A pickup and delivery collaborative scheduling method based on heuristic algorithm

Abstract

This specification discloses one or more embodiments of a harvesting and transportation coordination scheduling method, apparatus, and electronic device based on a heuristic algorithm. The method includes: acquiring attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle, wherein the task parameters are parameters describing the harvesting and transportation coordination task; based on a preset optimization objective, using the attribute parameters and the task parameters, constructing a harvesting and transportation coordination model for solving a target unloading strategy for the coordinated unloading of grain by the harvester and the grain transport vehicle, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to an unloading point in the harvesting path after receiving an unloading request; based on the harvesting path of the harvester, using a heuristic algorithm to solve the harvesting and transportation coordination model to obtain the target unloading strategy; and executing the target unloading strategy to schedule the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

Description

Technical Field

[0001] This specification relates to the field of data processing technology, and in particular to a collection and transportation collaborative scheduling method, apparatus, electronic device and storage medium based on heuristic algorithms. Background Technology

[0002] With the rapid development of my country's economy, the pace of rural land transfer has accelerated, gradually shifting from scattered operations to large-scale centralized operations, meaning the operational scenarios have become large-scale standardized farmland. Agricultural machinery socialized services typically involve one harvester paired with two or more grain transport vehicles to complete agricultural production operations. Generally, a suitable scheduling model is designed based on the farmland conditions and the harvester's path, predicting or analyzing the harvester's unloading time to employ a proximity strategy for scheduling between the combine harvester and the grain transport vehicles.

[0003] The aforementioned scheme simplifies the route planning of grain transport vehicles and reduces the complexity of the response between harvesters and grain transport vehicles, thus achieving overall scheduling planning for grain transport vehicles. However, ignoring the specific transfer paths of grain transport vehicles in farmland leads to insufficient accuracy in the scheduling process, reducing the environmental adaptability and safety of autonomous operation of grain transport vehicles. Therefore, there is an urgent need to provide a better scheme for coordinated harvesting and transportation scheduling. Summary of the Invention

[0004] This specification provides a heuristic algorithm-based collaborative scheduling method, apparatus, electronic device, and storage medium for collection and transportation, in order to provide a heuristic algorithm-based collaborative scheduling scheme for collection and transportation.

[0005] In a first aspect, one or more embodiments of this specification provide a harvesting and transportation coordination scheduling method based on a heuristic algorithm, comprising: acquiring attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle, wherein the attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task; based on a preset optimization objective, using the attribute parameters and the task parameters, constructing a harvesting and transportation coordination model for solving a target unloading strategy for the coordinated unloading of grain by the harvester and the grain transport vehicle, wherein the target unloading strategy includes at least a transfer path for the grain transport vehicle to move to an unloading point in the harvesting path after receiving an unloading request; solving the harvesting and transportation coordination model using a heuristic algorithm based on the harvester's harvesting path to obtain the target unloading strategy; and executing the target unloading strategy to schedule the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0006] Secondly, embodiments of this application provide a harvesting and transportation coordination scheduling device based on a heuristic algorithm, comprising: a parameter module, used to acquire attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle, wherein the attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task; a target module, used to construct a harvesting and transportation coordination model based on a preset optimization target, using the attribute parameters and the task parameters, for solving a target unloading strategy for the coordinated unloading of grain by the harvester and the grain transport vehicle, wherein the target unloading strategy includes at least a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving an unloading request; a solution module, used to solve the harvesting and transportation coordination model using a heuristic algorithm based on the harvesting path of the harvester to obtain the target unloading strategy; and a strategy module, used to execute the target unloading strategy to schedule the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0007] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0008] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0009] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0010] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model is constructed based on the optimization objective to solve the target unloading strategy for the coordinated unloading of harvesters and grain transport vehicles. Based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy and solve the harvesting and transportation coordination model to obtain the target unloading strategy. This target unloading strategy is then used to coordinate the unloading of grain transport vehicles and harvesters in the harvesting and transportation scheduling system. In the above process, both the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered. By combining the heuristic algorithm with the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, enhancing the adaptability of harvesters in dynamic agricultural environments. This solution will play an important role in improving agricultural machinery operation efficiency, improving agricultural production conditions, and ensuring food security. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in one or more embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of a heuristic algorithm-based collaborative scheduling method for collection and transportation according to an embodiment of this specification.

[0013] Figure 2 This is a schematic diagram of a harvesting path of a multi-machine internal spiral according to an embodiment of this specification.

[0014] Figure 3 This diagram illustrates a process of a particle swarm optimization algorithm based on an improved simulated annealing algorithm according to an embodiment of this specification.

[0015] Figure 4 This is a schematic diagram of a combination of graticles searched using the A* algorithm according to an embodiment of this specification.

[0016] Figure 5 This is a schematic diagram illustrating the determination of the entry and exit points of a grid based on relative positional relationships according to an embodiment of this specification.

[0017] Figure 6 This is a schematic diagram of a transfer path obtained based on a Dubins path according to an embodiment of this specification.

[0018] Figure 7 This diagram illustrates a process of a particle swarm optimization algorithm based on an improved simulated annealing algorithm according to an embodiment of this specification.

[0019] Figure 8 This is a schematic diagram of a collection and transportation collaborative scheduling device based on a heuristic algorithm according to an embodiment of this specification.

[0020] Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of this specification. Detailed Implementation

[0021] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments in this specification. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0022] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this specification can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] The following description, in conjunction with the accompanying drawings, details the heuristic algorithm-based collaborative scheduling method, apparatus, and electronic equipment provided in this specification through specific embodiments and application scenarios.

