purposes of the present invention is to provide a multi-domain intelligent supply chain decision system based on AI decision-making, aiming to solve the above-mentioned technical problems.

[0004] An embodiment of the present invention provides a multi-domain intelligent supply chain decision system based on AI decision-making, including:

[0005] The identification resolution module is used to resolve the identification carried by the materials when the materials circulate in different supply chain links and obtain the identification resolution data;

[0006] The procurement management module, logistics management module, inventory management module and production plan management module are used to perform procurement management, logistics management, inventory management and production plan management of the supply chain based on AI decision-making and identification resolution data.

[0007] Preferably, the identification and parsing data of the material at the raw material stage includes at least: supplier name, supply time, supply method, batch number and batch size;

[0008] The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP;

[0009] The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements;

[0010] The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price;

[0011] The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

[0012] Preferably, the procurement management module includes:

[0013] A demand identification unit is used to determine material requirements based on identification parsing data;

[0014] The supplier selection unit is used to determine the best supplier corresponding to the material demand based on the identification resolution data;

[0015] The order generation unit is used to establish supplier relationships with the best suppliers based on material requirements, generate purchase orders, and issue them;

[0016] Delivery tracking unit for delivery tracking of purchase orders based on identity resolution data and update inventory updates in real time.

[0017] Preferably, the logistics management module includes:

[0018] A transportation plan optimization unit, used to optimize the transportation plan based on the identification resolution data;

[0019] Tracking and monitoring unit, used to track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time;

[0020] The exception handling unit is used to make adaptive response adjustments to the transportation plan when abnormal logistics situations occur.

[0021] Preferably, the inventory management module includes:

[0022] Inventory optimization unit, used to optimize inventory management strategies based on identification resolution data;

[0023] The early warning reminder unit is used to give early warning reminders to relevant personnel when the inventory is below or above the inventory warning line based on the identification resolution data;

[0024] The inventory allocation unit is used to automatically allocate inventory based on the identification resolution data.

[0025] Preferably, the production planning management module includes:

[0026] The demand forecasting unit is used to determine product demand based on the identification resolution data and formulate corresponding production plans;

[0027] Production scheduling unit, used to optimize production planning and production scheduling based on identification resolution data;

[0028] The production monitoring unit is used to monitor the production progress and quality in real time based on the identification resolution data.

[0029] An embodiment of the present invention provides a multi-domain intelligent supply chain decision-making method based on AI decision-making, including:

[0030] Through the identification resolution module, when materials flow through different supply chain links, the identification carried by the materials is resolved to obtain identification resolution data;

[0031] Through the procurement management module, logistics management module, inventory management module and production plan management module based on AI decision-making, and according to the identification resolution data, procurement management, logistics management, inventory management and production plan management are carried out on the supply chain respectively.

[0032] Preferably, the identification and parsing data of the material at the raw material stage includes at least: supplier name, supply time, supply method, batch number and batch size;

[0033] The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP;

[0034] The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements;

[0035] The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price;

[0036] The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

[0037] Preferably, the procurement management module is used to manage the supply chain, including:

[0038] Determine material requirements based on identification parsing data through the requirement identification unit;

[0039] The supplier selection unit determines the best supplier corresponding to the material demand based on the identification parsing data;

[0040] Through the order generation unit, establish supplier relationships with the best suppliers based on material requirements, generate purchase orders and issue them;

[0041] The delivery tracking unit is used to track the purchase orders based on the identity resolution data and update the inventory updates in real time.

[0042] Preferably, performing logistics management on the supply chain through the logistics management module includes:

[0043] The transportation plan is optimized based on the identification resolution data through the transportation plan optimization unit;

[0044] Track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time through the tracking and monitoring unit;

[0045] When abnormal logistics situations occur, the transportation plan can be adaptively adjusted through the exception handling unit.

