Commercial and enterprise business management system and method based on artificial intelligence

Abstract

The invention provides a business and enterprise business management system and method based on artificial intelligence, and relates to the technical field of enterprise planning management, and the management system comprises an inventory management system, an intelligent data center, a core business module and an AI-driven supply chain center. According to the inventory management system, a plurality of storage and transportation devices are set up in a warehouse and used for carrying out statistical management on warehouse-in and warehouse-out of goods, and each storage and transportation device comprises a storage management recording platform, a driving mechanism, a conveying mechanism and a bottom surface recording assembly. Compared with a traditional afterward remedy mode, operation risks can be effectively reduced, weight information statistics and collection can be conducted on goods in and out of a warehouse, by means of a quick disassembly structure between the driving mechanism and the conveying mechanism, the transfer process of the goods can be facilitated, the content of image data in and out of the warehouse can be expanded, and more comprehensive clues are provided for subsequent goods traceability.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise planning and management, and in particular to an artificial intelligence-based business enterprise business management system and method. Background Art

[0002] Business management is the core mechanism for achieving strategic goals through systematic planning, organization, coordination, and control of enterprise resources. By optimizing resource allocation and leveraging automated systems, efficient cross-departmental collaboration is achieved. This modern management framework not only includes innovative modules such as risk early warning and smart contract execution, but also utilizes digital twin technology to create a virtual operational sandbox. This allows companies to rapidly iterate strategies in complex business environments, reducing decision-making response times from weeks to minutes, driving sustainable growth and reshaping agile competitiveness.

[0003] The business management solutions in existing technologies are slow to respond to various emergencies and are unable to conduct simulation drills for various risk scenarios such as supply chain disruptions and market mutations, thereby increasing the operational risks in business management. In the process of business management, an important part is the management of warehousing and logistics. Conventional warehousing management systems need to record the transportation process of goods, which is time-consuming and labor-intensive. The registration of each product is difficult and inefficient. At the same time, it is also impossible to provide comprehensive image records of the goods during the warehousing and outbound processes, which increases the difficulty of traceability. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide an artificial intelligence-based business management system and method to solve the problems raised in the above-mentioned background technology. The present invention can rehearse various risk scenarios such as supply chain disruptions and market mutations in a virtual sandbox, realize advance warning and adaptive strategy generation, and effectively reduce operational risks compared with the traditional post-remediation mode. It can collect weight information statistics of goods entering and leaving the warehouse, and with the help of the quick-detachable structure between the driving mechanism and the conveying mechanism, it can facilitate the transportation process of goods, further expand the image data of entering and leaving the warehouse in the process of goods management, and provide more comprehensive clues for subsequent goods traceability.

[0005] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: a business enterprise management system based on artificial intelligence, the management system includes an inventory management system, an intelligent data center, a core business module and an AI-driven supply chain center, in which multiple storage and transportation equipment are built in the warehouse for statistical management of the goods in and out of the warehouse, and the storage and transportation equipment includes a storage management recording platform, a driving mechanism, a conveying mechanism and a bottom recording component. The bottom of the storage management recording platform is embedded in the ground, and the storage management recording platform includes a weighing platform and an inclined plate. The inclined plate is arranged at both ends of the weighing platform, and a groove is opened in the middle of the weighing platform. The inside and side of the groove are installed with a bottom recording component. The side of the weighing platform is integrated with an extension plate, and the driving mechanism is pressed on the surface of the extension plate. The driving mechanism is used to control the conveying mechanism to move, and the bottom of the conveying mechanism is pressed on the surface of the weighing platform. The management system is also equipped with a human resources decision-making platform, a data governance module and a risk control module.

[0006] Furthermore, a first slide groove is provided on the surface of the weighing platform, a second slide groove is provided on the surface of the extension plate, and end plates are welded at both ends of the extension plate. The driving mechanism includes a first motor, a screw and a threaded sleeve. The output end of the first motor is connected to the screw. A bracket is installed on the outside of the warehouse management and recording platform, and a first acquisition camera is screwed to the middle position of the top of the bracket.

