Charging scheduling method, device and system for mine truck fleet and terminal equipment

Abstract

The invention provides a charging scheduling method, device and system of a mine truck fleet and terminal equipment. The method comprises the steps of determining a time interval and slicing duration of charging scheduling planning of a mine truck fleet, and performing time slicing on the time interval by using the slicing duration to obtain time slices; determining a decision variable according to each to-be-charged mine card and each time slice in the mine card fleet; constructing a charging scheduling decision optimization model of the mine truck fleet according to the optimization target, the constraint condition and the decision variable; wherein the constraint condition comprises the number limitation of the charging piles; and determining a charging scheduling planning result corresponding to each to-be-charged mine card according to the charging scheduling decision optimization model. According to the method, the charging time of each to-be-charged mine card can be reasonably planned, long-time queuing charging of the to-be-charged mine cards is avoided, and the production efficiency of a mine card motorcade is improved.

Description

Technical Field

[0001] The present application relates to the field of charging scheduling technology, and in particular to a charging scheduling method, device, system and terminal equipment for a mining truck fleet. Background Art

[0002] At present, in closed mining areas, the use of mining truck fleets for operation has gradually become popular. In order to ensure that each mining truck in the mining truck fleet can operate normally, it is currently necessary to periodically detect the remaining power of each mining truck in the mining truck fleet. When the remaining power of the mining truck is lower than the preset power threshold, the mining truck is dispatched to the charging pile for charging.

[0003] However, due to the limitation of operation cost and production site, mining areas are generally unable to equip each mining truck with a charging pile. In other words, if there are multiple mining trucks that need to be charged at the same time, that is, there are multiple mining trucks to be charged, when the number of mining trucks to be charged is greater than the number of charging piles, the long queue for charging of mining trucks to be charged will seriously affect production efficiency.

[0004] Therefore, how to improve the production efficiency of mining truck fleets is a technical problem that technicians in this field currently need to solve. Summary of the invention

[0005] The purpose of the present application is to provide a charging scheduling method, device, terminal equipment, system, charging pile and computer-readable storage medium for a mine truck fleet, aiming to improve the production efficiency of the mine truck fleet.

[0006] In a first aspect, the present application provides a charging scheduling method for a mining truck fleet. The method comprises:

[0007] Determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and use the slice duration to time slice the time interval to obtain a time slice;

[0008] Determine a decision variable according to each of the mine trucks to be charged in the mine truck fleet and each of the time slices;

[0009] Constructing a charging scheduling decision optimization model for the mining truck fleet according to the optimization objectives, constraints and the decision variables; wherein the constraints include a limit on the number of charging piles;

[0010] The charging scheduling planning result corresponding to each of the mining trucks to be charged is determined according to the charging scheduling decision optimization model.

[0011] In one embodiment, a charging scheduling method for a mining truck fleet further includes:

[0012] The average loading and unloading time of the mine truck fleet is obtained, and the average loading and unloading time is determined as the slicing time.

[0013] In one embodiment, determining the decision variables according to each mining truck to be charged in the mining truck fleet and each time slice includes:

[0014] Obtaining the remaining power corresponding to each mining truck in the mining truck fleet;

[0015] Determine the mining card whose remaining power is lower than a preset power threshold as a mining card to be charged;

[0016] The decision variables are determined according to each of the mining cards to be charged and each of the time slices.

[0017] In one embodiment, the constraints also include mining area production requirements.

[0018] In one of the embodiments, the optimization objective includes minimizing the charging cost of the mining truck fleet within the time interval.

[0019] In one embodiment, the determining of the charging scheduling planning result corresponding to each of the mining cards to be charged according to the charging scheduling decision optimization model includes:

[0020] A nonlinear integer optimization algorithm is used to solve the charging scheduling decision optimization model, and a charging scheduling planning result corresponding to each of the mining trucks to be charged is output.

[0021] In a second aspect, the present application also provides a charging scheduling device for a mining truck fleet. The device comprises:

[0022] A time slice determination module is used to determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and to time slice the time interval using the slice duration to obtain a time slice;

[0023] A decision variable determination module, used for determining a decision variable according to each mining truck to be charged in the mining truck fleet and each of the time slices;

[0024] A model determination module, used to construct a charging scheduling decision optimization model for the mine truck fleet according to the optimization goal, the constraint conditions and the decision variables; wherein the constraint conditions include a limit on the number of charging piles;

[0025] The scheduling planning result determination module is used to determine the charging scheduling planning result corresponding to each of the mining trucks to be charged according to the charging scheduling decision optimization model.