[0024] Figure 1 This illustration shows an embodiment of the present invention providing a heuristic algorithm-based collaborative scheduling method for collection and transportation. This method can be executed by an electronic device, which may include a server and / or a terminal device, wherein the terminal device may be, for example, a vehicle-mounted terminal or a mobile phone terminal. In other words, the method can be executed by software or hardware installed on the aforementioned electronic device, and the method includes the following steps:

[0025] S102: Obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle.

[0026] The harvesting and dispatching system is any system that coordinates the dispatching of multiple harvesters and grain transport vehicles. The harvesting and dispatching system may include multiple harvesters and multiple grain transport vehicles. This specification does not specify a particular number of harvesters and / or grain transport vehicles in the harvesting and dispatching system; this number can be determined based on actual circumstances.

[0027] The attribute parameters are parameters that describe the attributes of the harvester and / or the grain transport vehicle. Specifically, the attribute parameters may include a first attribute parameter corresponding to the harvester and / or a second attribute parameter corresponding to the grain transport vehicle.

[0028] The first attribute parameter describes the harvester's state at any given time. Specifically, it can be represented by a set. This refers to the harvester group in the harvesting and dispatching system. The harvester objects currently being studied are... To indicate, harvester The first attribute parameter can be represented as .in, Indicates the first i The maximum capacity of a harvester. VHi Indicates the first i The linear operating speed of a harvester. For the first i The turning speed of a harvester It is the first i Harvesting capacity per unit distance of a harvester Indicates the first i A harvester in t Coordinates of time, Indicates the first i A harvester in t The work journey at all times, Indicates the first i A harvester in t The remaining grain storage capacity at any given time Indicates the first i Total waiting time for a harvester.

[0029] The second attribute parameter describes the state of the grain transport vehicle during operation. Specifically, it can be represented by a set. This refers to a group of grain transport vehicles in the grain collection and dispatching system. The grain transport vehicles currently being studied are referred to as... To indicate, grain transport truck The second attribute parameter can be represented as .in, Indicates the first j The maximum capacity of a grain transport vehicle VT Indicates the first j The operating speed of the grain transport trucks XT j ( t ) indicates the first j A grain transport truck in t Coordinates of time, CT j ( t ) indicates the first j A grain transport truck in t The remaining capacity at any given time. It should be noted that... t =0 indicates the initial state of the harvester or grain transport vehicle.

[0030] The task parameters describe the parameters used in the harvesting and transportation coordination task. These parameters may include parameters related to the grain transport vehicle's response to the harvester's unloading request and the process of the grain transport vehicle assisting the harvester in unloading. Specifically, they can be used... Indicates in t At that moment, the grain transport truck Has the harvester been responded to? The request was made and the harvester was moved to its location to assist in unloading the grain. It can be represented by 0 and 1, where 0 represents a grain transport vehicle. Unresponsive harvester The request was made and the vehicle moved to the location of the harvester to prepare for assisting in unloading the grain. 1 indicates the grain transport vehicle. Harvesters have responded Upon receiving the request, they moved to the location of the harvester to prepare for assisting with unloading the grain. j The task sequence set is used to respond to the unloading time of each grain transport vehicle. This indicates that the task sequence set for starting grain unloading is... The unloading time is The auxiliary grain unloading process , R j , R’ j , U j These are the task parameters.

[0031] Step S104: Based on the preset optimization objective, using the attribute parameters and the task parameters, construct a harvesting and transportation coordination model for solving the target unloading strategy of the harvester and the grain transport vehicle for coordinated unloading.

[0032] Unlike existing schemes that only plan the harvester's harvesting path, the harvesting-transportation coordinated scheduling scheme in this specification includes not only the harvester's path but also the path planning of the grain transport vehicle. The optimization objective is the indicator that needs to be considered when implementing the harvesting-transportation coordinated scheduling scheme. In addition to existing routine indicators related to the harvester, the optimization objective may also include indicators describing the grain transport vehicle's performance of auxiliary unloading tasks, such as the time the harvester stops waiting for the grain transport vehicle and the path the grain transport vehicle takes. In one implementation, the optimization objective includes at least a first optimization objective and / or a second optimization objective.

[0033] The first optimization objective is to reduce the total non-operational travel distance of each grain transport vehicle in the scheduling system, and the second optimization objective is to reduce the total waiting time of each harvester in the scheduling system.

[0034] The non-operational movement distance can be the movement distance of the grain transport vehicle when it is not coordinating with the unloading. In one example, the distance the grain transport vehicle moves to the unloading point after responding to the harvester's unloading signal can be considered the non-operational movement distance. Specifically, the non-operational movement distance can be determined based on the transfer path, and then the non-operational movement distances corresponding to each transfer path can be summed to obtain the total non-operational movement distance. The total non-operational movement distance can be used to determine the first optimization objective (assuming that the state of the grain transport vehicle and the harvester must be consistent during unloading). Based on the above attribute parameters and task parameters, the first optimization objective can be expressed as Formula 1:

[0035] (Formula 1)

[0036] in, This indicates the distance the grain truck needs to move to the position of the harvester at the current moment. t z ∈ R j , t’ z ∈ R’ j Let i ∈ n, where n represents the number of harvesters, and j ∈ m, where m represents the number of grain transport vehicles. R j Indicates the first j The task sequence set is used to respond to the unloading time of each grain transport vehicle. R’ j Indicates the first j The task sequence set for the grain trucks to begin unloading grain. Indicates in t Grain transport trucks Has the harvester been responded to? The request was made and the harvester was moved to its location to assist in unloading the grain. XT j ( t z ) indicates the first j A grain truck in t z Coordinates of time, XH i ( t’ z ) indicates the first i A harvester in t’ z The coordinates of the moment.

