Task allocation management method applied to storage and transportation and related equipment

Abstract

The invention belongs to the technical field of multi-AGV scheduling optimization, and relates to a task allocation management method applied to storage and transportation and related equipment, and the method comprises the steps: calculating a shortest distance matrix and a shortest path matrix of storage map position data according to a Floyd algorithm; reading a task database, and obtaining a task order from the task database; determining passing working nodes and terminal nodes according to the task order; according to the shortest distance matrix and the shortest path matrix, calculating shortest path node vectors and required path costs of all the AGVs for executing the task order; according to an AGV density algorithm and the shortest path node vector, calculating AGV total density of each AGV path area; calculating the total cost of each AGV according to a bidding algorithm, the path cost and the AGV total density of each AGV path area; screening out a target AGV with the minimum total cost according to the total cost of each AGV; and distributing the task order to the target AGV. The bidding scheduling strategy is introduced, and it is ensured that tasks are distributed with the optimal efficiency.

Description

Technical Field

[0001] This application relates to the technical field of multi-AGV scheduling optimization, and particularly to a task allocation management method and related devices applied to warehousing transportation. Background Art

[0002] With the increasing growth of logistics demand, the AGV (Automated Guided Vehicle) technology is increasingly widely used in fields such as manufacturing and warehousing logistics. An efficient path planning algorithm is of great significance for reducing transportation costs and improving operation management efficiency. In an environment where multiple AGVs work together, in addition to point-to-point path planning, the problem of reasonable scheduling between AGVs also needs to be considered to improve the operation efficiency of the AGV system while avoiding conflicts.

[0003] In the currently more mainstream traditional AGV path planning algorithms, the Dijkstra algorithm is a commonly used single-source shortest path planning algorithm. This algorithm calculates the shortest path by gradually extending outward from the initial position on the map. Each calculation will explore the path outward based on the path node closest to the initial position, and repeat until the destination is included in the path; the A* algorithm uses a greedy strategy to solve the shortest path and introduces a heuristic function to evaluate the selectivity of the path between two nodes, enabling it to find the optimal solution in a relatively short time.

[0004] However, the applicant has found that existing research still faces problems such as low path planning accuracy and insufficient conflict handling efficiency when dealing with multi-AGV collaborative work in complex environments. Especially in scenarios where the task distribution is dense and there are moving obstacles, traditional algorithms often cannot effectively handle the interactive effects between AGVs and complex dynamic environment changes due to ignoring the regional congestion of AGVs. Summary of the Invention

[0005] The purpose of the embodiments of this application is to propose a task allocation management method and related devices applied to warehousing transportation to solve the problems of low path planning accuracy and insufficient conflict handling efficiency.

[0006] To solve the above technical problems, the embodiments of this application provide a task allocation management method applied to warehousing transportation, which adopts the following technical solutions:

[0007] Obtain the location data of the warehousing map;

[0008] Calculate the shortest distance matrix and the shortest path matrix of the warehousing map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes;

[0009] Read the task database and obtain task orders in the task database;

[0010] Determine the passing working nodes and the end node according to the task order;

[0011] Calculate the shortest path node vector and the required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix;

[0012] Calculate the total AGV density in the areas passed by each AGV respectively according to the AGV density algorithm and the shortest path node vector;

[0013] Calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total AGV density in the areas passed by each AGV;

[0014] Select the target AGV with the least total cost according to the total costs of each AGV;

[0015] Allocate the task order to the target AGV.

[0016] Furthermore, after the step of allocating the task order to the target AGV, the following steps are further included:

[0017] Perform a conflict detection operation when the target AGV executes the task order;

[0018] When the conflict detection operation detects a conflict situation, confirm the conflict type of the conflict situation;

[0019] Determine the AGV avoidance strategy according to the conflict type;

[0020] Perform a position data update operation on all AGVs according to the AGV avoidance strategy.

