Method and equipment for determining transport truck configuration and electric mining truck dispatching plan

Abstract

The present application proposes a method and device for determining a transport cart configuration and an electric mining truck scheduling scheme, wherein the method includes: establishing a traffic flow planning and transport cart configuration optimization model, and solving the traffic flow planning and transport cart configuration optimization model to obtain an optimal traffic flow planning scheme and an optimal transport cart quantity configuration scheme, and establishing an electric mining truck scheduling optimization model for an open-pit mine based on the transportation task set of heavy-load vehicles in the optimal vehicle planning scheme, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery swap stations in the open-pit mine, and solving the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the future time period. Thus, without manual participation, the target travel chain of each electric mining truck can be quickly determined by combining the traffic flow planning and transport cart configuration optimization model of the open-pit mine and the electric mining truck scheduling optimization model.

Description

Technical Field

[0001] The present application relates to the field of mining technology, and in particular to a method and device for determining a transport truck configuration and an electric mining truck dispatching plan. Background Art

[0002] At present, in open-pit mines, in order to reduce the impact on the environment, electric mining trucks are usually used to transport materials that need to be transported in open-pit mines.

[0003] In the related art, the itinerary chain of electric mining trucks in open-pit mines in the future time period is usually determined manually based on the dispatcher's experience and intuition, and the electric mining trucks are controlled according to the itinerary chain. However, this method of manually formulating the itinerary chain of electric mining trucks based on the dispatcher's experience and intuition lacks scientific data support, making it difficult to achieve optimal scheduling, and easily causing the electric mining trucks to have queue delays that cannot be ignored during the operation process, which in turn leads to low transportation efficiency of electric mining trucks in open-pit mines. Summary of the invention

[0004] The present application proposes a method and device for determining a transport truck configuration and an electric mining truck dispatching plan.

[0005] On one hand, an embodiment of the present application proposes a method for determining a transport cart configuration and an electric mining truck scheduling scheme, the method comprising: obtaining a first decision variable group of a traffic flow planning and transport cart configuration optimization model of an open-pit mine; establishing the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; solving the traffic flow planning and transport cart configuration optimization model to obtain an optimal traffic flow planning scheme and an optimal transport cart quantity configuration scheme for the open-pit mine in a future time period after a current moment, wherein the optimal traffic flow planning scheme comprises: a transport task set, wherein the transport task set comprises: transport tasks for each heavy-load vehicle trip; establishing an electric mining truck scheduling optimization model for the open-pit mine according to the transport task set, a set of dispatchable electric mining trucks in the open-pit mine, and a set of battery swap stations in the open-pit mine; solving the electric mining truck scheduling optimization model to obtain a target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0006] The method for determining the transport cart configuration and electric mining truck scheduling plan of the embodiment of the present application obtains the first decision variable group of the traffic flow planning and transport cart configuration optimization model of the open-pit mine; establishes the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; solves the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning plan and the optimal transport cart quantity configuration plan for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning plan includes: a transportation task set, wherein the transportation task set includes: the transportation tasks of each heavy-load vehicle trip; establishes an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery swap stations in the open-pit mine; solves the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period. Therefore, without human intervention, by combining the open-pit mine traffic planning and transport cart configuration optimization model and the electric mine truck scheduling optimization model, the target trip chain corresponding to each electric mine truck in the open-pit mine in the future time period can be quickly determined, which is convenient for the subsequent accurate scheduling of the electric mine trucks in the open-pit mine based on the target trip chain, thereby reducing the queuing phenomenon of electric mine trucks in the open-pit mine and improving the transportation efficiency of electric mine trucks in the open-pit mine.

[0007] On the other hand, an embodiment of the present application proposes a device for determining a transport cart configuration and an electric mining truck scheduling scheme, the device comprising: an acquisition module for acquiring a first decision variable group of a traffic flow planning and transport cart configuration optimization model of an open-pit mine; a first model building module for building the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; a first solution module for solving the traffic flow planning and transport cart configuration optimization model to obtain an optimal traffic flow planning scheme and an optimal transport cart quantity configuration scheme for the open-pit mine in a future time period after a current moment, wherein the optimal traffic flow planning scheme comprises a transport task set, wherein the transport task set comprises: transport tasks for each heavy-load vehicle trip; a second model building module for building an electric mining truck scheduling optimization model for the open-pit mine according to the transport task set, a set of dispatchable electric mining trucks in the open-pit mine, and a set of battery swap stations in the open-pit mine; a second solution module for solving the electric mining truck scheduling optimization model to obtain a target trip chain acquisition module corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0008] The device for determining the transport cart configuration and electric mining truck scheduling plan of the embodiment of the present application obtains the first decision variable group of the traffic flow planning and transport cart configuration optimization model of the open-pit mine; establishes the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; solves the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning plan and the optimal transport cart quantity configuration plan for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning plan includes: a transportation task set, wherein the transportation task set includes: the transportation tasks of each heavy-load vehicle trip; establishes the electric mining truck scheduling optimization model of the open-pit mine according to the transportation task set, the electric mining truck set that can be dispatched in the open-pit mine and the battery swap station set in the open-pit mine; solves the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period. Therefore, without human intervention, by combining the open-pit mine traffic planning and transport cart configuration optimization model and the electric mine truck scheduling optimization model, the target trip chain corresponding to each electric mine truck in the open-pit mine in the future time period can be quickly determined, which is convenient for the subsequent accurate scheduling of the electric mine trucks in the open-pit mine based on the target trip chain, thereby reducing the queuing phenomenon of electric mine trucks in the open-pit mine and improving the transportation efficiency of electric mine trucks in the open-pit mine.

[0009] On the other hand, an embodiment of the present application proposes an electronic device, including: a memory, a processor, and computer execution instructions stored in the memory and executable on the processor, and when the processor executes the computer execution instructions, it implements the method for determining the transport cart configuration and electric mining truck scheduling plan of the embodiment of the present application.

[0010] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer instructions are used to enable the computer to execute the method for determining the transport truck configuration and the electric mining truck scheduling plan disclosed in the embodiment of the present application.

[0011] Another aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining the transport truck configuration and the electric mining truck scheduling plan in the embodiment of the present application.

[0012] Other effects of the above optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0014] Figure 1 It is a flowchart of a method for determining a transport cart configuration and an electric mining truck scheduling plan according to an embodiment of the present application.

[0015] Figure 2 It is a detailed flowchart of step 102 according to an embodiment of the present application.

[0016] Figure 3 It is a detailed flowchart of step 104 according to an embodiment of the present application.

[0017] Figure 4 It is a flow chart for determining the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0018] Figure 5 It is a structural schematic diagram of a device for determining a transport cart configuration and an electric mining truck scheduling plan according to an embodiment of the present application.

