A collaborative method for low-carbon scheduling of autonomous mining trucks and refueling point location in open-pit mines

Abstract

The present invention discloses a collaborative method for low-carbon scheduling of autonomous mining trucks and fueling point location selection in open-pit mines, which relates to the technical field of open-pit mine production scheduling. In the present invention, road network information, production equipment information, and production plan data of the open-pit mine are input into the constructed truck flow distribution model to determine the transportation shifts between loading and unloading points and the transportation tasks of the mining trucks; by calculating the carbon emission costs generated by fuel consumption during transportation, a green transportation route planning model is constructed based on the multi-commodity network flow method to optimize the transportation routes and speeds of the mining trucks; according to the road nodes and fueling demand points in the route planning, an alternative set of fueling points is constructed and input into the constructed fueling point location selection optimization model to determine the fueling point location and the distribution of fueling demands for the mining trucks. The present invention can effectively improve the transportation efficiency of autonomous mining trucks in open-pit mines, reduce carbon emissions, and optimize the fueling point location selection, providing technical support for the green transportation of open-pit mines.

Description

Technical Field

[0001] The present invention belongs to the technical field of open-pit mine production scheduling, and specifically relates to a collaborative method for low-carbon scheduling of autonomous haul trucks and refueling point location selection in open-pit mines. Background Art

[0002] The low transportation efficiency in open-pit mines is a common problem, mainly because of the lack of accurate information on the operation status of open-pit mines and the inability to obtain optimal scheduling decisions.

[0003] Although the importance of carbon emissions caused by the consumption of fossil fuels in open-pit mines for the development of green mines has been realized, less attention has been paid to green and sustainable planning and scheduling methods in the production and transportation links. In practice, the research on haul truck scheduling mostly focuses on improving the transportation and shovel efficiency, ignoring the importance of refueling behavior for the continuous operation of haul trucks and the additional carbon emissions it brings.

[0004] In summary, how to reduce the transportation cost of haul trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines is a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention provides a collaborative method for low-carbon scheduling of autonomous haul trucks and refueling point location selection in open-pit mines, including haul truck traffic flow distribution, green route planning, and refueling point location selection planning, aiming to optimize decisions related to transportation, routes, and refueling, improve the working efficiency of shovels, reduce the transportation cost of haul trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: A collaborative method for low-carbon scheduling of autonomous haul trucks and refueling point location selection in open-pit mines, including:

[0007] S1. Input the obtained road network information, production equipment information, and production plan data into the constructed haul truck traffic flow distribution model, construct the objective function and related constraints of the haul truck traffic flow distribution model, and determine the transportation shifts and haul truck transportation tasks between loading and unloading points;

[0008] The objective function of the haul truck traffic flow distribution model is:

[0009] ;

[0010] Wherein, is the haul truck transportation time, ; is the shovel idle time, ; is the number of transportation shifts from the unloading point to the shovel ; is from the shovel To the unloading point The number of transportation shifts; Represents the set of loading points in the open-pit mine, and the location of the electric shovel is the location of the loading point; Represents the set of unloading points in the open-pit mine; is the electric shovel and the unloading point No-load transportation time; is the electric shovel and the unloading point Full-load transportation time; is the electric shovel Loading time; is the unloading point Unloading time; Is the working shift time range;

[0011] S2. Input the truck transportation tasks in S1 into the green transportation route planning model considering the carbon emission cost of truck fuel consumption, construct the objective function and related constraints of the green transportation route planning model, and obtain the transportation routes and section transportation speeds of the trucks;

[0012] The objective function of the green transportation route planning model is:

[0013] ;

[0014] Among them, is the transportation cost, ; is the carbon emission cost, ; is the unit distance transportation cost, is the unit carbon emission cost, is the conversion factor between fuel consumption and carbon emission, represents the section distance, represents the truck performing the transportation task on the section transportation volume; represents whether the transportation task 𝑢 passes through the section ; The function of is expressed as where is the truck's gross vehicle weight, , and are the fuel consumption terms related to the engine, speed, and load respectively; , , , is the engine friction coefficient (kJ / rev / liter); is the engine speed (rev / s); is the engine displacement (liter); is the front surface area (m 2 ); is the vehicle transmission coefficient; is the engine efficiency parameter; is the road slope; is the rolling resistance coefficient; is the fuel-to-air mass ratio; is the typical diesel calorific value (kJ / g); is the conversion coefficient (g / liter); is the air density (kg / m 3 ); is the gravitational constant (m / s 2 ); is the air resistance coefficient;

[0015] S3. According to the transportation route, construct a candidate set of refueling points, input the candidate set of refueling points and the refueling demand set into the constructed refueling point location optimization model, and determine the refueling point location and the distribution of mining truck refueling demands.