[0021] Furthermore, the conflict type includes a rear-end collision conflict and a vertical conflict. The step of determining the AGV avoidance strategy according to the conflict type specifically includes the following steps:

[0022] When the conflict type is a rear-end collision conflict, if the front AGV is in the shelf loading state, the rear AGV must stop and wait until the front AGV completes loading and leaves before it can continue to travel;

[0023] When the conflict type is a vertical conflict, compare the load conditions of the first conflict AGV and the second conflict AGV;

[0024] If the load of the first conflict AGV is less than the load of the second conflict AGV, limit that the first conflict AGV must avoid the second conflict AGV;

[0025] If the load of the first conflicting AGV is equal to the load of the second conflicting AGV, the remaining path costs of the first conflicting AGV and the second conflicting AGV are respectively obtained, and the conflicting AGV with a lower remaining path cost is restricted to avoid the conflicting AGV with a higher remaining path cost.

[0026] Furthermore, the shortest distance matrix D is expressed as:

[0027] D(i,j)=min{D(i,j),D(i,k)+D(k,j)}(1≤k≤N,k≠i≠j);

[0028] Among them, D represents the distance matrix between two points, i represents the starting point, j represents the end point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

[0029] Furthermore, the shortest path matrix P is expressed as:

[0030]

[0031] Among them, P represents the path matrix between two points, i represents the starting point, j represents the end point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

[0032] Furthermore, the total AGV density p(m) in the AGV route area is expressed as:

[0033] p(m)=∑ρ(n,t);

[0034]

[0035] Among them, s(n) represents the area of ​​the nth region, g(n,t) represents the number of AGVs in the nth region at time t, ρ(n,t) is the AGV density of the nth region at time t, and p(m) is the sum of the AGV densities of the regions passed by the mth AGV.

[0036] Furthermore, the total cost value(n) of each AGV is expressed as:

[0037] value(n)=route(n)×q1+p(m)×q2;

[0038] Among them, value(n) represents the bid of the nth AGV, route(n) represents the path cost of the nth AGV, p(m) represents the total AGV density in the area passed by the mth AGV, and the coefficients q1 and q2 represent the path weight and density weight respectively.

[0039] To solve the above technical problems, an embodiment of the present application further provides a task allocation management device for warehousing transportation, which adopts the following technical solutions:

[0040] A map data acquisition module, configured to acquire warehousing map location data;

[0041] A matrix calculation module, configured to calculate a shortest distance matrix and a shortest path matrix of the warehousing map location data according to the Floyd algorithm, wherein the shortest path matrix P includes path nodes;

[0042] A task order acquisition module, configured to read a task database and acquire a task order in the task database;

[0043] A node confirmation module, configured to determine a passing working node and an end node according to the task order;

[0044] A path cost calculation module, configured to calculate a shortest path node vector and a required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix;

[0045] An AGV total density calculation module, configured to calculate the total density of AGVs in each area passed by each AGV according to the AGV density algorithm and the shortest path node vector;

[0046] A total cost calculation module, configured to calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total density of AGVs in each area passed by each AGV;

[0047] A target AGV confirmation module, configured to screen out a target AGV with the least total cost according to the total cost of each AGV;

[0048] A task allocation module, configured to allocate the task order to the target AGV.

[0049] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

[0050] It includes a memory and a processor. Computer-readable instructions are stored in the memory, and when the processor executes the computer-readable instructions, the steps of the above-mentioned task allocation management method for warehousing transportation are implemented.

[0051] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

[0052] Computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the steps of the task allocation management method applied to warehousing and transportation as described above are implemented.

[0053] This application provides a task allocation management method applied to warehousing and transportation, including: obtaining warehousing map location data; calculating the shortest distance matrix and the shortest path matrix of the warehousing map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes; reading a task database and obtaining task orders in the task database; determining the passing work nodes and the end node according to the task orders; calculating the shortest path node vector and the required path cost for all AGVs to execute the task orders according to the shortest distance matrix and the shortest path matrix; calculating the total AGV density in the areas passed by each AGV according to the AGV density algorithm and the shortest path node vector respectively; calculating the total cost of each AGV according to the bidding algorithm, the path cost, and the total AGV density in the areas passed by each AGV; screening out the target AGV with the least total cost according to the total cost of each AGV; and allocating the task order to the target AGV. Compared with the prior art, this application introduces a bidding scheduling strategy, simulates the market mechanism, comprehensively considers the AGV regional density and path cost, allows AGVs to bid for tasks according to their own status and task requirements, ensures that tasks are allocated with the optimal efficiency, and additionally designs an obstacle avoidance priority strategy, which comprehensively determines the dynamic priority according to various factors such as the current task, load, and predetermined path of the AGV, reducing conflicts between AGVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the solutions in this application, the following will briefly introduce the drawings required for the description of the embodiments of this application. Obviously, the following described drawings are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 is an exemplary system architecture diagram to which this application can be applied;

