Logistics scheduling system and method based on multi-dimensional data collaboration

Abstract

The invention belongs to the technical field of logistics scheduling, and particularly relates to a logistics scheduling system and method based on multi-dimensional data collaboration, and the method comprises the following steps: carrying out the adjustment according to obtained multi-dimensional data; performing segmentation processing on the adjusted multi-dimensional data, distributing the segmented data to corresponding management modules for processing through a scheduling system, and distributing the segmented data to corresponding management systems; instruction execution and data feedback between the two ends are realized through data synchronization and scheduling between the management system and the execution end; and according to instruction execution and data feedback, distribution and management of the scheduling system after data segmentation are optimized so as to realize optimization of the scheduling system. According to the invention, complete collaboration of logistics and production operation can be realized, flow standardization is realized, and flow configuration, execution and deployment are carried out, so that the operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics scheduling, and particularly relates to a logistics scheduling system and method based on multi-dimensional data collaboration. Background Art

[0002] A logistics scheduling system is a key tool for optimizing the allocation of transportation resources and improving logistics efficiency. In modern logistics management, the scheduling system plays a crucial role. It not only ensures that goods are delivered to the destination in a timely and safe manner, but also significantly improves the operation efficiency of the entire supply chain.

[0003] Problems existing in the prior art: With the rapid development of e-commerce and modern supply chains, traditional logistics scheduling systems have become difficult to meet the requirements of high efficiency and precision. Existing logistics scheduling systems usually have problems such as data islands, information asynchronization, and poor real-time performance, resulting in low logistics efficiency and high costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a logistics scheduling system and method based on multi-dimensional data collaboration, which can achieve complete collaboration of logistics and production operations, standardize processes, and perform configuration, execution, and deployment of processes, thereby reducing operating costs.

[0005] The technical solutions adopted by the present invention are specifically as follows: A logistics scheduling method based on multi-dimensional data collaboration includes the following steps: Adjust according to the obtained multi-dimensional data; Perform segmentation processing on the adjusted multi-dimensional data, and distribute the segmented data to the corresponding management modules for processing through the scheduling system and allocate them to the corresponding management systems; Realize instruction execution and data feedback between the two ends through data synchronization and scheduling between the management system and the execution end; According to the instruction execution and data feedback, optimize the distribution and management of the data after segmentation by the scheduling system to achieve the optimization of the scheduling system.

[0006] Optionally, the multi-dimensional data acquisition and adjustment method includes the following steps: Input the obtained order information into the data acquisition module, and the data acquisition module calls the data set of the key associated information corresponding to the order information in the system according to the order information; Apply the data set of the key associated information corresponding to the order information to the data processing and analysis module; Generate a feasible strategy set by performing adaptability matching on multiple associated order information that matches the key associated information of the order information; If the relevance of the set of feasibility strategies meets the matching criteria, multiple order information is merged and allocated to the scheduling optimization module to verify the feasibility of the strategy set through the scheduling optimization module; After verification, the order information is allocated to the execution monitoring module.

[0007] Optionally, the multi-dimensional data segmentation method includes the following steps: Based on the multi-dimensional data obtained from at least one order information, a data extension starting point is established according to the weight value of a single data in the dataset; Using the starting point, multiple associated datasets are established, and the datasets related to the starting point in multiple order information are connected, and corresponding priority level labels are established for the multiple associated datasets; The priority level labels are used to establish multiple sub-element sets centered on the starting point; The priority level labels are attached to multiple corresponding sub-element sets; The sub-elements in multiple associated datasets are distributed to the execution monitoring module, the dynamic adjustment and execution module through the scheduling optimization module in sequence; According to the data verification and feedback obtained from the execution monitoring module, it is confirmed whether it conforms to the order information corresponding to the starting point. If not, it is determined according to the priority level label whether to match other starting points or generate a new starting point; If it conforms, the size of the dataset in the multi-dimensional data is further adjusted, and the adjusted dataset is continuously fed back to the scheduling optimization module to generate a new order information optimization plan through the data processing and analysis module.