[0037] In addition to the first and second optimization objectives mentioned above, a third optimization objective can be set to ensure a basic balance in the workload of grain transport vehicles. This third optimization objective is shown in Formula 2:

[0038] (Formula 2)

[0039] The second optimization objective can be determined based on the waiting time for the grain transport vehicle after each harvester arrives at the unloading point (unloading location). Specifically, in t When the harvester signals to the grain truck at 0:00, the grain truck... t Depart for the unloading point after 0:00. If the distance traveled by the grain truck is greater than that of the harvester... t When the distance traveled after time 0 is recorded, the harvester does not incur a waiting time, which is represented by 0; otherwise, the harvester will incur a waiting time. t z The waiting time corresponding to each harvester tz The second optimization objective can be determined by summing the results.

[0040] In the above process, the construction of the harvesting and transportation coordination model based on the first optimization objective and / or the second optimization objective can obtain a high-precision harvesting and transportation coordination scheduling model with a low total non-operational distance of the grain transport vehicle and a short waiting time of the harvester. In this way, a precise and reasonable target unloading strategy can be obtained based on the harvesting and transportation coordination scheduling model, thereby improving the field operation efficiency of the harvester and the grain transport vehicle.

[0041] Solving the harvesting and transportation coordination model constructed based on the above optimization objectives yields the target unloading strategy. Based on the aforementioned optimization objectives, which include indicators describing the grain transport vehicle's performance of auxiliary unloading tasks, the target unloading strategy may include at least a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving an unloading request. This transfer path needs to avoid running over areas not harvested by the combine harvester.

[0042] Step S106: Based on the harvesting path of the harvester, use a heuristic algorithm to solve the harvesting and transportation coordination model to obtain the target unloading strategy.

[0043] Step S108: Execute the target unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0044] Heuristic algorithms are a class of methods used to find approximations of complex optimization problems, suitable for solving large problems with limited computational resources. Vehicle scheduling is a non-deterministic polynomial hard (NP-Hard) problem, which is difficult to find the optimal solution. Heuristic algorithms can provide a method to find a satisfactory solution within a reasonable time. Even if the quality of the solution cannot be guaranteed, it can effectively find a sufficiently good solution within a finite time, suitable for agricultural field scheduling operations.

[0045] The harvesting path is the route taken by the harvester during the harvesting process. This path is determined based on the harvester's harvesting area and path planning information (such as the harvester's operating mode). The harvesting path includes unloading points where the harvester and unloading truck coordinate to unload grain. The transfer path is the route taken by the transport truck to the unloading points, determined based on these unloading points. In one example, based on the determined harvester path, a heuristic algorithm can be used to solve the established harvesting-transport coordination model, obtaining the specific values ​​of the aforementioned attribute and task parameters. Further processing of these values ​​yields the target unloading strategy. Executing this target unloading strategy enables coordinated unloading of grain by the transport truck and the harvester.

[0046] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model is constructed based on the optimization objective to solve the target unloading strategy for the coordinated unloading of harvesters and grain transport vehicles. Based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy and solve the harvesting and transportation coordination model to obtain the target unloading strategy. This target unloading strategy is then used to coordinate the unloading of grain transport vehicles and harvesters in the harvesting and transportation scheduling system. In the above process, both the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered. By combining the heuristic algorithm with the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, enhancing the adaptability of harvesters in dynamic agricultural environments. This solution will play an important role in improving agricultural machinery operation efficiency, improving agricultural production conditions, and ensuring food security.

[0047] For harvester path planning, most current methods employ global coverage path planning, which is based on covering the farm's operational environment and performing reciprocating parallel operations. Specifically, in the harvesting process corresponding to reciprocating parallel operations, parameters of the area to be harvested are first obtained, and the longest boundary of the area is selected as the baseline. Then, the harvester moves parallel to this baseline and at equal intervals according to its working width to generate multiple work rows until the harvester's path covers the entire area to be harvested. This method is typically used in regular areas such as dryland rice harvesting, where there are many turns. Especially in water harvesting operations, turning is difficult, energy consumption is high, safety is low, and repetition and incomplete coverage are prone to occur. In one implementation, before constructing the harvesting and transportation coordination model, the method further includes:

[0048] Based on the working width of the harvester, the harvesting path of the inner spiral shape executed by the harvester is determined in the harvesting area corresponding to the harvester.

[0049] Specifically, when planning the path for multiple harvesters in a rectangular plot, the plot can first be divided into multiple harvesting areas based on the number of harvesters. That is, the number of harvesting areas must match the number of harvesters. Within each harvesting area, starting from the outermost point, the harvester can spiral inwards to complete the area it needs to work on, using the harvester's working width as the starting point.

[0050] Figure 2 A schematic diagram of a harvesting path using a multi-rotor internal auger is shown. Figure 2 As shown, assuming the harvesters start from the midpoint of the farmland boundary and return to the starting point after completing the harvest, the resulting harvesting path planning for the two harvesters in the rectangular farmland is an inner spiral shape. During the harvesting process, when the harvester encounters the boundary or is a certain distance from the boundary, it first turns 90 degrees, then turns 90 degrees again, and travels in the opposite direction until it has cycled through all areas. Figure 2 As shown, compared with the aforementioned parallel operation method of reciprocating traversal, this method has fewer turns.

[0051] In the embodiments described in this specification, an inward spiral full-coverage path planning is used as the working path of the harvester. Starting from the innermost side, the harvester spirals inward parallel to the working boundary, ultimately covering the entire area. This process, through the inward spiral harvesting path, effectively reduces the number of turns and the difficulty of turning, minimizing unnecessary wear and tear, thereby improving operational efficiency.

[0052] In one implementation, the heuristic algorithm includes the A* algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. The step of using the heuristic algorithm to solve the harvesting and transportation coordination model based on the harvester's harvesting path to obtain the target unloading strategy includes:

[0053] Using a particle swarm optimization algorithm based on simulated annealing, multiple scheduling schemes are obtained. Each scheduling scheme includes unloading points located in the harvesting path of the harvester and grain transport vehicles matched with the unloading points.