[0056] Figure 2 is a flowchart of the implementation of the task allocation management method applied to warehousing and transportation provided by the embodiment of this application;

[0057] Figure 3 is a schematic diagram of the simulation map provided by the embodiment of this application;

[0058] Figure 4 is a schematic diagram of the AGV conflict type provided by the embodiment of this application;

[0059] Figure 5 It is a schematic diagram of map area division provided by an embodiment of the present application;

[0060] Figure 6 It is a schematic diagram provided by an embodiment of the present application;

[0061] Figure 7 It is a schematic diagram of the comparison of AGV speed inconsistency results provided by an embodiment of the present application;

[0062] Figure 8 It is a schematic structural diagram of a task assignment management device applied to warehousing transportation provided by an embodiment of the present application;

[0063] Figure 9 It is a schematic structural diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0065] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears at various positions in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0066] In order to enable those skilled in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0067] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0068] Users can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0069] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop portable computer, a desktop computer, and so on.

[0070] The server 103 can be a server that provides various services, such as a background server that provides support for the pages displayed on the terminal device 101.

[0071] It should be noted that the task assignment management method applied to warehousing and transportation provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the task assignment management device applied to warehousing and transportation is generally set in the server / terminal device.

[0072] It should be understood that Figure 1 the numbers of the terminal devices, networks, and servers in

[0073] Continue to refer to Figure 2 , which shows a flowchart of an embodiment of the task assignment management method applied to warehousing and transportation according to the present application. The task assignment management method applied to warehousing and transportation includes: step S201, step S202, step S203, step S204, step S205, step S206, step S207, step S208, and step S209.

[0074] In step S201, obtain the warehousing map location data.

[0075] In step S202, calculate the shortest distance matrix and the shortest path matrix of the warehousing map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes.

[0076] In some alternative implementation manners of the embodiments of the present application, the above-mentioned shortest distance matrix D is expressed as:

[0077] D(i,j) = min{D(i,j), D(i,k) + D(k,j)} (1 ≤ k ≤ N, k ≠ i ≠ j);

[0078] Wherein, D represents the distance matrix between two points, i represents the starting point, j represents the ending point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

[0079] In some alternative implementation manners of the embodiments of the present application, the above-mentioned shortest path matrix P is expressed as:

[0080]

[0081] Wherein, P represents the path matrix between two points, i represents the starting point, j represents the ending point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

[0082] In the embodiments of the present application, the shortest distance matrix D and the shortest path matrix P are iteratively calculated using the Floyd algorithm according to the warehouse map location information. Specifically, it can be:

[0083] (1) Initialization: Create an N×N adjacency matrix D according to the number of vertices N of the map. The element D(i,j) in the matrix represents the direct distance from vertex i to vertex j in the weight map. If there is no direct connection between vertices, the distance is set to infinity; at the same time, create a path matrix P of the same size. P(i,j) records the path information between vertices. Vertices that are not directly connected are represented by -1, and the values of the remaining elements are set to the row number where the element is located.

[0084] (2) Iterative update: For each pair of vertices i and j in the map, use k as the intermediate point for comparison. If D(i,k) + D(k,j) is smaller than the value of D(i,j) in the matrix, update the matrix, and at the same time update the value of P(i,j) to k, indicating that the shortest distance from node i to node j is via node k.

[0085] (3) Traversal: Use k to traverse all N vertices in the weight map. That is, after n iterations, the values in the distance matrix D will represent the shortest path lengths between all vertex pairs. When reading the path, only arrange the path nodes in the path matrix P in reverse order.

[0086] In the embodiments of the present application, the present application can divide the map according to the distribution of path nodes, and try to make the number of path nodes included in each area approximately equal.

[0087] In step S203, read the task database and obtain the task order from the task database.

[0088] In step S204, determine the passing work nodes and the end node according to the task order.

[0089] In step S205, calculate the shortest path node vector and the required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix.

[0090] In the embodiment of the present application, according to the calculated shortest distance matrix, calculate the path cost of each AGV participating in the current task bidding.

