An open-pit coal mine shovel coordination scheduling method considering device heterogeneity

Abstract

The present application relates to the technical field of open coal mine scheduling, and more particularly to a method for collaborative scheduling of truck and shovel in open coal mine considering equipment heterogeneity; comprising multi-source data acquisition, construction of equipment heterogeneity feature vector, calculation of multi-dimensional heterogeneity equipment matching index, construction of dynamic weighted comprehensive adaptation matrix, establishment of 0-1 integer programming collaborative scheduling optimization model, solution of scheduling model by genetic algorithm, generation of closed-loop collaborative scheduling scheme and dynamic rescheduling update. By characterizing the truck and shovel load capacity, truck bed or shovel capacity, loading and transportation efficiency, work power and energy consumption and other parameters, four types of indexes of volume matching degree, beat matching degree, efficiency matching degree and energy consumption matching degree are constructed, and the truck and shovel combination adaptation index is formed by weighted comprehensive, so as to realize the scientific quantification of the adaptation degree of truck and shovel combination, and improve the efficiency of truck and shovel collaborative operation.

Description

Technical Field

[0001] This invention relates to the field of open-pit coal mine scheduling technology, and in particular to a collaborative scheduling method for open-pit coal mine shovels that takes into account the heterogeneity of equipment. Background Technology

[0002] In the production process of open-pit coal mines, the mining and transportation system mainly consists of loaders and mining trucks, and its production efficiency directly determines the overall capacity and operating cost of the mine. With the development of digital mines, smart mines and unmanned mines, truck-loader collaborative scheduling has become an important research direction for intelligent production in open-pit coal mines.

[0003] Existing truck and shovel dispatching technologies mainly include the following categories: (1) Fixed vehicle allocation and dispatch mode Based on production experience, the number of trucks corresponding to each loader is determined in advance, and a relatively fixed truck-loader relationship is maintained during the scheduling process. This method is simple to implement, but it is difficult to adapt to changes in equipment status and production environment, and it is easy to cause equipment to be idle or to wait in queues.

[0004] (2) Dynamic scheduling method based on transport distance and queuing time Based on the truck's current location, transport distance, queue length, and loading requirements, the truck service targets are dynamically determined to reduce empty driving distance and waiting time, thereby improving transportation efficiency.

[0005] (3) A method for coordinated scheduling of shovel and truck based on optimization algorithms In recent years, a large number of studies have used intelligent optimization methods such as genetic algorithms, particle swarm optimization, ant colony optimization, and reinforcement learning to establish truck and shovel scheduling models in order to maximize output, minimize transportation time, or minimize operating costs.

[0006] However, the above-mentioned shovel and crane scheduling still has the following drawbacks: (1) The assumption of equipment homogeneity is obvious. Most existing scheduling models assume that all trucks have the same load capacity, transport speed and energy consumption level, and all forklifts have the same loading capacity and operating efficiency. However, in actual production, equipment of different models, service years and manufacturers are generally mixed and operated.

[0007] (2) Lack of equipment capacity matching mechanism Existing scheduling methods mainly allocate equipment based on transportation distance, queue length, or task requirements, lacking quantitative evaluation of the matching relationship between truck transportation capacity and loader loading capacity. In actual production, it is easy to match large-tonnage trucks with small-capacity loaders, resulting in excessive loading times; or match small-tonnage trucks with large-capacity loaders, resulting in decreased bucket utilization. The mismatch between truck transportation cycle and loader loading cycle leads to queuing or idle equipment.

[0008] (3) Lack of collaborative configuration for heterogeneous devices Existing solutions mainly address the question of "which vehicle serves which forklift," but fail to address how to achieve optimal capability matching between different types of equipment, making it difficult to achieve optimal collaborative configuration under heterogeneous equipment conditions.

[0009] Therefore, this application proposes a collaborative scheduling method for shovels and chaff in open-pit coal mines that takes into account the heterogeneity of equipment. Summary of the Invention

