CN-121974169-A - Bucket-wheel stacker reclaimer construction method optimization method, system and program product

CN121974169ACN 121974169 ACN121974169 ACN 121974169ACN-121974169-A

Abstract

The invention belongs to the technical field of intelligent control, and particularly discloses a method, a system and a program product for optimizing a bucket-wheel stacker-reclaimer, which are used for carrying out data fusion processing by collecting an operation monitoring data set of the bucket-wheel stacker-reclaimer under the current working condition and a geographic environment data set under the current working geographic environment, and then, optimizing a material taking method based on the neural network model to obtain an optimized control instruction for carrying out operation control feedback of the bucket-wheel stacker-reclaimer, so that the optimized control of material taking operation of the bucket-wheel stacker-reclaimer can be realized. The invention can adjust and optimize the working method of the bucket-wheel stacker-reclaimer according to real-time working conditions and environmental factors, effectively improve the anti-interference capability, adaptability and stability of the intelligent control of the bucket-wheel stacker-reclaimer, reduce the no-load operation and the invalid operation of equipment while improving the production efficiency, and save the resource consumption and the manpower input.

Inventors

LIU XIANGXIAO
WANG LEI
ZHOU YUWEI
HUANG TIAN
WANG DONGXU
CHEN LIANG
XING YONG
ZHU YONGXIN

Assignees

河北华电曹妃甸储运有限公司
华电煤业集团数智技术有限公司

Dates

Publication Date: 20260505
Application Date: 20251222

Claims (10)

1. The method for optimizing the working method of the bucket-wheel stacker reclaimer is characterized by comprising the following steps of: acquiring an operation monitoring data set of an unmanned system of the bucket-wheel stacker-reclaimer under the current working condition; Acquiring an initial geographic environment data set of the bucket-wheel stacker-reclaimer in the current operation geographic environment; Performing data preprocessing on the initial geographic environment data set to obtain a sampling geographic environment data set; Performing data fusion processing on the operation monitoring data set and the sampling geographic environment data set to obtain a fusion monitoring data set; Inputting the fusion monitoring data set into a pre-trained bucket-wheel stacker-reclaimer control optimization model to perform optimization treatment of a material taking method, and obtaining an optimization control instruction of the bucket-wheel stacker-reclaimer; And sending an optimal control instruction of the bucket-wheel stacker-reclaimer to an unmanned system of the bucket-wheel stacker-reclaimer, so that the unmanned system of the bucket-wheel stacker-reclaimer executes the optimal control instruction to control the bucket-wheel stacker-reclaimer to take materials.
2. The method of claim 1, wherein prior to inputting the fused monitoring dataset into the bucket-wheel stacker-reclaimer control optimization model, the method further comprises: Constructing an artificial neural network model; training the artificial neural network model by using a preset training set until the set training conditions are met, and obtaining a trained bucket-wheel stacker-reclaimer control optimization model, wherein the training set comprises a plurality of fused monitoring data set samples marked with corresponding optimization control instruction labels.
3. The method for optimizing a bucket-wheel stacker reclaimer process of claim 1, wherein said operational monitoring dataset comprises condition sensing data and stack scanning point cloud data.
4. The method of claim 3, wherein the operation monitoring data set further comprises a pick control command.
5. The method of claim 1, wherein the initial set of geographic environment data comprises RTK measurement data and environmental monitoring data.
6. The method for optimizing a bucket-wheel stacker reclaimer process of claim 1, wherein the performing data preprocessing on the initial geographic environment data set to obtain a sampled geographic environment data set comprises: and performing data cleaning, data alignment, data augmentation and data labeling on the initial geographic environment data set to obtain a sampling geographic environment data set.
7. The method for optimizing a bucket-wheel stacker reclaimer as defined in claim 1, wherein the performing a data fusion process on the operation monitoring dataset and the sampling geographical environment dataset to obtain a fused monitoring dataset comprises: Performing data standardization processing on the operation monitoring data set and the sampling geographic environment data set; And combining the operation monitoring data set after the data standardization processing and the sampling geographic environment data set based on the set data combination mode to obtain a fusion monitoring data set.
8. The utility model provides a bucket-wheel stacker reclaimer worker method optimizing system which characterized in that includes data acquisition unit, data processing unit, data fusion unit, control optimizing unit and instruction sending unit, wherein: The data acquisition unit is used for acquiring an operation monitoring data set of the unmanned system of the bucket-wheel stacker-reclaimer under the current working condition and acquiring an initial geographic environment data set of the bucket-wheel stacker-reclaimer under the current working geographic environment; The data processing unit is used for carrying out data preprocessing on the initial geographic environment data set to obtain a sampling geographic environment data set; the data fusion unit is used for carrying out data fusion processing on the operation monitoring data set and the sampling geographic environment data set to obtain a fusion monitoring data set; the control optimization unit is used for inputting the fusion monitoring data set into a pre-trained bucket-wheel stacker-reclaimer control optimization model to perform optimization treatment of a material taking method, so as to obtain an optimization control instruction of the bucket-wheel stacker-reclaimer; the instruction sending unit is used for sending the optimal control instruction of the bucket-wheel stacker-reclaimer to the unmanned system of the bucket-wheel stacker-reclaimer, so that the unmanned system of the bucket-wheel stacker-reclaimer executes the optimal control instruction to control the bucket-wheel stacker-reclaimer to take materials.
9. The utility model provides a bucket-wheel stacker reclaimer worker method optimizing system which characterized in that includes: a memory for storing instructions; the processor is used for reading the instructions stored in the memory and executing the bucket-wheel stacker-reclaimer construction method optimizing method according to the instructions.
10. A computer program product, characterized in that the method for optimizing the working method of a bucket-wheel stacker-reclaimer as defined in any one of claims 1-7 is performed when said computer program product is run on a computer.

