Intelligent logistics system, control method and device and medium
By acquiring logistics information in real time and planning routes through algorithms through intelligent logistics systems, the non-optimization problem of traditional logistics route planning is solved, accurate scheduling and efficient transportation of equipment are achieved, and the overall efficiency and resource utilization of the logistics system are improved.
Patent Information
- Application Number
- CN202510656768.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-21
- Publication Date
- 2025-09-23
AI Technical Summary
Traditional logistics distribution route planning relies on experienced dispatchers, resulting in suboptimal routes that are affected by human factors and lack consideration of real-time traffic conditions and dynamic factors, leading to congestion and detours, reducing transportation efficiency and increasing costs.
Through the intelligent logistics system, logistics information is obtained in real time, and algorithms are used to automatically plan routes. By combining the volume, weight and status information of the cargo and the handling equipment, the target equipment can be accurately matched to avoid equipment idleness or overload operation and optimize route planning.
It reduces congestion and detours, reduces transportation time and costs, and improves the overall efficiency and resource utilization of the logistics system.
Smart Images
Figure CN120688948A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to, but is not limited to, the technical field of intelligent logistics systems, and in particular to an intelligent logistics system, control method, device, and medium. Background Art
[0002] Traditional logistics distribution route planning often relies on experienced dispatchers, who plan delivery routes based on their subjective judgment and limited information. This approach is not only inefficient but also susceptible to human influence, resulting in suboptimal routes. In actual delivery processes, due to a lack of consideration for dynamic factors such as real-time traffic conditions, pedestrian flows, and special events, delivery vehicles often encounter congestion and take detours, which prolongs delivery times, increases transportation costs, and reduces efficiency.
[0003] Application Contents
[0004] The embodiments of the present application provide an intelligent logistics system, control method, device and medium, which can improve transportation efficiency.
[0005] In a first aspect, an embodiment of the present application provides a control method for an intelligent logistics system, wherein the intelligent logistics system includes a control module and a plurality of handling devices controlled by the control module. The control method includes:
[0006] In response to the cargo transportation task, determining the cargo starting point, cargo destination, cargo volume, and cargo weight according to the cargo transportation task;
[0007] Acquiring status information of each of the transporting devices, and determining a target transporting device based on the status information, the volume of the cargo, and the weight of the cargo;
[0008] Obtaining current logistics information, and determining a cargo route based on the current logistics information, the cargo starting point, the cargo destination, and preset warehouse map data;
[0009] According to the cargo path, the target handling equipment is controlled to transport the cargo.
[0010] According to the control method of the intelligent logistics system of the embodiment of the first aspect of the present application, there are at least the following beneficial effects: by obtaining the current logistics information (such as traffic conditions, warehouse maps, etc.) in real time and using algorithms to automatically plan routes, it overcomes the defects of traditional manual scheduling such as strong subjectivity and information lag, reduces congestion and detours, and reduces transportation time and costs. Based on the volume, weight and status information of the cargo, the target equipment is intelligently matched to achieve accurate scheduling of transportation resources, avoid idle or overloaded equipment, and improve overall logistics efficiency. In addition, by intelligently matching the target transportation equipment with the volume, weight and status information of the cargo, accurate scheduling of transportation resources is achieved, avoid idle or overloaded equipment, and improve overall logistics efficiency.
[0011] According to some embodiments of the first aspect of the present application, obtaining status information of each of the transport devices and determining a target transport device based on the status information, the cargo volume, and the cargo weight includes:
[0012] Obtaining the load capacity and transportable capacity of each of the transport equipment;
[0013] The transport equipment whose load capacity is greater than the weight of the cargo and whose transportable capacity is greater than the volume of the cargo is determined as the target transport equipment.
[0014] According to some embodiments of the first aspect of the present application, the control method further includes:
[0015] Obtaining the remaining movable distance of the target transport equipment;
[0016] When the cargo path is greater than or equal to the remaining movable distance, the load capacity, the transportable capacity, and the remaining movable distance of each of the transport devices are retrieved;
[0017] The transport equipment whose load capacity is greater than the cargo weight, whose transportable capacity is greater than the cargo volume, and whose remaining movable distance is greater than the cargo path is determined as a new target transport equipment.
[0018] According to some embodiments of the first aspect of the present application, the control method further includes:
[0019] Acquire real-time logistics information, and update the cargo route according to the real-time logistics information.