[0091] In step S206, calculate the total AGV density in the areas passed by each AGV according to the AGV density algorithm and the shortest path node vector respectively.

[0092] In the embodiment of the present application, according to the calculated shortest path matrix and the divided areas, calculate the AGV density in the areas passed by each AGV participating in the current task bidding.

[0093] In some alternative implementation manners of the embodiment of the present application, the total AGV density p(m) in the areas passed by the above AGV is expressed as:

[0094] p(m) = ∑ρ(n,t);

[0095]

[0096] Wherein, s(n) represents the area of the nth area, g(n,t) represents the number of AGVs in the nth area at time t, ρ(n,t) is the AGV density in the nth area at time t, and p(m) is the total AGV density in the mth area passed by the AGV.

[0097] In step S207, calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total AGV density in the areas passed by each AGV.

[0098] In the embodiment of the present application, combine the existing path cost and the AGV area density to calculate the bidding price of each AGV.

[0099] In some alternative implementation manners of the embodiment of the present application, the total cost value(n) of the above each AGV is expressed as:

[0100] value(n) = route(n) × q1 + p(m) × q2;

[0101] Among them, value(n) represents the bidding price of the nth AGV, route(n) represents the path cost of the nth AGV, p(m) represents the total AGV density of the area passed by the mth AGV, and the coefficients q1 and q2 represent the path weight and density weight respectively.

[0102] In step S208, the target AGV with the least total cost is selected according to the total cost of each AGV.

[0103] In the embodiment of the present application, the present application selects the AGV with the minimum bidding price to execute the task.

[0104] In step S209, the task order is assigned to the target AGV.

[0105] In practical applications, an application map example of the present application is as Figure 3 shown. The map consists of 100 shelves and 36 AGV docking areas. The roads in the map are distributed on both sides of the shelves. The roads are connected by 1224 fixed coordinate nodes. The abscissa range is from 0 to 35, and the ordinate is from 0 to 25. The roads are all designed as one-way streets. There are two roads between the shelves, corresponding to two running directions respectively. Each shelf consists of three working points, and the working coordinates of each working point are included in the 1224 nodes that make up the road. When the AGV works, it needs to reach the corresponding shelf working point in sequence according to the order requirements. Each order contains at most 3 working points.

[0106] In the embodiment of the present application, a task allocation management method applied to warehousing transportation is provided, including: obtaining warehousing map position data; calculating the shortest distance matrix and the shortest path matrix of the warehousing map position data according to the Floyd algorithm, where the shortest path matrix includes path nodes; reading the task database and obtaining task orders in the task database; determining the passing working nodes and the end node according to the task order; calculating the shortest path node vector and the required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix; calculating the total AGV density of the area passed by each AGV according to the AGV density algorithm and the shortest path node vector respectively; calculating the total cost of each AGV according to the bidding algorithm, the path cost and the total AGV density of the area passed by each AGV; selecting the target AGV with the least total cost according to the total cost of each AGV; and assigning the task order to the target AGV. Compared with the prior art, the present application introduces a bidding scheduling strategy, simulates the market mechanism, comprehensively considers the AGV area density and path cost, allows the AGV to bid for tasks according to its own status and task requirements, ensures that the tasks are allocated with the optimal efficiency. In addition, an obstacle avoidance priority strategy is designed, and the dynamic priority is comprehensively determined according to various factors such as the current task, load and predetermined path of the AGV, reducing the conflict between AGVs.

[0107] In some alternative implementation manners of the embodiments of the present application, after the step of allocating a task order to a target AGV, the following steps are further included:

[0108] Perform a conflict detection operation when the target AGV executes the task order;

[0109] When the conflict detection operation detects a conflict situation, confirm the conflict type of the conflict situation;

[0110] Determine an AGV avoidance strategy according to the conflict type;

[0111] Perform a position data update operation on all AGVs according to the AGV avoidance strategy.

[0112] In the embodiments of the present application, conflict detection is performed when the AGV is running. When dealing with a conflict situation, determine the AGVs that need to stop and wait for obstacle avoidance according to different conflict types, and update the positions of each AGV according to the processing result; detect the remaining situation of the task library and the positions of all AGVs, and end the program after the task library is emptied and all AGVs reach the destination.

