A mine car resource scheduling method, system, device and medium based on a second constraint convex optimization method

Abstract

A mine car resource scheduling method, system, device and medium based on a second constraint convex optimization method, the method comprising: establishing a scheduling model based on a second constraint convex optimization with the overall minimization of transportation time and processing time and the maximization of transportation volume as the optimization target; linearizing the constraint conditions of the optimized scheduling model; obtaining an optimal solution by KKT condition calculation of the convex optimization scheduling model through linearization transformation and genetic algorithm; and calculating the reasonable number of mine cars, the number of excavators and the optimal speed and optimal spacing of travel in real time to achieve the goal of minimum overall efficiency processing time and maximum workload of the mine car resources; the system, device and medium are used to implement the mine car resource scheduling method based on the second constraint convex optimization method; the reasonable number of mine cars, the number of excavators and the optimal speed of travel are calculated in real time by solving the multi-objective convex optimization problem through the construction of the scheduling model, so as to achieve the goal of minimum overall efficiency processing time and maximum workload, realize the resource matching and optimal scheduling of the mine car and the excavator, efficiently solve the problem, greatly improve the efficiency of the mine car resource scheduling, and improve the comprehensive management level of vehicle transportation and operation.

Description

Technical Field

[0001] This invention relates to the field of coal mine production technology, specifically to a method, system, equipment, and medium for scheduling mine car resources based on the quadratic constrained convex optimization method. Background Technology

[0002] In actual smart mine operations, the large number of mining trucks and excavators working on limited working faces and transport roads often leads to queue congestion. Due to the different performance and speeds of various vehicle types, as well as varying operator habits, especially in unforeseen circumstances, congestion is often exacerbated, resulting in reduced coal mining efficiency and directly impacting economic benefits. Patent application CN111160760A discloses an intelligent response scheduling system and method for mining excavators and trucks, utilizing communication protocols to achieve overall scheduling of various vehicles. However, this method cannot perform overall scheduling and often exhibits lag. Patent application CN110455290A discloses an optimal trajectory planning method for intelligent hydraulic excavators, employing path planning for scheduling. However, in open-pit mine operations, roads often change rapidly, making temporary road extraction and calculation difficult and resulting in low accuracy. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, the present invention aims to provide a mining truck resource scheduling method, system, equipment, and medium based on the quadratic constrained convex optimization method. By constructing a scheduling model to solve a multi-objective convex optimization problem, the reasonable number of mining trucks, the number of excavators, and the optimal travel speed are calculated in real time, thereby achieving the goal of minimizing overall efficiency processing time and maximizing workload. This achieves resource matching and optimal scheduling of mining trucks and excavators, saving time and increasing efficiency, ensuring safety and economy, and improving the comprehensive management level of vehicle transportation and operations.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A mining truck resource scheduling method based on quadratic constrained convex optimization includes the following steps:

[0006] Step 1: With the optimization objectives of minimizing overall transportation and processing time and maximizing transportation volume, establish a scheduling model based on quadratic constrained convex optimization:

[0007]

[0008]

[0009] The overall transportation and processing times are minimized as follows: The maximum transport volume is: -NL i ;

[0010] The constraints of the model are:

[0011] Among them, s n (t) represents the specific position of vehicle n at time t; κ is the sensitivity coefficient, Δs n (t)=s n-1 (t)-s n (t), while f(Δs) n (t) is the optimal velocity function; d s It's a safe distance, v max The maximum speed of this type of vehicle; rtt(i) is the round-trip time of the mining truck, C is the maximum processing capacity of the excavator vehicle working group, and η is the excavator's working efficiency; K max L represents the maximum number of excavators; L represents the transport capacity of the mining trucks.

[0012] Step 2: Solve the scheduling model using the optimization algorithm from Step 1. Linearize the constraints of the optimized scheduling model from Step 1 to obtain the following constraint equations:

[0013]

[0014] The following stability condition can be obtained:

[0015] f≤α / 2, where f is the derivative of the optimal velocity function when the intervals are equal:

[0016] Where h is the equidistant vehicle spacing in a stable traffic flow;

[0017] Step 3: Through linearization transformation and by using a genetic algorithm to calculate the KKT conditions for the convex optimization scheduling model, the optimal solution is obtained. The genetic algorithm calculation process is as follows:

[0018] First, a floating-point encoding method is used to solve a system of multivariate nonhomogeneous equations, which is then simplified to arranging all the unknown parameters to simulate a gene sequence.

[0019] Secondly, an initial population is generated. Assuming the initial population size is N, N individuals are generated using random numbers to serve as the initial gene sequence.

[0020] Finally, the fitness function is generated, and the merging of parent and child individuals is completed and a new parent population is generated. The distance between the individual adjacent to a certain individual in the new parent population and the objective function is determined by calculating the crowding degree, thereby filtering out solutions that are close in distance but ranked lower.