[0046] The present invention has achieved the following beneficial effects:

[0047] By using the identification resolution module, detailed data can be obtained in each link of the material flow, including procurement, production, logistics, inventory, sales and application. Through this system, each management module (procurement management, logistics management, inventory management and production planning management) can make intelligent decisions and optimize management based on AI decisions and identification resolution data, thereby improving the efficiency and transparency of the overall supply chain and optimizing the production rhythm of the supply chain. This modular design ensures full-chain optimization from demand identification, supplier selection, transportation optimization to inventory management and production scheduling, so that every link in the supply chain can be accurately connected and operate efficiently.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 A schematic diagram of a multi-domain intelligent supply chain decision system based on AI decision-making in an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the structure of a procurement management module in an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the logistics management module structure in an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the inventory management module structure in an embodiment of the present invention;

[0055] Figure 5 It is a schematic diagram of the production plan management module structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0057] The embodiment of the present invention provides a multi-domain intelligent supply chain decision system based on AI decision-making, such as Figure 1 As shown, including:

[0058] The identification resolution module 1 is used to resolve the identification carried by the materials when the materials circulate in different supply chain links, and obtain identification resolution data; wherein different supply chain links involve multiple fields;

[0059] The procurement management module 2, the logistics management module 3, the inventory management module 4 and the production plan management module 5 are used to perform procurement management, logistics management, inventory management and production plan management on the supply chain based on AI decision-making and identification resolution data.

[0060] The working principle and beneficial effects of the above technical solution are:

[0061] AI decision-making refers to the use of AI big models for decision management. When building AI big models, a large amount of experience in procurement management, logistics management, inventory management, and production planning management of the supply chain can be used as training samples to train the neural network and obtain the AI ​​big model.

[0062] By using the identification resolution module, detailed data can be obtained in each link of the material flow, including procurement, production, logistics, inventory, sales and application. Through this system, each management module (procurement management, logistics management, inventory management and production planning management) can make intelligent decisions and optimize management based on AI decisions and identification resolution data, thereby improving the efficiency and transparency of the overall supply chain and optimizing the production rhythm of the supply chain. This modular design ensures full-chain optimization from demand identification, supplier selection, transportation optimization to inventory management and production scheduling, so that every link in the supply chain can be accurately connected and operate efficiently.

[0063] In one embodiment, the multi-domain intelligent supply chain decision system based on AI decision-making further includes:

[0064] Auxiliary modules for:

[0065] S01. When a manager enters a sub-site of the supply chain, the manager's on-site management intention is determined based on the movement trajectory of the manager in the sub-site during the first time period and the on-site element map of the sub-site;

[0066] In step S01, in order to ensure the effectiveness of AI decision-making in the supply chain, managers need to conduct regular inspections and management, that is, go to the sub-sites of the supply chain for on-site management. The sub-site of the supply chain can be the production site of a certain supply chain product, etc. When the manager enters the sub-site, the movement trajectory generated by him is continuously recorded. The sub-site is preset with an on-site element map. The on-site element map shows the element position of each element in the sub-site. The element can be on-site personnel, on-site equipment, etc. The manager will patrol with a destination in the sub-site. Therefore, based on the movement trajectory and the on-site element map, his on-site management intention can be determined. For example, the movement trajectory reflects that the manager moves back and forth in the raw material quality inspection area of ​​the supply chain product, indicating that the manager has the intention to conduct on-site management of the raw material quality inspection area.

