Bucket wheel machine unattended operation system and method

Abstract

The invention discloses an unattended system and method for a bucket wheel machine, and relates to the technical field of bucket wheel machines. The bucket wheel machine comprises a bucket wheel machine main body, wherein the bucket wheel machine main body comprises a walking mechanism, a slewing mechanism, a bucket wheel device and a belt conveyor; the sensor module comprises a laser scanner, an encoder, a load sensor, a temperature sensor and a vibration sensor and is used for collecting operation state data and material pile shape data of the bucket wheel machine in real time; and the control module is connected with the sensor module and is used for executing automatic control logic according to the sensor data and generating an operation instruction of the bucket wheel machine. According to the invention, centralized management and task allocation are supported through the remote monitoring center, the management efficiency is improved, the system can replace manual operation, the manpower demand is reduced, the manpower cost and the management difficulty are reduced, the exposure of personnel in a dust environment is reduced through automatic operation, the health risk is reduced, the operation path and strategy are optimized, and the equipment energy consumption is reduced. The energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bucket wheel machines, and in particular to an unmanned system and method for bucket wheel machines. Background Art

[0002] Bucket wheel excavator, also known as bucket wheel stacker and reclaimer, is a high-efficiency loading and unloading machine used for continuous conveying of large dry bulk cargo yards that can both stack and retrieve materials. It is widely used in bulk material (ore, coal, coke, sand and gravel) storage yards such as ports, docks, metallurgy, cement, steel mills, coking plants, coal storage plants, and power plants for stacking and retrieving operations. It is mainly used for the storage and retrieval of materials such as coal and ore. Traditional bucket wheel excavator operation relies on manual operation. The operator needs to manually control the operation of the bucket wheel excavator in the control room or on site, including travel, rotation, bucket wheel excavation, belt conveyor and other actions.

[0003] Manual operation has the following problems: Operators need to concentrate for long periods of time, especially under complex working conditions, and are prone to operating errors due to fatigue. Manual operation relies on the operator's experience and skills, making it difficult to achieve efficient and accurate automated operations. Operators work in harsh environments (such as high temperature, dust, and noise) and face greater safety risks. Manual duty requires multiple operators, increasing labor costs and management difficulties.

[0004] Chinese patent publication number CN116281239A discloses an unmanned bucket wheel excavator system and method, including a 3D modeling subsystem and a central control station. The 3D modeling subsystem generates a 3D model corresponding to the stockpile, and the central control station generates operating instructions based on a preset bucket wheel excavator operation plan and the 3D model. With the development of automation and intelligent technology, unmanned systems are gradually being used in the industrial field. However, the existing unmanned systems for bucket wheel excavators still have the following shortcomings: Some systems only implement simple remote monitoring functions and cannot completely replace manual operations. Existing systems lack stability under complex working conditions (such as irregular material piles and harsh environments), lack real-time monitoring of equipment status and fault warning functions, and it is difficult to ensure the long-term stable operation of the system.

[0005] For this purpose, an unmanned system and method for a bucket wheel excavator are proposed. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems raised in the above background technology, and provide an unmanned bucket wheel excavator system and method.

[0007] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions: An unmanned bucket wheel machine system includes a bucket wheel machine body: wherein the bucket wheel machine body includes a traveling mechanism, a slewing mechanism, a bucket wheel device and a belt conveyor; Sensor modules, including laser scanners, encoders, load sensors, temperature sensors, and vibration sensors, are used to collect real-time data on the bucket wheel excavator's operating status and material pile shape; A control module, connected to the sensor module, for executing automated control logic based on sensor data to generate operating instructions for the bucket wheel excavator; A communication module, connected to the control module, for realizing data exchange between the system and the remote monitoring center; A fault diagnosis module, connected to the control module, for monitoring the status of the device in real time and identifying potential faults; An artificial intelligence module, connected to the control module, for optimizing operation path planning and material stacking strategies; The remote monitoring center is connected to the communication module and is used to send operation tasks and receive system status data.

