Intelligent carrying logistics system based on AGV

Abstract

The invention relates to the technical field of carrying logistics, and discloses an AGV-based intelligent carrying logistics system, which comprises a transportation data acquisition module, a storage environment data analysis module, a logistics transportation scheduling module and a control early warning module, and is characterized in that the transportation data acquisition module comprises a transportation path acquisition unit, an AGV carrying capacity acquisition unit and a freight volume acquisition unit. According to the system, image data of all AGV lines are collected, image data of AGV passing paths are analyzed, and whether a line scheduling instruction and a line early warning instruction are executed or not is judged; meanwhile, warehouse logistics scheduling is conducted on each AGV on the basis of all passing paths in the current warehouse, the loading capacity of each AGV and the loading capacity of each delivery node, then the AGVs are analyzed on the basis of operation parameters of each AGV, and whether an AGV early warning instruction is executed or not is judged; the AGV driving path and the running state of the AGV can be early warned in time, so that the timeliness of logistics transportation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of transport logistics, and in particular to an AGV-based intelligent transport logistics system. Background Art

[0002] AGV, also known as automatic guided vehicle, is a transport vehicle equipped with electromagnetic or optical automatic guidance devices. It can travel along a specified guidance path and has safety protection and various transfer functions. Logistics refers to the whole process of planning, implementing and managing the transportation of raw materials, semi-finished products, finished products or related information from the place of production to the place of consumption of the goods at the lowest cost through transportation, storage and distribution in order to meet customer needs. AGV is diverse, and its use environment involves companies with a high degree of automation. Another one is widely used in the service industry. Due to the different guidance systems, the scope of application is also different. During the installation and commissioning of existing AGV forklifts, the magnetic strips must be laid on the ground, resulting in the failure of the AGV forklift to avoid the road section in time when the magnetic strips are damaged or large obstacles appear, resulting in transportation obstruction. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides an intelligent handling and logistics system based on AGV, which has the advantages of timely handling and early warning of obstacles and risks in the handling and logistics process, thereby solving the above-mentioned technical problems.

[0004] (II) Technical solution To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent handling logistics system based on AGV, comprising a transportation data acquisition module, a storage environment data analysis module, a logistics transportation scheduling module and a control and early warning module; The transport data collection module includes a transport path collection unit, an AGV handling capacity collection unit and a cargo volume collection unit. The transport path collection unit is used to collect all the passage paths in the current warehouse, the AGV handling capacity collection unit is used to read the load on each AGV, and the cargo volume collection unit is used to collect the load of each delivery node; The storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of the AGV passage path, and determine whether to execute line scheduling instructions and line warning instructions; The logistics and transportation scheduling module includes a logistics and transportation analysis unit, a line scheduling unit and a transportation data analysis unit. The logistics and transportation analysis unit performs storage logistics scheduling for each AGV based on all the passages in the current warehouse, the load on each AGV and the load at each delivery node. The line scheduling unit dynamically adjusts the logistics line of the AGV based on the line scheduling instructions. The transportation data analysis unit analyzes the AGV based on the operating parameters of each AGV and determines whether to execute the AGV early warning instruction. When the storage environment data analysis module issues a line warning instruction or an AGV warning instruction, the control warning module executes corresponding line warning measures and AGV warning measures.

[0005] As a preferred technical solution of the present invention, the specific expression for the transportation path collection unit to collect all the passage paths in the current warehouse is as follows: ; in, represents the travel path dataset, They represent the first pass path, , No. Passage paths, , No. The passage path, and the Passageways The length of the pass path and the connectivity data with other pass paths are stored in .

[0006] As a preferred technical solution of the present invention, the specific expression of the load set read by the AGV handling capacity acquisition unit on each AGV is as follows: ; in, represents the AGV dataset, They represent the load of the first AGV, , No. AGV loading capacity, , No. AGV loading capacity.

