Intelligent collaborative operation control strategy for electro-hydraulic suspension system of high-horsepower tractor

An intelligent collaborative operation control strategy for an electro-hydraulic suspension system of a high-horsepower tractor, comprising the following steps:

• S1, establishing an operation task database, and defining control strategy parameters of the electro-hydraulic suspension system for different types of agricultural operation tasks in the operation task database;
• S2, collecting, in real time via a data acquisition module, a position of the electro-hydraulic suspension system, an operating pressure of a hydraulic system, an actual operating speed of the high-horsepower tractor, terrain data and environmental information during an operation of the high-horsepower tractor to obtain real-time data, and storing the real-time data in a data storage unit;
• S3, performing preprocessing, by a data processing module, on the real-time data stored in the data storage unit to generate a unified dataset, wherein the preprocessing comprises removing noise and abnormal data, and performing feature extraction and data fusion;
• S4, analyzing, by an intelligent decision-making module based on machine learning algorithms, the unified dataset to generate a preliminary control strategy for the electro-hydraulic suspension system based on the control strategy parameters in the operation task database, wherein the preliminary control strategy comprises control parameters for a suspension height, a hydraulic pressure, and an operating speed;
• wherein the step S4 comprises the following steps:
• S41, receiving, by the intelligent decision-making module, the unified dataset;
• S42, matching the unified dataset with the control strategy parameters in the operation task database to generate a matched dataset, thereby generating a target agricultural operation task;
• S43, analyzing the matched dataset based on a random forest-based machine learning algorithm to determine target control parameters for the electro-hydraulic suspension system consistent with current operating conditions of the high-horsepower tractor;
• and
• S44, generating the preliminary control strategy for the electro-hydraulic suspension system based on the target control parameters, wherein the preliminary control strategy comprises the control parameters for the suspension height, the hydraulic pressure, and the operating speed to adapt to the current operating conditions of the high-horsepower tractor;
• wherein the step S42 comprises the following steps:
• S421, performing an initial match between the unified dataset and the control strategy parameters in the operation task database;
• S422, calculating a different value between each parameter in the unified dataset and a corresponding control strategy parameter of the control strategy parameters in the operation task database as the following formula:
• D
• i
• ⁢
• j
• =
• ❘
• "\[LeftBracketingBar]"
• P
• i
• -
• T
• j
• ❘
• "\[RightBracketingBar]"
• where Dij represents a different value between an i-th parameter in the unified dataset and a j-th control strategy parameter of the control strategy parameters in the operation task dataset, Pi represents a value of the i-th parameter in the unified dataset, and Tj represents a value of the j-th control strategy parameter of the control strategy parameters in the operation task dataset;
• S423, calculating a total difference for each of the different types of agricultural operation tasks to determine the target agricultural operation task as per the following formula:
• D
• j
• =
• ∑
• i
• =
• 1
• n
• ⁢
• D
• i
• ⁢
• j
• where Dj represents a total difference for a j-th agricultural operation task of the different types of agricultural operation tasks, and n represents a total number of the control strategy parameters of each of the different types of agricultural operation tasks;
• S424, selecting an agricultural operation task with a minimum total difference from the different types of agricultural operation tasks as the target agricultural operation task as per the following formula:
• j
• best
• =
• arg
• min
• j
• {
• D
• j
• }
• where jbest represents an index of the target agricultural operation task with the minimum total difference Di, and argmin represents a minimum value function;
• and
• S425, using the control strategy parameters of the target agricultural operation task corresponding to the matched dataset as the preliminary control parameters;
• wherein the step S43 comprises the following steps:
• S431, constructing a training dataset comprising historical operating data and corresponding control parameters, wherein the training dataset comprises suspension heights, hydraulic pressures, operating speeds, and corresponding environmental conditions and operation results;
• S432, training a random forest model using the training dataset to obtain a trained random forest model, wherein the random forest model consists of a plurality of decision trees, and each of the plurality of decision trees is constructed as per the following formula:
• Tree
• ⁢
• (
• X
• )
• =
• ∑
• i
• =
• 1
• n
• ⁢
• w
• i
• ·
• h
• i
• (
• X
• )
• where Tree(X) represents a decision tree model, X represents an input feature vector, hi(X) represents an i-th decision tree, wi represents a corresponding weight of the i-th decision tree, and n represents a number of the plurality of decision trees;
• S433, inputting the matched dataset obtained in the step S42 into the trained random forest model for prediction, and outputting, by the trained random forest model, the target control parameters by integrating prediction results from all the plurality of decision trees as per the following formula:
• Y
• ^
• =
• 1
• N
• ⁢
• ∑
• j
• =
• 1
• N
• Tree
• j
• (
• X
• )
• where Ŷ represents a predicted target control parameter vector, N represents a total number of the plurality of decision trees, and Treej(X) represents a prediction result of a j-th decision tree of the plurality of decision trees;
• and
• S434, generating, by the trained random forest model, the control parameters for the electro-hydraulic suspension system based on the matched database, wherein the control parameters comprise the suspension height, the hydraulic pressure, and the operating speed;
• S5, receiving, by a collaborative operation module, the preliminary control strategy generated by the intelligent decision-making module, and performing data interaction and collaborative control with operation units to ensure coordination and consistency among the operation units;
• S6, receiving, by a feedback control module, operation status data collected in real time by the data acquisition module, and processing and analyzing the operation status data to identify deviations between the operation status data and the control parameters in the preliminary control strategy;
• and
• S7, dynamically adjusting, by the intelligent decision-making module, the preliminary control strategy based on the deviations identified by the feedback control module to respond to environmental changes and operating condition variations during an actual operation of the high-horsepower tractor.