[0026] In a third aspect, the present application further provides a terminal device, wherein the terminal device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0027] In a fourth aspect, the present application also provides a charging scheduling system for a mine truck fleet, the system comprising a mine truck fleet, a charging pile and a terminal device; the mine truck fleet comprises a plurality of mine trucks, each of which is respectively connected to the terminal device for communication; wherein the terminal device is used to obtain an average loading and unloading time of the mine truck fleet; time slices are performed on the time interval of the charging scheduling plan according to the average loading and unloading time to obtain a time slice; decision variables are determined according to each mine truck to be charged in the mine truck fleet and each time slice; a charging scheduling decision optimization model for the mine truck fleet is constructed according to the optimization objective, constraints and the decision variables; wherein the constraints include a limit on the number of charging piles; a charging scheduling planning result corresponding to each mine truck to be charged is determined according to the charging scheduling decision optimization model; the charging scheduling planning result is sent to the corresponding mine truck to be charged; the mine truck to be charged is used to perform work tasks in the mining area, and after receiving the charging scheduling planning result, a corresponding charging operation is performed according to the charging scheduling planning result; the charging pile is used to charge each mine truck to be charged.

[0028] In a fifth aspect, the present application also provides a charging pile, which includes a charging pile body and a controller, and the controller is used to execute the steps of the above method.

[0029] In a sixth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0030] The present application provides a charging scheduling method for a mine truck fleet. The method constructs a charging scheduling decision optimization model for the mine truck fleet according to optimization objectives, constraints and decision variables, wherein the constraints include a limit on the number of charging piles; a charging scheduling planning result that achieves the optimization objective under the condition of a limit on the number of charging piles is determined according to the charging scheduling decision optimization model; therefore, the method can reasonably plan the charging time of each mine truck to be charged, avoid long-term queuing for charging of the mine truck to be charged, and improve the production efficiency of the mine truck fleet.

[0031] It can be understood that the charging scheduling device, terminal equipment, system, charging pile and computer-readable storage medium for a mine truck fleet provided in the embodiments of the present application have the same beneficial effects as the charging scheduling method for the mine truck fleet mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A flowchart of a charging scheduling method for a mine truck fleet provided in an embodiment of the present application;

[0034] Figure 2 A schematic diagram of a decision variable provided in an embodiment of the present application;

[0035] Figure 3 A schematic diagram of an electricity price function provided in an embodiment of the present application;

[0036] Figure 4 A flowchart of another charging scheduling method for a mining truck fleet provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of a charging scheduling planning result provided in an embodiment of the present application;

[0038] Figure 6 A schematic diagram of the structure of a charging dispatching device for a mine truck fleet provided in an embodiment of the present application;

[0039] Figure 7 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application;

[0040] Figure 8 A schematic diagram of the structure of a charging dispatching system for a mine truck fleet provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0044] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0045] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0046] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. "Multiple" means "two or more".

[0047] The charging scheduling method for a mine truck fleet provided in the embodiment of the present application can be executed by the processor of the terminal device when running the corresponding computer program. In addition, in a specific implementation, the charging scheduling method for a mine truck fleet of the present application can be executed according to manual triggering by the user, or the execution interval can be pre-set, and the charging scheduling method for a mine truck fleet of the present application can be periodically executed according to the execution interval, which is not limited in the present embodiment.

[0048] Figure 1 This is a flow chart of a charging scheduling method for a mining truck fleet provided in an embodiment of the present application. For the sake of convenience, only the part related to this embodiment is shown. The method provided in this embodiment includes the following steps:

[0049] S100: Determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and use the slice duration to time slice the time interval to obtain a time slice.

[0050] Among them, the mining truck fleet refers to a collection of mining trucks operating in a mining area, that is, a mining truck fleet generally includes multiple mining trucks; this embodiment does not limit the type of mining truck, which can be an unmanned mining truck or a traditional fuel mining truck.

[0051] In actual operation, the time interval of the charging scheduling plan can be determined based on factors such as the current time of the charging scheduling plan, the maximum power value of each mining truck, and the distance between the loading and unloading point of the mining truck and the charging pile.

[0052] The slice duration refers to the duration corresponding to each time slice. Specifically, after obtaining the time interval and slice duration of the charging scheduling plan, the time interval is time sliced ​​using the slice duration to obtain multiple time slices; the duration of each time slice is equal to the slice duration.

