Strip mine automatic driving mine card low-carbon scheduling and refueling point site selection cooperation method

Abstract

The invention discloses a strip mine automatic driving mine card low-carbon scheduling and refueling site selection cooperation method, and relates to the technical field of strip mine production scheduling. The method comprises the following steps: inputting strip mine road network information, production equipment information and production plan data into a constructed mine truck traffic flow distribution model, and determining transportation shifts between loading and unloading points and transportation tasks of mine trucks; by accounting the carbon emission cost generated by fuel consumption in the transportation process, a green transportation route planning model is constructed based on a multi-commodity network flow method, and mine card transportation route and speed optimization is realized; and according to the road nodes and the refueling demand points in the route planning, constructing a refueling point alternative set, inputting the constructed refueling point site selection optimization model, and determining refueling point site selection and mine truck refueling demand distribution. According to the method, the strip mine automatic driving mine card transportation efficiency can be effectively improved, carbon emission is reduced, refueling site selection is optimized, and technical support is provided for green transportation of the strip mine.

Description

Technical Field

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

[0002] The mining industry is a key area for reducing carbon emissions, accounting for about 4-7% of global greenhouse gas emissions. Production activities in open-pit mines include drilling, blasting, loading, transportation and dumping, which directly generate 45-60% of carbon emissions in the mining life cycle. Among them, energy consumption in the transportation link can reach up to 35%, becoming the main source of carbon emissions in the production process, and also accounting for 50-60% of the total operating costs.

[0003] Low transportation efficiency in open-pit mines is a common problem. The main reason is the lack of accurate information on the operating conditions of open-pit mines, which makes it impossible to make optimal scheduling decisions.

[0004] Although the importance of carbon emissions caused by fossil fuel consumption in open-pit mines to the development of green mines is now recognized, there is less attention paid to green and sustainable planning and scheduling methods in the production and transportation links. In practice, research on mining truck scheduling is mostly focused on improving transportation and electric shovel efficiency, ignoring the importance of refueling behavior to the continuous operation of mining trucks and the additional carbon emissions it brings.

[0005] In summary, how to reduce the transportation cost of mining trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0006] In view of the above problems, the present invention provides a coordinated method for low-carbon scheduling of autonomous driving mining trucks in open-pit mines and site selection of refueling points, including mining truck traffic allocation, green route planning and refueling point site selection planning, aiming to optimize decisions related to transportation, routes and refueling, improve the working efficiency of electric shovels, reduce the transportation costs of mining trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solution: a method for low-carbon scheduling of open-pit mine autonomous driving mining trucks and site selection coordination of refueling points, comprising:

[0008] 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;

[0009] The objective function of the mine truck flow allocation model is:

[0010] ;

[0011] 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;

[0012] 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;

[0013] The objective function of the green transportation route planning model is:

[0014]

[0015] 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 transport volume; Indicates transportation task Passing the road section ; 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 (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 factor (g / liter); is the air density (kg / m 3 ); is the gravitational constant (m / s 2 ); is the air resistance coefficient;

[0016] S3. According to the transportation route, a candidate set of refueling points is constructed, and the candidate set of refueling points and the refueling demand set are input into the constructed refueling point location optimization model to determine the location of the refueling point and the allocation of refueling demand for mining trucks.

[0017] Furthermore, the constraints of the mine truck flow allocation model in S1 are:

[0018] 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;

[0019] 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;

[0020] Constraints (4) and constraints (5) Ensure that the number of mining trucks entering and leaving the shovel and unloading point must be balanced;

[0021] 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;

[0022] Constraints (7) Represents a continuous variable and The value range of .

[0023] Furthermore, 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, .

[0024] Furthermore, the constraints of the green transportation route planning model in S2 are:

[0025] 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;

[0026] Constraints (2) , the uninstall point will have a size of of ore transport entering, and no ore transport leaving from the unloading point;

[0027] 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.

[0028] Constraints (4) Represents the transport capacity limit of the road section, where For road section transportation capacity;

[0029] Constraints (5) Indicates the road segments between loading and unloading points , is a sufficiently large positive integer;

[0030] Constraints (6) Indicates the speed limit range of mining trucks;

[0031] Constraints (7) Represents a continuous variable The value range of .

[0032] Furthermore, the refueling point candidate set in S3 is composed of the unloading point and the top 10 road nodes with the highest passing frequency in the road network transportation path. , unloading point set As a refueling demand point.

[0033] Furthermore, the objective function of the refueling point location selection model in S3 is:

[0034]

[0035] 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.

[0036] Furthermore, the constraints of the gas station location optimization model in S3 are:

[0037] 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;

[0038] Constraints (2) Indicates that only The point is selected as a refueling point, and the mining card can go there Click to refuel;

[0039] Constraints (3) It means that all demand points will have designated refueling points;

[0040] 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 their designated refueling point;

[0041] Constraints (5) Represents a binary variable and The value range of .

[0042] Furthermore, 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 driving mining trucks, load, speed, model data, loading and unloading time of electric shovels and unloading points; the production plan data includes the length of the work shift, the planned throughput of the unloading point and the planned number of refueling points.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] The present invention can improve the working efficiency of electric shovels, reduce the transportation cost of mining trucks, reduce carbon emissions, and promote the sustainable development of open-pit mines.

[0045] 1. The present invention provides road network information, vehicle status, shovel status, and open-pit mine production plan based on the open-pit mine digital platform, fully considers the operating capacity of mining trucks, electric shovels, and unloading points, builds an autonomous driving mining truck logistics distribution model, and optimizes the number of transportation shifts between loading and unloading points;

[0046] 2. Based on the carbon emission cost caused by the fuel consumption of mining truck transportation and the vehicle speed limit of the road network, the present invention constructs a green route planning model for autonomous driving mining trucks based on multi-commodity network flow, and optimizes the transportation path of autonomous driving mining trucks and the driving speed of each road section;

[0047] 3. The present invention presets the location of the refueling point, constructs a candidate set of refueling points, and builds a refueling point site selection planning model based on the distribution of refueling demand points in open-pit mines, thereby optimizing the location of refueling points and the matching relationship between refueling demand and refueling points. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process of the present invention.

