Scheduling method and system for production-type transportation in mining area

Abstract

The invention discloses a mining area production type transportation scheduling method and system, and relates to the technical field of resource scheduling, and the method comprises the steps: obtaining loading data, consumption data, and mine card cost data, speed data and energy consumption data; establishing a cost objective function; a consumption constraint condition is established, and the consumption constraint condition is that the coal mine accumulation amount of each consumption point is smaller than or equal to an accumulation threshold value, and the coal mine accumulation amount is also larger than or equal to the consumption amount of two adjacent mine trucks in the unloading time difference of the consumption point; solving a cost objective function by considering a consumption constraint condition, and obtaining a cost optimal scheduling strategy which enables the total cost to be lowest; and the mine cards are scheduled according to the cost optimal scheduling strategy. According to the method, the consumption constraint condition is considered when the target function is solved, the constraint condition describes the matching relation between the consumption capacity of the consumption equipment and the coal mine accumulation amount, the consumption equipment cannot idle, excessive coal mines cannot be accumulated, and the scheduling strategy is optimal in a real sense.

Description

Technical Field

[0001] This application relates to the technical field of resource scheduling, and particularly relates to a scheduling method and system for production-oriented transportation in a mining area. Background Art

[0002] Coal production involves multiple links, and a large number of equipment and personnel are required for mining, transportation, and primary processing. Among them, transportation is the intermediate link connecting mining and primary processing, and has an important impact on ensuring the stable and efficient operation of coal production. At present, a large number of mining trucks, referred to as mining trucks for short, are required in the transportation link. After waiting to be fully loaded at the loading point, the mining trucks transport the coal to the consumption point, unload the coal at the consumption point, complete the current transportation task, and continue the next transportation task, and so on.

[0003] Since the number of mining trucks participating in transportation is large, in order to improve the utilization rate of each mining truck and the overall operation efficiency as much as possible, the starting point and destination of each mining truck in each task will be planned and scheduled at the global level to achieve the lowest cost and shortest time under the condition of meeting the production task. Based on this requirement, many scheduling methods have been developed in the prior art. These methods aim at the lowest cost or shortest time, and use optimization algorithms such as genetic algorithms and particle swarm algorithms to solve the objective function to obtain the optimal scheduling strategy.

[0004] However, the current scheduling methods for coal transportation aim to obtain the optimal route for each mining truck, and do not consider the relationship between the routes too much. For a mining truck, the transportation task is completed after unloading at the consumption point. However, from the perspective of the consumption point, the accumulation amount of coal at the consumption point also needs to be considered. This accumulation amount cannot be too large, otherwise it will occupy too much area. At the same time, it cannot be too small, otherwise it will cause primary processing equipment, such as ore dressing machines and coal washing machines, to idle for a period of time, resulting in waste of resources. Summary of the Invention

[0005] The embodiments of this application provide a scheduling method and system for production-oriented transportation in a mining area, which are used to solve the problem that the equipment utilization rate at the consumption point is not high due to the fact that the existing scheduling methods do not consider the relationship between the routes of each mining truck.

[0006] On the one hand, the embodiments of this application provide a scheduling method for production-oriented transportation in a mining area, including:

[0007] Obtain the loading data of each loading point, the consumption data of each consumption point, and the cost data of each mining truck. The cost data includes the depreciation unit price and the maintenance unit price;

[0008] Obtain the speed data and energy consumption data of each mining truck when driving in the mining area;

[0009] Determine the cost unit price of each mining truck according to the cost data and energy consumption data;

[0010] Establish a cost objective function with the lowest total cost as the goal. The total cost is determined according to the cost unit price of each mining truck and the length of the driving route;

[0011] Establish a consumption constraint condition. The consumption constraint condition is that the coal pile volume at each consumption point is less than or equal to the stacking threshold. At the same time, the coal pile volume is also greater than or equal to the consumption volume in the unloading time difference between two adjacent mining trucks at this consumption point. The consumption volume is determined according to the time difference and consumption data. The time difference is determined according to the driving time of each mining truck on a driving route and the waiting time at the loading point. The driving time is determined according to the speed data and the length of the driving route. The waiting time is determined according to the loading data and the capacity data of each mining truck;

[0012] Considering the consumption constraint condition, solve the cost objective function to obtain the cost-optimal scheduling strategy that minimizes the total cost. The cost-optimal scheduling strategy includes the driving route of each mining truck;

[0013] Schedule the mining trucks according to the cost-optimal scheduling strategy.

