A method for controlling the dredging of a multi-drive system of a grab unloader at a power plant dock.

Abstract

This invention relates to the field of transmission system control technology. It provides a method for controlling the dredging of a multi-transmission system in a grab unloader at a power plant dock. This method addresses issues such as data transmission instability susceptible to interference in complex industrial environments, control command delays, and motion errors caused by complex coupling relationships between various mechanisms in the multi-transmission system. The invention acquires and preprocesses basic system data, trains a dredging control model using a random forest model, receives dredging control tasks and generates simulated data, extracts data features and compares them with actual data for verification, replaces inconsistent data, and outputs real-time control tasks to the equipment for execution. This invention effectively improves the accuracy and stability of dredging control, reduces failure rates and maintenance costs, enhances system reliability, optimizes the work process, and is applicable to different material characteristics and operating environments.

Description

Technical Field

[0001] This invention relates to the field of transmission system control technology, and in particular to a method for controlling the dredging of a multi-transmission system of a grab unloader at a power plant dock. Background Technology

[0002] The multi-drive system of the grab unloader at the power plant dock is the core of its efficient operation. This system typically consists of a DC speed control system or an AC variable frequency speed control system, with the AC variable frequency system gradually becoming the mainstream choice due to its advantages such as high rated power and diverse control methods. The multi-drive system controls the lifting, opening and closing of the grab bucket, the trolley traveling mechanism (collectively referred to as the four-drum mechanism), the luffing mechanism, and the trolley traveling mechanism to achieve the entire process of the grab bucket excavating material from the ship's hold, lifting it to a safe height at the hatch opening, moving it laterally above the hopper to unload the material, and then returning to the top of the hold for a second grab. The multi-drive system also involves complex electrical drive control, including the coordinated actions of the lifting, opening and closing, and trolley traveling mechanisms, as well as the application of differential reducers, to ensure that the grab bucket can flexibly and accurately complete its tasks under various working conditions.

[0003] In the data processing of the multi-drive system of the grab unloader at the power plant dock, the stability of data transmission is easily affected by interference in the complex industrial environment, which leads to delays in control commands. There are complex coupling relationships between the various transmission mechanisms in the multi-drive system. If there are delays or errors in the data, errors will occur in the movement of each mechanism, causing errors in the dredging control of the multi-drive system of the grab unloader at the power plant dock. To solve this technical problem, this invention provides a dredging control method for the multi-drive system of the grab unloader at the power plant dock. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for controlling the dredging of a multi-drive system in a grab unloader at a power plant dock. This method solves the problem that in complex industrial environments, the stability of data transmission is easily affected by interference, leading to delays in control commands. Furthermore, there are complex coupling relationships between various transmission mechanisms in the multi-drive system. If data is delayed or erroneous, errors will occur in the movement of each mechanism, resulting in errors in the dredging control of the multi-drive system in the grab unloader at the power plant dock.

[0005] This invention discloses a method for controlling the dredging of a multi-drive system of a grab unloader at a power plant dock, comprising:

[0006] Step S101: Obtain the basic data of the multi-drive system of the grab unloader at the power plant dock. The basic data of the multi-drive system of the grab unloader at the power plant dock includes sensor data, position data, load data, system status data, material characteristic data, control command data, and historical data of the multi-drive system of the grab unloader at the power plant dock.

[0007] Step S102: Preprocess the basic data of the multi-transmission system of the grab unloader at the power plant wharf, divide the preprocessed basic data of the multi-transmission system of the grab unloader at the power plant wharf into training set and validation set, and use the training set and validation set to train the random forest model to obtain the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf.

[0008] Step S103: Receive the dredging control task of the multi-transmission system of the grab unloader at the power plant wharf, substitute the dredging control task of the multi-transmission system of the grab unloader at the power plant wharf into the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf, and obtain the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf.

[0009] Step S104: Extract data features from the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf to obtain the dredging data features of the multi-transmission system of the grab unloader at the power plant wharf. Collect corresponding data based on the dredging data features of the multi-transmission system of the grab unloader at the power plant wharf to obtain the data to be detected.

[0010] Step S105: Compare the data to be detected with the dredging control simulation data of the multi-drive system of the grab unloader at the power plant wharf. If the comparison results are inconsistent, replace the inconsistent data with the dredging control simulation data of the multi-drive system of the grab unloader at the power plant wharf. Use the replaced data as the dredging control data of the multi-drive system of the grab unloader at the power plant wharf. Substitute the dredging control data of the multi-drive system of the grab unloader at the power plant wharf into the dredging model of the multi-drive system of the grab unloader at the power plant wharf, output the real-time control task, and transmit the real-time control task to the equipment end. The equipment end executes the real-time control task.