[0020] According to some embodiments of the first aspect of the present application, obtaining current logistics information and determining a shipment path based on the current logistics information, the cargo starting point, the cargo destination, and preset warehouse map data includes:
[0021] Obtain current logistics information, input the cargo starting point, cargo destination, and preset warehouse map data of the current logistics information into a preset path planning model to obtain a cargo path.
[0022] According to some embodiments of the first aspect of the present application, after controlling the target handling equipment to transport cargo according to the cargo path, the control method further includes:
[0023] The operating time of the target transport equipment is accumulated, and the operating time of each transport equipment is counted, and the transport equipment whose operating time is greater than a preset time threshold is marked as equipment to be maintained.
[0024] According to some embodiments of the first aspect of the present application, it is characterized in that the control method further includes:
[0025] The cargo path is recorded in a preset cargo path library, and the path heat is calculated based on a plurality of cargo path records in the cargo path library.
[0026] In a second aspect, an embodiment of the present application provides an operation control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method as described in the first aspect.
[0027] In a third aspect, an embodiment of the present application provides an intelligent logistics system, comprising the operation control device as described in the embodiment of the second aspect above.
[0028] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the control method described in the embodiment of the first aspect above.
[0029] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] The accompanying drawings are used to provide a further understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.
[0031] Figure 1 A flow chart of a control method for an intelligent logistics system provided in an embodiment of the present application;
[0032] Figure 2 for Figure 1 Specific flow chart of step S200;
[0033] Figure 3 A flowchart of a control method for an intelligent logistics system provided in another embodiment of the present application;
[0034] Figure 4 A flowchart of a control method for an intelligent logistics system provided in another embodiment of the present application;
[0035] Figure 5 for Figure 1 Specific flow chart of step S300;
[0036] Figure 6 A flowchart of a control method for an intelligent logistics system provided in another embodiment of the present application;
[0037] Figure 7 A flowchart of a control method for an intelligent logistics system provided in another embodiment of the present application;
[0038] Figure 8 An operation control device is provided in an embodiment of the present application. DETAILED DESCRIPTION
[0039] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.
[0040] It is understood that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and the like in the specification, claims, or accompanying drawings are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.
[0041] Traditional logistics distribution route planning often relies on experienced dispatchers, who plan delivery routes based on their subjective judgment and limited information. This approach is not only inefficient but also susceptible to human influence, resulting in suboptimal routes. In actual delivery processes, due to a lack of consideration for dynamic factors such as real-time traffic conditions, pedestrian flows, and special events, delivery vehicles often encounter congestion and take detours, which prolongs delivery times, increases transportation costs, and reduces efficiency.
[0042] Based on this, the embodiments of the present application provide an intelligent logistics system, control method, device and medium. By acquiring the current logistics information (such as traffic conditions, warehouse maps, etc.) in real time and using algorithms to automatically plan routes, it overcomes the defects of traditional manual scheduling such as strong subjectivity and information lag, reduces congestion and detours, and reduces transportation time and costs. Based on the volume, weight and status information of the cargo, the target equipment is intelligently matched to achieve accurate scheduling of transportation resources, avoid idle or overloaded equipment, and improve overall logistics efficiency. In addition, by intelligently matching the target transportation equipment with the volume, weight and status information of the cargo, accurate scheduling of transportation resources is achieved, avoid idle or overloaded equipment, and improve overall logistics efficiency.
[0043] The embodiments of the present application are further described below with reference to the accompanying drawings.
[0044] Reference Figure 1 , Figure 1 The flow chart of the control method of the intelligent logistics system provided in the embodiment of the present application includes but is not limited to the following steps:
[0045] Step S100, in response to the cargo transportation task, determining the cargo starting point, cargo destination, cargo volume, and cargo weight according to the cargo transportation task;
[0046] Step S200, obtaining status information of each transport device, and determining the target transport device based on the status information, cargo volume, and cargo weight;
[0047] Step S300: obtaining current logistics information and determining the cargo route based on the current logistics information, cargo starting point, cargo destination, and preset warehouse map data;
[0048] Step S400: Control the target handling equipment to transport the cargo according to the cargo path.
[0049] It is understandable that the intelligent logistics system includes a control module and a plurality of handling equipment controlled by the control module. When the control module receives a cargo transportation task, it can first determine the cargo starting point, cargo end point, cargo volume and cargo weight according to the cargo transportation task. By obtaining the cargo starting point, cargo end point, cargo volume and cargo weight, the cargo information can be accurately grasped, so that the control module can accurately determine the target handling equipment according to the status information of each handling equipment in combination with the cargo volume and cargo weight, realize the precise scheduling of handling resources, avoid equipment idleness or overload operation, and improve the overall logistics efficiency. By obtaining the current logistics information (such as traffic conditions, etc.), and then according to the current logistics information, cargo starting point, cargo end point and preset warehouse map data, the cargo path can be determined, which can overcome the defects of traditional manual scheduling such as strong subjectivity and information lag, reduce congestion and detours, and reduce transportation time and cost. After determining the cargo path, the control module controls the target handling equipment to transport the cargo along the cargo path.