[0067] S02. Determine a first data screening condition based on the on-site management intention;

[0068] In step S02, the first data screening condition is a condition for screening out data corresponding to the on-site management intention that can assist the management personnel in on-site management. For example, if the on-site management intention is to conduct on-site management of the raw material quality inspection area, the first data screening condition is to screen out the personnel composition of the raw material quality inspection area, raw material traceability information, raw material flow information on the production line, etc.;

[0069] S03. Based on the first data screening condition, first data to be prompted is screened out from the target data; the target data at least includes: identification resolution data, and also includes management status data of procurement management, logistics management, inventory management and production plan management for the supply chain;

[0070] S04, determining the time maturity based on the movement trajectory of the management personnel in the sub-site during the second time period and the site element map;

[0071] In step S04, after the manager's on-site management intention is determined, if all the first data to be prompted are directly displayed to the manager, it may cause him to have no idea how to carry out on-site management and require targeted assistance. Therefore, it is necessary to determine the maturity of the time for assisting the manager; when determining the maturity of the time, it is determined based on the newly generated movement trajectory and the on-site element map during the second time period. For example: the movement trajectory reflects that the manager continues to stay next to a quality inspection equipment in the raw material quality inspection area of ​​the supply chain product, indicating that the manager wants to manage the quality inspection equipment, and the maturity of the time for on-site management assistance is 8; specifically, the system quantified into the maturity of the time can be set in advance by technical personnel according to actual needs.

[0072] S05. When the opportunity maturity is greater than or equal to the maturity threshold, execute in the third time period:

[0073] The maturity threshold can be 6; when the time maturity is greater than or equal to the maturity threshold, it means that the management personnel can be assisted at present;

[0074] S06, prompting the manager to check the smart terminal he / she carries; wherein the smart terminal includes at least: a mobile phone and a PDA device;

[0075] S07, prompting the manager to start the camera of the smart terminal and aim the camera at the management intended area;

[0076] In S07, the management intention area is the on-site area that the manager has the management intention to manage, for example: the area of ​​raw material quality inspection equipment;

[0077] S08, determining a second data screening condition based on the shooting history of the camera;

[0078] In S08, the shooting history of the camera will record the on-site situation of the management intention area, and the management personnel will also move the camera with the destination. Therefore, based on the shooting history, the second data screening condition can be determined. The second data screening condition is to screen out the second data to be prompted that can provide targeted assistance to the management personnel from the first data to be prompted. For example, the shooting history reflects that the quality inspection efficiency of the raw material quality inspection equipment cannot keep up with the needs of the raw material use link, and its quality inspection speed needs to be increased. Then the second data screening condition is to screen out the upper limit of the quality inspection speed adjustment of the raw material quality inspection equipment, the recommended adjustment speed of the AI ​​decision, etc.

[0079] S09, based on the second data screening condition, screening out the second data to be prompted from the first data to be prompted;

[0080] S10, based on the second data to be prompted, generating multiple sets of one-to-one corresponding auxiliary knowledge and mapping relationships; wherein the mapping relationship includes: a corresponding relationship or an associated relationship between the auxiliary knowledge and the management target in the management intention area;

[0081] In S10, the auxiliary knowledge is the knowledge of the auxiliary management personnel, which has a mapping relationship. For example, the auxiliary knowledge is to increase the speed of raw material quality inspection by 10%, and the mapping relationship is that the auxiliary knowledge corresponds to the raw material quality inspection equipment in the sub-site;

[0082] S11, traversing the auxiliary knowledge in sequence, and each time traversing, displaying the traversed auxiliary knowledge in the current shooting picture of the camera based on the mapping relationship corresponding to the traversed auxiliary knowledge;

[0083] In S11, when displaying in the current shooting picture of the camera, the traversed auxiliary knowledge is set next to the management target with which it has a mapping relationship;

[0084] S12. After the manager inputs the site management instruction, the site management instruction is sent to the instruction platform corresponding to the sub-site;

[0085] In S12, the command platform corresponding to the sub-site is used to provide site management instructions;

[0086] The second time period is after the first time period and adjacent to the first time period, and the third time period is after the second time period and adjacent to the first time period;

[0087] The length of the third time period is greater than the length of the first time period, which is greater than the length of the second time period. Setting this time length condition can ensure that the system can reasonably execute the auxiliary process within a reasonable time period. Specifically, the length of each time period can be set in advance by the technician according to actual needs.