[0008] Optionally, the data synchronization and scheduling method between the management system and the execution end includes the following steps: Based on the obtained order information, as well as the starting point and the corresponding sub-element sets after multi-dimensional data segmentation; Through the scheduling optimization module, the elements in the sub-element sets are marked with keyword labels and distributed to the corresponding execution monitoring module and the dynamic adjustment and execution module; First, by verifying the corresponding warehouse inventory and the expected production volume corresponding to the order date, the above information is fed back to the scheduling optimization module. If the expected order delivery time can be achieved, the production task and the warehouse distribution and delivery task are executed through the execution monitoring module; When the expected order delivery time is not reached, the corresponding production execution command is allocated according to the priority level label to implement the execution operation of the production process; The above information is synchronized to the scheduling optimization module.

[0009] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the above.

[0010] According to another aspect of the embodiments of the present invention, there is also provided a computer program product including a computer program, which, when executed by a processor, implements the method of any one of the above.

[0011] A logistics scheduling system based on multi-dimensional data collaboration includes: A data acquisition module for obtaining data from multi-dimensional data sources, including but not limited to order information, inventory information, vehicle status, and environmental information; A data processing and analysis module for processing the acquired multi-dimensional data information and outputting the analyzed key data information; A scheduling optimization module that, based on the analysis results of multi-dimensional data, uses an algorithm to generate an optimized logistics scheduling plan; An execution monitoring module for monitoring the execution status of logistics tasks in real time, collecting feedback information during the execution process, and dynamically adjusting the scheduling plan according to the feedback information; A dynamic adjustment and execution module providing an interactive interface for manually changing and optimizing data, providing a learning system for dynamically adjusting the plan based on the feedback information generated by the execution monitoring module, and learning the method of manually changing and optimizing data to achieve automatic optimization of the logistics scheduling system.

[0012] In the embodiments of the present invention, the data acquisition module is composed of upper-level systems such as ERP, MES, OMS, SAP, and MDS for issuing orders. After the order information is processed and analyzed by the data processing and analysis module, the production scheduling system of the scheduling optimization module realizes the scheduling optimization of the order information, converts it into orders executable by the warehouse management system and the industrial management system, issues an instruction set to the control system, and schedules the corresponding automated equipment to perform operations.

[0013] Optionally, the execution monitoring module realizes the control and monitoring of logistics equipment, feeds back the monitoring data in the logistics scheduling system, and issues an instruction set through the optimization scheduling module for the automated operation of various terminal devices in the dynamic adjustment and execution module.

[0014] Optionally, the instruction set includes a feasibility strategy set, feedback information, and a set of multiple sub-elements centered on the starting point.

[0015] The technical effects achieved by the present invention are: In the present invention, by optimizing and adjusting order information, the upstream and downstream processes can be standardized, so as to facilitate subsequent processes, provide accurate configurations, and facilitate the execution and deployment of subsequent processes. Similarly, it is also a prerequisite for subsequent execution and deployment processes.

[0016] In the present invention, the execution monitoring module realizes the control and monitoring of logistics equipment, and feeds back the monitoring data in the logistics scheduling system, so as to realize the coordinated operation between the execution end and the task distribution end, improve the operation efficiency and the timeliness of data feedback, and issue an instruction set through the feedback information through the optimized scheduling module for dynamically adjusting and automating the operation of various terminal devices in the execution module.

[0017] In the present invention, through the system operation of each system module, process standardization can be achieved, and processes can be configured, executed, and deployed, and performance can be observed, costs can be evaluated, feedback can be received, and control can be provided, thereby reducing operating costs.

[0018] In the present invention, through an integrated management system, the management of the production line and the warehouse operation site is emphasized, a large amount of real-time data generated during the logistics and production operations is automatically and quickly collected, and real-time events are processed in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of the method flow of the present invention; Figure 2 is a schematic diagram of the method flow for obtaining and adjusting multi-dimensional data in the present invention; Figure 3 is a schematic diagram of the method flow for dividing multi-dimensional data in the present invention; Figure 4 is a schematic diagram of the method flow for data synchronization and scheduling between the management system and the execution end in the present invention; Figure 5 is a schematic diagram of the structure of the logistics scheduling system in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] In order to make the purpose and advantages of the present invention clearer, the present invention will be specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific implementation manners of the present invention, and does not strictly limit the scope of protection of the specific claims of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] According to an embodiment of the present invention, there is provided a method embodiment of a logistics scheduling method based on multi-dimensional data collaboration. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0023] Furthermore, the corresponding markings in the drawings are as follows: ERP (Enterprise Resource Planning): Enterprise Resource Planning system; MES, the full name is MES Manufacturing Execution System (manufacturing execution system); OMS (Operational Method Sheet), operation instruction sheet, which realizes the management of enterprise resource information system with the ERP system; SAP (System Applications and Products) enterprise management solution system; MDS (Master Demand Schedules) master demand planning system, in this application, it corresponds to Figure 5 the production scheduling system in, and the production scheduling system is integrated in the scheduling optimization module; ESB (Enterprise Service Bus) enterprise service bus; IBS (Industrial Building System) industrial management system, which improves the efficiency and quality of industrial production through automation and informatization means; WMS (Warehouse Management System) warehouse management system, in this application, it is used to be integrated in the dynamic adjustment and execution module; EOS (Electronic Order System) Electronic Assistance System; ICS (Industrial Control Systems) Industrial Control System; RF (Radio Frequency) Sorting System; WCS (Warehouse Control System) Warehouse Control System.