[0019] Figure 6 is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] Embodiments of the present invention are described in detail below, and examples of the embodiments 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 application, and should not be construed as limiting the present application.

[0021] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0022] The following describes the method and equipment for determining the transport cart configuration and electric mining truck scheduling plan according to the embodiments of the present application with reference to the accompanying drawings.

[0023] Figure 1 It is a flowchart of a method for determining a transport cart configuration and an electric mining truck scheduling plan according to an embodiment of the present application.

[0024] Among them, it should be noted that the executor of the method for determining the transport cart configuration and the electric mine truck scheduling plan provided in this embodiment is a device for determining the transport cart configuration and the electric mine truck scheduling plan, and the device for determining the transport cart configuration and the electric mine truck scheduling plan can be implemented by software and / or hardware. The device for determining the transport cart configuration and the electric mine truck scheduling plan in this embodiment can be an electronic device, or can be configured in an electronic device.

[0025] Among them, the electronic device in this embodiment can be any device with computing capabilities, such as a personal computer, a mobile terminal, a server, etc. The mobile terminal can be, for example, a mobile phone, a tablet computer, a personal digital assistant, and other hardware devices with various operating systems, touch screens and / or display screens. This embodiment does not specifically limit the electronic device.

[0026] like Figure 1 As shown, the method for determining the transport truck configuration and the electric mining truck scheduling scheme may include:

[0027] Step 101, obtaining a first decision variable group of a traffic flow planning and transport truck configuration optimization model for an open-pit mine.

[0028] In this embodiment, the first decision variable group may include multiple decision variables.

[0029] Step 102: establishing a vehicle flow planning and transport truck configuration optimization model based on the first decision variable group.

[0030] In some embodiments, the first decision variable group in this embodiment includes the following decision variables:

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] in, represents the collection of loading areas of an open pit mine, It indicates the total number of loading areas in the open pit mine; represents the collection of unloading areas of an open-pit mine, represents the total number of unloading areas in the open-pit mine; decision variable Indicates the electric mining truck in the open pit from the loading area Heavy load to unloading area ; Decision variables Indicates the electric mining truck in the open pit from the loading area Heavy load does not go to unloading area ; Decision variables Indicates the electric mining truck in the open pit mine from the unloading area Go to the loading area without a load ; Decision variables Indicates the electric mining truck in the open pit mine from the unloading area No load, no loading area ; Decision variables From the loading area To the unloading area The number of heavy-load train trips; decision variables Indicates the unloading area To the loading area The number of empty train trips.

[0036] Correspondingly, when the first decision variable group includes the above decision variables, in order to accurately establish the vehicle flow planning and transport vehicle configuration optimization model, a possible implementation of the above step 102 is as follows: Figure 2 shown.

[0037] Figure 2 It is a detailed flowchart of step 102 according to an embodiment of the present application.

[0038] like Figure 2 As shown, the above step 102 may include:

[0039] Step 1021, based on the decision variables in the first decision variable group , establish the first objective function of the traffic flow planning and transport cart configuration optimization model.

[0040] Among them, the expression of the first objective function is:

[0041]

[0042] Among them, in formula (5) Indicates the revenue of a heavy-load trip. Indicates the cost per transport truck, It represents the ratio between the number of transport trucks and the total number of trips. The total number of trips = .

[0043] Among them, formula (5) represents maximizing profit.

[0044] Step 1022, based on the first decision variable group, establish multiple first constraint conditions of the vehicle flow planning and transport truck configuration optimization model.

[0045] The first constraints include the following constraints:

[0046]

[0047]

[0048] in, and Respectively indicate the loading area Single loading time and unloading area Single unloading time, represents the working time of each shift in the open-pit mine; Formula (6) represents the working time of each loading area The sum of the loading times of all electric mining trucks does not exceed the operating time of each shift in the open-pit mine, and formula (7) shows that in each unloading area The sum of the unloading time of all electric mining trucks does not exceed the operating time of each shift in the open pit mine;

[0049]

[0050]

[0051] Wherein, formula (8) represents the The number of loaded trains leaving is equal to the number of unloaded trains entering, and formula (9) indicates that The number of incoming loaded trains is equal to the number of outgoing empty trains;

[0052]

[0053] Among them, in formula (10) Represents an electric mining truck in an open pit mine moving from the loading area Run to unloading area the time required; Represents an electric mining truck in an open pit mine moving from the unloading area Run to loading area The time required; Formula (10) indicates that the sum of the operating time of all empty vehicle trips and loaded vehicle trips does not exceed the total operating time of all electric mining trucks in the open-pit mine, where the total operating time is ,in, express Multiply ,in, The number of electric mining trucks that can be dispatched in the open-pit mine;

[0054]

[0055]

[0056] Formula (11) specifies the decision variables and decision variables The relationship between ; Formula (12) specifies the decision variables and decision variables The relationship between , where M is an infinite positive integer;

[0057]

[0058]

[0059] Formula (13) represents the loading area The number of matches with all unloading areas does not exceed the specified value corresponding to loading area i , formula (14) represents the unloading area The number of matches of all loading areas does not exceed the specified value of unloading area j .

[0060] Among them, it should be noted that the electric mining trucks in the open-pit mines are Run to unloading area The required time can be obtained by: obtaining the loading area from the road network data of the open pit truck Unloading area The road distance between the two areas is calculated, and the average heavy-load driving speed of the electric mining truck in the open-pit mine is obtained. According to the road distance and the average heavy-load driving speed, the electric mining truck is determined to leave the loading area. Run to unloading area The time required.

[0061] Among them, it should be noted that the electric mining trucks in the open-pit mines are Run to loading area The time required is obtained by obtaining the time from the unloading area from the road network data of the open pit truck. With loading area The road distance between the two areas is calculated, and the average no-load speed of the electric mining truck in the open-pit mine is obtained, and the time required for the electric mining truck to leave the loading area is determined based on the road distance and the average no-load speed. Run to unloading area The time required.

[0062] Step 1023, establishing a vehicle flow planning and transport cart configuration optimization model including a first decision variable group, a first objective function and a plurality of first constraint conditions.

[0063] Step 103, solve the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning scheme includes: a transport task set, wherein the transport task set includes: the transport tasks of each heavy-load vehicle.

[0064] In this embodiment, the traffic flow planning and transport cart configuration optimization model can be solved based on the objective function and all the first constraints to obtain the optimal solution of each decision variable in the first decision variable group, and based on the optimal solution of each decision variable in the first decision variable group, the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment are obtained.

