A method, device, electronic device and storage medium for energy-saving allocation of logistics transportation tasks

Abstract

The invention discloses a method, device, electronic device and storage medium for allocating logistics transportation tasks in an energy-saving manner, and relates to the technical field of logistics transportation. For any access node pair in a transportation network diagram, the storage node and the access node of the access node pair are searched in opposite directions to obtain the initial route set of the access node pair; the effective path set of all access node pairs is determined by an ant colony algorithm; a plurality of chromosomes are randomly obtained according to a task list and all vehicles, and all chromosomes are iteratively optimized until a preset convergence condition is met and a result is output; the time for obtaining the initial route is shortened by searching in opposite directions, a relatively optimized effective path between two nodes is intelligently found by an ant colony algorithm, and then the optimal execution order of picking up and delivering operations is calculated by a chromosome algorithm, and the optimal transportation path for simultaneously executing each picking up and delivering operation is selected, thereby improving the transportation efficiency of logistics and reducing the energy consumption level of the logistics system.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics and transportation, and specifically relates to a method, device, electronic equipment and storage medium for energy-saving allocation of logistics and transportation tasks. Background Art

[0002] In the field of logistics and transportation, although certain progress has been made in logistics optimization, due to the lack of comprehensive consideration of road conditions and vehicle status, the current path planning system often cannot provide logistics vehicles with the truly optimal driving route. This means that in the face of a complex traffic network, logistics vehicles may encounter unnecessary detours and repeated runs. This unreasonable path selection has brought many negative impacts on logistics and transportation.

[0003] In particular, this unreasonable route selection leads to a huge waste of energy resources. In addition to the problem of route selection, unreasonable task allocation is also an important factor affecting the energy efficiency of logistics transportation. In logistics transportation, each vehicle has its specific load capacity, transportation distance and transportation time limit. However, in the actual task allocation process, these factors are often ignored or not fully considered, which may cause some vehicles to be inefficient due to excessive load or long driving distance, while other vehicles may be idle due to insufficient tasks. This unbalanced task allocation not only reduces the overall efficiency of logistics transportation, but also wastes precious transportation resources, resulting in excessive energy consumption of the logistics transportation system. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of excessive energy consumption in the logistics transportation system, and to propose a logistics transportation task energy-saving allocation method, device, electronic equipment and storage medium.

[0005] In a first aspect of the present invention, a method for energy-saving allocation of logistics transportation tasks is first proposed, the method comprising:

[0006] Obtain a transportation network diagram corresponding to the target area, and for any access node pair in the transportation network diagram, search the storage node and the pickup node of the access node pair in opposite directions to obtain an initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area;

[0007] Determine the valid paths in the initial route set by using an ant colony algorithm to obtain a node valid path set, obtain the node valid path sets corresponding to all access node pairs to obtain a valid path set; each access node pair has at least one valid path;

[0008] Multiple allocation plans are randomly generated based on the task list and all vehicles, and the driving path of each vehicle is determined based on the allocation plan; the driving path is a valid path from node to node;

[0009] A plurality of chromosomes are obtained for each allocation scheme and the corresponding driving path, and all chromosomes are iteratively optimized until a preset convergence condition is met and a result is output, and the allocation scheme is determined according to the output result.

[0010] Optionally, searching the storage node and the access node of the access node pair in opposite directions to obtain an initial route set of the access node pair includes:

[0011] Step 1: Randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node;

[0012] Step 2: For each sampling point, obtain the line from the initial point to the sampling point, and adjust the position of the sampling point according to whether the line passes through the non-road area;

[0013] Step 3: Generate a first starting tree and a second starting tree based on all the nodes in step 2; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node;

[0014] Step 4: Determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, output all paths to obtain the initial route set; if not, return to step 1 to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than a preset length, and there is no non-road area in the middle of the line connecting the last child nodes.