[0024] As Figure 1 shown, the present invention provides a logistics scheduling method based on multi-dimensional data collaboration, including the following steps: S1. Adjust according to the obtained multi-dimensional data; S2. Perform segmentation processing on the adjusted multi-dimensional data, S3. Distribute the segmented data to the corresponding management modules through the scheduling system for processing and allocate them to the corresponding management systems; S4. Through data synchronization and scheduling between the management system and the execution end, realize instruction execution and data feedback between the two ends.

[0025] S5. According to instruction execution and data feedback, optimize the distribution and management of the scheduling system after data segmentation to realize the optimization of the scheduling system.

[0026] Referring to the appendix Figure 2 , in step S1, it is executed by the data acquisition module, and the multi-dimensional data acquisition and adjustment method includes the following steps: S101. Input the obtained order information into the data acquisition module, and call the dataset of the key associated information corresponding to the order information in the system through the data acquisition module; S102. Apply the dataset of the key associated information corresponding to the order information to the data processing and analysis module; S103. Perform adaptability matching on multiple associated order information that matches the key associated information of the order information, and generate a set of feasible strategies; S104. If the relevance of the set of feasible strategies reaches the matching standard, merge multiple order information and allocate it to the scheduling optimization module to verify the feasibility of the strategy set through the scheduling optimization module; S105. After verification, allocate the order information to the execution monitoring module.

[0027] In step S102, retrieve the main information in the order in the form of keywords, distribute to each subsystem, and generate corresponding datasets according to the retrieval results.

[0028] Optionally, the data set is multiple data sets retrieved according to each keyword in the corresponding order information, and according to the corresponding items of the multiple data sets, it is simulated to run together with the order information, and the data sets or sub-data in the data sets that cannot be directly run with the order information are excluded to generate new executable multiple data sets.

[0029] Optionally, if the maximum load capacity of the vehicle obtained according to the order quantity of the goods to be transported in the order exceeds the threshold, the associated data of the corresponding other orders will be excluded.

[0030] For example, if there is a large deviation in the order delivery route in the order, or due to road blockage or other natural disaster factors on the delivery route and its branches, the corresponding other order information will be excluded, and the corresponding order information in multiple other data sets will also be excluded.

[0031] In steps S103 and S104, for other order information with stronger feasibility, a matching degree test is performed. The other order information can include one or more to achieve the maximum threshold for order information processing and optimize the order information. Among them, for the corresponding other order information that meets the requirements, if it is possible to batch transport the goods in the order information, the corresponding information will be entered into the order information and the other order information will be split and adapted.

[0032] In step S105, for the order information that meets the requirements, the determined order information is generated and distributed to the corresponding execution monitoring module.

[0033] According to the above steps, by optimizing and adjusting the order information, the upstream and downstream process standardization can be achieved, so as to provide accurate configuration for subsequent processes and facilitate the execution and deployment of subsequent processes. Similarly, it is also a prerequisite for subsequent execution and deployment processes.

[0034] Refer to Appendix Figure 3 , in step S2, the multi-dimensional data segmentation method includes the following steps: S201. Establish a data extension starting point for the multi-dimensional data obtained according to at least one order information according to the single data weight value in the data set; S202. Establish multiple associated data sets with the starting point, establish a connection relationship between the data sets related to the starting point in multiple order information, and establish corresponding priority level labels for the multiple associated data sets; S203. Establish multiple sub-element sets centered on the starting point with the priority level label; S204. Attach the priority level label to multiple corresponding sub-element sets; S205. Sequentially distribute the sub-elements in multiple associated data sets to the execution monitoring module, the dynamic adjustment and execution module through the scheduling optimization module; S206. According to the data verification and feedback obtained from the execution monitoring module, confirm whether it conforms to the order information corresponding to the starting point. If not, determine whether to match other starting points or generate a new starting point according to the priority level label; S207. If it conforms, further adjust the size of the data set in the multi-dimensional data, and continuously feedback the adjusted data set to the scheduling optimization module to generate a new order information optimization plan through the data processing and analysis module.