[0065] In some embodiments, in order to accurately obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment, when solving the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment, the current service capacity limitation information corresponding to the battery swap station in the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period can also be considered. Correspondingly, a possible implementation method of the above step 103 is: obtaining the current service capacity limitation information corresponding to the battery swap station of the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period; considering the current service capacity limitation information corresponding to the battery swap station of the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period, the traffic flow planning and transport cart configuration optimization model is solved to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment.

[0066] As an example, the objective function can be solved in combination with the current service capacity limitation information corresponding to the battery swap station in the open-pit mine at the current moment, the loading plan quantities corresponding to each loading area in the open-pit mine in the future time period, the unloading plan quantities corresponding to each unloading area in the open-pit mine in the future time period, and multiple first constraints to obtain the optimal solution of each decision variable in the first decision variable group, and based on the optimal solution of each decision variable in the first decision variable group, the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment are obtained.

[0067] In this embodiment, the length of the future time period may be one hour, or 30 minutes, etc. This embodiment does not specifically limit the length of the future time period.

[0068] It should be noted that the optimal traffic flow planning scheme in this embodiment includes not only the transportation tasks of each heavily loaded vehicle, but also other information, such as the transportation tasks of empty vehicles.

[0069] It should be noted that the optimal transport truck quantity configuration scheme in this embodiment refers to the number of transport trucks required when the value of the first objective function is optimal.

[0070] It should be noted that, in this embodiment, the open-pit mine includes a battery swap station and also includes a charging station matching the battery swap station.

[0071] Among them, it should be noted that the battery swap station in this embodiment is not equipped with a power supply. Correspondingly, a transport cart can be configured between the battery swap station and the charging station. The transport cart is used to transport the battery packs to be charged to the charging station so that the battery packs to be charged will be charged by the charging station. Correspondingly, the transport cart is also used to transport the charged battery packs in the charging station to the battery swap station for battery swapping by electric mining trucks in open-pit mines.

[0072] As an example, a charging station in a charging station in an open-pit mine can serve all battery packs in a transport truck. A transport truck can include five battery packs, where the charging time for each battery pack is 30 minutes on average. The battery pack is determined to be fully charged when the state of charge (SOC) of the battery pack is greater than 95%.

[0073] It should be noted that, in this embodiment, an example is given in which the transport truck is driven to a charging station for charging only after all battery packs in the truck are replaced.

[0074] Step 104, establishing an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery swap stations in the open-pit mine.

[0075] In this embodiment, in order to accurately obtain the electric mining truck scheduling optimization model for open-pit mines, the following is combined with Figure 3 A possible implementation of the above step 104 is described exemplarily.

[0076] Figure 3 It is a detailed flowchart of step 104 according to an embodiment of the present application.

[0077] like Figure 3 As shown, the above step 104 may include:

[0078] Step 1041, determining a second decision variable group of the electric mining truck scheduling optimization model according to the transportation task set, the set of dispatchable electric mining trucks in the open-pit mine, and the set of battery swap stations in the open-pit mine.

[0079] The second decision variable group includes the following decision variables:

[0080]

[0081]

[0082] in, represents a collection of electric mining trucks in an open-pit mine, represents a set of transport tasks, Indicates the collection of battery replacement stations in open-pit mines. represents the total number of battery swap stations in the open-pit mine; decision variable Represents an electric mining truck from the electric mining trucks collection Complete the transportation mission Later, it carried out transportation tasks , and the decision variable Indicates electric mining truck Complete the transportation mission Then choose to switch to a power station Replacement battery;

[0083] Step 1042, based on the decision variables in the second decision variable group , the second objective function of the electric mining truck scheduling optimization model is established.

[0084] Among them, the expression of the second objective function is:

[0085]

[0086] Among them, formula (17) means to maximize the number of completed transportation tasks;

[0087] It should be noted that formula (7) can be equivalently converted into minimizing the queuing waiting time.

[0088] Step 1043, establishing a plurality of second constraint conditions of the electric mining truck scheduling optimization model according to the second decision variable group.

[0089] The second constraints include the following constraints:

[0090]

[0091] Among them, formula (18) indicates that the number of vehicles performing any transportation task will not exceed one; that is, formula (18) can ensure the uniqueness of the vehicle performing the transportation task.

[0092]

[0093] Among them, formula (19) indicates that the vehicle that performs two adjacent transport tasks in the transport task set is the same vehicle;

[0094]

[0095] Among them, in formula (20) Indicates transportation task The starting point, Indicates transportation The end point of the previous transport task is the starting point of the next transport task;

[0096]

[0097]

[0098] in, It's an electric mining truck Carry out transportation tasks The previous power, It's an electric mining truck Carry out transportation tasks After that, the power It's an electric mining truck The power battery capacity, It is a transportation task The power consumption of electric mining trucks is shown in formula (21). Carry out transportation tasks Previous Power and Electric Mining Trucks Carry out transportation tasks The relationship between the subsequent power consumption; Formula (22) stipulates that the transportation task The power after that is If electric mining trucks After the battery is replaced, the power after the transportation task is , where M is an infinite positive integer;

[0099] ,

[0100] Among them, in formula (23) represents the minimum limit value of the battery state of charge, and formula (23) represents the electric mining truck Carry out transportation tasks The previous power should be no less than ;

[0101]

[0102]

[0103]

[0104] Wherein, formula (24) indicates that the battery inventory in the battery swap station s is equal to block; among them, Indicates battery replacement station Complete the transportation mission The number of fully charged batteries in the rear station, Indicates battery replacement station Complete the transportation mission The number of depleted batteries in the rear station; represents the number of fully charged batteries in the battery swap station s after it completes the previous transport task adjacent to transport task k; represents the number of depleted batteries in the battery swap station s after completing the previous transport task adjacent to transport task k; Formula (25) represents the number of depleted batteries in the battery swap station after the electric mining truck v is swapped The number of fully charged batteries in the battery is reduced by 1; Formula (26) represents the electric mining truck v after the battery is replaced by the battery replacement station The number of depleted batteries increases by 1. Step 1044, establishing an electric mining truck scheduling optimization model including a second decision variable group, a second objective function and a plurality of second constraints.

[0105] Step 105 , solving the electric mining truck scheduling optimization model to obtain the target trip chain corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0106] The target trip chain includes the trips of the corresponding electric mining truck at various time points in the future time period.

[0107] For example, the target trip chain corresponding to an electric mining truck t in the future time period can be expressed as ,in, , represents the travel distance of electric mining truck t at the kth time point in the future time period, where Indicates the future time period time points, among which The value of is the same as the total number of time points in the future time period, where it should be noted that the trip of the electric mining truck t at the kth time point in the future time period can be a transportation task or a battery replacement task.