[0050] Reference Figure 2 , Figure 2 for Figure 1 The specific flow chart of step S200 includes but is not limited to the following steps:
[0051] Step S210, obtaining the load capacity and transportable capacity of each transport equipment;
[0052] In step S220 , a transporting device having a load capacity greater than the weight of the cargo and a transportable capacity greater than the volume of the cargo is determined as a target transporting device.
[0053] It is understandable that since the load capacity and handling capacity of each handling equipment are different, if the handling equipment is randomly assigned, it may lead to a mismatch between the handling equipment and the cargo, resulting in reduced cargo handling efficiency or damage to the handling equipment. Therefore, based on the load capacity and handling capacity of each handling equipment, the handling equipment with a load capacity greater than the cargo weight and a handling capacity greater than the cargo volume can be determined as the target handling equipment to match the handling equipment with the cargo to be handled. The status information of the handling equipment includes the load capacity and handling capacity of the handling equipment.
[0054] Specifically, among multiple handling equipment, there may be multiple first handling equipment whose load capacity is greater than the weight of the goods and whose handling capacity is greater than the volume of the goods. Therefore, the difference between the load capacity and the weight of the goods and the difference between the handling capacity and the cargo capacity of each first handling equipment can also be calculated, and then the two differences are added to obtain the handling weight of the first handling equipment, and the first handling equipment with the smallest handling weight is selected as the target handling equipment.
[0055] Reference Figure 3 , Figure 3A flow chart of a control method for an intelligent logistics system provided in another embodiment of the present application includes but is not limited to the following steps:
[0056] Step S230, obtaining the remaining movable distance of the target transport equipment;
[0057] Step S240 , when the cargo path is greater than or equal to the remaining movable distance, the load capacity, the transport capacity, and the remaining movable distance of each transport device are retrieved;
[0058] In step S250 , a transporting device having a load capacity greater than the weight of the cargo, a transportable capacity greater than the volume of the cargo, and a remaining movable distance greater than the cargo path is determined as a new target transporting device.
[0059] It is understandable that if the transport equipment is powered by electricity, the total power of the transport equipment is fixed. When the energy of the transport equipment is exhausted, the transport equipment needs to be charged. When it is fully charged, the total movable distance of the transport equipment is fixed. Therefore, after determining the target equipment, the remaining movable distance of the target transport equipment can also be obtained. If the cargo path is greater than or equal to the remaining movable distance, it means that the target transport equipment cannot transport the cargo from the cargo starting point to the cargo end point. At this time, the load capacity, transportable capacity and remaining movable distance of each transport equipment can be re-obtained, and then the transport equipment with a load capacity greater than the cargo weight, a transportable capacity greater than the cargo volume and a remaining movable distance greater than the cargo path can be determined as the new target transport equipment to ensure that the target transport equipment can transport the cargo from the cargo starting point to the cargo end point.
[0060] Reference Figure 4 , Figure 4 A flow chart of a control method for an intelligent logistics system provided in another embodiment of the present application includes but is not limited to the following steps:
[0061] Step S500: Acquire real-time logistics information and update the cargo route according to the real-time logistics information.
[0062] It can be understood that the intelligent logistics system includes a sensor network composed of multiple sensors in the warehouse and an equipment monitoring system, which is used to collect real-time logistics information in the current warehouse in real time. Since the cargo route is planned according to the logistics information at the time of route planning, it has a lag, but the logistics information of the warehouse is dynamically changing. For example, whether there is congestion in the channel (possibly due to other equipment operations, temporary stacking of goods, etc.), whether there is equipment failure in a specific area causing traffic obstruction, the current real-time location and operating status of each handling equipment, etc. Therefore, real-time logistics information can be obtained, and the cargo route can be updated according to the real-time logistics information to ensure that the planned route can adapt to the real-time situation of the warehouse, thereby improving transportation efficiency.
[0063] Specifically, the cargo route is from storage area C3 to sorting area D7, and it needs to pass through area C5. However, when the handling equipment moves to area C4, the handling equipment in area C5 malfunctions. At this time, if the original cargo route is followed, the congestion in area C5 will be aggravated. Therefore, the cargo route can be updated to bypass area C5 to avoid congestion in area C5.
