Work terminal control method based on artificial intelligence
By introducing artificial intelligence-based control methods into the job terminal control system, integrating data acquisition, processing, feature extraction and deep learning model training modules, the problem of insufficient adaptive adjustment capabilities of traditional systems in complex environments is solved, and efficient and flexible work control and automation improvement are achieved.
Patent Information
- Application Number
- CN202411857548.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-05-06
AI Technical Summary
Traditional operation terminal control systems are difficult to achieve adaptive adjustments in complex environments and cannot meet personalized needs and efficiency requirements.
The operation terminal control method based on artificial intelligence is adopted, including data collection, data processing, feature extraction, deep learning model training, real-time prediction and decision-making execution modules. Through integrated perception technology, data processing technology, machine learning technology and automated control theory, it can automatically identify task requirements, plan action routes and accurately manipulate operations.
It improves the self-learning ability and adaptability of the homework terminal, can quickly respond to unprecedented situations, reduce the need for manual intervention, and improve the level of automation.
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of terminal control, and in particular to an operation terminal control method based on artificial intelligence. Background Art
[0002] With the rapid development of technologies such as the Internet of Things, cloud computing, and edge computing, intelligent control systems have gradually become the key to improving production efficiency and service quality. However, in practical applications, traditional operation terminal control systems mostly rely on preset rules or simple feedback mechanisms, and have limited adaptive adjustment capabilities in complex environments, making it difficult to meet the growing personalized needs and efficiency requirements.
[0003] In the existing technology, there are two main common methods to improve the intelligence level of the operation terminal. One is to increase the number and types of sensors, collect more data about the operation site, and then the central processing unit makes decisions based on a fixed algorithm; the other is to introduce an expert system, pre-write a large number of condition-action rule libraries, and call the corresponding instruction set to perform tasks according to different working conditions. Although the former can obtain more comprehensive information to support decision-making, it also brings high cost investment and complex maintenance problems; the latter is highly dependent on artificially set logic, which limits flexibility, especially when facing non-standard conditions, it often performs poorly. Summary of the invention
[0004] In order to solve the above-mentioned problems, the present invention provides an operation terminal control method based on artificial intelligence.
[0005] To achieve the above purpose, the technical solution provided by the present invention is:
[0006] An artificial intelligence-based operation terminal control method includes a data acquisition module, a data processing module, a feature extraction module, a deep learning model training module, a real-time prediction module and a decision execution module;
[0007] The data acquisition module detects and collects data on physical, chemical or biomass changes and environmental parameters through sensors, and then converts the data into digital signals for transmission;
[0008] The data processing module is used to perform noise filtering and smoothing on the digital signal;
[0009] The feature extraction module is used to extract useful feature information from the original data for subsequent classification, recognition, prediction and other tasks;
[0010] The deep learning model training module trains the model through specific algorithms and techniques, so that it can learn the inherent laws and characteristics of the data, thereby achieving accurate prediction and analysis of new data;
[0011] The real-time prediction module is used to deploy the trained model to the edge server for running and quickly generate prediction results;
[0012] The decision execution module is used to analyze the prediction output and convert it into specific control commands to be sent to the operation terminal.
[0013] Furthermore, the data acquisition module is configured as a MEMS sensor array.
[0014] Furthermore, the data processing module is configured as a digital filter.
[0015] Furthermore, the feature extraction module uses statistical methods such as PCA principal component analysis or LDA linear discriminant analysis to achieve dimensionality reduction.
[0016] Furthermore, the deep learning model training module uses the TensorFlow framework to build CNNs or LSTMs for learning.
[0017] Furthermore, the deep learning model training module uses gradient descent or Adam to adjust model parameters to reduce the loss function and improve the performance of the model.
[0018] Furthermore, the deep learning model training module uses the validation set to evaluate the performance of the model and determines whether the model is qualified based on the validation results. If the model is unqualified, retraining is required.
[0019] Furthermore, the data acquisition module captures raw signals from different sources in a timed or event-triggered manner; the data is then preprocessed to remove interference components to form a clean and effective time series data stream; subsequently, the feature extraction module selects the most representative attribute set as the basic input for subsequent modeling; next, the deep learning model training phase uses large-scale labeled samples to iteratively update weight parameters until the predetermined performance indicators are reached; when the system is online, the real-time prediction module receives the latest observation data and uses the trained model to instantly infer the best action plan in the current situation; finally, the decision execution module converts abstract action suggestions into clear operational instructions to guide the operation terminal to act according to plan.
[0020] The beneficial effects achieved by the present invention using the above structure are as follows:
[0021] The present invention integrates advanced perception technology, data processing technology, machine learning technology and automated control theory to enable the equipment to automatically identify task requirements, plan action routes and accurately control operations in various complex environments, thereby improving the self-learning and adaptability of the work terminal, and being able to quickly respond appropriately even when encountering unprecedented situations. By introducing advanced artificial intelligence technology, the work terminal can more flexibly respond to diverse work tasks, reducing the need for manual intervention and improving the level of automation. DETAILED DESCRIPTION
[0022] An artificial intelligence-based operation terminal control method includes a data acquisition module, a data processing module, a feature extraction module, a deep learning model training module, a real-time prediction module and a decision execution module;
[0023] The data acquisition module is set as a MEMS sensor array, and the data acquisition module detects and collects data on changes in physical, chemical or biological quantities and environmental parameters through sensors, and then converts the data into digital signals for transmission;
[0024] The data processing module is set as a digital filter, and the data processing module is used to perform noise filtering and smoothing on the digital signal;
[0025] The feature extraction module is used to extract useful feature information from the original data for subsequent classification, recognition, prediction and other tasks. The feature extraction module uses statistical methods such as PCA principal component analysis or LDA linear discriminant analysis to achieve dimensionality reduction;
[0026] The deep learning model training module trains the model through specific algorithms and technologies, so that it can learn the inherent laws and characteristics of the data, thereby achieving accurate prediction and analysis of new data. The deep learning model training module uses the TensorFlow framework to build CNNs or LSTMs for learning. The deep learning model training module uses gradient descent or Adam to adjust model parameters to reduce the loss function and improve the performance of the model. The deep learning model training module uses a validation set to evaluate the performance of the model and determines whether the model is qualified based on the validation results. If it is unqualified, retraining is required;
[0027] The real-time prediction module is used to deploy the trained model to the edge server for running and quickly generate prediction results;
[0028] The decision execution module is used to analyze the prediction output and convert it into specific control commands to be sent to the operation terminal.
