A plant logistics control method, device, equipment and medium

Abstract

The application discloses a kind of plant logistics control method, device, equipment and medium.Method includes: obtaining logistics task information in plant and at least one vehicle basic state information corresponding to distribution vehicle;According to logistics task information and each vehicle basic state information, each distribution vehicle is cooperatively scheduled, and the initial scheduling result corresponding to each distribution vehicle is determined;Each initial scheduling result is detected for exception, and the exception detection result is obtained;When the exception detection result is abnormal, each initial scheduling result is adjusted, the target task execution result corresponding to each distribution vehicle is obtained, and the target task execution result corresponding to each distribution vehicle is sent to corresponding distribution vehicle, to make each distribution vehicle execute logistics distribution work based on corresponding target task execution result.The application embodiment can improve the scheduling stability and distribution efficiency in plant logistics distribution process.

Description

Technical Field

[0001] This invention relates to the field of logistics control technology, and in particular to a method, device, equipment and medium for logistics control in a factory area. Background Technology

[0002] With the development of intelligent manufacturing and industrial automation technologies, more and more factories are adopting delivery vehicles to distribute production materials, semi-finished products, or production equipment in order to improve the efficiency of factory logistics transportation. During factory logistics distribution, it is usually necessary to uniformly schedule multiple delivery vehicles according to logistics task requirements and the operating status of the vehicles, so that each vehicle can complete the logistics distribution operation according to its corresponding delivery task and route.

[0003] Currently, existing technologies typically involve a scheduling system that allocates tasks and plans routes for delivery vehicles based on preset rules, and sends the corresponding delivery tasks and driving routes to the delivery vehicles in order to control the delivery vehicles to perform logistics delivery operations.

[0004] However, existing technologies suffer from low delivery efficiency and poor scheduling stability in the factory logistics and distribution process. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for controlling logistics in a factory area. The embodiments of this invention can improve the scheduling stability and delivery efficiency in the logistics distribution process in a factory area.

[0006] In a first aspect, embodiments of the present invention provide a factory logistics control method, the method comprising:

[0007] Obtain logistics task information within the factory area and basic vehicle status information corresponding to at least one delivery vehicle;

[0008] Based on logistics task information and basic status information of each vehicle, the delivery vehicles are coordinated and dispatched to determine the initial dispatch result for each delivery vehicle.

[0009] Anomaly detection is performed on each initial scheduling result to obtain the anomaly detection results;

[0010] When the anomaly detection result indicates the presence of an anomaly, the initial scheduling results are adjusted to obtain the target task execution results for each delivery vehicle. The target task execution results for each delivery vehicle are then sent to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution results.

[0011] Secondly, embodiments of the present invention also provide a factory logistics control device, the device comprising:

[0012] The data acquisition module is used to acquire logistics task information in the factory area and basic vehicle status information corresponding to at least one delivery vehicle.

[0013] The scheduling result determination module is used to coordinate the scheduling of each delivery vehicle based on logistics task information and basic status information of each vehicle, and to determine the initial scheduling result corresponding to each delivery vehicle.

[0014] The anomaly detection module is used to perform anomaly detection on each initial scheduling result and obtain the anomaly detection result;

[0015] The target task execution result generation module is used to adjust the initial scheduling results when the anomaly detection result indicates that an anomaly exists, to obtain the target task execution result corresponding to each delivery vehicle, and to send the target task execution result corresponding to each delivery vehicle to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution result.

[0016] Thirdly, embodiments of the present invention also provide a factory logistics control device, the factory logistics control device comprising:

[0017] At least one processor; and

[0018] A memory that is communicatively connected to at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the factory logistics control method of any embodiment of the present invention.

[0020] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the factory logistics control method of any embodiment of the present invention.

