Receiving and transporting cooperative scheduling method based on heuristic algorithm

Abstract

One or more embodiments of the invention disclose a heuristic algorithm-based receiving and transportation cooperative scheduling method and apparatus, and an electronic device. The method comprises the steps of obtaining attribute parameters and task parameters related to a collection and transportation cooperation task between the harvester and the grain transport vehicle, wherein the task parameters are parameters describing the collection and transportation cooperation task; based on a preset optimization target, using the attribute parameters and the task parameters to construct a collection and transportation cooperation model used for solving a target grain unloading strategy of cooperative grain unloading of the harvester and the grain transport vehicle, the target grain unloading strategy at least comprises a transfer path for the grain transporting vehicle to move to a grain unloading point in the harvesting path after receiving a grain unloading request; based on the harvesting path of the harvester, using a heuristic algorithm to solve the harvesting and transporting cooperation model to obtain a target grain unloading strategy; and executing the target grain unloading strategy to schedule the grain transport vehicle to assist the harvester to unload grains based on the transfer path.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a collection and transportation coordinated scheduling method, device, electronic device and storage medium based on a heuristic algorithm. Background Art

[0002] With the rapid development of my country's economy, the speed of rural land transfer has accelerated, gradually changing from decentralized management to large-scale centralized management, that is, the operation scene has become a large-scale standard farmland. Agricultural machinery social services usually involve a harvester equipped with two or more grain transport vehicles to complete agricultural production operations. Generally, a suitable scheduling model is designed based on the studied farmland conditions and the harvester's path, and the harvester's grain unloading time is predicted or analyzed, so as to adopt a proximity strategy to achieve scheduling between the combine harvester and the grain transport vehicle.

[0003] The above scheme simplifies the path planning of grain trucks and the response complexity of harvesters and grain trucks to achieve overall dispatch planning of grain trucks. However, if the specific transfer path of grain trucks in farmland is ignored, the dispatch process will be inaccurate and the environmental adaptability and safety of autonomous operation of grain trucks will be reduced. Therefore, there is an urgent need to provide a better solution for coordinated dispatch of harvesting and transportation. Summary of the invention

[0004] The embodiments of this specification provide a collection and transportation collaborative scheduling method and device based on a heuristic algorithm, an electronic device and a storage medium, so as to provide a collection and transportation collaborative scheduling solution based on a heuristic algorithm.

[0005] In a first aspect, one or more embodiments of the present specification provide a method for coordinated dispatching of harvesting and transportation based on a heuristic algorithm, comprising: obtaining attribute parameters and task parameters related to the coordinated harvesting and transportation task between the harvester and the grain transport vehicle, the attribute parameters being parameters describing the attributes of the harvester and / or the grain transport vehicle, and the task parameters being parameters describing the coordinated harvesting and transportation task; based on a preset optimization goal, using the attribute parameters and the task parameters, constructing a harvesting and transportation coordinated model for solving a target unloading strategy for the coordinated unloading of grain by the harvester and the grain transport vehicle, the target unloading strategy at least including 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 coordinated model to obtain a target unloading strategy; executing the target unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0006] In the second aspect, an embodiment of the present application provides a harvesting and transportation collaborative scheduling device based on a heuristic algorithm, including: a parameter module, used to obtain attribute parameters and task parameters related to the harvesting and transportation collaborative 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 collaborative task; a target module, used to construct a harvesting and transportation collaborative model for solving the target unloading strategy of the collaborative unloading of the harvester and the grain transport vehicle based on a preset optimization target, using the attribute parameters and the task parameters, the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; a solution module, used to solve the harvesting and transportation collaborative model based on the harvesting path of the harvester using a heuristic algorithm to obtain the target unloading strategy; a strategy module, used to execute the target unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0008] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0009] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method 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 for solving the target unloading strategy of the coordinated unloading of grain by the harvester and the grain transport vehicle is constructed based on the optimization goal, and based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy to solve the harvesting and transportation coordination model, and the target unloading strategy is obtained, so as to use the target unloading strategy to perform the coordinated unloading of grain by the grain transport vehicle and the harvester in the harvesting and transportation scheduling system. In the above process, the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered at the same time. Through the combination of the heuristic algorithm and the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, and the working adaptability of the harvester in a dynamic agricultural environment is enhanced. This solution will play an important role in improving the efficiency of agricultural machinery operations, improving agricultural production conditions, and ensuring food security. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of 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 ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 It is a schematic flowchart of a method for coordinated dispatching of collection and transportation based on a heuristic algorithm according to an embodiment of the present specification.

[0013] Figure 2 It is a schematic diagram of a harvesting path of a spiral in a multi-agricultural machine according to an embodiment of the present specification.

[0014] Figure 3 A schematic diagram of a process of a particle swarm optimization algorithm improved based on a simulated annealing algorithm according to an embodiment of the present specification is shown.

[0015] Figure 4 It is a schematic diagram of a combination of grids searched using an A* algorithm according to an embodiment of the present specification.

[0016] Figure 5 It is a schematic diagram of determining the entry point and exit point of a grid in relative position relationship according to an embodiment of the present specification.

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

[0018] Figure 7 A schematic diagram of a process of a particle swarm optimization algorithm improved based on a simulated annealing algorithm according to an embodiment of the present specification is shown.

[0019] Figure 8 It is a structural diagram of a collection and transportation collaborative scheduling device based on a heuristic algorithm according to an embodiment of the present specification.