[0014] On the other hand, the embodiment of the present application also provides a scheduling system for mining area production transportation, including:

[0015] The first data acquisition module is used to acquire the loading data of each loading point, the consumption data of each consumption point, and the cost data of each mining truck. The cost data includes the depreciation unit price and the maintenance unit price;

[0016] The second data acquisition module is used to acquire the speed data and energy consumption data when each mining truck travels in the mining area;

[0017] The cost unit price determination module is used to determine the cost unit price of each mining truck according to the cost data and energy consumption data;

[0018] The objective function establishment module is used to establish a cost objective function with the lowest total cost as the goal. The total cost is determined according to the cost unit price of each mining truck and the length of the driving route;

[0019] The constraint condition establishment module is used to establish a consumption constraint condition. The consumption constraint condition is that the coal pile volume at each consumption point is less than or equal to the stacking threshold. At the same time, the coal pile volume is also greater than or equal to the consumption volume in the unloading time difference between two adjacent mining trucks at this consumption point. The consumption volume is determined according to the time difference and consumption data. The time difference is determined according to the driving time of each mining truck on a driving route and the waiting time at the loading point. The driving time is determined according to the speed data and the length of the driving route. The waiting time is determined according to the loading data and the capacity data of each mining truck;

[0020] The objective function solving module is used to consider the accommodation constraint conditions, solve the cost objective function, and obtain the cost-optimal scheduling strategy that minimizes the total cost. The cost-optimal scheduling strategy includes the driving routes of each mining truck.

[0021] The mining truck scheduling module is used to schedule the mining trucks according to the cost-optimal scheduling strategy.

[0022] A scheduling method and system for mining area production transportation in this application have the following advantages:

[0023] When solving the objective function that minimizes the cost, the accommodation constraint conditions are considered. This constraint condition describes the matching relationship between the accommodation capacity of the accommodation equipment and the coal mine stacking volume, which neither makes the accommodation equipment run idly nor accumulates too much coal mine near the accommodation equipment, making the scheduling strategy reach the true optimal on the basis of the lowest cost. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of a scheduling method for mining area production transportation provided by an embodiment of the present application. Detailed Embodiments

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0027] Figure 1 It is a flowchart of a scheduling method for mining area production transportation provided by an embodiment of the present application. An embodiment of the present application provides a scheduling method for mining area production transportation, including:

[0028] S100, obtaining the loading data of each loading point, the accommodation data of each accommodation point, and the cost data of each mining truck. The cost data includes the depreciation unit price and the maintenance unit price.

[0029] Exemplarily, the mining truck in the present application can be a new energy mining truck driven by an electric motor or a traditional mining truck driven by an internal combustion engine, and these mining trucks can be manned vehicles or driverless vehicles. Regardless of the driving form or driving mode adopted, a communication device needs to be installed on each mining truck to send the status data of the mining truck to the dispatching center in real time and also receive the dispatching instructions issued by the dispatching center.

[0030] The loading point is the location where the mining truck loads coal, and the consumption point is the location where the mining truck unloads coal. There are multiple loading points and multiple consumption points in a mining area. Each loading point has corresponding loading equipment, and the loading speeds of these loading equipment are the same. The loading data mentioned in the present application refers to the loading speed, that is, the volume of coal that can be loaded per unit time. The loading volume can be controlled by the loading equipment. Generally, each mining truck will be filled with the entire carriage during one loading, and the loading volume of the vehicle is known. The weight of the mining truck together with the coal can be roughly estimated according to the compactness of the coal in the carriage and the density of the coal.

[0031] Consumption equipment such as ore dressing machines or coal washing machines is set at the consumption point. These consumption equipment use the coal unloaded by the mining truck as raw materials for rough processing. Therefore, the working state of the consumption equipment depends heavily on the amount of coal transported by the mining truck. After the mining truck unloads the coal, the coal will be temporarily piled up in the site near the consumption equipment and then transferred to the consumption equipment by equipment such as loaders. If it is necessary to ensure the continuous operation of the consumption equipment, it is necessary to make the consumption volume at the consumption point within the unloading time difference between two adjacent mining trucks unloading coal at the same consumption point less than or equal to the coal piling volume of the coal piled up in the site after the previous mining truck unloads coal among the two adjacent mining trucks. The consumption volume is determined according to the consumption data of the consumption point, that is, the consumption speed and the unloading time difference between two adjacent mining trucks. However, the coal piling volume should not be too large either. Excessive piled-up coal will cause serious occupation of the site and it is not easy for the mining truck to unload coal. Generally, a piling threshold will be set, and the coal piling volume in the site near the consumption equipment is not greater than, that is, less than or equal to this piling threshold at any time.