[0007] Furthermore, a threaded sleeve is sleeved on the surface of the screw rod, a rotating ring is sleeved on the surface of the threaded sleeve, a plug-in rod is integrally formed on the side of the rotating ring, balls are embedded on both sides of the plug-in rod, and the driving mechanism is symmetrically arranged on both sides of the conveying mechanism.

[0008] Furthermore, the first motor is screwed onto the outside of one of the end plates, the bottom of the threaded sleeve is integrally formed with a base plate, the bottom of the base plate is screwed onto a second roller, the second roller is embedded into the interior of the second slide groove, and the end of the screw rod is embedded into the interior of the end plate through a bearing.

[0009] Furthermore, the conveying mechanism includes a frame, a light-transmitting plate and an independent support plate, baffles are welded at both ends of the frame, a slot is opened on the inner side of the frame, the side edges of the light-transmitting plate and both ends of each independent support plate are embedded in the slot, a first roller is screwed to the bottom of the frame, the bottom of the first roller is embedded in the first slide groove, a clamping groove is welded on the outer side of the frame, and a lower hanging rod is welded to the bottom end of the independent support plate.

[0010] Furthermore, the outer side and top of the clamping groove are set to an open state, the bottom of the clamping groove is in a closed state, the plug-in rod is embedded into the interior of the clamping groove after rotation, and sliders are integrally formed at both ends of the support plate, and the sliders are used to be embedded in the interior of the slot, and each of the independent support plates is against the bottom surface of the light-transmitting plate.

[0011] Furthermore, the bottom surface recording assembly includes a second acquisition camera, a second motor and a push rod. The second motor is hidden inside the weighing platform. A drive shaft is inserted into the output end of the second motor, and the push rod is welded to the surface of the drive shaft.

[0012] Furthermore, a convex plate is screwed onto the surface of the weighing platform, the second acquisition camera is screwed onto the surface of the convex plate, the driving shaft is used to control the push rod to perform rotational movement, and the push rod is used to push the lower hanging rod to perform translational movement.

[0013] A management method using the above-mentioned business enterprise business management system comprises the following steps: S1. Establish a cross-departmental data center to integrate real-time data streams from ERP, CRM, financial systems, and IoT devices; S2. Collect back-end warehouse data to control real-time enterprise transactions and cargo transportation information; S3. Deploy a causal reasoning AI model to analyze the hidden correlations between historical sales data and external variables. S4. Introduce digital twin technology to build a virtual operating environment and simulate the response curves of different decisions to production line efficiency and market demand fluctuations; S5. Create an intelligent decision dashboard to convert AI-recommended solutions into a visual action list; S6. Launch a learning framework every quarter and update the knowledge base across branches while protecting business confidentiality.

[0014] Furthermore, the S1 identifies data outliers and constructs a dynamic data lineage map to ensure the spatiotemporal consistency of core data such as supply chain and customer portraits; the causal reasoning AI model uses knowledge graph technology to construct an enterprise operation relationship network and identify chain risk paths; the S5 constructs an LSTM neural network-driven prediction engine to provide early warning of cash flow fluctuations and deviations from key indicators of equipment failures, establishes an adaptive threshold system, and automatically adjusts inventory warning lines according to industry cycles.

[0015] Beneficial effects of the present invention: This AI-based business management method for commercial enterprises uses multimodal data fusion and causal reasoning algorithms to upgrade the basis for decision-making from empirical intuition to dynamic deduction, compressing the strategic response speed from "weekly manual analysis" to "minute-level real-time optimization"; with the help of digital twins and reinforcement learning technology, various risk scenarios such as supply chain disruptions and market mutations can be rehearsed in a virtual sandbox, achieving advance warning and adaptive strategy generation, which can effectively reduce operational risks compared to the traditional post-remediation model.

[0016] The present invention collects statistical weight information on incoming and outgoing goods through a warehouse management and recording platform, thereby building an accurate logistics data base and achieving dynamic optimization of the entire process. Through the digital accumulation of weight data, enterprises can accurately calculate transportation costs, optimize the three-dimensional layout of storage space, and dynamically monitor equipment load safety. The correlation analysis between weight data and inventory turnover rate can also assist in the precise configuration of packaging consumables and reduce resource waste. At the same time, the quick-release structure between the drive mechanism and the conveying mechanism can facilitate the transportation process of goods.