[0020] Fig. 9 It is a structural schematic diagram of an electronic device according to an embodiment of this specification. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of this specification can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0023] In conjunction with the accompanying drawings, the collection and transportation collaborative scheduling method, device and electronic device based on the heuristic algorithm provided in the embodiments of this specification are described in detail through specific embodiments and their application scenarios.

[0024] Figure 1 A collection and transportation coordination scheduling method based on a heuristic algorithm provided by an embodiment of the present invention is shown. The method can be executed by an electronic device, and the electronic device 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 in the aforementioned electronic device, and the method includes the following steps: S102: Acquire attribute parameters and task parameters related to the harvesting and transporting collaborative task between the harvester and the grain transport vehicle.

[0025] The harvesting and transportation dispatching system is any system for multi-machine coordinated dispatching of harvesters and grain transport vehicles, and the harvesting and transportation dispatching system may include multiple harvesters and multiple grain transport vehicles. The number of harvesters and / or grain transport vehicles in the harvesting and transportation dispatching system is not specifically limited in this specification and can be determined according to actual conditions.

[0026] The attribute parameters are parameters describing 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.

[0027] The first attribute parameter is used to describe the state of the harvester at the working time. Specifically, the set Represents the harvester group in the collection and transportation scheduling system. The harvester object currently studied is used To indicate that the harvester The first attribute parameter can be expressed as .in, Indicates i The maximum capacity of a harvester, VH iIndicates i The linear speed of the harvester, For the i The turning speed of a harvester, It is i The harvesting amount per unit distance of a harvester, Indicates i Harvester in t The coordinates of the moment, Indicates i Harvester in t The working journey of the time, Indicates i Harvester in t The remaining granary capacity at any time, Indicates i The total waiting time of the harvesters.

[0028] The second attribute parameter is used to describe the state of the grain truck at the working time. Represents the grain transport vehicle group in the collection and transportation dispatching system. The grain transport vehicle object currently studied is used To indicate that the grain truck The second attribute parameter can be expressed as .in, Indicates j The maximum capacity of a grain truck, VT Indicates j The operating speed of a grain truck, XT j ( t ) indicates the j Grain trucks in t The coordinates of the moment, CT j ( t ) indicates the j Grain trucks in t The remaining capacity at the moment. It should be noted that t =0 represents the moment of the initial state of the harvester or grain truck.

[0029] The task parameters are parameter descriptions of the harvesting and transporting collaborative task, and the task parameters may include parameters of the grain transport vehicle responding to the harvester's grain unloading request and the grain transport vehicle assisting the harvester in unloading grain. Indicated in t Time, grain truck Has the harvester responded? The request is made and the harvester is moved to the location to assist in unloading the grain. It can be represented by 0 and 1, 0 represents a grain truck No response harvester Request and move to the location of the harvester to assist in unloading grain, 1 represents the grain transport vehicle Responded to Harvester The request is made and the harvester is moved to the location to assist in unloading. j The grain truck responds to the moment of unloading grain using the task sequence set Indicates that the task sequence set for starting to unload grain is , unloading time is . , R j , R’ j , U j This is the task parameter.

[0030] Step S104: Based on a preset optimization target, 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 coordinated unloading of grain.

[0031] Unlike the existing schemes that only plan the harvesting path of the harvester, the harvesting and transportation coordinated scheduling scheme in this specification includes the path planning of the grain transport vehicle in addition to the harvesting path of the harvester. The optimization target is an indicator that needs to be considered when executing the harvesting and transportation coordinated scheduling scheme. In addition to the existing routine indicators related to the harvester, the optimization target can also include indicators that describe the grain transport vehicle in performing auxiliary grain unloading tasks, such as the time when the harvester stops working and waits for the grain transport vehicle, the path of the grain transport vehicle, etc. In one implementation, the optimization target includes at least the first optimization target and / or the second optimization target.

[0032] Among them, the first optimization goal is to reduce the total non-operating moving distance of each grain truck in the scheduling system, and the second optimization goal is to reduce the total waiting time of each harvester in the scheduling system.

[0033] Among them, the non-operating moving distance can be the moving distance of the grain transport vehicle when it does not coordinate unloading. In one example, the distance that the grain transport vehicle moves to the unloading point after responding to the unloading signal of the harvester can be used as the non-operating moving distance. Specifically, the non-operating moving distance can be determined according to the transfer path, and then the non-operating moving distances corresponding to each transfer path can be added to obtain the total non-operating moving distance. The first optimization goal can be determined by the total non-operating moving distance (the states of the grain transport vehicle and the harvester must be consistent when unloading). Based on the above attribute parameters and task parameters, the first optimization goal can be expressed as Formula 1: (Formula 1) in, Indicates the distance the grain truck needs to move to the harvester's location at the current moment. t z ∈ R j , t’ z ∈ R’ j , i∈n, n represents the number of harvesters, j∈m, m represents the number of grain trucks, R j Indicates j The grain truck responds to the moment of unloading grain using the task sequence set. R’ j Indicates j The task sequence set of a grain truck starts unloading grain, Indicated in t Grain truck at all times Has the harvester responded? The request is made and the harvester is moved to the location to assist in unloading the grain. XT j ( t z ) indicates the j Grain trucks in t z The coordinates of the moment, XH i ( t’ z ) indicates the i Harvester in t’ z The coordinates of the moment.

