Enterprise production information sharing logistics management system based on intelligent warehousing

Abstract

The application discloses an enterprise production information sharing logistics management system based on intelligent storage, relates to the technical field of intelligent storage and production logistics management, and comprises a disturbance event sensing module, a dynamic priority reconfiguration module, a vehicle fleet cooperative re-planning module and an execution feedback module, wherein: the disturbance event sensing module is used for real-time monitoring of workshop event signals sent by a production execution system, type identification and urgency quantification of the workshop event signals, identification of disturbance event types and output of disturbance event vectors; random disturbance events such as sudden failure of workshop equipment, urgent insertion of orders and temporary change of processes can be converted into structured driving signals in real time, the priority of material distribution tasks is dynamically reconfigured, the cooperative path re-planning of a plurality of AGV vehicle fleets is synchronously completed, and the problems of production line material supply stop and distribution time sequence dislocation caused by response lag after disturbance in the existing system are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and production logistics management technology, specifically to an enterprise production information sharing logistics management system based on intelligent warehousing. Background Technology

[0002] Existing enterprise production information sharing logistics management systems typically adopt an architecture paradigm of information integration platform and unified scheduling engine. By connecting the data interface of warehouse management system, production execution system and enterprise resource planning system, inventory balance, work order progress and material delivery instructions are synchronized in real time. AGV handling robots and automated warehouses are used to execute standard operating procedures of order picking, timed delivery and workstation delivery. To improve scheduling efficiency, conventional solutions generally adopt rule-based fixed priority scheduling strategy or offline optimization model trained on historical data to achieve static matching between logistics tasks and production rhythm, and set fixed rescheduling time windows to maintain the engineering balance between production continuity and warehouse turnover rate. However, in actual operation, the information sharing of the above architecture is essentially limited to data synchronization at the planned time point, lacking the ability to respond in real time to dynamic disturbances in the workshop. When disturbances such as sudden equipment failures, emergency orders, or temporary process changes occur on the production site, the system cannot complete the priority reconstruction of affected tasks and the replanning of the collaborative paths of the AGV fleet within the real-time window of the disturbance. Furthermore, the production end's cycle time guarantee target and the warehousing end's cost minimization target are strongly mutually exclusive in the underlying optimization function. The existing architecture lacks a unified coupling model to dynamically balance the two, which leads to frequent systemic resource mismatch problems such as production lines being forced to stop due to material supply interruptions and the timing of material delivery and workstation demand being out of sync. This seriously weakens the engineering effectiveness of the intelligent logistics management system in actual complex manufacturing scenarios.

[0003] Therefore, in view of this, the present invention proposes an enterprise production information sharing logistics management system based on intelligent warehousing to make up for and improve the shortcomings of the existing technology. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an enterprise production information sharing logistics management system based on intelligent warehousing, thereby resolving the corresponding technical issues raised in the background section.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: an enterprise production information sharing logistics management system based on intelligent warehousing, including a disturbance event perception module, a dynamic priority reconstruction module, a fleet collaborative replanning module, and an execution feedback module, wherein: The disturbance event perception module is used to monitor workshop event signals issued by the production execution system in real time, identify the type and urgency of the workshop event signals, identify disturbance event types including sudden equipment failure, emergency order insertion, and temporary process changes, and output a disturbance event vector containing event type code, occurrence timestamp and affected workstation identifier. The dynamic priority reconstructing module is used to receive disturbance event vectors, match them with a preset priority mapping rule library according to the event type code, calculate the dynamic priority weight of the affected materials in real time, and reorder all material delivery tasks to be executed within a preset rescheduling time window based on the dynamic priority weight, generating an updated task queue. The fleet coordination replanning module is used to obtain the updated task queue and the real-time position and load status of each AGV transport robot, call the preset multi-AGV cooperative path planning algorithm, assign new tasks to each AGV and plan the driving path under collision avoidance constraints, and output the execution instruction set containing the target task and target path of each AGV. The execution feedback module is used to send the execution instruction set to each AGV terminal and receive the position and status feedback of each AGV during the execution process in real time. When any AGV is detected to deviate from the target path, it triggers local path online correction until the affected material is delivered to the target workstation and synchronously transmits the completion status back to the production execution system.