[0007] As a preferred technical solution of the present invention, the specific expression of the loading volume of each delivery node collected by the freight volume collection unit is as follows: ; in, represents the delivery volume dataset, Respectively represent the loading volume of the first delivery node, , No. The loading volume of each delivery node, , No. The loading capacity of each delivery node.

[0008] As a preferred technical solution of the present invention, the storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of the AGV lines, and determine whether to execute line scheduling instructions and line warning instructions. The specific steps are as follows: Step A1: Obtain the corresponding travel path dataset The image data of all the passing paths, and the image data of each passing path, need to obtain the image data at three different times, and the image data at three different times are denoised and cropped to the same size for preprocessing, Step A2: Image data of three different time points of the passage , , Respectively with Initial image of the paths Compare and calculate the Risk factor of the travel path ; Step A3: If Risk factor of the travel path If it is greater than the risk threshold of the pass path, the line dispatch instruction is triggered to jump to step A4, otherwise, step A3 is terminated; Step A4: Route dispatching instruction: Read whether each AGV passes through the first Pass path, if any, Risk factor of the travel path Add one, and all the The AGV's path is replanned. Mark the passable path as impassable and calculate the Route risk factor of the traffic path ; Step A5: Determine Does the line risk factor of the first passage exceed the The line risk threshold of the travel path is exceeded. If it is exceeded, the line warning instruction is executed. If it is not exceeded, the line warning instruction is not executed.

[0009] As a preferred technical solution of the present invention, the step A2 calculates Risk factor of the travel path The specific expression is as follows: ; in, Indicates Risk factor of the travel path , , , Respectively represent The image data of the three different moments of the passage path and the initial image The difference area, Indicates Initial image of the paths total area.

[0010] As a preferred technical solution of the present invention, the step A4 calculates Route risk factor of the traffic path The specific expression is as follows: ; in, Indicates The route risk coefficient of the traffic path is Indicates The risk factor of the travel path is Represents a natural constant.

[0011] As a preferred technical solution of the present invention, the logistics and transportation analysis unit performs the following specific steps for performing storage logistics scheduling on each AGV based on all the passages in the current warehouse, the load on each AGV, and the load at each delivery node: Step B1: Based on Calculate the load of the delivery node The proportion of loading volume at each delivery node , and distribute AGVs in the same proportion according to the load ratio of each delivery node. The proportion of loading volume at each delivery node The specific expression is as follows: ; in, Indicates The proportion of loading volume at each delivery node, Indicates The loading volume of each delivery node, Represents the total load of all delivery nodes; Step B2: Use the A* algorithm to calculate the shortest path between each AGV from the delivery node to the storage point, and recalculate the shortest path between the storage points based on the current position of each AGV when receiving the route scheduling instruction.

[0012] As a preferred technical solution of the present invention, the specific steps of the transport data analysis unit analyzing the AGV based on the operating parameters of each AGV and determining whether to execute the AGV warning instruction are as follows: Step C1: Collect the operating parameters of AGV and combine them with The load on the AGV is calculated comprehensively. AGV risk factor , the specific expression is as follows: ; in, Indicates AGV risk factor , Indicates Rated value of the load on an AGV, Indicates the number of AGV errors; Step C2: Determine AGV risk factor Whether it exceeds the corresponding threshold, if it exceeds, execute the AGV warning instruction, if it does not exceed, terminate step C2.

[0013] As a preferred technical solution of the present invention, the control warning module executes the corresponding line warning measures as follows: The traffic path is cleaned and after the cleaning is completed, the The traffic path is marked as allowed to pass, and the control warning module executes the corresponding AGV warning measures to remind the staff There are risks with each AGV. AGVs were inspected and repaired.