[0011] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S101 includes:

[0012] Data is collected in real time from the multi-drive system of the grab unloader at the power plant dock through the placement of displacement sensors, force sensors, and speed sensors.

[0013] Using GPS, laser rangefinders, or encoder positioning devices, real-time position data of the grab bucket in three-dimensional space is collected;

[0014] The weight data of the material in the grab bucket is collected in real time through weighing sensors or load monitoring devices.

[0015] System status data is obtained from the control system of the multi-drive system. The system status data includes the voltage, current, and power factor of the electric drive system, the operating status of the drive mechanism, and alarm information.

[0016] Control command data is obtained from the operator console or automatic control system. The control command data includes commands for lifting, opening, closing, and moving the grab bucket, as well as the set value of the digging volume.

[0017] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S102 includes:

[0018] The preprocessed data is randomly divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to evaluate the model's performance.

[0019] Random forest model was selected as the basis for the dredging control model. The random forest model was trained using training set data and validated using validation set data.

[0020] The trained random forest model is saved in a loadable format to obtain the dredging control model of the multi-drive system of the grab unloader at the power plant dock.

[0021] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S103 includes:

[0022] Receive dredging control tasks from the control system or operator console of the grab unloader at the power plant dock. The dredging control tasks include the dredging depth, speed, position, material type, and grab size.

[0023] The received dredging control task is parsed to extract control parameters, including the starting position, ending position, target depth, and desired speed of the dredging.

[0024] Based on the dredging control task, sensor data, position data, and load data are extracted from the real-time data of the multi-transmission system of the grab unloader at the power plant wharf as the initial conditions for model input.

[0025] Load the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf, which was trained in step S102, and substitute the analyzed dredging control task parameters and the prepared real-time data into the dredging control model. The model will make decisions and predictions based on the input data and using the branch structure of the random forest algorithm.

[0026] The dredging control model generates simulation data of the multi-transmission system of the grab unloader at the power plant dock when performing dredging tasks, based on input data and internal algorithms. The simulation data includes the expected motion trajectory of each transmission mechanism, load changes, and system status.

[0027] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S104 includes:

[0028] Load the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf generated in step S103. The dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf includes the expected motion trajectory of each transmission mechanism, load changes and system status information when performing the dredging task.

[0029] The characteristics of the excavation control data are identified from the loaded simulation data. These characteristics include the movement speed and position of the grab bucket, the instantaneous changes in load, the system response time, and the coordination relationship between various transmission mechanisms.

[0030] The extracted data features are organized to form a feature dataset. Based on the extracted data features, data correspondence is established. The data correspondence includes data corresponding to these features collected in real time by sensors and monitoring systems during the actual operation of the grab unloader at the power plant dock.

[0031] The collected actual data is matched with the extracted data features to generate the dataset to be tested.

[0032] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S105 includes:

[0033] Align the data to be tested with the sag control simulation data on the timestamp, and compare the data to be tested with the sag control simulation data item by item. The item-by-item comparison includes the motion trajectory of each transmission mechanism, load changes and system state parameters.

[0034] During the comparison process, data that differed from the simulated data were identified. These differences were caused by data transmission errors, sensor malfunctions, or changes in system state.

[0035] For data where there are differences between the identified data to be detected and the simulated data, it is determined whether the fluctuation is within a reasonable range. If the fluctuation is not within a reasonable range, a data replacement decision is made.

[0036] Furthermore, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S105 includes:

[0037] For data that needs to be replaced, the corresponding part of the settlement control simulation data is replaced in the data to be tested to form the adjusted settlement control data;

[0038] The adjusted dredging control data is verified, and the verified adjusted dredging control data is substituted into the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf as the input data of the model.

[0039] Based on the model's processing results, real-time control tasks are generated, which include control commands and motion parameters for each transmission mechanism.

[0040] The generated real-time control tasks are transmitted to the device, which then executes the specific control operations.

[0041] The dredging control method for a multi-drive system of a grab unloader at a power plant dock, as described in this invention, has the following main advantages:

[0042] This invention constructs a random forest-based dredging control model, which can accurately predict the expected motion trajectory, load changes, and system state of each transmission mechanism of the grab unloader when performing dredging tasks, thereby significantly improving the accuracy of dredging control.

[0043] This invention effectively identifies and handles errors and delays in data transmission during data preprocessing and comparative verification, improving the accuracy of control data and reducing control errors caused by data transmission problems. By acquiring system data in real time, generating simulated data, conducting comparative verification, and generating real-time control tasks, this invention can significantly shorten system response time and improve the working efficiency of grab unloaders. By predicting and identifying potential problems in advance, this invention can reduce equipment failure rates and decrease downtime and maintenance costs caused by malfunctions.