[0035] In the above step S201, generate order information that meets the requirements according to step S105. When there is one other order information that matches the order information, directly implement pairing and directly match and distribute it to the subsequent production and warehousing ends.

[0036] Optionally, when there are multiple other order information that match the order information, set weight values for the multiple order information and establish a multi-dimensional order information with the order information as the starting point.

[0037] Furthermore, the associated data set at this time is the associated data set of multiple other order information associated with the order information; Optionally, based on step S202, establish corresponding priority level labels for multiple associated data sets according to information such as the delivery time limit, the order of goods loading, and the production or warehousing location of other order information.

[0038] Optionally, the judgment method includes machine judgment or manual judgment, and the priority of the mechanical judgment method is route, time limit, and warehousing location.

[0039] According to steps S203 to S205, after establishing the priority level label, generate multiple sub-element sets, and through the sub-element sets, realize the distribution of order tasks, that is, the order allocation of the sub-elements in multiple associated data sets.

[0040] According to steps S205 to S207, distribute to the execution monitoring module, the dynamic adjustment and execution module through the scheduling optimization module to realize warehouse shipping and factory production to coordinate the upstream and downstream of the order.

[0041] Optionally, according to the Figure 5 industrial management system and warehousing management system in the appendix, realize the reception and allocation of orders.

[0042] Optionally, by managing orders, production plans are generated and production orders are placed. Through the industrial control system, the production process is managed and optimized, and the production information of the industrial management system is fed back to the production scheduling system in real time.

[0043] Optionally, the order is managed through the warehouse management system, and the receiving and shipping tasks are allocated. Through the warehouse control system, the execution is controlled, and the execution status is fed back to the warehouse management system and the production scheduling system in sequence.

[0044] Optionally, in the industrial management system, the production plan is set according to the priority, and in the warehouse management system, the shipping order sequence is set according to the priority.

[0045] Optionally, for those that cannot be achieved or problems occur in the industrial management system and the warehouse management system, the information is fed back to the production scheduling system through the feedback channel, so as to further optimize the priority of multiple associated data sets through the production scheduling system.

[0046] Refer to Appendix Figure 4 , according to steps S4 and S5, the data synchronization and scheduling method between the management system and the execution end includes the following steps: S401. Based on the obtained order information, as well as the starting point and the corresponding sub-element set after multi-dimensional data segmentation; S402. The elements in the sub-element set are marked with keyword tags by the scheduling optimization module and distributed to the corresponding execution monitoring module and the dynamic adjustment and execution module; S403. First, by verifying the corresponding warehouse inventory and the expected production volume of the corresponding order date, the above information is fed back to the scheduling optimization module. If the expected order delivery time can be achieved, the production task and the warehouse distribution and delivery task are executed through the execution monitoring module; S404. When the expected order delivery time is not reached, the corresponding production execution command is assigned according to the priority level tag to implement the execution operation of the production process; S405. Synchronize the above information to the scheduling optimization module.

[0047] According to steps S401 and S402, the obtained order information is distributed to the corresponding management systems, and through the management systems, the order information is further distributed to the execution end. In order to improve the simplicity of the execution data, therefore, the starting point and the corresponding sub-elements after multi-dimensional data segmentation are distributed as key production scheduling information, and are monitored by the monitoring module, the dynamic adjustment and execution module.

[0048] According to steps S403 to S405, first verify the inventory in the warehouse, and feedback it to the industrial management system for production of the corresponding quantity. And through the above information, such as inventory in the warehouse, production time, production efficiency and other information, feedback it to the scheduling optimization module to achieve further optimization and adjustment of production scheduling.

[0049] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium storing a computer program, which when executed by a processor implements the method of any one of the above.

[0050] According to another aspect of the embodiments of the present invention, there is also provided a computer program product including a computer program, which when executed by a processor implements the method of any one of the above.