[0034] In addition to the first and second optimization objectives mentioned above, a third optimization objective can also be set to ensure that the workload of the grain transport vehicle is basically balanced. The third optimization objective is shown in Formula 2: (Formula 2) The second optimization goal can be determined based on the time each harvester waits for the grain truck after arriving at the grain unloading point (grain unloading location). t When the harvester sends a signal to the grain truck at time 0, the grain truck t After 0:00, the grain truck will set out for the grain unloading point. t If the distance traveled after time 0 is greater than 0, the harvester will not generate any waiting time and it will be represented by 0; otherwise, the harvester will generate any waiting time. t z , the waiting time corresponding to each harvester t z The second optimization target can be determined by adding them together.

[0035] In the above process, the harvesting and transportation coordination model is constructed based on the first optimization objective and / or the second optimization objective, and a high-precision harvesting and transportation coordination scheduling model with a low total non-operating distance of the grain transport vehicle and a short waiting time of the harvester can be obtained. In this way, an accurate and reasonable target grain 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.

[0036] The target grain unloading strategy can be obtained by solving the harvesting and transportation coordination model constructed based on the above optimization objectives. Based on the aforementioned optimization objectives including indicators describing the grain transport vehicle in performing auxiliary grain unloading tasks, the target grain unloading strategy can at least include a transfer path for the grain transport vehicle to move to the grain unloading point in the harvesting path after receiving the grain unloading request, and the transfer path needs to avoid running over the unharvested area of ​​the harvester.

[0037] Step S106: Based on the harvesting path of the harvester, a heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain a target grain unloading strategy.

[0038] Step S108: Execute the target grain unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0039] Among them, heuristic algorithms are a type of method used to find approximations of complex optimization problems, and are suitable for solving large problems with limited computing resources. Vehicle scheduling is a non-deterministic polynomial hard (NP-Hard) problem, and it is difficult to find the optimal solution. Heuristic algorithms can provide a method to find a satisfactory solution in a reasonable time. Even if the quality of the solution cannot be guaranteed, it can effectively find a good enough solution in a limited time to suit agricultural field scheduling operations.

[0040] The harvesting path is the path that the harvester walks during the harvesting process. The harvesting path can be determined based on the harvesting area of ​​the harvester and the path planning information of the harvester (such as the operation walking mode of the harvester, etc.). The harvesting path includes the unloading point where the harvester and the grain unloading vehicle unload grain together. The transfer path is the path that the grain transport vehicle transfers to the unloading point determined on the basis of the unloading point in the harvesting path. In one example, based on the determined harvesting path of the harvester, a heuristic algorithm can be used to solve the established harvesting and transportation coordination model to obtain the specific values ​​of the aforementioned attribute parameters and task parameters. Further, these values ​​are processed to obtain the target unloading strategy. Executing the target unloading strategy can realize the coordinated unloading of grain by the grain transport vehicle and the harvester.

[0041] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model for solving the target unloading strategy of the coordinated unloading of grain by the harvester and the grain transport vehicle is constructed based on the optimization goal, and based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy to solve the harvesting and transportation coordination model, and the target unloading strategy is obtained, so as to use the target unloading strategy to perform the coordinated unloading of grain by the grain transport vehicle and the harvester in the harvesting and transportation scheduling system. In the above process, the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered at the same time. Through the combination of the heuristic algorithm and the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, and the working adaptability of the harvester in a dynamic agricultural environment is enhanced. This solution will play an important role in improving the efficiency of agricultural machinery operations, improving agricultural production conditions, and ensuring food security.

[0042] As for the path planning of harvesters, most of the global coverage path planning methods are currently adopted, which are mainly based on covering the farm operating environment, and reciprocating traversal parallel operations are performed based on this. Specifically, in the harvesting process corresponding to the reciprocating traversal parallel operation, the parameters of the area to be harvested are first obtained, and the longest boundary among the boundaries of the area to be harvested is selected as the baseline. Then the harvester is parallel to the baseline and is translated at equal intervals according to the working width of the harvester to generate multiple working rows, until the harvester's path covers the entire area to be harvested. This method is usually used in regular areas such as dryland rice harvesting, and the number of turns is relatively large. Especially when harvesting in water areas, turning is difficult, energy consumption requirements are large, safety is low, and duplication and incomplete coverage are prone to occur. In one implementation method, before constructing the harvesting and transportation coordination model, the method also includes: According to the working width of the harvester, an inner spiral shaped harvesting path executed by the harvester is determined in the to-be-harvested area corresponding to the harvester.

[0043] Specifically, in the process of path planning for multiple harvesters in a rectangular plot, the rectangular plot can be divided into multiple areas to be harvested according to the number of harvesters. That is, the number of areas to be harvested must match the number of harvesters. In each harvesting area, the outermost point can be used as the starting point, and the working width of the harvester can be used as the width, and the area to be operated by the harvester can be walked inward in a spiral.

[0044] Figure 2 A schematic diagram of a harvesting path of a multi-agricultural machine inner spiral is shown. Figure 2 As shown in the figure, assuming that the harvester starts from the midpoint of the farmland boundary and returns to the starting point after completing the harvesting task, the harvesting path planning result of 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 away from the boundary, it first turns 90 degrees and then turns 90 degrees to drive in the opposite direction until it has cycled through all areas, as shown in the figure. Figure 2As shown, compared with the aforementioned reciprocating traversal parallel operation method, this method has fewer turns.

[0045] In the embodiment of this specification, an inward spiral full coverage path planning is used as the working path of the harvester, so that the harvester starts from the innermost side and spirals inward parallel to the working boundary, and finally covers the entire area. This process can effectively reduce the number of turns and difficulty of the harvester through the inner spiral harvesting path, reduce unnecessary losses, and thus achieve the purpose of improving work efficiency.