[0010] To overcome the shortcomings of existing technologies and solve the technical problems existing in the background art, this invention proposes a collaborative scheduling method for shovels in open-pit coal mines that considers the heterogeneity of equipment, comprising the following steps: Step 1: Acquiring multi-source data Real-time collection of dynamic operating parameters of trucks and loaders and retrieval of static factory parameters of equipment from the mining equipment management database; synchronous collection of relevant information on mining and loading operation area, transportation distance, and unloading point; classification and storage; construction of open-pit coal mine truck and loader scheduling information database. Step 2: Construct device heterogeneity feature vectors We established truck feature vectors containing parameters of load, speed, and energy consumption, and loader feature vectors containing parameters of bucket capacity, loading time, and operating energy consumption, to complete the unified quantitative characterization of heterogeneous equipment. Step 3: Calculation of the Matching Index for Multidimensional Heterogeneous Equipment Based on two types of feature vectors, four single-item matching indices are calculated in sequence: volume matching degree, beat matching degree, efficiency matching degree, and energy consumption matching degree. The values ​​of the four indices are all in the range of 0-1. Step 4: Construct a dynamic weighted comprehensive adaptation matrix The entropy weighting method of time series data is used to update the weights of four types of matching indicators in real time, and the comprehensive matching index of each truck-shovel combination is calculated by weighting. Based on the index of all combinations, a truck-shovel matching matrix is ​​generated. Step 5: Establish a 0-1 integer programming collaborative scheduling optimization model The optimization objective is to maximize the sum of the comprehensive adaptation index, while constraining that each truck is assigned only one loader and the total loading time of a single loader does not exceed the scheduling cycle. Step Six: Solve the scheduling model using a genetic algorithm. The optimal combination of truck and shovel is output by solving the problem through integer encoding, elite retention roulette wheel selection, single-point intersection, and constrained repair mutation iteration. Step 7: Generate a closed-loop collaborative scheduling scheme The operation priority queue is constructed in descending order of the comprehensive adaptability index, transportation tasks are allocated and loading queues for each forklift and truck are generated, and equipment operation data is transmitted back in real time to complete the scheduling loop. Step 8: Dynamic Rescheduling Update Set trigger conditions for adding or removing equipment, changing transport distance, and switching unloading points. When the changes in operating conditions reach the threshold, re-execute steps one through seven to generate a new scheduling scheme.

[0011] Preferably, the volume matching degree in step three is calculated as follows: the scoop capacity ratio is calculated by combining the truck bed volume and rated load capacity as dual constraints; the optimal number of loading scoops is determined by combining the transportation distance; and the volume matching degree is calculated by the difference between the actual scoop capacity ratio and the optimal number of scoops. .

[0012] Preferably, in step three, the cycle time matching degree is calculated based on the total complete transportation time of the truck and the single loading time of the forklift, and the transportation cycle margin is quantified by the synchronization deviation between the margin and the loading cycle time. It is used to characterize the degree of timing coordination in the operation of the shovel and crane.

[0013] Preferably, the efficiency matching degree calculation method in step three is as follows: first calculate the transport capacity per unit time of each shovel combination. The maximum unit transport capacity is obtained by traversing all combinations. Minimum value After range normalization, we obtain ,in, For trucks With bulldozer Transport capacity per unit time when combined (unit: ), For the first Rated load capacity of a truck (unit: ).

[0014] Preferably, the energy consumption matching degree calculation method in step three is as follows: the combined energy consumption of truck round-trip transportation and loader loading is superimposed to obtain the comprehensive energy consumption of a single cycle. Iterate through all combinations of shovels to obtain the extreme energy consumption values ​​and perform comprehensive energy consumption normalization. To obtain standardized energy consumption indicators, and then through The energy consumption matching degree is obtained.

[0015] Preferably, the dynamic weighting process of the time series data entropy weighting method in step four is as follows: select four types of matching indicators for continuous multi-periods to construct a time series matrix, and calculate the information entropy and difference coefficient of each indicator after dimensionless processing of the indicator data. The indicator weights are allocated according to the difference coefficients, and the weights are updated on a rolling basis after each scheduling cycle is completed.

[0016] Preferably, the decision variables in step five are 0-1 integers. This is used to characterize whether truck i is assigned to forklift j, and the optimization objective is to maximize the sum of the comprehensive fit index of all matching combinations.

[0017] Preferably, in step six, the genetic algorithm uses integer encoding of the number of trucks corresponding to the chromosome length, verifies the model constraints after mutation, and reallocates trucks to individuals that violate the constraints to complete the repair.

[0018] Preferably, the closed-loop scheduling process in step seven is as follows: real-time equipment operation data is transmitted back to the scheduling information database as the data input for step one of the next scheduling cycle, thereby realizing a closed loop of perception-matching evaluation-scheduling optimization-task execution.

[0019] Preferably, the rescheduling triggering conditions in step eight include any of the following: the number of trucks / forklifts increases or decreases exceeding a preset threshold, the rate of change in transport distance exceeds the limit, or the unloading point is switched.

[0020] The beneficial effects of this invention are: 1. This invention addresses the common problem in existing technologies that assume homogeneity between trucks and forklifts and ignore equipment heterogeneity. It proposes a truck-forklift matching evaluation mechanism based on multi-dimensional heterogeneous characteristics. This mechanism characterizes parameters such as the load capacity, cargo box or bucket capacity, loading and transportation efficiency, operating power, and energy consumption of trucks and forklifts, constructing four categories of indicators: volume matching degree, cycle time matching degree, efficiency matching degree, and energy consumption matching degree. A weighted comprehensive index is then formed to form a truck-forklift combination matching index. This addresses the problem of quantifying the matching relationship between trucks and forklifts under various heterogeneous equipment conditions, enabling scheduling decisions to reflect actual operating efficiency. This allows for the scientific quantification of the suitability of truck-forklift combinations, improving the efficiency of truck-forklift collaborative operations.