[0044] This invention ensures the stable operation of grab unloaders in complex industrial environments, improving system reliability and safety. The dredging control method of this invention is applicable to different material characteristics and operating environments. By adjusting model parameters and control strategies, it can flexibly respond to various operational needs, improving system adaptability. Through automated dredging control and data processing, this invention optimizes the operational process, reduces manual intervention, and improves operational efficiency and quality.

[0045] In summary, this invention significantly improves the dredging control accuracy of the multi-drive system of the grab unloader at the power plant wharf by constructing a dredging control model based on random forest and combining real-time data acquisition, simulation data generation, data comparison and verification, and real-time control task generation. This reduces the failure rate and maintenance costs, and enhances the reliability of the system. Attached Figure Description

[0046] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0047] Figure 1 This invention provides a schematic flowchart of a dredging control method for a multi-drive system of a grab unloader at a power plant dock. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0049] To better understand the purpose of this invention, the invention will now be described in further detail.

[0050] Please see Figure 1 The present invention discloses a method for controlling the dredging of a multi-drive system of a grab unloader at a power plant dock, comprising:

[0051] Step S101: Obtain the basic data of the multi-drive system of the grab unloader at the power plant dock. The basic data of the multi-drive system of the grab unloader at the power plant dock includes sensor data, position data, load data, system status data, material characteristic data, control command data, and historical data of the multi-drive system of the grab unloader at the power plant dock.

[0052] Acquire various fundamental data from the multi-drive system of the grab unloader at the power plant dock, including sensor data, position data, load data, system status data, material characteristic data, control command data, and historical data. Comprehensive data acquisition provides a rich information foundation for subsequent steps.

[0053] Step S102: Preprocess the basic data of the multi-transmission system of the grab unloader at the power plant wharf, divide the preprocessed basic data of the multi-transmission system of the grab unloader at the power plant wharf into training set and validation set, and use the training set and validation set to train the random forest model to obtain the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf.

[0054] The acquired basic data is preprocessed and then divided into training and validation sets. The random forest model is trained using the training and validation sets to obtain the dredging control model for the multi-drive system of the grab unloader at the power plant dock. Preprocessing improves data quality and ensures the accuracy of model training. The random forest model can handle complex data relationships and has strong generalization ability, enabling it to predict system behavior based on input data.

[0055] Step S103: Receive the dredging control task of the multi-transmission system of the grab unloader at the power plant wharf, substitute the dredging control task of the multi-transmission system of the grab unloader at the power plant wharf into the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf, and obtain the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf.

[0056] The system receives dredging control tasks from the multi-drive system of the grab unloader at the power plant dock, inputs task parameters and real-time data into a trained dredging control model, and generates simulation data. This simulation data reflects the expected motion trajectory and load changes of each drive mechanism during the actual dredging process. This helps to identify potential problems in advance and provides a basis for subsequent comparison and adjustments.

[0057] Step S104: Extract data features from the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf to obtain the dredging data features of the multi-transmission system of the grab unloader at the power plant wharf. Collect corresponding data based on the dredging data features of the multi-transmission system of the grab unloader at the power plant wharf to obtain the data to be detected.

[0058] Data feature extraction is performed on the simulated data to obtain the submerged data features. Then, corresponding data is collected based on these features to obtain the data to be tested. Through feature extraction, key factors that significantly influence system behavior can be identified. The data to be tested is collected during actual operation and is used to compare with the simulated data to verify the accuracy of the model.

[0059] Step S105: Compare the data to be detected with the dredging control simulation data of the multi-drive system of the grab unloader at the power plant wharf. If the comparison results are inconsistent, replace the inconsistent data with the dredging control simulation data of the multi-drive system of the grab unloader at the power plant wharf. Use the replaced data as the dredging control data of the multi-drive system of the grab unloader at the power plant wharf. Substitute the dredging control data of the multi-drive system of the grab unloader at the power plant wharf into the dredging model of the multi-drive system of the grab unloader at the power plant wharf, output the real-time control task, and transmit the real-time control task to the equipment end. The equipment end executes the real-time control task.

[0060] The data to be tested is compared with simulated data. If inconsistencies are found, the inconsistent parts of the data to be tested are replaced with simulated data. Then, the replaced data is substituted into the excavation control model, outputting real-time control tasks, which are then transmitted to the equipment for execution. Data comparison and replacement can promptly detect and correct data errors or delays, ensuring the accuracy and stability of control data. The generation and execution of real-time control tasks enable precise control of the system, avoiding motion errors caused by data transmission stability issues or complex system coupling relationships.