[0008] Furthermore, the laser scanner is installed on the traveling mechanism and bucket wheel device to scan the material pile shape and generate three-dimensional point cloud data; the encoder is installed on the traveling motor, rotary motor and bucket wheel motor to measure position and speed; the load sensor is installed on the bucket wheel device and belt conveyor to monitor load changes; the temperature sensor and vibration sensor are installed on various components of the bucket wheel machine body to monitor the equipment operation status.

[0009] Furthermore, the control module includes: Data processing module, used for filtering, calibrating and fusing sensor data; A logic control module is used to generate operating instructions for the bucket wheel excavator based on the processed data; The path planning module is used to combine material pile shape data and task requirements and use artificial intelligence algorithms to plan the optimal operation path.

[0010] Furthermore, the communication module supports 4G / 5G wireless communication or optical fiber communication for real-time transmission of sensor data and reception of remote commands.

[0011] Furthermore, the fault diagnosis module includes: Data acquisition module, used to collect temperature, vibration and load data in real time; Feature extraction module, used to extract key features from data; Fault identification module, which uses machine learning algorithms to classify features and identify potential faults; The warning processing module is used to trigger warnings and suspend operations.

[0012] Furthermore, the artificial intelligence module includes: Data acquisition and preprocessing module, used to collect and process historical operation data and sensor data; Model training module, used to train the path planning model using deep learning algorithms; The real-time optimization module is used to generate the optimal operation path based on the current material pile shape and task requirements.

[0013] Furthermore, the remote monitoring center includes: Monitoring terminal, used to display the operating status of the bucket wheel excavator; Task management module, used to assign job tasks; The data storage and analysis module is used to store historical data and analyze operation efficiency.

[0014] A bucket wheel excavator unmanned operation method comprises the following steps: Step S1: System initialization, the sensor module collects the initial state data of the bucket wheel excavator; Step S2: The remote monitoring center sends the job task; Step S3: The control system uses the artificial intelligence module to plan the optimal operation path based on the material pile shape data and task requirements; Step S4: The bucket wheel excavator automatically completes the actions of walking, rotating, bucket wheel excavation, belt conveying, etc. according to the planned path; Step S5: The sensor module continuously collects operating data, and the control system adjusts the operating parameters according to the data; Step S6: The fault diagnosis module monitors the equipment status in real time and triggers an early warning and suspends the operation when an abnormality is found; Step S7: After the operation is completed, the system sends a completion report to the remote monitoring center.

[0015] Furthermore, the path planning in step 3 includes: Step S31: using a laser scanner to obtain three-dimensional point cloud data of the material pile shape; Step S32: Generate the optimal operation path using deep learning algorithms based on task requirements.

[0016] Furthermore, the fault diagnosis in step 6 includes: Step S61: collecting temperature, vibration and load data in real time; Step S62: extract key features from the data; Step S63: using a machine learning algorithm to identify potential faults; Step S64: trigger an early warning and suspend the operation, and send an alarm message to the remote monitoring center.

[0017] The beneficial effects of the present invention are as follows: By integrating advanced sensor technology, automated control technology, and artificial intelligence algorithms, the system can achieve fully automated operation of bucket wheel excavators, reduce manual intervention, and improve operating efficiency. It uses artificial intelligence modules to optimize operation path planning in real time and dynamically adjust operation strategies based on material pile shape and task requirements, significantly improving operating efficiency. The sensor module collects real-time operating status data of the bucket wheel excavator, including position, speed, load, temperature, and vibration, to ensure that the system can respond to changes in a timely manner. The fault diagnosis module monitors the equipment status in real time, identifies potential faults, and issues early warnings to ensure the long-term stable operation of the system. The remote monitoring center monitors the bucket wheel excavator's operating status in real time, supports remote operation and management, and reduces operator risks in harsh environments. The fault diagnosis module automatically triggers an early warning and suspends operations when an anomaly is detected, avoiding equipment damage and safety accidents. The laser scanner generates 3D point cloud data of the material pile shape, solving the adaptability problem of the existing system in complex working conditions, such as irregular material pile shapes. The system can dynamically adjust the operation path and strategy based on real-time data to adapt to different material pile shapes and environmental conditions. Encoders and load sensors are used to ensure the operating accuracy of bucket wheel excavators, avoiding material waste due to operational errors. The data processing module filters, calibrates, and integrates sensor data to ensure data accuracy and reliability. The system supports centralized management and task allocation through a remote monitoring center, improving management efficiency. It can replace manual operations, reduce manpower requirements, reduce labor costs and management difficulty. Automated operations reduce personnel exposure to dusty environments, reduce health risks, optimize operation paths and strategies, reduce equipment energy consumption, and improve energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the system architecture of the present invention; Figure 2 This is a control flow chart of the unmanned bucket wheel excavator system of the present invention; Figure 3 Schematic diagram of the arrangement of the sensor module of the present invention; Figure 4 This is a flow chart of the path planning algorithm of the artificial intelligence module of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. In addition, the terms "first," "second," etc. are used only to distinguish the descriptions and are not to be understood as indicating or implying relative importance.

