Mine vehicle scheduling method, device and equipment and storage medium

Abstract

The invention provides a mine vehicle scheduling method, device and equipment and a storage medium, and relates to the technical field of vehicle scheduling, and the method comprises the steps: carrying out the symbol coding of an unloading point, an excavator and a truck through computer equipment based on the obtained scheduling information, and obtaining a first symbol code, a second symbol code and a third symbol code corresponding to the information; generating an initial population based on the obtained symbol codes, wherein individuals in the initial population represent a scheduling scheme formed by scheduling path sequences corresponding to all third symbol codes; constructing a target function under a first constraint condition by taking minimization of the total transportation cost of the scheduling scheme as an optimization target; and performing iterative evolution on the initial population by adopting a genetic algorithm based on a target function to obtain an optimal scheduling scheme and outputting the optimal scheduling scheme. According to the invention, automation and intelligence of vehicle scheduling are realized, the real-time performance and accuracy of scheduling are improved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and vehicle dispatching, and in particular to a mine vehicle dispatching method, device, equipment and storage medium. Background Art

[0002] Open-pit mining is widely used in the mining industry due to its advantages such as rapid construction, high productivity, low cost and high safety. However, in open-pit mining and large-scale earthwork projects, dispatching management is often lagging behind, and it is mostly dependent on on-site managers driving command vehicles for inspection and manual dispatching. This method is difficult to discover and solve dispatching problems in real time, and is limited by the vision and experience of managers, making it difficult to achieve optimal dispatching, resulting in limited efficiency of construction machinery and increased fuel consumption. Summary of the invention

[0003] The present invention provides a mine vehicle dispatching method, device, equipment and storage medium, which are used to solve the defects in the prior art that open-pit mining vehicle dispatching mostly relies on on-site managers driving command vehicles for inspection and manual dispatching, resulting in difficulty in real-time discovery and resolution of dispatching problems, limited efficiency of engineering machinery, and increased fuel consumption.

[0004] In one aspect, the present invention provides a mine vehicle dispatching method, which is applied to a computer device, and the method comprises:

[0005] Obtaining dispatching information of a loading point, an excavator, and a truck, and respectively performing symbol coding on the loading point, the excavator, and the truck according to the dispatching information, to obtain a first symbol coding corresponding to the loading point, a second symbol coding corresponding to the excavator, and a third symbol coding corresponding to the truck;

[0006] Based on the first symbol code, the second symbol code and the third symbol code, an initial population is generated, wherein the individuals in the initial population represent a scheduling scheme composed of scheduling path sequences corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each third symbol code includes at least one scheduling path composed of the first symbol code and the second symbol code;

[0007] Taking minimizing the total transportation cost of the scheduling scheme as the optimization goal, constructing an objective function under a first constraint condition, where the first constraint condition is a constraint condition of the scheduling scheme;

[0008] Based on the objective function, a genetic algorithm is used to iteratively evolve the initial population until a preset termination condition is reached, and an optimal scheduling plan is obtained and output in a preset output mode, so as to schedule the trucks according to the optimal scheduling plan.

[0009] Another aspect of the present invention further provides a mining vehicle dispatching device, the device comprising:

[0010] A code acquisition module, used to acquire the dispatch information of the unloading point, the excavator and the truck, and to perform symbol encoding on the unloading point, the excavator and the truck respectively according to the dispatch information, so as to obtain a first symbol code corresponding to the unloading point, a second symbol code corresponding to the excavator and a third symbol code corresponding to the truck;

[0011] A population generation module, configured to generate an initial population based on the first symbol code, the second symbol code, and the third symbol code, wherein the individuals in the initial population represent a scheduling solution composed of a scheduling path sequence corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each of the third symbol codes includes at least one scheduling path composed of a pair of the first symbol code and the second symbol code;

[0012] A function construction module, used to construct an objective function under a first constraint condition with minimizing the total transportation cost of the scheduling scheme as the optimization goal, where the first constraint condition is a constraint condition of the scheduling scheme;

[0013] A scheme acquisition module is used to iteratively evolve the initial population using a genetic algorithm based on the objective function until a preset termination condition is reached to obtain an optimal scheduling scheme; and

[0014] The scheduling module is used to output the optimal scheduling plan in a preset output mode so as to schedule the trucks according to the optimal scheduling plan.

[0015] In another aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned mine vehicle dispatching method when executing the program.

[0016] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the mine vehicle dispatching method as described above is implemented.

[0017] The mine vehicle scheduling method, device, equipment and storage medium provided by the present invention perform symbol encoding on the unloading point, excavator and truck, and generate an initial population containing multiple scheduling path sequences based on these symbol encodings, each scheduling path sequence represents a possible truck scheduling scheme, including the detailed path of the truck from the excavator loading to the unloading point, then minimize the total transportation cost of the scheduling scheme as the optimization goal, construct the objective function of the first constraint condition, and finally combine this objective function, by adopting a genetic algorithm to iteratively evolve the initial population, under the action of the genetic algorithm, the scheduling path of the truck in the scheduling scheme will undergo operations such as selection, crossover and mutation, so as to gradually approach the optimal solution. When the preset termination condition is reached, an optimal scheduling scheme will be output. Thereby, the automation and intelligence of vehicle scheduling in open-pit mining and large earthwork projects are realized, which not only improves the real-time and accuracy of scheduling, but also reduces costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the flow of the mine vehicle dispatching method provided by the present invention;

[0020] Figure 2 is a schematic diagram of a cross-operation scenario provided by the present invention;

[0021] Figure 3 is a table of distances between mine loading and unloading points in one embodiment of the present invention;

[0022] Figure 4 is a comparison diagram of iterative convergence of truck transportation cost when different genetic algorithms are applied in one embodiment of the present invention;

[0023] Figure 5 is a comparison diagram of iterative convergence of truck transportation cost when different genetic algorithms are applied in another embodiment of the present invention;

[0024] Figure 6 is a comparison diagram of iterative convergence of truck transportation cost when different genetic algorithms are applied in another embodiment of the present invention;