[0016] Furthermore, the constraint conditions of the mining truck traffic flow distribution model in S1 are:

[0017] Constraint (1) and Constraint (2) respectively represent the maximum number of mining truck shifts served by the electric shovel and the unloading point within one work shift ;

[0018] Constraint (3) ensures the planned throughput of the unloading point, and the transportation volume cannot exceed the processing capacity limit of the unloading point; where is the planned throughput of the unloading point ; is the unloading point processing capacity;

[0019] Constraint (4) and Constraint (5) guarantee that the number of mining trucks entering and leaving the electric shovel and the unloading point must be balanced;

[0020] Constraint (6) indicates that the minimum number of mining trucks required to achieve the target throughput is limited by the total number of mining trucks; where is the total number of available mining trucks;

[0021] Constraint (7) represents the continuous variable and Value range

[0022] Furthermore, taking the number of transport shifts between each loading and unloading point obtained by solving the ore truck flow distribution model, with each loading and unloading point as the starting and ending points of the transport task and the load capacity of the ore truck as the transport volume, a transport task set is constructed , each transport task is expressed as , where is the transport volume of the transport task. When the ore truck is fully loaded , when the ore truck is empty , is the number of transport tasks, .

[0023] Furthermore, the constraint conditions of the green transport route planning model in S2 are as follows:

[0024] Constraint (1) , there will be ore materials with a size of transported out from the loading point, and no ore materials will be transported to the loading point;

[0025] Constraint (2) , there will be ore materials with a size of transported into the unloading point, and no ore materials will be transported out from the unloading point;

[0026] Constraint (3) represents the flow conservation of the intermediate nodes of the road. The amount of ore materials transported into and out of the intermediate nodes of the road network for any transport task is equal;

[0027] Constraint (4) represents the transport capacity limit of the road section, where is the transport capacity of road section ;

[0028] Constraint (5) represents the road sections passed between the loading and unloading points , is a sufficiently large positive integer;

[0029] Constraint (6) represents the speed limit range of the ore truck;

[0030] Constraint (7) represents the value range of the continuous variable Value range

[0031] Furthermore, the candidate set of refueling points in S3 consists of the unloading points and the top 10 road nodes with the highest passing frequencies in the road network transport path to form the candidate set of refueling points , the set of unloading points As a refueling demand point.

[0032] Furthermore, the objective function of the refueling point location model in S3 is:

[0033] ;

[0034] Wherein, is the maximum value of the distances from all refueling demand points to their respective facilities; is a binary variable. If point is assigned to refueling point then it is 1, otherwise it is 0; is a road node and is the shortest distance.

[0035] Furthermore, the constraint conditions of the refueling point location optimization model in S3 are:

[0036] Constraint (1) indicates that the total number of refueling point locations is the same as the planned number of refueling points. Wherein, is a binary variable. If point is selected as a refueling point, then it is 1, otherwise it is 0; is the number of refueling points;

[0037] Constraint (2) indicates that only when point is selected as a refueling point can the mining truck go to point for refueling;

[0038] Constraint (3) indicates that all demand points will have designated refueling points;

[0039] Constraint (4) indicates that the distance from any demand point to the designated refueling point cannot exceed the maximum value of the distances from all demand points to their designated refueling points;

[0040] Constraint (5) indicates the value range of the binary variables and ;

[0041] Furthermore, in S1, the road network information includes the locations, distances, transportation times, section passing capacities, and section speed limits of the loading and unloading points; the production equipment information includes the number of autonomous mining trucks, load capacities, speeds, model data, loading and unloading times of the electric shovels and unloading points; the production plan data includes the working shift durations, planned throughput of the unloading points, and planned number of refueling points.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] The present invention can improve the working efficiency of electric shovels, reduce the transportation cost of ore trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines.

[0044] 1. Based on the digital platform of the open-pit mine, the present invention provides road network information, vehicle status, and electric shovel status, as well as the production plan of the open-pit mine. It fully considers the operating capabilities of ore trucks, electric shovels, and unloading points, constructs an automatic driving ore truck flow distribution model, and optimizes the number of transportation shifts between loading and unloading points.

[0045] 2. Based on the carbon emission cost caused by the fuel consumption of ore truck transportation and combined with the vehicle speed limit of the road network, the present invention constructs a green route planning model for automatic driving ore trucks based on multi-commodity network flow, and optimizes the transportation path of automatic driving ore trucks and the driving speed of each section of the road.