[0006] As a preferred method, the specific process for outputting the disturbance event vector is as follows: S101. Establish a real-time data path with the production execution system through a preset listening interface, and poll the workshop event logs issued by the production execution system at a fixed sampling period. S102. Perform structured parsing on the obtained workshop event logs, and extract the event type field, the event occurrence timestamp field, and the workstation number field associated with the event; S103. Match the extracted event type field with a preset disturbance type feature library, which pre-stores three disturbance types: sudden equipment failure, emergency order insertion, and temporary process change, as well as their corresponding feature codes. If a match is successful, the preset urgency quantification weight value corresponding to the disturbance type is read, and the event type code, occurrence timestamp, and affected workstation identifier are encapsulated into a disturbance event vector and output. If a match fails, the event is marked as a non-disturbance regular event and will not trigger the subsequent dynamic reconstruction process.

[0007] As a preferred method, the specific process for generating the updated task queue is as follows: S201. Obtain the disturbance event vector and retrieve all unexecuted material distribution tasks and their corresponding original priority levels from the currently running warehouse management system. S202. Based on the event type encoding in the disturbance event vector, index the preset priority mapping rule library to obtain the priority weight adjustment coefficient corresponding to the disturbance type. Different disturbance types in the priority mapping rule library correspond to different weight adjustment coefficients. S203. For each affected material delivery task, its original priority level and priority weight adjustment coefficient are weighted and integrated to obtain the dynamic priority weight of the task. S204. Based on the preset rescheduling time window parameters, within the rescheduling time window, arrange all material delivery tasks to be executed in descending order according to the dynamic priority weight, and generate an updated task queue containing the execution order of each task.

[0008] As a preferred approach, the specific process for outputting the execution instruction set is as follows: S301. Obtain the generated updated task queue, and at the same time obtain the real-time position coordinates and current load status of all AGV handling robots currently in the online state through the wireless communication link; S302. Extract the target workstation coordinates and material type information of each delivery task from the updated task queue, and combine them with the real-time position coordinates of each AGV to construct a multi-AGV collaborative scheduling optimization model with task allocation variables and path sequence variables as decision variables. S303. Under collision avoidance constraints, the preset multi-AGV cooperative path planning algorithm is invoked to iteratively solve the multi-AGV cooperative scheduling optimization model. The collision avoidance constraints include that at most one AGV is allowed to occupy the same path node at the same time, and the safe distance between any two AGVs is not less than the preset safe distance threshold. S304. The optimal solution obtained by iterative solution is parsed into the target task identifier and target path sequence corresponding to each AGV. The target task identifier and target path sequence of each AGV are bound one by one and encapsulated to generate an execution instruction set and output it.

[0009] As a preferred approach, the specific process for issuing the execution instruction set is as follows: S401: The execution instruction set is sent to the vehicle controller of each AGV terminal through the wireless communication network, driving each AGV to travel to the target workstation according to its own target path; S402. While executing the instruction set, start the real-time monitoring thread to receive the position coordinates and driving status data transmitted back by each AGV terminal at fixed intervals. S403. Compare the position coordinates transmitted back by each AGV in real time with the target path in the corresponding execution instruction set point by point, and calculate the position deviation. S404. When the position deviation of any AGV exceeds the preset allowable deviation threshold, it is determined that the AGV has deviated from the target path, triggering the local path online correction process. Without changing the tasks already assigned to other AGVs, the correction path of the AGV from its current position to the target workstation is replanned and issued. S405. Continue executing steps S402 to S404 until each AGV delivers its loaded materials to the corresponding target workstation, and then synchronously transmit the completion status of each task back to the production execution system.

[0010] Preferably, in the dynamic priority reconstruction module, the duration of the preset rescheduling time window is dynamically determined based on the urgency metric value in the disturbance event vector. The higher the urgency metric value, the shorter the corresponding rescheduling time window duration.

[0011] Preferably, in the fleet collaborative replanning module, the multi-AGV collaborative path planning algorithm adopts a time window-based conflict avoidance algorithm. The time window-based conflict avoidance algorithm uses the time interval of each AGV arriving at each path node as a constraint variable, and eliminates node conflicts by staggering the time windows of different AGVs at the same path node.