[0054] The A* algorithm is used to determine candidate transfer paths between the unloading point and the grain transport vehicle matching the unloading point in each of the scheduling schemes.

[0055] The simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, and the optimal transfer path is used as the solution result of the collection and transportation coordination model.

[0056] The target unloading strategy is determined based on the optimal transfer path.

[0057] Among them, the transfer path is the path that the grain transport vehicle takes to the unloading point after receiving the unloading signal from the harvester. The candidate transfer path is the transfer path of the grain transport vehicle determined based on the scheduling scheme before determining the optimal solution of the harvesting and transportation coordination model. There are multiple candidate transfer paths, and the optimal transfer path is the transfer path corresponding to the optimal solution of the harvesting and transportation coordination model.

[0058] The A* algorithm is a heuristic search algorithm widely used for pathfinding and graph traversal. Its core lies in using an evaluation function to estimate the cost from any node to the target node. Because it relies on the guidance of a heuristic function, it can achieve better performance.

[0059] Figure 3 A flowchart illustrating the scheduling algorithm for harvesters and grain transport vehicles is shown, such as... Figure 3As shown, after the harvesting and transportation coordination model randomly generates an initial solution, a particle swarm optimization algorithm based on simulated annealing can be used to obtain multiple scheduling schemes corresponding to the initial solution. These scheduling schemes include the harvester's unloading point (unloading location) and the grain transport vehicle matching that unloading point. Then, based on the unloading location, the A* algorithm can be used to determine candidate transfer paths for the grain transport vehicle. It should be understood that... Figure 3 In addition to the optimal transfer path corresponding to the optimal removal of the grain transport vehicle, the data can also include multiple data related to harvesting and transportation coordination, such as the harvester's waiting time and the grain transport vehicle's response time. These data can be the specific values ​​of the aforementioned attribute parameters and task parameters.

[0060] The A* algorithm is a grid-based search algorithm. Before determining candidate transfer paths, the harvesting area corresponding to the harvester can be rasterized. In one implementation, using the A* algorithm to determine the candidate transfer path between the unloading point and the grain transport vehicle matching the unloading point in each scheduling scheme includes:

[0061] Based on the working width of the harvester, the area to be harvested by the harvester is gridded to obtain multiple grids;

[0062] The A* algorithm is used to determine the combination of grids corresponding to the candidate transfer paths from the plurality of grids;

[0063] Based on the combination of the grids, the candidate transfer paths corresponding to the scheduling scheme are obtained.

[0064] Specifically, the area to be harvested can be rasterized based on the harvester's working width, with each grid being a square roughly the same width as the working width. In one example, the process of using the A* algorithm to determine candidate transfer paths may include:

[0065] ① Define nodes and edges: Treat each pixel in the rasterized grid as a node, and the line connecting adjacent pixels as an edge. If there is an obstacle between two pixels, remove the edge between them.

[0066] ② Define the start and end points: Select the start and end points in the rasterized grid. The start and end points must be within the passable area, and the orientation of the grain truck at the start point needs to be calculated.

[0067] ③ Calculate the heuristic cost of each node (i.e., the estimated distance from that node to the destination): Simple distance calculation methods such as Euclidean distance or Manhattan distance can be used.

[0068] ④ Implement the A* algorithm using a priority queue: Place the starting point in the priority queue, then repeat the following steps until the destination is found or the queue is empty: Remove the node with the minimum heuristic cost from the queue; if this node is the destination, path planning is complete; otherwise, expand the adjacent nodes that match the direction of the grain truck, calculate their heuristic costs, and add them to the queue. If adjacent nodes have already been visited, update their costs and predecessor nodes; if the new cost is smaller, update the corresponding node in the queue.

[0069] ⑤ Backtracking path: Once the destination is found, backtrack along the predecessor nodes of each node from the destination until the starting point is reached to obtain the candidate transition path.

[0070] Figure 4 A schematic diagram of the combination of rasters searched using the A* algorithm is shown, such as... Figure 4 As shown, the black frame represents the boundary and the area not harvested by the harvester. The grid combination can only indicate the approximate path direction, not the actual path. After determining the grid combination, the transfer path needs to be determined based on the grid combination. In one implementation, obtaining the transfer path corresponding to each grain transport vehicle in the harvesting and dispatching system based on the grid combination includes:

[0071] Based on the Dubins algorithm, the entry and exit points of each grid in the grid combination are determined according to the relative positional relationship between each grid and the two grids before and after it.

[0072] The transfer path is obtained by connecting the entry and exit points of each grid in the grid combination.

[0073] For a given grid combination, excluding the first and last grids, we can first determine the relative positional relationship between each of the remaining grids and the two grids before and after it. Then, based on this relative positional relationship, we can determine the entry and exit points of the grids. Specifically, after determining the midpoints of the four sides and the four vertices of each grid, we can determine the entry and exit points within the current grid based on the relative positional relationships. Figure 5 A schematic diagram illustrating the determination of the entry and exit points of a grid based on relative positional relationships is shown, such as... Figure 5 As shown, the middle grid is the target grid. For different relative positional relationships, the entry point and exit point of the target grid are different, and both the entry point and exit point are either vertices or midpoints.

[0074] In one example, the Dubins path can be used to determine the transfer path from the combination of grids. The real-time calculation method of the transfer path using the "grid method + A* algorithm + Dubins path" can significantly improve the calculation accuracy of the shortest distance corresponding to the optimization target of the grain transport vehicle. Figure 6A schematic diagram of the transfer path obtained based on the Dubins path is shown. Figure 6 As shown, compared to the aforementioned grid combination, the transfer path obtained based on the Dubins path is smoother.