Description

Bucket-wheel stacker reclaimer construction method optimization method, system and program product Technical Field The invention belongs to the technical field of intelligent control, and particularly relates to a bucket-wheel stacker-reclaimer construction method optimization method, a bucket-wheel stacker-reclaimer construction method optimization system and a program product. Background The bucket-wheel stacker reclaimer is an efficient loading and unloading machine for a large-scale dry bulk storage yard, and can realize continuous stacking and reclaiming functions. The automatic control of the bucket-wheel stacker-reclaimer means that all operation mechanisms of the bucket-wheel stacker-reclaimer are controlled by an automatic control system, and the mode utilizes advanced technologies such as stacking detection, anti-collision detection, accurate positioning, visual monitoring and the like, so that unmanned full-automatic control of the bucket-wheel stacker-reclaimer is realized. The control mode of the bucket-wheel stacker reclaimer directly influences the operation efficiency, the safety and the economy. The existing bucket-wheel stacker reclaimer control system automatically completes a material taking task according to a single construction method, and the fixed material piling and taking construction method cannot realize stable and efficient production efficiency due to the task working condition of a material piling and material taking field and the complexity of the environment (such as uneven material pile, different sedimentation degrees of a row area, weather conditions and the like), so that a plurality of challenges are brought to the automatic control of the bucket-wheel stacker reclaimer, and sometimes an operator is required to continuously correct control parameters before or during the task according to actual conditions, so that the aim of optimizing the production efficiency is achieved. Therefore, the control construction method of the existing bucket-wheel stacker reclaimer needs to be improved. ‌ ‌ A Disclosure of Invention The invention aims to provide a bucket-wheel stacker reclaimer construction method optimization method, a bucket-wheel stacker reclaimer construction method optimization system and a program product, which are used for solving the problems in the prior art. In order to achieve the above purpose, the present invention adopts the following technical scheme: in a first aspect, a method for optimizing a bucket-wheel stacker reclaimer is provided, including: acquiring an operation monitoring data set of an unmanned system of the bucket-wheel stacker-reclaimer under the current working condition; Acquiring an initial geographic environment data set of the bucket-wheel stacker-reclaimer in the current operation geographic environment; Performing data preprocessing on the initial geographic environment data set to obtain a sampling geographic environment data set; Performing data fusion processing on the operation monitoring data set and the sampling geographic environment data set to obtain a fusion monitoring data set; Inputting the fusion monitoring data set into a pre-trained bucket-wheel stacker-reclaimer control optimization model to perform optimization treatment of a material taking method, and obtaining an optimization control instruction of the bucket-wheel stacker-reclaimer; And sending an optimal control instruction of the bucket-wheel stacker-reclaimer to an unmanned system of the bucket-wheel stacker-reclaimer, so that the unmanned system of the bucket-wheel stacker-reclaimer executes the optimal control instruction to control the bucket-wheel stacker-reclaimer to take materials. In one possible design, before inputting the fused monitoring dataset into the bucket-wheel stacker-reclaimer control optimization model, the method further comprises: Constructing an artificial neural network model; training the artificial neural network model by using a preset training set until the set training conditions are met, and obtaining a trained bucket-wheel stacker-reclaimer control optimization model, wherein the training set comprises a plurality of fused monitoring data set samples marked with corresponding optimization control instruction labels. In one possible design, the operational monitoring dataset includes condition sensing data and stockpile scanning point cloud data. In one possible design, the operational monitoring data set further includes take-off control instructions. In one possible design, the initial geographic environment data set includes RTK measurement data and environmental monitoring data. In one possible design, the performing data preprocessing on the initial geographic environment data set to obtain a sampled geographic environment data set includes: and performing data cleaning, data alignment, data augmentation and data labeling on the initial geographic environment data set to obtain a sampling geographic environment data set. In on