[0022] In the description of the embodiments of the present invention, it should be noted that the terms "inside", "outside", "upper", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0023] like Figures 1 to 4 As shown, an unmanned system for a bucket wheel excavator includes a bucket wheel excavator body: wherein the bucket wheel excavator body includes a walking mechanism, a slewing mechanism, a bucket wheel device and a belt conveyor; a sensor module, including a laser scanner, an encoder, a load sensor, a temperature sensor and a vibration sensor, for real-time acquisition of operating status data and material pile shape data of the bucket wheel excavator; a control module, connected to the sensor module, for executing automatic control logic according to sensor data and generating operating instructions for the bucket wheel excavator; a communication module, connected to the control module, for realizing data interaction between the system and a remote monitoring center; a fault diagnosis module, connected to the control module, for real-time monitoring of equipment status and identification of potential faults; an artificial intelligence module, connected to the control module, for optimizing operation path planning and material stacking strategies; a remote monitoring center, connected to the communication module, for sending operation tasks and receiving system status data.

[0024] More specifically, by integrating sensor modules, control modules, communication modules, fault diagnosis modules, artificial intelligence modules and remote monitoring centers, the system can achieve fully automated operation. The sensor module collects the operating status and material pile shape data of the bucket wheel excavator in real time. The control module executes automatic control logic based on these data and generates operating instructions. The communication module realizes data interaction between the system and the remote monitoring center, supports remote management and monitoring, and the fault diagnosis module monitors the equipment status in real time, identifies potential faults and issues early warnings to ensure the safety and reliability of the system. The artificial intelligence module optimizes operation path planning and material stacking strategies to improve operation efficiency. The remote monitoring center is responsible for task allocation and status monitoring to reduce manual intervention.

[0025] The laser scanner is installed on the traveling mechanism and bucket wheel device to scan the material pile shape and generate three-dimensional point cloud data; the encoder is installed on the traveling motor, rotary motor and bucket wheel motor to measure position and speed; the load sensor is installed on the bucket wheel device and belt conveyor to monitor load changes; the temperature sensor and vibration sensor are installed on various components of the bucket wheel machine body to monitor the equipment operation status.

[0026] More specifically, laser scanners are installed on the traveling mechanism and bucket wheel device to scan the material pile shape and generate three-dimensional point cloud data, solving the adaptability problems of the existing system under complex working conditions such as irregular material pile shape. Encoders are installed on the traveling motor, rotary motor and bucket wheel motor to measure position and speed to ensure operation accuracy. Load sensors are installed on the bucket wheel device and belt conveyor to monitor load changes and prevent overload. Temperature sensors and vibration sensors are installed on key components to monitor the equipment operation status in real time, identify potential faults, and improve the stability and safety of the system.

[0027] The control module includes: a data processing module for filtering, calibrating and fusing sensor data; a logic control module for generating operating instructions for the bucket wheel excavator based on the processed data; and a path planning module for combining material pile shape data and task requirements to plan the optimal operation path using an artificial intelligence algorithm.

