CN117092918A - Cloud edge cooperation-based intelligent gas sensing control method - Google Patents
Cloud edge cooperation-based intelligent gas sensing control method Download PDFInfo
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Abstract
The invention belongs to the technical field of gas sensing, and particularly relates to a cloud edge cooperation-based intelligent control method for gas sensing, which comprises the following steps: step 1, collecting sensor data; step 2, deploying edge equipment; step 3, the cloud platform and the data are stored; step 4, data preprocessing and enhancement; step 5, data analysis and algorithm modeling; step 6, generating an advanced control strategy; step 7, issuing and executing a control instruction; step 8, monitoring and feeding back in real time; and 9, autonomous fault diagnosis and maintenance. According to the invention, through the edge equipment and the data preprocessing, the rapid processing, the real-time monitoring and the control are realized; machine learning and deep learning improve the gas system state recognition capability; the advanced control strategy automatically adjusts parameters and strategies; cloud fault diagnosis and prediction are maintained in advance; the cloud data storage ensures the safety; edge calculation reduces cost and flexibly expands the system.
Description
Technical Field
The invention belongs to the technical field of gas sensing, and particularly relates to a cloud edge cooperation-based intelligent gas sensing control method.
Background
The gas sensor is a device for detecting gas leakage in an indoor or industrial place, and recognizes whether gas leakage exists by sensing the concentration of gas in the environment, when detecting that the concentration of gas exceeds a preset threshold, the sensor can give an alarm and trigger corresponding safety measures, such as closing a gas valve or starting an exhaust system, so as to prevent accidents, and the gas sensor is widely applied to gas equipment, heating systems, kitchens, factories and other places in home and business environments, can help to protect life and property safety and reduce the occurrence of accidents by timely and accurately detecting the gas leakage, and is very important for gas users to periodically check and maintain the working state of the gas sensor so as to ensure the normal operation and reliability of the gas sensor;
the existing intelligent control method for gas sensing generally only carries out simple intelligent control on gas sensing, has poor state identification capability of a gas system, is inconvenient to carry out cloud fault diagnosis and predictive early maintenance, and therefore the intelligent control method for gas sensing based on cloud-edge cooperation is provided for solving the problems.
Disclosure of Invention
The invention aims to provide a cloud-edge cooperation-based intelligent control method for gas sensing, which can realize rapid processing, real-time monitoring and control through edge equipment and data preprocessing; machine learning and deep learning improve the gas system state recognition capability; the advanced control strategy automatically adjusts parameters and strategies; cloud fault diagnosis and prediction are maintained in advance; the cloud data storage ensures the safety; edge calculation reduces cost and flexibly expands the system.
The technical scheme adopted by the invention is as follows:
a cloud-edge cooperation-based intelligent control method for gas sensing comprises the following steps:
step 1, collecting sensor data;
step 2, deploying edge equipment;
step 3, the cloud platform and the data are stored;
step 4, data preprocessing and enhancement;
step 5, data analysis and algorithm modeling;
step 6, generating an advanced control strategy;
step 7, issuing and executing a control instruction;
step 8, monitoring and feeding back in real time;
and 9, autonomous fault diagnosis and maintenance.
In a preferred embodiment, the sensor data collection is to install a plurality of gas sensors to monitor a plurality of parameters of the gas system, wherein the gas sensors are one or more of gas flow sensor, pressure sensor, temperature sensor and humidity sensor, and the sensor is ensured to have high precision, low power consumption and long service life by using an integrated circuit and advanced sensing technology, and appropriate sampling frequencies are set for different parameters so as to obtain accurate data in real-time and historical data analysis.
In a preferred scheme, the edge equipment is deployed to deploy edge equipment with calculation and communication capabilities, the edge equipment comprises an internet of things gateway and an edge server, the edge equipment processes sensor data and performs data compression and lightweight preliminary analysis, the pressure of data transmission and cloud computing is reduced, and the edge equipment is combined with an edge algorithm model and used for real-time anomaly detection and automatic triggering of an alarm mechanism.