[0088] When the management personnel of the embodiment of the present invention conduct on-site management in a sub-site, the system can cooperate with the management personnel in a humane and intelligent manner, determine the advance preparation of the first data to be prompted when the management personnel needs it, and also determine the precise assistance of the second data to be prompted when the management personnel needs it. The management personnel only needs to take out their mobile phones and accept the system prompts to quickly get assistance, thereby improving their efficiency in on-site management.

[0089] In one embodiment, the identification parsing data of the material at the raw material stage includes at least: supplier name, supply time, supply method, batch number and batch size;

[0090] The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP;

[0091] The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements;

[0092] The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price;

[0093] The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

[0094] The working principle and beneficial effects of the above technical solution are:

[0095] By recording detailed identification and parsing data at different stages of the material, tracking and management of the entire life cycle are achieved; in the raw material stage, supplier information, delivery time and method, batch number and batch are recorded to ensure that the source and quality of the raw materials are traceable; in the manufacturing stage, manufacturer information, manufacturing time and data server IP are recorded to ensure the transparency of the production process and data security; in the logistics and transportation stage, the storage time, the outbound time and storage requirements are recorded to ensure the accuracy of the logistics stage and the proper handling of materials; in the distribution and sales stage, the sales commodity name, sales time and transaction price are recorded to help monitor market circulation and sales data; in the application stage, the user identity, location, credentials and access IP are recorded to ensure that the use of materials complies with the specifications and can be traced. The embodiment of the present invention provides a complete data chain from raw materials to final application, which improves the transparency of the supply chain; by recording the data of each stage in detail, the quality of materials can be more effectively monitored and managed; when problems are found, they can be quickly traced back to the source to shorten the problem solving time; help enterprises better manage inventory, sales and production, improve operational efficiency; ensure that the operations of all stages comply with regulations and reduce compliance risks; through these detailed data records, enterprises can more effectively manage the supply chain, improve overall operational efficiency, and enhance control over quality and compliance.

[0096] In one embodiment, Figure 2 As shown, the procurement management module 2 includes:

[0097] A demand identification unit 21, used to determine material demand based on the identification parsing data;

[0098] A supplier selection unit 22, used to determine the best supplier corresponding to the material demand based on the identification resolution data;

[0099] The order generation unit 23 is used to establish a supplier relationship with the best supplier according to the material demand, generate a purchase order and issue it;

[0100] The delivery tracking unit 24 is used to track the delivery of the purchase order based on the identification resolution data and update the inventory in real time.

[0101] The working principle and beneficial effects of the above technical solution are:

[0102] The demand identification unit 21 analyzes and determines the material demand based on the identification resolution data (such as the production, supply and use records of the material); collects and analyzes the material-related data, evaluates the current inventory, predicts future demand and calculates the required material quantity. The supplier selection unit 22 determines the most suitable supplier based on the material demand data provided by the demand identification unit; evaluates the supplier's historical performance, delivery capacity, price and other relevant indicators to select the supplier that best meets the demand. The order generation unit 23: creates and sends a purchase order based on the material demand and the selected best supplier; generates a detailed purchase order, including material specifications, quantity and delivery date, etc., establishes a supply relationship with the best supplier, and issues an order to start the procurement process. The delivery tracking unit 24 tracks the delivery of the purchase order in real time and updates the inventory information; monitors the transportation and delivery status of the order based on the identification resolution data, updates the inventory system in a timely manner to reflect the latest inventory level, and monitors the delivery process to ensure on-time delivery. By accurately analyzing the identification resolution data, the demand identification unit 21 can more accurately predict material demand and reduce excess or shortage situations. The supplier selection unit 22 evaluates suppliers based on comprehensive data to select the best supplier, which helps reduce procurement costs, improve supply chain efficiency, and ensure timely supply of materials. The order generation unit 23 automatically generates and issues purchase orders, reduces manual intervention, improves order processing efficiency, and reduces error rates. The delivery tracking unit 24 implements real-time tracking and inventory updates of purchase orders, allowing companies to keep abreast of the delivery status of materials, quickly respond to any logistics issues, and maintain inventory accuracy. Through real-time tracking and data-driven decision-making, companies can optimize the entire supply chain process, improve overall operational efficiency and responsiveness, and thus enhance market competitiveness.