[0015] Optionally, for each sampling point, obtaining a line from the initial point to the sampling point, and adjusting the position of the sampling point according to whether the line passes through a non-road area includes:

[0016] For each sampling point, obtain a line from the initial point to the end point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated by the first target node;

[0017] If the line passes through a non-road area, a first adjustment point is determined with a preset length and an initial point, and the end point is removed; a collision test is performed on the line from the initial point to the first adjustment point, and if no collision occurs, the first adjustment point is recorded as the end point, and a preset angle range when the end point is used as the starting point is increased;

[0018] If the line does not pass through the non-road area, the second adjustment point is determined using the preset length and the line. If the second adjustment point exists, a collision test is performed on the line from the initial point to the second adjustment point. If no collision occurs, the second adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is reduced.

[0019] Optionally, all chromosomes are iteratively optimized until the preset convergence conditions are met. The output results include:

[0020] Step 1: Perform a legitimacy check on all chromosomes to determine the first legal chromosome group and the illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed the preset load, the vehicle cannot perform cargo pickup operations for a preset number of consecutive times, the cargo must be loaded and unloaded within the specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle.

[0021] Step 2: adjusting the transport order of the chromosomes in the illegal chromosome group so that the chromosomes satisfy the constraint conditions to obtain a second legal chromosome group;

[0022] Step 3: Initialize the first legal chromosome group and the second legal chromosome group by merging them to obtain an initialized population;

[0023] Step 4: The initialized population performs selection, crossover and mutation operations to generate an offspring population;

[0024] Step 5: Return the generated offspring population to step 1 for iterative optimization until the preset convergence conditions are met and the results are output.

[0025] In a second aspect of the present invention, a device for energy-saving allocation of logistics transportation tasks is provided, the device comprising:

[0026] An initial route set determination module is used to obtain a transportation network diagram corresponding to a target area, and for any access node pair in the transportation network diagram, search the storage node and the pickup node of the access node pair in opposite directions to obtain an initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area;

[0027] An effective path set determination module is used to determine the effective paths in the initial route set by using an ant colony algorithm to obtain a node effective path set, and obtain the node effective path sets corresponding to all access node pairs to obtain an effective path set; each access node pair has at least one effective path;

[0028] A driving path determination module is used to randomly generate multiple allocation schemes based on the task list and all vehicles, and determine the driving path of each vehicle according to the allocation scheme; the driving path is a valid path from node to node;

[0029] The allocation scheme determination module is used to obtain multiple chromosomes for each allocation scheme and the corresponding driving path, iteratively optimize all chromosomes until a preset convergence condition is met to output a result, and determine the allocation scheme according to the output result.

[0030] Optionally, the initial route set determination module includes:

[0031] A sampling point generation module, used to randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node;

[0032] A sampling point position adjustment module is used to obtain a line from the initial point to the sampling point for each sampling point, and adjust the position of the sampling point according to whether the line passes through a non-road area;

[0033] A starting tree establishment module, used to generate a first starting tree and a second starting tree according to all nodes in the sampling point position adjustment module; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node;

[0034] The path network establishment module is used to determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, all paths are output to obtain an initial route set; if not, the sampling point generation module is returned to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than a preset length, and there is no non-road area in the middle of the line connecting the last child nodes.

[0035] Optionally, the sampling point position adjustment module includes:

[0036] A connection generation module, used for obtaining a connection from an initial point to an end point for each sampling point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated from the first target node;

[0037] A first adjustment point determination module is used to determine the first adjustment point with a preset length and an initial point if the connection line passes through a non-road area, and remove the end point; perform a collision test on the connection line from the initial point to the first adjustment point, and if no collision occurs, record the first adjustment point as the end point, and increase the preset angle range when the end point is used as the starting point;

[0038] The second adjustment point determination module is used to determine the second adjustment point based on the preset length and the connection line if the connection line does not pass through the non-road area. If the second adjustment point exists, a collision test is performed on the connection line from the initial point to the second adjustment point. If no collision occurs, the second adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is reduced.