[0064] Reference Figure 5 , Figure 5 for Figure 1 The specific flowchart of step S300 includes but is not limited to the following steps:
[0065] Step S310, obtaining current logistics information, inputting the current logistics information, cargo starting point, cargo destination and preset warehouse map data into a preset path planning model to obtain a cargo path.
[0066] It is understandable that planning a cargo route based on the user's subjective judgment and limited information is inefficient and susceptible to human factors, resulting in a suboptimal planned route. Therefore, current logistics information can be obtained and the cargo origin and destination information, as well as pre-set warehouse map data, can be input into a preset route planning model to determine the cargo route. The route planning model can be an A* algorithm. Furthermore, after determining the cargo route, real-time logistics information can be obtained and then input into the route planning model to update the cargo route.
[0067] Specifically, the A* algorithm calculates the path cost as f(n) = g(n) + h(n). g(n) is the actual cost (e.g., distance, time) from the cargo origin to the current node n, and h(n) is the estimated cost (a heuristic function using Euclidean distance) from the current node n to the cargo destination. f(n) is the total cost and is used for priority sorting (smaller values indicate higher priority). For example, the starting point of the goods is the A3 shelf, and the end point of the goods is the D7 sorting area, A3 (coordinates [2,3]), D7 (coordinates [7,4]), the warehouse map data is the warehouse grid map (channel = 0, shelf = obstacle), the current logistics status is that the E5 channel is temporarily congested, and the path planning model is calculated as follows: Step 1: h(A3) = |7-2| + |4-3| = 6, Step 2: Expand the adjacent nodes B3 and A4, update g(n) and f(n), Step 3, E5 is congested, g(E5) increases (such as +2), forcing the algorithm to detour, and the output path is A3-B3-C3-D3-D5-D6-D7 (avoiding E5, total cost f(n) = 8).
[0068] Reference Figure 6 , Figure 6A flow chart of a control method for an intelligent logistics system provided in another embodiment of the present application includes but is not limited to the following steps:
[0069] Step S600: accumulating the operating time of the target transport equipment, and counting the operating time of each transport equipment, and marking the transport equipment with an operating time greater than a preset time threshold as equipment to be maintained.
[0070] It is understandable that handling equipment may wear out during long-term operation. The higher the degree of wear, the greater the probability of failure of the handling equipment. Therefore, the operating time of the target handling equipment can be accumulated, and the operating time of each handling equipment can be counted. The handling equipment with an operating time greater than the preset time threshold can be marked as equipment to be maintained. Users can maintain the equipment to be maintained, reduce the sudden failure rate, and thus improve the overall operating efficiency of the intelligent logistics system.
[0071] Reference Figure 7 , Figure 7 A flow chart of a control method for an intelligent logistics system provided in another embodiment of the present application includes but is not limited to the following steps:
[0072] In step S700 , the cargo path is recorded in a preset cargo path library, and the path heat is calculated based on a plurality of cargo path records in the cargo path library.
[0073] It is understandable that the higher the repetition of the cargo path, the more handling equipment there are on the path, that is, the greater the probability of congestion. Therefore, the cargo path can be recorded in the preset cargo path library, and the path heat can be calculated based on the multiple cargo path records in the cargo path library. Among them, the higher the path heat, the higher the repetition of the cargo path and the more handling equipment there are on the path. In addition, after calculating the path heat, the warehouse can be adjusted. For example, more handling equipment or charging piles can be deployed on high-heat paths to improve overall efficiency; path heat can be added as a calculation variable when planning cargo paths, such as giving priority to low-heat paths for diversion and balancing system loads. In addition, future demand can be predicted through heat trends to guide warehouse expansion or equipment procurement.
[0074] Secondly, refer to Figure 8 The embodiment of the present invention provides an operation control device 800, including a memory 810, a processor 820, and a computer program stored in the memory 810 and executable on the processor 820. The processor 820 executes the program to implement the control method of the intelligent logistics system according to the first embodiment of the present invention, for example, Figure 1 Method steps S100 to S400, Figure 2 Steps S210 to S220 of the method, Figure 3Steps S230 to S250 of the method, Figure 4 Method step S500, Figure 5 In method step 310, Figure 6 Method step S600 and Figure 7 Method step S700 in .