[0103] In one embodiment, Figure 3 As shown, the logistics management module 3 includes:

[0104] A transport plan optimization unit 31, configured to optimize the transport plan based on the identification resolution data;

[0105] A tracking and monitoring unit 32 is used to track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time;

[0106] The exception handling unit 33 is used to make adaptive response adjustments to the transportation plan when a logistics abnormality occurs.

[0107] The working principle and beneficial effects of the above technical solution are:

[0108] The transport plan optimization unit 31 uses the identification resolution data to optimize the transport plan to improve efficiency and reduce costs; analyzes the source, destination, transport route and time of the goods, calculates the optimal transport plan, and optimizes the transport route and time arrangement. The tracking and monitoring unit 32 monitors the status of the goods in the transport process in real time to ensure that the goods arrive at the destination as planned; tracks the location and transport status of the goods through the identification resolution data, checks whether the transportation complies with the predetermined plan, and provides real-time updates. The exception handling unit 33 handles abnormal situations that occur during transportation and adjusts the transportation plan in a timely manner; monitors abnormal events (such as delays, damage or loss), evaluates their impact, and adjusts the transportation plan or takes remedial measures to ensure that the goods arrive on time. The transport plan optimization unit 31 reduces transportation time and cost and improves overall transportation efficiency by optimizing the transportation route and time arrangement. The tracking and monitoring unit 32 provides real-time updates on the status of the goods, so that the enterprise can understand the progress of transportation in real time, improve the transparency and management level of transportation. The exception handling unit 33 can quickly handle abnormal situations in transportation, reduce the negative impact on the transportation plan, ensure that the goods can arrive at the destination as soon as possible, and reduce potential risks. Through effective transportation planning optimization and real-time tracking, we ensure that goods arrive on time and improve customer satisfaction and trust.

[0109] In one embodiment, Figure 4 As shown, the inventory management module 4 includes:

[0110] An inventory optimization unit 41, configured to optimize inventory management strategies based on the identification resolution data;

[0111] The early warning reminder unit 42 is used to give early warning reminders to relevant personnel when the inventory is lower than or higher than the inventory warning line based on the identification resolution data;

[0112] The inventory allocation unit 43 is used to automatically allocate inventory based on the identification resolution data.

[0113] The working principle and beneficial effects of the above technical solution are:

[0114] The inventory optimization unit 41 optimizes the inventory management strategy based on the identification resolution data; analyzes the inventory data, sales trends and demand forecasts, and adjusts the inventory level and replenishment strategy to improve the inventory turnover rate and reduce the inventory cost. The early warning reminder unit 42 monitors the inventory level and issues early warning reminders; tracks the inventory data in real time, and automatically sends reminder notifications to relevant personnel when the inventory is lower than or higher than the set warning line, prompting timely action. The inventory allocation unit 43 automatically allocates inventory; based on the real-time identification resolution data, automatically adjusts the inventory distribution, optimizes the inventory configuration, and ensures that the inventory level of warehouses in various places meets the demand. The inventory optimization unit 41 reduces inventory backlogs and out-of-stock situations through optimization strategies, improves the overall efficiency of inventory management; accurately adjusts inventory levels and replenishment strategies to reduce inventory holding costs and capital occupation. The early warning reminder unit 42 can notify relevant personnel in a timely manner to prevent insufficient or excessive inventory and reduce losses caused by inventory problems. The inventory allocation unit 43 improves the allocation efficiency by automatically adjusting the inventory configuration, ensuring that the inventory in various places meets the actual demand; the real-time data analysis and automatic allocation functions improve the response speed of the supply chain and enhance the ability to adapt to market changes.

