Coal mine vehicle scheduling method

Abstract

The invention provides a coal mine vehicle scheduling method, and relates to the technical field of mine engineering and intelligent scheduling, and the method comprises the steps: obtaining to-be-executed task data of a target mine and an initial vehicle scheduling model; determining task execution parameters based on the to-be-executed task data and the vehicle data; and adjusting the initial vehicle scheduling model based on the task execution parameters to generate a target vehicle scheduling model, and performing vehicle scheduling on the target mine based on the target vehicle scheduling model. Therefore, the vehicle scheduling model is adjusted through the to-be-executed task data, the method can adapt to a continuously changing scene of a transportation task such as mine transportation, meanwhile, the characteristics of the to-be-executed task and the actual bearing capacity and state of the vehicle are analyzed, the optimal execution parameter of each task is calculated, the coal mine vehicle scheduling effect is improved, and the vehicle scheduling efficiency is improved. And scheduling cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical fields of mining engineering and intelligent scheduling, and in particular to a coal mine vehicle scheduling method. Background Art

[0002] As the coal mining industry develops toward intelligent and unmanned operations, auxiliary transportation systems, as a crucial component of underground coal mine operations, face greater demands for efficiency and flexibility. The primary function of auxiliary transportation is to transport personnel, equipment, and materials from the surface to the underground work area, and its efficiency directly impacts the mine's overall production efficiency and operating costs. Although traditional rail-based transportation systems were once the core method of auxiliary transportation in coal mines, they have been gradually replaced by trackless rubber-tyred vehicles due to their poor flexibility and limited adaptability. Trackless rubber-tyred vehicles, with their greater flexibility and efficiency, have become the mainstream choice for auxiliary transportation in underground coal mines. However, with the diversification of transportation needs and the increasing complexity of tasks, the scheduling and optimization of trackless transportation systems has become a key issue that needs to be urgently addressed in the construction of intelligent mines.

[0003] The underground transportation environment is complex and ever-changing, with narrow lanes, multiple intersections, and multiple tasks intersecting, which poses severe challenges to transportation scheduling. Existing scheduling methods mostly use time priority or location priority as core strategies, and usually rely on simple rules or manual experience to allocate tasks. These methods lack comprehensiveness and systematicness in handling task time windows, vehicle load restrictions, and route optimization, resulting in low vehicle utilization, high transportation costs, and often reduced production efficiency due to task conflicts or delays. In addition, faced with the dynamic scheduling requirements of multiple vehicles and multiple tasks, the computing power and response speed of traditional systems cannot meet the needs of efficient and refined management. Summary of the Invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, one purpose of the present disclosure is to propose a coal mine vehicle scheduling method.

[0006] The second objective of the present disclosure is to provide a coal mine vehicle dispatching device.

[0007] A third objective of the present disclosure is to provide an electronic device.

[0008] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present disclosure is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a coal mine vehicle scheduling method, including: obtaining the task data to be executed and the initial vehicle scheduling model of the target mine, wherein the initial vehicle scheduling model is established and generated based on the transportation node data, transportation path data and vehicle data of the target mine; determining the task execution parameters based on the task data to be executed and the vehicle data; adjusting the initial vehicle scheduling model based on the task execution parameters to generate a target vehicle scheduling model, and performing vehicle scheduling on the target mine based on the target vehicle scheduling model.

[0011] According to one embodiment of the present disclosure, establishing the initial vehicle scheduling model includes: establishing the objective function, penalty function and constraint function of the initial vehicle scheduling model based on the transportation node data, the transportation path data and the vehicle data; and establishing the initial vehicle scheduling model based on the objective function, the penalty function and the constraint function.

[0012] According to one embodiment of the present disclosure, the objective function of the initial vehicle scheduling model is established based on the transportation node data, the transportation path data and the vehicle data, including: establishing a transportation attendance time minimization function and an attendance quantity minimization function based on the transportation node data, the transportation path data and the vehicle data; and establishing the objective function based on the transportation attendance time minimization function and the attendance quantity minimization function.

[0013] According to one embodiment of the present disclosure, the penalty function is established based on the transport node data, the transport path data and the vehicle data, including: establishing a timeout penalty function and a vehicle quantity excess penalty function based on the transport node data, the transport path data and the vehicle data; establishing the penalty function based on the timeout penalty function and the vehicle quantity excess penalty function.