[0053] It should be noted that the purpose of using the slice duration to slice the time interval to obtain the time slice is to determine the specific charging time point of the mining card to be charged. For example, if the time interval of the charging scheduling plan is from 8:00 to 9:00, it is determined whether it is necessary to arrange the charging of the mining card to be charged at 8:00-9:00, and if it is necessary to arrange the charging of the mining card to be charged, at which time point between 8:00-9:00 should the charging be carried out.

[0054] S200: Determine a decision variable according to each mining truck to be charged in the mining truck fleet and each time slice.

[0055] Among them, decision variables are controllable factors in optimization problems.

[0056] In this embodiment, the decision variables are determined according to each mining truck to be charged in the mining truck fleet and each time slice. Specifically, for each time slice, the decision variables corresponding to each mining truck to be charged are determined. Figure 2 A schematic diagram of a decision variable provided in an embodiment of the present application; determining that the decision variable is x i,j ; where i represents the mining card number, j represents the time slice number; x i,j When it is 0, it means that mining card i is not charged in time slice j; i,j When it is 1, it means that mining card i is charging in time slice j. Figure 2 In the decision variable diagram shown, the number of mining cards is 10 and the number of time slices is 18.

[0057] S300: Constructing a charging scheduling decision optimization model for a mine truck fleet according to optimization objectives, constraints and decision variables; wherein the constraints include a limit on the number of charging piles.

[0058] The optimization target is the objective function, which is a function that represents the target to be achieved by the charging scheduling of the mining truck fleet. The optimization target can be the maximum output of the mining area within the time interval or the minimum charging cost of the mining truck fleet within the time interval. This embodiment does not limit the specific type of the optimization target.

[0059] Among them, constraints refer to the limiting conditions that decision variables need to meet, which can be set according to the actual operating scenarios of the mining area.

[0060] In this embodiment, in order to avoid the long waiting time for the mining cards to be charged, the constraint conditions include the number of charging piles, that is, the maximum number of mining cards to be charged that can be scheduled to be charged at the same time. Specifically, for any time slice j, the constraint conditions can be set:

[0061]

[0062] That is, for any time slice j, the number of mining cards scheduled to be charged at the same time should be less than P. In a specific implementation, P can be the number of charging piles.

[0063] It can be understood that the optimization objectives, constraints and decision variables are the three elements of the charging scheduling decision optimization model. Therefore, the charging scheduling decision optimization model of the mine truck fleet is constructed based on the optimization objectives, constraints and decision variables; the charging scheduling decision optimization model in this embodiment is a model for solving the corresponding decision variables when the optimization objective is achieved under the condition of limiting the number of charging piles.

[0064] S400: Determine the charging scheduling planning result corresponding to each mining card to be charged according to the charging scheduling decision optimization model.

[0065] Specifically, after constructing the charging scheduling decision optimization model of the mine truck fleet according to the optimization objectives, constraints and decision variables, the corresponding charging scheduling planning results are calculated based on the charging scheduling decision optimization model, that is, the charging scheduling planning results corresponding to each mine truck to be charged are determined.

[0066] Specifically, when it is determined according to the charging scheduling planning result that the mining truck to be charged needs to be charged in a certain time slice, the mining truck to be charged completes the corresponding loading and unloading tasks within the time slice and then drives to the charging pile position for charging operation.

[0067] Specifically, after the charging scheduling planning result is determined, the charging scheduling planning result can be immediately sent to each mining card to be charged. Each mining card to be charged can drive to the charging pile position for charging operation in the time slice where charging is required and after completing the current loading and unloading task according to the planning result corresponding to itself in the charging scheduling planning result; each mining card to be charged can also be monitored separately, and in the time slice where the mining card to be charged needs to be charged and after the mining card to be charged completes the current loading and unloading task, a charging instruction can be sent to the mining card to be charged, so that the mining card to be charged drives to the charging pile position for charging operation according to the charging instruction; this embodiment does not limit the specific process of charging the mining card to be charged according to the charging scheduling planning result.

[0068] An embodiment of the present application provides a charging scheduling method for a mine truck fleet. The method constructs a charging scheduling decision optimization model for the mine truck fleet according to optimization objectives, constraints and decision variables, wherein the constraints include a limit on the number of charging piles; a charging scheduling planning result that achieves the optimization objective under the condition of a limit on the number of charging piles is determined according to the charging scheduling decision optimization model; therefore, the method can reasonably plan the charging time of each mine truck to be charged, avoid long-term queuing for charging of the mine truck to be charged, and improve the production efficiency of the mine truck fleet.