[0021] 2. Based on the truck-loader matching evaluation mechanism, this application constructs a truck-loader adaptation matrix, clarifies the optimal matching relationship between each truck and loader, and proposes a collaborative scheduling optimization model with the goal of maximizing the comprehensive adaptation index. This model generates the optimal matching scheme between trucks and loaders, solves the problem of optimizing collaborative scheduling of trucks and loaders under the condition of considering equipment heterogeneity, ensures the continuity of operation between loaders and multiple trucks, improves transportation efficiency and reduces energy consumption, thereby achieving the optimal combination configuration of different types of trucks and loaders and improving operational efficiency. Attached Figure Description

[0022] Figure 1 This is an example diagram of the collaborative operation of heterogeneous shovels in open-pit coal mines according to the present invention; Figure 2 This is the collaborative scheduling process for the shovel and chaff in this invention; Figure 3 This is a flowchart of the genetic algorithm in this invention. Detailed Implementation

[0023] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of skill in the art. The reagents and raw materials used in this invention are readily available through conventional means, and unless otherwise specified, they shall be used in accordance with conventional methods in the art or as per the product instructions. Furthermore, any methods and materials similar to or equivalent to those described herein may be applied to the methods of this invention. The invention will now be further described with reference to the accompanying drawings and specific embodiments. The preferred embodiments and materials described herein are for illustrative purposes only.

[0025] A collaborative scheduling method for shovels in open-pit coal mines that considers equipment heterogeneity, such as Figures 1-3 As shown, it includes the following steps: Step 1: Acquiring multi-source data Real-time collection of dynamic operating parameters of trucks and loaders and retrieval of static factory parameters of equipment from the mining equipment management database; synchronous collection of relevant information on mining and loading operation area, transportation distance, and unloading point; classification and storage; construction of open-pit coal mine truck and loader scheduling information database. Specifically as follows: Real-time operational data of the open-pit coal mine production site is obtained through multi-source collaboration. The real-time operational data includes truck equipment information, loader equipment information, mining and loading operation area information, transportation distance information, and unloading point information.

[0026] The truck equipment information is obtained by collecting dynamic operating parameters in real time through the vehicle-mounted CAN bus data terminal and by retrieving static factory configurations from the mining equipment management database, including: total number of trucks, rated load, cargo box volume, average empty transport speed, average fully loaded transport speed, empty unit transport energy consumption, and fully loaded unit transport energy consumption.

[0027] The information of the loader equipment is collected in real time through the vehicle-mounted CAN bus data terminal, and the static factory configuration is retrieved in conjunction with the mining equipment management database, including: total number of loaders, bucket capacity, single bucket loading time, loading cycle, working power, unit loading energy consumption, etc.

[0028] Information on the mining and loading operation area is obtained through the mine production management system, geographic information system, or mine geological database, including parameters such as the spatial coordinates of the mining and loading points, ore type, ore density, and ore loosening coefficient.

[0029] The transportation distance information is calculated based on the actual transportation path between the loading / unloading point and the unloading point. The acquired operational data is classified and stored according to equipment information, work area information, transportation road information, and unloading point information to establish a dispatch information database for open-pit coal mine trucks and loaders.

[0030] Step 2: Construct device heterogeneity feature vectors We established truck feature vectors containing parameters of load, speed, and energy consumption, and loader feature vectors containing parameters of bucket capacity, loading time, and operating energy consumption, to complete the unified quantitative characterization of heterogeneous equipment. Specifically as follows: Based on the equipment operation parameters in the open-pit coal mine truck-loader collaborative scheduling information database established in step one, unified feature representations are performed on different types of trucks and loaders to construct equipment heterogeneity feature vectors.

[0031] For the The heterogeneous feature vectors of the trucks are represented as follows:

[0032] in: Rated load capacity (unit: ); The volume of the carriage (unit: ); Average unloaded transport speed (unit: ); Average transport speed at full load (unit: ); Energy consumption per unit of unloaded transport (unit: ); Energy consumption per unit of full-load transportation (unit: ).

[0033] For the The heterogeneous feature vector of the loader is represented as follows:

[0034] in: Bucket capacity (unit: ); Loader bucket full capacity coefficient (value between 0 and 1); Loading time per bucket (unit: ); Operating power (unit: ); Energy consumption per unit load (unit: ).

[0035] Step 3: Calculation of the Matching Index for Multidimensional Heterogeneous Equipment Based on two types of feature vectors, four single-item matching indices are calculated in sequence: volume matching degree, beat matching degree, efficiency matching degree, and energy consumption matching degree. The values ​​of the four indices are all in the range of 0-1. In one specific embodiment of the present invention, the volume matching degree in step three is calculated as follows: the scoop capacity ratio is calculated by combining the truck bed volume and rated load capacity as dual constraints; the optimal number of loading scoops is determined by combining the transportation distance; and the volume matching degree is calculated by the difference between the actual scoop capacity ratio and the optimal number of scoops. .