[0028] More specifically, the data processing module filters, calibrates and fuses sensor data to ensure data accuracy and reliability. The logic control module generates operating instructions for the bucket wheel excavator based on the processed data to achieve automated control. The path planning module combines material pile shape data and task requirements and uses artificial intelligence algorithms to plan the optimal operation path, solving the path planning problems of the existing system under complex working conditions and improving operation efficiency and accuracy.

[0029] The communication module supports 4G / 5G wireless communication or optical fiber communication for real-time transmission of sensor data and reception of remote commands.

[0030] More specifically, the communication module supports 4G / 5G wireless communication or fiber optic communication, ensuring real-time data transmission between the remote monitoring center. Through high-speed and stable communication connections, it can receive remote instructions and upload operating data in a timely manner, support remote management and monitoring, reduce manual intervention, and improve the system's degree of automation.

[0031] The fault diagnosis module includes: a data acquisition module for collecting temperature, vibration and load data in real time; a feature extraction module for extracting key features from the data; a fault identification module for classifying features using a machine learning algorithm to identify potential faults; and an early warning processing module for triggering an early warning and suspending operations.

[0032] More specifically, the data acquisition module collects temperature, vibration and load data in real time, the feature extraction module extracts key features from the data, the fault identification module uses machine learning algorithms to classify features and identify potential faults, and the early warning processing module triggers an early warning and suspends operations when an anomaly is detected, and sends an alarm message to the remote monitoring center to ensure the safety and reliability of the system and solve the problem that the existing system lacks real-time monitoring and fault warning functions.

[0033] The artificial intelligence module includes: a data acquisition and preprocessing module for collecting and processing historical operation data and sensor data; a model training module for training the path planning model using a deep learning algorithm; and a real-time optimization module for generating the optimal operation path based on the current material pile shape and task requirements.

[0034] More specifically, the data acquisition and preprocessing module collects and processes historical operation data and sensor data to provide basic data support for path planning. The model training module uses deep learning algorithms to train the path planning model to improve the intelligence level of path planning. The real-time optimization module generates the optimal operation path according to the current material pile shape and task requirements, dynamically adjusts the operation strategy, solves the adaptability problems of the existing system under complex working conditions, and improves operation efficiency and accuracy.

[0035] The remote monitoring center includes: a monitoring terminal for displaying the operating status of the bucket wheel machine; a task management module for allocating work tasks; and a data storage and analysis module for storing historical data and analyzing work efficiency.

[0036] More specifically, the operating status of the bucket wheel excavator is displayed through the monitoring terminal, supporting remote monitoring and operation. The task management module allocates work tasks and realizes automated task management. The data storage and analysis module stores historical data and analyzes work efficiency, providing data support for system optimization, reducing manual intervention and improving management efficiency.

[0037] A bucket wheel excavator unmanned operation method comprises the following steps: Step S1: System initialization, the sensor module collects the initial state data of the bucket wheel excavator; Step S2: The remote monitoring center sends the job task; Step S3: The control system uses the artificial intelligence module to plan the optimal operation path based on the material pile shape data and task requirements; Step S4: The bucket wheel excavator automatically completes the actions of walking, rotating, bucket wheel excavation, belt conveying, etc. according to the planned path; Step S5: The sensor module continuously collects operating data, and the control system adjusts the operating parameters according to the data; Step S6: The fault diagnosis module monitors the equipment status in real time and triggers an early warning and suspends the operation when an abnormality is found; Step S7: After the operation is completed, the system sends a completion report to the remote monitoring center.

[0038] More specifically, after the system is initialized, the sensor module collects the initial status data of the bucket wheel excavator to ensure that the system is in normal operation. The remote monitoring center sends the operation task, and the control system uses the artificial intelligence module to plan the optimal operation path based on the material pile shape data and task requirements. The bucket wheel excavator automatically completes walking, rotation, bucket wheel excavation, belt conveying and other actions according to the planned path to achieve fully automated operation. The sensor module continuously collects operation data, and the control system adjusts the operation parameters according to the data to ensure operation accuracy. The fault diagnosis module monitors the equipment status in real time, triggers an early warning and suspends the operation when an abnormality is found, to ensure system safety. After the operation is completed, the system sends a completion report to the remote monitoring center to support remote management and monitoring.