[0061] In summary, through the coordinated operation of these five steps, this invention can effectively solve the problems of data transmission stability being easily interfered with, control command delays, and motion errors caused by the complex coupling relationship between various transmission mechanisms in a multi-transmission system. It can not only improve the operating efficiency and accuracy of the multi-transmission system of the grab unloader at the power plant dock, but also reduce maintenance costs and safety risks.

[0062] Specifically, the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention includes step S101, which comprises:

[0063] Data is collected in real time from the multi-drive system of the grab unloader at the power plant dock through the placement of displacement sensors, force sensors, and speed sensors.

[0064] Using GPS, laser rangefinders, or encoder positioning devices, real-time position data of the grab bucket in three-dimensional space is collected;

[0065] The weight data of the material in the grab bucket is collected in real time through weighing sensors or load monitoring devices.

[0066] System status data is obtained from the control system of the multi-drive system. The system status data includes the voltage, current, and power factor of the electric drive system, the operating status of the drive mechanism, and alarm information.

[0067] Control command data is obtained from the operator console or automatic control system. The control command data includes commands for lifting, opening, closing, and moving the grab bucket, as well as the set value of the digging volume.

[0068] In step S101, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf according to the present invention specifically implements the following data acquisition process:

[0069] Data is collected in real time from the multi-drive system of the grab unloader at the power plant dock using displacement, force, and speed sensors. This data reflects the real-time motion status of the grab and its transmission mechanism, such as displacement, force, and velocity. Positioning devices such as GPS, laser rangefinders, or encoders are used to accurately collect the grab's real-time position data in three-dimensional space. This facilitates precise positioning and navigation of the grab. Weighing sensors or load monitoring devices are used to collect the weight data of the material inside the grab in real time. This is crucial for controlling the grab's lifting and dredging depth, ensuring that the grab can stably grab and unload materials during operation.

[0070] System status data is acquired from the control system of multiple drive systems, including electrical parameters such as voltage, current, and power factor of the electric drive system, as well as the operating status and alarm information of the drive mechanism. This data reflects the overall health and performance of the system, providing a basis for fault prediction and timely handling.

[0071] Control command data is obtained from the operator console or automatic control system, including commands for the lifting, opening, closing, and movement of the grab bucket, as well as the setpoint for the digging volume. These commands are the core of controlling the grab bucket operation, ensuring the accuracy and efficiency of the operation.

[0072] By comprehensively collecting this basic data, step S101 provides a reliable information foundation for subsequent data preprocessing, model training, and real-time control, thereby effectively solving the problems of data transmission stability being easily disturbed in complex industrial environments, leading to control command delays, and motion errors caused by complex coupling relationships between various transmission mechanisms in multi-transmission systems.

[0073] Specifically, the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention includes step S102, which comprises:

[0074] The preprocessed data is randomly divided into a training set and a validation set. The training set is used to train the model, and the validation set is used to evaluate the model's performance.

[0075] Random forest model was selected as the basis for the dredging control model. The random forest model was trained using training set data and validated using validation set data.

[0076] The trained random forest model is saved in a loadable format to obtain the dredging control model of the multi-drive system of the grab unloader at the power plant dock.

[0077] In step S102, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf described in this invention performs in-depth data processing and successfully constructs a dredging control model. The specific process is as follows:

[0078] The preprocessed data is randomly divided into training and validation sets. The training set is used to train the model, enabling it to learn patterns and regularities in the data; the validation set is used to evaluate the model's performance, ensuring that the model has good generalization ability and accuracy.

[0079] Random forest model was chosen as the basis for the dredging control model. Random forest is an ensemble learning method that improves the accuracy and stability of the model by constructing multiple decision trees and combining their predictions. This model performs well when handling complex, high-dimensional data and is well-suited for dredging control of multi-drive systems in grab unloaders at power plant docks.

[0080] The random forest model is trained using training data to learn various characteristics and patterns in the dredging process of the multi-drive system of the grab unloader. Then, the trained model is validated using validation data to evaluate its performance. By continuously adjusting and optimizing the model parameters, optimal predictive performance can be ensured.

[0081] The trained random forest model is saved in a loadable format to obtain the dredging control model for the multi-drive system of the grab unloader at the power plant dock. This allows the model to be directly loaded for prediction and control in subsequent practical applications without retraining, thus significantly improving efficiency and accuracy.

[0082] Through the processing in step S102, this invention successfully constructed a multi-drive system dredging control model for a grab unloader at a power plant wharf based on random forest. This model can accurately predict key parameters such as the grab's motion trajectory and load changes during the dredging process, providing strong support for subsequent real-time control. Simultaneously, through data partitioning and model verification, the model's accuracy was improved, effectively solving problems such as data transmission instability being easily interfered with, control command delays, and motion errors caused by complex coupling relationships between various transmission mechanisms in the multi-drive system.