In a preferred scheme, the cloud platform and the data storage are cloud computing and storage platforms with elasticity and high availability, and the cloud computing and storage platforms comprise cloud servers and cloud databases, a data storage architecture is designed, the data storage architecture comprises a time sequence database and a big data storage, a large amount of sensor data is processed and stored, advanced data security policies are implemented, the data encryption, the authentication and the access control are included, and the privacy and the integrity of the data are protected.
In a preferred scheme, the data preprocessing and enhancement is to further preprocess the sensor data, the preprocessing is to remove one or more of noise, fill in missing values and data interpolation, and the machine learning and deep learning technology is utilized to perform feature extraction, dimension reduction and enhancement on the data so as to capture hidden modes and relevance.
In a preferred scheme, the data analysis and algorithm modeling is enclosed as utilizing cloud high-performance computing resources, complex data analysis and modeling are carried out on the preprocessed data, machine learning, deep learning, statistics and optimization algorithms are applied to determine the state of a gas system, find abnormal behaviors, conduct trend prediction and performance optimization, continuously optimize the accuracy and robustness of the model, and adapt to actual system changes and new data modes through online learning and migration learning technologies.
In a preferred scheme, the advanced control strategy is generated based on the results of data analysis and modeling, and a higher-level control strategy is generated, including fuzzy control, model predictive control and application of reinforcement learning technology, so as to realize optimized gas system control and performance.
In a preferred scheme, the control instruction is issued and executed to convert the generated advanced control strategy into an executable control instruction, and the control instruction is issued through a cloud application program and edge equipment, and the edge equipment controls actual gas equipment according to the instruction by utilizing an automation and adjustment technology, wherein the gas equipment is a valve and an electric actuator.
In a preferred scheme, the real-time monitoring and feedback is to monitor the state and the running condition of the gas system in real time, push and feedback of data in time are realized through the edge equipment and the cloud platform, and the real-time monitoring and control strategy evaluation result is combined to perform closed-loop control, adjust control parameters and strategies in real time, respond to system change and optimize control performance.
In a preferred scheme, the autonomous fault diagnosis and maintenance is implemented by utilizing a cloud algorithm and a model, monitoring and diagnosing sensor faults, equipment faults and abnormal working conditions, and triggering early warning, alarming and maintenance operations according to analysis results.
The invention has the technical effects that:
by the arrangement of the edge equipment and the data preprocessing, the sensor data can be rapidly processed and analyzed, and the state of the gas system can be monitored and controlled in real time, so that the real-time responsiveness of the system is improved;
the machine learning and deep learning algorithm is utilized to analyze and model the data, and through data preprocessing and feature extraction, the recognition capability of the state and the behavior of the gas system is improved, and a more accurate control strategy is provided;
advanced control strategy generation and application, such as fuzzy control, model predictive control, reinforcement learning and the like, can automatically adjust control parameters and strategies according to different application scenes and changes of a gas system, and realize more intelligent and self-adaptive control;
by means of cloud algorithm and model, autonomous fault diagnosis and prediction of the gas system are realized, faults and abnormal conditions are identified and predicted through real-time monitoring and analysis, so that maintenance measures are taken in advance, and loss caused by system faults is avoided;
through the cloud platform and the application of data storage, the centralized storage, backup and safety of sensor data are ensured, data loss and information leakage are avoided, and the integrity and privacy of the data are ensured;
by the deployment of the edge equipment and the application of edge calculation, the dependence on cloud computing resources is reduced, the communication and calculation cost is reduced, and the calculation efficiency and response speed of the system are improved;
by adopting the distributed architecture, the edge equipment and cloud computing resources can be flexibly expanded according to actual requirements so as to adapt to gas systems with different scales and complexity.