[0069] On the basis of the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, a charging scheduling method for a mine truck fleet further includes:

[0070] Get the average loading and unloading time of the mine truck fleet, and determine the average loading and unloading time as the slicing time.

[0071] Among them, loading and unloading time refers to the total time from the start of loading to the completion of unloading of the mining truck; the average loading and unloading time of the mining truck fleet refers to the average value calculated based on the loading and unloading time of each mining truck in the mining truck fleet.

[0072] Specifically, the loading and unloading time of each truck in the truck fleet can be counted, and the average loading and unloading time of the truck fleet can be calculated based on each loading and unloading time. For example, the loading and unloading time of 10 trucks in the truck fleet is:

[0073] [22min,18min,19min,21min,20min,24min,17min,18min,20min,21min];

[0074] The average loading and unloading time is calculated based on the loading and unloading time:

[0075] (22+18+19+21+20+24+17+18+20+21) / 10=20min.

[0076] In addition, multiple loading and unloading times may be obtained for each mining truck in the mining truck fleet, and then the average loading and unloading time is calculated based on each loading and unloading time. This embodiment does not limit the specific method for calculating the average loading and unloading time.

[0077] In this embodiment, according to the scenario restriction that the mining truck can only go to the charging pile location to charge after completing the current loading and unloading task, the time is sliced ​​on the time axis, that is, the time interval of the charging scheduling plan is time sliced, and the slice duration is the average loading and unloading time. For example, if the average loading and unloading time is 20 minutes, the time interval of the charging scheduling plan is time sliced, and the corresponding time slice is [8:00-8:20, 8:20-8:40, 8:40:9:00]; if the average loading and unloading time is 30 minutes, the time interval of the charging scheduling plan is time sliced, and the corresponding time slice is [8:00-8:30, 8:30-9:00]).

[0078] According to the method of this embodiment, the mining truck to be charged is charged after completing the current loading and unloading task, thereby avoiding charging the mining truck to be charged in the loading state and improving the charging efficiency.

[0079] On the basis of the above embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the decision variables are determined according to each mining truck to be charged and each time slice in the mining truck fleet, including:

[0080] Get the remaining power of each mining truck in the mining truck fleet;

[0081] A mining card whose remaining power is lower than a preset power threshold is determined as a mining card to be charged;

[0082] The decision variables are determined according to each mining card to be charged and each time slice.

[0083] Specifically, the terminal device sends a power acquisition instruction to each mining card in the mining truck fleet. After receiving the power acquisition instruction, each mining card feeds back its own remaining power to the terminal device. After obtaining the remaining power corresponding to each mining card in the mining truck fleet, the terminal device determines whether the remaining power is lower than the preset power threshold. The mining card with the remaining power lower than the preset power threshold is determined as a mining card to be charged. For each mining card to be charged, the decision variable is determined according to each time slice. In this embodiment, the specific value of the preset power threshold is not limited. For example, it can be taken as 20% of the available power of the mining card.

[0084] In this embodiment, mining cards to be charged are screened out according to the remaining power of each mining card in the mining truck fleet, that is, charging scheduling is only performed on mining cards whose remaining power is lower than a preset power threshold, so that the mining cards to be charged can be charged efficiently and timely.

[0085] On the basis of the above-mentioned embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the constraint conditions also include the production requirements of the mining area.

[0086] In this embodiment, in order to ensure the output of the mining area after the mining trucks to be charged are scheduled to be charged, the actual output of the mining trucks in the transport state should be greater than the output requirement of the mining area (minimum output guarantee), that is, the mining trucks that are not scheduled to be charged need to meet the output requirements of the mining area, and the number of mining trucks to be charged that are scheduled to be charged at the same time cannot be greater than the total number of mining trucks in the mining truck fleet minus the number of mining trucks corresponding to the output requirements of the mining area. Therefore, this embodiment also includes setting constraints according to the output requirements of the mining area. For example, in any time slice, at least Q mining trucks are required to run operations at the same time (in the transport state) to meet the output requirements of the mining area, so the maximum number of mining trucks to be charged that are scheduled to be charged at the same time is limited to (nQ). Specifically, for any time slice j, constraints can be set:

[0087]

[0088] Among them, Q can be set according to the total number of mining trucks in the mining truck fleet and the production requirements of the mining area. This embodiment does not limit the specific value of Q.