[0075] Particle Swarm Optimization (PSO) is a swarm intelligence algorithm characterized by fast convergence, few parameters, and simple implementation. It performs exceptionally well in searching for optimal parameters in continuous domains, but its application in discrete domains is limited and requires modifications. The velocity and position update formulas in PSO are Equations 3 and 4.

[0076] (Formula 3)

[0077] (Formula 4)

[0078] in, i ∈[1,n], where m is the number of search particles and n is the node dimension. V The current velocity of the particle (for) m × n (matrix) V n The velocity of the particle after the update. c 1. c 2 represents accelerated long-range (removing normal numbers). X This represents the particle's current position (size is the same as velocity). X’ The position updated for the particle. ω For inertial weights, P i The best position in the history of each particle (size and velocity). P g This is the best historical position among all particles.

[0079] By introducing commutators and commutation sequences, the velocity-position update formula can be redefined (as shown in formulas 5 and 6), enabling the PSO algorithm to handle discrete domains. Each element in the commutation sequence is called a commutator. For example, given the commutation sequence V = [2 0 0] and the discrete sequence X = [1 2 3], then X + V = [2 1 3]. This is because... V The number 2 at index 1 represents X The object to be swapped at index 1 is index 2, therefore the swap is performed. X The values ​​of indices 1 and 2. To ensure that addition and subtraction are reversible operations under the commutative order definition, let [2 1 3] = P Then there is PX=V .

[0080] (Formula 5)

[0081] (Formula 6)

[0082] in, α and β It is a random positive number less than 1. α ( P id -X id ) represents the basic commutative order ( P id -X id All commutators in ) are retained with probability α; β ( P gd -X id ) represents the basic commutative order ( P gd -X id All commutators in ) with probability β reserve.

[0083] Formula 6 can actually be broken down into a superposition of three position update formulas, which changes the objects affected by the latter two exchange orders, rendering the definition of exchange order meaningless. To solve this problem, the position update formula can be modified (as shown in Formula 7).

[0084] (Formula 7)

[0085] in, ωV id Show basic commutation order V id All commutators with probability ω Retained. The new position formula (Formula 7) ensures that the particle's interaction with the corresponding commutative order is additive.

[0086] Because the PSO algorithm is prone to getting trapped in local optima and exhibiting oscillations in its later stages, a simulated annealing mechanism (SA algorithm) can be introduced. This allows the algorithm to accept both optimized and deteriorated solutions with a certain probability, thereby improving performance and achieving an effective balance between global and local convergence. Using the standard PSO algorithm for optimization in the early stages of the computation increases the search speed and adds diversity to the population for later random initialization. Introducing simulated annealing in the later stages enhances the global search capability of the particle swarm optimization algorithm, significantly mitigating its local convergence problem.

[0087] Figure 7The specific steps of the particle swarm optimization algorithm (SAPSO algorithm) based on the simulated annealing algorithm are shown:

[0088] ① Parameter initialization: Set population size m Minimum particle velocity V min and maximum speed V max Inertia weight control factor q c Learning factor c 1 and c 2. Annealing start and end temperatures T s and T e Annealing speed β Initial values ​​for parameters;

[0089] ② Randomly generated n The initial solution population of each particle, i.e. n Initial solutions X i ( t ) and initial velocity V j ( t );

[0090] ③ Calculate the fitness value of each particle in the population. f ( X i ( t And the fitness value of each particle. f ( X i ( t The results are compared with the individual extreme value and the global extreme value, and the optimal value is updated to become the individual extreme value and the global extreme value.

[0091] ④ Update the particle velocity and position according to Formulas 3 and 4, and limit its velocity to... V max Within;

[0092] ⑤ Calculate the updated fitness value for each particle. f ( X i ( t+ 1)), and calculate the change in fitness value Δ caused by the two positions. E If Δ E< If 0, then accept the new position; if They also accept new positions, among which ε The result must be a random number in the interval [0,1]; otherwise, the application will be rejected.

[0093] ⑥ If the new solution is accepted, then proceed according to... Ts → βTe The cooling process is performed in one step, and the optimal solution to the problem is found iteratively; otherwise, no cooling process is performed, and the process returns to step ③.

[0094] like Figure 3 As shown, the scheduling algorithm in this specification includes Figure 7 The SAPSO algorithm is shown, and it combines the A* algorithm with the SAPSO algorithm. Specifically, in the scheduling algorithm of this scheme, the A* algorithm can calculate the grid combination of the transfer paths of the grain transport vehicles given the unloading point and the scheduling scheme. Then, Duinbs path calculation is used to calculate the candidate transfer paths under this grid combination. Furthermore, the SAPSO algorithm is used for multiple iterations to determine the optimal solution among several candidate transfer paths, thus obtaining the target unloading strategy. The SAPSO algorithm is used to design a reasonable response path optimization scheme for combine harvester grain transport vehicles, simplifying the search process and improving scheduling efficiency.

[0095] In constructing a grain collection and transportation coordination model, in addition to optimizing the target, attribute parameters, and task parameters, it is also necessary to consider some constraints to limit the target unloading strategy of the grain collection and transportation scheduling system. In one implementation, the simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, and the optimal transfer path is used as the solution result of the grain collection and transportation coordination model, including:

[0096] Construct the first penalty term and / or the second penalty term of the collection and transportation coordination model;

[0097] Based on the first penalty term and / or the second penalty term, the simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, so as to use the optimal transfer path as the solution result of the collection and transportation coordination model.

[0098] The first penalty item is used to prevent the grain volume in the grain bin of the grain transport vehicle and / or the harvester from exceeding a preset value at any time, and the second penalty item is used to prevent the state of the grain transport vehicle from not being consistent with that of the harvester after arriving at the unloading point.