[0083] Specifically, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S103 includes:

[0084] Receive dredging control tasks from the control system or operator console of the grab unloader at the power plant dock. The dredging control tasks include the dredging depth, speed, position, material type, and grab size.

[0085] The received dredging control task is parsed to extract control parameters, including the starting position, ending position, target depth, and desired speed of the dredging.

[0086] Based on the dredging control task, sensor data, position data, and load data are extracted from the real-time data of the multi-transmission system of the grab unloader at the power plant wharf as the initial conditions for model input.

[0087] Load the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf, which was trained in step S102, and substitute the analyzed dredging control task parameters and the prepared real-time data into the dredging control model. The model will make decisions and predictions based on the input data and using the branch structure of the random forest algorithm.

[0088] The dredging control model generates simulation data of the multi-transmission system of the grab unloader at the power plant dock when performing dredging tasks, based on input data and internal algorithms. The simulation data includes the expected motion trajectory of each transmission mechanism, load changes, and system status.

[0089] In step S103, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf described in this invention realizes the process of converting the dredging control task into specific simulation data. The specific steps are as follows:

[0090] The system receives dredging control tasks from the control system or operator console of the grab unloader at the power plant dock. These tasks typically include key information such as dredging depth, speed, location, material type, and grab size.

[0091] The received digging control task is analyzed to extract specific control parameters. These parameters include the starting position, ending position, target depth, and desired speed of the digging, which directly determine the trajectory and speed of the grab bucket during the digging process.

[0092] Based on the requirements of the dredging control task, relevant sensor data, position data, and load data are extracted from the real-time data of the multi-drive system of the grab unloader at the power plant dock. These data serve as the initial conditions for model input, reflecting the current state of the grab and its drive system.

[0093] Load the dredging control model of the multi-drive system of the grab unloader at the power plant wharf, which was trained in step S102. This model has learned various characteristics and patterns in the dredging process of the multi-drive system of the grab unloader and can make accurate predictions and decisions based on the input data.

[0094] The analyzed dredging control task parameters and prepared real-time data are then fed into the dredging control model. Based on this data, the model will use the branching structure of the random forest algorithm to make decisions and predictions, generating simulation data of the grab bucket performing the dredging task.

[0095] The dredging control model generates simulation data of the multi-drive system of the grab unloader at the power plant dock during the dredging task, based on input data and internal algorithms. This simulation data includes key information such as the expected motion trajectory of each drive mechanism, load changes, and system status, which can reflect the actual movement of the grab bucket during the dredging process.

[0096] Through the processing in step S103, the present invention successfully transforms the dredging control task into specific simulation data.

[0097] Specifically, the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention includes step S104, which comprises:

[0098] Load the dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf generated in step S103. The dredging control simulation data of the multi-transmission system of the grab unloader at the power plant wharf includes the expected motion trajectory of each transmission mechanism, load changes and system status information when performing the dredging task.

[0099] The characteristics of the excavation control data are identified from the loaded simulation data. These characteristics include the movement speed and position of the grab bucket, the instantaneous changes in load, the system response time, and the coordination relationship between various transmission mechanisms.

[0100] The extracted data features are organized to form a feature dataset. Based on the extracted data features, data correspondence is established. The data correspondence includes data corresponding to these features collected in real time by sensors and monitoring systems during the actual operation of the grab unloader at the power plant dock.

[0101] The collected actual data is matched with the extracted data features to generate the dataset to be tested.

[0102] In step S104, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf described in this invention further extracts features from the simulated data and matches and corresponds with the actual collected data. The specific steps are as follows:

[0103] Load the dredging control simulation data of the multi-drive system of the grab unloader at the power plant wharf, generated in step S103. This data includes the expected motion trajectory of each drive mechanism, load changes, and system status information during the dredging task, and forms the basis for subsequent feature extraction.

[0104] In-depth analysis of the loaded simulation data identifies the characteristics of the excavation control data, including the movement speed and position of the grab bucket, instantaneous changes in load, system response time, and coordination relationships between various transmission mechanisms. These characteristics comprehensively reflect the dynamic behavior of the grab bucket during the excavation process.

[0105] The extracted data features were organized to form a feature dataset. This dataset contains all data features that have a significant impact on excavation control, facilitating subsequent data mapping and matching.

[0106] Based on the extracted data features, a data correspondence is established. This correspondence indicates which real-time collected data corresponds to these features during the actual operation of the grab unloader at the power plant dock. For example, the grab's movement speed data collected by sensors corresponds to the movement speed features in the feature dataset.