[0046] In one implementation, the heuristic algorithm includes an A* algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm. Based on the harvesting path of the harvester, the heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain a target grain unloading strategy, including: Using a particle swarm optimization algorithm based on a simulated annealing algorithm, a plurality of scheduling schemes are obtained, wherein the scheduling schemes include a grain unloading point located in a harvesting path of the harvester and a grain transport vehicle matched with the grain unloading point; Using the A* algorithm, determining candidate transfer paths between the grain unloading point in each of the scheduling schemes and the grain transport vehicle matching the grain unloading point; Using a simulated annealing algorithm, determine an optimal transfer path from the candidate transfer paths, and use the optimal transfer path as a solution result of the collection and transportation coordination model; The target grain unloading strategy is determined based on the optimal transfer path.

[0047] Among them, the transfer path is the path that the grain truck takes to transfer to the unloading point after receiving the unloading signal from the harvester. The candidate transfer path is the transfer path of the grain truck determined based on the scheduling plan before determining the optimal solution of the collection 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 collection and transportation coordination model.

[0048] The A* algorithm is a heuristic search algorithm widely used in path finding and graph traversal. Its core is to use a valuation function to estimate the cost from any node to the target node. Because it is guided by the heuristic function, it can have better performance.

[0049] Figure 3 The flowchart of the scheduling algorithm for harvesters and grain trucks is shown in FIG. Figure 3 As shown in the figure, after the harvesting and transportation coordination model randomly generates an initial solution, a particle swarm optimization algorithm based on a simulated annealing algorithm can be used to obtain multiple scheduling schemes corresponding to the initial solution. The scheduling scheme includes the unloading point (unloading position) of the harvester and the grain transport vehicle matching the unloading point. Then, based on the unloading position, the candidate transfer path of the grain transport vehicle can be determined using the A* algorithm. It should be understood that Figure 3 In addition to the optimal transfer path corresponding to the optimal release of the grain transport vehicle, it can also include multiple data related to the coordination of harvesting and transportation, such as the waiting time of the harvester and the response time of the grain transport vehicle. These data can be the specific values ​​of the aforementioned attribute parameters and task parameters.

[0050] The A* algorithm is a grid-based search algorithm. Before determining the candidate transfer path, the harvesting area corresponding to the harvester can be gridded. In one implementation, the A* algorithm is used to determine the candidate transfer path between the grain unloading point in each scheduling scheme and the grain transport vehicle matching the grain unloading point, including: Based on the working width of the harvester, the area to be harvested of the harvester is gridded to obtain a plurality of grids; Using an A* algorithm, determining a combination of grids corresponding to the candidate transfer path from the plurality of grids; According to the combination of the grids, a candidate transfer path corresponding to the scheduling scheme is obtained.

[0051] Specifically, the area to be harvested may be gridded based on the harvester's working width, and each grid may be a square substantially the same as the working width. In one example, the process of determining the candidate transfer path using the A* algorithm may include: ① Define nodes and edges: Each pixel in the rasterized grid is regarded as a node, and the line between adjacent pixels is regarded as an edge. If there is an obstacle between two pixels, the edge between them is removed.

[0052] ② Define the starting point and end point: Select the starting point and end point in the rasterized grid. The starting point and end point must be within the passable area, and the direction of the grain truck at the starting point needs to be calculated.

[0053] ③ Calculate the heuristic cost of each node (i.e. the estimated distance from the node to the end point): You can use simple distance calculation methods such as Euclidean distance or Manhattan distance.

[0054] ④ Implement the A* algorithm using a priority queue: put the starting point into the priority queue, and then repeat the following steps until the end point is found or the queue is empty: take out the node with the smallest heuristic cost from the queue; if the node is the end point, the path planning is complete; otherwise, expand the nodes adjacent to the node that match the grain truck's direction, calculate their heuristic costs, and add them to the queue. If the adjacent nodes have been visited, update their costs and predecessor nodes. If the new cost is smaller, update the corresponding nodes in the queue.

[0055] ⑤ Backtracking path: When the end point is found, backtrack along the predecessor node of each node from the end point until backtracking to the starting point to obtain the candidate transfer path.

[0056] Figure 4 A schematic diagram showing the combination of grids searched using the A* algorithm is shown in FIG. Figure 4 As shown, the black box represents the boundary and the area not harvested by the harvester. The combination of grids can only represent the approximate path direction, but not the actual path. After determining the combination of grids, the transfer path needs to be determined based on the combination of grids. In one implementation, the transfer path corresponding to each grain transport vehicle in the collection and transportation scheduling system is obtained according to the combination of grids, including: Based on the Dubins algorithm, according to the relative position relationship between each grid in the combination of grids and the two grids before and after the grid, the entry point and the exit point of each grid in the combination of grids are determined; The entry point and the exit point of each grid in the combination of grids are connected to obtain the transfer path.

[0057] For the obtained grid combination, except for the first and last grids, the relative position relationship between each remaining grid and the two grids before and after can be determined, and then the grid entry point and exit point can be determined based on this relative position relationship. Specifically, after determining the midpoints of the four sides and the four vertices of each grid, the entry point and exit point in the current grid can be determined based on the relative position relationship. Figure 5 A schematic diagram of determining the entry and exit points of a grid based on a relative position relationship is shown. Figure 5 As shown, the grid in the middle is the target grid. For different relative position relationships, the entry point and exit point of the target grid are different, and both the entry point and the exit point are vertices or midpoints.