[0115] In one embodiment, Figure 5 As shown, the production plan management module 5 includes:

[0116] Demand forecasting unit 51, used to perform product demand requirements based on the identification resolution data and formulate corresponding production plans;

[0117] A production scheduling unit 52, used to optimize production planning and production scheduling based on the identification resolution data;

[0118] The production monitoring unit 53 is used to monitor the production progress and quality in real time based on the identification resolution data.

[0119] The working principle and beneficial effects of the above technical solution are:

[0120] The demand forecasting unit 51 forecasts product demand based on the identification resolution data and formulates corresponding production plans; analyzes historical sales data, market trends, customer needs and other relevant information to predict future product demand; and formulates detailed production plans based on the forecast results to ensure that production capacity and resource allocation can meet the expected demand.

[0121] The production scheduling unit 52 optimizes production plans and production schedules; based on real-time identification resolution data, such as the availability of production resources, production progress, and demand changes, the production plan and production schedule are adjusted. By optimizing production scheduling and resource allocation, the production process is ensured to be efficient and flexible to respond to changes.

[0122] The production monitoring unit 53 monitors the production progress and quality in real time; it uses the identification resolution data to track the work progress and product quality on the production line in real time. By monitoring the production status, problems can be discovered and solved in a timely manner to ensure that the production process proceeds smoothly and meets quality standards. The demand forecasting unit 51 improves the accuracy of the production plan and reduces the risk of overproduction or underproduction through data-driven demand forecasting, thereby optimizing resource utilization and inventory levels. The production scheduling unit 52 can adjust the production plan in real time according to actual conditions, improve production efficiency, reduce production costs, and reduce production stagnation and resource waste. The production monitoring unit 53 ensures that the product meets quality standards at every step in the production process by monitoring the production progress and quality in real time, thereby reducing the defect rate and rework rate and improving the overall quality of the product.

[0123] The embodiment of the present invention provides a multi-domain intelligent supply chain decision-making method based on AI decision-making, including:

[0124] Through the identification resolution module, when materials flow through different supply chain links, the identification carried by the materials is resolved to obtain identification resolution data;

[0125] Through the procurement management module, logistics management module, inventory management module and production plan management module based on AI decision-making, and according to the identification resolution data, procurement management, logistics management, inventory management and production plan management are carried out on the supply chain respectively.

[0126] The identification and parsing data of materials at the raw material stage shall at least include: supplier name, delivery time, delivery method, batch number and batch size;

[0127] The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP;

[0128] The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements;

[0129] The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price;

[0130] The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

[0131] Procurement management of the supply chain is carried out through the procurement management module, including:

[0132] Determine material requirements based on identification parsing data through the requirement identification unit;

[0133] The supplier selection unit determines the best supplier corresponding to the material demand based on the identification parsing data;

[0134] Through the order generation unit, establish supplier relationships with the best suppliers based on material requirements, generate purchase orders and issue them;

[0135] The delivery tracking unit is used to track the purchase orders based on the identity resolution data and update the inventory updates in real time.

[0136] Logistics management of the supply chain through the logistics management module includes:

[0137] The transportation plan is optimized based on the identification resolution data through the transportation plan optimization unit;

[0138] Track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time through the tracking and monitoring unit;

[0139] When abnormal logistics situations occur, the transportation plan can be adaptively adjusted through the exception handling unit.

[0140] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-domain intelligent supply chain decision system based on AI decision-making, characterized by: include: The identification resolution module is used to resolve the identification carried by the materials when the materials circulate in different supply chain links and obtain the identification resolution data; The procurement management module, logistics management module, inventory management module and production plan management module are used to perform procurement management, logistics management, inventory management and production plan management of the supply chain based on AI decision-making and identification resolution data.