[0025] Figure 7 is a comparison table of truck waiting time and excavator hook time using different algorithms of the present invention;

[0026] Figure 8It is a structural schematic diagram of a mine vehicle dispatching device provided by the present invention;

[0027] Fig. 9 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The improved genetic algorithm (also referred to as the improved genetic algorithm) used in each embodiment of the present invention creatively adopts a selection method combining roulette selection and elite retention strategy to perform selection operations. Among them, the roulette selection mechanism allocates selection opportunities according to the fitness ratio of the individual. The higher the fitness of the individual, the greater the probability of being selected, which helps to guide the algorithm to evolve in the direction of higher fitness. The elite retention mechanism is a strategy designed to protect the optimal solution from being lost. During the iterative process of the genetic algorithm, the mechanism will screen out the top individuals (or several individuals with the highest fitness) in the current population and pass them directly to the next generation, skipping the crossover and mutation links. Doing so can ensure that the algorithm continues to move towards a better solution domain during the evolutionary journey, while preventing the accidental loss of the current optimal solution due to crossover and mutation operations. Combining roulette selection with the elite retention strategy can ensure that the algorithm can converge to the global optimal solution while maintaining population diversity.

[0030] Figure 1 is a flow chart of a mine vehicle dispatching method provided by an embodiment of the present invention, such as Figure 1 As shown, the method is applied to a computer device, and includes the following steps performed by the computer device:

[0031] Step 110, obtaining the dispatching information of the unloading point, the excavator and the truck through the computer device, and respectively performing symbol coding on the unloading point, the excavator and the truck according to the dispatching information to obtain a first symbol coding corresponding to the unloading point, a second symbol coding corresponding to the excavator and a third symbol coding corresponding to the truck;

[0032] In this embodiment, a genetic algorithm is used to solve the open pit truck scheduling problem. Specifically, when using a genetic algorithm to solve the open pit truck scheduling problem, it is first necessary to determine a coding method for each component of the problem (i.e., unloading point, excavator, and truck) so that the genetic algorithm can process them. Among them, the excavator is equivalent to the digging point. A mine usually has multiple digging points, and one digging point is equipped with an excavator. The digging point is also the loading point of the truck.

[0033] The dispatch information of the unloading point, excavator and truck may include, but is not limited to: identification information of the unloading point, identification information of the loading point, one or more of the number, name, model, load and other information of the excavators that can be configured at the loading point, and one or more of the number, model, name and other information of the dispatchable trucks. The dispatch information may be input by the user through the input / output device of the computer device (such as a keyboard, microphone, or touch screen, etc.). Alternatively, the dispatch information may also be stored in a cloud server or a central control device of a mine vehicle dispatch platform, and the computer device uses its own network communication interface to obtain the above dispatch information from the cloud server or the central control device through a wireless or wired network.

[0034] The symbol encoding used in the encoding process, in addition to {0,1}, also includes symbols without actual numerical meaning. These symbols can usually be selected from an alphabet, a number table or a code table, for example: {a,b,c,d…}, {1,2,3,4…} or {a1,a2,a3,a4…}. Symbol encoding can be used to achieve subsequent steps without the need for decoding.

[0035] In one example, a unique symbol is assigned to each unloading point, and these symbols are usually represented by lowercase letters to form the symbol code of the unloading point, that is, the first symbol code. For example, if there are three unloading points, they can be represented by 'a', 'b' and 'c' respectively.

[0036] In one example, a unique symbol is also assigned to each excavator (i.e., loading point). Different from the unloading point, the symbol code of the excavator can be represented by capital letters to form the symbol code of the excavator, i.e., the second symbol code. For example, if there are three excavators, they can be represented by 'A', 'B', and 'C' respectively.

[0037] In one example, a unique symbol is assigned to each truck to form a symbol code of the truck, i.e., the third symbol code. For example, if there are four trucks, they can be represented by numbers 1, 2, 3, and 4.

[0038] According to the actual transportation scheduling situation, the truck travels back and forth between the loading point and the unloading point. Therefore, the symbol encoding corresponds to 3 vector sets:

[0039] Unloading point set: x_name = ['abc'];

[0040] Excavator collection: z_name = ['ABC'];

[0041] Truck set: k_name = [1, 2, 3, 4].

[0042] Based on the above steps, the symbol codes corresponding to the unloading point, excavator and truck are obtained. These symbol codes will be used as the genes of the chromosomes in the genetic algorithm for subsequent genetic operations (such as selection, crossover and mutation) and fitness evaluation. In this way, the genetic algorithm can handle the truck scheduling problem more efficiently without worrying about the complicated decoding process.

[0043] Step 120: generating an initial population based on the first symbol code, the second symbol code, and the third symbol code, wherein the individuals in the initial population represent a scheduling solution composed of scheduling path sequences corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each third symbol code includes at least one scheduling path composed of the first symbol code and the second symbol code;

[0044] Here, the initial population is composed of multiple individuals, each of which represents a possible scheduling solution. Each individual (i.e., chromosome) is composed of the scheduling path sequence corresponding to all trucks (third symbol code), and the scheduling path sequence of each truck contains at least a pair of scheduling paths composed of the first symbol code (unloading point) and the second symbol code (excavator), which describes the transportation process of the truck from a certain excavator (loading point) to a certain unloading point.

[0045] For example, a scheduling scheme for a truck with the third symbol coded as 1 within a specific time period can be expressed as "AaBbCc", which means that the truck with the third symbol coded as 1 loads from excavator A and unloads at unloading point a, then goes to excavator B to load and unloads at unloading point b, finally goes to excavator C to load and finally unloads at unloading point c.

[0046] For example, a scheduling plan for all 4 trucks within a specific time period can be expressed as:

[0047]

[0048] Among them, each row is the scheduling plan for each truck, and the number of rows is equal to the number of trucks; each column is the loading point (excavator) and unloading point passed by each truck.