[0017] The present invention is provided with a double-layer support structure at the bottom of the conveying mechanism. The bottom recording component cooperates with the first acquisition camera on the top to provide image acquisition from the outer surface and bottom of the goods when the goods are put in and out of the warehouse, further expanding the in-and-out image data in the process of goods management, and providing more comprehensive clues for subsequent goods traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a principle block diagram of an artificial intelligence-based business management system of the present invention; Figure 2 This is a flow chart of a business enterprise management method based on artificial intelligence of the present invention; Figure 3 This is a structural diagram of warehousing and transportation equipment used in an artificial intelligence-based business management system for commercial enterprises of the present invention; Figure 4 This is a structural diagram of the storage management recording platform in the storage and transportation equipment of the present invention; Figure 5 It is a structural schematic diagram of the bottom surface recording component part of the present invention; Figure 6 Schematic diagram of the driving mechanism structure of the present invention; Figure 7 It is a structural schematic diagram of the conveying mechanism part of the present invention; Figure 8 This is an exploded view of the conveying mechanism of the present invention; Figure 9 This is a schematic structural diagram of the independent support plate portion of the present invention; In the figure: 1. Warehouse management recording platform; 2. Driving mechanism; 3. Conveying mechanism; 4. Groove; 5. Bottom recording assembly; 6. Weighing platform; 7. Inclined plate; 8. First slide; 9. Extension plate; 10. Second slide; 11. End plate; 12. Convex plate; 13. Second acquisition camera; 14. Second motor; 15. Driving shaft; 16. Push rod; 17. Second motor; 18. Screw; 19. Threaded sleeve; 20. Rotating ring; 21. Connecting rod; 22. Ball; 23. Bottom plate; 24. Second roller; 25. Frame; 26. Baffle; 27. Transparent plate; 28. Clamping groove; 29. ​​First roller; 30. Slot; 31. Independent support plate; 32. Slider; 33. Lower hanging rod; 34. Bracket; 35. First acquisition camera. DETAILED DESCRIPTION

[0019] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0020] See also Figures 1 to 9 The present invention provides the following technical solutions: a business management system based on artificial intelligence, the management system includes an inventory management system, an intelligent data center, a core business module and an AI-driven supply chain center, in which multiple storage and transportation equipment are built in the warehouse for statistical management of the goods in and out of the warehouse, and the storage and transportation equipment includes a storage management recording platform 1, a drive mechanism 2, a conveying mechanism 3 and a bottom recording component 5. The bottom of the storage management recording platform 1 is embedded in the ground, and the storage management recording platform 1 includes a weighing platform 6 and an inclined plate 7. The inclined plate 7 is arranged at both ends of the weighing platform 6. A groove 4 is opened in the middle of the weighing platform 6, and the bottom recording component 5 is installed inside and on the side of the groove. The side of the weighing platform 6 is integrated with an extension plate 9, and the drive mechanism 2 is pressed on the surface of the extension plate 9. The drive mechanism 2 is used to control the conveying mechanism 3 to move, and the bottom of the conveying mechanism 3 is pressed on the surface of the weighing platform 6. The management system is also equipped with a human resources decision-making platform, a data governance module and a risk control module. When unexpected market fluctuations occur, the business management system can trigger dynamic replenishment strategies through the supply chain module, simultaneously adjust customer tiering criteria within the CRM, automatically generate financial cash flow stress test reports, and deliver multi-dimensional decision-making solutions to management via a digital assistant. This cross-module collaborative response represents a core breakthrough of AI systems compared to traditional ERP.

[0021] In the present invention, storage and transportation equipment is established in the warehouse to obtain the weight information of the goods entering and leaving the warehouse, so as to carry out statistical management of the goods. In this process, no matter whether the goods are entering or leaving the warehouse, the goods are first placed in the conveying mechanism 3. After the conveying mechanism 3 is connected to the driving mechanism 2, the conveying mechanism 3 can be controlled to move by starting the driving mechanism 2 until it moves over the surface of the warehouse management recording platform 1. During the movement, the weight of the goods transported this time can be collected and counted through the warehouse management recording platform 1, and the surface image of the goods can also be collected with the help of a collection camera. After the transportation is completed, the weight data of the goods entering and leaving the warehouse can be uploaded to the intelligent data center for storage.