[0039] The path planning in step 3 includes: Step S31: using a laser scanner to obtain three-dimensional point cloud data of the material pile shape; Step S32: Generate the optimal operation path using deep learning algorithms based on task requirements.

[0040] More specifically, by using a laser scanner to obtain three-dimensional point cloud data of the material pile shape, combining it with task requirements, and using a deep learning algorithm to generate the optimal operation path, by collecting material pile shape data in real time and dynamically adjusting the path planning, the adaptability problem of the existing system under complex working conditions is solved, and the operation efficiency and accuracy are improved.

[0041] The fault diagnosis in step 6 includes: Step S61: collecting temperature, vibration and load data in real time; Step S62: extract key features from the data; Step S63: using a machine learning algorithm to identify potential faults; Step S64: trigger an early warning and suspend the operation, and send an alarm message to the remote monitoring center.

[0042] More specifically, by collecting temperature, vibration and load data in real time, extracting key features from the data, and using machine learning algorithms to identify potential faults, an early warning is triggered and the operation is suspended when an anomaly is detected, and an alarm message is sent to the remote monitoring center to ensure the safety and reliability of the system and solve the problem that the existing system lacks real-time monitoring and fault warning functions.

[0043] Implementation Cases The following is an application case of the present invention in a thermal power plant: A sensor module, a control system, a communication module and a fault diagnosis module are installed on the bucket wheel excavator and connected to a remote monitoring center.

[0044] The remote monitoring center sends reclaiming tasks to the bucket wheel excavator based on the coal inventory situation.

[0045] The control system uses a laser scanner to obtain three-dimensional point cloud data of the coal pile and plans the optimal material removal path based on task requirements.

[0046] The bucket wheel excavator automatically completes actions such as walking, rotation, bucket wheel excavation, and belt conveying according to the planned path to transport the coal to the designated location.

[0047] The sensor module continuously collects operating data, and the control system adjusts operating parameters based on the data to ensure material picking accuracy.

[0048] The fault diagnosis module monitors the equipment status in real time and automatically triggers an early warning and suspends operations when an abnormality is detected.

[0049] After the material is taken, the system sends a completion report to the remote monitoring center and enters standby mode.

[0050] Through the implementation of the present invention, the bucket wheel excavator operating efficiency of the thermal power plant is improved by 30%, the labor cost is reduced by 50%, and the equipment failure rate is reduced by 40%, which significantly improves the production efficiency and management level.

[0051] In summary: By integrating advanced sensor technology, automation control technology and artificial intelligence algorithms, the system can realize the fully automated operation of bucket wheel excavators, reduce manual intervention, improve operation efficiency, use artificial intelligence modules to optimize operation path planning in real time, dynamically adjust operation strategies according to material pile shape and task requirements, and significantly improve operation efficiency; the sensor module collects the operation status data of bucket wheel excavators in real time, including position, speed, load, temperature and vibration, to ensure that the system can respond to changes in a timely manner; the fault diagnosis module monitors the equipment status in real time, identifies potential faults and issues early warnings to ensure the long-term stable operation of the system; the remote monitoring center monitors the operation status of bucket wheel excavators in real time, supports remote operation and management, and reduces the risk of operators working in harsh environments. The fault diagnosis module automatically triggers an early warning and suspends the operation when an abnormality is detected, avoiding Equipment damage and safety accidents; Generate three-dimensional point cloud data of material pile shape through laser scanner to solve the adaptability problem of existing system under complex working conditions, such as irregular material pile shape. The system can dynamically adjust the operation path and strategy according to real-time data to adapt to different material pile shapes and environmental conditions; Ensure the operation accuracy of bucket wheel machine through encoder and load sensor to avoid material waste due to operating errors, and filter, calibrate and fuse sensor data through data processing module to ensure data accuracy and reliability; Support centralized management and task allocation through remote monitoring center to improve management efficiency. The system can replace manual operation, reduce manpower requirements, reduce labor costs and management difficulty. Automated operation reduces personnel exposure to dust environment, reduces health risks, optimizes operation path and strategy, reduces equipment energy consumption, and improves energy utilization efficiency.