[0032] The depreciation unit price is the depreciation price caused by the mining truck per unit distance traveled, and it can usually be estimated from the purchase price, selling price and driving mileage of the mining truck. The maintenance price is the maintenance price caused by the mining truck per unit distance traveled, and it can usually be estimated from the maintenance cost of the mining truck and the driving mileage.

[0033] S110, obtain the speed data and energy consumption data of each mining truck when driving in the mining area.

[0034] Exemplarily, the speed data and energy consumption data can be determined after the mining truck actually travels in the mining area. During the driving process, the mining truck can pass through all positions in the mining area and drive in two different directions, uphill and downhill, at any position, and finally obtain the speed data and energy consumption data of the mining truck. The speed data referred to in this application is the average speed, and the energy consumption data is the energy consumed by the mining truck per unit distance. For new energy mining trucks, this energy consumption data is the power consumption, and for traditional mining trucks, it is the fuel consumption.

[0035] S120. Determine the cost unit price of each mining truck according to the cost data and energy consumption data.

[0036] Exemplarily, when obtaining the energy consumption data, obtain the energy consumption data per unit distance when the mining truck travels at different position points and in different directions in the mining area, establish a corresponding relationship between the energy consumption data and the position points and directions, and when determining the cost unit price, determine the cost unit price at each position point and direction according to the corresponding relationship.

[0037] Specifically, each road in the mining area that allows the mining truck to pass can be divided into multiple position points. When dividing the position points, a position point can be set every certain distance, such as 5m or 10m. The driving route is a directed curve formed by connecting multiple position points in sequence. The starting point of this directed curve is the current position or a loading point, and the ending point is a consumption point.

[0038] After dividing the position points, the mining truck will drive along a pre-set route, which will cover all position points in the mining area. During the driving process, the speed data and energy consumption data will be recorded at all times. Every time it passes through a position point, the current speed data and energy consumption data of the mining truck will be used as the speed data and energy consumption data of this position point, and thus the corresponding relationship between the energy consumption data and the position points is obtained. After the mining truck drives in the opposite direction at the same position point, the corresponding relationship between the energy consumption data, the position points, and the directions can be obtained.

[0039] Since the cost data includes the depreciation unit price and the maintenance unit price, the cost data has nothing to do with the usage environment of the mining truck and is a fixed value. Therefore, the cost unit price is the sum of the fixed cost data and the energy consumption data at a certain position point.

[0040] S130. Establish a cost objective function with the lowest total cost as the goal. The total cost is determined according to the cost unit price of each mining truck and the length of the driving route.

[0041] Exemplarily, the cost objective function can be expressed as:

[0042]

[0043] In the formula, F c is the cost objective function, min is to take the minimum value, Ctotal Let \(C\) be the total cost, \(M\) be the number of transportation tasks performed by the mining truck, \(j\) be the label of the transportation task performed by the mining truck, \(N\) be the number of mining trucks, \(i\) be the label of the mining truck, and \(O\) ij be the number of position points on the driving route when the \(i\)-th mining truck performs the \(j\)-th transportation task, \(s\) be the label of the position point, and \(P\) sij be the unit cost of the \(s\)-th position point on the driving route when the \(i\)-th mining truck performs the \(j\)-th transportation task, and \(D\) sij be the distance between the \(s\)-th position point and the next position point on the driving route when the \(i\)-th mining truck performs the \(j\)-th transportation task. That is, it is the length of the driving route when the \(i\)-th mining truck performs the \(j\)-th transportation task.

[0044] It should be understood that since the direction is considered, the cost objective function also needs to clarify the direction corresponding to the unit cost \(P\) sij and select the accurate unit cost according to the actual direction of the driving route.

[0045] Furthermore, when obtaining the energy consumption data of the mining truck at different position points and different directions, the mining truck is also in a variety of different loading weight states. According to the energy consumption data of the mining truck in different loading weight states, the energy consumption function corresponding to each position point and direction is fitted. When determining the total cost, the actual unit cost of the mining truck at each position point is determined according to the weight of the mining truck, the direction of the driving route, the position points passed through, and the energy consumption function, and then the total cost is determined according to the length of the driving route and the actual unit cost.