[0022] In this embodiment, a first slide groove 8 is provided on the surface of the weighing platform 6, a second slide groove 10 is provided on the surface of the extension plate 9, and end plates 11 are welded to both ends of the extension plate 9. The drive mechanism 2 includes a first motor, a screw 18, and a threaded sleeve 19. The output end of the first motor is connected to the screw 18. A bracket 34 is installed on the outside of the warehouse management and recording platform 1, and a first acquisition camera 35 is screwed to the top middle position of the bracket 34. The surface of the screw 18 is sleeved with a threaded sleeve 19, and the surface of the threaded sleeve 19 is sleeved with a rotating ring 20. The side of the rotating ring 20 is integrally formed with a plug rod 21, and both sides of the plug rod 21 are embedded with balls 22. The drive is symmetrically arranged on both sides of the conveying mechanism 3. The first motor is screwed to the outside of one of the end plates 11, and the bottom of the threaded sleeve 19 is integrally formed with a base plate 23. The bottom of the base plate 23 is screwed with a second roller 24, and the second roller 24 is embedded in the interior of the second slide groove 10. The end of the screw rod 18 is embedded in the interior of the end plate 11 through a bearing.

[0023] Weight statistics and data collection for incoming and outgoing goods build a precise logistics data foundation and enable dynamic optimization of the entire process. Through the digital accumulation of weight data, companies can accurately calculate transportation costs, optimize the three-dimensional layout of storage space, and dynamically monitor equipment load safety. Correlation analysis between weight data and inventory turnover rates also assists in the precise allocation of packaging consumables, reducing resource waste. Furthermore, the quick-release structure between drive mechanism 2 and conveyor mechanism 3 facilitates the transfer of goods.

[0024] Specifically, by starting the first motor in the driving mechanism 2, the screw rod 18 can be driven to rotate, and the entire conveying mechanism 3 can be controlled to move in conjunction with the threaded sleeve 19. When the conveying mechanism 3 is docked with the driving mechanism 2, the conveying mechanism 3 is first moved to the side clamping groove 28 to align with the plug-in rod 21, and the plug-in rod 21 is rotated to embed into the inside of the clamping groove 28. After the first motor is started, the bottom plate 23 of the threaded sleeve 19 is pressed on the surface of the extension plate 9 through the second roller 24, which can directly cooperate with the plug-in rod 21 and the clamping groove 28 to pull the entire conveying mechanism 3 to move, and after moving along the inclined plate 7 to the weighing platform 6, the purpose of weighing the conveying mechanism 3 can be achieved.

[0025] In this embodiment, the conveying mechanism 3 includes a frame 25, a light-transmitting plate 27, and an independent support plate 31. Baffles 26 are welded to both ends of the frame 25. A slot 30 is provided on the inner side of the frame 25. The side edges of the light-transmitting plate 27 and both ends of each independent support plate 31 are embedded in the slot 30. A first roller 29 is screwed to the bottom of the frame 25, and the bottom of the first roller 29 is embedded in the first slide groove 8. A clamping groove 28 is welded to the outer side of the frame 25, and a lower hanging rod 33 is welded to the bottom end of the independent support plate 31. The outer side and top of the clamping groove 28 are both set to an open state, and the bottom of the clamping groove 28 is closed. The plug-in rod 21 is embedded in the clamping groove 28 by rotation. Sliders 32 are integrally formed at both ends of the support plate. The slides 32 are used to embed in the slot 30. Each independent support plate 31 rests against the bottom surface of the light-transmitting plate 27.

[0026] Specifically, the bottom of the conveying mechanism 3 is supported by a light-transmitting plate 27 and an independent support plate 31 to support the surface goods. The light-transmitting plate 27 is made of a hard acrylic plate material, and additional support is provided by multiple independent support plates 31 made of metal material at the bottom. After the plug-in rod 21 is embedded in the clamping groove 28, the entire conveying mechanism 3 can be pulled by the plug-in rod 21 to perform a translational movement until it moves over the surface of the warehouse management recording platform 1. During this process, the conveying mechanism 3 and the goods placed on the surface are weighed by the weighing platform 6.