[0108] In one embodiment of the present application, in order to accurately solve the electric mine truck scheduling optimization model, the electric mine truck scheduling optimization model can be solved in combination with the current relevant information of the open-pit mine at the current moment. Correspondingly, a possible implementation method of the above step 105 is: obtaining the current travel information and current status information corresponding to each electric mine truck in the electric mine truck set at the current moment; obtaining the completion status data of the optimal vehicle planning scheme of the open-pit mine in the target time period, wherein the target time period is the time period of the current moment, wherein the target time period and the future time period are adjacent time periods; obtaining the information of each battery swap station in the open-pit mine. The current service capacity limitation information corresponding to the current moment is obtained, and the current service capacity limitation information corresponding to the charging station bound to each battery swap station in the open-pit mine is obtained; taking into account the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information corresponding to each electric mine truck in the electric mine truck set at the current moment, and the current status information, the electric mine truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mine truck in the electric mine truck set in the future time period.

[0109] Specifically, the electric mine truck scheduling optimization model can be solved in combination with the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information and current status information corresponding to each electric mine truck in the electric mine truck set at the current moment, and multiple second constraints to obtain the target travel chain corresponding to each electric mine truck in the electric mine truck set in the future time period.

[0110] In this embodiment, in combination with the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information and current status information corresponding to each electric mining truck in the electric mining truck set at the current moment, and multiple second constraints, a parallel genetic algorithm is used to solve the second objective function to obtain the optimal solution of each decision variable in the second decision variable group, and in combination with the optimal solution of each decision variable in the second decision variable group, the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period is obtained.

[0111] Among them, the current service capacity limitation information corresponding to the battery swap station at the current moment is used to indicate the battery swap capacity limitation corresponding to the battery swap station at the current moment. For example, the current service capacity limitation information may include but is not limited to: the current battery inventory corresponding to the battery swap station at the current moment.

[0112] The method for determining the transport cart configuration and electric mining truck scheduling scheme provided in the embodiment of the present application obtains the first decision variable group of the traffic flow planning and transport cart configuration optimization model of the open-pit mine; establishes the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; solves the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning scheme includes: a transportation task set, wherein the transportation task set includes: the transportation tasks of each heavy-load vehicle trip; establishes the electric mining truck scheduling optimization model of the open-pit mine according to the transportation task set, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery swap stations in the open-pit mine; solves the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period. Therefore, without human intervention, by combining the open-pit mine traffic planning and transport cart configuration optimization model and the electric mine truck scheduling optimization model, the target trip chain corresponding to each electric mine truck in the open-pit mine in the future time period can be quickly determined, which is convenient for the subsequent accurate scheduling of the electric mine trucks in the open-pit mine based on the target trip chain, thereby reducing the queuing phenomenon of electric mine trucks in the open-pit mine and improving the transportation efficiency of electric mine trucks in the open-pit mine.

[0113] In order to clearly understand the process of combining the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current trip information and current state information corresponding to each electric mining truck in the electric mining truck set at the current moment, and multiple second constraints, a parallel genetic algorithm is used to solve the second objective function to obtain the target trip chain corresponding to each electric mining truck in the electric mining truck set in the future time period. Figure 4 , an exemplary description is given of the process of determining the target trip chains corresponding to each electric mining truck in the electric mining truck set in the future time period:

[0114] like Figure 4 As shown, this may include:

[0115] Step 401, obtain the current travel information and current status information corresponding to each electric mining truck in the electric mining truck set at the current moment, the completion data of the optimal vehicle planning scheme of the open-pit mine in the target time period, the current service capacity limitation information corresponding to each battery swap station in the open-pit mine at the current moment, and the current service capacity limitation information corresponding to the charging station bound to each battery swap station in the open-pit mine at the current moment.

[0116] The target time period is the time period of the current moment, and the target time period and the future time period are adjacent time periods.

[0117] Step 402, combining the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current trip information corresponding to each electric mining truck in the electric mining truck set at the current moment, and the current status information, determines the initial trip chain corresponding to each electric mining truck in the future time period.

[0118] Step 403, using a parallel genetic algorithm, solves the second objective function according to the initial trip chain and multiple second constraints, so as to use the trip chains corresponding to each electric mining truck in the future time period when the second objective function is optimal as the target trip chains corresponding to each electric mining truck in the future time period.

[0119] In this embodiment, the second objective function can be solved based on a parallel genetic algorithm according to the initial trip chain and multiple second constraints, so that the trip chain corresponding to each electric mining truck in the future time period when the second objective function is optimized can be used as the target trip chain corresponding to each electric mining truck in the future time period.

[0120] Specifically, in the parallel genetic algorithm, the crossover between populations and the mutation rules within populations are defined: 1) the trips assigned to different electric mining trucks are exchanged; 2) the loading area or unloading area assigned to the electric mining trucks is randomly replaced. After that, the algorithm continuously iterates to evolve the population, and outputs the optimal solution when the iteration limit is reached.

[0121] Step 404 : Based on the target travel chains corresponding to the electric mining trucks in the future time period, corresponding control instructions are sent to the corresponding electric mining trucks to control the corresponding electric mining trucks.

[0122] Corresponding to the methods for determining the transport cart configuration and the electric mine truck scheduling plan provided in the above-mentioned embodiments, an embodiment of the present application also provides a device for determining the transport cart configuration and the electric mine truck scheduling plan. Since the device for determining the transport cart configuration and the electric mine truck scheduling plan provided in the embodiment of the present application corresponds to the methods for determining the transport cart configuration and the electric mine truck scheduling plan provided in the above-mentioned embodiments, the implementation method of the method for determining the transport cart configuration and the electric mine truck scheduling plan is also applicable to the device for determining the transport cart configuration and the electric mine truck scheduling plan provided in this embodiment, and will not be described in detail in this embodiment.

[0123] Figure 5 It is a structural schematic diagram of a device for determining a transport cart configuration and an electric mining truck scheduling plan according to an embodiment of the present application.

[0124] like Figure 5 As shown, the device 500 for determining the transport truck configuration and the electric mining truck scheduling scheme includes: an acquisition module 501, a first model building module 502, a first solution module 503, a second model building module 504 and a second solution module 505, wherein:

[0125] The acquisition module 501 is used to acquire the first decision variable group of the traffic flow planning and transport truck configuration optimization model of the open-pit mine.

[0126] The first model building module 502 is used to build a vehicle flow planning and transport truck configuration optimization model according to the first decision variable group.

[0127] The first solution module 503 is used to solve the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning scheme includes: a transport task set, wherein the transport task set includes: the transport tasks of each heavy-load vehicle.

[0128] The second model building module 504 is used to build an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery replacement stations in the open-pit mine.