[0107] During the actual operation of the grab unloader at the power plant dock, data corresponding to the features is collected in real time through sensors and monitoring systems. Then, this actual data is matched with the extracted data features to generate a dataset for testing. This dataset contains all the key data from the actual operation and will be used for subsequent data comparison and verification.

[0108] Through the processing in step S104, the present invention successfully extracted key data features for dredging control from the simulated data and matched them with the actual collected data. This provides strong support for subsequent data comparison, model verification, and real-time control, ensuring the accuracy of the multi-drive system of the grab unloader at the power plant dock when performing dredging tasks.

[0109] Specifically, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S105 includes:

[0110] Align the data to be tested with the sag control simulation data on the timestamp, and compare the data to be tested with the sag control simulation data item by item. The item-by-item comparison includes the motion trajectory of each transmission mechanism, load changes and system state parameters.

[0111] During the comparison process, data that differed from the simulated data were identified. These differences were caused by data transmission errors, sensor malfunctions, or changes in system state.

[0112] For data where there are differences between the identified data to be detected and the simulated data, it is determined whether the fluctuation is within a reasonable range. If the fluctuation is not within a reasonable range, a data replacement decision is made.

[0113] In step S105, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf described in this invention underwent data comparison and verification to ensure consistency between actual operating data and simulated data. The specific steps are as follows:

[0114] The data to be tested is aligned with the dredging control simulation data in terms of timestamps to ensure the accuracy and reliability of the comparison. Then, the data to be tested and the dredging control simulation data are compared item by item, including key information such as the motion trajectory of each transmission mechanism, load changes, and system state parameters.

[0115] During the comparison process, careful identification of differences between the data to be tested and the simulated data is crucial. These differences may arise from various reasons, such as data transmission errors, sensor malfunctions, and changes in system state. By identifying these differences, potential problems can be discovered in a timely manner, providing a basis for subsequent judgments and decisions.

[0116] The identified discrepancies are assessed for reasonableness. It is determined whether these discrepancies fall within a reasonable range of fluctuation. For example, minor differences caused by normal factors such as environmental conditions or equipment wear and tear are generally acceptable. However, if the discrepancies exceed the pre-defined reasonable range, further analysis of the causes is required.

[0117] If the discrepancies in the data are outside the reasonable range of fluctuation, a data replacement decision needs to be implemented. This means replacing outliers in the data to be monitored with corresponding values ​​in the simulated data to ensure data accuracy. Data replacement decisions need to be implemented carefully to avoid introducing new errors or problems.

[0118] Through step S105, the present invention successfully achieved the comparison and verification of the data to be tested and the dredging control simulation data. This process is crucial for ensuring the dredging control accuracy and stability of the multi-drive system of the grab unloader at the power plant dock. By promptly identifying and addressing data discrepancies, control errors caused by data transmission errors, sensor malfunctions, and other reasons can be effectively avoided, thereby improving the overall system operating efficiency.

[0119] Specifically, in the dredging control method for a multi-drive system of a grab unloader at a power plant wharf according to the present invention, step S105 includes:

[0120] For data that needs to be replaced, the corresponding part of the settlement control simulation data is replaced in the data to be tested to form the adjusted settlement control data;

[0121] The adjusted dredging control data is verified, and the verified adjusted dredging control data is substituted into the dredging control model of the multi-transmission system of the grab unloader at the power plant wharf as the input data of the model.

[0122] Based on the model's processing results, real-time control tasks are generated, which include control commands and motion parameters for each transmission mechanism.

[0123] The generated real-time control tasks are transmitted to the device, which then executes the specific control operations.

[0124] In the detailed description of step S105, the dredging control method for the multi-drive system of the grab unloader at the power plant wharf described in this invention elaborates on the processes of data replacement, verification, model input, and control task generation and transmission. The specific steps are as follows:

[0125] For data that needs to be replaced, the corresponding portion of the excavation control simulation data is precisely replaced in the data to be tested. This step ensures data accuracy and eliminates the impact of abnormal data caused by data transmission errors, sensor malfunctions, etc., on control precision.

[0126] The adjusted dredging control data undergoes rigorous verification. The verification process includes checking the completeness, consistency, and rationality of the data to ensure that the replaced data accurately reflects the actual operating status of the grab unloader's multi-drive system. Verified data will be used for subsequent model input and control task generation.

[0127] The validated and adjusted dredging control data is substituted into the dredging control model of the multi-drive system of the grab unloader at the power plant wharf as input data. Based on the input data, the model, combined with its internal algorithms and decision-making mechanisms, simulates and predicts the dredging process of the grab bucket, generating a real-time control strategy.