[0039] Optionally, the allocation scheme determination module includes:

[0040] A legality detection module is used to perform legality detection on all chromosomes to determine a first legal chromosome group and an illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed a preset load, the vehicle cannot continuously perform cargo pickup operations for a preset number of times, cargo loading and unloading must be completed within a specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle;

[0041] A second legal chromosome group determination module is used to adjust the transport order of the chromosomes in the illegal chromosome group so that the chromosomes satisfy the constraint conditions to obtain a second legal chromosome group;

[0042] An initialization population determination module, used for merging and initializing the first legal chromosome group and the second legal chromosome group to obtain an initialization population;

[0043] A offspring population determination module, used for performing selection, crossover and mutation operations on the initialization population to generate an offspring population;

[0044] The iterative optimization module is used to return the generated offspring population to the legitimacy detection module for iterative optimization until the preset convergence conditions are met and the results are output.

[0045] In a third aspect of the present invention, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0046] Memory, used to store computer programs;

[0047] The processor is used to implement any of the steps of the above-mentioned method for energy-saving allocation of logistics transportation tasks when executing the program stored in the memory.

[0048] In a fourth aspect of the implementation of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the steps of the above-mentioned method for energy-saving allocation of logistics transportation tasks is implemented.

[0049] Beneficial effects of the present invention:

[0050] The present invention proposes an energy-saving allocation method for logistics transportation tasks. By obtaining a transportation network diagram corresponding to a target area, for any access node pair in the transportation network diagram, the storage node and the access node of the access node pair are searched in opposite directions to obtain the initial route set of the access node pair; the transportation network diagram consists of cargo storage points, pickup points and connection paths in the target area; the effective paths in the initial route set are determined by an ant colony algorithm to obtain a node effective path set, and the node effective path sets corresponding to all access node pairs are obtained to obtain an effective path set; each access node pair has at least one effective path; a plurality of allocation schemes are randomly generated according to a task list and all vehicles, and a driving path of each vehicle is determined according to the allocation scheme; the driving path is a valid path from node to node; a plurality of chromosomes are obtained for each allocation scheme and the corresponding driving path, and all chromosomes are iteratively optimized until a preset convergence condition is met to output a result, and the allocation scheme is determined according to the output result. By searching the storage nodes and the fetching nodes in opposite directions, the time efficiency of obtaining the initial route is reduced. The effective path in the initial route set is determined by the ant colony algorithm, which can intelligently find a more optimized effective path between the two nodes. The chromosome algorithm is then used to find the optimal execution order of the pickup and delivery operations, and the best transportation path for simultaneously executing the pickup and delivery operations is selected, which improves the transportation efficiency of logistics and reduces the energy consumption level of the logistics system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings. Figure 1 A flow chart of a method for energy-saving allocation of logistics transportation tasks is provided for an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a collection area is provided for an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of a device for energy-saving allocation of logistics and transportation tasks is provided for an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The embodiment of the present invention provides a method for energy-saving allocation of logistics transportation tasks. Figure 1 , Figure 1 A flow chart of a method for energy-saving allocation of logistics transportation tasks provided by an embodiment of the present invention. The method comprises the following steps:

[0056] S101, obtaining a transportation network diagram corresponding to a target area, and for any access node pair in the transportation network diagram, searching the storage node and the access node of the access node pair in opposite directions to obtain an initial route set of the access node pair.

[0057] S102, determining the valid paths in the initial route set by using an ant colony algorithm to obtain a node valid path set, and obtaining the node valid path sets corresponding to all access node pairs to obtain a valid path set.

[0058] S103, randomly generating multiple allocation plans according to the task list and all vehicles, and determining the driving path of each vehicle according to the allocation plan.

[0059] S104, obtaining multiple chromosomes for each allocation scheme and the corresponding driving path, iteratively optimizing all chromosomes until a preset convergence condition is met and a result is output, and determining the allocation scheme according to the output result.