[0129] The second solving module 505 is used to solve the electric mining truck scheduling optimization model to obtain the target trip chain corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0130] In one embodiment of the present application, the first decision variable group includes the following decision variables:

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] in, represents the collection of loading areas of an open pit mine, It indicates the total number of loading areas in the open pit mine; represents the collection of unloading areas of an open-pit mine, represents the total number of unloading areas in the open-pit mine; decision variable Indicates the electric mining truck in the open pit from the loading area Heavy load to unloading area ; Decision variables Indicates the electric mining truck in the open pit from the loading area Heavy load does not go to unloading area ; Decision variables Indicates the electric mining truck in the open pit mine from the unloading area Go to the loading area without a load ; Decision variables Indicates the electric mining truck in the open pit mine from the unloading area No load, no loading area ; Decision variables From the loading area To the unloading area The number of heavy-load train trips; decision variables Indicates the unloading area To the loading area The number of empty train trips;

[0136] The first model building module 502 is specifically used for:

[0137] According to the decision variables in the first decision variable group , the first objective function of the vehicle flow planning and transport cart configuration optimization model is established, where the expression of the first objective function is:

[0138]

[0139] Among them, in formula (5) Indicates the revenue of a heavy-load trip. Indicates the cost per transport truck, It represents the ratio between the number of transport trucks and the total number of trips. The total number of trips = ;

[0140] According to the first decision variable group, a plurality of first constraint conditions of the vehicle flow planning and transport vehicle configuration optimization model are established, wherein the plurality of first constraint conditions include the following constraint conditions:

[0141]

[0142]

[0143] in, and Respectively indicate the loading area Single loading time and unloading area Single unloading time, represents the working time of each shift in the open-pit mine; Formula (6) represents the working time of each loading area The sum of the loading times of all electric mining trucks does not exceed the operating time of each shift in the open-pit mine, and formula (7) shows that in each unloading area The sum of the unloading time of all electric mining trucks does not exceed the operating time of each shift in the open pit mine;

[0144]

[0145]

[0146] Wherein, formula (8) represents the The number of loaded trains leaving is equal to the number of unloaded trains entering, and formula (9) indicates that The number of incoming loaded trains is equal to the number of outgoing empty trains;

[0147]

[0148] Among them, in formula (10) Represents an electric mining truck in an open pit mine moving from the loading area Run to unloading area the time required; Represents an electric mining truck in an open pit mine moving from the unloading area Run to loading area The time required; Formula (10) indicates that the sum of the operating time of all empty vehicle trips and loaded vehicle trips does not exceed the total operating time of all electric mining trucks in the open-pit mine, where the total operating time is ,in, express Multiply ,in, The number of electric mining trucks that can be dispatched in the open-pit mine;

[0149]

[0150]

[0151] Formula (11) specifies the decision variables and decision variables The relationship between ; Formula (12) specifies the decision variables and decision variables The relationship between , where M is an infinite positive integer;

[0152]

[0153]

[0154] Formula (13) represents the loading area The number of matches with all unloading areas does not exceed the specified value corresponding to loading area i , formula (14) represents the unloading area The number of matches of all loading areas does not exceed the specified value of unloading area j ;

[0155] A vehicle flow planning and transport cart configuration optimization model including a first decision variable group, a first objective function and a plurality of first constraint conditions is established.

[0156] In one embodiment of the present application, the first solution module 503 is specifically used to:

[0157] Obtain the current service capacity limitation information corresponding to the battery swap station in the open-pit mine at the current moment, the loading plan corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan corresponding to each unloading area in the open-pit mine in the future time period;

[0158] Taking into account the current service capacity limitation information of the battery swap station in the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period, the traffic flow planning and transport cart configuration optimization model is solved to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment.

[0159] In one embodiment of the present application, the second model building module 504 is specifically used to:

[0160] According to the transport task set, the set of dispatchable electric mining trucks in the open-pit mine, and the set of battery swap stations in the open-pit mine, a second decision variable group of the electric mining truck scheduling optimization model is determined, wherein the second decision variable group includes the following decision variables:

[0161]

[0162]

[0163] in, represents a collection of electric mining trucks in an open-pit mine, represents a set of transport tasks, Indicates the collection of battery replacement stations in open-pit mines. represents the total number of battery swap stations in the open-pit mine; decision variable Represents an electric mining truck from the electric mining trucks collection Complete the transportation mission Later, it carried out transportation tasks , and the decision variable Indicates electric mining truck Complete the transportation mission Then choose to switch to a power station Replacement battery;

[0164] According to the decision variables in the second decision variable group , the second objective function of the electric mining truck scheduling optimization model is established, where the expression of the second objective function is:

[0165]

[0166] Among them, formula (17) means to maximize the number of completed transportation tasks;

[0167] According to the second decision variable group, a plurality of second constraints of the electric mining truck scheduling optimization model are established, wherein the plurality of second constraints include the following constraints:

[0168]

[0169] Among them, formula (18) indicates that the number of vehicles performing any transportation task will not exceed one;

[0170]

[0171] Among them, formula (19) indicates that the vehicle that performs two adjacent transport tasks in the transport task set is the same vehicle;

[0172]

[0173] Among them, in formula (20) Indicates transportation task The starting point, Indicates transportation The end point of the previous transport task is the starting point of the next transport task;

[0174]

[0175]

[0176] in, It's an electric mining truck Carry out transportation tasks The previous power, It's an electric mining truck Carry out transportation tasks After that, the power It's an electric mining truck The power battery capacity, It is a transportation task The power consumption of electric mining trucks is shown in formula (21). Carry out transportation tasks Previous Power and Electric Mining Trucks Carry out transportation tasks The relationship between the subsequent power consumption; Formula (22) stipulates that the transportation task The power after that is If electric mining trucks After the battery is replaced, the power after the transportation task is , where M is an infinite positive integer;

[0177] ,

[0178] Among them, in formula (23) represents the minimum limit value of the battery state of charge, and formula (23) represents the electric mining truck Carry out transportation tasks The previous power should be no less than ;

[0179]

[0180]

[0181]

[0182] Wherein, formula (24) indicates that the battery inventory in the battery swap station s is equal to block; among them, Indicates battery replacement station Complete the transportation mission The number of fully charged batteries in the rear station, Indicates battery replacement station Complete the transportation mission The number of depleted batteries in the rear station; represents the number of fully charged batteries in the battery swap station s after it completes the previous transport task adjacent to transport task k; represents the number of depleted batteries in the battery swap station s after completing the previous transport task adjacent to transport task k; Formula (25) represents the number of depleted batteries in the battery swap station after the electric mining truck v is swapped The number of fully charged batteries in the battery is reduced by 1; Formula (26) represents the electric mining truck v after the battery is replaced by the battery replacement station The number of internal depleted batteries increases by 1;

[0183] An electric mining truck scheduling optimization model including a second decision variable group, a second objective function and a plurality of second constraint conditions is established.