[0021] The technical solution of this invention provides basic data support for the coordinated scheduling of delivery vehicles by acquiring logistics task information and the basic vehicle status information of the delivery vehicles. By coordinating the scheduling of delivery vehicles based on the logistics task information and the basic vehicle status information, unified task allocation and route planning for multiple delivery vehicles can be achieved, thereby improving the scheduling efficiency in the factory logistics delivery process. By detecting anomalies in the initial scheduling results, abnormal situations in the scheduling results can be identified before the delivery vehicles perform logistics delivery operations. By adjusting the initial scheduling results when anomalies are detected and controlling the delivery vehicles to perform logistics delivery operations based on the adjusted target task execution results, the impact of path conflicts, vehicle anomalies, or task conflicts on the logistics delivery process can be reduced, thereby improving the scheduling stability and efficiency in the factory logistics delivery process. This solves the technical problems of low delivery efficiency and poor scheduling stability in the existing technology for factory logistics delivery, thus improving the scheduling stability and delivery efficiency in the factory logistics delivery process.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of a factory logistics control method provided in an embodiment of the present invention;

[0025] Figure 2 A flowchart of a factory logistics control method provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of a factory logistics control device provided in an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of a factory logistics control device provided in an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] In the technical solutions of this invention, the acquisition, storage, and application of logistics task information and vehicle basic status information, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Figure 1 This is a flowchart illustrating a factory logistics control method provided in an embodiment of the present invention. This embodiment is applicable to factory logistics control situations, and the method can be executed by a factory logistics control device, which can be implemented in hardware and / or software.

[0032] See Figure 1 The factory logistics control methods shown include:

[0033] S101. Obtain logistics task information in the factory area and basic vehicle status information corresponding to at least one delivery vehicle.

[0034] In this invention, logistics task information refers to task data that characterizes the logistics and distribution needs of a factory area, describing the logistics and distribution content that needs to be completed by delivery vehicles. Logistics task information typically reflects the transportation needs of materials, equipment, semi-finished products, or finished products between different locations within the factory area. Logistics task information may include task number, task type, starting location, destination location, task priority, task generation time, material type, transportation time limit, and task status. In this invention, logistics task information provides a basis for the coordinated scheduling of delivery vehicles, enabling the cloud scheduling server to clearly define the logistics and distribution content that each delivery vehicle needs to perform. For example, a logistics task might be: Material X located in warehouse area A needs to be transported to production workshop B, and the delivery must be completed within 10 minutes.

[0035] Delivery vehicles can refer to transportation equipment used to perform logistics operations within a factory area, for transporting materials, equipment, or products between different areas. Delivery vehicles can include automated guided vehicles, unmanned delivery vehicles, forklifts, or rail transport vehicles, etc.

[0036] The vehicle basic status information refers to the fundamental data characterizing the current operating status of delivery vehicles, reflecting whether the vehicles are currently capable of performing logistics delivery tasks. This information may include the vehicle's current location, operating speed, battery level, vehicle load status, communication status, idle status, and fault status. By acquiring the vehicle basic status information corresponding to each delivery vehicle, the cloud-based dispatch server can analyze the current operating capacity and dispatchability of each vehicle, and rationally allocate logistics tasks based on the differences in the status of different vehicles. In this invention, the vehicle basic status information provides a basis for the collaborative dispatch of delivery vehicles, thereby improving the matching between logistics tasks and delivery vehicles.

[0037] S102. Based on the logistics task information and the basic status information of each vehicle, coordinate the scheduling of each delivery vehicle and determine the initial scheduling result corresponding to each delivery vehicle.

[0038] Collaborative scheduling refers to the unified coordination and allocation of operational tasks for multiple delivery vehicles, enabling them to collaboratively complete logistics delivery operations according to corresponding task requirements. In this invention, collaborative scheduling is used to achieve unified coordination and control among multiple delivery vehicles, thereby improving the overall scheduling efficiency of the factory's logistics delivery process. For example, a cloud-based scheduling server can assign delivery vehicles closer to the target area to corresponding logistics tasks and plan different driving routes for different delivery vehicles.

[0039] The initial scheduling result refers to the preliminary scheduling result generated by the cloud scheduling server after completing collaborative scheduling, which characterizes the task execution content corresponding to the delivery vehicle. The initial scheduling result may include the target delivery task, target travel route, task execution order, and target arrival area. In this invention, the initial scheduling result serves as a preliminary execution plan before the delivery vehicle performs logistics delivery operations. After completing task allocation and route planning, the cloud scheduling server combines the corresponding task information and route information to form the initial scheduling result, which serves as the basis for subsequent anomaly detection and task execution.

[0040] S103. Perform anomaly detection on each initial scheduling result to obtain the anomaly detection result.