[0113] In some alternative implementation manners of the embodiments of the present application, the conflict types include a rear-end collision conflict and a vertical conflict. The step of determining the AGV avoidance strategy according to the conflict type specifically includes the following steps:

[0114] When the conflict type is a rear-end collision conflict, if the front AGV is in a shelf loading state, the rear AGV must stop and wait until the front AGV finishes loading and leaves before continuing to drive;

[0115] When the conflict type is a vertical conflict, compare the load conditions of the first conflict AGV and the second conflict AGV;

[0116] If the load of the first conflict AGV is less than the load of the second conflict AGV, it is specified that the first conflict AGV must avoid the second conflict AGV;

[0117] If the load of the first conflict AGV is equal to the load of the second conflict AGV, respectively obtain the remaining path costs of the first conflict AGV and the second conflict AGV, and specify that the conflict AGV with the lower remaining path cost avoids the conflict AGV with the higher remaining path cost.

[0118] In the embodiments of the present application, under the one-way traffic rule of the present application, there will be two vehicle conflict types as Figure 4 in: rear-end collision conflict and vertical conflict. The AGV avoidance rules are as follows:

[0119] (1) First, an avoidance priority needs to be assigned to the AGV, and the priority is composed of the AGV load state, the remaining task path cost, and the AGV working state.

[0120] (2) When a rear-end collision occurs, if the leading vehicle is in the shelf loading state, the following vehicle must stop and wait until the leading vehicle completes loading and leaves before it can continue to move forward.

[0121] (3) When a vertical collision occurs, considering that the start and stop of a heavy-load AGV consume a large amount of energy, it is necessary to first compare the load conditions of the two vehicles. The AGV with a lighter load stops to give way to the AGV with a heavier load. If the loads of the two vehicles are the same, then consider the remaining path costs of the two vehicles, and the AGV with a lower cost takes the initiative to stop and give way to the AGV with a higher cost.

[0122] In the embodiment of the present application, when designing the avoidance priority strategy, considering that the energy consumed by the start and stop of an AGV is different under different loads, and the higher the load, the higher the energy consumption during startup. Therefore, the load of the AGV is taken as the primary factor to be considered. In any case, the AGV with a heavier load enjoys the right of priority passage. To facilitate the comparison of loads, the AGV load levels are divided into three levels: level one, level two, and level three, where level three is the maximum load. Each time an AGV arrives at a shelf working point, it is regarded as an increase in the load level by one level; at the same time, the startup time of the AGV will also increase with the increase of the load level. The startup time of the AGV without load is 1 time step, that is, when the AGV detects that the surrounding conflicting vehicles have left the conflict range, it needs to stay in place for 1 time step before it can continue to move forward. When the load level is level 1, it takes 2 time steps to complete the startup action. Similarly, level two and level three loads require 3 and 4 time steps for startup. When the load levels of the two conflicting parties are the same, then compare the estimated time required for both parties to complete the task. To shorten the overall task time, it is stipulated that the party with the shorter remaining task time among the two conflicting parties gives way to the party with the longer remaining task time.

[0123] In practical applications, compared with the single Floyd path planning algorithm, the algorithm of the present application can better solve the scheduling problem when the AGV has regional congestion. Specifically:

[0124] Tests are carried out with 15 and 20 task quantities respectively, and the speed of the AGV is set to 1, and the time step is 0.1. The simulation map is divided into regions as shown in Figure 5 When issuing tasks, at least one of the nodes included in each task is set to be located in Figure 5 area 5 in to simulate the situation where AGVs are concentrated and congested in area 5.

[0125] The algorithms considering AGV density and not considering AGV density are used for experiments respectively, and the total time used to complete the tasks is recorded for each. The experimental results are shown in Table 1 below:

[0126]

[0127] Table 1

[0128] It can be seen from the experimental results that using the regional AGV density as a reference for task scheduling can effectively reduce the overall task time. Moreover, according to the observation of the AGV movement, most of the reduced task time is due to the system task scheduling avoiding the peak of regional AGV density, dispersing the task nodes concentrated in Area 5 in time, and avoiding the time loss caused by AGV avoidance due to congestion. Thus, it can be seen that the AGV regional density has a certain optimization effect on task scheduling and can improve task efficiency.