[0049] That is, the vehicle dispatching plan of the truck numbered 1 is represented as 'AaBbCc', and its dispatching path sequence is AaBbCc, which includes the dispatching paths composed of 3 pairs of first symbol codes and second symbol codes: excavator A and unloading point a, excavator B and unloading point b, and excavator C and unloading point c;

[0050] The vehicle dispatching plan of the truck numbered 2 is represented as 'AbCaBc', and its dispatching path sequence is AbCaBc, which includes the dispatching paths composed of 3 pairs of first symbol codes and second symbol codes: excavator A and unloading point b, excavator C and unloading point a, and excavator B and unloading point c;

[0051] The vehicle dispatching scheme of the truck numbered 3 is represented as 'BcAaCb', and its dispatching path sequence is BcAaCb, which includes the dispatching paths composed of 3 pairs of first symbol codes and second symbol codes: excavator B and unloading point c, excavator A and unloading point a, and excavator C and unloading point b;

[0052] The vehicle dispatching plan of the truck numbered 4 is represented as 'CbAa', and its dispatching path sequence is CbAa, which includes dispatching paths consisting of 2 pairs of first symbol codes and second symbol codes: excavator C and unloading point b, and excavator A and unloading point a.

[0053] The specific number of columns can be determined according to the actual parameters of the transportation scheduling process. The scheduling scheme composed of the scheduling path sequence of all trucks determined in this way is an individual.

[0054] Step 130: Taking minimizing the total transportation cost of the scheduling scheme as the optimization goal, constructing an objective function under a first constraint condition, where the first constraint condition is a constraint condition of the scheduling scheme;

[0055] It should be understood that the optimization goal of open pit truck scheduling is to reduce the total transportation cost, including but not limited to: reducing empty driving distance, waiting time, and idle time of trucks and excavators, etc. By optimizing the scheduling plan, it can ensure that resources are used most effectively, thereby reducing the overall transportation cost.

[0056] Specifically, in order to quantify the transportation cost, an objective function is constructed. The objective function is usually constructed based on multiple variables, such as the truck's travel distance, travel time, waiting time, loading and unloading time, etc.

[0057] In addition, when constructing the objective function, a series of preset first constraint conditions also need to be considered to ensure the feasibility and effectiveness of the scheduling plan. The first constraint conditions include but are not limited to: time constraints (working hours, rest hours, etc. of trucks and excavators), capacity constraints (loading capacity of trucks, production capacity of excavators, etc.), path constraints (path selection for trucks to travel, etc.), and resource constraints (number of available trucks and excavators, etc.).

[0058] Step 140: Based on the objective function, use the genetic algorithm to iteratively evolve the initial population until a preset termination condition is reached, obtain the optimal scheduling plan and output it in a preset output manner, so as to schedule the trucks according to the optimal scheduling plan.

[0059] In this embodiment, the objective function is selected as the fitness function to evaluate the quality of feasible solutions in the truck transportation scheduling problem.

[0060] Specifically, according to the objective function, evaluate the fitness of individuals in the population, and select individuals with higher fitness (i.e., scheduling plans with lower costs) as parents for subsequent crossover and mutation operations. Randomly select multiple individuals from the selected parents, and exchange some of the scheduling paths in their scheduling path sequences to generate new offspring individuals. Perform mutation operations on the new offspring individuals to increase the diversity of the population. Finally, combine the new offspring individuals generated after the crossover and mutation operations with some individuals in the parent generation (such as individuals with higher fitness) to form a new population. This new population will be used for the next round of iterative evolution.

[0061] Specifically, in the improved genetic algorithm in this application, new individuals are generated through three mechanisms: selection, crossover, and mutation, which together constitute a new population of size N'. If the condition N' > N is satisfied, select the top N individuals with the highest fitness to form a new population; if the condition N' < N holds, randomly generate an additional (N - N') individuals and add them to the new population to ensure that the size of the new population is the same as that of the previous generation.

[0062] Repeat the above processes of selection, crossover, mutation, and new population generation until a preset termination condition is reached, such as reaching the maximum number of iterations, or the fitness of individuals in the population reaches a stable state after consecutive iterations, then select the individual with the highest fitness from the current population as the optimal scheduling plan.

[0063] Furthermore, the computer device outputs the obtained optimal scheduling scheme in accordance with a preset output method, so as to schedule the truck according to the optimal scheduling scheme. Specifically, the preset output method may include, but is not limited to: outputting the optimal scheduling scheme to a cloud server or a central control device of a mine vehicle scheduling platform, so that the user can obtain the optimal scheduling scheme through the cloud server or the central control device, and schedule the truck based on the optimal scheduling scheme; or, when the truck and / or the excavator are unmanned intelligent vehicles, the computer device may also send a scheduling instruction to the truck and / or the excavator according to the optimal scheduling scheme, so that the truck and / or the excavator perform the corresponding task according to the scheduling instruction, thereby further realizing the all-round intelligent control of the mine vehicle; or, the computer device may also send the optimal scheduling scheme to a communication device specified by the user according to the reserved communication information.

[0064] The mine vehicle scheduling method proposed in this embodiment performs symbol encoding on the unloading point, excavator and truck, and generates an initial population containing multiple scheduling path sequences based on these symbol encodings. Each scheduling path sequence represents a possible truck scheduling scheme, which contains the detailed path of the truck from the excavator loading to the unloading point. Then, the objective function of the first constraint condition is constructed with the minimization of the total transportation cost of the scheduling scheme as the optimization goal. Finally, combined with this objective function, the initial population is iteratively evolved by using a genetic algorithm. Under the action of the genetic algorithm, the scheduling path of the truck in the scheduling scheme will undergo operations such as selection, crossover and mutation, so as to gradually approach the optimal solution. When the preset termination condition is reached, an optimal scheduling scheme will be output. Thereby, the automation and intelligence of vehicle scheduling in open-pit mining and large-scale earthwork projects are realized, which not only improves the real-time and accuracy of scheduling, but also reduces costs.

[0065] It should be noted that each implementation method of the present application can be freely combined, the order can be changed, or it can be executed separately, and does not need to rely on or depend on a fixed execution order.