[0041] Anomaly detection refers to the process of identifying and judging whether there are any abnormalities in the initial scheduling results. Anomaly detection can include path conflict detection, vehicle anomaly detection, and communication anomaly detection. Path conflict detection is used to detect whether there is a risk of path overlap between different delivery vehicles; vehicle anomaly detection is used to detect whether delivery vehicles have malfunctions or abnormal operating conditions. In this invention, anomaly detection is used to identify scheduling anomalies before delivery vehicles perform logistics delivery operations, thereby reducing the possibility of abnormal scheduling results being directly implemented.

[0042] The anomaly detection result refers to the detection result obtained after performing anomaly detection on the initial scheduling result, which is used to characterize whether there is an anomaly in the current scheduling result. In this invention, the anomaly detection result is used as the basis for determining whether the initial scheduling result needs to be adjusted.

[0043] S104. When the anomaly detection result indicates the existence of an anomaly, adjust each initial scheduling result to obtain the target task execution result corresponding to each delivery vehicle, and send the target task execution result corresponding to each delivery vehicle to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution result.

[0044] In this context, adjustment refers to the process of re-optimizing or reallocating the initial scheduling results after detecting anomalies. The adjustment process may include task reallocation, path replanning, adjusting the task execution order, or vehicle replacement. In this invention, adjustment is used to correct abnormal initial scheduling results, thereby improving the executability of the scheduling results.

[0045] The target task execution result refers to the final task execution result determined after adjustments for abnormalities, used to guide delivery vehicles in performing logistics and delivery operations. The target task execution result may include the reallocated target delivery tasks, the replanned target driving routes, and the updated task execution order. In this invention, the target task execution result serves as the control basis for the final execution of logistics and delivery operations by the delivery vehicles.

[0046] As can be seen, in this embodiment, by acquiring logistics task information and the basic vehicle status information corresponding to the delivery vehicles, basic data support can be provided for the collaborative scheduling of delivery vehicles; by coordinating the scheduling of delivery vehicles based on logistics task information and basic vehicle status information, unified task allocation and path planning for multiple delivery vehicles can be achieved, thereby improving the scheduling efficiency in the factory logistics delivery process; by detecting anomalies in the initial scheduling results, abnormal situations in the scheduling results can be identified before the delivery vehicles perform logistics delivery operations; by adjusting the initial scheduling results when anomalies are detected, and controlling the delivery vehicles to perform logistics delivery operations based on the adjusted target task execution results, the impact of path conflicts, vehicle anomalies, or task conflicts on the logistics delivery process can be reduced, thereby improving the scheduling stability and efficiency in the factory logistics delivery process; this solves the technical problems of low delivery efficiency and poor scheduling stability in the factory logistics delivery process of existing technologies, thereby improving the scheduling stability and delivery efficiency in the factory logistics delivery process.

[0047] In an optional embodiment, Figure 2 The flowchart of a factory logistics control method provided in this embodiment of the invention refines the process of "coordinated scheduling of each delivery vehicle based on logistics task information and basic status information of each vehicle to determine the initial scheduling result corresponding to each delivery vehicle" into "determining the vehicle execution capability information corresponding to each delivery vehicle based on the basic status information of each vehicle; obtaining the target delivery task corresponding to each delivery vehicle based on the logistics task information and the vehicle execution capability information corresponding to each delivery vehicle; determining the target driving path corresponding to each delivery vehicle based on the target delivery task; and determining each target delivery task and each target driving path as the initial scheduling result corresponding to each delivery vehicle," thereby improving the operation of factory logistics control.

[0048] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments.

[0049] See Figure 2 The factory logistics control methods shown include:

[0050] S201. Obtain logistics task information in the factory area and basic vehicle status information corresponding to at least one delivery vehicle.

[0051] S202. Based on the basic status information of each vehicle, determine the vehicle execution capability information corresponding to each delivery vehicle.

[0052] Vehicle execution capability information refers to data characterizing the current task execution capability of a delivery vehicle, reflecting whether the vehicle possesses the ability to perform the corresponding logistics delivery task. Essentially, vehicle execution capability information is a comprehensive description of the delivery vehicle's operational capacity, transportation capacity, and task adaptability. This information may include remaining battery power, vehicle load capacity, current idle status, operating speed, range, communication status, fault status, and current location. In this invention, vehicle execution capability information serves as a crucial basis for logistics task allocation, enabling the cloud-based scheduling server to adaptively allocate logistics tasks based on the actual operational capabilities of different delivery vehicles, thereby improving the matching degree between logistics tasks and delivery vehicles.