[0129] In practical applications, the algorithm of this application has better performance than the single Floyd algorithm. Specifically:

[0130] In the simulation map, 10 groups of tests are carried out for the number of tasks from 10 to 100 at an interval of 10. The AGV speed is uniformly set to 1, that is, in the normal driving state, the distance that the AGV moves in the simulation map within each time step is 1. This group is recorded as the experimental group. To test the algorithm efficiency, a control experiment using only the single Floyd algorithm is set up at the same time, recorded as the control group. The same task orders are issued to the experimental group and the control group, and the efficiency of the two algorithms in path planning and task scheduling is compared through the final task time. The comparison results are as Figure 6 (a) and Figure 6 (b) shown.

[0131] When only 10 tasks are issued, the total time difference between the experimental group and the control group to complete the tasks is only 0.6 s, and the saved time is only 0.7326%. This shows that when the number of tasks is small, the improvement of this algorithm in the system path planning and task scheduling efficiency is not obvious. As the number of tasks gradually increases, the time spent by this algorithm is significantly shorter than that of the single Floyd algorithm, and the optimization efficiency also increases significantly. When the number of tasks reaches a certain limit, although the saved time still increases with the number of tasks, due to the increase in the number of AGVs required to call the tasks and the aggravation of the path occupancy in the map, the optimization efficiency of this algorithm finally stabilizes at about 9.5%.

[0132] In practical applications, the algorithm of this application can still maintain good performance when the AGV speeds are inconsistent. Specifically:

[0133] In practical applications, the speeds of different AGVs may vary due to factors such as task types, path conditions, and obstacles. The inconsistent speeds will increase the probability of AGV conflicts. At the same time, when the system calculates the path cost, due to different speeds, the path cost changes more flexibly.

[0134] 8 sets of tests are carried out in the simulation map, and the number of test tasks is 30 - 100, increasing at intervals of 10. The same tasks are used in each set of tests, and the simulation is carried out for two cases where the AGV speeds are the same and the AGV speeds are different respectively. When each set of test tasks is issued, the speed of each AGV in the experimental group is set to a random value among 0.75, 1, and 1.25 and remains unchanged in this set of test tasks. The speed of the AGVs in the control group is uniformly set to the average speed of the AGVs in the corresponding experimental group; when the next set of test tasks is issued, the speeds of the AGVs in the experimental group are randomly reset again and their average speed is assigned to the control group. The same task orders are issued to the experimental group and the control group in each set of tests, and the execution effect of the algorithm is judged by comparing the task time. The comparison results are as Figure 7 (a) and Figure 7 (b) shown.

[0135] The different AGV speeds will cause additional conflicts in the system during operation. Therefore, the experimental groups with different speeds require longer time to complete the tasks compared to the control group with the same speed. The increased duration is the time spent by the system to handle the additional conflicts. Although the additional time increases with the increase in the number of tasks, its time increase ratio remains stable within a certain range. The above results indicate that the algorithm can also achieve task planning when the AGV running speeds are different, reasonably adjust the task scheduling and path planning strategies, and minimize the waiting time due to additional conflicts, so as to control the error between the time to complete the task goal and the ideal time within 10%, effectively controlling the AGV operation cost.

[0136] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0137] Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technologies mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0138] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0139] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0140] Further reference Figure 8 to Figure 2 As an implementation of the method shown above, an embodiment of a task assignment management device applied to warehousing and transportation is provided in this application. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0141] As Figure 8 shown, the task assignment management device 200 applied to warehousing and transportation in the embodiment of this application includes:

[0142] A map data acquisition module 210, configured to acquire warehousing map location data;

[0143] A matrix calculation module 220, configured to calculate the shortest distance matrix and the shortest path matrix of the warehousing map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes;

[0144] A task order acquisition module 230, configured to read a task database and acquire task orders in the task database;

[0145] A node confirmation module 240, configured to determine the passing working nodes and the end node according to the task order;

[0146] A path cost calculation module 250, configured to calculate the shortest path node vectors and required path costs for all AGVs to execute task orders according to the shortest distance matrix and the shortest path matrix;

[0147] An AGV total density calculation module 260, configured to calculate the total density of AGVs in each area passed by an AGV according to the AGV density algorithm and the shortest path node vectors respectively;

[0148] A total cost calculation module 270, configured to calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total density of AGVs in each area passed by an AGV;

[0149] A target AGV confirmation module 280, configured to screen out the target AGV with the least total cost according to the total costs of each AGV;

[0150] A task assignment module 290, configured to assign task orders to the target AGV.

