Trackless rubber-tyred vehicle scheduling method and auxiliary transportation system based on modular auxiliary transportation mode

Abstract

The present invention provides a method and system for dispatching trackless rubber-tyred vehicles based on a modular auxiliary transportation mode, which relates to the field of auxiliary transportation in coal mines. The method includes: obtaining material demand information and modular loading container information; generating material transportation tasks based on the material demand information and modular loading container information; using the Floyd algorithm to store the shortest distance path matrix between the ground and each node in the coal mine; based on an improved adaptive genetic algorithm, generating a vehicle dispatching plan based on the shortest distance path matrix, vehicle information and material transportation tasks; and performing auxiliary transportation dispatching according to the above vehicle dispatching plan. In the embodiment of the present invention, the trackless rubber-tyred vehicles are combined with modular transportation of multiple vehicles. Through the loading and unloading of the vehicles, the vehicles do not need to wait for a long time, and multiple trackless rubber-tyred vehicles can efficiently complete the auxiliary transportation tasks in the coal mines. A combined strategy genetic algorithm that conforms to the modular auxiliary transportation mode is designed, which significantly improves the efficiency of material auxiliary transportation in coal mines.

Description

Technical Field

[0001] The present invention relates to the technical field of auxiliary transportation in underground coal mines, and in particular to a method for dispatching trackless rubber-tyred vehicles and an auxiliary transportation system based on a modular auxiliary transportation mode. Background Art

[0002] Currently, there are two main modes of auxiliary material transportation in traditional coal mines: monorail cranes and containers, and trackless rubber-tyred vehicles used to transport bulk materials. A scheduling method based on these two traditional modes is used to manage and control auxiliary transportation, thereby improving the efficiency of auxiliary material transportation and coal mine production.

[0003] However, both auxiliary material transport modes have certain drawbacks. Monorail crane transport lacks flexibility, while trackless rubber-wheeled vehicles are time-consuming and costly to transport bulk materials, and have insufficient capacity. Furthermore, the scheduling methods for these traditional auxiliary transport modes operate independently from the actual production process, resulting in limited improvements in auxiliary material transport efficiency in coal mines. Summary of the Invention

[0004] To solve the above problems, an embodiment of the present invention provides a method for scheduling trackless rubber-tyred vehicles based on a modular auxiliary transportation mode, the method comprising: obtaining material demand information and modular loading container information; the modular loading containers comprise box-type carriers, plate-type carriers and container carriers, each of the carriers being used to load onto or unload from the trackless rubber-tyred vehicle; generating material transportation tasks according to the material demand information and the modular loading container information; adopting the Floyd algorithm to store the shortest distance path matrix of each node on the ground and underground in a coal mine; generating a vehicle scheduling plan based on the shortest distance path matrix, vehicle information and the material transportation tasks based on an improved adaptive genetic algorithm; the improved adaptive genetic algorithm comprises constraints of an objective function constructed based on the vehicle information, the material demand information and the modular loading container information, and an objective function constructed based on the travel distance, transportation task completion time and operating cost of the trackless rubber-tyred vehicle; performing auxiliary transportation scheduling according to the vehicle scheduling plan.

[0005] In the embodiment of the present invention, the modular transportation of trackless rubber-tyred vehicles with multiple carriers, the flexible combination of vehicle + carrier + material, and the loading and unloading of the carriers do not require the vehicles to wait for a long time. Multiple trackless rubber-tyred vehicles can efficiently complete the auxiliary transportation tasks in the coal mine. A combination strategy genetic algorithm that conforms to the modular auxiliary transportation mode is designed. Compared with the traditional trackless rubber-tyred vehicle transportation of bulk materials and rail locomotive transportation in the coal mine, a new auxiliary transportation mode is established, the traditional scheduling process is updated, and the efficiency of the auxiliary transportation of materials in the coal mine is significantly improved.

[0006] Optionally, the constraints of the improved adaptive genetic algorithm include:

[0007]

[0008]

[0009] T={T1,T2,…,T m}

[0010] R={R1,R2,…,R m}

[0011] Among them, T represents the set of container transport tasks, R represents the set of trackless rubber-tyred vehicle transport tasks, k represents the number of material transport tasks, n represents the number of material loading and unloading points, m represents the required number of trackless rubber-tyred vehicles, a represents the number of trackless rubber-tyred vehicle transport tasks, and cm represents the transport tasks to which the trackless rubber-tyred vehicle is assigned.

[0012] The embodiment of the present invention provides constraint conditions for constructing an improved adaptive genetic algorithm objective function based on vehicle information, material demand information and loading container information, establishes a new auxiliary transportation mode, and significantly improves the auxiliary transportation efficiency of coal mine materials.

[0013] Optionally, the objective function of the improved adaptive genetic algorithm is expressed as:

[0014] G=αs+βt+λc

[0015]

[0016]

[0017] c=p y +c t +g v +c f +c oh

[0018] Among them, G represents the total objective function, s represents the travel distance of the trackless rubber-tyred vehicle, t represents the time it takes for the trackless rubber-tyred vehicle to complete the transportation task, c represents the operating cost, and s ij represents the distance traveled by each rubber-tyred trackless vehicle to complete the transportation task, t l Indicates the time the rubber-tyred trackless vehicle waits for loading at the material yard, t u Indicates the time the rubber-tyred trackless vehicle waits for unloading in the well, p y represents the total salary expenditure of personnel, c t represents the purchase cost of rubber-tyred trackless vehicles, g v Indicates the fuel cost of the rubber-tyred trackless vehicle, c f represents the monthly maintenance cost of rubber-tyred trackless vehicles, c oh Indicates the overhaul cost of rubber-tyred trackless vehicles.