[0051] Refer to the appendix Figure 5 , a logistics scheduling system based on multi-dimensional data collaboration, including: A data acquisition module for obtaining data from multi-dimensional data sources, including but not limited to order information, inventory information, vehicle status and environmental information; A data processing and analysis module for processing the acquired multi-dimensional data information and outputting the analyzed key data information; A scheduling optimization module, which generates an optimized logistics scheduling plan based on the analysis results of multi-dimensional data using an algorithm; An execution monitoring module for monitoring the execution status of logistics tasks in real time, collecting feedback information during the execution process, and dynamically adjusting the scheduling plan according to the feedback information; A dynamic adjustment and execution module, which provides an interactive interface for manually optimizing the data, provides a learning system for dynamically adjusting the plan according to the feedback information generated by the execution monitoring module, and learning the method of manually optimizing the data, for realizing the automatic optimization of the logistics scheduling system.

[0052] Further, refer to the appendix Figure 2 , the data acquisition module executes steps S101 to S105 corresponding to step S1. The data acquisition module is composed of upper-level systems such as ERP, MES, OMS, SAP, and MDS for placing orders. By inputting the placed orders, and the data in the orders and multi-dimensional data retrieved according to the data in the system corresponding to the orders, it is used to generate a data set associated with the orders.

[0053] Optionally, after placing a logistics order, based on the time, location, route and other information of the order, retrieve information such as the weather in the area related to the route, other orders matching the order delivery time or orders from other organizations in the related system, other orders with corresponding order delivery time in the related storage area, the vehicle path and maximum cargo capacity used to match the order, and the scheduled cargo capacity that has been placed. By entering the corresponding information in the corresponding management modules of the ERP, MES, OMS, SAP, and MDS systems and starting operation, it is responsible for organizing and distributing the corresponding order information.

[0054] Furthermore, referring to Figure 2 The data processing and analysis module executes steps S201 to S207 corresponding to step S2. The order information is processed and analyzed by the data processing and analysis module. The production scheduling system of the scheduling optimization module realizes the scheduling optimization of the order information, converts it into an order that can be executed by the warehouse management system and the industrial management system, issues an instruction set to the control system, and schedules the corresponding automation equipment to perform the operation.

[0055] The instruction set includes a feasible strategy set, feedback information, and a plurality of sub-element sets centered on a starting point.

[0056] By segmenting the data, we can achieve accuracy in information processing and further improve the accuracy of subsequent multi-dimensional data processing.

[0057] Optionally, the data processing and analysis module corresponds to the attached Figure 2 The enterprise service bus in the system, after obtaining the order information, further extracts keywords from the processed order information and issues it to the corresponding warehousing and production stages according to the keywords.

[0058] See attached Figure 5 The execution monitoring module realizes the control and monitoring of logistics equipment, and feeds back the monitoring data in the logistics scheduling system to achieve the coordinated operation between the execution end and the task distribution end, improve the operation efficiency and the timeliness of data feedback, and through the feedback information, issue the instruction set through the optimization scheduling module, which is used for dynamic adjustment and automatic operation of various terminal equipment in the execution module.

[0059] Furthermore, the execution monitoring module corresponds to the warehouse management system, and the dynamic adjustment and execution module corresponds to the industrial management system, so as to realize the operation of executable orders.

[0060] Optionally, various mid-to-high-end equipment including but not limited to PLC, aisle stacker, destacker, transmission line, shuttle car, etc. are monitored by executing the monitoring module.

[0061] Optionally, the monitoring includes but is not limited to production capacity monitoring, storage area monitoring, logistics monitoring, video monitoring, order monitoring, task monitoring, operation monitoring, equipment monitoring, equipment status monitoring, equipment performance, etc.

[0062] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention are implemented by conventional means in the art without special instructions and limitations.

Claims

1. A logistics scheduling method based on multi-dimensional data collaboration, characterized in that: The steps include: Make adjustments based on the multi-dimensional data obtained; The adjusted multi-dimensional data is segmented and processed, and the segmented data is distributed to the corresponding management modules through the scheduling system for processing and allocated to the corresponding management system; Through data synchronization and scheduling between the management system and the execution end, instruction execution and data feedback between the two ends are realized; According to instruction execution and data feedback, it is used to optimize the scheduling system's distribution and management of data after segmentation to achieve optimization of the scheduling system.