[0089] According to the method of this embodiment, it is not only possible to avoid the long queues for charging of the mining trucks to be charged, but also possible to ensure the production requirements of the mining area during the charging scheduling of the mining truck fleet by setting constraints according to the production requirements of the mining area.

[0090] On the basis of the above-mentioned embodiments, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the optimization goal includes minimizing the charging cost of the mine truck fleet within the time interval.

[0091] Figure 3 A schematic diagram of an electricity price function provided in an embodiment of the present application. Figure 3 As shown, f(x i,j The corresponding relationship between the electricity price and the time period is shown in Table 1:

[0092] Table 1 The correspondence between electricity prices and time periods;

[0093]

[0094]

[0095] Among them, the electricity price function f(x i,j ) represents the decision variable x i,j The corresponding charging fee is the charging fee of mining card i in time slice j. i,j =1, it means that mining card i is charging in time slice j, so according to xi,j *f(x i,j ) calculates the corresponding charging cost; if the slice duration corresponding to time slice j is 10 minutes, and the charging price during the off-peak period is 1 yuan / hour, then the decision variable x ij The corresponding charging cost is 1 / 6. i,j = 0, it means that mining card i is not charged in time slice j, so the charging cost x is determined i,j *f(x i,j )=0.

[0096] In this embodiment, the charging cost of the entire mining truck fleet is minimized to reduce operating costs, so the optimization objective is determined as:

[0097]

[0098] Among them, n represents the number of mining trucks in the mining truck fleet, and m is the number of time slices.

[0099] According to the method of this embodiment, the problem of high operating costs caused by mining trucks being frequently scheduled to charge during high electricity price periods can be avoided. Therefore, this method can reduce the charging costs of the mining truck fleet.

[0100] On the basis of the above-mentioned embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, in this embodiment, the charging scheduling planning result corresponding to each mining card to be charged is determined according to the charging scheduling decision optimization model, including:

[0101] A nonlinear integer optimization algorithm is used to solve the charging scheduling decision optimization model, and the charging scheduling planning results corresponding to each mining truck to be charged are output.

[0102] It can be understood that the charging scheduling decision optimization model determined in this embodiment is a nonlinear integer programming model. Therefore, a nonlinear integer optimization algorithm is used to solve the charging scheduling decision optimization model, and the output result is the charging scheduling planning result corresponding to each mining card to be charged.

[0103] Specifically, the charging scheduling decision optimization model value is a planning model in which the decision variables only take 0 or 1, that is, 0-1 integer programming; therefore, the implicit enumeration method, Hungarian method or Monte Carlo method can be used to solve the 0-1 integer programming to obtain the charging scheduling planning result corresponding to the charging scheduling decision optimization model.

[0104] According to the method of this embodiment, the charging scheduling planning result can be determined efficiently and conveniently.

[0105] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application are described in detail below in combination with actual application scenarios. In this embodiment of the application, a mine truck fleet in a certain mining area includes 10 mine trucks and 2 charging piles. To ensure production quality, it is necessary to ensure that 7 mine trucks are in a transport state (non-charging state) at any time. The current time is 9 o'clock, and the charging scheduling of each mine truck in the next 4 hours (9:00-15:00) needs to be considered. Figure 4 Flow chart of another charging scheduling method for a mine truck fleet is shown. In an embodiment of the present application, the specific steps of a charging scheduling method for a mine truck fleet are as follows:

[0106] Step 1: Get the loading and unloading time of the mining trucks in the mining truck fleet to complete 10 loading and unloading. The loading and unloading time is [22min, 18min, 19min, 21min, 20min, 24min, 17min, 18min, 20min, 21min]. According to the loading and unloading time, the average loading and unloading time of the mining truck fleet is calculated to be 20min.