[0014] According to one embodiment of the present disclosure, the constraint function is established based on the transportation node data, the transportation path data and the vehicle data, including: establishing driving time constraints, time window constraints, load constraints, vehicle total constraints and transportation task constraints based on the transportation node data, the transportation path data and the vehicle data; establishing the constraint function based on the driving time constraints, the time window constraints, the load constraints, the vehicle total constraints and the transportation task constraints.

[0015] According to one embodiment of the present disclosure, determining the task execution parameters based on the task data to be executed and the vehicle data includes: processing the task data to be executed and the vehicle data to generate the earliest start time, the latest start time, the unloading time, the start time, the transport volume, the starting node, and the driving time as the task execution parameters.

[0016] According to one embodiment of the present disclosure, establishing the initial vehicle scheduling model based on the objective function, the penalty function and the constraint function includes: obtaining the function weights corresponding to the objective function and the penalty function respectively; and establishing the initial vehicle scheduling model based on the function weights, the objective function, the penalty function and the constraint function.

[0017] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes a coal mine vehicle scheduling device, including: an acquisition module, used to obtain the task data to be executed and the initial vehicle scheduling model of the target mine, wherein the initial vehicle scheduling model is established and generated based on the transportation node data, transportation path data and vehicle data of the target mine; a determination module, used to determine the task execution parameters based on the task data to be executed and the vehicle data; an adjustment module, used to adjust the initial vehicle scheduling model based on the task execution parameters to generate a target vehicle scheduling model, and perform vehicle scheduling on the target mine based on the target vehicle scheduling model.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the coal mine vehicle dispatching method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the coal mine vehicle dispatching method as described in the first embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the coal mine vehicle scheduling method as described in the first embodiment of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of a coal mine vehicle dispatching method according to one embodiment of the present disclosure;

[0022] Figure 2is a schematic diagram of another coal mine vehicle dispatching method according to an embodiment of the present disclosure;

[0023] Figure 3 is a schematic diagram of a coal mine vehicle dispatching device according to one embodiment of the present disclosure;

[0024] Figure 4 is a schematic diagram of an electronic device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0026] The acquisition, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of relevant laws and regulations.

[0027] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0028] Figure 1 Schematic diagram of a coal mine vehicle dispatching method according to an embodiment of the present disclosure. Figure 1 As shown, the coal mine vehicle dispatching method includes the following steps:

[0029] S101, obtaining the task data to be executed and the initial vehicle scheduling model of the target mine, wherein the initial vehicle scheduling model is established and generated based on the transportation node data, transportation route data and vehicle data of the target mine.

[0030] It should be noted that the target mine is the mine that currently needs to be scheduled or managed.

[0031] The coal mine vehicle dispatching method of the embodiment of the present application can be applied to the scenario of target mine vehicle dispatching. The executor of the coal mine vehicle dispatching of the embodiment of the present application can be the coal mine vehicle dispatching device of the embodiment of the present application, and the coal mine vehicle dispatching device can be set on an electronic device.

[0032] In the embodiment of the present disclosure, the initial vehicle scheduling model may be a neural network model, a heuristic algorithm model, etc., and no limitation is made here.

[0033] It should be noted that pending task data refers to the design parameters for tasks that are currently or will be executed in the future. This data can include a variety of items, and is not limited here. For example, for a target mine transportation task, pending task data may include the type of cargo to be transported, the quantity or weight of the cargo, the volume or dimensions of the cargo, and may also include the destination and actual location of the transport.

[0034] The data of tasks to be executed may be manually set up or automatically generated based on the tasks that need to be executed currently, and there is no limitation here.

[0035] Mine transportation node data typically includes the marked or annotated nodes and their corresponding parameters between the mining site and the final destination (such as a processing plant, warehouse, or port). This data is crucial for optimizing transportation efficiency, reducing costs, and ensuring safe operations. Mine transportation node data can include the location, distance, and connectivity of each marked or annotated node.

[0036] Transportation route data refers to parameters or data related to roads within the target mine or to other transportation destinations. Transportation route data can include a variety of information, not limited herein. For example, transportation route data may include data such as the identifier of each route, its length, and a description of the route.

[0037] Vehicle data plays a vital role in logistics management, transportation scheduling, and fleet management, among other areas. This data not only helps improve operational efficiency, but also enhances safety and reduces costs. Vehicle data can include a variety of factors, such as vehicle identification, model, payload, and fuel consumption.

[0038] In the embodiment of the present disclosure, the transportation node data, transportation route data and vehicle data of the target mine can be obtained by analyzing the historical transportation data or scheduling data of the target mine.