[0060] The transportation network diagram consists of cargo storage points, pickup points and connection paths within the target area; each storage and access node pair has at least one valid path; the driving path is a valid path from node to node;

[0061] A method for energy-saving allocation of logistics transportation tasks provided by an embodiment of the present invention reduces the time efficiency of obtaining an initial route by searching in opposite directions with storage nodes and fetching nodes, determines the effective path in the initial route set through an ant colony algorithm, and can intelligently find a more optimized effective path between two nodes. Then, the chromosome algorithm is used to find the optimal execution order of the pickup and delivery operations, and the best transportation path for simultaneously executing the pickup and delivery operations is selected, thereby improving the transportation efficiency of logistics and reducing the energy consumption level of the logistics system.

[0062] In one implementation, by searching for access node pairs in opposite directions to obtain an initial route set, multiple possible driving routes can be quickly generated, thereby improving efficiency.

[0063] In one implementation, an ant colony algorithm is used to determine valid paths from an initial route set, which can fully consider the feasibility, efficiency and cost of the path, thereby generating a high-quality node valid path set.

[0064] In one implementation, multiple allocation schemes are randomly generated based on the task list and all vehicles, which increases the possibility of finding the optimal solution. The driving path of each vehicle is determined by the allocation scheme. These paths are based on the previously determined valid path set to avoid energy consumption caused by the paths between nodes.

[0065] In one implementation, the preset convergence condition is that the maximum number of iterations or the quality improvement of the solution is less than a preset value, the maximum number of iterations is determined by the staff, and the preset value is determined by the staff.

[0066] In one embodiment, searching the storage node and the access node of the access node pair in opposite directions to obtain an initial route set of the access node pair includes:

[0067] Step 1: Randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node;

[0068] Step 2: For each sampling point, obtain the line from the initial point to the sampling point, and adjust the position of the sampling point according to whether the line passes through the non-road area;

[0069] Step 3: Generate a first starting tree and a second starting tree based on all the nodes in step 2; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node;

[0070] Step 4: Determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, output all paths to obtain the initial route set; if not, return to step 1 to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than the preset length, and there is no non-road area in the middle of the line connecting the last child nodes.

[0071] In one implementation, the preset angle range is to take the first target node as the foot of the perpendicular, the path from the first target node to the second target node as the vertical line, take the initial length as the radius, rotate 90 degrees to the left, and rotate 90 degrees to the right. The initial length is determined by the operator, see Figure 2 A1 and L1 are the initial lengths.

[0072] In one implementation, the preset length is determined by an operator, and the preset length is less than or equal to the initial length.

[0073] In one implementation, random sampling can be performed by uniform random sampling, importance sampling, adaptive sampling, Markov chain Monte Carlo, sampling guided by probability density function, etc.

[0074] In one implementation, by checking whether the link passes through a non-road area and adjusting the sampling point position accordingly, the algorithm can adapt to changes in the environment, avoid obstacles, and ensure that the generated path does not violate any driving rules or safety restrictions.

[0075] In one implementation, generating two starting trees provides a structured way to explore and expand the path. The two trees are rooted at the storage node and the fetch node respectively, which maintains the directionality of the search and helps to directly connect to the target point.

[0076] In one implementation, by judging whether the distance between the last child nodes of the two trees meets the connection condition, it is possible to efficiently determine whether a feasible path is found. If the two trees are not successfully connected, the algorithm will return and resample until a path that meets the conditions is found, thus ensuring the integrity of the path network.

[0077] In one embodiment, for each sampling point, a line from the initial point to the sampling point is obtained, and according to whether the line passes through a non-road area, adjusting the position of the sampling point includes:

[0078] For each sampling point, obtain the line from the initial point to the end point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated by the first target node;

[0079] If the line passes through a non-road area, the first adjustment point is determined by the preset length and the initial point, and the end point is removed; a collision test is performed on the line from the initial point to the first adjustment point, and if no collision occurs, the first adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is increased;

[0080] If the line does not pass through the non-road area, the second adjustment point is determined by the preset length and the line. If the second adjustment point exists, a collision test is performed on the line from the initial point to the second adjustment point. If no collision occurs, the second adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is reduced.