2. A logistics scheduling method based on multi-dimensional data collaboration according to claim 1, characterized in that: The multi-dimensional data acquisition and adjustment method comprises the following steps: The acquired order information is input into the data collection module, and the data collection module calls the data set of key related information of the order information in the system according to the order information; Applying the data set corresponding to the key associated information of the order information to the data processing and analysis module; By adaptively matching a plurality of associated order information that matches the key associated information of the order information, and generating a feasible strategy set; If the relevance of the feasible strategy set meets the matching standard, multiple order information is merged and distributed to the scheduling optimization module, and the feasibility of the strategy set is verified by the scheduling optimization module; After verification is completed, the order information is assigned to the execution monitoring module.

3. The logistics scheduling method based on multi-dimensional data collaboration according to claim 1 is characterized in that: The multi-dimensional data segmentation method comprises the following steps: The multi-dimensional data obtained according to at least one order information establishes a data extension starting point according to a single data weight value in the data set; Establishing multiple associated data sets with the starting point, establishing connection relationships between data sets related to the starting point in multiple order information, and establishing corresponding priority level labels for the multiple associated data sets; The priority level label is used to establish a plurality of sub-element sets centered at the starting point; attaching the priority level labels to a plurality of corresponding sub-element sets; Distribute the sub-elements in the multiple associated data sets to the execution monitoring module, the dynamic adjustment and execution module in sequence through the scheduling optimization module; According to the acquired execution monitoring module data verification and feedback, confirm whether it matches the order information corresponding to the starting point. If not, determine whether to match other starting points or generate new starting points according to the priority level label; If it meets the requirements, the size of the data set in the multi-dimensional data is further adjusted, and the adjusted data set is continuously fed back to the scheduling optimization module to generate a new order information optimization plan through the data processing and analysis module.

4. The logistics scheduling method based on multi-dimensional data collaboration according to claim 1 is characterized in that: The data synchronization and scheduling method between the management system and the execution end comprises the following steps: Based on the acquired order information, the starting point after multi-dimensional data segmentation and the corresponding sub-element set; The elements in the sub-element set are marked with keyword tags through the scheduling optimization module and distributed to the corresponding execution monitoring module and dynamic adjustment and execution module; First, by verifying the corresponding warehouse inventory and the expected production volume on the corresponding order date, the above information is fed back to the scheduling optimization module. If the expected order delivery time can be achieved, the production task and warehouse distribution delivery task are executed through the execution monitoring module; When the expected order delivery time is not reached, the corresponding production execution command is assigned according to the priority level label to implement the execution operation of the production process; Synchronize the above information to the scheduling optimization module.

5. A logistics scheduling system based on multi-dimensional data collaboration, using a logistics scheduling method based on multi-dimensional data collaboration as claimed in any one of claims 1 to 4, characterized in that: include: Data collection module, used to obtain data from multi-dimensional data sources, including but not limited to order information, inventory information, vehicle status and environmental information; The data processing and analysis module processes the collected multi-dimensional data information and outputs the analyzed key data information; A scheduling optimization module, which generates an optimized logistics scheduling plan using an algorithm based on the analysis results of multi-dimensional data; An execution monitoring module, which is used to monitor the execution status of logistics tasks in real time, collect feedback information during the execution process, and dynamically adjust the scheduling plan according to the feedback information; The dynamic adjustment and execution module provides an interactive interface for manually changing and optimizing data, and provides a learning system for dynamically adjusting the feedback information generated by the execution monitoring module, as well as learning the manual data change optimization method, to realize automatic optimization of the logistics scheduling system.

6. A logistics scheduling system based on multi-dimensional data collaboration according to claim 5, characterized in that: The data acquisition module is composed of ERP, MES, OMS, SAP, and MDS systems, which are upper-level systems for placing orders. The order information is processed and analyzed by the data processing and analysis module, and the production scheduling system of the scheduling optimization module realizes the scheduling optimization of the order information, converts it into an order that can be executed by the warehouse management system and the industrial management system, issues an instruction set to the control system, and schedules the corresponding automated equipment to perform operations.

7. The logistics scheduling system based on multi-dimensional data collaboration according to claim 1 is characterized by: The execution monitoring module realizes the control and monitoring of logistics equipment, and feeds back the monitoring data in the logistics scheduling system. Through the feedback information, the optimization scheduling module issues an instruction set for dynamic adjustment and automated operation of various terminal devices in the execution module.

8. The logistics scheduling system based on multi-dimensional data collaboration according to claim 1 is characterized by: The instruction set includes a feasible strategy set, feedback information, and a plurality of sub-element sets centered around the starting point.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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