[0046] Specifically, since the mining truck is not always fully loaded during each driving process, it may not be fully loaded after leaving the loading point, or it may be fully loaded at the loading point but has unloaded a part of the coal after passing through a consumption point, and the remaining part of the coal will be transported to another consumption point. Therefore, the mining truck during the driving process may be of any weight, and its speed and energy consumption are not exactly the same under different weights. Therefore, the least squares method will be used to fit the energy consumption function at each position point and direction, and this energy consumption function can be expressed in the form of the following polynomial:

[0047] \(E = aW\) 2 + \(bW + c\)

[0048] In the formula, \(E\) is the energy consumption function, \(W\) is the weight of the mining truck, and \(a\), \(b\), and \(c\) are the coefficients of the quadratic term, linear term, and constant term respectively.

[0049] After establishing the above energy consumption function, the weight of the mining truck can be substituted into it, and finally the energy consumption data of the mining truck at each position point can be calculated. Based on this energy consumption data, the actual unit cost of this position point can be determined, and then the total cost can be finally determined.

[0050] S140. Establish consumption constraint conditions. The consumption constraint conditions are that the coal mine accumulation volume at each consumption point is less than or equal to the accumulation threshold, and at the same time, the coal mine accumulation volume is also greater than or equal to the consumption volume in the unloading time difference between two adjacent mining trucks at this consumption point. The consumption volume is determined according to the time difference and consumption data. The time difference is determined according to the driving time of each mining truck on a driving route and the waiting time at the loading point. The driving time is determined according to the speed data and the length of the driving route. The waiting time is determined according to the loading data and the capacity data of each mining truck.

[0051] Exemplarily, the consumption constraint conditions can be expressed as:

[0052] V a ≤V max

[0053] V a ≥V c

[0054] V c =v c ·Δt

[0055] Δt=(t ii +t di +t wi )-(t i(i+1) +t d(i+1) +t w(i+1) )

[0056]

[0057] In the formula, V a is the coal mine accumulation volume, V max is the accumulation threshold, V c is the consumption volume, v c is the consumption data, Δt is the time difference, t ii is the initial time of the i-th mining truck, t di is the driving time of the i-th mining truck, t wi is the waiting time of the i-th mining truck, t i(i+1) is the initial time of the (i + 1)-th mining truck, t d(i+1) is the driving time of the (i + 1)-th mining truck, t w(i+1) is the waiting time of the (i + 1)-th mining truck, V v is the capacity data of the mining truck, v l is the loading data.

[0058] Further, when obtaining the speed data, obtain the speed data of each mining truck under different loading weight states, and fit to obtain a speed function reflecting the relationship between the loading weight and the speed. When determining the travel time, determine the actual speed of the mining truck according to the weight of the mining truck and the speed function, and then determine the travel time according to the actual speed and the length of the travel route.

[0059] Specifically, similar to the energy consumption data, the driving speeds of mining trucks with different weights are also different. Therefore, the least squares method can be used to fit and obtain the speed function, and the speed function can be expressed in the following polynomial form:

[0060] V = dW 2 + eW + f

[0061] In the formula, V is the speed function, and d, e, and f are the coefficients of the quadratic term, the linear term, and the constant term, respectively.

[0062] After establishing the above speed function, the weight of the mining truck can be substituted into it, and finally the actual speed of the mining truck can be calculated. Based on this actual speed, the travel time can be finally determined.

[0063] Further, after obtaining the loading data, the consumption data, the speed data, and the cost unit price, a comprehensive objective function is established with the goal of minimizing the weighted sum of the total cost and the total time. Considering the consumption constraint conditions, the comprehensive objective function is solved to obtain a comprehensive optimal scheduling strategy that minimizes the weighted sum of the total cost and the total time. The comprehensive optimal scheduling strategy includes the travel route of each mining truck.

[0064] Specifically, the comprehensive objective function can be expressed as:

[0065] F s = min(αC n + βT n )

[0066] In the formula, F s is the comprehensive objective function, α and β are the weights of C n and T n , respectively. The value ranges of α and β are [0, 1], and the sum of the two weights is 1. C n and T n are the normalized total cost and the execution time, respectively, and are expressed as follows:

[0067]

[0068] In the formula, C min and C max are the minimum and maximum values of the total cost among all scheduling strategies, respectively. T l is the longest time required for the mining truck to execute a scheduling strategy, and T minand T max are respectively the minimum and maximum values of the longest time among all scheduling strategies.