[0039] During implementation, if the roads, vehicles and transportation nodes of the target mine change, such as when some vehicles, roads or transportation nodes are added or abandoned, the transportation node data, transportation route data and vehicle data can also be adjusted.

[0040] S102: Determine task execution parameters based on the task data to be executed and the vehicle data.

[0041] It is understandable that, due to the differences in mining period, development status, etc. of the target mine, the execution task data may be variable data.

[0042] In the disclosed embodiment, the task execution parameters are the execution parameters for the current target mine to execute the current task based on the current vehicle configuration, etc. For example, the task execution parameters are related to the current vehicle load and displacement of the target mine, as well as the volume and weight of the cargo to be transported in the current task.

[0043] S103 , adjusting the initial vehicle scheduling model based on the task execution parameters to generate a target vehicle scheduling model, and performing vehicle scheduling for the target mine based on the target vehicle scheduling model.

[0044] In the embodiment of the present disclosure, since the task execution parameters are related to the task that needs to be currently executed, the target vehicle scheduling model needs to be changed according to the task that is currently executed.

[0045] In another possible implementation, when the currently executed task is similar or identical to a task executed in the past, the target vehicle dispatch model generated in the past can be directly called to perform vehicle dispatch. This can reduce the cost of data processing and model generation.

[0046] In the above embodiment, the target mine's pending task data and initial vehicle scheduling model are first obtained, wherein the initial vehicle scheduling model is established and generated based on the target mine's transportation node data, transportation path data, and vehicle data. Then, the task execution parameters are determined based on the pending task data and vehicle data. Finally, the initial vehicle scheduling model is adjusted based on the task execution parameters to generate a target vehicle scheduling model, and the target mine is scheduled based on the target vehicle scheduling model. Thus, by adjusting the vehicle scheduling model based on the pending task data, it can adapt to the scenario of mine transportation, where transportation tasks are constantly changing. At the same time, the characteristics of the pending tasks and the actual carrying capacity and status of the vehicles are analyzed to calculate the optimal execution parameters for each task, thereby improving the effect of coal mine vehicle scheduling and reducing scheduling costs.

[0047] In the above embodiment, the initial vehicle scheduling model is established, and Figure 2 Explaining further, the method includes:

[0048] S201, establishing an objective function, a penalty function, and a constraint function of an initial vehicle scheduling model based on transportation node data, transportation route data, and vehicle data.

[0049] The objective function is the transportation goal that the task aims to achieve. It also quantifies the performance that needs to be achieved to achieve this goal. The objective function can include the function that we want to minimize or maximize during the optimization process. It quantifies the desired goal, such as minimizing costs, maximizing profits, or optimizing a specific performance metric.

[0050] Constraint functions define the space of feasible solutions, that is, which solutions meet the actual conditions. Constraints can be equality constraints or inequality constraints, which are used to limit the range of values ​​of decision variables.

[0051] Penalty function is a technique that incorporates constraints into the objective function by adding extra terms to the objective function to "penalize" solutions that violate the constraints.

[0052] In one possible implementation, the penalty function in the embodiment of the present disclosure may have two forms:

[0053] Internal penalty function (also called barrier function): When the solution approaches the boundary, the penalty term becomes very large, forcing the search process to remain within the feasible region.

[0054] External penalty function: directly imposes penalties on violations of constraints, making the objective function value of infeasible solutions worse.

[0055] In an embodiment of the present disclosure, the objective function of the initial vehicle scheduling model is established based on the transportation node data, the transportation route data and the vehicle data. First, a transportation attendance time minimization function and an attendance quantity minimization function are established based on the transportation node data, the transportation route data and the vehicle data, and then an objective function is established based on the transportation attendance time minimization function and the attendance quantity minimization function.

[0056] In the disclosed embodiment, transport attendance time refers to the total time required for all transport tasks from initiation to completion. Minimizing transport attendance time helps improve transport efficiency, reduce vehicle idle time and waiting time, and maximize system utilization. The objective function is as follows:

[0057]

[0058] In the disclosed embodiment, the number of vehicles on duty refers to the number of vehicles required to complete all transport tasks. Minimizing the number of vehicles on duty helps reduce resource usage and operating costs, improving the economy and sustainability of the system. The objective function is as follows:

[0059]

[0060] In an embodiment of the present disclosure, a penalty function is established based on transportation node data, transportation route data and vehicle data. First, a timeout penalty function and a vehicle quantity excess penalty function are established based on the transportation node data, transportation route data and vehicle data, and then a penalty function is established based on the timeout penalty function and the vehicle quantity excess penalty function.