[0036] Specifically as follows: To evaluate the matching degree between the carrying capacity of trucks and the loading capacity of forklifts, a volume matching degree index is constructed.

[0037] First, based on the relationship between the truck bed volume and the effective loading capacity of a single bucket of the forklift, calculate the truck... With bulldozer Spoon volume ratio under volume constraints during combination :

[0038] In the formula: For the first Truck cargo box volume (unit: ); For the first Bucket capacity of a loader (unit: ); For the first The full bucket coefficient of the loader (value between 0 and 1).

[0039] Then, based on the relationship between the truck's rated load capacity and the ore / rock loading mass, the truck's... With bulldozer Spoon capacity ratio under load constraints during combination :

[0040] in, For the first Rated load capacity of a truck (unit: ); The loosening coefficient of the ore and rock in the working area; For the first Bucket capacity of a loader (unit: ); The unit weight of the ore and rock in the work area; For the first The full bucket coefficient of the loader (value between 0 and 1).

[0041] Take the smaller value under the two constraints as the truck With bulldozer Actual production spoon capacity ratio during assembly :

[0042] Optimal number of loading loops The actual transport distance is set as a fixed value, and the specific rules are as follows: when the transport distance is less than... At that time, take =3; when the transport distance is 1-3km, take =4; when the transport distance is 3-5km, take =5; when the transport distance is greater than 6km, take =6.

[0043] To measure how close the actual number of loading buckets is to the optimal number of loading cycles, a truck is defined. With bulldozer Volume matching degree during combination for:

[0044] in: ,when When the actual number of buckets loaded is close to the optimal number of loading cycles, the loading capacity of the truck and the loader is well matched; this can effectively avoid the problems of excessive loading cycles caused by configuring a large-capacity truck with a small-capacity loader, and low bucket utilization caused by configuring a small-capacity truck with a large-capacity loader.

[0045] In one specific embodiment of the present invention, the cycle time matching degree in step three is calculated based on the total transportation time of the truck and the single loading time of the forklift to obtain the transportation cycle margin. The result is obtained by quantifying the synchronization deviation between the margin and the loading cycle time. It is used to characterize the degree of timing coordination in the operation of the shovel and crane.

[0046] Specifically as follows: To evaluate the synergy between truck transportation cycle and forklift loading cycle, a cycle matching index is constructed.

[0047] truck With bulldozer When combined with other operations, the time required for a truck to complete one full transport cycle. for:

[0048] In the formula: The full-load transport time of the truck (unit: ); For trucks With bulldozer Transportation distance between (unit: ); For trucks Average transport speed at full load (unit: ); , is the truck's empty transport time (unit: ); For trucks With bulldozer Transportation distance between (unit: ); Average unloaded transport speed (unit: ); Loading time (unit: ),in, The number of loading buckets is rounded up. The actual production spoon capacity ratio calculated for S1; For the first Loading time per bucket of loader (unit: ); Unloading time (unit: ),in, For the first Load capacity of a truck (unit: ); The unloading capacity of the equipment at the unloading point (unit: ); Define the margin of the transportation cycle relative to the loading cycle time. for:

[0049] in: Indicates rounding down; ,when The time indicates that the transportation cycle is completely synchronized with the loading cycle. When the synchronization deviation is at its maximum, The time indicates that the next synchronization cycle is approaching.

[0050] Define beat matching degree for:

[0051] in ,when This indicates that the transportation cycle is highly synchronized with the loading rhythm, and the arrival time of the trucks is highly matched with the loading rhythm of the forklift, which can effectively reduce the truck queuing time and the idle time of the forklift.

[0052] In one specific embodiment of the present invention, the efficiency matching degree calculation method in step three is as follows: first calculate the unit time transportation capacity of each shovel combination. The maximum unit transport capacity is obtained by traversing all combinations. Minimum value After range normalization, we obtain ,in, For trucks With bulldozer Transport capacity per unit time when combined (unit: ), For the first Rated load capacity of a truck (unit: ).

[0053] Specifically as follows: To evaluate the differences in actual production capacity of different types of truck and forklift combinations in completing transportation tasks, an efficiency matching index is constructed.

[0054] For trucks With bulldozer The combined operations have a complete transport cycle time of 1 hour. Then the transportation capacity per unit time for:

[0055] in, For trucks With bulldozer Transport capacity per unit time when combined (unit: ), For the first Rated load capacity of a truck (unit: ); To eliminate the influence of dimensional differences between different truck-shovel combinations, the unit-time transport capacity of all truck-shovel combinations is normalized to obtain the efficiency matching degree. for:

[0056] in, The maximum transport capacity per unit time among all truck-shovel combinations (unit: ); The minimum transport capacity per unit time among all truck-shovel combinations (unit: ).

[0057] but ,when When the time is specified, it indicates that the shovel and crane combination has a high transport capacity per unit time, and can complete more ore and rock transport tasks in the same amount of time, with the highest production efficiency.