[0069] S150. Considering the accommodation constraint conditions, solve the cost objective function to obtain the cost-optimal scheduling strategy that minimizes the total cost. The cost-optimal scheduling strategy includes the driving routes of each mining truck.

[0070] Exemplarily, the driving route includes at least one loading point and at least one accommodation point. Therefore, this application allows the mining truck to load coal at multiple loading points and then unload the coal at multiple accommodation points, without having to limit that it must be fully loaded at one loading point and then fully unloaded at one accommodation point, improving the flexibility of the scheduling strategy.

[0071] Further, after establishing the accommodation constraint conditions, a grade matching constraint condition is also established. The grade matching constraint condition is that the grade of the coal loaded by the mining truck at the loading point matches the grade of the coal that the accommodation point can receive.

[0072] Specifically, the grade is the quality of the coal. Although in the same mining area, the quality of the coal produced at different loading points is not the same, and the requirements for the quality of the coal at different accommodation points are also different. It is necessary to ensure that the coal loaded by the mining truck at the loading point is unloaded at the accommodation point with a matching grade to avoid the problem of incorrect coal scheduling.

[0073] Further, a genetic algorithm is used to solve the cost objective function or the comprehensive objective function.

[0074] Specifically, the genetic algorithm is a kind of heuristic algorithm, which generally needs to go through multiple steps such as determining the fitness value, selection, crossover, and mutation. These steps simulate the intergenerational inheritance of organisms in the natural environment and select the scheduling strategy with the highest fitness value after multiple rounds of iteration according to the principle of survival of the fittest.

[0075] S160. Schedule the mining trucks according to the cost-optimal scheduling strategy.

[0076] Exemplarily, the scheduling strategy includes the driving routes of each mining truck in each task, and each driving route is in the form of a series of position lines connected in sequence. Therefore, after the mining truck obtains the scheduling strategy, it will extract the order of the position points of the driving route in its own task and complete the transportation task by driving in sequence according to these position points.

[0077] The embodiment of this application also provides a scheduling system for mining area production transportation. This system includes the following functional modules:

[0078] The first data acquisition module is used to acquire the loading data of each loading point, the accommodation data of each accommodation point, and the cost data of each mining truck. The cost data includes the depreciation unit price and the maintenance unit price;

[0079] A second data acquisition module, configured to acquire speed data and energy consumption data when each mining truck travels in a mining area;

[0080] A cost unit price determination module, configured to determine the cost unit price of each mining truck according to cost data and energy consumption data;

[0081] An objective function establishment module, configured to establish a cost objective function with the lowest total cost as the goal, where the total cost is determined according to the cost unit price of each mining truck and the length of the driving route;

[0082] A constraint condition establishment module, configured to establish a consumption constraint condition, where the consumption constraint condition is that the coal pile quantity at each consumption point is less than or equal to the pile threshold, and at the same time, the coal pile quantity is also greater than or equal to the consumption quantity in the unloading time difference between two adjacent mining trucks at this consumption point. The consumption quantity is determined according to the time difference and consumption data, the time difference is determined according to the driving time of each mining truck on a driving route and the waiting time at the loading point, the driving time is determined according to the speed data and the length of the driving route, and the waiting time is determined according to the loading data and the capacity data of each mining truck;

[0083] An objective function solving module, configured to consider the consumption constraint condition, solve the cost objective function, and obtain a cost-optimal scheduling strategy that minimizes the total cost. The cost-optimal scheduling strategy includes the driving route of each mining truck;

[0084] A mining truck scheduling module, configured to schedule mining trucks according to the cost-optimal scheduling strategy.

[0085] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0086] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for dispatching production-type transportation in a mining area, characterized in that: include: Obtaining the loading data of each loading point, the consumption data of each consumption point and the cost data of each mining truck, wherein the cost data includes the depreciation unit price and the maintenance unit price; Obtain the speed and energy consumption data of each mining truck when it is traveling in the mining area; Determine the unit cost price of each mining truck according to the cost data and the energy consumption data; A cost objective function is established with the goal of minimizing the total cost, wherein the total cost is determined according to the cost unit price of each mining truck and the length of the driving route; Establish a consumption constraint condition, wherein the coal mine accumulation amount at each consumption point is less than or equal to the accumulation threshold, and the coal mine accumulation amount is also greater than or equal to the consumption amount in the unloading time difference between two adjacent mining trucks at the consumption point, the consumption amount is determined according to the time difference and the consumption data, the time difference is determined according to the driving time of each mining truck on one of the driving routes and the waiting time at the loading point, the driving time is determined according to the speed data and the length of the driving route, and the waiting time is determined according to the loading data and the capacity data of each mining truck; Considering the consumption constraint condition, solving the cost objective function, and obtaining a cost-optimal scheduling strategy that minimizes the total cost, wherein the cost-optimal scheduling strategy includes the driving route of each mining truck; The mining trucks are dispatched according to the cost-optimal dispatching strategy.