[0151] In an embodiment of the present application, a task assignment management device 200 applied to warehousing transportation is provided, including: a map data acquisition module 210, configured to acquire warehousing map location data; a matrix calculation module 220, configured to calculate the shortest distance matrix and the shortest path matrix of the warehousing map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes; a task order acquisition module 230, configured to read a task database and acquire task orders in the task database; a node confirmation module 240, configured to determine passing working nodes and end nodes according to the task orders; a path cost calculation module 250, configured to calculate the shortest path node vectors and required path costs for all AGVs to execute task orders according to the shortest distance matrix and the shortest path matrix; an AGV total density calculation module 260, configured to calculate the total density of AGVs in each area passed by an AGV according to the AGV density algorithm and the shortest path node vectors respectively; a total cost calculation module 270, configured to calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total density of AGVs in each area passed by an AGV; a target AGV confirmation module 280, configured to screen out the target AGV with the least total cost according to the total costs of each AGV; a task assignment module 290, configured to assign task orders to the target AGV. Compared with the prior art, the present application introduces a bidding scheduling strategy, simulates a market mechanism, comprehensively considers the AGV area density and path cost, allows AGVs to bid for tasks according to their own states and task requirements, ensures that tasks are assigned with optimal efficiency, and in addition, designs an obstacle avoidance priority strategy, and comprehensively determines the dynamic priority according to various factors such as the current tasks, load, and predetermined paths of AGVs, reducing conflicts between AGVs.

[0152] To solve the above technical problems, an embodiment of the present application also provides a computer device. For details, please refer toFigure 9 , Figure 9 This is the basic structural block diagram of the computer device according to the embodiment of the present application.

[0153] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are communicatively connected to each other through a system bus. It should be noted that only the computer device 300 with components 310-330 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0154] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device, etc.

[0155] The memory 310 includes at least one type of readable storage medium, which includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 310 may be an internal storage unit of the computer device 300, such as the hard disk or memory of the computer device 300. In other embodiments, the memory 310 may also be an external storage device of the computer device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the computer device 300. Of course, the memory 310 may also include both the internal storage unit and the external storage device of the computer device 300. In the embodiments of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions for the task assignment management method applied to warehousing and transportation. In addition, the memory 310 may also be used to temporarily store various data that have been output or will be output.

[0156] In some embodiments, the processor 320 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiments of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as running the computer-readable instructions for the task assignment management method applied to warehousing and transportation.

[0157] The network interface 330 may include a wireless network interface or a wired network interface, and the network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0158] The computer device provided by the present application introduces a bidding scheduling strategy, simulates the market mechanism, comprehensively considers the AGV area density and path cost, allows the AGV to conduct task bidding according to its own status and task requirements, ensures that tasks are allocated with the optimal efficiency, and in addition, designs an obstacle avoidance priority strategy, and comprehensively determines the dynamic priority according to various factors such as the current task, load, and predetermined path of the AGV, reducing the conflicts between AGVs.

[0159] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor, so that the at least one processor executes the steps of the task allocation management method applied to warehousing and transportation as described above.

[0160] The computer-readable storage medium provided by the present application introduces a competitive bidding scheduling strategy, simulates the market mechanism, comprehensively considers the AGV regional density and path cost, allows AGVs to bid for tasks according to their own status and task requirements, ensures that tasks are allocated with the optimal efficiency. In addition, an obstacle avoidance priority strategy is designed to comprehensively determine the dynamic priority according to various factors such as the current task, load, and predetermined path of the AGV, reducing conflicts between AGVs.

[0161] Through the description of the above implementation manners, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0162] Obviously, the embodiments described above are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific implementation manners, or perform equivalent replacements for some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields is similarly within the scope of the patent protection of the present application.