[0053] S203. Based on the logistics task information and the vehicle execution capacity information of each delivery vehicle, obtain the target delivery task corresponding to each delivery vehicle.

[0054] The target delivery task refers to the logistics delivery task to be executed for delivery vehicles after collaborative scheduling, which characterizes the specific logistics transportation content that the corresponding delivery vehicle needs to complete. The target delivery task may include the target transport materials, the starting delivery area, the target delivery area, the task priority, the task execution time limit, the task execution order, and the vehicle corresponding to the task. In this invention, the target delivery task is used to clarify the logistics delivery content that each delivery vehicle needs to perform, enabling different delivery vehicles to complete the logistics transportation operations within the factory area according to the corresponding tasks.

[0055] S204. Determine the target driving route for each delivery vehicle based on the target delivery task corresponding to each delivery vehicle.

[0056] The target travel path refers to the planned route for delivery vehicles to perform target delivery tasks, guiding them from their current location to the target delivery area. The target travel path can include multiple path nodes, road areas, turning nodes, and the target arrival area. Path nodes can represent multiple locations the delivery vehicle passes through on the factory roads; turning nodes can represent locations where the delivery vehicle needs to turn or change routes. Optionally, the target travel path may also include attributes such as path length, estimated travel time, path priority, and path traffic restrictions.

[0057] S205. Determine the initial scheduling results for each target delivery task and each target driving route as the initial scheduling results for each delivery vehicle.

[0058] S206. Perform anomaly detection on each initial scheduling result and obtain the anomaly detection results.

[0059] S207. When the anomaly detection result indicates the existence of an anomaly, adjust each initial scheduling result to obtain the target task execution result corresponding to each delivery vehicle, and send the target task execution result corresponding to each delivery vehicle to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution result.

[0060] As can be seen, in this embodiment, by determining the vehicle execution capability information corresponding to the delivery vehicles based on the basic vehicle status information, the current operating capability and task execution capability of different delivery vehicles can be reflected, thus providing a basis for vehicle capability for subsequent logistics task allocation. By combining logistics task information and vehicle execution capability information to determine the target delivery task corresponding to each delivery vehicle, the adaptability of logistics tasks and delivery vehicles can be achieved, thereby improving the rationality of logistics task allocation. By determining the target driving path corresponding to the delivery vehicle based on the target delivery task, the delivery vehicle can be provided with a corresponding logistics delivery route, enabling the delivery vehicle to perform logistics delivery operations according to the preset path. By determining the target delivery task and target driving path as the initial scheduling result, a preliminary task execution plan corresponding to the delivery vehicle can be formed, thereby providing basic scheduling data for subsequent anomaly detection and scheduling adjustment. Thus, the rationality of logistics task allocation and the coordination of delivery vehicles in performing logistics delivery operations are improved.

[0061] In some embodiments, anomaly detection is performed on each initial scheduling result to obtain anomaly detection results, including:

[0062] Obtain the path association between the target driving routes of each delivery vehicle, as well as the vehicle operation status information of each delivery vehicle.

[0063] Based on the path association and the operating status information of each vehicle, anomaly detection is performed on each initial scheduling result to obtain the anomaly detection results.

[0064] In this invention, path association refers to the positional, interactive, and overlapping relationships between the target travel paths of different delivery vehicles. Essentially, path association reflects the degree of correlation between different target travel paths, rather than the path information of a single delivery vehicle. In this invention, path association reflects the path association between different delivery vehicles, thus providing a basis for path analysis for anomaly detection. By analyzing path association, potential path conflict risks between multiple delivery vehicles can be identified in advance, thereby avoiding congestion, collisions, or traffic obstructions during actual operation.

[0065] Vehicle operation status information refers to data characterizing the current actual operating status of delivery vehicles, reflecting their operational status during logistics and delivery operations. Essentially, vehicle operation status information describes whether the delivery vehicle is currently operating normally and its current operational behavior. During logistics and delivery operations, delivery vehicles can collect real-time data on their current operating status through onboard sensors, positioning modules, communication modules, or vehicle control modules, and send the corresponding vehicle operation status information to the cloud dispatch server.