[0066] In some embodiments, the objective function under the first constraint condition is constructed with minimizing the total transportation cost of the scheduling scheme as the optimization goal, including: determining the total transportation cost of the scheduling scheme based on the heavy-load transportation cost and the empty-load transportation cost of the truck corresponding to each of the third symbol codes, and the heavy-load transportation cost and the empty-load transportation cost of the excavator corresponding to each of the second symbol codes in the scheduling path sequence corresponding to each of the third symbol codes; constructing the objective function under the first constraint condition with minimizing the total transportation cost of the total transportation cost of the scheduling scheme as the optimization goal; here, the heavy-load transportation cost of the truck refers to the transportation cost of the truck when it is loaded with goods, and the empty-load transportation cost of the truck refers to the transportation cost of the truck when it is not loaded with goods. The heavy-load transportation cost of the excavator refers to the transportation cost of the excavator when it is loaded with goods, and the empty-load transportation cost of the excavator refers to the transportation cost of the excavator when it is not loaded with goods.

[0067] In this embodiment, the total transportation cost is calculated using the heavy-load transportation cost and the empty-load transportation cost of the truck, and the heavy-load transportation cost and the empty-load transportation cost of the excavator.

[0068] First, define the variables is the unit heavy-load transportation cost of truck k, is the unit heavy-load transportation cost of the excavator g, is the unit empty transport cost of truck k, is the unit heavy-load transportation cost of excavator g, d ij is the distance between nodes i and j.

[0069] Next, define the following decision variables:

[0070] If the heavily loaded truck k goes from node i to node j, the value is 1; if the heavily loaded truck k does not go from node i to node j, the value is 0;

[0071] If the empty truck k goes from node i to node j, the value is 1; if the empty truck k does not go from node i to node j, the value is 0;

[0072] If the overloaded excavator g goes from node i to node j, the value is 1; if the overloaded excavator g does not go from node i to node j, the value is 0;

[0073] If the unloaded excavator g moves from node i to node j, the value is 1; if the unloaded excavator g does not move from node i to node j, the value is 0.

[0074] Based on the above costs, the total transportation cost F1(S) of the scheduling scheme is determined as:

[0075]

[0076] Among them, (i, j)∈D represents all possible node pairs.

[0077] Furthermore, in order to minimize the total transportation cost, the objective function under the first constraint is constructed as follows:

[0078]

[0079] Specifically, the first constraint condition includes at least one of the following:

[0080] (1) Path uniqueness constraint: ensure that each demand point is served only once and each path is traversed only once in each mission. Demand points can include unloading points and loading points.

[0081]

[0082] (2) Vehicle load constraint: ensure that the load of each vehicle does not exceed its maximum load, and the load when leaving point i is not less than the load when arriving at point j;

[0083]

[0084]

[0085]

[0086] in, is the loading quantity of truck k at point i, is the loading capacity of excavator g at point i, Q i k is the cargo load of truck k when it leaves point i, Q i g is the loading capacity of excavator g when it leaves point i, M is an infinite number, is the maximum load of truck k, is the maximum load of the excavator g, is the load of truck k arriving at point j, is the cargo volume of truck k arriving at point j.

[0087] (3) Transportation demand constraint: the total shipping volume of all vehicles should not be less than the total demand of the demand point;

[0088]

[0089] (4) Vehicle quantity constraint: the number of assigned vehicles shall not exceed the total number of vehicles;

[0090]

[0091] Among them, K is the total number of trucks, G is the total number of excavators, For truck k from the starting point to point i, is the excavator g from the starting point to point i, For truck k from point j to the unloading point, For truck k from point j to the unloading point.

[0092] (5) Time allocation constraint: the excavator can only be assigned to one area during each period of time;

[0093]

[0094] Where a∈A represents a mining area in the set of all mining areas, t∈T represents the time planning range, g∈G represents the excavator in operation in the set of all excavators, It means that if the excavator g is located in area a during time period t, it is 1, otherwise it is 0.

[0095] (6) Operating area constraints: restricting the excavator to operate only within one area;

[0096]

[0097] In the formula Indicates the maximum number of excavators operating in the same area.

[0098] (7) Vehicle type quantity constraint: the number of trucks that need to be dispatched is limited;

[0099]

[0100] Where K Min , K Max Indicates the maximum and minimum number of trucks that need to be dispatched in any time period.

[0101] The mine vehicle dispatching method proposed in this embodiment realizes the coordinated operation of trucks and excavators and saves transportation costs through the objective function and the first constraint condition constructed in the above manner.

[0102] In some embodiments, based on the objective function, the initial population is iteratively evolved using a genetic algorithm until a preset termination condition is reached, including: determining the fitness of each individual in the initial population based on the objective function; determining the first individual in the initial population using an elite retention strategy based on the fitness of each individual in the initial population; determining the second individual in the initial population other than the first individual using a roulette wheel selection strategy based on the fitness of each individual in the initial population; performing crossover and mutation operations on the second individual to obtain a third individual; obtaining a new population based on the third individual and the first individual, and using the new population as the initial population, and returning to continue executing the step of determining the fitness of each individual in the initial population based on the objective function until the preset termination condition is reached.

[0103] In this embodiment, a selection method combining a roulette wheel selection strategy and an elite retention strategy is adopted in the selection operation of the genetic algorithm for truck transportation scheduling.

[0104] Specifically, the elite retention strategy is a strategy that prevents the optimal solution from being lost during the evolution process. In each generation of the genetic algorithm, the elite retention strategy retains the best individuals (or the individuals with the highest fitness) in the current population and directly copies them to the next generation population without crossover and mutation operations. This ensures that the algorithm continuously approaches a better solution space during the evolution process, while avoiding the destruction of the current optimal solution due to crossover and mutation operations.

[0105] First, the fitness of each individual is calculated by the objective function f(x i ), where i = 1, 2, ..., n, n is the population size. According to the fitness of each individual f(x i ), select the best individual (or the individuals with the highest fitness) in the current population as the elite individual, that is, the first individual, which will be directly retained in the next generation population.