Claims

1. A task allocation management method applied to warehousing and transportation, characterized in that Including the following steps: Obtain the location data of the warehouse map; Calculate the shortest distance matrix and the shortest path matrix of the warehouse map location data according to the Floyd algorithm, where the shortest path matrix P includes path nodes; Read the task database and obtain task orders in the task database; Determine the passing working nodes and the end node according to the task order; Calculate the shortest path node vector and the required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix; Calculate the total AGV density in the areas passed by each AGV according to the AGV density algorithm and the shortest path node vector; Calculate the total cost of each AGV according to the bidding algorithm, the path cost, and the total AGV density in the areas passed by each AGV; Select the target AGV with the least total cost according to the total costs of each AGV; Assign the task order to the target AGV.

2. The task assignment management method applied to warehousing and transportation according to claim 1, wherein After the step of assigning the task order to the target AGV, the following steps are further included: Perform a conflict detection operation when the target AGV executes the task order; When the conflict detection operation detects a conflict situation, confirm the conflict type of the conflict situation; Determine the AGV avoidance strategy according to the conflict type; Perform a location data update operation on all AGVs according to the AGV avoidance strategy.

3. The task allocation management method for warehousing and transportation according to claim 2, characterized in that The conflict type includes a rear-end collision conflict and a vertical conflict. The step of determining the AGV avoidance strategy according to the conflict type specifically includes the following steps: When the conflict type is a rear-end collision conflict, if the front AGV is in a shelf loading state, the rear AGV must stop and wait until the front AGV finishes loading and leaves before continuing to drive; When the conflict type is a vertical conflict, compare the load conditions of the first conflict AGV and the second conflict AGV; If the load of the first conflict AGV is less than the load of the second conflict AGV, limit that the first conflict AGV must avoid the second conflict AGV; If the load of the first conflict AGV is equal to the load of the second conflict AGV, respectively obtain the remaining path costs of the first conflict AGV and the second conflict AGV, and limit that the conflict AGV with the lower remaining path cost avoids the conflict AGV with the higher remaining path cost.

4. The task allocation management method applied to warehousing and transportation according to claim 1, characterized in that, The shortest distance matrix D is expressed as: D(i,j)=min{D(i,j),D(i,k)+D(k,j)}(1≤k≤N,k≠i≠j); Where, D represents the distance matrix between two points, i represents the starting point, j represents the end point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

5. The task allocation management method applied to warehousing and transportation according to claim 1, characterized in that, The shortest path matrix P is expressed as: Where, P represents the path matrix between two points, i represents the starting point, j represents the end point, N represents the number of vertices in the weight map, and k represents the intermediate point in the iteration process.

6. The task assignment management method applied to warehousing and transportation according to claim 1, characterized in that, The total AGV density p(m) in the area passed by the AGV is expressed as: p(m)=∑ρ(n,t); Among them, s(n) represents the area of the nth region, g(n, t) represents the number of AGVs in the nth region at time t, ρ(n, t) is the AGV density in the nth region at time t, and p(m) is the total AGV density of the regions passed by the mth AGV.

7. The task assignment management method applied to warehousing and transportation according to claim 1, characterized in that, The total cost value(n) of each AGV is expressed as: value(n) = route(n) × q1 + p(m) × q2; Among them, value(n) represents the bidding price of the nth AGV, route(n) represents the path cost of the nth AGV, p(m) represents the total AGV density of the regions passed by the mth AGV, and the coefficients q1 and q2 represent the path weight and density weight respectively.

8. A task allocation management device applied to warehousing and transportation, characterized in that, Including: A map data acquisition module for acquiring the location data of the warehouse map; A matrix calculation module for calculating the shortest distance matrix and the shortest path matrix of the warehouse map location data according to the Floyd algorithm, where the shortest path matrix includes path nodes; A task order acquisition module for reading the task database and obtaining task orders in the task database; A node confirmation module for determining the passing working nodes and the end node according to the task order; A path cost calculation module for calculating the shortest path node vector and the required path cost for all AGVs to execute the task order according to the shortest distance matrix and the shortest path matrix; An AGV total density calculation module for calculating the total AGV density of the regions passed by each AGV respectively according to the AGV density algorithm and the shortest path node vector; A total cost calculation module for calculating the total cost of each AGV according to the bidding algorithm, the path cost, and the total AGV density of the regions passed by each AGV; A target AGV confirmation module for screening out the target AGV with the least total cost according to the total cost of each AGV; A task assignment module for assigning the task order to the target AGV.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the task assignment management method for warehouse transportation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the task assignment management method for warehouse transportation as described in any one of claims 1 to 7 are implemented.

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