[0066] As can be seen, in this embodiment, by acquiring the path association between the target driving paths corresponding to each delivery vehicle and the vehicle operation status information corresponding to each delivery vehicle, the path association between delivery vehicles and the real-time operation status of vehicles can be obtained simultaneously, thereby providing path data and vehicle status data for anomaly detection. By performing anomaly detection on the initial scheduling results based on the path association and vehicle operation status information, path anomalies or vehicle operation anomalies in the initial scheduling results can be identified, thereby reducing the possibility of anomaly scheduling results being directly put into logistics and delivery operations. This improves the anomaly identification capability of the initial scheduling results and the scheduling stability during the logistics and delivery process.

[0067] In some embodiments, anomaly detection is performed on each initial scheduling result based on path association and vehicle operating status information to obtain anomaly detection results, including:

[0068] Based on the path association relationship, path conflict detection is performed to obtain conflict anomaly detection results;

[0069] Based on the operating status information of each vehicle, vehicle anomaly detection is performed, and multiple vehicle anomaly detection results are obtained;

[0070] Based on the conflict anomaly detection results and the anomaly detection results of each vehicle, the anomaly detection results are obtained.

[0071] Among them, path conflict detection refers to the detection process of identifying and judging whether there is a risk of path conflict between the target driving paths of multiple delivery vehicles. It is used to determine whether different delivery vehicles may have path intersections, traffic conflicts or operational interference during the logistics delivery process.

[0072] Among them, vehicle anomaly detection refers to the detection process of identifying and judging whether there are any abnormalities in the current operating status of delivery vehicles, in order to determine whether delivery vehicles can perform logistics delivery operations normally.

[0073] As can be seen, in this embodiment, by detecting path conflicts based on path associations, it is possible to identify whether there are path intersections or path occupancy conflicts between different delivery vehicles, thereby improving the ability to detect path anomalies; by detecting vehicle anomalies based on vehicle operating status information, it is possible to identify whether delivery vehicles have operational anomalies, malfunctions, or communication anomalies, thereby improving the ability to identify anomalies in the operating status of delivery vehicles; by combining the conflict anomaly detection results and the anomaly detection results of each vehicle to determine the anomaly detection results, a comprehensive anomaly judgment can be made on the initial scheduling results, thereby improving the accuracy of the anomaly detection results.

[0074] In some embodiments, anomaly detection results are obtained based on conflict anomaly detection results and anomaly detection results for each vehicle, including:

[0075] When the conflict anomaly detection result is no anomaly, and the anomaly detection results for each vehicle are also no anomaly, the anomaly detection result is determined to be no anomaly.

[0076] When there are abnormalities in the conflict anomaly detection results or the anomaly detection results of each vehicle, the anomaly detection results are determined to be abnormal.

[0077] As can be seen, in this embodiment, by determining that the anomaly detection result is not abnormal when the conflict anomaly detection result is not abnormal and the anomaly detection results of each vehicle are not abnormal, it can be indicated that there are no path conflict anomalies or vehicle operation anomalies in the current initial scheduling result, thereby improving the clarity of the anomaly detection result judgment; by determining that the anomaly detection result is abnormal when there are anomalies in the conflict anomaly detection result or vehicle anomaly detection result, it is possible to identify abnormal situations in the initial scheduling result in a timely manner, thereby reducing the possibility that abnormal scheduling results will directly execute logistics and distribution operations.

[0078] In some embodiments, when the anomaly detection result indicates the presence of an anomaly, the initial scheduling results are adjusted to obtain the target task execution result for each delivery vehicle, including:

[0079] When the anomaly detection result indicates the presence of an anomaly, determine the anomaly type corresponding to the anomaly detection result and at least one vehicle associated with the anomaly;

[0080] Based on the anomaly type and the vehicles associated with each anomaly, the target delivery tasks corresponding to each vehicle associated with anomaly are reassigned to obtain the execution results of the target tasks for each vehicle associated with anomaly.

[0081] Here, "anomaly type" refers to the anomaly category formed by classifying the anomalies corresponding to the anomaly detection results. It is used to characterize the specific type of anomalies occurring in the current logistics and distribution process. In this invention, anomaly types are used to classify anomalies, thereby providing a basis for anomaly handling in subsequent scheduling adjustments.

[0082] Among them, "abnormally associated vehicles" refers to delivery vehicles that are related to the current abnormal situation, and is used to characterize delivery vehicles affected by or causing the abnormal situation. Abnormally associated vehicles can include delivery vehicles that have experienced an anomaly, delivery vehicles affected by the anomaly, or delivery vehicles associated with the abnormal route.