[0029] The data acquisition module captures raw signals from different sources in a timed or event-triggered manner; the data is then preprocessed to remove interference components to form a clean and effective time series data stream; subsequently, the feature extraction module selects the most representative attribute set as the basic input for subsequent modeling; next, the deep learning model training phase uses large-scale labeled samples to iteratively update weight parameters until the predetermined performance indicators are reached; when the system is online, the real-time prediction module receives the latest observation data and uses the trained model to instantly infer the best action plan in the current situation; finally, the decision execution module converts abstract action suggestions into clear operational instructions to guide the operation terminal to act according to plan.
[0030] When deploying necessary sensor nodes and communication facilities, the present invention needs to ensure that the data transmission link is unobstructed, select appropriate data preprocessing algorithms and technologies according to business characteristics, reduce interference from invalid information, adjust hyperparameter settings during deep learning, monitor training progress, and prevent overfitting. After importing the optimized model into the edge computing platform, configure appropriate computing resources, and then write a decision script to ensure that the output meets the requirements of safety specifications. When starting the control system, it is necessary to continuously observe the operating status and promptly discover potential risk points.
[0031] In summary: The present invention integrates advanced perception technology, data processing technology, machine learning technology and automation control theory, so that the equipment can automatically identify task requirements, plan action routes and accurately control operations in various complex environments, thereby improving the self-learning and adaptability of the work terminal, and can quickly make appropriate responses even when encountering unprecedented situations. By introducing advanced artificial intelligence technology, the work terminal can more flexibly respond to diverse work tasks, reduce the need for manual intervention, and improve the level of automation.
[0032] The present invention and its implementation methods are described above, which is not restrictive and is not limited to this. In short, if ordinary technicians in this field are inspired by it and design structural methods and embodiments similar to the technical solution without creativity without departing from the purpose of the invention, they should all fall within the protection scope of the present invention.
Claims
1. An artificial intelligence-based operation terminal control method, characterized in that: It includes data acquisition module, data processing module, feature extraction module, deep learning model training module, real-time prediction module and decision execution module; The data acquisition module detects and collects data on physical, chemical or biomass changes and environmental parameters through sensors, and then converts the data into digital signals for transmission; The data processing module is used to perform noise filtering and smoothing on the digital signal; The feature extraction module is used to extract useful feature information from the original data for subsequent classification, recognition, prediction and other tasks; The deep learning model training module trains the model through specific algorithms and techniques, so that it can learn the inherent laws and characteristics of the data, thereby achieving accurate prediction and analysis of new data; The real-time prediction module is used to deploy the trained model to the edge server for running and quickly generate prediction results; The decision execution module is used to analyze the prediction output and convert it into specific control commands to be sent to the operation terminal.
2. The method for controlling an operation terminal based on artificial intelligence according to claim 1, characterized in that: The data acquisition module is configured as a MEMS sensor array.
3. The method for controlling an operation terminal based on artificial intelligence according to claim 2, characterized in that: The data processing module is configured as a digital filter.
4. The method for controlling an operation terminal based on artificial intelligence according to claim 3, characterized in that: The feature extraction module uses statistical methods such as PCA principal component analysis or LDA linear discriminant analysis to achieve dimensionality reduction.
5. The method for controlling an operation terminal based on artificial intelligence according to claim 4, characterized in that: The deep learning model training module uses the TensorFlow framework to build CNNs or LSTMs for learning.
6. The method for controlling an operation terminal based on artificial intelligence according to claim 5, characterized in that: The deep learning model training module uses gradient descent or Adam to adjust model parameters to reduce the loss function and improve the performance of the model.
7. The method for controlling an operation terminal based on artificial intelligence according to claim 6, characterized in that: The deep learning model training module uses the validation set to evaluate the performance of the model and determines whether the model is qualified based on the validation results. If it is unqualified, it needs to be retrained.
8. The method for controlling an operation terminal based on artificial intelligence according to claim 7, characterized in that: The data acquisition module captures raw signals from different sources in a timed or event-triggered manner; the data is then pre-processed to remove interference components and form a clean and effective time series data stream; Subsequently, the feature extraction module selects the most representative set of attributes as the basic input for subsequent modeling; next, the deep learning model training phase uses large-scale labeled samples to iteratively update weight parameters until the predetermined performance indicators are reached; when the system is online, the real-time prediction module receives the latest observation data and uses the trained model to instantly infer the best action plan under the current situation; finally, the decision execution module converts abstract action suggestions into clear operational instructions to guide the operation terminal to act according to plan.