2. A method for dispatching production-type transportation in a mining area according to claim 1, characterized in that: When acquiring the energy consumption data, the energy consumption data per unit distance when the mining truck travels at different locations and in different directions in the mining area is acquired, a correspondence is established between the energy consumption data and the location points and directions, and when determining the unit cost price, the unit cost price at each location point and direction is determined according to the correspondence.

3. A method for dispatching production-type transportation in a mining area according to claim 2, characterized in that: When obtaining the energy consumption data of the mining truck at different positions and in different directions, the mining truck is also placed in a variety of different loading weight states, and the energy consumption function corresponding to each position point and direction is fitted according to the energy consumption data of the mining truck under the different loading weight states. When determining the total cost, the actual cost unit price of the mining truck at each position point is determined according to the weight of the mining truck, the direction of the driving route, the passed position points, and the energy consumption function, and then the total cost is determined according to the length of the driving route and the actual cost unit price.

4. The method for dispatching production-type transportation in a mining area according to claim 1, characterized in that: When acquiring the speed data, the speed data of each mining truck under different loading weight states is acquired, and a speed function reflecting the relationship between the loading weight and the speed is fitted. When determining the travel time, the actual speed of the mining truck is determined according to the weight of the mining truck and the speed function, and then the travel time is determined according to the actual speed and the length of the travel route.

5. The method for dispatching production-type transportation in a mining area according to claim 1, characterized in that: The driving route includes at least one loading point and at least one disposal point.

6. The method for dispatching production-type transportation in a mining area according to claim 1, characterized in that: After establishing the consumption constraint condition, a grade matching constraint condition is also established, and the grade matching constraint condition is that the grade of the coal loaded by the mining truck at the loading point matches the grade of the coal that the consumption point can receive.

7. The method for dispatching production-type transportation in a mining area according to claim 1, characterized in that: After obtaining the loading data, the consumption data, the speed data and the unit cost, a comprehensive objective function is established with the minimum weighted sum of the total cost and the total time as the goal, and the consumption constraint is considered to solve the comprehensive objective function to obtain a comprehensive optimal scheduling strategy that minimizes the weighted sum of the total cost and the total time. The comprehensive optimal scheduling strategy includes the driving route of each mining truck.

8. A method for dispatching production-type transportation in a mining area according to claim 7, characterized in that: A genetic algorithm is used to solve the cost objective function or the comprehensive objective function.

9. A system using the method for dispatching mining area production transportation according to any one of claims 1 to 8, characterized in that: include: A first data acquisition module is used to acquire the loading data of each loading point, the consumption data of each consumption point and the cost data of each mining truck, wherein the cost data includes a depreciation unit price and a maintenance unit price; The second data acquisition module is used to acquire the speed data and energy consumption data of each mining truck when it is traveling in the mining area; A cost unit price determination module, used to determine the cost unit price of each mining truck according to the cost data and the energy consumption data; An objective function establishment module is used to establish a cost objective function with the goal of minimizing the total cost, wherein the total cost is determined according to the cost unit price of each mining truck and the length of the driving route; A constraint condition establishment module is used to establish a consumption constraint condition, wherein the coal mine accumulation amount at each consumption point is less than or equal to the accumulation threshold, and the coal mine accumulation amount is also greater than or equal to the consumption amount in the unloading time difference between two adjacent mining trucks at the consumption point, and the consumption amount is determined according to the time difference and the consumption data, and the time difference is determined according to the driving time of each mining truck on a driving route and the waiting time at the loading point, and the driving time is determined according to the speed data and the length of the driving route, and the waiting time is determined according to the loading data and the capacity data of each mining truck; An objective function solving module, used to consider the consumption constraint condition, solve the cost objective function, and obtain a cost-optimal scheduling strategy that minimizes the total cost, wherein the cost-optimal scheduling strategy includes the driving route of each mining truck; The mining truck scheduling module is used to schedule the mining truck according to the cost-optimal scheduling strategy.

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