[0081] In one implementation, a collision test is performed on the line from the initial point to the first adjustment point, and if a collision occurs, the first adjustment point is deleted.

[0082] In one implementation, in a line connecting an initial point to an end point, the initial point is used as a starting point, and a position in the line is intercepted with a preset length to obtain a first adjustment point.

[0083] In one implementation, the preset angle range when adding the end point as the starting point is: with the end point as the foot and the path from the end point to the second target node as the vertical line, with the preset length as the radius, rotate 45 degrees to 120 degrees to the left, and rotate 45 degrees to 120 degrees to the right, which is recorded as the preset angle range, see Figure 2 A2 and L2 are preset lengths.

[0084] In one implementation, if the second adjustment point is not present, no change occurs.

[0085] In one implementation, the second adjustment point is determined by the preset length and the connecting line, and the length obtained by adding the preset length in the direction of the connecting line is recorded as the second adjustment point.

[0086] In one implementation, the preset angle range when the end point is used as the starting point is reduced as follows:

[0087] , take the end point as the foot and the path from the end point to the second target node as the vertical line, take the length of the line plus the preset length as the radius, rotate 45 degrees to the left and 45 degrees to the right, record it as the preset angle range, see Figure 2 A3 and L3 in the figure are the length of the connection plus the preset length.

[0088] In one implementation, the angle range is dynamically adjusted according to the result of the collision test, so that the search is more intelligent and purposeful, and the child nodes corresponding to each node can be obtained more quickly.

[0089] In one embodiment, all chromosomes are iteratively optimized until a preset convergence condition is met, and the output results include:

[0090] Step 1: Perform a legitimacy check on all chromosomes to determine the first legal chromosome group and the illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed the preset load, the vehicle cannot perform cargo pickup operations for a preset number of consecutive times, the cargo must be loaded and unloaded within the specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle.

[0091] Step 2: Adjust the transport order of chromosomes in the illegal chromosome group so that the chromosomes meet the constraints and obtain the second legal chromosome group;

[0092] Step 3: Initialize the first legal chromosome group and the second legal chromosome group by merging them to obtain an initialized population;

[0093] Step 4: Initialize the population and perform selection, crossover and mutation operations to generate the offspring population;

[0094] Step 5: Return the generated offspring population to step 1 for iterative optimization until the preset convergence conditions are met and the results are output.

[0095] In one implementation, a chromosome group obtained by merging the first legal chromosome group and the second legal chromosome group is initialized by a heuristic method.

[0096] In one implementation, all chromosomes are tested for legality, thus avoiding invalid searches. Chromosomes that do not meet the conditions are transformed into legal solutions through adjustments, thus increasing the diversity of the population.

[0097] In one implementation, legitimate and adjusted chromosomes are combined to provide a diverse starting point for subsequent genetic manipulations.

[0098] In one implementation, a chromosome with strong adaptability is selected through fitness evaluation to increase the probability of the chromosome appearing in the next generation. The fitness evaluation of the chromosome can be obtained through methods such as roulette and tournaments. Different chromosomes are exchanged for fragments to produce new offspring and increase the diversity of the population. The exchange can be carried out through methods such as single-point crossover, multi-point crossover or uniform crossover.

[0099] Based on the same inventive concept, the embodiment of the present invention also provides a logistics transportation task energy-saving allocation device. Figure 3 , Figure 3 A schematic diagram of a logistics transportation task energy-saving allocation device provided by an embodiment of the present invention includes:

[0100] The initial route set determination module is used to obtain the transportation network diagram corresponding to the target area, and for any access node pair in the transportation network diagram, the storage node and the pickup node of the access node pair are searched in opposite directions to obtain the initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area;

[0101] The effective path set determination module is used to determine the effective paths in the initial route set by using an ant colony algorithm to obtain a node effective path set, and obtain the node effective path sets corresponding to all access node pairs to obtain an effective path set; each access node pair has at least one effective path;

[0102] A driving path determination module is used to randomly generate multiple allocation schemes based on the task list and all vehicles, and determine the driving path of each vehicle according to the allocation scheme; the driving path is a valid path from node to node;

[0103] The allocation scheme determination module is used to obtain multiple chromosomes for each allocation scheme and the corresponding driving path, iteratively optimize all chromosomes until the preset convergence conditions are met and the results are output, and the allocation scheme is determined according to the output results.