[0046] 3. The present invention presets the location of the refueling points, constructs a candidate set of refueling points, and combines the distribution of refueling demand points in the open-pit mine to construct a refueling point location planning model, and optimizes the location of the refueling points and the matching relationship between refueling demand and refueling points. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a schematic flow chart of the present invention.

[0048] Figure 2 is a schematic diagram of the road network of the open-pit mine provided by the embodiment of the present invention.

[0049] Figure 3 is a schematic diagram of the refueling point selection and refueling demand distribution results provided by the present invention.

[0050] Figure 4 is a schematic diagram of the result comparison of considering / not considering carbon emission cost provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0052] See Figures 1-4 As shown, the embodiment of the present invention provides a collaborative method for low-carbon scheduling of automatic driving ore trucks and refueling point location selection in open-pit mines. This method is applicable to the application scenarios of automatic driving ore truck transportation planning and ore truck refueling point planning in the production scheduling process of open-pit mines; specifically includes the following steps:

[0053] S1. Obtain the road network information, production equipment information, and production plan data, and input them into the constructed ore truck flow distribution model to determine the transportation shifts between loading and unloading points and the ore truck transportation tasks.

[0054] S2. Input the ore truck transportation tasks into the green transportation route planning model considering the carbon emission cost of ore truck fuel consumption to obtain the transportation routes of the ore trucks and the transportation speeds of the road sections.

[0055] S3. According to the transportation routes, construct a candidate set of refueling points, and input the candidate set of refueling points and the refueling demand set into the constructed refueling point location optimization model to determine the refueling point location and the distribution of ore truck refueling demands.

[0056] The following will separately elaborate on each of the above steps in detail.

[0057] In the above step S1, it specifically includes:

[0058] First, obtain the road network information, production equipment information, and production plan data from the intelligent mine digital platform of the open-pit mine; the road network information includes the locations, distances, transportation times, road section passing capacities, and road section speed limits of the loading and unloading points; the production equipment information includes the number, load capacity, speed, model data of the autonomous ore trucks, and the loading and unloading times between the electric shovels and the unloading points. The production plan data includes the working shift duration, the planned throughput of the unloading points, and the planned number of refueling points.

[0059] Then, input the data into the ore truck flow distribution model. The objective function of the ore truck flow distribution model is:

[0060]

[0061] Among them, is the ore truck transportation time, ; is the idle time of the electric shovel, ; is the number of transportation shifts from the unloading point to the electric shovel ; is the number of transportation shifts from the electric shovel to the unloading point ; represents the set of loading points in the open-pit mine, and the location where the electric shovel is located is the loading point location; represents the set of unloading points in the open-pit mine; is the empty-load transportation time between the electric shovel and the unloading point ; is the full-load transportation time between the electric shovel and the unloading point ; is the electric shovel Loading time; is the unloading point Unloading time; is the working shift time range.

[0062] In this embodiment, the constraint conditions of the target of the ore truck flow distribution model are as follows:

[0063] Constraint (1) and Constraint (2) respectively represent the maximum number of ore truck shifts served by the electric shovel and the unloading point within one working shift ;

[0064] Constraint (3) ensures the planned throughput of the unloading point, and the transportation volume cannot exceed the processing capacity limit of the unloading point; where is the unloading point Planned throughput; is the unloading point Processing capacity;

[0065] Constraint (4) and Constraint (5) ensure that the number of ore trucks entering and leaving the electric shovel and the unloading point must be balanced;

[0066] Constraint (6) indicates that the minimum number of ore trucks required to achieve the target throughput is limited by the total number of ore trucks; where is the total number of available ore trucks;

[0067] In this embodiment, the ore truck flow distribution model is solved by the Gurobi optimization solver to determine the number of transportation shifts between each loading and unloading point.

[0068] In this embodiment, according to the number of transportation shifts between each loading and unloading point, with each loading and unloading point as the starting and ending points of the transportation task, and the load of the ore truck as the transportation volume, a transportation task set is constructed , each transportation task is expressed as , where is the transportation volume of the transportation task, when the ore truck is fully loaded , when the ore truck is empty , is the number of transportation tasks, .