[0061] In practical applications, some constraints may be difficult to satisfy, such as an excessive number of tasks, an insufficient number of vehicles, or an overly restrictive time window. To ensure that the model can still find a viable solution under these circumstances, a penalty function is often introduced. This penalizes constraint violations, allowing the optimization algorithm to gradually improve and find the optimal solution that satisfies the constraints during the iterative process.

[0062] Their significance lies in that when the penalty function value for violating the time window in the final optimization result is greater than 0, the scheduling manager should comprehensively consider whether the task is reasonably arranged; when the penalty function for violating the maximum number of vehicles is greater than 0, the mine should consider increasing the number of vehicles.

[0063] In the embodiment of the present disclosure, the timeout penalty function is used to execute each transport task within its allowed time range. m Exceeded its latest start time het m , then it will be penalized. The penalty function is as follows:

[0064]

[0065] The vehicle excess penalty function ensures that each vehicle can only perform one task at a time. If the number of tasks for a single vehicle exceeds its maximum carrying capacity, it will be penalized. The penalty function is as follows:

[0066]

[0067] In an embodiment of the present disclosure, a constraint function is established based on transportation node data, transportation route data and vehicle data. First, driving time constraints, time window constraints, load constraints, total vehicle quantity constraints and transportation task constraints are established based on the transportation node data, transportation route data and vehicle data. Then, a constraint function is established based on the driving time constraints, time window constraints, load constraints, total vehicle quantity constraints and transportation task constraints.

[0068] Constraints ensure that the solution in the optimization process is feasible, that is, it meets all practical operational requirements.

[0069] The driving time constraint of the transport task ensures that the driving time between each task is reasonably calculated and scheduled. a To transport mission h b The driving time is:

[0070]

[0071] The time window constraint ensures that each task is executed within its allowed time range to avoid task delays. The specific constraint conditions are as follows:

[0072] ht m ≤het m

[0073] ht m ≥hst m

[0074] The load constraint ensures that the load of each transport vehicle does not exceed its maximum load capacity, ensuring transportation safety and efficiency. The specific constraint conditions are as follows:

[0075]

[0076] The total number of vehicles constraint ensures that each transport vehicle can only perform one task at a time to avoid resource conflicts. The specific constraint conditions are as follows:

[0077]

[0078] The transport task constraint ensures that each transport task is completed by only one vehicle. The specific constraint conditions are as follows:

[0079]

[0080]

[0081] In one possible implementation method, an initial vehicle scheduling model is established based on the objective function, penalty function and constraint function. First, the function weights corresponding to the objective function and penalty function are obtained, and then the initial vehicle scheduling model is established based on the function weights, objective function, penalty function and constraint function.

[0082] It should be noted that the function weights corresponding to the objective function and the penalty function can be designed in advance or obtained by analyzing historical operating data, and no limitation is made here.

[0083] Taking into account the above objective function, penalty function and constraint function, the final comprehensive objective function can be constructed to achieve the optimal scheduling of transportation tasks. The comprehensive objective function is as follows:

[0084]

[0085] Among them, the penalty function coefficients α and β are used to adjust the weights of each objective and penalty function to ensure the adjustability and flexibility of the model.

[0086] G = (N, E) represents a graph, where N is the set of nodes and E is the set of paths.

[0087] N={n1,n2,…,n i ,…,n IAll transportation nodes in the mine, including loading points, unloading points, and transfer points in the mine.

[0088] E={e1,e2,…,e j ,…,e J All transportation routes within the mine. These routes connect different nodes and form the transportation network within the mine. Each route has a specific distance and road type.

[0089] V={v1,v2,…,v k ,…,v K All available rubber-tyred trackless vehicles.

[0090] H={h1,h2,…,h m ,…,h M} represents all the transport tasks that need to be performed.

[0091] R={r1,r2,…,r l ,…,r L} represents different types of roads in the mine.

[0092] Input parameters:

[0093] conn ab A Boolean parameter indicating whether there is a direct connection between node a and node b. If there is a connection, conn ab =1, and Otherwise, conn ab =0.

[0094] dist j Represents path e j The distance, in kilometers, used to calculate driving time.

[0095] rtype j Represents path e j The road type affects the speed of rubber-tyred trackless vehicles. j ∈R.