[0058] In one example, the Dubins path can be used to determine the transfer path from the combination of grids. The real-time transfer path calculation method of "grid method + A* algorithm + Dubins path" can significantly improve the calculation accuracy of the shortest distance corresponding to the optimization target of the grain truck. Figure 6 FIG. 4 shows a schematic diagram of the transfer path obtained based on the Dubins path. Figure 6 As shown, compared with the aforementioned combination of grids, the transfer path obtained based on the Dubins path is smoother.

[0059] Particle Swarm Optimization (PSO) is a swarm intelligence algorithm with fast convergence speed, few parameters, simple and easy-to-implement algorithm. It has very good performance in searching for optimal parameters in continuous domains, but its application in discrete domains is relatively limited and requires some modifications before it can be applied in discrete domains. The speed and position update formulas in PSO are Formula 3 and Formula 4.

[0060] (Formula 3) (Formula 4) in, i ∈[1,n], m is the number of search particles, n is the node dimension, V is the current velocity of the particle ( m × n ), V n is the updated velocity of the particle, c 1. c 2 is the accelerated long speed (negative normal number), X is the current position of the particle (size and speed), X’ is the updated position of the particle, ω is the inertia weight, P i is the best historical position (size and speed) of each particle, P g is the best historical position of all particles.

[0061] By introducing the commutator and the commutation sequence, the speed and position update formula can be redefined (as shown in formulas 5 and 6), so that the PSO algorithm has the ability to process discrete domains. Each element in the commutation sequence is called a commutator. For example, if there is a commutation sequence V=[2 0 0] and a discrete sequence X=[1 2 3], then X+V=[2 1 3], because V The number 2 at index 1 means X The object to be exchanged at index 1 is index 2, so X The values ​​of indexes 1 and 2. To ensure that addition and subtraction are reversible operations under the definition of commutative order, let [2 1 3] = P , then PX=V .

[0062] (Formula 5) (Formula 6) in, α and β is a random positive number within 1. α ( P id -X id ) represents the basic exchange sequence ( P id -X id ) are retained with probability α; β ( P gd -X id ) represents the basic exchange sequence (P gd -X id ) with probability β reserve.

[0063] Formula 6 can actually be broken down into the superposition of three position update formulas, which results in the change of the objects of the last two exchange orders, making the definition of the exchange order meaningless. To solve this problem, the position update formula can be modified (as shown in Formula 7).

[0064] (Formula 7) in, ωV id Show the basic exchange sequence V id All commutators in ω The new position formula (Formula 7) ensures the addition of the particles and the corresponding exchange order effects.

[0065] Since the PSO algorithm is prone to fall into local optimality and oscillation in the later stage of the algorithm, the simulated annealing mechanism (SA algorithm) can be introduced to achieve acceptance of the optimized solution while accepting the deteriorated solution with a certain probability to improve the performance of the algorithm, thereby achieving an effective balance between the global convergence and local convergence of the algorithm. Using the standard PSO algorithm to find the best solution in the early stage of the calculation process can increase the rate of the algorithm search throughout the process and increase the diversity of the population for the random initialization work in the later stage; and introducing the simulated annealing principle in the later stage enhances the global search ability of the particle swarm and largely solves the local convergence of the PSO algorithm.

[0066] Figure 7 The specific steps of the particle swarm optimization algorithm (SAPSO algorithm) improved based on the simulated annealing algorithm are shown: ①Parameter initialization: setting population size m , minimum particle speed V min and maximum speed V max , inertia weight control factor q c , learning factor c 1 and c 2. Annealing start and end temperature T s and T e , annealing rate β Initial values ​​of parameters such as ② Random generation n The initial solution population of particles is n Initial solutionX i ( t ) and initial velocity V j ( t ); ③Calculate the fitness value of each particle in the population f ( X i ( t )), and the fitness value of each particle f ( X i ( t )) are compared with the individual extreme value and the global extreme value respectively, and the best one is updated as the individual extreme value and the global extreme value; ④ Update the particle speed and position according to Formula 3 and Formula 4, and limit its speed to V max within; ⑤Calculate the updated fitness value of each particle f ( X i ( t+ 1)), and calculate the change in fitness value Δ caused by the two positions E If Δ E< 0, then accept the new position; if , also accepts new positions, where ε is a random number in the interval [0,1], otherwise it is rejected; ⑥ If you accept the new solution, then follow Ts → βTe The cooling operation is performed in this way, and the optimal solution of the optimization problem is iteratively optimized; otherwise, no cooling process is performed and the process returns to step ③.

[0067] like Figure 3 As shown, the scheduling algorithm in this specification includes Figure 7 The SAPSO algorithm shown in the figure is combined with the A* algorithm on the basis of the SAPSO algorithm. Specifically, in the scheduling algorithm of this scheme, the A* algorithm can be used to calculate the grid combination of the transfer path of the grain truck under the premise of known unloading points and scheduling schemes, and then the Duinbs path is used to calculate the candidate transfer path under this grid combination. Furthermore, the SAPSO algorithm is iterated multiple times to determine the optimal solution among several candidate transfer paths, and the target unloading strategy is obtained. The SAPSO algorithm is used to design a reasonable response path optimization scheme for the harvester grain truck, which simplifies the search process and improves the scheduling efficiency.