[0106] Next, for the individuals in the current population except the first individual, the roulette wheel selection strategy is used for selection.

[0107] Specifically, first calculate the sum of the fitness of all individuals:

[0108]

[0109] Calculate the probability of each individual being selected and inherited into the next generation population:

[0110]

[0111] Calculate the cumulative probability for each individual:

[0112]

[0113] Where j = 1, 2, ..., m, m is the number of iterations, and the cumulative probability q i Represents the sum of the selection probabilities from the first individual to the i-th individual (including the i-th individual).

[0114] Generate a uniformly distributed random number r in the interval [0,1]. Then, starting from the first individual, compare the random number r with the cumulative probability q one by one. i If r is less than or equal to q i , then select the i-th individual as the second individual.

[0115] Next, a crossover operation is performed on the selected second individuals. Specifically, one or more crossover points are randomly selected on the two second individuals. At the crossover points, the third symbol coding sequences of the two second individuals are exchanged to generate a new individual. Then, a random mutation operation is performed on certain sites in the new individual to generate a third individual.

[0116] Finally, the third individual generated by the crossover and mutation operations is combined with the first individual directly transferred through the elite retention strategy to form a new population of the new generation. Repeat the above steps for the new population, that is, recalculate the fitness of each individual, and then perform selection, crossover and mutation operations until the preset termination condition is met.

[0117] The mine vehicle scheduling method proposed in this embodiment selects individuals by combining roulette wheel selection and elite retention strategy when iteratively evolving the population through a genetic algorithm. This effectively balances the diversity of the population and the convergence of the algorithm, which can prevent the algorithm from falling into a local optimal solution and ensure that the algorithm can converge to the global optimal solution, thereby improving the performance and efficiency of the algorithm.

[0118] In some embodiments, the method further includes: performing a second constraint check on the fourth individual generated after the crossover operation; if the fourth individual does not satisfy the second constraint check, cyclically moving the position of the third symbol code in the fourth individual until the second constraint is satisfied; wherein the second constraint is that the task start time of the truck corresponding to the third symbol code at the last position in the individual is within a preset time window. The fourth individual may be obtained by performing a crossover operation on the second individual, for example.

[0119] It should be noted that, since there may be mission conflicts between vehicles, in order to reduce the probability of conflicts, in this embodiment, only crossover is performed among individuals of the same type, rather than the traditional direct crossover. After the individual crossover operation is completed, the fourth individual generated after the crossover operation is tested for the second constraint. Specifically, the second constraint is to test whether the mission start time of the truck corresponding to each third symbol code in the individual is within the preset time window.

[0120] If the fourth entity is found to not satisfy the second constraint during the inspection process, that is, the task start time of at least one truck corresponding to the third symbol code exceeds the preset time window, then the position of the third symbol code in the fourth entity needs to be cyclically moved. Here, cyclic movement refers to changing the task execution order of the trucks corresponding to the third symbol code in the fourth entity, trying different permutations and combinations of the third symbol code, until a permutation and combination is found so that the task start times of all trucks corresponding to the third symbol code are completed within the preset time window.

[0121] If the last truck's trip start time is still not within the preset time window after one round of circular movement, then the fourth entity is deleted and a new fourth entity is generated. Figure 2 As shown, after the previous generation individual 1 and the previous generation individual 2 are cross-operated, the contemporary individual 1 and the illegal individual 2 are obtained. If the task start time of the truck with the third symbol code #1 located at the last position in the illegal individual 2 exceeds the preset time window, it is necessary to cyclically move the position of the third symbol code in the illegal individual 2. After one circle of cyclic movement, if the task start time of the truck with the third symbol code located at the last position in the illegal individual 2 still exceeds the preset time window, the illegal individual 2 is deleted.

[0122] In this embodiment, the above method ensures that the generated individuals not only inherit the characteristics of the parent generation, but also meet the constraints in actual operation, thereby improving the quality and feasibility of the scheduling plan.

[0123] In some embodiments, the method also includes: determining a fifth individual selected for mutation operation, and randomly selecting a mutation point from the scheduling path sequence corresponding to the fifth individual for mutation; wherein, when the mutation point is the first symbol code corresponding to the first unloading point, the first symbol code corresponding to the first unloading point is randomly replaced with the first symbol code corresponding to the second unloading point; and when the mutation point is the second symbol code corresponding to the first excavator, the second symbol code corresponding to the first excavator is randomly replaced with the second symbol code corresponding to the second excavator.

[0124] Wherein, the fifth individual is determined from the fourth individual. Optionally, in other embodiments of the present application, determining the fifth individual selected to perform the mutation operation from the fourth individual includes: randomly generating a random number between (0, 1) for the fourth individual obtained after the crossover operation, and comparing the random number with a preset mutation probability; if the random number is less than the preset mutation probability, determining the fourth individual as the fifth individual to perform the mutation operation.

[0125] Specifically, a random number a between (0,1) is first randomly generated for the individual after the crossover operation, and the random number is compared with the preset mutation probability Pm. If the random number is less than Pm, the corresponding individual is selected for the mutation operation, that is, the fifth individual.

[0126] For example, in the application of improved genetic algorithm in truck transportation scheduling, the mutation operation is as follows: the population of genetic algorithm is set to N, and N random numbers a are randomly generated in (0,1). On this basis, the N a values ​​are combined with the mutation probability P m For comparison, when it is less than P m , then any loading point or unloading point in the dispatching routes of all trucks is mutated.

[0127] For the fifth individual, a position in the individual is randomly selected as a mutation point. This mutation point corresponds to a specific demand point on the scheduling path sequence, such as a unloading point or an excavator.

[0128] If the selected variation point is a unloading point corresponding to the first symbol code (for example, an unloading point with the first symbol code a), then the first symbol code corresponding to another unloading point (for example, an unloading point with the first symbol code b) is randomly selected, and then the original first symbol code is replaced with the new first symbol code.