[0058] In one specific embodiment of the present invention, the energy consumption matching degree calculation method in step three is as follows: the combined energy consumption of truck round-trip transportation and loader loading is obtained by superimposing the energy consumption of the truck and the energy consumption of the loader to obtain the comprehensive energy consumption of a single cycle. Iterate through all combinations of shovels to obtain the extreme energy consumption values ​​and perform comprehensive energy consumption normalization. To obtain standardized energy consumption indicators, and then through The energy consumption matching degree is obtained.

[0059] Specifically as follows: To evaluate the energy efficiency of different types of truck and forklift combinations in completing a unit transportation task, an energy consumption matching index is constructed.

[0060] For trucks With bulldozer The combined operations first calculate the transportation energy consumption of the truck to complete one transportation cycle. for:

[0061] in: For trucks With bulldozer Transportation distance between (unit: ); , Trucks Unit energy consumption under full load and no load (unit: ); Then calculate the truck. With bulldozer When assembling, the forklift Loading energy consumption to complete the loading task of this vehicle for:

[0062] in: For trucks With bulldozer The actual production spoon capacity ratio during assembly (calculated in step S3); For loader Single bucket loading time (unit: ); For loader Operating power (unit: ); Then truck With bulldozer The combined energy consumption to complete a transportation task for:

[0063] The overall energy consumption is normalized:

[0064] in: This represents the maximum energy consumption per unit transport volume for all truck-shovel combinations. This represents the minimum energy consumption per unit transport volume for all truck-shovel combinations.

[0065] Define energy consumption matching degree:

[0066] but ,when The lower the energy consumption of the shovel and loader assembly to complete a unit transportation task, the higher the energy utilization efficiency.

[0067] Step 4: Construct a dynamic weighted comprehensive adaptation matrix The entropy weighting method of time series data is used to update the weights of four types of matching indicators in real time, and the comprehensive matching index of each truck-shovel combination is calculated by weighting. Based on the index of all combinations, a truck-shovel matching matrix is ​​generated. As a specific implementation of the present invention, the dynamic weighting process of the time series data entropy weighting method in step four is as follows: select four types of matching indicators for continuous multi-periods to construct a time series matrix, and calculate the information entropy and difference coefficient of each indicator after dimensionless processing of the indicator data. The indicator weights are allocated according to the difference coefficients, and the weights are updated on a rolling basis after each scheduling cycle is completed.

[0068] Specifically as follows: Based on the volume matching degree obtained in step three Beat matching degree Efficiency matching degree Energy consumption matching degree Construct a comprehensive compatibility index for shovel and chaff combinations. :

[0069] in: Weights are matched to the volume; Weights are assigned to the beat. Match weights to efficiency; For energy consumption matching weights, the following conditions must be met: .

[0070] The initial values ​​of each weight are determined using the mean weighting method, i.e. To eliminate subjective human intervention and enable the scheduling system to adaptively adjust to time-varying mine conditions, an objective dynamic weighting method based on time-series data entropy weighting is adopted to determine the weights of each evaluation index.

[0071] First, select continuous Based on the evaluation index data within each production cycle, construct an index time series matrix:

[0072] in, =1,2,3…T , indicating the production cycle number; These correspond to volume matching degree, cycle time matching degree, efficiency matching degree and energy consumption matching degree, respectively; Indicates the first Within the production cycle, the first The values ​​of each evaluation indicator.

[0073] Dimensionless processing of indicator data:

[0074] Calculate the first The first indicator in the Proportion in each production cycle:

[0075] Further calculate the information entropy of the indicator:

[0076] when At that time, it was stipulated

[0077] Calculate the coefficient of variation of the indicators:

[0078] Calculate the dynamic weights of each indicator based on the coefficient of difference:

[0079] After each scheduling cycle is completed, the latest production cycle data is added to the time series data window, the oldest historical data is removed, and the information entropy and dynamic weight of each evaluation indicator are recalculated to achieve rolling updates of the evaluation weights.

[0080] The greater the fluctuation of an indicator and the richer the information it provides, the higher its corresponding weight; the smaller the fluctuation of an indicator, the lower its weight. By updating historical data of evaluation indicators in real time, the weights can be dynamically adjusted, enabling the comprehensive adaptation index to more accurately reflect the importance of each indicator under current production conditions.

[0081] but ,when The higher the overall compatibility index of the shovel and excavator combination, the better.

[0082] Based on the comprehensive compatibility index among all trucks and forklifts, establish a truck-forklift compatibility matrix:

[0083] in: For the number of trucks; This refers to the number of forklifts; Indicates truck With bulldozer The overall compatibility index between them.

[0084] The larger the value of the matrix element, the higher the degree of matching between the corresponding truck and loader in terms of loading capacity, working cycle, production efficiency and energy consumption level, and the more suitable it is as a priority collaborative operation combination. The established truck-loader adaptation matrix serves as the basic input for the subsequent collaborative scheduling optimization model, providing a decision basis for optimal matching and dynamic scheduling under heterogeneous equipment conditions.