Drawings
FIG. 1 is a schematic diagram of a cloud-edge cooperation-based intelligent control method for gas sensing.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
Example 1
Referring to fig. 1, the invention provides a cloud-edge cooperation-based intelligent control method for gas sensing, which comprises the following steps:
step 1, collecting sensor data;
step 2, deploying edge equipment;
step 3, the cloud platform and the data are stored;
step 4, data preprocessing and enhancement;
step 5, data analysis and algorithm modeling;
step 6, generating an advanced control strategy;
step 7, issuing and executing a control instruction;
step 8, monitoring and feeding back in real time;
step 9, autonomous fault diagnosis and maintenance;
the sensor data acquisition is to install a plurality of gas sensors to monitor a plurality of parameters of a gas system, wherein the gas sensors are a gas flow sensor, a pressure sensor and a temperature sensor, an integrated circuit and an advanced sensing technology are used to ensure that the sensor has high precision, low power consumption and long service life, and proper sampling frequencies are set for different parameters so as to obtain accurate data in real-time and historical data analysis;
the edge equipment is deployed as edge equipment with calculation and communication capabilities, the edge equipment comprises an Internet of things gateway and an edge server, data compression and lightweight preliminary analysis are carried out while the edge equipment processes sensor data, the pressure of data transmission and cloud computing is reduced, the edge equipment is combined with an edge algorithm model, and the edge equipment is used for real-time anomaly detection and automatically triggering an alarm mechanism;
the cloud platform and the data storage are cloud computing and storage platforms with elasticity and high availability, the cloud computing and storage platform comprises a cloud server and a cloud database, a data storage architecture is designed, the data storage architecture comprises a time sequence database and a big data storage, a large amount of sensor data is processed and stored, and advanced data security policies comprising data encryption, identity verification and access control are implemented to protect the privacy and integrity of the data;
the data preprocessing and enhancement is to further preprocess the sensor data, the preprocessing is to remove noise and fill missing values, and the machine learning and deep learning technology is utilized to extract, reduce and enhance the characteristics of the data so as to capture hidden modes and relevance;
the data analysis and algorithm modeling are enclosed as utilizing cloud high-performance computing resources, complex data analysis and modeling are carried out on the preprocessed data, machine learning, deep learning, statistics and optimization algorithms are applied to determine the state of a gas system, find abnormal behaviors, conduct trend prediction and performance optimization, the accuracy and robustness of a model are continuously optimized, and adaptation to actual system changes and new data modes is achieved through online learning and migration learning technologies;
the advanced control strategy is generated based on the data analysis and modeling result, and a higher control strategy is generated, which comprises fuzzy control, model predictive control and reinforcement learning technology application, so as to realize optimized gas system control and performance;
the control command is issued and executed to convert the generated advanced control strategy into an executable control command, and the executable control command is issued through a cloud application program and edge equipment, and the edge equipment controls actual gas equipment according to the command by utilizing an automation and adjustment technology, wherein the gas equipment is a valve and an electric actuator;
the real-time monitoring and feedback is to monitor the state and the running condition of the gas system in real time, realize the timely pushing and feedback of data through the edge equipment and the cloud platform, combine the real-time monitoring and control strategy evaluation result, carry out closed-loop control, adjust the control parameters and strategies in real time, respond to the system change and optimize the control performance;
the autonomous fault diagnosis and maintenance is to use cloud algorithm and model to implement autonomous fault diagnosis and maintenance function, monitor and diagnose sensor fault, equipment fault and abnormal working condition, and trigger early warning, alarming and maintenance operation according to analysis result.