[0021] Step 4: Solve the scheduling model in Step 1 to calculate the reasonable number of mine trucks, the number of excavators, and the optimal speed and spacing in real time, so as to achieve the goal of minimizing the overall efficiency of mine truck resources and maximizing the workload; reorganize and allocate excavators according to the optimal solution obtained in Step 3 to achieve the reuse of excavators and combine it with the mine truck operation scheduling optimization model to finally achieve a collaborative optimization model with the goal of minimizing the total delay time and meeting the constraints of maintaining a safe distance and allowing overtaking when mine trucks pass.

[0022] A mining truck resource scheduling system based on quadratic constrained convex optimization method includes:

[0023] The algorithm calculation module is used for calculating KKT conditions in convex optimization.

[0024] The data storage module is used for recording and calculating process data;

[0025] The visualization module is used to display the calculation results.

[0026] A mining truck resource scheduling device based on quadratic constrained convex optimization method, comprising:

[0027] Memory, used to store computer programs;

[0028] The processor is used to implement the mine car resource scheduling method based on the quadratic constrained convex optimization method described in steps 1 to 4 when executing the computer program.

[0029] A computer-readable storage medium storing a computer program that, when executed by a processor, enables the scheduling of mining truck resources based on a quadratic constrained convex optimization method.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] By proposing a quadratic constrained convex optimization scheduling model, and solving the scheduling model using optimization algorithms, the optimal number of mine cars, excavators, and travel speeds are calculated in real time through linearization transformation and genetic algorithms. This achieves the goal of minimizing overall efficiency processing time and maximizing workload. It addresses the problem of weak rail transport capacity due to the underutilization of excavators when the ratio of mine cars to excavators in a coal mine is known. By reorganizing and allocating excavators to achieve their reuse, combined with a mine car operation scheduling optimization model, a collaborative optimization model is ultimately achieved that minimizes total delay time while satisfying constraints such as maintaining safe distances and allowing overtaking by mine cars. This model can be solved efficiently, significantly improving the efficiency of mine car resource scheduling. Attached Figure Description

[0032] Figure 1This is a basic traffic flow diagram.

[0033] Figure 2 This is a flowchart of the genetic algorithm solution of the present invention. Detailed Implementation

[0034] The present invention will be further described in detail below with reference to the accompanying drawings.

[0035] A mining truck resource scheduling method based on quadratic constrained convex optimization includes the following steps:

[0036] Step 1: With the optimization objectives of minimizing overall transportation and processing time and maximizing transportation volume, establish a scheduling model based on quadratic constrained convex optimization:

[0037]

[0038]

[0039] The overall transportation and processing times are minimized as follows: The maximum transport volume is: -NL i ;

[0040] The constraints of the model are:

[0041] The constraint model consists of two parts: the optimal speed model and the constraint on the number of mining cars. In a homogeneous mining car traffic flow, all transportation equipment and drivers have the same behavioral characteristics. However, in actual mining operations, all equipment and drivers exhibit different behavioral patterns, such as... Figure 1 As shown. The optimal speed model assumes that the driver's choice will allow the vehicle to travel at the best speed possible given the current vehicle distance.

[0042] Among them, s n (t) represents the specific position of vehicle n at time t; κ is the sensitivity coefficient, Δs n (t)=s n-1 (t)-s n (t), while f(Δs) n (t) is the optimal velocity function; d s It's a safe distance, v max The maximum speed of this type of vehicle; rtt(i) is the round-trip time of the mining truck, C is the maximum processing capacity of the excavator vehicle working group, and η is the excavator's working efficiency; K max L represents the maximum number of excavators; L represents the mining truck transportation capacity.

[0043] Step 2: Solve the scheduling model using the optimization algorithm from Step 1. Linearize the constraints of the optimized scheduling model from Step 1 to obtain the following constraint equations:

[0044]

[0045] The following stability condition can be obtained:

[0046] f≤α / 2, where f is the derivative of the optimal velocity function when the intervals are equal:

[0047] Where h is the equidistant distance between vehicles in a stable traffic flow.

[0048] Step 3: Through linearization transformation, and by using a genetic algorithm to calculate the KKT conditions for the convex optimization scheduling model, the optimal solution is obtained. The genetic algorithm calculation process is as follows: Figure 2 As shown. First, there are two main encoding methods for genetic algorithms: binary encoding and floating-point encoding. Floating-point encoding is used to solve the multivariate non-homogeneous equation system. The problem is simplified to arranging all unknown parameters, simulating a gene sequence. Second, the initial population can be generated in several ways. Assuming the initial population size is N, N individuals can be generated randomly as the initial gene sequence. Finally, the fitness function is generated. The fitness function is analogous to an organism's adaptability to its environment, serving as the basis for natural selection. Genes that adapt to the environment are more likely to be preserved. The fitness function is derived from the objective function. The process involves merging parent and offspring individuals and generating a new parent population. By calculating crowding, the distance between an individual adjacent to a given individual and the objective function is determined, thus filtering out solutions that are close in distance but ranked lower. Crowding calculation ensures the correctness of solutions in the solution set while increasing population diversity.