[0069] In the above step S2, the transportation shifts, transportation tasks, and ore truck vehicle information are input into the green transportation route planning model. The objective function of the green transportation route planning model is:

[0070] ;

[0071] Among them, is the transportation cost, ; is the carbon emission cost, ; is the transportation cost per unit distance, is the carbon emission cost per unit, is the conversion factor between fuel consumption and carbon emissions, represents the road section distance, represents that the mining truck performs the transportation task on the road section transport volume; represents whether the transportation task 𝑢 passes through the road section ; The function of is expressed as where is the total mass of the mining truck, , and are the fuel consumption items related to the engine, speed, and load respectively; , , . is the engine friction coefficient (kJ / rev / liter); is the engine speed (rev / s); is the engine displacement (liter); is the front surface area (m 2 ); is the vehicle transmission coefficient; is the engine efficiency parameter; is the road slope; is the rolling resistance coefficient; is the fuel - air mass ratio; is the typical diesel calorific value (kJ / g); is the conversion coefficient (g / liter); is the air density (kg / m 3 ); is the universal gravitational constant (m / s 2 ); is the air resistance coefficient.

[0072] In this embodiment, the constraint conditions of the green transportation route planning model target are:

[0073] Constraint (1) , there will be ore materials with a size of transported out from the loading point, and no ore materials will be transported to the loading point;

[0074] Constraint (2) , there will be aggregate transportation of size entering the unloading point, and no aggregate transportation will be transported out of the unloading point;

[0075] Constraint (3) represents the flow conservation of the intermediate nodes of the road. The amount of aggregate transported into and out of the intermediate nodes of the road network for any transportation task is equal;

[0076] Constraint (4) represents the transportation capacity limit of the road section, where is the transportation capacity of road section ;

[0077] Constraint (5) represents the road section passed between the loading and unloading points , is a sufficiently large positive integer;

[0078] Constraint (6) represents the speed limit range of the mining truck;

[0079] Constraint (7) represents the value range of the continuous variable ;

[0080] In this embodiment, the Gurobi optimization solver is used to solve the green transportation route planning model to determine the transportation route of the mining truck and the transportation speed on each road section.

[0081] In the above step S3, first, the unloading point and the top 10 points with the highest passing frequency in the road network transportation path are taken to form a refueling point candidate set , and the unloading point set is used as the refueling demand point.

[0082] In this embodiment, in the said S3, the objective function of the refueling point location optimization model is:

[0083] ;

[0084] where, is the maximum value of the distance from all demand points to their respective facilities; is a binary variable. If point is assigned to refueling point then it is 1, otherwise it is 0; is the shortest distance between road nodes and ;

[0085] In this embodiment, the constraint conditions of the refueling point location optimization model objective are:

[0086] Constraint (1) indicates that the total number of selected refueling points is the same as the planned number of refueling points, where is a binary variable, which is 1 if the point is selected as a refueling point and 0 otherwise; is the number of refueling points;

[0087] Constraint (2) indicates that only when the point is selected as a refueling point can the mining truck go to the point for refueling;

[0088] Constraint (3) indicates that all demand points will have designated refueling points;

[0089] Constraint (4) indicates that the distance from any demand point to the designated refueling point cannot exceed the maximum value of the distances from all demand points to their designated refueling points;

[0090] Constraint (5) indicates the value range of the binary variables and .

[0091] Furthermore, the refueling point location optimization model is solved by the Gurobi optimization solver to determine the refueling point selection and refueling demand allocation plan.

[0092] In the embodiments of the present invention, a solution algorithm program including a haulage distance penalty strategy is written in Python. The optimization model of the embodiment can obtain the mining truck transportation plan, transportation route, transportation speed, refueling point selection and refueling demand allocation plan, where the refueling point selection and refueling demand allocation results are as shown in Figure 3 , and the result comparison considering or not considering the carbon emission cost is as shown in Figure 4 .