[0083] Reassignment refers to the process of readjusting and redistributing existing target delivery tasks after an anomaly is detected, in order to redetermine the logistics delivery tasks corresponding to each delivery vehicle. After determining the type of anomaly and the vehicles associated with it, the cloud scheduling server can reanalyze the current logistics tasks in the factory area and the operating status of each delivery vehicle, and then reselect suitable delivery vehicles to perform the corresponding logistics tasks.

[0084] As can be seen, in this embodiment, by determining the anomaly type and at least one associated vehicle when the anomaly detection result indicates an anomaly, the anomaly category and the objects affected by the anomaly can be clearly identified, thus providing a basis for subsequent scheduling adjustments. By reallocating the target delivery tasks corresponding to the associated vehicles based on the anomaly type and associated vehicles, the logistics delivery tasks affected by the anomaly can be readjusted, thereby reducing the impact of the anomaly on the logistics delivery process.

[0085] In some embodiments, the anomaly types include: vehicle anomaly, route anomaly, and communication anomaly.

[0086] Among them, vehicle abnormality can refer to an abnormality in the operating status of the delivery vehicle itself, which indicates that the delivery vehicle is unable to perform logistics delivery operations normally.

[0087] Among them, route anomaly can refer to an abnormal situation in the target driving route corresponding to the delivery vehicle, which is used to indicate that the target driving route cannot normally meet the logistics and delivery needs.

[0088] Among them, communication anomaly can refer to an abnormality in the communication status between the delivery vehicle and the cloud dispatch server, which indicates that the delivery vehicle is unable to conduct data communication normally.

[0089] In some optional embodiments, when the exception type is a communication exception, the edge collaboration node deployed locally in the factory area automatically takes over the scheduling authority within the area when the network is interrupted. Based on the locally cached production task information and vehicle status data, it independently completes task allocation and multi-vehicle collaboration to ensure that logistics tasks are not interrupted.

[0090] As can be seen, in this embodiment, by classifying anomalies into vehicle anomalies, route anomalies, and communication anomalies, anomalies from different sources can be classified and processed, thereby improving the ability to classify and process anomalies and the targeting of anomaly detection.

[0091] Figure 3 This invention provides a schematic diagram of a factory logistics control device. This invention is applicable to factory logistics control situations; the device can execute factory logistics control methods and can be implemented in hardware and / or software.

[0092] See Figure 3 The factory logistics control device shown includes: a data acquisition module 301, a scheduling result determination module 302, an anomaly detection module 303, and a target task execution result generation module 304, wherein...

[0093] Data acquisition module 301 is used to acquire logistics task information in the factory area and basic vehicle status information corresponding to at least one delivery vehicle.

[0094] The scheduling result determination module 302 is used to coordinate the scheduling of each delivery vehicle based on the logistics task information and the basic status information of each vehicle, and to determine the initial scheduling result corresponding to each delivery vehicle.

[0095] Anomaly detection module 303 is used to perform anomaly detection on each initial scheduling result and obtain anomaly detection results;

[0096] The target task execution result generation module 304 is used to adjust each initial scheduling result when the anomaly detection result indicates that an anomaly exists, to obtain the target task execution result corresponding to each delivery vehicle, and to send the target task execution result corresponding to each delivery vehicle to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution result.

[0097] The technical solution of this invention provides basic data support for the coordinated scheduling of delivery vehicles by acquiring logistics task information and the basic vehicle status information of the delivery vehicles. By coordinating the scheduling of delivery vehicles based on the logistics task information and the basic vehicle status information, unified task allocation and route planning for multiple delivery vehicles can be achieved, thereby improving the scheduling efficiency in the factory logistics delivery process. By detecting anomalies in the initial scheduling results, abnormal situations in the scheduling results can be identified before the delivery vehicles perform logistics delivery operations. By adjusting the initial scheduling results when anomalies are detected and controlling the delivery vehicles to perform logistics delivery operations based on the adjusted target task execution results, the impact of path conflicts, vehicle anomalies, or task conflicts on the logistics delivery process can be reduced, thereby improving the scheduling stability and efficiency in the factory logistics delivery process. This solves the technical problems of low delivery efficiency and poor scheduling stability in the existing technology for factory logistics delivery, thus improving the scheduling stability and delivery efficiency in the factory logistics delivery process.