[0128] Based on the model's processing results, real-time control tasks are generated. These tasks include control commands and motion parameters for each transmission mechanism, such as motor speed, direction, and torque, as well as the grab bucket's trajectory and speed. The generation of control tasks ensures that the grab bucket unloader can perform dredging operations according to predetermined goals and requirements.

[0129] The generated real-time control tasks are transmitted to the device via a communication network or control system. Upon receiving the control task, the device executes specific control operations according to the task requirements, such as adjusting the motor speed or controlling the grab's trajectory. This step achieves seamless transfer and execution of control tasks from the model to the actual equipment.

[0130] Through the refined processing in step S105, the dredging control method of the multi-transmission system of the power plant wharf grab unloader described in this invention realizes timely processing of data anomalies, accurate generation and effective execution of control tasks, thereby improving the control accuracy, stability and operating efficiency of the entire system.

[0131] The technical solution of this invention solves the problem of errors in the dredging control of multi-drive systems of grab unloaders at power plant docks in complex industrial environments through the following steps:

[0132] First, in step S101, comprehensive basic data of the multi-drive system of the grab unloader at the power plant dock is acquired, including but not limited to sensor data, position data, load data, system status data, material characteristic data, control command data, and historical data. This data provides a solid foundation for subsequent model training and dredging control.

[0133] In step S102, the acquired basic data is preprocessed and divided into training and validation sets. These data are used to train a random forest model, resulting in a dredging control model for the multi-drive system of the grab unloader at the power plant dock. This model can predict various parameters during the dredging process based on the input data, such as the motion trajectory of the transmission mechanism and load changes.

[0134] Then, in step S103, the dredging control task is received, and the task parameters and real-time data are substituted into the trained dredging control model to obtain simulation data. The simulation data simulates the expected motion trajectory and load changes of each transmission mechanism during the actual dredging process, providing a basis for subsequent comparison and adjustment.

[0135] In step S104, data features are extracted from the simulated data to obtain the excavation data features, and corresponding data are collected based on these features to obtain the data to be tested. These data are collected during actual operation and are used for comparison with the simulated data.

[0136] Finally, in step S105, the data to be tested is compared with the simulated data. If inconsistencies are found, the inconsistencies in the data to be tested are replaced with simulated data to improve the accuracy of the control data. The adjusted data is then substituted into the excavation control model to generate real-time control tasks, which are then transmitted to the equipment for execution.

[0137] Through this series of steps, the present invention effectively solves the problems of data transmission stability being easily interfered with, control command delays, and motion errors caused by the complex coupling relationships between various transmission mechanisms in a multi-drive system. The method of the present invention can adjust control data in real time and accurately, ensuring precise and error-free dredging control of the multi-drive system of the grab unloader at the power plant dock.

Claims

1. A method for excavating control of a power plant wharf grab ship unloader multi-drive system, characterized in that, The method comprises the following steps: Step S101, obtaining the basic data of the power plant wharf grab ship unloader multi-drive system, the basic data of the power plant wharf grab ship unloader multi-drive system including sensor data, position data, load data, system state data, material characteristic data, control instruction data, and historical data of the power plant wharf grab ship unloader multi-drive system; Step S102, preprocessing the basic data of the power plant wharf grab ship unloader multi-drive system, dividing the preprocessed basic data of the power plant wharf grab ship unloader multi-drive system into a training set and a verification set, training a random forest model using the training set and the verification set, and obtaining a sinking and digging control model of the power plant wharf grab ship unloader multi-drive system; Step S103, receiving a sinking and digging control task of the power plant wharf grab ship unloader multi-drive system, substituting the sinking and digging control task of the power plant wharf grab ship unloader multi-drive system into the sinking and digging control model of the power plant wharf grab ship unloader multi-drive system, obtaining sinking and digging control simulation data of the power plant wharf grab ship unloader multi-drive system, and the sinking and digging control simulation data of the power plant wharf grab ship unloader multi-drive system including expected motion trajectories of each transmission mechanism, load changes, and system state information when the sinking and digging task is performed; Step S104, extracting data features from the sinking and digging control simulation data of the power plant wharf grab ship unloader multi-drive system, obtaining sinking and digging data features of the power plant wharf grab ship unloader multi-drive system, collecting corresponding data according to the sinking and digging data features of the power plant wharf grab ship unloader multi-drive system, and obtaining detection data; Step S105, comparing the detection data with the sinking and digging control simulation data of the power plant wharf grab ship unloader multi-drive system, if the comparison data results are inconsistent, replacing the inconsistent data with the sinking and digging control simulation data of the power plant wharf grab ship unloader multi-drive system, taking the replaced data as sinking and digging control data of the power plant wharf grab ship unloader multi-drive system, substituting the sinking and digging control data of the power plant wharf grab ship unloader multi-drive system into the sinking and digging control model of the power plant wharf grab ship unloader multi-drive system, and outputting real-time control tasks, and transmitting the real-time control tasks to the equipment end, and the equipment end executes the real-time control tasks.