2. The multi-domain intelligent supply chain decision system based on AI decision-making as claimed in claim 1, characterized in that: The identification and parsing data of materials at the raw material stage shall at least include: supplier name, delivery time, delivery method, batch number and batch size; The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP; The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements; The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price; The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

3. The multi-domain intelligent supply chain decision system based on AI decision-making as claimed in claim 1, characterized in that: The procurement management module includes: A demand identification unit is used to determine material requirements based on identification parsing data; The supplier selection unit is used to determine the best supplier corresponding to the material demand based on the identification resolution data; The order generation unit is used to establish supplier relationships with the best suppliers based on material requirements, generate purchase orders, and issue them; Delivery tracking unit for delivery tracking of purchase orders based on identity resolution data and update inventory updates in real time.

4. The multi-domain intelligent supply chain decision system based on AI decision-making as claimed in claim 1, characterized in that: The logistics management module includes: A transportation plan optimization unit, used to optimize the transportation plan based on the identification resolution data; Tracking and monitoring unit, used to track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time; The exception handling unit is used to make adaptive response adjustments to the transportation plan when abnormal logistics situations occur.

5. The multi-domain intelligent supply chain decision system based on AI decision-making as claimed in claim 1, characterized in that: The inventory management module includes: Inventory optimization unit, used to optimize inventory management strategies based on identification resolution data; The early warning reminder unit is used to give early warning reminders to relevant personnel when the inventory is below or above the inventory warning line based on the identification resolution data; The inventory allocation unit is used to automatically allocate inventory based on the identification resolution data.

6. The multi-domain intelligent supply chain decision system based on AI decision-making as claimed in claim 1, characterized in that: The production planning management module includes: The demand forecasting unit is used to determine product demand based on the identification resolution data and formulate corresponding production plans; Production scheduling unit, used to optimize production planning and production scheduling based on identification resolution data; The production monitoring unit is used to monitor the production progress and quality in real time based on the identification resolution data.

7. A multi-domain intelligent supply chain decision-making method based on AI decision-making, characterized in that: include: Through the identification resolution module, when materials flow through different supply chain links, the identification carried by the materials is resolved to obtain identification resolution data; Through the procurement management module, logistics management module, inventory management module and production plan management module based on AI decision-making, and according to the identification resolution data, procurement management, logistics management, inventory management and production plan management are carried out on the supply chain respectively.

8. The multi-domain intelligent supply chain decision-making method based on AI decision-making as claimed in claim 7, characterized in that: The identification and parsing data of materials at the raw material stage shall at least include: supplier name, delivery time, delivery method, batch number and batch size; The identification and resolution data of materials in the manufacturing process shall at least include: manufacturer name, manufacturing time and manufacturing data server IP; The identification and parsing data of materials in the logistics and transportation link shall at least include: storage time, storage time and storage requirements; The identification and resolution data of materials in the distribution and sales stage shall at least include: the name of the product sold, the time of sale, and the transaction price; The identification and resolution data of materials in the application stage shall at least include: the user's identity, location, credentials and access IP.

9. The multi-domain intelligent supply chain decision-making method based on AI decision-making as claimed in claim 7, characterized in that: Procurement management of the supply chain is carried out through the procurement management module, including: Determine material requirements based on identification parsing data through the requirement identification unit; The supplier selection unit determines the best supplier corresponding to the material demand based on the identification parsing data; Through the order generation unit, establish supplier relationships with the best suppliers based on material requirements, generate purchase orders and issue them; The delivery tracking unit is used to track the purchase orders based on the identity resolution data and update the inventory updates in real time.

10. The multi-domain intelligent supply chain decision-making method based on AI decision-making as claimed in claim 7, characterized in that: Logistics management of the supply chain through the logistics management module includes: The transportation plan is optimized based on the identification resolution data through the transportation plan optimization unit; Track and monitor whether the cargo transportation is complete and whether it arrives at the destination on time through the tracking and monitoring unit; When abnormal logistics situations occur, the transportation plan can be adaptively adjusted through the exception handling unit.

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