[0085] Step 5: Establish a 0-1 integer programming collaborative scheduling optimization model The optimization objective is to maximize the sum of the comprehensive adaptation index, while constraining that each truck is assigned only one loader and the total loading time of a single loader does not exceed the scheduling cycle. As a specific embodiment of the present invention, the 0-1 integer decision variables in step five This is used to characterize whether truck i is assigned to forklift j, and the optimization objective is to maximize the sum of the comprehensive fit index of all matching combinations.

[0086] Specifically as follows: Assuming the current decision-making moment has the following set of operational trucks across the entire mining area: The workable forklift assembly is Introduce discrete 0 and 1 integer decision variables. This is used to characterize the job matching relationship between trucks and forklifts:

[0087] in: ; .

[0088] In the current decision-making round, the optimization objective is to maximize the overall compatibility index of truck-loader combinations by globally allocating the matching combinations of trucks and loaders.

[0089] In the formula: The comprehensive fit index is dynamically calculated by the temporal data entropy weight method in step S4.

[0090] Constraints: (1) Each truck can only serve one forklift within a dispatch cycle:

[0091] (2) Within a scheduling cycle, the first one is allocated to the second one. The total loading time of all trucks using the loader must not exceed the length of the dispatch cycle.

[0092]

[0093] in, The number of loading buckets is rounded up. The actual production spoon capacity ratio calculated for S1; For the first Loading time per bucket of loader (unit: ), Scheduling cycle length.

[0094] Step Six: Solve the scheduling model using a genetic algorithm. The optimal combination of truck and shovel is output by solving the problem through integer encoding, elite retention roulette wheel selection, single-point intersection, and constrained repair mutation iteration. In one specific embodiment of the present invention, in step six, the genetic algorithm uses an integer encoding of the number of trucks corresponding to the chromosome length. After the mutation operation, the model constraints are checked, and trucks are reassigned to individuals that violate the constraints to complete the repair.

[0095] Specifically as follows: A genetic algorithm is used to solve the truck-loader cooperative scheduling optimization model constructed in step five to obtain the truck-loader matching scheme with the largest comprehensive fitness index. The specific steps include: (1) Parameter initialization Set the parameters for the genetic algorithm, including the population size. Maximum number of iterations Crossover probability and mutation probability An initial population that satisfies the constraints is randomly generated, with each individual representing a shovel-vehicle matching scheme.

[0096] (2) Chromosome coding Chromosomes are constructed using integer encoding, and their length is equal to the number of trucks. :

[0097] Among them, genes Indicates the first Each truck is assigned a corresponding forklift number.

[0098] (3) Fitness calculation Based on the shovel adaptation matrix constructed in step four Calculate the fitness value for each individual:

[0099] in: Indicates truck With bulldozer The overall compatibility index between them Match decision variables to the shovel.

[0100] (4) Select operation A hybrid rule combining roulette wheel selection and elite retention is employed. First, the top 5% of the most fit individuals in the current population are directly copied to the next generation without crossover or mutation. The remaining 95% of individuals are allocated selection probabilities according to their fitness as a percentage of the total fitness, and then roulette wheel selection is performed using pseudo-random number emission to ensure rapid convergence of high-yield genes in the population.

[0101] (5) Cross operations According to crossover probability Two parent individuals are randomly selected. and Perform single-point intersection; within the interval Randomly generated intersection points Place the two parent individuals in positions The gene sequence is cut off and the second half is exchanged, resulting in two new offspring individuals.

[0102] (6) Mutation operation According to mutation probability Randomly select a gene locus in the chromosome, and reassign the corresponding truck to another working loader to generate a new matching scheme; after the mutation is completed, perform a constraint feasibility check on the individual; if the constraint conditions in step five are violated, repair is carried out by reassigning the overloaded loader to the corresponding truck until the constraint requirements are met.

[0103] (7) Population renewal Elite individuals are merged with new individuals generated through selection, crossover, and mutation to form the next generation of the population, and the fitness values ​​of each individual in the population are recalculated.

[0104] (8) Termination of judgment Determine whether any of the following termination conditions are met: ① Reaching the maximum number of iterations ; ② Continuous The change in optimal fitness is less than a preset threshold. .

[0105] If the termination condition is met, the iteration stops; otherwise, return to (3) and continue execution.

[0106] (9) Output the optimal scheduling scheme The individual with the highest fitness is output as the optimal truck-loader matching result, and a truck-loader collaborative scheduling scheme is generated.

[0107] Step 7: Generate a closed-loop collaborative scheduling scheme The operation priority queue is constructed in descending order of the comprehensive adaptability index, transportation tasks are allocated and loading queues for each forklift and truck are generated, and equipment operation data is transmitted back in real time to complete the scheduling loop. As a specific embodiment of the present invention, the closed-loop scheduling process in step seven is as follows: the real-time operation data of the equipment is transmitted back to the scheduling information database as the data input for step one of the next scheduling cycle, thereby realizing a closed loop of perception-matching evaluation-scheduling optimization-task execution.