[0129] If the selected variation point is an excavator corresponding to the second symbol code (for example, a unloading point with the second symbol code A), then the second symbol code corresponding to another unloading point (for example, a unloading point with the first symbol code B) is randomly selected, and then the new second symbol code is used to replace the original second symbol code.

[0130] Mutation can recover and supplement genetic information that may be lost in the crossover operation, while preventing the above algorithm from falling into the local optimal solution, thereby ensuring that the above algorithm has effective local search capabilities.

[0131] In some embodiments, the method further includes: randomly generating a first scheduling path sequence for a first truck based on a third constraint; determining a second scheduling path sequence for a second truck other than the first truck based on the first scheduling path sequence for the first truck, the second scheduling path sequence being the same as the first scheduling path sequence; and generating an initial population based on the first scheduling path sequence for the first truck and the second scheduling path sequence for the second truck.

[0132] Among them, the third constraint condition includes: when the truck leaves the loading point corresponding to the current excavator, it goes to the unloading point that is not currently occupied by other trucks; when the truck leaves the current unloading point, it goes to the loading point corresponding to the excavator that is not currently occupied by other trucks, or, goes to the loading point corresponding to the excavator that meets the preset loading waiting time.

[0133] In this embodiment, before generating the first dispatching path sequence of the first truck, a series of initial data needs to be collected and determined, including the number of trucks, the number of loading points (excavators), the number of unloading points, various distances, carrying capacity, speed, work efficiency, etc.

[0134] The starting site of the first truck is included in the loading point in the first scheduling path sequence, and then a first matching code is randomly selected from the first symbol code corresponding to the unloading point, and then a second matching code is randomly selected from the second symbol code corresponding to the excavator, and then a first matching code is randomly selected from the first symbol code corresponding to the unloading point..., and this arrangement is repeated to randomly generate the first scheduling path sequence for the first truck.

[0135] Here, when randomly generating the first dispatching path sequence of the first truck, the third constraint condition needs to be followed, and the third constraint condition includes the following two constraints.

[0136] When a truck leaves the loading point corresponding to the current excavator, it must go to an unloading point that is not currently occupied by other trucks to avoid resource conflicts and ensure that each unloading point will not be visited by multiple trucks at the same time, resulting in task saturation.

[0137] When a truck leaves the current unloading point, it can choose to go to a loading point corresponding to an excavator that is not currently occupied by other trucks, or to a loading point corresponding to an excavator that meets the preset loading waiting time. This ensures that the truck can efficiently return to the loading point after completing the task, taking into account the availability and waiting time of the loading point.

[0138] In this embodiment, the above method is used to ensure that each individual in the initial population meets the actual operation restrictions.

[0139] The following will be combined Figures 3 to 7, giving an example to illustrate the advantages of the above mine vehicle scheduling method. Specifically, in an example of the above mine vehicle scheduling method being applied, a mining area of ​​an open-pit coal mine has a total of 5 loading points and 3 unloading points. Each loading point is equipped with an excavator, and the three unloading points are required to handle 3,500 tons (t) per shift. The distance between each loading point and unloading point is as follows: Figure 3 As shown, the unit of distance is kilometers (km). There are 60 trucks in the garage (different numbers of trucks are assigned according to the daily work schedule), the maximum load of each truck is 50t, the cost of empty and loaded trucks is 40 yuan / km and 60 yuan / km respectively, the average speed of empty and loaded trucks is 30km / h (hour) and 20km / h respectively, and the cost of empty and loaded excavators is 50 yuan / km and 70 yuan / km respectively (since the position of the excavator is basically fixed, generally speaking, only the cost of one round trip is considered). According to the collected data, the loading time of the excavator is 6 minutes (min), the unloading time of the truck is 3 minutes, the average waiting time of the truck per shift is 1.25h, and the average hook time of the excavator per shift is 0.5h.

[0140] Assume that the population size is 100, the crossover rate is 0.7, the mutation rate is 0.05, and the maximum number of iterations is 100. The number of trucks is 30, 45, and 60, and the number of excavators is 5. Assume that the position of the excavator is fixed, that is, it is not scheduled at any time. Use MATLAB programming to implement the improved genetic algorithm and obtain the scheduling plan.

[0141] The comparison of iterative convergence of the genetic algorithm transportation cost with 30 trucks is shown in the figure below. Figure 4 As shown in the figure, the total transportation cost per shift is 136,582 yuan using the traditional genetic algorithm, 129,545 yuan using the improved genetic algorithm, and 141,500 yuan using the on-site fixed solution. The improved genetic algorithm saves 8.4% compared to the on-site fixed solution and 5.2% compared to the ordinary genetic algorithm. As can be seen from the figure, the improved genetic algorithm converges to the minimum value when the number of iterations is about 40, while the traditional genetic algorithm converges to the minimum value when the number of iterations is about 60; the initial values ​​of the two algorithms are different when the number of iterations is 0, because different genetic algorithms use different strategies or methods to generate initial solutions when initializing the population. These initial solutions may have differences in fitness, resulting in different initial values ​​of the iterative convergence graph.

[0142] The comparison of iterative convergence of the genetic algorithm transportation cost with 45 trucks is shown in the figure below. Figure 5As shown in the figure, using the traditional genetic algorithm, the total transportation cost per shift is 202034 yuan, using the improved genetic algorithm, the total transportation cost per shift is 194298 yuan, and using the on-site fixed solution, the total transportation cost per shift is 212250 yuan. The improved genetic algorithm saves 8.5% compared to the on-site fixed solution and 3.8% compared to the ordinary genetic algorithm. It can be seen from the figure that the improved genetic algorithm converges to the minimum value when the number of iterations is about 40, while the traditional genetic algorithm converges to the minimum value when the number of iterations is about 80.