[0019] The embodiment of the present invention provides an improved objective function of an adaptive genetic algorithm, establishes a new auxiliary transportation mode, and significantly improves the auxiliary transportation efficiency of coal mine materials.

[0020] Optionally, the improved adaptive genetic algorithm includes multi-sequence coding; the multi-sequence coding includes a first sequence: a global transport route, a second sequence: a multi-vehicle material transport route, and a third sequence: a material vehicle transport route; using l to represent a transport task and i to represent the i-th transport task, the global transport route obtained by coding is as follows:

[0021] {l1,l2,l3,…,l i}

[0022] In the global transportation route, a, b, c... are used to represent underground material demand points, a i represents the i-th transportation task to the material demand point a, b i represents the i-th transportation task to the material demand point b;

[0023] The global transport route is “split by 0”, that is, 0 is used as the starting point to split the global transport route into specific multi-vehicle transport paths. Then, after encoding, a multi-vehicle material transport route can be obtained as follows:

[0024] {0,a1,b1,…,0,a2,c1,…,0,a i ,…,0,b i ,…,0,c i ,…,0}

[0025] The material transportation routes of the multiple vehicles are sorted and recombined, and based on the time constraint of the global transportation task, a material transportation route for each trackless rubber-tyred vehicle is generated.

[0026] The embodiment of the present invention provides an improved multi-sequence encoding process of an adaptive genetic algorithm, which innovates and improves the modular material auxiliary transportation link in coal mines, thereby improving accuracy and flexibility.

[0027] Optionally, the improved adaptive genetic algorithm includes initializing a population; the initializing population includes: marking all nodes that need to transport materials and the demand for materials; taking the upper limit of the loading capacity of the rubber-tyred trackless vehicle as m units as a constraint, calculating the material demand of each node based on m units to require at least k trips, and supplementing d trips as a margin for nodes with high material demand, that is, nodes with high material demand require at least k+d trips; generating initial target nodes according to the required trips for each node; randomly sorting the generated initial target locations to generate n initial individuals; inserting the starting point 0 into the initial individuals with the upper limit of the loading capacity as a constraint to generate a complete initial population.

[0028] The embodiment of the present invention provides an improved population initialization process of the adaptive genetic algorithm, which provides a good foundation for subsequent optimization iterations of the genetic algorithm.

[0029] Optionally, the improved adaptive genetic algorithm includes fitness calculation, and the fitness calculation formula of the population is:

[0030]

[0031] Among them, f i represents the fitness value of the i-th individual, G i represents the objective function of the i-th individual.

[0032] Optionally, the improved adaptive genetic algorithm includes crossover mutation, and the crossover probability formula is as follows:

[0033]

[0034] Among them, P c is the crossover probability, P c1 For a given crossover probability, P c2 is the self-determined crossover probability, f=max(f1,f2) is the larger fitness value of parent individual 1 and parent individual 2, f avg is the average fitness value of the contemporary population, f max is the maximum fitness value of the contemporary population.

[0035] Optionally, a crossover degree value negatively correlated with the evolutionary generation number is adopted, and the crossover probability decay function is set as follows:

[0036]

[0037] Among them, P c ′ is the crossover probability attenuation function, μ and λ are related factors that affect the crossover probability attenuation function, μ affects the overall change of the function, λ is related to the process of genetic iteration, and λ∈1,2, n is the current iteration number, N is the total number of genetic iterations;

[0038] After introducing the crossover probability attenuation function, the crossover probability is improved to:

[0039]

[0040] After introducing the mutation probability attenuation function, the mutation probability in the population genetic process is improved to:

[0041]

[0042] Subformula P′ m is the mutation probability attenuation function, P m is the mutation probability, Pm1 For a given mutation probability, P m2 is the self-determined mutation probability, and f′ is the fitness value of the parent individual to be mutated.

[0043] The embodiment of the present invention provides an improved adaptive genetic algorithm fitness evaluation and selection process, the goal of which is to reduce the transportation distance, transportation time and transportation cost of the entire vehicle auxiliary transportation task.