[0104] An energy-saving allocation device for logistics transportation tasks provided by an embodiment of the present invention reduces the time efficiency of obtaining an initial route by searching in opposite directions with storage nodes and fetching nodes, determines the effective path in the initial route set through an ant colony algorithm, and can intelligently find a more optimized effective path between two nodes. Then, the chromosome algorithm is used to find the optimal execution order of the pickup and delivery operations, and the best transportation path for simultaneously executing the pickup and delivery operations is selected, thereby improving the transportation efficiency of logistics and reducing the energy consumption level of the logistics system.

[0105] In one embodiment, the initial route set determination module includes:

[0106] A sampling point generation module, used to randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node;

[0107] A sampling point position adjustment module is used to obtain a line from the initial point to the sampling point for each sampling point, and adjust the position of the sampling point according to whether the line passes through a non-road area;

[0108] A starting tree establishment module, used to generate a first starting tree and a second starting tree according to all nodes in the sampling point position adjustment module; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node;

[0109] The path network establishment module is used to determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, all paths are output to obtain the initial route set; if not, the sampling point generation module is returned to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than a preset length, and there is no non-road area in the middle of the connection line of the last child node.

[0110] In one embodiment, the sampling point position adjustment module includes:

[0111] A connection generation module is used to obtain a connection from an initial point to an end point for each sampling point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated from the first target node;

[0112] A first adjustment point determination module is used to determine the first adjustment point with a preset length and an initial point if the connection line passes through a non-road area, and remove the end point; perform a collision test on the connection line from the initial point to the first adjustment point, and if no collision occurs, record the first adjustment point as the end point, and increase the preset angle range when the end point is used as the starting point;

[0113] The second adjustment point determination module is used to determine the second adjustment point based on the preset length and the connection line if the connection line does not pass through the non-road area. If the second adjustment point exists, a collision test is performed on the connection line from the initial point to the second adjustment point. If no collision occurs, the second adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is reduced.

[0114] In one embodiment, the allocation scheme determination module includes:

[0115] The legality detection module is used to perform legality detection on all chromosomes to determine the first legal chromosome group and the illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed the preset load, the vehicle cannot continuously perform the cargo pickup operation for a preset number of times, the cargo is loaded and unloaded within the specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle;

[0116] A second legal chromosome group determination module is used to adjust the transport order of chromosomes in the illegal chromosome group so that the chromosomes meet the constraint conditions to obtain a second legal chromosome group;

[0117] An initialization population determination module is used to merge and initialize the first legal chromosome group and the second legal chromosome group to obtain an initialization population;

[0118] The offspring population determination module is used to initialize the population and perform selection, crossover and mutation operations to generate the offspring population;

[0119] The iterative optimization module is used to return the generated offspring population to the legitimacy detection module for iterative optimization until the preset convergence conditions are met and the results are output.

[0120] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device. Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention includes:

[0121] Processor 401, communication interface 402, memory 403 and communication bus 404, wherein processor 401, communication interface 402, memory 403 communicate with each other via communication bus 404.

[0122] Memory 403, used for storing computer programs;

[0123] The processor 401 is used to execute the program stored in the memory 403 to implement the following steps:

[0124] Obtain a transportation network diagram corresponding to the target area, and for any access node pair in the transportation network diagram, search the storage node and the pickup node of the access node pair in opposite directions to obtain an initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area;

[0125] Determine the valid paths in the initial route set by using an ant colony algorithm to obtain a node valid path set, obtain the node valid path sets corresponding to all access node pairs to obtain a valid path set; each access node pair has at least one valid path;

[0126] Multiple allocation plans are randomly generated based on the task list and all vehicles, and the driving path of each vehicle is determined based on the allocation plan; the driving path is a valid path from node to node;

[0127] A plurality of chromosomes are obtained for each allocation scheme and the corresponding driving path, and all chromosomes are iteratively optimized until a preset convergence condition is met and a result is output, and the allocation scheme is determined according to the output result.