[0093] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for low-carbon dispatching of autonomous driving mining trucks in open-pit mines and site selection coordination of refueling points, characterized in that: include: S1. Input the acquired road network information, production equipment information and production plan data into the constructed mine truck logistics allocation model, construct the objective function and related constraints of the mine truck logistics allocation model, and determine the transportation shifts and mine truck transportation tasks between loading and unloading points; The objective function of the mine truck flow allocation model is: ； in, The transportation time of the mining truck. ; is the idle time of the shovel, ; From the uninstall point To the electric shovel The number of transport flights; For electric shovel To the unloading point The number of transport flights; Represents an open pit mine loading point collection, electric shovel The current location is the loading point location; Represents a collection of open pit mine unloading points; For electric shovel With uninstall point Empty transport time; For electric shovel With uninstall point Full load transportation time; For electric shovel Loading time; Unload point Unloading time; The time range for work shifts; S2, input the mining truck transportation task in S1 into the green transportation route planning model that considers the mining truck fuel consumption and carbon emission costs, construct the objective function and related constraints of the green transportation route planning model, and obtain the transportation path and section transportation speed of the mining truck; The objective function of the green transportation route planning model is: ； in, For transportation costs, ; is the carbon emission cost, ; is the transportation cost per unit distance, is the unit carbon emission cost, is the conversion factor between fuel consumption and carbon emissions, Indicates road segment The distance Indicates that the mining truck performs the transportation task On the road The transportation volume; Indicates whether the transport task 𝑢 passes through the road segment ; The function is expressed as ,in It is the quality of the mining truck. , and They are the fuel consumption items related to the engine, speed, and load; , , , is the engine friction coefficient; is the engine speed; is the engine displacement; is the front surface area; is the vehicle transmission coefficient; is the engine efficiency parameter; is the road slope; is the rolling resistance coefficient; is the fuel-air mass ratio; is the typical diesel calorific value; is the conversion factor; is the air density; is the gravitational constant; is the air resistance coefficient; The constraints of the green transportation route planning model in S2 are: Constraints (1) , the mount point will have a size of of the ore is transported out, and no ore is transported to the loading point; constraint (2) , the uninstall point will have a size of of ore transport entering, and no ore transport leaving from the unloading point; Constraints (3) It indicates the conservation of flow at the intermediate nodes of the road. The amount of mineral materials transported into and out of the intermediate nodes of the road network for any transportation task is equal. Constraints (4) Represents the transport capacity limit of the road section, where For road section transportation capacity; Constraints (5) Indicates the road segments between loading and unloading points , is a positive integer; Constraints (6) Indicates the speed limit range of mining trucks; Constraints (7) Represents a continuous variable The value range of S3. Construct a candidate set of refueling points based on the transportation route, input the candidate set of refueling points and the refueling demand set into the constructed refueling point location optimization model, and determine the location of the refueling point and the allocation of refueling demand for mining trucks; The objective function of the refueling point location selection model in S3 is: ； in, The maximum distance from all refueling demand points to their respective facilities; If is a binary variable Points allocated to refueling points If yes, it is 1, otherwise it is 0; Road Node and The shortest distance; The constraints of the gas station location optimization model in S3 are: Constraints (1) Indicates that the total number of gas station locations is the same as the planned number of gas stations, where is a binary variable. If If the point is a refueling point, it is 1, otherwise it is 0; The number of refueling points; Constraints (2) Indicates that only The point is selected as a refueling point, and the mining card can go there Click to refuel; Constraints (3) It means that all demand points will have designated refueling points; Constraints (4) Indicates that the distance from any demand point to the designated refueling point cannot exceed the maximum distance from all demand points to its designated refueling point; Constraints (5) Represents a binary variable and The value range of .

2. The open-pit mine autonomous driving mining truck low-carbon scheduling and refueling point site selection collaborative method as claimed in claim 1, characterized in that: The constraints of the mine truck flow allocation model in S1 are: Constraints (1) and constraint (2) Respectively represent the shovel and unloading point in a working shift The maximum number of mining truck shifts served within the company; Constraints (3) The planned throughput at the unloading point is ensured, and the transport volume cannot exceed the handling capacity limit of the unloading point; Unload point Plan throughput; Unload point Processing power; Constraints (4) and constraints (5) Ensure that the number of mining trucks entering and leaving the shovel and unloading point must be balanced; Constraints (6) It shows that the minimum number of mining cards required to achieve the target throughput is limited by the total number of mining cards; is the total number of available mining cards; Constraints (7) Represents a continuous variable and The value range of .

3. The open-pit mine autonomous driving mining truck low-carbon scheduling and refueling point site selection collaborative method as claimed in claim 1, characterized in that: By solving the mine truck flow distribution model to obtain the number of transport shifts between each loading and unloading point, each loading and unloading point is used as the starting and ending point of the transport task, and the load of the mine truck is used as the transport volume to construct a transport task set , each transport task Expressed as ,in is the transportation volume of the transportation task. When the mining truck is fully loaded , when the mining card is unloaded , is the number of transport tasks, .

4. The open-pit mine autonomous driving mining truck low-carbon scheduling and refueling point site selection collaborative method as claimed in claim 1, characterized in that: The candidate set of refueling points in S3 is composed of the unloading point and the top 10 road nodes with the highest frequency in the road network transportation path. , unloading point set As a refueling demand point.

5. The open-pit mine autonomous driving mining truck low-carbon scheduling and refueling point site selection collaborative method as claimed in claim 1, characterized in that: In S1, the road network information includes the location, distance, transportation time, road section capacity and road section speed limit of the loading and unloading points; the production equipment information includes the number of autonomous mining trucks, load, speed, model data, electric shovel and loading and unloading time of the unloading point; Production planning data includes the length of work shifts, the planned throughput at unloading points, and the planned number of refueling points.

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