[0068] In the process of constructing the collection and transportation coordination model, in addition to optimizing the objectives, attribute parameters and task parameters, some constraints need to be considered to restrict the target unloading strategy of the collection and transportation scheduling system. In one implementation, the use of a simulated annealing algorithm 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, includes: Constructing a first penalty term and / or a second penalty term of the collection and transportation coordination model; Based on the first penalty term and / or the second penalty term, the optimal transfer path is determined from the candidate transfer paths using a simulated annealing algorithm, so that the optimal transfer path is used as a solution result of the collection and transportation coordination model.

[0069] Among them, the first penalty item is used to prevent the volume of grain 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 being inconsistent with that of the harvester after arriving at the grain unloading point.

[0070] Specifically, when establishing the constraints of the harvesting and transporting coordination model, the amount of grain stored in the grain truck and / or harvester's granary (or the remaining capacity of the granary) can be restricted to avoid the situation where the stored crops overflow the grain truck and / or harvester, so as to improve the feasibility of the target grain unloading strategy. Specifically, the first penalty term can be: 0< CH i ( t )≤ CH imax and 0< CT j ( t )≤ CT jmax .

[0071] In addition to the amount of grain stored in the granary, the states of the harvester and the grain truck at the beginning of grain unloading can also be restricted, so that the target grain unloading strategy is that the grain truck reaches the set unloading point and the harvester and the grain truck are in the same state before grain unloading can be performed, further improving the feasibility of the target grain unloading strategy, where the state consistency can include the grain truck and the harvester having the same direction, speed, and basically the same coordinates (the grain truck and the harvester both reach the preset unloading point). Specifically, the second penalty item can be: .

[0072] In addition to the contents restricted by the first and second penalty items mentioned above, relevant information of the grain unloading point can also be limited. For example, when the harvester arrives at the grain unloading point, the volume of grain stored in the grain bin of the harvester is limited to 80%~100% of the maximum capacity of the grain bin of the harvester, so as to improve the operating efficiency of the grain transport vehicle and / or harvester.

[0073] It should be noted that the execution subject of the method for collection and transportation collaborative scheduling based on a heuristic algorithm provided in the embodiments of this specification may be a device for collection and transportation collaborative scheduling based on a heuristic algorithm, or a control module in the device for collection and transportation collaborative scheduling based on a heuristic algorithm for executing the method for collection and transportation collaborative scheduling based on a heuristic algorithm. In the embodiments of this specification, the device for collection and transportation collaborative scheduling based on a heuristic algorithm provided in the embodiments of this specification is explained by taking the device for collection and transportation collaborative scheduling based on a heuristic algorithm executing the method for collection and transportation collaborative scheduling based on a heuristic algorithm as an example.

[0074] Figure 8 is a schematic diagram of the structure of a collection and transportation coordination scheduling device based on a heuristic algorithm according to an embodiment of the present invention. Figure 8 As shown, the collection and transportation coordinated scheduling device 800 based on the heuristic algorithm includes: The parameter module 810 is used to obtain attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; The target module 820 is used to construct a harvesting and transporting coordination model for solving the target unloading strategy of the harvester and the grain transport vehicle for coordinated grain unloading based on a preset optimization target, using the attribute parameters and the task parameters, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; A 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 a target grain unloading strategy; The strategy module 840 is used to execute the target grain unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0075] 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-operating moving 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.

[0076] In one embodiment, the heuristic algorithm includes an A* algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm, and the solution module 830 includes: A scheduling scheme unit, used for obtaining a plurality of scheduling schemes by using a particle swarm optimization algorithm based on a simulated annealing algorithm, wherein the scheduling schemes include a grain unloading point located in a harvesting path of the harvester and a grain transport vehicle matched with the grain unloading point; A candidate path unit, used for determining a candidate transfer path between the grain unloading point and the grain transport vehicle matching the grain unloading point in each of the scheduling schemes by using an A* algorithm; An optimization unit, used for determining an optimal transfer path from the candidate transfer paths by using a simulated annealing algorithm, so as to use the optimal transfer path as a solution result of the collection and transportation coordination model; A strategy unit is used to determine the target grain unloading strategy based on the optimal transfer path.

[0077] In one embodiment, the candidate path unit is used to: Based on the working width of the harvester, the area to be harvested of the harvester is gridded to obtain a plurality of grids; Using an A* algorithm, determining a combination of grids corresponding to the candidate transfer path from the plurality of grids; According to the combination of the grids, a candidate transfer path corresponding to the scheduling scheme is obtained.

[0078] In one embodiment, obtaining the transfer path corresponding to each grain transport vehicle in the collection and transportation scheduling system according to the combination of the grids includes: Based on the Dubins algorithm, according to the relative position relationship between each grid in the combination of grids and the two grids before and after the grid, the entry point and the exit point of each grid in the combination of grids are determined; The entry point and the exit point of each grid in the combination of grids are connected to obtain the transfer path.

[0079] In one embodiment, the optimization unit is used to: Constructing the first penalty item and / or the second penalty item of the harvesting and transportation coordination model, wherein 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 grain transport vehicle from reaching a state consistent with that of the harvester after arriving at the grain unloading point; Based on the first penalty term and / or the second penalty term, the optimal transfer path is determined from the candidate transfer paths using a simulated annealing algorithm, so that the optimal transfer path is used as a solution result of the collection and transportation coordination model.

[0080] In one embodiment, the collection and transportation coordinated scheduling device 800 based on the heuristic algorithm further includes: The harvesting path module is used to determine the inner spiral harvesting path executed by the harvester in the to-be-harvested area corresponding to the harvester according to the working width of the harvester before constructing the harvesting and transportation coordination model.