[0184] In one embodiment of the present application, the second solution module 505 is specifically used to: obtain the current travel information and current status information corresponding to each electric mining truck in the electric mining truck set at the current moment; obtain the completion data of the optimal vehicle planning scheme for the open-pit mine in the target time period, wherein the target time period is the time period in which the current moment is located, wherein the target time period and the future time period are adjacent time periods; obtain the current service capacity limitation information corresponding to each battery swap station in the open-pit mine at the current moment, and obtain the current service capacity limitation information corresponding to the charging station bound to each battery swap station in the open-pit mine at the current moment; considering the completion data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information corresponding to each electric mining truck in the electric mining truck set at the current moment, and the current status information, the electric mining truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period.

[0185] It should be noted that, in this embodiment, the time lengths corresponding to the target time period and the future time period are the same.

[0186] The device for determining the transport cart configuration and electric mining truck scheduling plan of the embodiment of the present application obtains the first decision variable group of the traffic flow planning and transport cart configuration optimization model of the open-pit mine; establishes the traffic flow planning and transport cart configuration optimization model according to the first decision variable group; solves the traffic flow planning and transport cart configuration optimization model to obtain the optimal traffic flow planning plan and the optimal transport cart quantity configuration plan for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning plan includes: a transportation task set, wherein the transportation task set includes: the transportation tasks of each heavy-load vehicle trip; establishes the electric mining truck scheduling optimization model of the open-pit mine according to the transportation task set, the electric mining truck set that can be dispatched in the open-pit mine and the battery swap station set in the open-pit mine; solves the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period. Therefore, without human intervention, by combining the open-pit mine traffic planning and transport cart configuration optimization model and the electric mine truck scheduling optimization model, the target trip chain corresponding to each electric mine truck in the open-pit mine in the future time period can be quickly determined, which is convenient for the subsequent accurate scheduling of the electric mine trucks in the open-pit mine based on the target trip chain, thereby reducing the queuing phenomenon of electric mine trucks in the open-pit mine and improving the transportation efficiency of electric mine trucks in the open-pit mine.

[0187] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0188] like Figure 6 , which is a block diagram of an electronic device according to an embodiment of the present application.

[0189] like Figure 6 As shown, the electronic device includes:

[0190] Memory 601 , processor 602 , and computer instructions stored in the memory 601 and executable on the processor 602 .

[0191] When the processor 602 executes the instructions, the method for determining the transport truck configuration and the electric mining truck scheduling scheme provided in the above embodiments is implemented.

[0192] Furthermore, the electronic device further comprises:

[0193] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0194] The memory 601 is used to store computer instructions that can be executed on the processor 602 .

[0195] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0196] The processor 602 is used to implement the method for determining the transport truck configuration and the electric mining truck scheduling plan of the above embodiment when executing the program.

[0197] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0198] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0199] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0200] The present application also proposes a computer program product, including a computer program, which, when executed by a processor, implements a method for determining a transport truck configuration and an electric mining truck scheduling plan in an embodiment of the present application.

[0201] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representations 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, without contradiction.

[0202] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0203] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 not be performed in 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 technicians in the technical field to which the embodiments of the present application belong.

[0204] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, 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 otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0205] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0206] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0207] In addition, each functional unit in each embodiment 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 one 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.