[0012] Preferably, when reordering all material delivery tasks currently to be executed, the dynamic priority reconstruction module further includes: S501. Obtain the physical distance between the current storage location of the material and the target workstation corresponding to each material delivery task. S502. The physical distance and the dynamic priority weight of each task are weighted and combined to calculate the comprehensive scheduling score of each task. S503. Sort all material delivery tasks to be executed in descending order according to the comprehensive scheduling score from high to low, and generate an optimized task queue.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: By real-time monitoring of workshop event signals issued by the production execution system, the workshop event signals are type-identified and urgency-quantified, the disturbance event type is identified, and a disturbance event vector is output. Based on the event type code, a preset priority mapping rule base is matched to calculate the dynamic priority weight of the affected materials in real time. Based on the dynamic priority weight, within a preset rescheduling time window, all currently pending material delivery tasks are reordered to generate an updated task queue. The real-time position and load status of each AGV handling robot are obtained. A preset multi-AGV cooperative path planning algorithm is invoked to assign new tasks to each AGV and plan travel paths under collision avoidance constraints. The output includes information about each AGV. The system executes the target task and target path instruction set and sends it to each AGV terminal. At the same time, it receives the position and status feedback of each AGV during the execution process in real time. When any AGV is detected to deviate from the target path, it triggers local path online correction until the affected material is delivered to the target workstation. The completion status is then synchronously transmitted back to the production execution system. It can convert random disturbance events such as sudden equipment failures, emergency orders, and temporary process changes in the workshop into structured driving signals in real time. It dynamically reconstructs the priority of material delivery tasks and synchronously completes the collaborative path replanning of multiple AGV fleets. It effectively solves the problem of production line material supply interruption and machine stoppage and delivery sequence disconnection caused by the delayed response after the disturbance occurs in the existing system. It significantly improves the real-time performance and robustness of production logistics scheduling in discrete manufacturing scenarios. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of a preferred embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of issuing an execution instruction set as shown in this invention; Figure 3 This is a flowchart illustrating the process of reordering all material delivery tasks currently to be executed, as shown in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Embodiments of the present invention: Please refer to Figures 1 to 3 As shown, the enterprise production information sharing logistics management system based on intelligent warehousing includes a disturbance event perception module, a dynamic priority reconstruction module, a fleet collaborative replanning module, and an execution feedback module, wherein: The disturbance event perception module is used to monitor workshop event signals issued by the production execution system in real time, identify the type and urgency of the workshop event signals, identify disturbance event types including sudden equipment failure, emergency order insertion, and temporary process changes, and output a disturbance event vector containing event type code, occurrence timestamp and affected workstation identifier. The dynamic priority reconstructing module is used to receive disturbance event vectors, match them with a preset priority mapping rule library according to the event type code, calculate the dynamic priority weight of the affected materials in real time, and reorder all material delivery tasks to be executed within a preset rescheduling time window based on the dynamic priority weight, generating an updated task queue. The fleet coordination replanning module is used to obtain the updated task queue and the real-time position and load status of each AGV transport robot, call the preset multi-AGV cooperative path planning algorithm, assign new tasks to each AGV and plan the driving path under collision avoidance constraints, and output the execution instruction set containing the target task and target path of each AGV. The execution feedback module is used to send the execution instruction set to each AGV terminal and receive the position and status feedback of each AGV during the execution process in real time. When any AGV is detected to deviate from the target path, it triggers local path online correction until the affected material is delivered to the target workstation and synchronously transmits the completion status back to the production execution system.

[0017] The specific process of outputting the disturbance event vector is as follows: S101. Establish a real-time data path with the production execution system through a preset listening interface, and poll the workshop event logs issued by the production execution system at a fixed sampling period. S102. Perform structured parsing on the obtained workshop event logs, and extract the event type field, the event occurrence timestamp field, and the workstation number field associated with the event; S103. Match the extracted event type field with the preset disturbance type feature library. The disturbance type feature library contains three disturbance types and their corresponding feature codes: sudden equipment failure, emergency order insertion, and temporary process change. If a match is successful, the preset urgency quantification weight value corresponding to the disturbance type is read, and the event type code, occurrence timestamp, and affected workstation identifier are encapsulated into a disturbance event vector and output. If a match fails, the event is marked as a non-disturbance regular event and will not trigger the subsequent dynamic reconstruction process.