[0049] Step 4: Solve the scheduling model in Step 1 to calculate the reasonable number of mine trucks, the number of excavators, and the optimal speed and spacing in real time, so as to achieve the goal of minimizing the overall efficiency of mine truck resources and maximizing the workload; reorganize and allocate excavators according to the optimal solution obtained in Step 3 to achieve the reuse of excavators and combine it with the mine truck operation scheduling optimization model to finally achieve a collaborative optimization model with the goal of minimizing the total delay time and meeting the constraints of maintaining a safe distance and allowing overtaking when mine trucks pass.

[0050] This invention is not limited to the preferred embodiments described above. Anyone can derive other products in various forms under the guidance of this invention. However, regardless of any changes in shape or structure, any product with the same or similar technical solution as this application falls within the protection scope of this invention.

Claims

1. A mining truck resource scheduling method based on quadratic constrained convex optimization, characterized in that: Specifically, the following steps are included: Step 1: With the optimization objectives of minimizing overall transportation and processing time and maximizing transportation volume, establish a scheduling model based on quadratic constrained convex optimization: · The overall transportation and processing times are minimized as follows: The maximum transport volume is: ; The constraints of the model are: in, It is the specific position of vehicle n at time t; It is the sensitivity coefficient. ,and It is the optimal velocity function; It is a safe distance. This is the maximum speed of this type of vehicle; The round-trip time of the mine car. To maximize the processing capacity of the excavator vehicle working group To improve the working efficiency of excavators; This represents the maximum number of excavators. For mining truck transportation capacity; Step 2: Solve the scheduling model using the optimization algorithm from Step 1. Linearize the constraints of the optimized scheduling model from Step 1 to obtain the following constraint equations: The following stability condition can be obtained: Where h is the equidistant vehicle spacing in a stable traffic flow; Step 3: Through linearization transformation and by using a genetic algorithm to calculate the KKT conditions for the convex optimization scheduling model, the optimal solution is obtained. The genetic algorithm calculation process is as follows: First, a floating-point encoding method is used to solve a system of multivariate nonhomogeneous equations, which is then simplified to arranging all the unknown parameters to simulate a gene sequence. Secondly, an initial population is generated. Assuming the initial population size is N, N individuals are generated using random numbers to serve as the initial gene sequence. Finally, the fitness function is generated, and the merging of parent and child individuals is completed and a new parent population is generated. The distance between the individual adjacent to a certain individual in the new parent population and the objective function is determined by calculating the crowding degree, thereby filtering out solutions that are close in distance but ranked lower. Step 4: Solve the scheduling model in Step 1 to calculate the reasonable number of mine trucks, the number of excavators, and the optimal speed and spacing in real time, so as to achieve the goal of minimizing the overall efficiency of mine truck resources and maximizing the workload; reorganize and allocate the excavators according to the optimal solution obtained in Step 3 to achieve the reuse of excavators and combine it with the mine truck operation scheduling optimization model to finally achieve a collaborative optimization model with the goal of minimizing the total delay time and meeting the constraints of maintaining a safe distance for mine truck operation and allowing overtaking.

2. A mining truck resource scheduling system based on quadratic constrained convex optimization method, characterized in that: To implement the method of claim 1, including The algorithm calculation module is used to calculate the KKT conditions for convex optimization: First, it uses floating-point encoding to solve the multivariate non-homogeneous equation system and simplifies it by arranging all the unknown parameters to simulate a gene sequence; Secondly, an initial population is generated. Assuming the initial population size is N, N individuals are generated using random numbers to serve as the initial gene sequence. Finally, the fitness function is generated, and the merging of parent and child individuals is completed and a new parent population is generated. The distance between the individual adjacent to a certain individual in the new parent population and the objective function is determined by calculating the crowding degree, thereby filtering out solutions that are close in distance but ranked lower. The data storage module is used for recording and calculating process data; The visualization module is used to display the calculation results.

3. A mining truck resource scheduling device based on quadratic constrained convex optimization method, characterized in that: To implement the method of claim 1, comprising: Memory, used to store computer programs; The processor is used to implement the mine car resource scheduling method based on the quadratic constrained convex optimization method described in steps 1 to 4 when executing the computer program.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it can perform mining truck resource scheduling based on the method described in claim 1.

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