[0099] Specifically, when establishing constraints for the harvesting and transportation coordination model, the amount of grain stored in the grain bins of the grain trucks and / or harvesters (or the remaining capacity of the grain bins) can be limited to prevent stored crops from overflowing the grain trucks and / or harvesters, thereby improving the feasibility of the target unloading strategy. Specifically, the first penalty term could be: 0 < CH i ( t )≤ CH imax and 0< CT j ( t )≤ CT jmax .

[0100] In addition to the amount of grain stored in the granary, the states of the harvester and the grain transport vehicle at the start of unloading can be restricted. This ensures that the target unloading strategy requires the harvester and the grain transport vehicle to be in the same state after the grain transport vehicle reaches the designated unloading point before unloading can proceed. This further improves the feasibility of the target unloading strategy. "Inconsistent state" can include the grain transport vehicle and the harvester being in the same direction, at the same speed, and with basically the same coordinates (both the grain transport vehicle and the harvester have reached the preset unloading point). Specifically, the second penalty could be: .

[0101] In addition to the restrictions imposed by the first and second penalty items mentioned above, relevant information regarding the unloading point can also be limited. For example, when the harvester arrives at the unloading point, the volume of grain stored in the harvester's grain bin should be limited to 80% to 100% of the maximum capacity of the harvester's grain bin, in order to improve the operating efficiency of the grain transport vehicle and / or the harvester.

[0102] It should be noted that the heuristic-based collaborative scheduling method for collection and transportation provided in the embodiments of this specification can be executed by a device for heuristic-based collaborative scheduling of collection and transportation, or by a control module within that device for executing the heuristic-based collaborative scheduling method. This specification uses the example of a device for heuristic-based collaborative scheduling of collection and transportation executing the heuristic-based collaborative scheduling method to illustrate the device provided in the embodiments of this specification.

[0103] Figure 8 This is a schematic diagram of a collection and transportation collaborative scheduling device based on a heuristic algorithm according to an embodiment of the present invention. Figure 8 As shown, the collection and transportation collaborative scheduling device 800 based on heuristic algorithms includes:

[0104] The parameter module 810 is used to acquire attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task.

[0105] The target module 820 is used to construct a harvesting and transportation coordination model based on a preset optimization target, using the attribute parameters and the task parameters, for solving the target unloading strategy of the harvester and the grain transport vehicle in coordinated unloading. The target unloading strategy includes at least the transfer path of the grain transport vehicle to the unloading point in the harvesting path after receiving the unloading request.

[0106] The solution module 830 is used to solve the harvesting and transportation coordination model based on the harvesting path of the harvester using a heuristic algorithm to obtain the target unloading strategy.

[0107] The strategy module 840 is used to execute the target unloading strategy to schedule the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0108] In one embodiment, the optimization objective includes at least a first optimization objective and / or a second optimization objective, wherein the first optimization objective is used to reduce the total non-operational travel distance of each grain transport vehicle in the scheduling system, and the second optimization objective is used to reduce the total waiting time of each harvester in the scheduling system.

[0109] In one embodiment, the heuristic algorithm includes the A* algorithm, particle swarm optimization algorithm, and simulated annealing algorithm, and the solution module 830 includes:

[0110] The scheduling scheme unit is used to obtain multiple scheduling schemes using a particle swarm optimization algorithm based on simulated annealing. The scheduling scheme includes unloading points located in the harvesting path of the harvester and grain transport vehicles matched with the unloading points.

[0111] Candidate path unit, used to determine, using the A* algorithm, a candidate transfer path between the unloading point and the grain transport vehicle matching the unloading point in each of the scheduling schemes;

[0112] The optimal unit is used to determine the optimal transfer path from the candidate transfer paths using the simulated annealing algorithm, so as to use the optimal transfer path as the solution result of the collection and transportation coordination model.

[0113] The strategy unit is used to determine the target unloading strategy based on the optimal transfer path.

[0114] In one embodiment, the candidate path unit is used for:

[0115] Based on the working width of the harvester, the area to be harvested by the harvester is gridded to obtain multiple grids;

[0116] The A* algorithm is used to determine the combination of grids corresponding to the candidate transfer paths from the plurality of grids;

[0117] Based on the combination of the grids, the candidate transfer paths corresponding to the scheduling scheme are obtained.

[0118] In one embodiment, obtaining the transfer path corresponding to each grain transport vehicle in the collection and dispatching system based on the combination of the grids includes:

[0119] Based on the Dubins algorithm, the entry and exit points of each grid in the grid combination are determined according to the relative positional relationship between each grid and the two grids before and after it.

[0120] The transfer path is obtained by connecting the entry and exit points of each grid in the grid combination.

[0121] In one embodiment, the optimal unit is used for:

[0122] The first penalty term and / or the second penalty term are constructed in the harvesting and transportation coordination model. The first penalty term is used to prevent the grain volume in the grain bin of the grain transport vehicle and / or the harvester from exceeding a preset value at any time. The second penalty term is used to prevent the state of the grain transport vehicle and the harvester from not being consistent after the grain transport vehicle arrives at the unloading point.

[0123] Based on the first penalty term and / or the second penalty term, the simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, so as to use the optimal transfer path as the solution result of the collection and transportation coordination model.

[0124] In one embodiment, the heuristic algorithm-based collection and transportation collaborative scheduling device 800 further includes:

[0125] The harvesting path module is used to determine the inner spiral shape of the harvester in the harvesting area corresponding to the harvester, based on the working width of the harvester, before constructing the harvesting and transportation coordination model.

[0126] The heuristic algorithm-based collection and transportation coordination scheduling device in the embodiments of this specification can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. The embodiments of this specification do not impose specific limitations.

[0127] The heuristic algorithm-based collection and transportation collaborative scheduling device in the embodiments of this specification can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; the embodiments of this specification do not specifically limit it.