[0044] Optionally, the method further comprises a scheduling task change step, wherein the scheduling task change adopts a global optimization scheduling strategy, a supplementary optimization scheduling strategy or a dynamic insertion scheduling strategy; the global optimization scheduling strategy comprises: if the number of tasks at time t changes, judging whether the trackless rubber-tyred vehicle has started the auxiliary transport task; if it has not yet started, changing the demand matrix according to the changed material auxiliary transport demand, the demand matrix corresponds to the transportation location, and regenerating the vehicle scheduling plan; or, the supplementary optimization scheduling strategy comprises: if the number of tasks at time t changes, judging whether the trackless rubber-tyred vehicle has started the auxiliary transport task; if it has already started, changing the demand matrix according to the changed material auxiliary transport demand, the demand matrix corresponds to the transportation location, and regenerating the vehicle scheduling plan. The transportation demand changes the demand matrix, which corresponds to the transportation location, and adds a new trackless rubber-tyred vehicle to complete the temporary auxiliary transportation task on the basis of the previous task sequence; or, the dynamic insertion scheduling strategy includes: if the number of tasks at time t changes, judging whether the trackless rubber-tyred vehicle has started the auxiliary transportation task; if it has not started, judging for each trackless rubber-tyred vehicle whether the remaining load of the old task sequence is sufficient to complete the added auxiliary transportation task; if it has started, judging the number of rounds of transportation of the trackless rubber-tyred vehicle, and then judging whether the old task sequence that has not been transported has sufficient remaining load to complete the added auxiliary transportation task, and inserting the new task into the old task sequence.

[0045] In the embodiment of the present invention, a complex scenario scheduling method is designed to implement a modular scheduling solution of static global output + dynamic real-time response.

[0046] An embodiment of the present invention provides a modular auxiliary transport system, including a trackless rubber-tyred vehicle scheduling system, multiple trackless rubber-tyred vehicles and multiple modular loading containers; the modular loading containers include box-type carriers, plate-type carriers and container carriers, each of the carriers is used to load on or unload from the trackless rubber-tyred vehicle; the plate-type carriers are used to load materials or container carriers; the trackless rubber-tyred vehicle scheduling system is used to execute any of the above-mentioned trackless rubber-tyred vehicle scheduling methods based on the modular auxiliary transport mode.

[0047] The modular auxiliary transport system provided by the embodiment of the present invention can achieve the same technical effect as the above-mentioned trackless rubber-tyred vehicle scheduling method based on the modular auxiliary transport mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0049] Figure 1 1 is a schematic flow chart of a method for dispatching rubber-tyred trackless vehicles based on a modular auxiliary transport mode in an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a loading container in an embodiment of the present invention;

[0051] Figure 3 Schematic diagram of a vehicle loading a plate-type carrier and a plate-type carrier loading a container carrier in an embodiment of the present invention;

[0052] Figure 4 Schematic diagram of transporting materials to multiple destinations in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] The embodiment of the present invention provides a method for dispatching trackless rubber-tyred vehicles using a modular transport auxiliary mode to achieve efficient operation of coal mine auxiliary transportation.

[0055] Figure 1 The following is a schematic flow chart of a method for dispatching rubber-tyred trackless vehicles based on a modular auxiliary transport mode in an embodiment of the present invention, the method comprising the following steps:

[0056] S102: Obtain material demand information and modular loading container information.

[0057] The modular loading container includes a box-type carrier, a plate-type carrier, and a container carrier, each of which can be loaded onto or unloaded from a trackless rubber-tyred vehicle. The material demand information may include information such as the node of material demand, the type of material, and the quantity of the material.

[0058] Compared with the traditional rubber-tyred trackless vehicle that loads materials in one bucket, this embodiment proposes modular transportation. Three loading containers are designed, namely box-type carriers, plate-type carriers and container carriers, which are matched with the new rubber-tyred trackless vehicle to form a modular auxiliary transportation mode, which can be flexibly matched and transported to multiple locations.

[0059] Loading containers are designed with corresponding material size specifications. Box-type carriers are used to carry bulk materials, plate-type carriers are used to directly carry long materials such as anchor cables and anchor nets, and containers are used to carry bulk and small materials. Containers can be loaded onto multiple rubber-tyred trackless vehicles, enabling multi-destination unloading. Table 1 shows the material transport compatibility table, which shows the size of each material and whether it can be transported in containers. Due to the limitations of the transport capacity unit, materials are classified into loading methods suitable for container transportation and unsuitable for container transportation to match the transport vehicle.

[0060]

[0061]

[0062] Table 1

[0063] Figure 2 A schematic diagram of loading containers is shown, showing, from left to right: a box-type carrier, a plate-type carrier, and a container-type carrier, enabling flexible configuration of vehicle, carrier, and material. These three types of loading containers can be used flexibly in practice: the box-type carrier can be used to load bulk materials, while the plate-type carrier can directly carry materials unsuitable for containerized transport, such as anchor cables and anchor nets, or it can be loaded with containers for auxiliary transportation. Figure 3 It shows a schematic diagram of a vehicle loading a plate-type carrier and a plate-type carrier loading a container carrier.

[0064] For example, the loading unit design is as follows: the vehicle load capacity is designed to carry 6 units, the container 3 units, the box carrier 6 units, and the plate carrier is designed to carry long anchor bolts and cables or containers. This embodiment adopts a flexible combination of vehicle + carrier + material, which allows for immediate pick-up and delivery. The carrier is loaded when the material is picked up. The material can be pre-loaded into the carrier without waiting for the material to be loaded. After the material is delivered, the carrier is unloaded and the vehicle can directly proceed to the next transportation task without waiting for unloading. Multiple trackless rubber-wheeled vehicles efficiently complete the auxiliary transportation tasks in the coal mine.

[0065] S104: Generate a material transportation task based on the material demand information and modular loading container information.