[0014] Compared with the prior art, the present invention provides an intelligent handling logistics system based on AGV, which has the following beneficial effects: The present invention collects image data of all AGV lines, analyzes the image data of AGV travel paths, and determines whether to execute line scheduling instructions and line warning instructions. At the same time, based on all travel paths in the current warehouse, the load capacity of each AGV and the load capacity of each delivery node, the present invention performs warehousing logistics scheduling on each AGV. Then, the present invention analyzes the AGV based on the operating parameters of each AGV, and determines whether to execute the AGV warning instruction. The present invention can timely warn the AGV travel path and the operating status of the AGV itself, thereby ensuring the timeliness of logistics transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 , an AGV-based intelligent handling logistics system, including a transportation data acquisition module, a storage environment data analysis module, a logistics transportation scheduling module and a control and early warning module; The transportation data collection module includes a transportation path collection unit, an AGV handling capacity collection unit and a cargo volume collection unit. The transportation path collection unit is used to collect all the passages in the current warehouse, the AGV handling capacity collection unit is used to read the load on each AGV, and the cargo volume collection unit is used to collect the load of each delivery node. The specific expression of the transportation path collection unit collecting all the passages in the current warehouse is as follows: ; in, represents the travel path dataset, They represent the first pass path, , No. Passage paths, , No. The passage path, and the Passageways The length of the passage path and the connectivity data with other passage paths are stored in the AGV handling capacity collection unit. The specific expression of the load set on each AGV is as follows: ; in, represents the AGV dataset, They represent the load of the first AGV, , No. AGV loading capacity, , No. The specific expression of the loading volume of each AGV and the loading volume collection unit for each delivery node is as follows: ; in, represents the delivery volume dataset, Respectively represent the loading volume of the first delivery node, , No. The loading volume of each delivery node, , No. The loading volume of each delivery node; The storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of AGV passage paths, and determine whether to execute line scheduling instructions and line warning instructions. The storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of AGV lines, and determine whether to execute line scheduling instructions and line warning instructions. The specific steps are as follows: Step A1: Obtain the corresponding travel path dataset The image data of all the passing paths, and the image data of each passing path, need to obtain the image data at three different times, and the image data at three different times are denoised and cropped to the same size for preprocessing, Step A2: Image data of three different time points of the passage , , Respectively with Initial image of the paths Compare and calculate the Risk factor of the travel path , calculate the Risk factor of the travel path The specific expression is as follows: ; in, Indicates Risk factor of the travel path , , , Respectively represent The image data of the three different moments of the passage path and the initial image The difference area, Indicates Initial image of the paths Total area; Step A3: If Risk factor of the travel path If it is greater than the risk threshold of the pass path, the line dispatch instruction is triggered to jump to step A4, otherwise, step A3 is terminated; Step A4: Route dispatching instruction: Read whether each AGV passes through the first Pass path, if any, Risk factor of the travel path Add one, and all the The AGVs in the first path are re-planned. If there is a passable path, it will return to the midpoint of the last path and re-plan. Mark the passable path as impassable and calculate the Route risk factor of the traffic path , calculate the Route risk factor of the traffic path The specific expression is as follows: ; in, Indicates The route risk coefficient of the traffic path is Indicates The risk factor of the travel path is represents a natural constant; Step A5: Determine Does the line risk factor of the first passage exceed the The line risk threshold of the travel path is exceeded. If it is exceeded, the line warning instruction is executed. If it is not exceeded, the line warning instruction is not executed.

[0018] The logistics and transportation scheduling module includes a logistics and transportation analysis unit, a line scheduling unit and a transportation data analysis unit. The logistics and transportation analysis unit performs storage logistics scheduling for each AGV based on all the passages in the current warehouse, the load on each AGV and the load on each delivery node. The line scheduling unit dynamically adjusts the logistics line of the AGV based on the line scheduling instructions. The transportation data analysis unit analyzes the AGV based on the operating parameters of each AGV and determines whether to execute the AGV early warning instruction. The logistics and transportation analysis unit performs storage logistics scheduling for each AGV based on all the passages in the current warehouse, the load on each AGV and the load on each delivery node. The specific steps are as follows: Step B1: Based on Calculate the load of the delivery node The proportion of loading volume at each delivery node , and distribute AGVs in the same proportion according to the load ratio of each delivery node. The proportion of loading volume at each delivery node The specific expression is as follows: ; in, Indicates The proportion of loading volume at each delivery node, Indicates The loading volume of each delivery node, Represents the total load of all delivery nodes; Step B2: Use the A* algorithm to calculate the shortest path between each AGV from the delivery node to the storage point, and recalculate the shortest path between the storage points based on the current position of each AGV when receiving the route scheduling instruction.