[0107] Step 2: Get the time interval of charging scheduling plan 9:00-15:00, slice the time interval according to the average loading and unloading time of 20 minutes, and obtain each time slice;

[0108] Step 3: Obtain the remaining power of each mining card in the mining truck fleet, and determine the mining card with a remaining power lower than the preset power threshold as the mining card to be charged;

[0109] Step 4: Determine the decision variable x based on each mining card to be charged and each time slice i,j ;

[0110] Step 5: Considering the limit on the number of charging piles (2 charging piles), in any time slice, the number of mining cards to be charged at the same time cannot be greater than the number of charging piles. The following constraint condition 1 is established:

[0111]

[0112] Where n represents the number of mining trucks in the mining truck fleet;

[0113] Step 6: Considering the production requirements of the mining area (in any time slice, at least 3 mining trucks need to be in the transport state at the same time), that is, the number of mining trucks charging at the same time needs to be less than (10-7=3), and the following constraint condition 2 is established:

[0114]

[0115] Step 7: With the goal of minimizing the charging cost of the entire mining truck fleet, determine the optimization goal:

[0116]

[0117] Among them, f(x i,j ) represents the electricity price function; m is the number of time slices.

[0118] Step 8: Construct a charging scheduling decision optimization model for the fleet based on decision variables, optimization objectives and constraints; that is, the charging cost of the mining truck fleet within the time interval of the charging scheduling plan is taken as the optimization goal, and the production requirements of the mining area and the number of charging piles are taken as constraints; the corresponding charging scheduling decision optimization model is as follows:

[0119]

[0120] st Any time slice j

[0121] Any time slice j

[0122] Step 9: Use integer nonlinear optimization algorithm to solve the charging scheduling decision optimization model to obtain the optimal charging scheduling planning result for the entire fleet; Figure 5 Schematic diagram of a charging scheduling planning result in an embodiment of the present application; Figure 5 As shown, mining card No. 1 has sufficient power during the time period of 9:00-15:00 in the charging scheduling plan, so it does not need to be charged.

[0123] Step 10: After determining the charging scheduling planning results, the mining cards to be charged are scheduled for charging in sequence according to the time sequence of the time slices in which the mining cards to be charged need to be charged. For example, mining card No. 2 is scheduled to go to charging pile 1 for charging after completing the current loading and unloading tasks between 9:00 and 9:20 (any charging pile can be selected from all idle charging piles), mining card No. 3 is scheduled to go to charging pile 2 for charging after completing the current loading and unloading tasks between 9:40 and 10:00, mining card No. 3 is scheduled to go to charging pile 1 for charging after completing the current loading and unloading tasks between 10:00 and 10:20, and mining card No. 4 is scheduled to go to charging pile 2 for charging after completing the current loading and unloading tasks between 10:40 and 11:00.

[0124] An embodiment of the present application provides a charging scheduling method for a mine truck fleet. The method constructs a charging scheduling decision optimization model for the mine truck fleet according to optimization objectives, constraints and decision variables, wherein the constraints include a limit on the number of charging piles; a charging scheduling planning result that achieves the optimization objective under the condition of a limit on the number of charging piles is determined according to the charging scheduling decision optimization model; therefore, the method can reasonably plan the charging time of each mine truck to be charged, avoid long-term queuing for charging of the mine truck to be charged, and improve the production efficiency of the mine truck fleet.

[0125] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] Figure 6 A schematic diagram of the structure of a charging scheduling device for a mine truck fleet provided in an embodiment of the present application.

[0127] like Figure 6 As shown, the charging scheduling device for the mine truck fleet of this embodiment includes:

[0128] The time slice determination module 610 is used to determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and use the slice duration to time slice the time interval to obtain the time slice;

[0129] A decision variable determination module 620 is used to determine a decision variable according to each mining truck to be charged and each time slice in the mining truck fleet;

[0130] The model determination module 630 is used to construct a charging scheduling decision optimization model for the mining truck fleet according to the optimization objectives, constraints and decision variables; wherein the constraints include the limit on the number of charging piles;

[0131] The scheduling planning result determination module 640 is used to determine the charging scheduling planning result corresponding to each mining card to be charged according to the charging scheduling decision optimization model.

[0132] A charging scheduling device for a mine truck fleet provided in an embodiment of the present application has the same beneficial effects as the charging scheduling method for a mine truck fleet described above.

[0133] In one embodiment, a charging scheduling device for a mining truck fleet further includes:

[0134] The slicing time determination module is used to obtain the average loading and unloading time of the mine truck fleet and determine the average loading and unloading time as the slicing time.

[0135] In one embodiment, the decision variable determination module 620 includes:

[0136] The remaining power acquisition submodule is used to obtain the remaining power corresponding to each mining truck in the mining truck fleet;

[0137] A submodule for determining a mining card to be charged, used to determine a mining card whose remaining power is lower than a preset power threshold as a mining card to be charged;

[0138] The decision variable determination submodule is used to determine the decision variables according to each mining card to be charged and each time slice.