[0208] 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for determining a transport truck configuration and an electric mining truck scheduling scheme, characterized in that: The method comprises: Obtain the first decision variable group of the open-pit mine traffic planning and transport truck configuration optimization model; Establishing the vehicle flow planning and transport truck configuration optimization model according to the first decision variable group; Solve the traffic flow planning and transport truck configuration optimization model to obtain the optimal traffic flow planning scheme and the optimal transport truck quantity configuration scheme for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning scheme includes: a transport task set, wherein the transport task set includes: transport tasks of each heavy-load vehicle trip; Establishing an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the dispatchable electric mining truck set in the open-pit mine, and the battery swap station set in the open-pit mine; The electric mining truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period; the first decision variable group includes the following decision variables: ； ； ； ； in, represents the set of loading areas of the open-pit mine, represents the total number of loading areas in the open pit mine; represents the set of unloading areas of the open-pit mine, represents the total number of unloading areas in the open-pit mine; decision variables In the open-pit mine, the electric mining truck is loaded from the loading area. Heavy load to unloading area ; Decision variables In the open-pit mine, the electric mining truck is loaded from the loading area. Heavy load does not go to unloading area ; Decision variables Indicates that the electric mining truck in the open-pit mine is unloaded from the unloading area Go to the loading area without a load ; Decision variables Indicates that the electric mining truck in the open-pit mine is unloaded from the unloading area No load, no loading area ; Decision variables From the loading area To unloading area The number of heavy-load train trips; decision variables Indicates the unloading area To the loading area The number of empty train trips; Wherein, establishing the vehicle flow planning and transport vehicle configuration optimization model according to the first decision variable group includes: According to the decision variables in the first decision variable group , establish the first objective function of the vehicle flow planning and transport cart configuration optimization model, wherein the expression of the first objective function is: Among them, in formula (5) Indicates the revenue of a heavy-load trip. Indicates the cost per transport truck, It represents the ratio between the number of transport trucks and the total number of trips, where the total number of trips = ; According to the first decision variable group, a plurality of first constraint conditions of the vehicle flow planning and transport cart configuration optimization model are established, wherein the plurality of first constraint conditions include the following constraint conditions: in, and Respectively indicate the loading area Single loading time and unloading area Single unloading time, represents the working time of each shift in the open-pit mine; Formula (6) represents the working time of each loading area The sum of the loading times of all electric mining trucks does not exceed the operating time of each shift in the open-pit mine, and formula (7) indicates that in each unloading area The sum of the unloading times of all electric mining trucks does not exceed the operating hours of each shift in the open-pit mine; Wherein, formula (8) represents the The number of loaded trains leaving is equal to the number of unloaded trains entering, and formula (9) indicates that The number of incoming loaded trains is equal to the number of outgoing empty trains; Among them, in formula (10) The electric mining truck in the open-pit mine is shown in FIG. Run to unloading area the time required; The electric mining truck in the open-pit mine is shown in FIG. Run to loading area The time required; Formula (10) indicates that the sum of the operating time of all empty vehicle trips and loaded vehicle trips does not exceed the total operating time of all electric mining trucks in the open-pit mine, where the total operating time is ,in, express Multiply ,in, The number of electric mining trucks that can be dispatched in the open-pit mine; Formula (11) specifies the decision variables and decision variables The relationship between ; Formula (12) specifies the decision variables and decision variables The relationship between , where M is an infinite positive integer; Formula (13) represents the loading area The number of matches with all unloading areas does not exceed the specified value corresponding to loading area i , formula (14) represents the unloading area The number of matches of all loading areas does not exceed the specified value of unloading area j ; Establishing a vehicle flow planning and transport cart configuration optimization model including the first decision variable group, the first objective function and the plurality of first constraints; The method of establishing an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the dispatchable electric mining truck set in the open-pit mine, and the battery replacement station set in the open-pit mine comprises: According to the transport task set, the set of dispatchable electric mining trucks in the open-pit mine, and the set of battery swap stations in the open-pit mine, a second decision variable group of the electric mining truck scheduling optimization model is determined, wherein the second decision variable group includes the following decision variables: in, represents the set of electric mining trucks in the open-pit mine, represents the transport task set, represents the set of battery swap stations in the open-pit mine, represents the total number of battery swap stations in the open-pit mine; decision variables represents an electric mining truck in the electric mining truck set Complete the transportation mission Later, it carried out transportation tasks , and the decision variable Electric mining truck Complete the transportation mission Then choose to switch to a power station Replacement battery; According to the decision variables in the second decision variable group , establish the second objective function of the electric mining truck scheduling optimization model, wherein the expression of the second objective function is: Among them, formula (17) means to maximize the number of completed transportation tasks; According to the second decision variable group, a plurality of second constraints of the electric mining truck scheduling optimization model are established, wherein the plurality of second constraints include the following constraints: Among them, formula (18) indicates that the number of vehicles performing any transportation task will not exceed one; Among them, formula (19) indicates that the vehicle that performs two adjacent transport tasks in the transport task set is the same vehicle; Among them, in formula (20) Indicates transportation task The starting point, Indicates transportation The end point of the previous transport task is the starting point of the next transport task; in, It's an electric mining truck Carry out transportation tasks The previous power, It's an electric mining truck Carry out transportation tasks After that, the power It's an electric mining truck The power battery capacity, It is a transportation task The power consumption of electric mining trucks is shown in formula (21). Carry out transportation tasks Previous Power and Electric Mining Trucks Carry out transportation tasks The relationship between the subsequent power consumption; Formula (22) stipulates that the transportation task The power after that is If electric mining trucks After the battery is replaced, the power after the transportation task is , where M is an infinite positive integer; , Among them, in formula (23) represents the minimum limit value of the battery state of charge, and formula (23) represents the electric mining truck Carry out transportation tasks The previous power should be no less than ; Among them, formula (24) indicates that the battery inventory in the battery swap station s is equal to block; among them, Indicates battery replacement station Complete the transportation mission The number of fully charged batteries in the rear station, Indicates battery replacement station Complete the transportation mission The number of depleted batteries in the rear station; represents the number of fully charged batteries in the battery swap station s after it completes the previous transport task adjacent to transport task k; represents the number of depleted batteries in the battery swap station s after completing the previous transport task adjacent to transport task k; Formula (25) represents the number of depleted batteries in the battery swap station after the electric mining truck v is swapped The number of fully charged batteries in the battery is reduced by 1; Formula (26) represents the electric mining truck v after the battery is replaced by the battery replacement station The number of internal depleted batteries increases by 1; The electric mining truck scheduling optimization model including the second decision variable group, the second objective function and the plurality of second constraints is established.

2. The method according to claim 1, characterized in that The vehicle flow planning and transport truck configuration optimization model is solved to obtain the optimal vehicle flow planning scheme and the optimal transport truck quantity configuration scheme for the open-pit mine in the future time period after the current moment, including: Obtaining the current service capacity limitation information corresponding to the battery swap station of the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period; Taking into account the current service capacity limitation information of the battery swap station of the open-pit mine at the current moment, the loading plan quantities corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantities corresponding to each unloading area in the open-pit mine in the future time period, the traffic flow planning and transport cart configuration optimization model is solved to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment.

3. The method according to claim 1, characterized in that The electric mining truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period, including: Obtaining current travel information and current state information corresponding to each electric mining truck in the electric mining truck set at the current moment; Acquire completion status data of the optimal vehicle planning scheme of the open-pit mine in a target time period, wherein the target time period is the time period of the current moment, and wherein the target time period and the future time period are adjacent time periods; Obtaining the current service capacity limitation information corresponding to each battery swap station in the open-pit mine at the current moment, and obtaining the current service capacity limitation information corresponding to the charging station bound to each battery swap station in the open-pit mine at the current moment; Taking into account the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information and current status information corresponding to each electric mine truck in the electric mine truck set at the current moment, the electric mine truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mine truck in the electric mine truck set in the future time period.