[0128] The heuristic algorithm-based collaborative scheduling device for collection and transportation provided in the embodiments of this specification can achieve... Figure 1 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0129] Based on the same idea, one or more embodiments of this specification also provide an electronic device, such as... Figure 9 As shown. Electronic devices can vary considerably due to differences in configuration or performance, and may include one or more processors 901 and memory 902. Memory 902 may store one or more application programs or data. Memory 902 may be temporary or persistent storage. The application programs stored in memory 902 may include one or more modules (not shown), each module may include a series of computer-executable instructions for the electronic device. Furthermore, processor 901 may be configured to communicate with memory 902 and execute the series of computer-executable instructions in memory 902 on the electronic device. The electronic device may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input / output interfaces 905, and one or more keyboards 906.

[0130] In one specific embodiment, the electronic device is used in a harvesting and dispatching system comprising multiple harvesters and multiple grain transport vehicles, including a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the electronic device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0131] Obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task.

[0132] Based on the preset optimization objective, using the attribute parameters and the task parameters, a harvesting and transportation coordination model is constructed to solve the target unloading strategy of the harvester and the grain transport vehicle in coordinating unloading. The target unloading strategy includes at least the transfer path of the grain transport vehicle to the unloading point in the harvesting path after receiving the unloading request.

[0133] Based on the harvesting path of the harvester, a heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain the target unloading strategy.

[0134] The target unloading strategy is executed to dispatch the grain transport vehicles to assist the harvester in unloading grain based on the transfer path.

[0135] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model is constructed based on the optimization objective to solve the target unloading strategy for the coordinated unloading of harvesters and grain transport vehicles. Based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy and solve the harvesting and transportation coordination model to obtain the target unloading strategy. This target unloading strategy is then used to coordinate the unloading of grain transport vehicles and harvesters in the harvesting and transportation scheduling system. In the above process, both the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered. By combining the heuristic algorithm with the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, enhancing the adaptability of harvesters in dynamic agricultural environments. This solution will play an important role in improving agricultural machinery operation efficiency, improving agricultural production conditions, and ensuring food security.

[0136] It should be noted that the embodiments concerning electronic devices in this application and the embodiments concerning the collection and transportation collaborative scheduling method based on heuristic algorithms in this application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the corresponding collection and transportation collaborative scheduling method based on heuristic algorithms mentioned above, and the repeated parts will not be described again.

[0137] Furthermore, corresponding to the heuristic algorithm-based harvesting and transportation collaborative scheduling method described above, based on the same technical concept, one or more embodiments of this application also provide a storage medium for storing computer-executable instructions for a harvesting and transportation scheduling system comprising multiple harvesters and multiple grain transport vehicles. In a specific embodiment, the storage medium can be a USB flash drive, optical disc, hard disk, etc. When the computer-executable instructions stored in the storage medium are executed by a processor, they can achieve the following process:

[0138] Obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task.

[0139] Based on the preset optimization objective, using the attribute parameters and the task parameters, a harvesting and transportation coordination model is constructed to solve the target unloading strategy of the harvester and the grain transport vehicle in coordinating unloading. The target unloading strategy includes at least the transfer path of the grain transport vehicle to the unloading point in the harvesting path after receiving the unloading request.

[0140] Based on the harvesting path of the harvester, a heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain the target unloading strategy.

[0141] The target unloading strategy is executed to dispatch the grain transport vehicles to assist the harvester in unloading grain based on the transfer path.

[0142] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model is constructed based on the optimization objective to solve the target unloading strategy for the coordinated unloading of harvesters and grain transport vehicles. Based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy and solve the harvesting and transportation coordination model to obtain the target unloading strategy. This target unloading strategy is then used to coordinate the unloading of grain transport vehicles and harvesters in the harvesting and transportation scheduling system. In the above process, both the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered. By combining the heuristic algorithm with the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, enhancing the adaptability of harvesters in dynamic agricultural environments. This solution will play an important role in improving agricultural machinery operation efficiency, improving agricultural production conditions, and ensuring food security.

[0143] It should be noted that the embodiments concerning storage media in this application and the heuristic algorithm-based collaborative scheduling method for collection and transportation in this application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding heuristic algorithm-based collaborative scheduling method for collection and transportation mentioned above, and the repeated parts will not be described again.

[0144] Furthermore, corresponding to the heuristic algorithm-based collaborative scheduling method for harvesting and transportation described above, and based on the same technical concept, one or more embodiments of this application also provide a computer program product for a harvesting and transportation scheduling system comprising multiple harvesters and multiple grain transport vehicles. This computer program product includes a computer program that, when executed by a processor, can implement the following process:

[0145] Obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task.

[0146] Based on the preset optimization objective, using the attribute parameters and the task parameters, a harvesting and transportation coordination model is constructed to solve the target unloading strategy of the harvester and the grain transport vehicle in coordinating unloading. The target unloading strategy includes at least the transfer path of the grain transport vehicle to the unloading point in the harvesting path after receiving the unloading request.

[0147] Based on the harvesting path of the harvester, a heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain the target unloading strategy.

[0148] The target unloading strategy is executed to dispatch the grain transport vehicles to assist the harvester in unloading grain based on the transfer path.

[0149] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model is constructed based on the optimization objective to solve the target unloading strategy for the coordinated unloading of harvesters and grain transport vehicles. Based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy and solve the harvesting and transportation coordination model to obtain the target unloading strategy. This target unloading strategy is then used to coordinate the unloading of grain transport vehicles and harvesters in the harvesting and transportation scheduling system. In the above process, both the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered. By combining the heuristic algorithm with the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, enhancing the adaptability of harvesters in dynamic agricultural environments. This solution will play an important role in improving agricultural machinery operation efficiency, improving agricultural production conditions, and ensuring food security.