[0096] vcap k Indicates trackless rubber-tyred vehicle v k The maximum loading capacity in kg.

[0097] Indicates trackless rubber-tyred vehicle v k On road type r l The speed on the vehicle is measured in kilometers per hour.

[0098] hst m Represents the transportation task h mThe earliest start time is set to ensure that the task can be started within the specified time.

[0099] het m Represents the transportation task h m The latest start time to prevent task delays.

[0100] hdt m Transport mission m Unloading time, which is the time required to unload the cargo from the vehicle.

[0101] ht m Represents the transportation task h m The starting time, that is, the time when the vehicle starts to load the cargo.

[0102] hvol m Represents the transportation task h m The transport volume is in kg.

[0103] hdest m Represents the transportation task h m The starting node.

[0104] htravel ab Indicates that from the transport task h a Drive from the current location to the transport task h b The driving time between locations.

[0105] slt mk Is a Boolean variable representing the transport task h m Whether it is a trackless rubber-wheeled vehicle k Execute. If yes, then slt mk =1, otherwise slt mk = 0. Used to indicate task allocation.

[0106] Is a Boolean variable representing the trackless rubber-tyred vehicle v k After completing the transport mission a Whether to execute the transport task immediately after h b If yes, then otherwise Used to indicate the sequence and connection relationship between tasks.

[0107] S202: Establish an initial vehicle scheduling model based on the objective function, penalty function, and constraint function.

[0108] In the disclosed embodiment, the objective function, penalty function, and constraint function of the initial vehicle scheduling model are first established based on transportation node data, transportation route data, and vehicle data. Then, the initial vehicle scheduling model is established based on the objective function, penalty function, and constraint function. This allows for the rational allocation of tasks and route planning to maximize the utilization of each vehicle, reduce idle driving rates and unnecessary waiting time, and simultaneously reduce travel distance and time, thereby improving overall transportation efficiency.

[0109] In the above embodiment, the task execution parameters are determined based on the task data to be executed and the vehicle data. The task data to be executed and the vehicle data can be processed to generate the earliest start time, the latest start time, the unloading time, the start time, the transport quantity, the starting node, and the driving time as the task execution parameters.

[0110] There are many methods for processing the data of the task to be executed and the vehicle data. For example, the data of the task to be executed and the vehicle data can be input into a pre-trained processing model to generate task execution parameters.

[0111] Corresponding to the coal mine vehicle dispatching methods provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a coal mine vehicle dispatching device. Since the coal mine vehicle dispatching device provided in the embodiment of the present disclosure corresponds to the coal mine vehicle dispatching methods provided in the above-mentioned embodiments, the implementation method of the above-mentioned coal mine vehicle dispatching method is also applicable to the coal mine vehicle dispatching device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0112] Figure 3 Schematic diagram of a coal mine vehicle dispatching device according to one embodiment of the present disclosure. Figure 3 As shown, the coal mine vehicle dispatching device 300 includes: an acquisition module 310 , a determination module 320 and an adjustment module 330 .

[0113] The acquisition module 310 is used to acquire the to-be-executed task data and the initial vehicle scheduling model of the target mine, wherein the initial vehicle scheduling model is established and generated based on the transportation node data, transportation route data and vehicle data of the target mine.

[0114] The determination module 320 is configured to determine task execution parameters based on the task data to be executed and the vehicle data.

[0115] The adjustment module 330 is configured to adjust the initial vehicle scheduling model based on the task execution parameters to generate a target vehicle scheduling model, and perform vehicle scheduling for the target mine based on the target vehicle scheduling model.

[0116] According to one embodiment of the present disclosure, establishing an initial vehicle scheduling model includes: establishing an objective function, a penalty function, and a constraint function of the initial vehicle scheduling model based on transportation node data, transportation path data, and vehicle data; and establishing the initial vehicle scheduling model based on the objective function, the penalty function, and the constraint function.

[0117] According to one embodiment of the present disclosure, the objective function of the initial vehicle scheduling model is established based on the transportation node data, the transportation route data and the vehicle data, including: establishing a transportation attendance time minimization function and an attendance quantity minimization function based on the transportation node data, the transportation route data and the vehicle data; establishing an objective function based on the transportation attendance time minimization function and the attendance quantity minimization function.

[0118] According to one embodiment of the present disclosure, a penalty function is established based on transportation node data, transportation route data and vehicle data, including: establishing a timeout penalty function and a vehicle quantity exceeding penalty function based on transportation node data, transportation route data and vehicle data; establishing a penalty function based on the timeout penalty function and the vehicle quantity exceeding penalty function.