[0098] In some embodiments, in coordinating the scheduling of each delivery vehicle based on logistics task information and basic vehicle status information to determine the initial scheduling result for each delivery vehicle, the scheduling result determination module 302 is specifically used for:

[0099] Based on the basic status information of each vehicle, determine the vehicle execution capability information corresponding to each delivery vehicle;

[0100] Based on the logistics task information and the vehicle execution capacity information of each delivery vehicle, the target delivery task corresponding to each delivery vehicle is obtained.

[0101] Based on the target delivery tasks corresponding to each delivery vehicle, determine the target driving route for each delivery vehicle.

[0102] Each target delivery task and each target driving route are determined as the initial scheduling result for each delivery vehicle.

[0103] In some embodiments, the anomaly detection module 303 is specifically used for: performing anomaly detection on each initial scheduling result to obtain anomaly detection results.

[0104] Obtain the path association between the target driving routes of each delivery vehicle, as well as the vehicle operation status information of each delivery vehicle.

[0105] Based on the path association and the operating status information of each vehicle, anomaly detection is performed on each initial scheduling result to obtain the anomaly detection results.

[0106] In some embodiments, in order to perform anomaly detection on each initial scheduling result based on the path association and the operating status information of each vehicle, and to obtain anomaly detection results, the anomaly detection module 303 is specifically used for:

[0107] Based on the path association relationship, path conflict detection is performed to obtain conflict anomaly detection results;

[0108] Based on the operating status information of each vehicle, vehicle anomaly detection is performed, and multiple vehicle anomaly detection results are obtained;

[0109] Based on the conflict anomaly detection results and the anomaly detection results of each vehicle, the anomaly detection results are obtained.

[0110] In some embodiments, in obtaining the anomaly detection result based on the conflict anomaly detection result and the anomaly detection results of each vehicle, the anomaly detection module 303 is specifically used for:

[0111] When the conflict anomaly detection result is no anomaly, and the anomaly detection results for each vehicle are also no anomaly, the anomaly detection result is determined to be no anomaly.

[0112] When there are abnormalities in the conflict anomaly detection results or the anomaly detection results of each vehicle, the anomaly detection results are determined to be abnormal.

[0113] In some embodiments, when an anomaly detection result indicates the presence of an anomaly, adjusting each initial scheduling result to obtain the target task execution result corresponding to each delivery vehicle, the target task execution result generation module 304 is specifically used for:

[0114] When the anomaly detection result indicates the presence of an anomaly, determine the anomaly type corresponding to the anomaly detection result and at least one vehicle associated with the anomaly;

[0115] Based on the anomaly type and the vehicles associated with each anomaly, the target delivery tasks corresponding to each vehicle associated with anomaly are reassigned to obtain the execution results of the target tasks for each vehicle associated with anomaly.

[0116] In some embodiments, the anomaly types include: vehicle anomaly, route anomaly, and communication anomaly.

[0117] The factory logistics control device provided in the embodiments of the present invention can execute the factory logistics control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the factory logistics control method.

[0118] Figure 4 This is a schematic diagram of the structure of a factory logistics control device provided in an embodiment of the present invention.

[0119] like Figure 4As shown, the plant logistics control equipment 400 includes at least one processor 401 and a memory, such as a read-only memory (ROM) 402 and a random access memory (RAM) 403, communicatively connected to the at least one processor 401. The memory stores computer programs executable by the at least one processor. The processor 401 can perform various appropriate actions and processes based on the computer program stored in the ROM 402 or loaded into the RAM 403 from the storage unit 408. The RAM 403 can also store various programs and data required for the operation of the plant logistics control equipment 400. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0120] Multiple components in the factory logistics control equipment 400 are connected to the I / O interface 405, including: input units 406, such as keyboards and mice; output units 407, such as various types of displays and speakers; storage units 408, such as disks and optical discs; and communication units 409, such as network interface cards, modems, and wireless transceivers. The communication unit 409 allows the factory logistics control equipment 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processor 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 401 performs the various methods and processes described above, such as plant logistics control methods.

[0122] In some embodiments, the plant logistics control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the plant logistics control device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by processor 401, one or more steps of the plant logistics control method described above may be performed. Alternatively, in other embodiments, processor 401 may be configured to perform the plant logistics control method by any other suitable means (e.g., by means of firmware).