[0150] It should be noted that the embodiments of the computer program product in this application and the embodiments of the collection and transportation collaborative scheduling method based on heuristic algorithms in this application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the corresponding collection and transportation collaborative scheduling method based on heuristic algorithms mentioned above, and the repeated parts will not be described again.

[0151] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0152] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0153] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0154] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0155] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0156] Those skilled in the art will understand that one or more embodiments of this application can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0160] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0161] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0162] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0163] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0164] One or more embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. One or more embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0165] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0166] The above are merely embodiments of this document and are not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A collection and transportation cooperative scheduling method based on a heuristic algorithm, characterized in that, A harvesting and dispatching system for multiple harvesters and multiple grain transport vehicles, including: Obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task. Based on the preset optimization objective, using the attribute parameters and the task parameters, a harvesting and transportation coordination model is constructed to solve the target unloading strategy of the harvester and the grain transport vehicle in coordinating unloading. The target unloading strategy includes at least the transfer path of the grain transport vehicle moving to the unloading point in the harvesting path after receiving the unloading request. The transfer path is used to avoid crushing the area not harvested by the harvester. Based on the harvesting path of the harvester, a heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain the target unloading strategy. The target unloading strategy is executed to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path; The heuristic algorithms include the A* algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. The step of using heuristic algorithms to solve the harvesting and transportation coordination model based on the harvester's harvesting path to obtain the target unloading strategy includes: Using a particle swarm optimization algorithm based on simulated annealing, multiple scheduling schemes are obtained. Each scheduling scheme includes unloading points located in the harvesting path of the harvester and grain transport vehicles matched with the unloading points. The A* algorithm is used to determine candidate transfer paths between the unloading point and the grain transport vehicle matching the unloading point in each of the scheduling schemes. The simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, and the optimal transfer path is used as the solution result of the collection and transportation coordination model. Based on the optimal transfer path, the target grain unloading strategy is determined.

2. The method according to claim 1, characterized in that, The optimization objectives include at least a first optimization objective and / or a second optimization objective. The first optimization objective is used to reduce the total non-operational travel distance of each grain transport vehicle in the scheduling system, and the second optimization objective is used to reduce the total waiting time of each harvester in the scheduling system.

3. The method according to claim 1, characterized in that, The step of using the A* algorithm to determine the candidate transfer path between the unloading point and the grain transport vehicle matching the unloading point in each scheduling scheme includes: Based on the working width of the harvester, the area to be harvested by the harvester is gridded to obtain multiple grids; The A* algorithm is used to determine the combination of grids corresponding to the candidate transfer paths from the plurality of grids; Based on the combination of the grids, the candidate transfer paths corresponding to the scheduling scheme are obtained.

4. The method according to claim 3, characterized in that, The step of obtaining the transfer path corresponding to each grain transport vehicle in the collection and dispatch system based on the combination of the grids includes: Based on the Dubins algorithm, the entry and exit points of each grid in the grid combination are determined according to the relative positional relationship between each grid and the two grids before and after it. The transfer path is obtained by connecting the entry and exit points of each grid in the grid combination.

5. The method according to claim 1, characterized in that, The step of using simulated annealing to determine the optimal transfer path from the candidate transfer paths, and using the optimal transfer path as the solution result of the collection and transportation coordination model, includes: The first penalty term and / or the second penalty term are constructed in the harvesting and transportation coordination model. The first penalty term is used to prevent the grain volume in the grain bin of the grain transport vehicle and / or the harvester from exceeding a preset value at any time. The second penalty term is used to prevent the state of the grain transport vehicle and the harvester from not being consistent after the grain transport vehicle arrives at the unloading point. Based on the first penalty term and / or the second penalty term, the simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, so as to use the optimal transfer path as the solution result of the collection and transportation coordination model.

6. The method according to claim 1, characterized in that, Before constructing the collection and transportation coordination model, the method further includes: Based on the working width of the harvester, the harvesting path of the inner spiral shape executed by the harvester is determined in the harvesting area corresponding to the harvester.

7. A device for cooperative scheduling of collection and transportation based on a heuristic algorithm, comprising: The parameter module is used to obtain attribute parameters and task parameters related to the harvesting and transportation coordination task between the harvester and the grain transport vehicle. The attribute parameters are parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters are parameters describing the harvesting and transportation coordination task. The target module is used to construct a harvesting and transportation coordination model based on a preset optimization target, using the attribute parameters and the task parameters, to solve the target unloading strategy of the harvester and the grain transport vehicle in coordinated unloading. The target unloading strategy includes at least a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request. The transfer path is used to avoid crushing the area not harvested by the harvester. The solution module is used to solve the harvesting and transportation coordination model based on the harvesting path of the harvester using a heuristic algorithm to obtain the target unloading strategy. The strategy module is used to execute the target unloading strategy to schedule the grain transport vehicle to assist the harvester in unloading grain based on the transfer path; The heuristic algorithms include the A* algorithm, particle swarm optimization algorithm, and simulated annealing algorithm. The solution module is used for: Using a particle swarm optimization algorithm based on simulated annealing, multiple scheduling schemes are obtained. Each scheduling scheme includes unloading points located in the harvesting path of the harvester and grain transport vehicles matched with the unloading points. The A* algorithm is used to determine candidate transfer paths between the unloading point and the grain transport vehicle matching the unloading point in each of the scheduling schemes. The simulated annealing algorithm is used to determine the optimal transfer path from the candidate transfer paths, and the optimal transfer path is used as the solution result of the collection and transportation coordination model. Based on the optimal transfer path, the target grain unloading strategy is determined.

8. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions configured to be executed by the processor, the executable instructions including steps for performing the method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is used to store computer-executable instructions that cause the computer to perform the method as described in any one of claims 1 to 6.

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