[0143] The comparison of iterative convergence of the genetic algorithm transportation cost with 60 trucks is shown in the figure below. Figure 6 As shown in the figure, using the traditional genetic algorithm, the total transportation cost per shift is 259,581 yuan, using the improved genetic algorithm, the total transportation cost per shift is 249,651 yuan, and using the on-site fixed solution, the total transportation cost per shift is 280,000 yuan. The improved genetic algorithm saves 10.8% compared to the on-site fixed solution and 3.8% compared to the ordinary genetic algorithm. It can be seen from the figure that the improved genetic algorithm converges to the minimum value when the number of iterations is about 50, while the traditional genetic algorithm converges to the minimum value when the number of iterations is about 60.

[0144] Truck waiting time and excavator hook time using the three different methods mentioned above are as follows: Figure 7 As shown, when the number of trucks is 30, the use of the improved genetic algorithm saves 11.3% compared with the on-site fixed solution and 6.5% compared with the traditional genetic algorithm; when the number of trucks is 45, the use of the improved genetic algorithm saves 12.3% compared with the on-site fixed solution and 5.1% compared with the traditional genetic algorithm; when the number of trucks is 60, the use of the improved genetic algorithm saves 11.5% compared with the on-site fixed solution and 6.2% compared with the traditional genetic algorithm.

[0145] In summary, the improved genetic algorithm of the present invention has obvious improvements in transportation cost, truck waiting time and excavator hook time compared with the on-site fixed solution, and also has significant improvements compared with the traditional genetic algorithm. Figures 4 to 6 It can also be seen that the improved genetic algorithm converges faster than the traditional genetic algorithm.

[0146] Therefore, compared with the traditional scheduling method, the present invention has the following advantages:

[0147] First, for the open-pit mine truck transportation scheduling problem, truck scheduling schemes were established with the goal of minimizing the total transportation cost and reducing the truck waiting time and excavator hook time, achieving the minimization of truck transportation costs and truck waiting time and excavator hook time. This scheduling scheme takes into account the problem of long truck waiting time for loading caused by improper optimization of truck-shovel collaboration, maximizes production and minimizes equipment idleness, so as to achieve the goal of optimizing the collaborative efficiency of trucks and excavators;

[0148] Second, in the process of solving the truck dispatching scheme using the improved genetic algorithm, variable-length symbol coding is used. This coding method allows the length of the code to be flexibly adjusted according to the characteristics and needs of the problem. Compared with fixed-length coding, variable-length coding can make more efficient use of space, especially for problems with large changes in the number of features. For problems that require high-precision representation, variable-length coding can provide a more sophisticated encoding method. By increasing the length of the code, the accuracy of the representation can be improved, thereby more accurately reflecting the characteristics of the problem;

[0149] Third, in the selection process, a combination of roulette wheel selection and elite retention strategy is used. This method can effectively balance the diversity of the population and the convergence of the algorithm in the genetic algorithm, which can not only prevent the algorithm from falling into the local optimal solution, but also ensure that the algorithm can converge to the global optimal solution, thereby improving the performance and efficiency of the algorithm.

[0150] Finally, the problem was solved through simulation, and experiments proved that the above scheduling scheme can better solve the open-pit mine truck scheduling problem.

[0151] Based on any of the above embodiments, the present invention further provides a mining vehicle dispatching device, Figure 8 Schematic diagram of the structure of the mine vehicle dispatching device provided by the present invention, such as Figure 8 As shown, the device comprises:

[0152] The code acquisition module 310 is used to acquire the dispatch information of the unloading point, the excavator and the truck, and to perform symbol encoding on the unloading point, the excavator and the truck according to the dispatch information, so as to obtain a first symbol code corresponding to the unloading point, a second symbol code corresponding to the excavator and a third symbol code corresponding to the truck;

[0153] A population generation module 320 is used to generate an initial population based on the first symbol code, the second symbol code and the third symbol code, wherein the individuals in the initial population represent a scheduling scheme composed of scheduling path sequences corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each third symbol code includes at least one scheduling path composed of the first symbol code and the second symbol code;

[0154] A function construction module 330 is used to construct an objective function under a first constraint condition with minimizing the total transportation cost of the scheduling solution as the optimization goal, where the first constraint condition is a constraint condition of the scheduling solution;

[0155] A solution acquisition module 340 is used to iteratively evolve the initial population using a genetic algorithm based on the objective function until a preset termination condition is reached to obtain an optimal scheduling solution; and

[0156] The scheduling module 350 is used to output the optimal scheduling plan in a preset output mode, so as to schedule the trucks according to the optimal scheduling plan.

[0157] The device provided by the embodiment of the present invention performs symbol encoding on the unloading point, excavator and truck, and generates an initial population containing multiple scheduling path sequences based on these symbol encodings. Each scheduling path sequence represents a possible truck scheduling scheme, including the detailed path of the truck from the excavator loading to the unloading point. Then, the objective function of the first constraint condition is constructed with minimizing the total transportation cost of the scheduling scheme as the optimization goal. Finally, combined with this objective function, the initial population is iteratively evolved by using a genetic algorithm. Under the action of the genetic algorithm, the scheduling path of the truck in the scheduling scheme will undergo operations such as selection, crossover and mutation, so as to gradually approach the optimal solution. When the preset termination condition is reached, an optimal scheduling scheme will be output. Thereby, the automation and intelligence of vehicle scheduling in open-pit mining and large-scale earthwork projects are realized, which not only improves the real-time and accuracy of scheduling, but also reduces costs.

[0158] The mine vehicle dispatching device described in this embodiment and the mine vehicle dispatching method provided by the present invention described above can refer to each other, and will not be described in detail here.

[0159] Fig. 9 An example of a physical structure diagram of an electronic device is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the mine vehicle dispatching method in the above-mentioned embodiments.

[0160] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0161] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the mine vehicle scheduling method in the above-mentioned embodiments.

[0162] In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the mine vehicle dispatching method in the above-mentioned embodiments is implemented.