[0123] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0126] To provide user interaction, the systems and techniques described herein can be implemented on the operational detection equipment. This plant logistics control equipment includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the plant logistics control equipment. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0127] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0128] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0129] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0130] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling logistics in a factory area, characterized in that, The method, applied to a cloud-based scheduling server, includes: Obtain logistics task information within the factory area and basic vehicle status information corresponding to at least one delivery vehicle; Based on the logistics task information and the basic status information of each vehicle, the delivery vehicles are coordinated and scheduled to determine the initial scheduling result for each delivery vehicle. Anomaly detection is performed on each of the initial scheduling results to obtain anomaly detection results; When the anomaly detection result indicates the presence of an anomaly, the initial scheduling results are adjusted to obtain the target task execution results corresponding to each delivery vehicle. The target task execution results corresponding to each delivery vehicle are then sent to the corresponding delivery vehicle so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution results.

2. The method according to claim 1, characterized in that, The step of coordinating and scheduling each delivery vehicle based on the logistics task information and the basic status information of each vehicle, and determining the initial scheduling result for each delivery vehicle, includes: Based on the basic status information of each vehicle, determine the vehicle execution capability information corresponding to each delivery vehicle; Based on the logistics task information and the vehicle execution capability information corresponding to each of the delivery vehicles, the target delivery task corresponding to each of the delivery vehicles is obtained; Based on the target delivery task corresponding to each of the delivery vehicles, determine the target driving route corresponding to each of the delivery vehicles; The target delivery tasks and the target driving routes are determined as the initial scheduling results corresponding to the delivery vehicles.

3. The method according to claim 1, characterized in that, The step of performing anomaly detection on each of the initial scheduling results to obtain anomaly detection results includes: Obtain the path association relationship between the target driving routes corresponding to each of the delivery vehicles, as well as the vehicle operation status information corresponding to each of the delivery vehicles; Based on the path association and the vehicle operation status information, anomaly detection is performed on each of the initial scheduling results to obtain anomaly detection results.

4. The method according to claim 3, characterized in that, The step of performing anomaly detection on each of the initial scheduling results based on the path association and the vehicle operation status information to obtain anomaly detection results includes: Based on the path association relationship, path conflict detection is performed to obtain conflict anomaly detection results; Based on the vehicle operating status information, vehicle anomaly detection is performed, resulting in multiple vehicle anomaly detection results; Based on the conflict anomaly detection results and the anomaly detection results of each vehicle, the anomaly detection results are obtained.

5. The method according to claim 4, characterized in that, The step of obtaining the anomaly detection result based on the conflict anomaly detection result and the anomaly detection results of each vehicle includes: When the conflict anomaly detection result is no anomaly, and the anomaly detection results for each vehicle are all no anomaly, the anomaly detection result is determined to be no anomaly. When the conflict anomaly detection result or the vehicle anomaly detection result is abnormal, the anomaly detection result is determined to be abnormal.

6. The method according to claim 1, characterized in that, When the anomaly detection result indicates the presence of an anomaly, the initial scheduling results are adjusted to obtain the target task execution result for each delivery vehicle, including: When the anomaly detection result indicates the presence of an anomaly, the anomaly type corresponding to the anomaly detection result and at least one anomaly-associated vehicle are determined. Based on the anomaly type and each anomaly-associated vehicle, the target delivery tasks corresponding to each anomaly-associated vehicle are reassigned to obtain the target task execution results for each anomaly-associated vehicle.

7. The method according to claim 6, characterized in that, The anomaly types include: vehicle anomaly, route anomaly, and communication anomaly.

8. A factory logistics control device, characterized in that, include: The data acquisition module is used to acquire logistics task information in the factory area and basic vehicle status information corresponding to at least one delivery vehicle. The scheduling result determination module is used to coordinate the scheduling of each delivery vehicle based on the logistics task information and the basic status information of each vehicle, and determine the initial scheduling result corresponding to each delivery vehicle. An anomaly detection module is used to perform anomaly detection on each of the initial scheduling results and obtain anomaly detection results; The target task execution result generation module is used to adjust the initial scheduling results when the anomaly detection result indicates that an anomaly exists, to obtain the target task execution result corresponding to each delivery vehicle, and to send the target task execution result corresponding to each delivery vehicle to the corresponding delivery vehicle, so that each delivery vehicle can perform logistics delivery operations based on the corresponding target task execution result.

9. A factory logistics control device, characterized in that, The plant area logistics control equipment includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the factory logistics control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the factory logistics control method according to any one of claims 1-7.

Images