[0049] Figure 2 A schematic diagram of an open-pit mine road network provided in an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the refueling point selection and refueling demand allocation results provided by the present invention.

[0051] Figure 4 A schematic diagram comparing the results of considering / not considering carbon emission costs provided by the present invention. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] See also Figure 1-Figure 4 As shown, an embodiment of the present invention provides a method for low-carbon scheduling of autonomous driving mining trucks in open-pit mines and coordinated site selection of refueling points. The method is applicable to application scenarios of autonomous driving mining truck transportation planning and mining truck refueling point planning in the process of open-pit mine production scheduling; specifically, the following steps are included:

[0054] S1. The acquired road network information, production equipment information, and production plan data are input into the constructed mine truck logistics allocation model to determine the transportation shifts and mine truck transportation tasks between loading and unloading points;

[0055] S2. Input the mining truck transportation task into the green transportation route planning model that considers the mining truck fuel consumption and carbon emission cost, and obtain the mining truck transportation path and road section transportation speed;

[0056] S3. According to the transportation route, a candidate set of refueling points is constructed, and the candidate set of refueling points and the refueling demand set are input into the constructed refueling point location optimization model to determine the location of the refueling point and the allocation of refueling demand for mining trucks.

[0057] The following is a detailed description of each of the above steps.

[0058] In the above step S1, it specifically includes:

[0059] First, the road network information, production equipment information and production plan data are obtained from the open-pit mine's smart mine digital platform; 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 self-driving mining trucks, load, speed, model data, and loading and unloading time of electric shovels and unloading points. The production plan data includes the length of the work shift, the planned throughput of the unloading point and the planned number of refueling points.

[0060] Then, the data is input into the mine truck flow allocation model. The objective function of the mine truck flow allocation model is:

[0061]

[0062] 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.

[0063] In this embodiment, the constraints of the mine truck flow allocation model objective are:

[0064] 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;

[0065] 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;

[0066] Constraints (4) and constraints (5) Ensure that the number of mining trucks entering and leaving the shovel and unloading point must be balanced;

[0067] 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;

[0068] In this embodiment, the Gurobi optimization solver is used to solve the mine truck flow distribution model to determine the number of transportation shifts between each loading and unloading point.

[0069] In this embodiment, according to 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 mining 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, .

[0070] In the above step S2, the transport shift, transport task, and mining truck information are input into the green transport route planning model. The objective function of the green transport route planning model is:

[0071]

[0072] 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 transport volume; Indicates transportation task Passing the road section ; 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 (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 factor (g / liter); is the air density (kg / m 3 ); is the gravitational constant (m / s 2 ); is the air resistance coefficient.

[0073] In this embodiment, the constraints of the green transportation route planning model objective are:

[0074] 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;

[0075] Constraints (2) , the uninstall point will have a size of of ore transport entering, and no ore transport leaving from the unloading point;

[0076] 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.

[0077] Constraints (4) Represents the transport capacity limit of the road section, where For road section transportation capacity;

[0078] Constraints (5) Indicates the road segments between loading and unloading points , is a sufficiently large positive integer;

[0079] Constraints (6) Indicates the speed limit range of mining trucks;

[0080] Constraints (7) Represents a continuous variable The value range of .

[0081] In this embodiment, the green transportation route planning model is solved by Gurobi optimization solver to determine the transportation route of the mining truck and the transportation speed in each section.

[0082] In the above step S3, the top 10 points with the highest frequency of passing through the unloading point and the road network transportation path are first selected to form a candidate set of refueling points. , unloading point set As a refueling demand point.

[0083] In this embodiment, in S3, the objective function of the gas station location optimization model is:

[0084]

[0085] in, The maximum distance from all demand points to their 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.

[0086] In this embodiment, the constraints of the gas station location optimization model objective are:

[0087] 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;

[0088] Constraints (2) Indicates that only The point is selected as a refueling point, and the mining card can go there Click to refuel;

[0089] Constraints (3) It means that all demand points will have designated refueling points;

[0090] 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 their designated refueling point;

[0091] Constraints (5) Represents a binary variable and The value range of .

[0092] Furthermore, the refueling point location optimization model is solved by Gurobi optimization solver to determine the refueling point selection and refueling demand allocation plan.

[0093] In the embodiment of the present invention, Python is used to write and execute a solution algorithm program including a transport distance penalty strategy. The optimization model of the embodiment can obtain a mining truck transportation plan, transportation route, transportation speed, refueling point selection and refueling demand allocation scheme, where the refueling point selection and refueling demand allocation results are as follows: Figure 3 The results of considering or not considering carbon emission costs are shown in Figure 4 shown.

[0094] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may 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 transportation task Passing the road section ; 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 (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 factor (g / liter); is the air density (kg / m 3 ); is the gravitational constant (m / s 2 ); is the air resistance coefficient; S3. According to the transportation route, a candidate set of refueling points is constructed, and the candidate set of refueling points and the refueling demand set are input into the constructed refueling point location optimization model to determine the location of the refueling point and the allocation of refueling demand for mining trucks.

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 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; Constraints (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 sufficiently large positive integer; Constraints (6) Indicates the speed limit range of mining trucks; Constraints (7) Represents a continuous variable The value range of .

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: 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.

6. 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 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.

7. 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 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 .

8. 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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