[0052] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A bucket wheel excavator unmanned system, characterized in that: It includes a bucket wheel machine body: wherein the bucket wheel machine body includes a walking mechanism, a slewing mechanism, a bucket wheel device and a belt conveyor; Sensor modules, including laser scanners, encoders, load sensors, temperature sensors, and vibration sensors, are used to collect real-time data on the bucket wheel excavator's operating status and material pile shape; A control module, connected to the sensor module, for executing automated control logic based on sensor data to generate operating instructions for the bucket wheel excavator; A communication module, connected to the control module, for realizing data exchange between the system and the remote monitoring center; A fault diagnosis module, connected to the control module, for monitoring the status of the device in real time and identifying potential faults; An artificial intelligence module, connected to the control module, for optimizing operation path planning and material stacking strategies; The remote monitoring center is connected to the communication module and is used to send operation tasks and receive system status data.

2. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The laser scanner is installed on the traveling mechanism and bucket wheel device to scan the material pile shape and generate three-dimensional point cloud data; the encoder is installed on the traveling motor, rotary motor and bucket wheel motor to measure position and speed; the load sensor is installed on the bucket wheel device and belt conveyor to monitor load changes; the temperature sensor and vibration sensor are installed on various components of the bucket wheel machine body to monitor the equipment operation status.

3. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The control module includes: Data processing module, used for filtering, calibrating and fusing sensor data; A logic control module is used to generate operating instructions for the bucket wheel excavator based on the processed data; The path planning module is used to combine material pile shape data and task requirements and use artificial intelligence algorithms to plan the optimal operation path.

4. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The communication module supports 4G / 5G wireless communication or optical fiber communication for real-time transmission of sensor data and reception of remote commands.

5. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The fault diagnosis module includes: Data acquisition module, used to collect temperature, vibration and load data in real time; Feature extraction module, used to extract key features from data; Fault identification module, which uses machine learning algorithms to classify features and identify potential faults; The warning processing module is used to trigger warnings and suspend operations.

6. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The artificial intelligence module includes: Data acquisition and preprocessing module, used to collect and process historical operation data and sensor data; Model training module, used to train the path planning model using deep learning algorithms; The real-time optimization module is used to generate the optimal operation path based on the current material pile shape and task requirements.

7. The unmanned bucket wheel excavator system according to claim 1, characterized in that: The remote monitoring center includes: Monitoring terminal, used to display the operating status of the bucket wheel excavator; Task management module, used to assign job tasks; The data storage and analysis module is used to store historical data and analyze operation efficiency.

8. A bucket wheel excavator unmanned operation method, characterized in that: The following steps are involved: Step S1: System initialization, the sensor module collects the initial state data of the bucket wheel excavator; Step S2: The remote monitoring center sends the job task; Step S3: The control system uses the artificial intelligence module to plan the optimal operation path based on the material pile shape data and task requirements; Step S4: The bucket wheel excavator automatically completes the actions of walking, rotating, bucket wheel excavation, belt conveying, etc. according to the planned path; Step S5: The sensor module continuously collects operating data, and the control system adjusts the operating parameters according to the data; Step S6: The fault diagnosis module monitors the equipment status in real time and triggers an early warning and suspends the operation when an abnormality is found; Step S7: After the operation is completed, the system sends a completion report to the remote monitoring center.

9. The unmanned bucket wheel excavator method according to claim 8, characterized in that: The path planning in step 3 includes: Step S31: using a laser scanner to obtain three-dimensional point cloud data of the material pile shape; Step S32: Generate the optimal operation path using deep learning algorithms based on task requirements.

10. The unmanned bucket wheel excavator method according to claim 8, characterized in that: The fault diagnosis in step 6 includes: Step S61: collecting temperature, vibration and load data in real time; Step S62: extract key features from the data; Step S63: using a machine learning algorithm to identify potential faults; Step S64: trigger an early warning and suspend the operation, and send an alarm message to the remote monitoring center.

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