[0128] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0129] The communication interface is used for communication between the above terminal and other devices.

[0130] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0131] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0132] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, an energy-saving allocation method for logistics transportation tasks described in any of the above embodiments is implemented.

[0133] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute a method for energy-saving allocation of logistics transportation tasks as described in any one of the above embodiments.

[0134] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0135] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0136] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for energy-saving allocation of logistics transportation tasks, characterized in that: The method comprises: Obtain a transportation network diagram corresponding to the target area, and for any access node pair in the transportation network diagram, search the storage node and the pickup node of the access node pair in opposite directions to obtain an initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area; Determine the valid paths in the initial route set by using an ant colony algorithm to obtain a node valid path set, obtain the node valid path sets corresponding to all access node pairs to obtain a valid path set; each access node pair has at least one valid path; A plurality of allocation schemes are randomly generated according to the task list and all vehicles, and the driving path of each vehicle is determined according to the allocation schemes; The driving path is a valid path from node to node; A plurality of chromosomes are obtained for each allocation scheme and the corresponding driving path, and all chromosomes are iteratively optimized until a preset convergence condition is met and a result is output, and an allocation scheme is determined according to the output result; The initial route set of the access node pair obtained by searching the storage node and the access node of the access node pair in opposite directions includes: Step 1: Randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node; Step 2: For each sampling point, obtain the line from the initial point to the sampling point, and adjust the position of the sampling point according to whether the line passes through the non-road area; Step 3: Generate a first starting tree and a second starting tree based on all the nodes in step 2; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node; Step 4: Determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, output all paths to obtain the initial route set; if not, return to step 1 to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than a preset length, and there is no non-road area in the middle of the line connecting the last child nodes.

2. A method for energy-saving allocation of logistics transportation tasks according to claim 1, characterized in that: For each sampling point, a line from the initial point to the sampling point is obtained, and according to whether the line passes through a non-road area, the position of the sampling point is adjusted, including: For each sampling point, obtain a line from the initial point to the end point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated by the first target node; If the line passes through a non-road area, a first adjustment point is determined with a preset length and an initial point, and the end point is removed; a collision test is performed on the line from the initial point to the first adjustment point, and if no collision occurs, the first adjustment point is recorded as the end point, and a preset angle range when the end point is used as the starting point is increased; If the line does not pass through the non-road area, the second adjustment point is determined using the preset length and the line. If the second adjustment point exists, a collision test is performed on the line from the initial point to the second adjustment point. If no collision occurs, the second adjustment point is recorded as the end point, and the preset angle range when the end point is used as the starting point is reduced.

3. The method for energy-saving allocation of logistics transportation tasks according to claim 1 is characterized in that: Iterate and optimize all chromosomes until the preset convergence conditions are met. The output results include: Step 1: Perform a legality check on all chromosomes to determine the first legal chromosome group and the illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed the preset load, the vehicle cannot continuously perform the cargo pickup operation for a preset number of times, the cargo is loaded and unloaded within the specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle; Step 2: adjusting the transport order of the chromosomes in the illegal chromosome group so that the chromosomes satisfy the constraint conditions to obtain a second legal chromosome group; Step 3: Initialize the first legal chromosome group and the second legal chromosome group by merging them to obtain an initialized population; Step 4: The initialized population performs selection, crossover and mutation operations to generate an offspring population; Step 5: Return the generated offspring population to step 1 for iterative optimization until the preset convergence conditions are met and the results are output.