[0081] The device for coordinated dispatching of collection and transportation based on heuristic algorithms in the embodiments of this specification may be a device, or a component, integrated circuit, or chip in a terminal. The device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device may be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., which is not specifically limited in the embodiments of this specification.

[0082] The device for coordinated dispatching of collection and transportation based on heuristic algorithms in the embodiments of this specification may be a device with an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of this specification.

[0083] The device for coordinated dispatching of collection and transportation based on heuristic algorithm provided in the embodiments of this specification can achieve Figure 1 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0084] Based on the same idea, one or more embodiments of this specification also provide an electronic device, such as Fig. 9 As shown. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors 901 and memory 902, and the memory 902 may store one or more storage applications or data. Among them, the memory 902 may be a temporary storage or a permanent storage. The application stored in the memory 902 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the electronic device. Furthermore, the processor 901 can be configured to communicate with the memory 902 to execute a series of computer executable instructions in the 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 and output interfaces 905, and one or more keyboards 906.

[0085] In a specific embodiment, the electronic device is used for a harvesting and transportation scheduling system including multiple harvesters and multiple grain transport vehicles, including a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer executable instructions in the electronic device, and the one or more programs are configured to be executed by one or more processors, including the following computer executable instructions: Acquire attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; Based on the preset optimization goal, using the attribute parameters and the task parameters, a harvesting and transporting collaborative model for solving the target unloading strategy of the harvester and the grain transport vehicle for collaborative unloading of grain is constructed, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; Based on the harvesting path of the harvester, the harvesting and transportation coordination model is solved using a heuristic algorithm to obtain a target grain unloading strategy; The target grain unloading strategy is executed to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0086] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model for solving the target unloading strategy of the coordinated unloading of grain by the harvester and the grain transport vehicle is constructed based on the optimization goal, and based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy to solve the harvesting and transportation coordination model, and the target unloading strategy is obtained, so as to use the target unloading strategy to perform the coordinated unloading of grain by the grain transport vehicle and the harvester in the harvesting and transportation scheduling system. In the above process, the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered at the same time. Through the combination of the heuristic algorithm and the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, and the working adaptability of the harvester in a dynamic agricultural environment is enhanced. This solution will play an important role in improving the efficiency of agricultural machinery operations, improving agricultural production conditions, and ensuring food security.

[0087] It should be noted that the embodiment of the electronic device in the present application and the embodiment of the collection and transportation collaborative scheduling method based on heuristic algorithm in the present application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the collection and transportation collaborative scheduling method based on heuristic algorithm, and the repeated parts will not be repeated.

[0088] Further, corresponding to the above-described heuristic algorithm-based harvesting and transportation coordinated scheduling method, based on the same technical concept, one or more embodiments of the present application further provide a storage medium for storing computer executable instructions for a harvesting and transportation scheduling system including multiple harvesters and multiple grain transport vehicles. In a specific embodiment, the storage medium may be a USB flash drive, an optical disk, a hard disk, etc. When the computer executable instructions stored in the storage medium are executed by the processor, the following process can be implemented: Acquire attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; Based on the preset optimization goal, using the attribute parameters and the task parameters, a harvesting and transporting collaborative model for solving the target unloading strategy of the harvester and the grain transport vehicle for collaborative unloading of grain is constructed, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; Based on the harvesting path of the harvester, the harvesting and transportation coordination model is solved using a heuristic algorithm to obtain a target grain unloading strategy; The target grain unloading strategy is executed to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0089] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model for solving the target unloading strategy of the coordinated unloading of grain by the harvester and the grain transport vehicle is constructed based on the optimization goal, and based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy to solve the harvesting and transportation coordination model, and the target unloading strategy is obtained, so as to use the target unloading strategy to perform the coordinated unloading of grain by the grain transport vehicle and the harvester in the harvesting and transportation scheduling system. In the above process, the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered at the same time. Through the combination of the heuristic algorithm and the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, and the working adaptability of the harvester in a dynamic agricultural environment is enhanced. This solution will play an important role in improving the efficiency of agricultural machinery operations, improving agricultural production conditions, and ensuring food security.

[0090] It should be noted that the embodiment of the storage medium in the present application and the collection and transportation collaborative scheduling method based on heuristic algorithm in the present application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the collection and transportation collaborative scheduling method based on heuristic algorithm, and the repeated parts will not be repeated.

[0091] Furthermore, corresponding to the above-described harvesting and transportation coordinated scheduling method based on the heuristic algorithm, based on the same technical concept, one or more embodiments of the present application further provide a computer program product for a harvesting and transportation scheduling system including multiple harvesters and multiple grain transport vehicles. The computer program product includes a computer program. When the computer program is executed by a processor, the following process can be implemented: Acquire attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; Based on the preset optimization goal, using the attribute parameters and the task parameters, a harvesting and transporting collaborative model for solving the target unloading strategy of the harvester and the grain transport vehicle for collaborative unloading of grain is constructed, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; Based on the harvesting path of the harvester, the harvesting and transportation coordination model is solved using a heuristic algorithm to obtain a target grain unloading strategy; The target grain unloading strategy is executed to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

[0092] In the embodiments of this specification, after obtaining the attribute parameters and task parameters, a harvesting and transportation coordination model for solving the target unloading strategy of the coordinated unloading of grain by the harvester and the grain transport vehicle is constructed based on the optimization goal, and based on the harvesting path, a heuristic algorithm is used to determine the target unloading strategy to solve the harvesting and transportation coordination model, and the target unloading strategy is obtained, so as to use the target unloading strategy to perform the coordinated unloading of grain by the grain transport vehicle and the harvester in the harvesting and transportation scheduling system. In the above process, the transfer path of the grain transport vehicle and the harvesting path of the harvester are considered at the same time. Through the combination of the heuristic algorithm and the collaborative operation scheduling model, the overall optimization of the harvesting and transportation scheduling system is achieved, and the working adaptability of the harvester in a dynamic agricultural environment is enhanced. This solution will play an important role in improving the efficiency of agricultural machinery operations, improving agricultural production conditions, and ensuring food security.