[0108] Specifically as follows: Based on the optimal matching result of the vehicle and shovel obtained in step six, a collaborative scheduling scheme for the current scheduling cycle is generated.

[0109] (1) Extraction of matching results between shovel and vehicle Determine the optimal matching relationship between the truck and the forklift based on step six:

[0110] in Indicates truck Assigned to forklift Perform the task.

[0111] (2) Priority queue construction For the matching set All shovel and blade combinations are based on the comprehensive compatibility index. Sort in descending order:

[0112] Form a priority matching queue for shovels and excavators:

[0113] The higher the overall compatibility index, the higher the priority of the corresponding truck-shovel combination in the scheduling queue.

[0114] (3) Task allocation According to the priority matching queue for shovels The transport tasks are assigned sequentially to the shovel-truck assembly. For any shovel-truck assembly (ta, sb), the corresponding transport task is generated:

[0115] in: Number the truck; Number the forklift; This is the loading and unloading area; This is the unloading point.

[0116] Based on the priority matching queue order, the transportation tasks are assigned to the corresponding trucks and forklifts in sequence, forming a task execution list:

[0117] When multiple trucks are simultaneously servicing the same forklift, they enter the loading queue in descending order of their overall compatibility index:

[0118] Indicates the first The loading service queue corresponding to each forklift; Indicates service to the The first loader A truck.

[0119] (4) Status feedback and task closure During task execution, the heterogeneity feature vector parameter data of the equipment in step two is collected in real time and recorded in the scheduling information database. This data serves as the input for step one of the next scheduling cycle, realizing a closed-loop scheduling process of equipment status perception, heterogeneous matching evaluation, collaborative scheduling optimization, and task execution feedback.

[0120] Step 8: Dynamic Rescheduling Update Set trigger conditions for adding or removing equipment, changing transport distance, and switching unloading points. When the changes in operating conditions reach the threshold, re-execute steps one through seven to generate a new scheduling scheme.

[0121] As a specific embodiment of the present invention, the rescheduling triggering conditions in step eight include any of the following: the number of trucks / forklifts increases or decreases exceeds a preset threshold, the rate of change of transportation distance exceeds the limit, or the unloading point is switched.

[0122] Specifically as follows: To adapt to the dynamic changes in equipment status and production environment during open-pit coal mine production, a dynamic update and rescheduling mechanism is established. When a change in equipment status or production environment is detected to reach a preset trigger threshold, a rescheduling procedure is initiated to update the equipment information database in real time and regenerate the collaborative scheduling scheme.

[0123] (1) Determination of rescheduling trigger conditions Real-time monitoring of equipment operating status and production environment status; construction of a set of rescheduling trigger events:

[0124] The triggering events include: truck malfunction; truck outage; new truck deployment; forklift malfunction; forklift outage; new forklift deployment; change in transport distance; and change in unloading point.

[0125] (2) Rescheduling is triggered when any of the following conditions are met: ① The number of trucks adding or removing equipment exceeds a preset threshold. ; ② The number of new or removed forklifts exceeds the preset threshold. ; ③ The rate of change in transport distance exceeds the threshold. : ④ Switching the unloading point; (3) Adjustment of scheduling strategy When the rescheduling trigger condition is met, the device information database is updated, and steps one through seven are re-executed.

[0126] In summary, this invention overcomes the limitations of the assumption of equipment homogeneity by incorporating heterogeneous characteristics such as truck load capacity, transport speed, transport energy consumption, and loader loading capacity, loading efficiency, and operating energy consumption into a unified characterization system, thereby achieving accurate characterization of the capabilities of different types of equipment. By constructing volume matching degree, cycle time matching degree, efficiency matching degree, and energy consumption matching degree, it can comprehensively reflect the synergistic relationship between truck and loader combinations, improving the accuracy and rationality of equipment matching.

[0127] In addition, a collaborative scheduling optimization model is established based on the integrated matching matrix of the vehicle and shovel to achieve optimal matching configuration under heterogeneous equipment conditions. This effectively reduces equipment waiting time and loading mismatch, avoids unreasonable configurations such as "large vehicle with small shovel" or "small vehicle with large shovel", and improves equipment utilization, loading efficiency and transportation efficiency. At the same time, the matching matrix can be updated in real time according to changes in equipment status and production environment and the scheduling scheme can be regenerated, improving the scheduling system's dynamic response capability and operational stability to complex working conditions.