4. A device for determining the configuration of transport carts and the dispatching plan of electric mining trucks, characterized in that: The device comprises: An acquisition module, used to acquire a first decision variable group of a vehicle flow planning and transport truck configuration optimization model of an open-pit mine; A first model building module, used to build the vehicle flow planning and transport truck configuration optimization model according to the first decision variable group; The first solution module is used to solve the traffic flow planning and transport truck configuration optimization model to obtain the optimal traffic flow planning scheme and the optimal transport truck quantity configuration scheme for the open-pit mine in the future time period after the current moment, wherein the optimal traffic flow planning scheme includes: a transport task set, wherein the transport task set includes: transport tasks of each heavy-load vehicle trip; A second model building module is used to build an electric mining truck scheduling optimization model for the open-pit mine according to the transportation task set, the set of electric mining trucks that can be dispatched in the open-pit mine, and the set of battery swap stations in the open-pit mine; The second solution module is used to solve the electric mining truck scheduling optimization model to obtain the target travel chain corresponding to each electric mining truck in the electric mining truck set in the future time period; the first decision variable group includes the following decision variables: ； ； ； ； in, represents the set of loading areas of the open-pit mine, represents the total number of loading areas in the open pit mine; represents the set of unloading areas of the open-pit mine, represents the total number of unloading areas in the open-pit mine; decision variables In the open-pit mine, the electric mining truck is loaded from the loading area. Heavy load to unloading area ; Decision variables In the open-pit mine, the electric mining truck is loaded from the loading area. Heavy load does not go to unloading area ; Decision variables Indicates that the electric mining truck in the open-pit mine is unloaded from the unloading area Go to the loading area without a load ; Decision variables Indicates that the electric mining truck in the open-pit mine is unloaded from the unloading area No load, no loading area ; Decision variables From the loading area To unloading area The number of heavy-load train trips; decision variables Indicates the unloading area To the loading area The number of empty train trips; Wherein, the first model building module is specifically used for: According to the decision variables in the first decision variable group , establish the first objective function of the vehicle flow planning and transport cart configuration optimization model, wherein the expression of the first objective function is: Among them, in formula (5) Represents the revenue of a heavy-load trip. Indicates the cost per transport truck, It represents the ratio between the number of transport trucks and the total number of trips, where the total number of trips = ; According to the first decision variable group, a plurality of first constraint conditions of the vehicle flow planning and transport cart configuration optimization model are established, wherein the plurality of first constraint conditions include the following constraint conditions: in, and Respectively indicate the loading area Single loading time and unloading area Single unloading time, represents the working time of each shift in the open-pit mine; Formula (6) represents the working time of each loading area The sum of the loading times of all electric mining trucks does not exceed the operating time of each shift in the open-pit mine, and formula (7) indicates that in each unloading area The sum of the unloading times of all electric mining trucks does not exceed the operating hours of each shift in the open-pit mine; Wherein, formula (8) represents the The number of loaded trains leaving is equal to the number of unloaded trains entering, and formula (9) indicates that The number of incoming loaded trains is equal to the number of outgoing empty trains; Among them, in formula (10) The electric mining truck in the open-pit mine is shown in FIG. Run to unloading area the time required; The electric mining truck in the open-pit mine is shown in FIG. Run to loading area The time required; Formula (10) indicates that the sum of the operating time of all empty vehicle trips and loaded vehicle trips does not exceed the total operating time of all electric mining trucks in the open-pit mine, where the total operating time is ,in, express Multiply ,in, The number of electric mining trucks that can be dispatched in the open-pit mine; Formula (11) specifies the decision variables and decision variables The relationship between ; Formula (12) specifies the decision variables and decision variables The relationship between , where M is an infinite positive integer; Formula (13) represents the loading area The number of matches with all unloading areas does not exceed the specified value corresponding to loading area i , formula (14) represents the unloading area The number of matches of all loading areas does not exceed the specified value of unloading area j ; Establishing a vehicle flow planning and transport cart configuration optimization model including the first decision variable group, the first objective function and the plurality of first constraints; The second model building module is specifically used for: According to the transport task set, the set of dispatchable electric mining trucks in the open-pit mine, and the set of battery swap stations in the open-pit mine, a second decision variable group of the electric mining truck scheduling optimization model is determined, wherein the second decision variable group includes the following decision variables: in, represents the set of electric mining trucks in the open-pit mine, represents the transport task set, represents the set of battery swap stations in the open-pit mine, represents the total number of battery swap stations in the open-pit mine; decision variables represents an electric mining truck in the electric mining truck set Complete the transportation mission Later, it carried out transportation tasks , and the decision variable Electric mining truck Complete the transportation mission Then choose to switch to a power station Replacement battery; According to the decision variables in the second decision variable group , establish the second objective function of the electric mining truck scheduling optimization model, wherein the expression of the second objective function is: Among them, formula (17) means to maximize the number of completed transportation tasks; According to the second decision variable group, a plurality of second constraints of the electric mining truck scheduling optimization model are established, wherein the plurality of second constraints include the following constraints: Among them, formula (18) indicates that the number of vehicles performing any transportation task will not exceed one; Among them, formula (19) indicates that the vehicle that performs two adjacent transport tasks in the transport task set is the same vehicle; Among them, in formula (20) Indicates transportation task The starting point, Indicates transportation The end point of the previous transport task is the starting point of the next transport task; in, It's an electric mining truck Carry out transportation tasks The previous power, It's an electric mining truck Carry out transportation tasks After that, the power It's an electric mining truck The power battery capacity, It is a transportation task The power consumption of electric mining trucks is shown in formula (21). Carry out transportation tasks Previous Power and Electric Mining Trucks Carry out transportation tasks The relationship between the subsequent power consumption; Formula (22) stipulates that the transportation task The power after that is If electric mining trucks After the battery is replaced, the power after the transportation task is , where M is an infinite positive integer; , Among them, in formula (23) represents the minimum limit value of the battery state of charge, and formula (23) represents the electric mining truck Carry out transportation tasks The previous power should be no less than ; Wherein, formula (24) indicates that the battery inventory in the battery swap station s is equal to block; among them, Indicates battery replacement station Complete the transportation mission The number of fully charged batteries in the rear station, Indicates battery replacement station Complete the transportation mission The number of depleted batteries in the rear station; represents the number of fully charged batteries in the battery swap station s after it completes the previous transport task adjacent to transport task k; represents the number of depleted batteries in the battery swap station s after completing the previous transport task adjacent to transport task k; Formula (25) represents the number of depleted batteries in the battery swap station after the electric mining truck v is swapped The number of fully charged batteries in the battery is reduced by 1; Formula (26) represents the electric mining truck v after the battery is replaced by the battery replacement station The number of internal depleted batteries increases by 1; The electric mining truck scheduling optimization model including the second decision variable group, the second objective function and the plurality of second constraints is established.

5. The device according to claim 4, characterized in that The first solution module is specifically used for: Obtaining the current service capacity limitation information corresponding to the battery swap station of the open-pit mine at the current moment, the loading plan quantity corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantity corresponding to each unloading area in the open-pit mine in the future time period; Taking into account the current service capacity limitation information of the battery swap station of the open-pit mine at the current moment, the loading plan quantities corresponding to each loading area in the open-pit mine in the future time period, and the unloading plan quantities corresponding to each unloading area in the open-pit mine in the future time period, the traffic flow planning and transport cart configuration optimization model is solved to obtain the optimal traffic flow planning scheme and the optimal transport cart quantity configuration scheme for the open-pit mine in the future time period after the current moment.

6. The device according to claim 4, characterized in that The second solution module is specifically used for: Obtaining current travel information and current state information corresponding to each electric mining truck in the electric mining truck set at the current moment; Acquire completion status data of the optimal vehicle planning scheme of the open-pit mine in a target time period, wherein the target time period is the time period of the current moment, and wherein the target time period and the future time period are adjacent time periods; Obtaining the current service capacity limitation information corresponding to each battery swap station in the open-pit mine at the current moment, and obtaining the current service capacity limitation information corresponding to the charging station bound to each battery swap station in the open-pit mine at the current moment; Taking into account the completion status data, the current service capacity limitation information corresponding to each battery swap station at the current moment, the current service capacity limitation information corresponding to the charging station at the current moment, the current travel information and current status information corresponding to each electric mine truck in the electric mine truck set at the current moment, the electric mine truck scheduling optimization model is solved to obtain the target travel chain corresponding to each electric mine truck in the electric mine truck set in the future time period.

7. An electronic device, characterized in that: include: A memory, a processor, and computer-executable instructions stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 3 when executing the computer-executable instructions.

8. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by a processor, the method according to any one of claims 1 to 3 is implemented.

Images