[0019] The specific steps of the transport data analysis unit analyzing the AGV based on the operating parameters of each AGV and determining whether to execute the AGV warning instruction are as follows: Step C1: Collect the operating parameters of AGV and combine them with The load on the AGV is calculated comprehensively. AGV risk factor , the specific expression is as follows: ; in, Indicates AGV risk factor , Indicates Rated value of the load on an AGV, Indicates the number of AGV errors. The number of AGV errors here can be obtained from the AGV sensor to ensure the stability of the number; Step C2: Determine AGV risk factor Whether it exceeds the corresponding threshold, if it exceeds, the AGV warning instruction is executed, if it does not exceed, the step C2 is terminated. When it is less than 0, the AGV warning instruction can also be executed. The combined risk of a serious overload of an AGV is that the load exceeds twice the rated value. Avoid lead to The clock is 0.

[0020] When the storage environment data analysis module issues a line warning instruction or an AGV warning instruction, the control warning module executes the corresponding line warning measures and AGV warning measures. The corresponding line warning measures executed by the control warning module are: The traffic path is cleaned and after the cleaning is completed, the The traffic path is marked as allowed to pass, and the control warning module executes the corresponding AGV warning measures to remind the staff There are risks with each AGV. AGVs were inspected and repaired.

[0021] Example: The delivery nodes and loading capacity in this embodiment, and there are 11 AGVs, are shown in Table 1 below: Table 1 Total load = 100 + 200 + 150 + 50 + 300 = 800; Calculate the loading ratio of each delivery node, and distribute the AGVs in the same proportion according to the loading ratio of each delivery node. See Table 2 Table 2 Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent handling logistics system based on AGV, characterized by: It includes transportation data collection module, storage environment data analysis module, logistics transportation scheduling module and control and early warning module; The transport data collection module includes a transport path collection unit, an AGV handling capacity collection unit and a cargo volume collection unit. The transport path collection unit is used to collect all the passage paths in the current warehouse, the AGV handling capacity collection unit is used to read the load on each AGV, and the cargo volume collection unit is used to collect the load of each delivery node; The storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of the AGV passage path, and determine whether to execute line scheduling instructions and line warning instructions; The logistics and transportation scheduling module includes a logistics and transportation analysis unit, a line scheduling unit and a transportation data analysis unit. The logistics and transportation analysis unit performs storage logistics scheduling for each AGV based on all the passages in the current warehouse, the load on each AGV and the load at each delivery node. The line scheduling unit dynamically adjusts the logistics line of the AGV based on the line scheduling instructions. The transportation data analysis unit analyzes the AGV based on the operating parameters of each AGV and determines whether to execute the AGV early warning instruction. When the storage environment data analysis module issues a line warning instruction or an AGV warning instruction, the control warning module executes corresponding line warning measures and AGV warning measures.

2. The AGV-based intelligent handling logistics system according to claim 1, characterized in that: The specific expression for the transportation path collection unit to collect all the passage paths in the current warehouse is as follows: ； in, represents the travel path dataset, They represent the first pass path, , No. Passage paths, , No. The passage path, and the Passageways The length of the pass path and the connectivity data with other pass paths are stored in .

3. The AGV-based intelligent handling logistics system according to claim 2, characterized in that: The specific expression of the AGV handling capacity collection unit reading the load set on each AGV is as follows: ； in, represents the AGV dataset, They represent the load of the first AGV, , No. AGV loading capacity, , No. AGV loading capacity.