[0119] According to one embodiment of the present disclosure, a constraint function is established based on transportation node data, transportation path data and vehicle data, including: establishing driving time constraints, time window constraints, load constraints, total vehicle quantity constraints and transportation task constraints based on transportation node data, transportation path data and vehicle data; establishing a constraint function based on driving time constraints, time window constraints, load constraints, total vehicle quantity constraints and transportation task constraints.

[0120] According to one embodiment of the present disclosure, task execution parameters are determined based on the task data to be executed and the vehicle data, including: processing the task data to be executed and the vehicle data to generate the earliest start time, the latest start time, the unloading time, the start time, the transport volume, the starting node, and the driving time as the task execution parameters.

[0121] According to one embodiment of the present disclosure, an initial vehicle scheduling model is established based on an objective function, a penalty function, and a constraint function, including: obtaining function weights corresponding to the objective function and the penalty function; and establishing the initial vehicle scheduling model based on the function weights, the objective function, the penalty function, and the constraint function.

[0122] In order to implement the above embodiment, the present disclosure further provides an electronic device 400. Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 4 As shown, the electronic device 400 includes: a processor 401 and a memory 402 in communication with the processor, the memory 402 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 401 to implement the present disclosure. Figure 1 and Figure 2 A coal mine vehicle dispatching method according to an embodiment.

[0123] In order to implement the above embodiment, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiment. Figure 1 and Figure 2 A coal mine vehicle dispatching method according to an embodiment.

[0124] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program, which implements the above embodiments when executed by a processor. Figure 1 and Figure 2 A coal mine vehicle dispatching method according to an embodiment.

[0125] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0126] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0127] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0131] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0134] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A coal mine vehicle dispatching method, characterized in that: include: Obtaining pending task data and an initial vehicle scheduling model of a target mine, wherein the initial vehicle scheduling model is established and generated based on transportation node data, transportation route data, and vehicle data of the target mine; determining task execution parameters based on the to-be-executed task data and the vehicle data; The initial vehicle scheduling model is adjusted based on the task execution parameters to generate a target vehicle scheduling model, and vehicles are scheduled for the target mine based on the target vehicle scheduling model.

2. The method according to claim 1, characterized in that Establishing the initial vehicle scheduling model includes: Establishing the objective function, penalty function and constraint function of the initial vehicle scheduling model based on the transportation node data, the transportation path data and the vehicle data; The initial vehicle scheduling model is established based on the objective function, the penalty function and the constraint function.

3. The method according to claim 2, characterized in that The objective function of the initial vehicle scheduling model is established based on the transportation node data, the transportation path data, and the vehicle data, including: Establishing a transport attendance time minimization function and an attendance quantity minimization function based on the transport node data, the transport route data and the vehicle data; The objective function is established based on the transportation attendance time minimization function and the attendance quantity minimization function.

4. The method according to claim 2, characterized in that Establishing the penalty function based on the transportation node data, the transportation route data, and the vehicle data includes: Establishing a timeout penalty function and a vehicle quantity exceeding penalty function based on the transportation node data, the transportation route data and the vehicle data; The penalty function is established based on the timeout penalty function and the vehicle quantity excess penalty function.

5. The method according to claim 2, characterized in that Establishing the constraint function based on the transportation node data, the transportation path data, and the vehicle data includes: Establishing driving time constraints, time window constraints, load constraints, vehicle total constraints and transportation task constraints based on the transportation node data, the transportation route data and the vehicle data; The constraint function is established based on the driving time constraint, the time window constraint, the load constraint, the total vehicle quantity constraint and the transportation task constraint.

6. The method according to any one of claims 1 to 5, characterized in that The determining of the task execution parameters based on the to-be-executed task data and the vehicle data includes: The data of the task to be executed and the vehicle data are processed to generate the earliest start time, the latest start time, the unloading time, the start time, the transport volume, the starting node, and the driving time, and the earliest start time, the latest start time, the unloading time, the start time, the transport volume, the starting node, and the driving time are used as the task execution parameters.

7. The method according to claim 2, characterized in that The establishing of the initial vehicle scheduling model based on the objective function, the penalty function and the constraint function includes: Obtaining function weights corresponding to the objective function and the penalty function; The initial vehicle scheduling model is established based on the function weight, the objective function, the penalty function and the constraint function.

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