[0139] In one of the embodiments, the constraints also include a mine production requirement.

[0140] In one embodiment, the optimization objective includes minimizing the charging cost of the mining truck fleet within a time interval.

[0141] In one embodiment, the scheduling planning result determination module 640 includes:

[0142] The calculation submodule is used to solve the charging scheduling decision optimization model using a nonlinear integer optimization algorithm and output the charging scheduling planning results corresponding to each mining truck to be charged.

[0143] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0144] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0145] Figure 7 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 7 As shown, the terminal device 700 of this embodiment includes a memory 701, a processor 702, and a computer program 703 stored in the memory 701 and executable on the processor 702; when the processor 702 executes the computer program 703, the steps in the above-mentioned charging scheduling method embodiments for each mine truck fleet are implemented; or when the processor 702 executes the computer program 703, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0146] Exemplarily, the computer program 703 may be divided into one or more modules / units, one or more modules / units are stored in the memory 701, and are executed by the processor 702 to implement the method of the embodiment of the present application. One or more modules / units may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program 703 in the terminal device 700. For example, the computer program 703 may be divided into a time slice determination module, a decision variable determination module, a model determination module, and a scheduling planning result determination module, and the specific functions of each module are as follows:

[0147] The time slice determination module is used to determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and use the slice duration to time slice the time interval to obtain the time slice;

[0148] A decision variable determination module is used to determine the decision variables according to each mining truck to be charged and each time slice in the mining truck fleet;

[0149] The model determination module is used to construct a charging scheduling decision optimization model for the mining truck fleet according to the optimization objectives, constraints and decision variables; wherein the constraints include the limit on the number of charging piles;

[0150] The scheduling planning result determination module is used to determine the charging scheduling planning result corresponding to each mining truck to be charged according to the charging scheduling decision optimization model.

[0151] In application, the terminal device 700 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 700 may include but is not limited to a memory 701 and a processor 702. Those skilled in the art will appreciate that Figure 7 It is only an example of a terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.; among which, the input and output devices may include cameras, audio acquisition / playback devices, display screens, etc.; the network access device may include a communication module for wireless communication with external devices.

[0152] In applications, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0153] In applications, the memory can be an internal storage unit of a terminal device, such as a hard disk or memory of the terminal device; it can also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.; it can also include both the internal storage unit and the external storage device of the terminal device. The memory is used to store operating systems, applications, boot loaders, data, and other programs, such as program codes of computer programs. The memory can also be used to temporarily store data that has been output or is about to be output.

[0154] Figure 8 This is a schematic diagram of the structure of a charging dispatching system for a mining truck fleet provided in an embodiment of the present application. Figure 8 As shown, the charging dispatching system of the mine truck fleet of this embodiment includes a mine truck fleet 810, a charging pile 820 and a terminal device 830; the mine truck fleet 810 includes a plurality of mine trucks, each of which is respectively connected to the terminal device 830 for communication; wherein,

[0155] The terminal device 830 is used to obtain the average loading and unloading time of the mine truck fleet 810; time slice the time interval of the charging scheduling plan according to the average loading and unloading time to obtain the time slice; determine the decision variables according to each mine truck to be charged in the mine truck fleet 810 and each time slice; construct a charging scheduling decision optimization model for the mine truck fleet 810 according to the optimization goal, constraint conditions and decision variables; wherein the constraint conditions include the number limit of charging piles 820; determine the charging scheduling planning results corresponding to each mine truck to be charged according to the charging scheduling decision optimization model; and send the charging scheduling planning results to the corresponding mine trucks to be charged;

[0156] The mining card to be charged is used to perform work tasks in the mining area, and after receiving the charging scheduling planning results, it performs corresponding charging operations according to the charging scheduling planning results;

[0157] The charging pile 820 is used to charge each mining card to be charged.

[0158] In this embodiment, the mining truck fleet 810 includes multiple mining cards, and the number of mining cards is greater than the number of charging piles 820; the terminal device 830 is connected to each mining card for communication, and information can be exchanged between the terminal device 830 and each mining card.

[0159] The embodiment of the present application provides a charging scheduling system for a mine truck fleet. Since the terminal device can construct a charging scheduling decision optimization model for the mine truck fleet according to the optimization objectives, constraints and decision variables, the constraints include the limit on the number of charging piles; the charging scheduling planning result that achieves the optimization objective under the condition of the limit on the number of charging piles is determined according to the charging scheduling decision optimization model; therefore, the system can reasonably plan the charging time of each mine truck to be charged, avoid the mine trucks to be charged queuing for a long time to charge, and improve the production efficiency of the mine truck fleet.