[0027] In this embodiment, the bottom surface recording component 5 includes a second acquisition camera 13, a second motor 14, and a push rod 16. The second motor 14 is hidden inside the weighing platform 6. The output end of the second motor 14 is plugged with a drive shaft 15, and the push rod 16 is welded to the surface of the drive shaft 15. A convex plate 12 is screwed onto the surface of the weighing platform 6, and the second acquisition camera 13 is screwed onto the surface of the convex plate 12. The drive shaft 15 is used to control the push rod 16 to perform rotational movement, and the push rod 16 is used to push the lower hanging rod 33 to perform translational movement. A double-layer support structure is provided at the bottom of the conveying mechanism 3. The bottom surface recording component 5 cooperates with the first acquisition camera 35 at the top to provide image acquisition from both the outer surface and the bottom of the goods when the goods are in and out of the warehouse, further expanding the image data of goods in and out of the warehouse during management, and providing more comprehensive clues for subsequent goods traceability.

[0028] Specifically, after starting the second motor 14 at the bottom, the drive shaft 15 is rotated by the second electric drive, and then the push rod 16 on the surface is driven to rotate. When the push rod 16 rotates, it pushes the lower hanging plate under the independent pallet 31 that has moved over the top, and controls the independent pallet 31 to move toward one side, thereby forming a gap between the independent pallet 31 and the independent pallet 31 on the side. At this time, with the help of the second acquisition camera 13, the bottom surface of the goods placed on the conveying mechanism 3 can be imaged through the light-transmitting plate 27 from the gap. Repeat the above process, move the independent pallet 31 at the bottom in turn, change the position of the gap, and finally obtain the bottom surface data of all goods. In conjunction with the first acquisition camera 35, more comprehensive cargo image data can be obtained.

[0029] This embodiment also provides a management method using the above-mentioned business enterprise business management system, comprising the following steps: S1. Establish a cross-departmental data center to integrate real-time data streams from ERP, CRM, financial systems, and IoT devices. Identify data outliers, build a dynamic data lineage map, and ensure the temporal and spatial consistency of core data such as supply chain and customer profiles. S2. Collect back-end warehouse data to control real-time enterprise transactions and cargo transportation information; S3. Deploy a causal reasoning AI model to analyze the hidden associations between historical sales data and external variables. The causal reasoning AI model uses knowledge graph technology to construct a business operation relationship network and identify chain risk paths. S4. Introduce digital twin technology to build a virtual operating environment and simulate the response curves of different decisions to production line efficiency and market demand fluctuations; S5. Create an intelligent decision dashboard to convert AI-recommended solutions into a visual action list. Build an LSTM neural network-driven forecasting engine to provide early warning of cash flow fluctuations, equipment failures, and deviations from key indicators. Establish an adaptive threshold system to automatically adjust inventory warning levels based on industry cycles. S6. Launch the learning framework 25 every quarter and update the knowledge base across branches while protecting business confidentiality.

[0030] This method upgrades the decision-making basis from empirical intuition to dynamic deduction through multimodal data fusion and causal reasoning algorithms, compressing the strategic response speed from "weekly manual analysis" to "minute-level real-time optimization"; with the help of digital twins and reinforcement learning technology, various risk scenarios such as supply chain disruptions and market mutations can be rehearsed in a virtual sandbox, realizing advance warning and adaptive strategy generation, which can effectively reduce operational risks compared to the traditional post-remediation model.

[0031] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0032] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. An artificial intelligence-based business management system, characterized by: The management system includes an inventory management system, an intelligent data center, a core business module and an AI-driven supply chain center. In the inventory management system, multiple storage and transportation equipment are built in the warehouse for statistical management of the goods in and out of the warehouse. The storage and transportation equipment includes a storage management recording platform, a driving mechanism, a conveying mechanism and a bottom recording component. The bottom of the storage management recording platform is embedded in the ground. The storage management recording platform includes a weighing platform and an inclined plate. The inclined plate is arranged at both ends of the weighing platform. A groove is opened in the middle of the weighing platform. The inside and side of the groove are installed with a bottom recording component. The side of the weighing platform is integrated with an extension plate. The driving mechanism is pressed on the surface of the extension plate. The driving mechanism is used to control the movement of the conveying mechanism. The bottom of the conveying mechanism is pressed on the surface of the weighing platform. The management system is also equipped with a human resources decision-making platform, a data governance module and a risk control module.