[0066] Specifically, after determining the material requirements, different types of vehicles can be matched according to the material type and quantity to generate material transportation tasks.

[0067] S106, using the Floyd algorithm to store the shortest distance path matrix between the ground and each node in the coal mine.

[0068] In this embodiment, the Floyd algorithm is used to store the shortest distance path matrix between the ground and the coal mine. The specific steps include:

[0069] Initialization: Modeling and map initialization of the ground scene and underground tunnel scene, and subsequent target nodes can be marked;

[0070] Iterative optimization: Generate an initial distance matrix based on the initialization map, and use the Floyd algorithm to iteratively optimize the initialization distance matrix to generate the shortest distance matrix between each node;

[0071] Store data: Storing the above shortest distance matrix will facilitate subsequent genetic algorithm optimization to be called at any time.

[0072] S108 , based on the improved adaptive genetic algorithm, a vehicle scheduling plan is generated according to the shortest distance path matrix, vehicle information and material transportation tasks.

[0073] Among them, the improved adaptive genetic algorithm includes the constraints of the objective function constructed based on vehicle information, material demand information and modular loading container information, as well as the objective function constructed based on the driving distance of the rubber-tyred trackless vehicle, the time to complete the transportation task and the operating cost.

[0074] The constraints of the improved adaptive genetic algorithm include:

[0075]

[0076]

[0077] T={T1,T2,…,T m}

[0078] R={R1,R2,…,R m}

[0079] Among them, T represents the set of container transport tasks, R represents the set of trackless rubber-tyred vehicle transport tasks, k represents the number of material transport tasks, n represents the number of material loading and unloading points, m represents the required number of trackless rubber-tyred vehicles, a represents the number of trackless rubber-tyred vehicle transport tasks, and cm represents the transport tasks to which the trackless rubber-tyred vehicle is assigned.

[0080] Furthermore, the above constraints may also include the following:

[0081] Loading constraints: the carrying capacity of a material truck is divided into material loading units according to the specifications of the loading container, enabling flexible matching of carriers and vehicles;

[0082] Vehicle number constraints: limit the number of vehicles required to complete the task based on the total material demand and the material vehicle carrying capacity;

[0083] Material occupancy unit constraint: In modular transportation mode, the material occupancy unit of the material cart is constrained according to the size of each material.

[0084] For example, a special modular combination method is used, requiring that the total number of loading units of vehicles loaded on each vehicle is less than or equal to 6, as follows

[0085]

[0086] Among them, z is the type of carrier, z1 is the loading unit of box-type carrier, z2 is the loading unit of plate-type carrier, z3 is the loading unit of container, and n is the corresponding number of carriers.

[0087] The objective function of the improved adaptive genetic algorithm is expressed as:

[0088] G=αs+βt+λc

[0089]

[0090]

[0091] c=p y +c t +g v +c f +c oh

[0092] Among them, G represents the total objective function, s represents the travel distance of the trackless rubber-tyred vehicle, t represents the time it takes for the trackless rubber-tyred vehicle to complete the transportation task, c represents the operating cost, and s ij represents the distance traveled by each rubber-tyred trackless vehicle to complete the transportation task, t l Indicates the time the rubber-tyred trackless vehicle waits for loading at the material yard, t u Indicates the time the rubber-tyred trackless vehicle waits for unloading in the well, p y represents the total salary expenditure of personnel, c t represents the purchase cost of rubber-tyred trackless vehicles, g v Indicates the fuel cost of the rubber-tyred trackless vehicle, c f represents the monthly maintenance cost of rubber-tyred trackless vehicles, c oh Indicates the overhaul cost of rubber-tyred trackless vehicles.

[0093] Methods for solving the objective function using genetic algorithms based on the Floyd algorithm include:

[0094] (1)Multiple sequence encoding:

[0095] Traditional binary encoding in genetic algorithms is relatively simple in subsequent genetic operations, facilitating iteration, crossover, and mutation. However, the underground coal mine container auxiliary vehicle scheduling problem studied in this example is not suitable for traditional binary encoding methods because it can be simplified to a multi-vehicle routing optimization problem, which is related to overall scheduling and order.

[0096] In the material transportation phase of underground coal mining, multi-vehicle routing optimization requires consideration of not only overall scheduling and order factors but also high demand at a single location. For this purpose, a dual-sequence encoding scheme, combining real-number encoding and string encoding, is designed to differentiate it from traditional methods.

[0097] Optionally, the multi-sequence coding includes a first sequence: global transport route, a second sequence: multi-vehicle material transport route, and a third sequence: material vehicle transport route;

[0098] Assuming that l represents a transport task and i represents the i-th transport task, the global transport route obtained by encoding is as follows:

[0099] {l1,l2,l3,…,l i}

[0100] In the global transportation route, a, b, c... are used to represent underground material demand points. i represents the i-th transportation task to the material demand point a, b i represents the i-th transportation task to the material demand point b;

[0101] Perform "0-split" on the global transport route, that is, use 0 as the starting point to split the global transport route into specific multi-vehicle transport paths. After encoding, the multi-vehicle material transport route can be obtained as follows:

[0102] {0,a1,b1,…,0,a2,c1,…,0,a i ,…,0,b i ,…,0,c i ,…,0}

[0103] The material transportation routes of multiple vehicles are sorted and recombined, and the material transportation route of each trackless rubber-tyred vehicle is generated based on the time constraint of the global transportation task.