[0163] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0164] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical coding feature diagrams therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mine vehicle dispatching method, applied to computer equipment, characterized in that: include: Obtaining dispatching information of a loading point, an excavator, and a truck, and respectively performing symbol coding on the loading point, the excavator, and the truck according to the dispatching information, to obtain a first symbol coding corresponding to the loading point, a second symbol coding corresponding to the excavator, and a third symbol coding corresponding to the truck; Based on the first symbol code, the second symbol code and the third symbol code, an initial population is generated, wherein the individuals in the initial population represent a scheduling scheme composed of scheduling path sequences corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each third symbol code includes at least one scheduling path composed of the first symbol code and the second symbol code; Taking minimizing the total transportation cost of the scheduling scheme as the optimization goal, constructing an objective function under a first constraint condition, where the first constraint condition is a constraint condition of the scheduling scheme; Based on the objective function, a genetic algorithm is used to iteratively evolve the initial population until a preset termination condition is reached, and an optimal scheduling plan is obtained and output in a preset output mode, so as to schedule the trucks according to the optimal scheduling plan.

2. The mine vehicle dispatching method according to claim 1, characterized in that: The optimization goal is to minimize the total transportation cost of the scheduling scheme, and to construct an objective function under the first constraint condition, including: Determine the total transportation cost of the scheduling solution based on the heavy-load transportation cost and the empty-load transportation cost of the truck corresponding to each of the third symbol codes, and the heavy-load transportation cost and the empty-load transportation cost of the excavator corresponding to each of the second symbol codes in the scheduling path sequence corresponding to each of the third symbol codes; Taking the total transportation cost of minimizing the total transportation cost of the scheduling plan as the optimization goal, constructing the objective function under the first constraint condition; Among them, the first constraint condition includes at least one of path uniqueness constraint, vehicle load constraint, transportation demand constraint, vehicle quantity constraint, unloading point selection constraint, time allocation constraint, operation area constraint, vehicle type quantity constraint, loading point compatibility constraint, area allocation constraint, excavator allocation uniqueness constraint and pit digging compatibility constraint.

3. The mine vehicle dispatching method according to claim 1, characterized in that: The step of iteratively evolving the initial population using a genetic algorithm based on the objective function until a preset termination condition is reached includes: Based on the objective function, determining the fitness of each individual in the initial population; Based on the fitness of each individual in the initial population, an elite retention strategy is adopted to determine the first individual in the initial population; Based on the fitness of each individual in the initial population, a roulette wheel selection strategy is used to determine a second individual in the initial population except the first individual; Performing a crossover operation and a mutation operation on the second individual to obtain a third individual; Based on the third individual and the first individual, a new population is obtained, and the new population is used as the initial population. The step of determining the fitness of each individual in the initial population based on the objective function is returned to continue until a preset termination condition is reached.

4. The mine vehicle dispatching method according to claim 3, characterized in that: The method further comprises: Performing a crossover operation on the second individual to obtain a fourth individual, and performing a second constraint condition test on the fourth individual; When the fourth individual does not satisfy the second constraint condition test, cyclically moving the position of the third symbol code in the fourth individual until the second constraint condition is satisfied; The second constraint condition is that the task start time of the truck corresponding to the third symbol code located at the last position in the individual is within a preset time window.

5. The mine vehicle dispatching method according to claim 4, characterized in that: The method further comprises: Determining a fifth individual selected for mutation operation from the fourth individual, and randomly selecting a mutation point from the scheduling path sequence corresponding to the fifth individual for mutation; Among them, when the variation point is the first symbol code corresponding to the first unloading point, the first symbol code corresponding to the first unloading point is randomly replaced by the first symbol code corresponding to the second unloading point; when the variation point is the second symbol code corresponding to the first excavator, the second symbol code corresponding to the first excavator is randomly replaced by the second symbol code corresponding to the second excavator.

6. The mine vehicle dispatching method according to claim 5, characterized in that: The fifth individual selected to perform the mutation operation from the fourth individual comprises: Randomly generate a random number between (0, 1) for the fourth individual obtained after the crossover operation, and compare the random number with a preset mutation probability; If the random number is less than the preset mutation probability, the fourth individual is determined as the fifth individual to be subjected to the mutation operation.

7. The mine vehicle dispatching method according to claim 1, characterized in that: Also includes: Based on the third constraint condition, randomly generate a first dispatch path sequence for the first truck; Determine, based on the first dispatch path sequence of the first truck, a second dispatch path sequence for a second truck other than the first truck, wherein the second dispatch path sequence is the same as the first dispatch path sequence; generating an initial population based on the first dispatching path sequence of the first truck and the second dispatching path sequence of the second truck; The third constraint condition includes: After the current truck leaves the loading point corresponding to the current excavator, it goes to the unloading point that is not currently occupied by other trucks; After the current truck leaves the current unloading point, it goes to the loading point corresponding to the excavator that is not currently occupied by other trucks, or goes to the loading point corresponding to the excavator that meets the preset loading waiting time.

8. A mine vehicle dispatching device, characterized in that: include: A code acquisition module, used to acquire the dispatch information of the unloading point, the excavator and the truck, and to perform symbol encoding on the unloading point, the excavator and the truck respectively according to the dispatch information, so as to obtain a first symbol code corresponding to the unloading point, a second symbol code corresponding to the excavator and a third symbol code corresponding to the truck; A population generation module, configured to generate an initial population based on the first symbol code, the second symbol code, and the third symbol code, wherein the individuals in the initial population represent a scheduling solution composed of a scheduling path sequence corresponding to all the third symbol codes, wherein the scheduling path sequence corresponding to each of the third symbol codes includes at least one scheduling path composed of a pair of the first symbol code and the second symbol code; A function construction module, used to construct an objective function under a first constraint condition with minimizing the total transportation cost of the scheduling scheme as the optimization goal, where the first constraint condition is a constraint condition of the scheduling scheme; A scheme acquisition module is used to iteratively evolve the initial population using a genetic algorithm based on the objective function until a preset termination condition is reached to obtain an optimal scheduling scheme; and The scheduling module is used to output the optimal scheduling plan in a preset output mode so as to schedule the trucks according to the optimal scheduling plan.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the mine vehicle dispatching method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the mine vehicle dispatching method according to any one of claims 1 to 7 is implemented.

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