4. A logistics transportation task energy-saving allocation device, characterized in that: The device comprises: An initial route set determination module is used to obtain a transportation network diagram corresponding to a target area, and for any access node pair in the transportation network diagram, search the storage node and the pickup node of the access node pair in opposite directions to obtain an initial route set of the access node pair; the transportation network diagram is composed of cargo storage points, pickup points and connection paths in the target area; An effective path set determination module is used to determine the effective paths in the initial route set by using an ant colony algorithm to obtain a node effective path set, and obtain the node effective path sets corresponding to all access node pairs to obtain an effective path set; each access node pair has at least one effective path; A driving path determination module is used to randomly generate multiple allocation plans based on the task list and all vehicles, and determine the driving path of each vehicle according to the allocation plan; The driving path is a valid path from node to node; An allocation scheme determination module is used to obtain multiple chromosomes for each allocation scheme and the corresponding driving path, iteratively optimize all chromosomes until a preset convergence condition is met and a result is output, and determine the allocation scheme according to the output result; The initial route set determination module includes: A sampling point generation module, used to randomly sample the path from the first target node to the second target node within a preset angle range to generate sampling points; when the first target node is a storage node, the second target node is a fetching node; when the first target node is a fetching node, the second target node is a storage node; A sampling point position adjustment module is used to obtain a line from the initial point to the sampling point for each sampling point, and adjust the position of the sampling point according to whether the line passes through a non-road area; A starting tree establishment module, used to generate a first starting tree and a second starting tree according to all nodes in the sampling point position adjustment module; the first starting tree is a tree established with the storage node as the root node; the second starting tree is a tree established with the extraction node as the root node; The path network establishment module is used to determine whether the first starting tree and the second starting tree meet the connection condition; if the connection is successful, all paths are output to obtain an initial route set; if not, the sampling point generation module is returned to generate sampling points until the first starting tree and the second starting tree meet the connection condition to generate a path network; the connection condition is that the distance between the last child nodes of the first starting tree and the second starting tree is less than a preset length, and there is no non-road area in the middle of the line connecting the last child nodes.

5. The energy-saving distribution device for logistics and transportation tasks according to claim 4 is characterized in that: The sampling point position adjustment module comprises: A connection generation module, used for obtaining a connection from an initial point to an end point for each sampling point; if it is the first sampling, the initial point is the first target node, and the end point is any sampling point generated from the first target node; A first adjustment point determination module is used to determine the first adjustment point with a preset length and an initial point if the connection line passes through a non-road area, and remove the end point; perform a collision test on the connection line from the initial point to the first adjustment point, and if no collision occurs, record the first adjustment point as the end point, and increase the preset angle range when the end point is used as the starting point; The module for determining a second adjustment point is used to determine a second adjustment point using the preset length and the connection line if the connection line does not pass through a non-road area; if the second adjustment point exists, a collision test is performed on the connection line from the initial point to the second adjustment point; if no collision occurs, the second adjustment point is recorded as an end point, and the preset angle range when the end point is used as a starting point is reduced.

6. The energy-saving distribution device for logistics and transportation tasks according to claim 4 is characterized in that: The allocation scheme determination module includes: A legality detection module is used to perform legality detection on all chromosomes to determine a first legal chromosome group and an illegal chromosome group; the constraints of the legal chromosome group are that the vehicle's load cannot exceed a preset load, the vehicle cannot continuously perform cargo pickup operations for a preset number of times, cargo loading and unloading must be completed within a specified time, and the vehicle's driving length cannot exceed the maximum driving distance of its own vehicle; A second legal chromosome group determination module is used to adjust the transport order of the chromosomes in the illegal chromosome group so that the chromosomes satisfy the constraint conditions to obtain a second legal chromosome group; An initialization population determination module, used for merging and initializing the first legal chromosome group and the second legal chromosome group to obtain an initialization population; A offspring population determination module, used for performing selection, crossover and mutation operations on the initialization population to generate an offspring population; The iterative optimization module is used to return the generated offspring population to the legitimacy detection module for iterative optimization until the preset convergence conditions are met and the results are output.

7. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 3 when executing a program stored in a memory.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 3 are implemented.

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