[0128] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A method for coordinated scheduling of shovels and chaff in open-pit coal mines, considering the heterogeneity of equipment, characterized in that... Includes the following steps: Step 1: Acquiring multi-source data Real-time collection of dynamic operating parameters of trucks and loaders and retrieval of static factory parameters of equipment from the mining equipment management database; synchronous collection of relevant information on mining and loading operation area, transportation distance, and unloading point; classification and storage; construction of open-pit coal mine truck and loader scheduling information database. Step 2: Construct device heterogeneity feature vectors We established truck feature vectors containing parameters of load, speed, and energy consumption, and loader feature vectors containing parameters of bucket capacity, loading time, and operating energy consumption, to complete the unified quantitative characterization of heterogeneous equipment. Step 3: Calculation of the Matching Index for Multidimensional Heterogeneous Equipment Based on two types of feature vectors, four single-item matching indices are calculated in sequence: volume matching degree, beat matching degree, efficiency matching degree, and energy consumption matching degree. The values ​​of the four indices are all in the range of 0-1. Step 4: Construct a dynamic weighted comprehensive adaptation matrix The entropy weighting method of time series data is used to update the weights of four types of matching indicators in real time, and the comprehensive matching index of each truck-shovel combination is calculated by weighting. Based on the index of all combinations, a truck-shovel matching matrix is ​​generated. Step 5: Establish a 0-1 integer programming collaborative scheduling optimization model The optimization objective is to maximize the sum of the comprehensive adaptation index, while constraining that each truck is assigned only one loader and the total loading time of a single loader does not exceed the scheduling cycle. Step Six: Solve the scheduling model using a genetic algorithm. The optimal vehicle-shovel matching combination is output by solving the problem through integer encoding, elite retention roulette wheel selection, single-point intersection, and constrained repair mutation iteration. Step 7: Generate a closed-loop collaborative scheduling scheme The operation priority queue is constructed in descending order of the comprehensive adaptability index, transportation tasks are allocated and loading queues for each forklift and truck are generated, and equipment operation data is transmitted back in real time to complete the scheduling loop. Step 8: Dynamic Rescheduling Update Set trigger conditions for adding or removing equipment, changing transport distance, and switching unloading points. When the changes in operating conditions reach the threshold, re-execute steps one through seven to generate a new scheduling scheme.

2. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity as described in claim 1, characterized in that, The calculation method for volume matching degree in step three is as follows: The scoop capacity ratio is calculated by combining the truck bed volume and rated load capacity as dual constraints; the optimal number of loading scoops is determined by considering the transportation distance; and the volume matching degree is calculated by the difference between the actual scoop capacity ratio and the optimal number of scoops. .

3. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity as described in claim 1, characterized in that, In step three, the cycle time matching degree is calculated based on the total time of the truck's complete transportation and the time of a single loading by the forklift. The transportation cycle margin is then quantified by the synchronization deviation between the margin and the loading cycle time. It is used to characterize the degree of timing coordination in the operation of the shovel and crane.

4. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity as described in claim 1, characterized in that, The efficiency matching degree calculation method in step three is as follows: first calculate the transport capacity per unit time of each shovel combination. The maximum unit transport capacity is obtained by traversing all combinations. Minimum value After range normalization, we obtain ,in, For trucks With bulldozer Transport capacity per unit time when combined (unit: ), For the first Rated load capacity of a truck (unit: ).

5. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity according to claim 1, characterized in that, The energy consumption matching degree calculation method in step three is as follows: the combined energy consumption of truck round-trip transportation and loader loading is obtained by superimposing the energy consumption of the truck and the energy consumption of the loader. Iterate through all combinations of shovels to obtain the extreme energy consumption values ​​and perform comprehensive energy consumption normalization. To obtain standardized energy consumption indicators, and then through The energy consumption matching degree is obtained.

6. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity according to claim 1, characterized in that, The dynamic weighting process of the time series data entropy weighting method in step four is as follows: select four types of matching indicators for continuous multi-periods to construct a time series matrix, and calculate the information entropy and difference coefficient of each indicator after dimensionless processing of the indicator data. The indicator weights are assigned according to the difference coefficients, and the weights are updated on a rolling basis after each scheduling cycle is completed.

7. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity according to claim 1, characterized in that, Step 5: 0-1 integer decision variables This is used to characterize whether truck i is assigned to forklift j, and the optimization objective is to maximize the sum of the comprehensive fit index of all matching combinations.

8. The method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity according to claim 1, characterized in that, In step six, the genetic algorithm uses integer encoding of the number of trucks corresponding to the chromosome length. After mutation, the model constraints are checked, and trucks are reassigned to individuals that violate the constraints to complete the repair.

9. A method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity, as described in claim 1, is characterized in that... The closed-loop scheduling process in step seven is as follows: real-time equipment operation data is transmitted back to the scheduling information database as the data input for step one of the next scheduling cycle, realizing a closed loop of perception-matching evaluation-scheduling optimization-task execution.

10. A method for coordinated scheduling of shovels and chaff in open-pit coal mines considering equipment heterogeneity, as described in claim 1, is characterized in that... The rescheduling trigger conditions in step eight include any of the following: the number of trucks / forklifts increases or decreases exceeding a preset threshold, the rate of change in transport distance exceeds the limit, or the unloading point is switched.

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