[0104] Aiming at the problem of containerized auxiliary transport vehicle scheduling in underground coal mines, a new coding method is formed, in which the global transport route is the first sequence, the multi-carriage material transport route is the second sequence, and the material vehicle transport route is the third sequence.

[0105] (2) Initialization: Initialize the object and population. The object includes the population size, running generations and crossover mutation probability, and the population includes the path sequence and sequence number.

[0106] This embodiment generates an optimal initialization population based on the intensive vehicle transportation rule. The specific steps are as follows:

[0107] Step 1: Mark all locations where materials need to be transported and the required quantities of materials;

[0108] Step 2: With the upper limit of the rubber-tyred trackless vehicle's loading capacity as m units as a constraint, calculate the material demand of each node based on m units to require at least k trips, and add d trips as a margin for nodes with high material demand, that is, nodes with high material demand require at least k + d trips;

[0109] Step 3: Generate the initial target location based on the number of trains required for each location;

[0110] Step 4: Randomly sort the generated target locations to generate n initial individuals;

[0111] Step 5: Using the above-mentioned upper limit of loading as a constraint, insert the starting point 0 into the individual to generate a complete initial population.

[0112] (3) Fitness evaluation and selection: Calculate the fitness value of each individual according to the objective function, and select individuals with high fitness values ​​in the population according to the proportion.

[0113] This example studies the coal mine container auxiliary transport vehicle scheduling problem. The goal is to reduce the transportation distance, transportation time, and transportation cost of the entire material vehicle auxiliary transport task. Therefore, the inverse of the objective function is used as the fitness value of the population. The fitness of the population is calculated as:

[0114]

[0115] Among them, f i represents the fitness value of the i-th individual, G i represents the objective function of the i-th individual.

[0116] (4) Adaptive crossover and mutation: Set the crossover probability and mutation probability, and perform crossover and mutation processing on the path sequence in the population.

[0117] Selecting some chromosomes of the parent samples from the current generation to exchange in order to create chromosomes representing the new generation is a process called crossover, the probability of which is determined by the crossover operator. Compared with the fixed crossover probability of the traditional genetic algorithm, this embodiment adopts an adaptive genetic algorithm, the improvement of which is to adaptively adjust the genetic parameters so as to maintain the diversity of the population while ensuring the convergence of the algorithm. For example, for the basic genetic algorithm, the probability of crossover and mutation is fixed, while the adaptive strategy requires adaptive adjustment during the evolution process: in the initial stage, a larger crossover and mutation probability is selected. Such a rough search process is conducive to maintaining population diversity, and in the later stage, it is adjusted to a smaller value for detailed search to prevent the destruction of the optimal solution and speed up the convergence. Therefore, the crossover probability formula is introduced in the coal mine underground container auxiliary vehicle scheduling problem as follows:

[0118]

[0119] Among them, P c is the crossover probability, P c1 For a given crossover probability, P c2 is the self-determined crossover probability, f=max(f1,f2) is the larger fitness value of parent individual 1 and parent individual 2, f avg is the average fitness value of the contemporary population, f max is the maximum fitness value of the contemporary population, that is, the fitness value of the optimal individual.

[0120] When entering the late iteration stage, the fixed probability may lead to the loss of excellent individuals, so the crossover effect should be reduced. The corresponding measure is to reduce the crossover probability. This embodiment adopts a crossover degree value that is negatively correlated with the evolutionary generation number, that is, the attenuation function is set as follows:

[0121]

[0122] Among them, P c ′ is the crossover probability attenuation function, μ and λ are related factors that affect the crossover probability attenuation function, μ affects the overall change of the function, λ is related to the process of genetic iteration, and λ∈1,2, n is the current iteration number, N is the total number of genetic iterations;

[0123] After the introduction of the crossover probability attenuation function, the crossover probability in the population genetic process is improved to:

[0124]

[0125] The process of randomly changing one or more chromosome values ​​to form a new generation of individuals is called mutation. The mutation operation, like the crossover operation, also has the risk of affecting the stability of the population in the later stages of iteration. After introducing the mutation probability attenuation function, the mutation probability in the population inheritance process is improved to:

[0126] After introducing the mutation probability attenuation function, the mutation probability in the population genetic process is improved to:

[0127]

[0128] in,

[0129] Subformula P′ m is the mutation probability attenuation function, P m is the mutation probability, P m1 For a given mutation probability, P m2 is the self-determined mutation probability, and f′ is the fitness value of the parent individual to be mutated.

[0130] (5) Iteration: Select the individual with the highest fitness in the population as the optimal modular vehicle scheduling solution.

[0131] S110: Perform auxiliary transport scheduling according to the above vehicle scheduling plan.

[0132] After determining the vehicle dispatch plan based on the above method, the corresponding dispatch instructions can be sent to each rubber-tyred trackless vehicle. The dispatch instructions can include information such as the auxiliary transport task route, target location, and estimated time for each rubber-tyred trackless vehicle.