2. The artificial intelligence-based business management system according to claim 1, characterized in that: A first slide groove is provided on the surface of the weighing platform, a second slide groove is provided on the surface of the extension plate, and end plates are welded at both ends of the extension plate. The driving mechanism includes a first motor, a screw rod and a threaded sleeve. The output end of the first motor is connected to the screw rod. A bracket is installed on the outside of the warehouse management and recording platform, and a first acquisition camera is screwed to the middle position of the top of the bracket.

3. The artificial intelligence-based business management system according to claim 2, characterized in that: The surface of the screw rod is sleeved with a threaded sleeve, the surface of the threaded sleeve is sleeved with a rotating ring, the side of the rotating ring is integrally formed with a plug rod, both sides of the plug rod are embedded with balls, and the driving mechanism is symmetrically arranged on both sides of the conveying mechanism.

4. The artificial intelligence-based business management system according to claim 3, characterized in that: The first motor is screwed onto the outside of one of the end plates, the bottom of the threaded sleeve is integrally formed with a base plate, the bottom of the base plate is screwed onto a second roller, the second roller is embedded in the inside of the second slide groove, and the end of the screw rod is embedded in the inside of the end plate through a bearing.

5. The artificial intelligence-based business management system according to claim 3, characterized in that: The conveying mechanism includes a frame, a light-transmitting plate and an independent support plate. Baffles are welded at both ends of the frame. A slot is opened on the inner side of the frame. The side edges of the light-transmitting plate and both ends of each independent support plate are embedded in the slot. A first roller is screwed to the bottom of the frame. The bottom of the first roller is embedded in the first slide groove. A clamping groove is welded on the outer side of the frame. A lower hanging rod is welded to the bottom end of the independent support plate.

6. The artificial intelligence-based business management system according to claim 5, characterized in that: The outer side and top of the clamping groove are set to an open state, and the bottom of the clamping groove is in a closed state. The plug-in rod is embedded into the interior of the clamping groove after rotation. Sliders are integrally formed at both ends of the support plate, and the sliders are used to be embedded in the interior of the slot. Each of the independent support plates is against the bottom surface of the light-transmitting plate.

7. The artificial intelligence-based business management system according to claim 5, characterized in that: The bottom surface recording assembly includes a second acquisition camera, a second motor and a push rod. The second motor is hidden inside the weighing platform. A drive shaft is inserted into the output end of the second motor. The push rod is welded to the surface of the drive shaft.

8. The artificial intelligence-based business management system according to claim 7, characterized in that: A convex plate is screwed onto the surface of the weighing platform, and the second acquisition camera is screwed onto the surface of the convex plate. The driving shaft is used to control the push rod to perform rotational motion, and the push rod is used to push the lower hanging rod to perform translational motion.

9. A management method using the business management system according to claim 1, characterized in that: The following steps are involved: S1. Establish a cross-departmental data center to integrate real-time data streams from ERP, CRM, financial systems, and IoT devices; S2. Collect back-end warehouse data to control real-time enterprise transactions and cargo transportation information; S3. Deploy a causal reasoning AI model to analyze the hidden correlations between historical sales data and external variables. S4. Introduce digital twin technology to build a virtual operating environment and simulate the response curves of different decisions to production line efficiency and market demand fluctuations; S5. Create an intelligent decision dashboard to convert AI-recommended solutions into a visual action list; S6. Launch a learning framework every quarter and update the knowledge base across branches while protecting business confidentiality.

10. The management method according to claim 9, characterized in that: In S1, data outliers are identified and a dynamic data lineage map is constructed to ensure the spatiotemporal consistency of the core data of the supply chain and customer profiles. The causal reasoning AI model uses knowledge graph technology to construct an enterprise operation relationship network and identify chain risk paths. The S5 constructs a prediction engine driven by an LSTM neural network to provide early warning of cash flow fluctuations and deviations from key indicators of equipment failures, and establishes an adaptive threshold system to automatically adjust inventory warning lines according to industry cycles.

Images