2. A method of cut-off control of a power plant quay grab ship unloader multi-drive system according to claim 1, characterized in that, The step S101 comprises: Real-time data acquisition from the multi-drive system of the power plant wharf grab ship unloader through the displacement sensor, force sensor, and speed sensor arranged; Real-time position data acquisition of the grab bucket in three-dimensional space using the global positioning system, laser range finder, or encoder positioning device; Real-time weight data acquisition of the material in the grab bucket through the weighing sensor or load monitoring device; System state data acquisition from the control system of the multi-drive system, including voltage, current, power factor of the electrical transmission system, running state of the transmission mechanism, and alarm information; Control instruction data acquisition from the operator console or automatic control system, including lifting, opening and closing, and moving action instructions of the grab bucket, and setting value of the sinking and digging amount.

3. A method of cut-off control of a power plant quay grab ship unloader multi-drive system according to claim 1, characterized in that, The step S102 comprises: Randomly dividing the preprocessed data into a training set and a verification set, the training set being used for training the model, and the verification set being used for evaluating the performance of the model; The random forest model is selected as the basis of the excavation control model, the random forest model is trained using the training set data, and the trained random forest model is verified using the verification set data; The trained random forest model is saved in a loadable format to obtain the excavation control model of the multi-drive system of the grab ship unloader of the power plant wharf.

4. A method of cut-off control of a power plant quay grab ship unloader multi-drive system according to claim 1, characterized in that, The step S103 comprises: Receiving the excavation control task from the control system or operator console of the grab ship unloader of the power plant wharf, the excavation control task including the depth, speed, position, material type and grab size of the excavation; Analyzing the received excavation control task to extract the control parameters, including the starting position, ending position, target depth and expected speed of the excavation; According to the excavation control task, extracting sensor data, position data and load data from the real-time data of the multi-drive system of the grab ship unloader of the power plant wharf as the initial conditions of the model input; Load the excavation control model of the multi-drive system of the grab ship unloader of the power plant wharf trained in step S102, and substitute the parsed excavation control task parameters and prepared real-time data into the excavation control model. The model will make decisions and predictions based on the input data using the branch structure of the random forest algorithm; The excavation control model generates simulation data of the multi-drive system of the grab ship unloader of the power plant wharf during the execution of the excavation task according to the input data and internal algorithm, including the expected motion trajectory, load change and system state of each transmission mechanism during the execution of the excavation task.

5. A method of cut-off control of a power plant quay grab ship unloader multi-drive system according to claim 1, characterized in that, The step S104 comprises: Loading the excavation control simulation data of the multi-drive system of the grab ship unloader of the power plant wharf generated in step S103; Identifying the excavation control data features from the loaded simulation data, including the motion speed, position, instantaneous load change, system response time and coordination relationship between each transmission mechanism of the grab; Organize the extracted data features to form a feature data set, and establish a data correspondence relationship based on the extracted data features, including the real-time collection of data corresponding to these features through sensors and monitoring systems during the actual operation of the grab ship unloader of the power plant wharf; Correspond and match the collected actual data with the extracted data features to generate a detection data set.

6. A method of cut-off control of a power plant quay grab ship unloader multi-drive system according to claim 1, characterized in that, The step S105 comprises: Align the detection data with the excavation control simulation data in the time stamp, and compare the detection data with the excavation control simulation data item by item, including the motion trajectory, load change and system state parameters of each transmission mechanism; In the comparison process, identify the data that exists between the detection data and the simulation data, which is caused by data transmission errors, sensor failures or system state changes; Judge whether the identified data that exists between the detection data and the simulation data belongs to the reasonable range of fluctuations, and if not, execute the data replacement decision.

7. A method of cut-out control of a power plant quay grab ship unloader multi-drive system as claimed in claim 6, characterized in that, The step S105 comprises: For the data needing to be replaced, the corresponding part of the simulation data of the dredging control is replaced into the to-be-detected data to form adjusted dredging control data; The adjusted dredging control data is checked, and the adjusted dredging control data that passes the check is substituted into the dredging control model of the multi-drive system of the electric power terminal grab ship unloader as input data of the model; According to the processing result of the model, real-time control tasks are generated, and the real-time control tasks include control instructions and motion parameters of each transmission mechanism; The generated real-time control tasks are transmitted to the equipment end, and the equipment end performs specific control operations.

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