4. The AGV-based intelligent handling logistics system according to claim 3 is characterized by: The specific expression for the cargo volume collection unit to collect the loading volume of each delivery node is as follows: ； in, represents the delivery volume dataset, Respectively represent the loading volume of the first delivery node, , No. The loading volume of each delivery node, , No. The loading capacity of each delivery node.

5. The AGV-based intelligent handling logistics system according to claim 4, characterized in that: The storage environment data analysis module is used to collect image data of all AGV lines, analyze the image data of the AGV lines, and determine whether to execute line scheduling instructions and line warning instructions. The specific steps are as follows: Step A1: Obtain the corresponding travel path dataset The image data of all the passing paths, and the image data of each passing path, need to obtain the image data at three different times, and the image data at three different times are denoised and cropped to the same size for preprocessing; Step A2: Image data of three different time points of the passage , , Respectively with Initial image of the paths Compare and calculate the Risk factor of the travel path ; Step A3: If Risk factor of the travel path If it is greater than the risk threshold of the pass path, the line dispatch instruction is triggered to jump to step A4, otherwise, step A3 is terminated; Step A4: Route dispatching instruction: Read whether each AGV passes through the first Pass path, if any, Risk factor of the travel path Add one, and all the The AGV's path is replanned. Mark the passable path as impassable and calculate the Route risk factor of the traffic path ; Step A5: Determine Does the line risk factor of the first passage exceed the The line risk threshold of the travel path is exceeded. If it is exceeded, the line warning instruction is executed. If it is not exceeded, the line warning instruction is not executed.

6. The AGV-based intelligent handling logistics system according to claim 5, characterized in that: In step A2, the calculation Risk factor of the travel path The specific expression is as follows: ； in, Indicates Risk factor of the travel path , , , Respectively represent The image data of the three different moments of the passage path and the initial image The difference area, Indicates Initial image of the paths total area.

7. The AGV-based intelligent handling logistics system according to claim 6, characterized in that: In step A4, the calculation Route risk factor of the traffic path The specific expression is as follows: ； in, Indicates The route risk coefficient of the traffic path is Indicates The risk factor of the travel path is Represents a natural constant.

8. The AGV-based intelligent handling logistics system according to claim 7, characterized in that: The specific steps of the logistics and transportation analysis unit for performing storage logistics scheduling for each AGV based on all the passages in the current warehouse, the load on each AGV, and the load at each delivery node are as follows: Step B1: Based on Calculate the load of the delivery node The proportion of loading volume at each delivery node , and distribute AGVs in the same proportion according to the load ratio of each delivery node. The proportion of loading volume at each delivery node The specific expression is as follows: ； in, Indicates The proportion of loading volume at each delivery node, Indicates The loading volume of each delivery node, Represents the total load of all delivery nodes; Step B2: Use the A* algorithm to calculate the shortest path between each AGV from the delivery node to the storage point, and recalculate the shortest path between the storage points based on the current position of each AGV when receiving the route scheduling instruction.

9. The AGV-based intelligent handling logistics system according to claim 8, characterized in that: The specific steps of the transport data analysis unit analyzing the AGV based on the operating parameters of each AGV and determining whether to execute the AGV warning instruction are as follows: Step C1: Collect the operating parameters of AGV and combine them with The load on the AGV is calculated comprehensively. AGV risk factor , the specific expression is as follows: ； in, Indicates AGV risk factor , Indicates Rated value of the load on an AGV, Indicates the number of AGV errors; Step C2: Determine AGV risk factor Whether it exceeds the corresponding threshold, if it exceeds, execute the AGV warning instruction, if it does not exceed, terminate step C2.

10. The AGV-based intelligent handling logistics system according to claim 9, characterized in that: The control warning module executes the corresponding line warning measures as follows: The traffic path is cleaned and after the cleaning is completed, the The traffic path is marked as allowed to pass, and the control warning module executes the corresponding AGV warning measures to remind the staff There are risks with each AGV. AGVs were inspected and repaired.

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