[0075] Memory 810, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the control method for the intelligent logistics system in the above-mentioned embodiments of the present invention. Processor 820 implements the control method for the intelligent logistics system in the above-mentioned embodiments of the present invention by executing the non-transitory software programs and instructions stored in memory 810.
[0076] The memory 810 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data required to execute the control method of the intelligent logistics system in the above embodiment, etc. In addition, the memory 810 may include a high-speed random access memory 810, and may also include a non-volatile memory 810, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. It should be noted that the memory 810 may optionally include a memory 810 remotely arranged relative to the processor 820, and these remote memories 810 may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.
[0077] On the third aspect, an embodiment of the present invention provides an intelligent logistics system, which includes an operation control device 800 as in the embodiment of the second aspect, which obtains current logistics information (such as traffic conditions, warehouse maps, etc.) in real time and uses algorithms to automatically plan routes, overcoming the defects of traditional manual scheduling such as strong subjectivity and information lag, significantly reducing congestion and detours, and reducing transportation time and costs. Based on the volume, weight and status information of the cargo, the target equipment is intelligently matched to achieve accurate scheduling of transportation resources, avoid idle or overloaded equipment, and improve overall logistics efficiency. In addition, by intelligently matching the target transportation equipment with the volume, weight and status information of the cargo, accurate scheduling of transportation resources is achieved, avoid idle or overloaded equipment, and improve overall logistics efficiency.
[0078] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the control method of the intelligent logistics system of the embodiment of the first aspect above, such as executing Figure 1 Method steps S100 to S400, Figure 2Steps S210 to S220 of the method, Figure 3 Steps S230 to S250 of the method, Figure 4 Method step S500, Figure 5 In method step 310, Figure 6 Method step S600 and Figure 7 Method step S700 in .
[0079] Those skilled in the art will appreciate that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer-readable media, which can include computer storage media or non-transitory media and communication media or transient media. As a term well known to those skilled in the art, computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.
[0080] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0081] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.
Claims
1. A control method for an intelligent logistics system, characterized in that: The intelligent logistics system includes a control module and a plurality of handling devices controlled by the control module, and the control method includes: In response to the cargo transportation task, determining the cargo starting point, cargo destination, cargo volume, and cargo weight according to the cargo transportation task; Acquiring status information of each of the transporting devices, and determining a target transporting device based on the status information, the volume of the cargo, and the weight of the cargo; Obtaining current logistics information, and determining a cargo route based on the current logistics information, the cargo starting point, the cargo destination, and preset warehouse map data; According to the cargo path, the target handling equipment is controlled to transport the cargo.
2. The control method according to claim 1, characterized in that: The acquiring of status information of each transport device and determining a target transport device according to the status information, the cargo volume, and the cargo weight includes: Obtaining the load capacity and transportable capacity of each of the transport equipment; The transport equipment whose load capacity is greater than the weight of the cargo and whose transportable capacity is greater than the volume of the cargo is determined as the target transport equipment.
3. The control method according to claim 2, characterized in that: The control method further includes: Obtaining the remaining movable distance of the target transport equipment; When the cargo path is greater than or equal to the remaining movable distance, the load capacity, the transportable capacity, and the remaining movable distance of each of the transport devices are retrieved; The transport equipment whose load capacity is greater than the cargo weight, whose transportable capacity is greater than the cargo volume, and whose remaining movable distance is greater than the cargo path is determined as a new target transport equipment.
4. The control method according to claim 1, wherein: The control method further includes: Acquire real-time logistics information, and update the cargo route according to the real-time logistics information.
5. The control method according to claim 1, characterized in that: The obtaining of current logistics information and determining a shipment route based on the current logistics information, the shipment starting point, the shipment destination, and preset warehouse map data includes: The current logistics information is obtained, and the current logistics information, the cargo starting point, the cargo destination, and the preset warehouse map data are input into a preset path planning model to obtain the cargo path.
6. The control method according to claim 1, characterized in that: After controlling the target transport device to transport the cargo according to the cargo path, the control method further includes: The operating time of the target transport equipment is accumulated, and the operating time of each transport equipment is counted, and the transport equipment whose operating time is greater than a preset time threshold is marked as equipment to be maintained.
7. The control method according to claim 1, characterized in that: The control method further includes: The cargo path is recorded in a preset cargo path library, and the path heat is calculated based on a plurality of cargo path records in the cargo path library.
8. An operation control device, characterized in that: The system comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the control method according to any one of claims 1 to 7.
9. An intelligent logistics system, characterized in that: Comprising the operation control device as claimed in claim 8.
10. A computer storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the control method according to any one of claims 1 to 7.