[0018] The specific process for generating the updated task queue is as follows: S201. Obtain the disturbance event vector and retrieve all unexecuted material distribution tasks and their corresponding original priority levels from the currently running warehouse management system. S202. Based on the event type encoding in the disturbance event vector, index the preset priority mapping rule base and obtain the priority weight adjustment coefficient corresponding to the disturbance type. Different disturbance types in the priority mapping rule base correspond to different weight adjustment coefficients. S203. For each affected material delivery task, its original priority level and priority weight adjustment coefficient are weighted and combined to obtain the dynamic priority weight of the task. The calculation formula is as follows:

[0019] in, The original priority level of the k-th material delivery task; This is the priority weight adjustment coefficient corresponding to the current disturbance type; This refers to the updated dynamic priority weight for this task. S204. Based on the preset rescheduling time window parameters, within the rescheduling time window, arrange all material delivery tasks to be executed in descending order according to the dynamic priority weight, and generate an updated task queue containing the execution order of each task.

[0020] The specific process of outputting the instruction set is as follows: S301. Obtain the generated updated task queue, and at the same time obtain the real-time position coordinates and current load status of all AGV handling robots currently in the online state through the wireless communication link; S302. Extract the target workstation coordinates and material type information of each delivery task from the updated task queue, and combine them with the real-time position coordinates of each AGV to construct a multi-AGV collaborative scheduling optimization model with task allocation variables and path sequence variables as decision variables. S303. Under collision avoidance constraints, the preset multi-AGV cooperative path planning algorithm is invoked to iteratively solve the multi-AGV cooperative scheduling optimization model. The collision avoidance constraints include that at most one AGV is allowed to occupy the same path node at the same time, and the safe distance between any two AGVs is not less than the preset safe distance threshold. S304. The optimal solution obtained by iterative solution is parsed into the target task identifier and target path sequence corresponding to each AGV. The target task identifier and target path sequence of each AGV are bound one by one and encapsulated to generate an execution instruction set and output it.

[0021] The specific process of issuing the execution instruction set is as follows: S401: The execution instruction set is sent to the vehicle controller of each AGV terminal through the wireless communication network, driving each AGV to travel to the target workstation according to its own target path; S402. While executing the instruction set, start the real-time monitoring thread to receive the position coordinates and driving status data transmitted back by each AGV terminal at fixed intervals. S403. Compare the position coordinates transmitted back by each AGV in real time with the target path in the corresponding execution instruction set point by point, and calculate the position deviation. S404. When the position deviation of any AGV exceeds the preset allowable deviation threshold, it is determined that the AGV has deviated from the target path, triggering the local path online correction process. Without changing the tasks already assigned to other AGVs, the correction path of the AGV from its current position to the target workstation is replanned and issued. The goal of correcting the path is to minimize the cost of path deviation, and its objective function is:

[0022] in, To correct the total length of the path; To correct the coordinate vectors of each node on the path; This represents the coordinate vector of the corresponding node on the original target path; and These are preset weighting coefficients; S405. Continue executing steps S402 to S404 until each AGV delivers its loaded materials to the corresponding target workstation, and then synchronously transmit the completion status of each task back to the production execution system.

[0023] In the dynamic priority reconfiguration module, the duration of the preset rescheduling time window is dynamically determined based on the urgency metric value in the disturbance event vector. The higher the urgency metric value, the shorter the corresponding rescheduling time window duration.

[0024] In the fleet collaborative replanning module, the multi-AGV collaborative path planning algorithm adopts a time window-based conflict avoidance algorithm. The time window-based conflict avoidance algorithm uses the time interval of each AGV arriving at each path node as the constraint variable, and eliminates node conflicts by staggering the time windows of different AGVs at the same path node.