[0133] The brief operation process in this embodiment is as follows:

[0134] Apply for orders - approve mine material orders - pre-pack containers - intelligent scheduling to optimize transportation routes - assign trackless rubber-wheeled vehicles - load vehicles with modular vehicles - perform auxiliary transportation tasks - unload vehicles at multiple destination points underground - complete auxiliary transportation tasks.

[0135] The embodiment of the present invention provides a method for dispatching trackless rubber-tyred vehicles based on a modular auxiliary transport mode, modular transportation of trackless rubber-tyred vehicles with multiple carriers, and a flexible combination of vehicle + carrier + material. Through the loading and unloading of carriers, vehicles do not need to wait for a long time, and multiple trackless rubber-tyred vehicles can efficiently complete the auxiliary transport tasks in coal mines; a combination strategy genetic algorithm that conforms to the modular auxiliary transport mode is designed. Compared with the traditional trackless rubber-tyred vehicle transportation of bulk materials and rail locomotive transportation in coal mines, a new auxiliary transport mode is established, the traditional dispatching process is updated, and the efficiency of material auxiliary transportation in coal mines is significantly improved.

[0136] This embodiment also designs a complex scenario scheduling method to implement a modular scheduling method of static global output + dynamic real-time response.

[0137] In order to enable real-time response and dynamic scheduling, this embodiment adopts a combination of static and dynamic scheduling. When the scheduling task changes, a global optimization scheduling strategy, a supplementary optimization scheduling strategy, or a dynamic insertion scheduling strategy is adopted.

[0138] The global optimization scheduling strategy includes: if the number of tasks changes at time t, determine whether the trackless rubber-tyred vehicle has started the auxiliary transportation task according to time t; if it has not yet started, change the demand matrix according to the changed material auxiliary transportation demand, correspond the demand matrix to the transportation location, and regenerate the vehicle scheduling plan; or,

[0139] The supplementary optimization scheduling strategy includes: if the number of tasks changes at time t, determine whether the trackless rubber-tyred vehicle has started the auxiliary transportation task according to time t; if it has already started, change the demand matrix according to the changed material auxiliary transportation demand, the demand matrix corresponds to the transportation location, and add a new trackless rubber-tyred vehicle to complete the temporary auxiliary transportation task based on the previous task sequence; or,

[0140] The dynamic insertion scheduling strategy includes: if the number of tasks changes at time t, determine whether the trackless rubber-tyred vehicle has started the auxiliary transport task according to time t; if it has not started, determine for each trackless rubber-tyred vehicle whether the remaining load of the old task sequence is sufficient to complete the added auxiliary transport task; if it has started, determine the number of rounds of transportation of the trackless rubber-tyred vehicle, and then determine whether the old task sequence that has not yet been transported has sufficient remaining load to complete the added auxiliary transport task, and insert the new task into the old task sequence.

[0141] Figure 4 A schematic diagram of multi-destination material transport is shown. Arrows indicate the direction of vehicle travel, and the vehicle is loaded with Container 1, Container 2, and Container 3. Loading the carrier onto the vehicle on the ground saves ground loading time. After the vehicle delivers the designated material container to the work location (Container 1 stacking point, Container 2 stacking point, etc.), the carrier is unloaded and the vehicle can be raised to the well. It can transport materials to multiple destinations, making it more efficient than traditional trackless rubber-tyred vehicles.

[0142] For example, the scheduling process of this embodiment is as follows:

[0143] The production team applies for orders in advance - the mine material order module approves the orders - the materials are allocated to different loading containers according to the loading units for pre-packing - intelligent scheduling optimizes the transportation routes - the loading containers are matched with the corresponding vehicles - and whether there are any task changes;

[0144] If there is no such task, the system will execute the auxiliary transport task—loading and unloading vehicles at multiple underground destinations—to complete the auxiliary transport task. If there is such a task, the system will perform a complex scenario assessment and then complete the auxiliary transport task. Furthermore, the system will continuously detect task changes during the auxiliary transport task.

[0145] For example, the route for efficient underground containerized material transportation is: garage - containerized material supermarket - auxiliary inclined shaft - auxiliary transport main tunnel - 6th joint tunnel - auxiliary transport main tunnel - auxiliary transport drift of 112205 fully mechanized mining working face - auxiliary transport main tunnel - rubberized transport drift of 112206 heading working face - auxiliary transport main tunnel - auxiliary inclined shaft - containerized material supermarket - garage. The specific process is as follows:

[0146] Departure from the garage: After receiving the task dispatch instruction, the material truck goes to the container material supermarket to load the materials;

[0147] Loading: After loading the three types of material containers, the staff set off for the designated unloading location according to the dispatch task instructions;

[0148] Arriving at designated unloading point 1: the material truck passes through the auxiliary inclined shaft and auxiliary transport tunnel, arrives at the 6th tunnel, unloads container-1, loads the empty container, and continues to the next unloading location;

[0149] Arrive at designated unloading point 2: Pass through the auxiliary transport tunnel and arrive at the auxiliary transport chute of the 112205 fully mechanized mining working face, unload container-2, load the empty container, and prepare for the return trip;

[0150] If the location is changed to unloading point 3, then go to unloading point 3: pass through the auxiliary transport tunnel, arrive at the 112206 excavation working face rubber transport chute, unload container-2, load the empty container, and prepare to return;

[0151] Return to the container material supermarket: reach the container material supermarket through the auxiliary transport tunnel and auxiliary inclined shaft to unload the empty containers.