[0160] An embodiment of the present application also provides a charging pile, which includes a charging pile body and a controller, and the controller is used to execute the steps in the above-mentioned method embodiments.

[0161] A charging pile provided in an embodiment of the present application has the same beneficial effects as the above-mentioned charging scheduling method for a mining truck fleet.

[0162] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0163] A computer-readable storage medium provided in an embodiment of the present application has the same beneficial effects as the above-mentioned charging scheduling method for a mining truck fleet.

[0164] The present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0165] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0166] Those of ordinary skill in the art will appreciate that the devices and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices, which can be electrical, mechanical or other forms.

[0168] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 application, and should all be included in the protection scope of the present application.

Claims

1. A charging scheduling method for a mine truck fleet, characterized in that: The method comprises: Determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and use the slice duration to time slice the time interval to obtain a time slice; Determine a decision variable according to each of the mine trucks to be charged in the mine truck fleet and each of the time slices; Constructing a charging scheduling decision optimization model for the mining truck fleet according to the optimization objectives, constraints and the decision variables; wherein the constraints include a limit on the number of charging piles; The charging scheduling planning result corresponding to each of the mining trucks to be charged is determined according to the charging scheduling decision optimization model.

2. The method according to claim 1, characterized in that The method further comprises: The average loading and unloading time of the mine truck fleet is obtained, and the average loading and unloading time is determined as the slicing time.

3. The method according to claim 1, characterized in that The determining of decision variables according to each mining truck to be charged in the mining truck fleet and each time slice includes: Obtaining the remaining power corresponding to each mining truck in the mining truck fleet; Determine the mining card whose remaining power is lower than a preset power threshold as a mining card to be charged; The decision variables are determined according to each of the mining cards to be charged and each of the time slices.

4. The method according to claim 1, characterized in that: The constraints also include mine production requirements.

5. The method according to claim 1, characterized in that The optimization objective includes minimizing the charging cost of the mining truck fleet within the time interval.

6. The method according to any one of claims 1 to 5, characterized in that: The determining of the charging scheduling planning result corresponding to each of the mining trucks to be charged according to the charging scheduling decision optimization model includes: A nonlinear integer optimization algorithm is used to solve the charging scheduling decision optimization model, and a charging scheduling planning result corresponding to each of the mining trucks to be charged is output.

7. A charging dispatching device for a mine truck fleet, characterized in that: The device comprises: A time slice determination module is used to determine the time interval and slice duration of the charging scheduling plan of the mining truck fleet, and to time slice the time interval using the slice duration to obtain a time slice; A decision variable determination module, used for determining a decision variable according to each mining truck to be charged in the mining truck fleet and each of the time slices; A model determination module, used to construct a charging scheduling decision optimization model for the mine truck fleet according to the optimization goal, the constraint conditions and the decision variables; wherein the constraint conditions include a limit on the number of charging piles; The scheduling planning result determination module is used to determine the charging scheduling planning result corresponding to each of the mining trucks to be charged according to the charging scheduling decision optimization model.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A charging dispatching system for a mining truck fleet, characterized in that: The system includes a mining truck fleet, a charging pile and a terminal device; the mining truck fleet includes a plurality of mining cards, each of which is respectively connected to the terminal device for communication; wherein, The terminal device is used to obtain the average loading and unloading time of the mine truck fleet; time slice the time interval of the charging scheduling plan according to the average loading and unloading time to obtain the time slice; determine the decision variables according to each mine truck to be charged in the mine truck fleet and each time slice; construct the charging scheduling decision optimization model of the mine truck fleet according to the optimization goal, constraint conditions and the decision variables; wherein the constraint conditions include the limit on the number of charging piles; determine the charging scheduling planning results corresponding to each mine truck to be charged according to the charging scheduling decision optimization model; and send the charging scheduling planning results to the corresponding mine trucks to be charged; The mining card to be charged is used to perform work tasks in the mining area, and after receiving the charging scheduling planning result, performs corresponding charging operations according to the charging scheduling planning result; The charging pile is used to charge each of the mining cards to be charged.

10. A charging pile, comprising a charging pile body, characterized in that: It also includes a controller, which is used to execute the steps of the method according to any one of claims 1 to 6.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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