[0093] It should be noted that the embodiment of the computer program product in the present application and the embodiment of the collection and transportation collaborative scheduling method based on heuristic algorithm in the present application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the collection and transportation collaborative scheduling method based on heuristic algorithm, and the repeated parts will not be repeated.

[0094] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, 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, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0096] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0097] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0098] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0099] Those skilled in the art will appreciate that one or more embodiments of the present application may be provided as a method, system or computer program product. Therefore, one or more embodiments of the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented 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.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0104] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0105] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0107] One or more embodiments of the present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present application may also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0108] Each embodiment in this application is described in a progressive 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 system 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.

[0109] The above are only embodiments of this document and are not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A collection and transportation coordinated scheduling method based on heuristic algorithm, characterized in that: A harvesting and transporting dispatching system for multiple harvesters and multiple grain transporters, including: Acquire attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; Based on the preset optimization goal, using the attribute parameters and the task parameters, a harvesting and transporting collaborative model for solving the target unloading strategy of the harvester and the grain transport vehicle for collaborative unloading of grain is constructed, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to the unloading point in the harvesting path after receiving the unloading request; Based on the harvesting path of the harvester, the harvesting and transportation coordination model is solved using a heuristic algorithm to obtain a target grain unloading strategy; The target grain unloading strategy is executed to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

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-operating moving 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 heuristic algorithm includes an A* algorithm, a particle swarm optimization algorithm and a simulated annealing algorithm. Based on the harvesting path of the harvester, the heuristic algorithm is used to solve the harvesting and transportation coordination model to obtain a target grain unloading strategy, including: Using a particle swarm optimization algorithm based on a simulated annealing algorithm, a plurality of scheduling schemes are obtained, wherein the scheduling schemes include a grain unloading point located in a harvesting path of the harvester and a grain transport vehicle matched with the grain unloading point; Using the A* algorithm, determining candidate transfer paths between the grain unloading point in each of the scheduling schemes and the grain transport vehicle matching the grain unloading point; Using a simulated annealing algorithm, determine an optimal transfer path from the candidate transfer paths, and use the optimal transfer path as a solution result of the collection and transportation coordination model; The target grain unloading strategy is determined based on the optimal transfer path.

4. The method according to claim 3, characterized in that The method of using the A* algorithm to determine a candidate transfer path between the grain unloading point in each of the scheduling schemes and a grain transport vehicle matching the grain unloading point includes: Based on the working width of the harvester, the area to be harvested of the harvester is gridded to obtain a plurality of grids; Using an A* algorithm, determining a combination of grids corresponding to the candidate transfer path from the plurality of grids; According to the combination of the grids, a candidate transfer path corresponding to the scheduling scheme is obtained.

5. The method according to claim 4, characterized in that The transfer path corresponding to each grain transport vehicle in the collection and transportation scheduling system is obtained according to the combination of the grids, including: Based on the Dubins algorithm, according to the relative position relationship between each grid in the combination of grids and the two grids before and after the grid, the entry point and the exit point of each grid in the combination of grids are determined; The entry point and the exit point of each grid in the combination of grids are connected to obtain the transfer path.

6. The method according to claim 3, characterized in that The using of a simulated annealing algorithm to determine an optimal transfer path from the candidate transfer paths, so as to use the optimal transfer path as a solution result of the collection and transportation coordination model, includes: Constructing the first penalty item and / or the second penalty item of the harvesting and transportation coordination model, wherein 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 grain transport vehicle from reaching a state consistent with that of the harvester after arriving at the grain unloading point; Based on the first penalty term and / or the second penalty term, the optimal transfer path is determined from the candidate transfer paths using a simulated annealing algorithm, so that the optimal transfer path is used as a solution result of the collection and transportation coordination model.

7. The method according to claim 1, characterized in that Before constructing the collection and transportation coordination model, the method further includes: According to the working width of the harvester, an inner spiral shaped harvesting path executed by the harvester is determined in the to-be-harvested area corresponding to the harvester.

8. A device for coordinated dispatching of collection and transportation based on a heuristic algorithm, comprising: A parameter module, used to obtain attribute parameters and task parameters related to the harvesting and transporting collaborative 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 transporting collaborative task; A target module is used to construct a harvesting and transporting coordination model for solving a target unloading strategy for coordinated unloading of grain by the harvester and the grain transport vehicle based on a preset optimization target, using the attribute parameters and the task parameters, wherein the target unloading strategy at least includes a transfer path for the grain transport vehicle to move to a grain unloading point in the harvesting path after receiving a grain unloading request; A solution module, used for solving the harvesting and transportation coordination model based on the harvesting path of the harvester using a heuristic algorithm to obtain a target grain unloading strategy; A strategy module is used to execute the target grain unloading strategy to dispatch the grain transport vehicle to assist the harvester in unloading grain based on the transfer path.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, the executable instructions being configured to be executed by the processor, the executable instructions comprising instructions for performing the steps in the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is used to store computer-executable instructions, and the executable instructions enable a computer to execute the method according to any one of claims 1 to 7.

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