[0152] Return to the garage: park in the corresponding parking space.

[0153] Advantages of the embodiments of the present invention include:

[0154] (1) The modular transportation of trackless rubber-tyred vehicles with multiple carriers, the flexible combination of vehicle + carrier + material, pick up and deliver immediately, and after the carrier is unloaded, the vehicle can directly proceed to the next transportation task without waiting for unloading. Multiple trackless rubber-tyred vehicles can efficiently complete the auxiliary transportation task in the coal mine;

[0155] (2) For the first new model, a combined strategy genetic algorithm that conforms to the modular auxiliary transportation model is designed. Compared with the traditional trackless rubber-wheeled vehicle transportation of bulk materials and rail locomotive transportation in coal mines, a new auxiliary transportation model is established, the traditional scheduling process is updated, and a complex scenario scheduling method is designed to realize a modular scheduling method of static global output + dynamic real-time response.

[0156] The embodiment of the present invention further provides a modular auxiliary transport system, including a trackless rubber-tyred vehicle dispatching system, a plurality of trackless rubber-tyred vehicles, and a plurality of modular loading containers; the modular loading containers include box-type carriers, plate-type carriers, and container carriers, each carrier being used for loading onto or unloading from the trackless rubber-tyred vehicle; the plate-type carrier is used for loading materials or container carriers;

[0157] The trackless rubber-tyred vehicle dispatching system is used to execute the above-mentioned trackless rubber-tyred vehicle dispatching method based on the modular auxiliary transport mode.

[0158] Specifically, the above-mentioned trackless rubber-tyred vehicle dispatching system may include:

[0159] The material transport module divides the carriers according to the size and quantity of materials required for coal mine production, and provides different loading containers according to the characteristics of the materials.

[0160] The vehicle data information module displays real-time status data for rubber-tyred trackless vehicles, including vehicle location, health status, and information about the material containers they carry, and obtains dispatch instructions. Dispatch instructions include each vehicle's auxiliary transport task route, target location, and estimated time. Dispatch instructions are issued by the dispatch optimization management module.

[0161] The mine material order module allocates corresponding loading containers according to the material needs of coal mine production, and manages data such as the daily order number, material details, transportation task allocation, and real-time status of transportation vehicles.

[0162] The scheduling optimization management module adopts a combination of genetic algorithms and multi-heuristic algorithms to optimize vehicle scheduling for daily transportation tasks, match different destinations and different types of materials loaded on the same vehicle, intelligently plan travel routes, and realize information-based intelligent management of the entire transportation process.

[0163] The modular auxiliary transport system provided in the above embodiment can realize each process in the embodiment of the trackless rubber-tyred vehicle scheduling method based on the modular auxiliary transport mode. To avoid repetition, it will not be described here.

[0164] The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various processes of the embodiment of the trackless rubber-tyred vehicle scheduling method based on the modular auxiliary transport mode are implemented, and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0165] Of course, those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the control device through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above-mentioned method embodiments, wherein the storage medium may be a memory, a disk, an optical disk, etc.

[0166] In this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0167] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0168] 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 readily apparent to those 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 is not limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for dispatching rubber-tyred trackless vehicles based on a modular auxiliary transport mode, characterized in that: The method comprises: Obtaining material demand information and modular loading container information; the modular loading container includes a box-type carrier, a plate-type carrier, and a container carrier, each of which is used to load onto or unload from the trackless rubber-tyred vehicle; generating a material transportation task according to the material demand information and the modular loading container information; The Floyd algorithm is used to store the shortest distance path matrix between the ground and each node in the coal mine; Based on an improved adaptive genetic algorithm, a vehicle scheduling plan is generated according to the shortest distance path matrix, vehicle information, and the material transportation task; the improved adaptive genetic algorithm includes constraints of an objective function constructed based on the vehicle information, the material demand information, and the modular loading container information, and an objective function constructed based on the travel distance of the rubber-tyred trackless vehicle, the time to complete the transportation task, and the operating cost; Perform auxiliary transport dispatch according to the vehicle dispatch plan; The improved adaptive genetic algorithm includes multiple sequence coding; The multi-sequence code includes a first sequence: global transport route, a second sequence: multi-carriage material transport route, and a third sequence: material vehicle transport route; Using l to represent the transport task and i to represent the i-th transport task, the global transport route obtained after encoding is as follows: {l1,l2,l3,…，l i} In the global transportation route, a, b, c... are used to represent underground material demand points, a i represents the i-th transportation task to the material demand point a, b i represents the i-th transportation task to the material demand point b; The global transport route is "split by 0", that is, 0 is used as the starting point to split the global transport route into specific multi-vehicle transport paths. After encoding, the multi-vehicle material transport route can be obtained as follows: <h2 style=";text-align:left;direction:ltr">{0,a1,b1,…,0,a2,c1,…,0,a<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ,…,0,b<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ,…,0,c<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ,…,0} Sorting and recombining the material transport routes of the multiple vehicles, and generating a material transport route for each of the trackless rubber-tyred vehicles based on the time constraint of the global transport task; Using the crossover degree value that is negatively correlated with the evolutionary generation number, the crossover probability attenuation function is set as follows: Among them, P′ c is the crossover probability attenuation function, μ and λ are the related factors that affect the crossover probability attenuation function, μ affects the overall change of the function, λ is related to the process of genetic iteration, and λ∈1,2, n is the current iteration number, N is the total number of genetic iterations; After introducing the crossover probability attenuation function, the crossover probability is improved to: After introducing the mutation probability attenuation function, the mutation probability in the population genetic process is improved to: in, Subformula P′ m is the mutation probability attenuation function, P m is the mutation probability, P m1 For a given mutation probability, P m2 is the self-determined mutation probability, and f′ is the fitness value of the parent individual to be mutated.