[0025] The dynamic priority reordering module, when reordering all material delivery tasks currently pending execution, also includes: S501. Obtain the physical distance between the current storage location of the material and the target workstation corresponding to each material delivery task. S502. The physical distance and the dynamic priority weight of each task are weighted and combined to calculate the comprehensive scheduling score of each task. The calculation formula is as follows:

[0026] in, The overall scheduling score for the k-th task; This refers to the physical distance between the current storage location of the material corresponding to this task and the target workstation. and These are preset weighting coefficients; This represents the dynamic priority weight for the task. S503. Sort all material delivery tasks to be executed in descending order according to the comprehensive scheduling score from high to low, and generate an optimized task queue.

[0027] By monitoring workshop event signals from the production execution system in real time, the system performs type identification and urgency quantification on these signals. It identifies disturbance event types and outputs disturbance event vectors. Based on the event type code, it matches a preset priority mapping rule base to calculate the dynamic priority weight of affected materials in real time. Using this dynamic priority weight as a basis, within a preset rescheduling time window, it reorders all currently pending material delivery tasks, generating an updated task queue. It also obtains the real-time position and load status of each AGV (Automated Guided Vehicle) robot, calls a preset multi-AGV cooperative path planning algorithm, assigns new tasks to each AGV and plans travel paths under collision avoidance constraints, and outputs the target tasks and target paths for each AGV. The system executes the instruction set and sends it to each AGV terminal. At the same time, it receives the position and status feedback of each AGV during the execution process in real time. When any AGV is detected to deviate from the target path, it triggers local path online correction until the affected material is delivered to the target workstation. The completion status is synchronously transmitted back to the production execution system. It can transform random disturbance events such as sudden equipment failures, emergency orders, and temporary process changes in the workshop into structured driving signals in real time. It dynamically reconstructs the priority of material delivery tasks and synchronously completes the collaborative path replanning of multiple AGV fleets. It effectively solves the problem of production line material supply interruption and machine stoppage and delivery sequence disconnection caused by the delayed response after the disturbance occurs in the existing system. It significantly improves the real-time performance and robustness of production logistics scheduling in discrete manufacturing scenarios.

[0028] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0029] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the coupling or direct coupling or communication connection between the shown or discussed mutuals can be through some interfaces, and the indirect coupling or communication connection between the apparatus or modules can be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart warehousing-based enterprise production information sharing logistics management system, characterized in that: It includes a disturbance event perception module, a dynamic priority reconstruction module, a fleet collaborative replanning module, and an execution feedback module, among which: The disturbance event perception module is used to monitor workshop event signals issued by the production execution system in real time, identify the type and urgency of the workshop event signals, identify disturbance event types including sudden equipment failure, emergency order insertion, and temporary process changes, and output a disturbance event vector containing event type code, occurrence timestamp and affected workstation identifier. The dynamic priority reconstructing module is used to receive disturbance event vectors, match them with a preset priority mapping rule library according to the event type code, calculate the dynamic priority weight of the affected materials in real time, and reorder all material delivery tasks to be executed within a preset rescheduling time window based on the dynamic priority weight, generating an updated task queue. The fleet coordination replanning module is used to obtain the updated task queue and the real-time position and load status of each AGV transport robot, call the preset multi-AGV cooperative path planning algorithm, assign new tasks to each AGV and plan the driving path under collision avoidance constraints, and output the execution instruction set containing the target task and target path of each AGV. The execution feedback module is used to send the execution instruction set to each AGV terminal and receive the position and status feedback of each AGV during the execution process in real time. When any AGV is detected to deviate from the target path, it triggers local path online correction until the affected material is delivered to the target workstation and synchronously transmits the completion status back to the production execution system.

2. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, The specific process of outputting the disturbance event vector is as follows: S101. Establish a real-time data path with the production execution system through a preset listening interface, and poll the workshop event logs issued by the production execution system at a fixed sampling period. S102. Perform structured parsing on the obtained workshop event logs, and extract the event type field, the event occurrence timestamp field, and the workstation number field associated with the event; S103. Match the extracted event type field with a preset disturbance type feature library, which pre-stores three disturbance types: sudden equipment failure, emergency order insertion, and temporary process change, as well as their corresponding feature codes. If a match is successful, the preset urgency quantification weight value corresponding to the disturbance type is read, and the event type code, occurrence timestamp, and affected workstation identifier are encapsulated into a disturbance event vector and output. If a match fails, the event is marked as a non-disturbance regular event and will not trigger the subsequent dynamic reconstruction process.

3. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, The specific process for generating the updated task queue is as follows: S201. Obtain the disturbance event vector and retrieve all unexecuted material distribution tasks and their corresponding original priority levels from the currently running warehouse management system. S202. Based on the event type encoding in the disturbance event vector, index the preset priority mapping rule library to obtain the priority weight adjustment coefficient corresponding to the disturbance type. Different disturbance types in the priority mapping rule library correspond to different weight adjustment coefficients. S203. For each affected material delivery task, its original priority level and priority weight adjustment coefficient are weighted and integrated to obtain the dynamic priority weight of the task. S204. Based on the preset rescheduling time window parameters, within the rescheduling time window, arrange all material delivery tasks to be executed in descending order according to the dynamic priority weight, and generate an updated task queue containing the execution order of each task.

4. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, The specific process of outputting the execution instruction set is as follows: S301. Obtain the generated updated task queue, and at the same time obtain the real-time position coordinates and current load status of all AGV handling robots currently in the online state through the wireless communication link; S302. Extract the target workstation coordinates and material type information of each delivery task from the updated task queue, and combine them with the real-time position coordinates of each AGV to construct a multi-AGV collaborative scheduling optimization model with task allocation variables and path sequence variables as decision variables. S303. Under collision avoidance constraints, the preset multi-AGV cooperative path planning algorithm is invoked to iteratively solve the multi-AGV cooperative scheduling optimization model. The collision avoidance constraints include that at most one AGV is allowed to occupy the same path node at the same time, and the safe distance between any two AGVs is not less than the preset safe distance threshold. S304. The optimal solution obtained by iterative solution is parsed into the target task identifier and target path sequence corresponding to each AGV. The target task identifier and target path sequence of each AGV are bound one by one and encapsulated to generate an execution instruction set and output it.

5. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, The specific process of issuing the execution instruction set is as follows: S401: The execution instruction set is sent to the vehicle controller of each AGV terminal through the wireless communication network, driving each AGV to travel to the target workstation according to its own target path; S402. While executing the instruction set, start the real-time monitoring thread to receive the position coordinates and driving status data transmitted back by each AGV terminal at fixed intervals. S403. Compare the position coordinates transmitted back by each AGV in real time with the target path in the corresponding execution instruction set point by point, and calculate the position deviation. S404. When the position deviation of any AGV exceeds the preset allowable deviation threshold, it is determined that the AGV has deviated from the target path, triggering the local path online correction process. Without changing the tasks already assigned to other AGVs, the correction path of the AGV from its current position to the target workstation is replanned and issued. S405. Continue executing steps S402 to S404 until each AGV delivers its loaded materials to the corresponding target workstation, and then synchronously transmit the completion status of each task back to the production execution system.

6. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, In the dynamic priority reconstruction module, the duration of the preset rescheduling time window is dynamically determined based on the urgency metric value in the disturbance event vector. The higher the urgency metric value, the shorter the corresponding rescheduling time window duration.

7. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 1, characterized in that, In the vehicle fleet collaborative replanning module, the multi-AGV collaborative path planning algorithm adopts a time window-based conflict avoidance algorithm. The time window-based conflict avoidance algorithm uses the time interval of each AGV arriving at each path node as a constraint variable, and eliminates node conflicts by staggering the time windows of different AGVs at the same path node.

8. The enterprise production information sharing logistics management system based on intelligent warehousing according to claim 3, characterized in that, The dynamic priority reconfiguration module, when reordering all material delivery tasks currently to be executed, also includes: S501. Obtain the physical distance between the current storage location of the material and the target workstation corresponding to each material delivery task. S502. The physical distance and the dynamic priority weight of each task are weighted and combined to calculate the comprehensive scheduling score of each task. S503. Sort all material delivery tasks to be executed in descending order according to the comprehensive scheduling score from high to low, and generate an optimized task queue.

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