2. The method according to claim 1, characterized in that The constraints of the improved adaptive genetic algorithm include: T={T1,T2,…,T m } R={R1,R2,…,R m } Among them, T represents the set of container transport tasks, R represents the set of trackless rubber-tyred vehicle transport tasks, k represents the number of material transport tasks, n represents the number of material loading and unloading points, m represents the required number of trackless rubber-tyred vehicles, a represents the number of trackless rubber-tyred vehicle transport tasks, and cm represents the transport tasks to which the trackless rubber-tyred vehicle is assigned.

3. The method according to claim 1, characterized in that The objective function of the improved adaptive genetic algorithm is expressed as: G=αs+βt+λc c=p y +c t +g v +c f +c oh Among them, G represents the total objective function, s represents the travel distance of the trackless rubber-tyred vehicle, t represents the time it takes for the trackless rubber-tyred vehicle to complete the transportation task, c represents the operating cost, and s ij represents the distance traveled by each rubber-tyred trackless vehicle to complete the transportation task, t l Indicates the time the rubber-tyred trackless vehicle waits for loading at the material yard, t u Indicates the time the rubber-tyred trackless vehicle waits for unloading in the well, p y represents the total salary expenditure of personnel, c t represents the purchase cost of rubber-tyred trackless vehicles, g v Indicates the fuel cost of the rubber-tyred trackless vehicle, c f represents the monthly maintenance cost of rubber-tyred trackless vehicles, c oh Indicates the overhaul cost of rubber-tyred trackless vehicles.

4. The method according to claim 1, wherein The improved adaptive genetic algorithm includes initializing a population; the initializing population includes: Mark all nodes that need to transport materials and the required quantity of materials; Taking the upper limit of the loading capacity of the rubber-tyred trackless vehicle as m units as a constraint, the material demand of each node is calculated based on m units to require at least k trips, and d trips are added as a margin for nodes with high material demand, that is, nodes with high material demand require at least k+d trips; Generate the initial target node based on the number of trains required for each node; Randomly sort the generated initial target locations to generate n initial individuals; The starting point 0 is inserted into the initial individuals with the upper limit of the loading amount as a constraint to generate a complete initial population.

5. The method according to claim 1, wherein The improved adaptive genetic algorithm includes fitness calculation, and the fitness calculation formula of the population is: Among them, f i represents the fitness value of the i-th individual, G i represents the objective function of the i-th individual.

6. The method according to claim 1, characterized in that The method further comprises a scheduling task change step, wherein the scheduling task change adopts a global optimization scheduling strategy, a supplementary optimization scheduling strategy or a dynamic insertion scheduling strategy; The global optimization scheduling strategy includes: if the number of tasks changes at time t, determining whether the trackless rubber-tyred vehicle has started the auxiliary transportation task; if it has not yet started, changing the demand matrix according to the changed material auxiliary transportation demand, the demand matrix corresponds to the transportation location, and regenerating the vehicle scheduling plan; or, The supplementary optimization scheduling strategy includes: if the number of tasks changes at time t, determining whether the trackless rubber-tyred vehicle has started the auxiliary transportation task; if it has already started, changing the demand matrix according to the changed material auxiliary transportation demand, the demand matrix corresponds to the transportation location, and adding a new trackless rubber-tyred vehicle to complete the temporary auxiliary transportation task based on the previous task sequence; or, The dynamic insertion scheduling strategy includes: if the number of tasks changes at time t, determining whether the trackless rubber-tyred vehicle has started the auxiliary transport task; if it has not started, determining for each trackless rubber-tyred vehicle whether the remaining load of the old task sequence is sufficient to complete the added auxiliary transport task; if it has started, determining the number of rounds of transportation of the trackless rubber-tyred vehicle, and then determining whether the old task sequence that has not yet been transported has sufficient remaining load to complete the added auxiliary transport task, and inserting the new task into the old task sequence.

7. A modular auxiliary transport system, characterized in that: It includes a trackless rubber-tyred vehicle dispatching system, multiple trackless rubber-tyred vehicles and multiple modular loading containers; the modular loading containers include box-type carriers, plate-type carriers and container carriers, each of which is used to load or unload from the trackless rubber-tyred vehicle; the plate-type carrier is used to load materials or container carriers; The trackless rubber-tyred vehicle dispatching system is used to execute the trackless rubber-tyred vehicle dispatching method based on the modular auxiliary transport mode as described in any one of claims 1 to 6.

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