Example two
Referring to fig. 1, the invention provides a cloud-edge cooperation-based intelligent control method for gas sensing, which comprises the following steps:
step 1, collecting sensor data;
step 2, deploying edge equipment;
step 3, the cloud platform and the data are stored;
step 4, data preprocessing and enhancement;
step 5, data analysis and algorithm modeling;
step 6, generating an advanced control strategy;
step 7, issuing and executing a control instruction;
step 8, monitoring and feeding back in real time;
step 9, autonomous fault diagnosis and maintenance;
the sensor data acquisition is to install a plurality of gas sensors to monitor a plurality of parameters of a gas system, wherein the gas sensors are a gas flow sensor, a pressure sensor and a humidity sensor, an integrated circuit and an advanced sensing technology are used to ensure that the sensor has high precision, low power consumption and long service life, and proper sampling frequencies are set for different parameters so as to obtain accurate data in real-time and historical data analysis;
the edge equipment is deployed as edge equipment with calculation and communication capabilities, the edge equipment comprises an Internet of things gateway and an edge server, data compression and lightweight preliminary analysis are carried out while the edge equipment processes sensor data, the pressure of data transmission and cloud computing is reduced, the edge equipment is combined with an edge algorithm model, and the edge equipment is used for real-time anomaly detection and automatically triggering an alarm mechanism;
the cloud platform and the data storage are cloud computing and storage platforms with elasticity and high availability, the cloud computing and storage platform comprises a cloud server and a cloud database, a data storage architecture is designed, the data storage architecture comprises a time sequence database and a big data storage, a large amount of sensor data is processed and stored, and advanced data security policies comprising data encryption, identity verification and access control are implemented to protect the privacy and integrity of the data;
the data preprocessing and enhancement is to further preprocess the sensor data, wherein the preprocessing is to fill in missing values and data interpolation, and the machine learning and deep learning technology is utilized to extract, reduce and enhance the characteristics of the data so as to capture hidden modes and relevance;
the data analysis and algorithm modeling are enclosed as utilizing cloud high-performance computing resources, complex data analysis and modeling are carried out on the preprocessed data, machine learning, deep learning, statistics and optimization algorithms are applied to determine the state of a gas system, find abnormal behaviors, conduct trend prediction and performance optimization, the accuracy and robustness of a model are continuously optimized, and adaptation to actual system changes and new data modes is achieved through online learning and migration learning technologies;
the advanced control strategy is generated based on the data analysis and modeling result, and a higher control strategy is generated, which comprises fuzzy control, model predictive control and reinforcement learning technology application, so as to realize optimized gas system control and performance;
the control command is issued and executed to convert the generated advanced control strategy into an executable control command, and the executable control command is issued through a cloud application program and edge equipment, and the edge equipment controls actual gas equipment according to the command by utilizing an automation and adjustment technology, wherein the gas equipment is a valve and an electric actuator;
the real-time monitoring and feedback is to monitor the state and the running condition of the gas system in real time, realize the timely pushing and feedback of data through the edge equipment and the cloud platform, combine the real-time monitoring and control strategy evaluation result, carry out closed-loop control, adjust the control parameters and strategies in real time, respond to the system change and optimize the control performance;
the autonomous fault diagnosis and maintenance is to use cloud algorithm and model to implement autonomous fault diagnosis and maintenance function, monitor and diagnose sensor fault, equipment fault and abnormal working condition, and trigger early warning, alarming and maintenance operation according to analysis result.
Example III
Referring to fig. 1, the invention provides a cloud-edge cooperation-based intelligent control method for gas sensing, which comprises the following steps:
step 1, collecting sensor data;
step 2, deploying edge equipment;
step 3, the cloud platform and the data are stored;
step 4, data preprocessing and enhancement;
step 5, data analysis and algorithm modeling;
step 6, generating an advanced control strategy;
step 7, issuing and executing a control instruction;
step 8, monitoring and feeding back in real time;
step 9, autonomous fault diagnosis and maintenance;
the sensor data acquisition is to install a plurality of gas sensors to monitor a plurality of parameters of a gas system, wherein the gas sensors are a gas flow sensor, a temperature sensor and a humidity sensor, and an integrated circuit and an advanced sensing technology are used to ensure that the sensor has high precision, low power consumption and long service life, and proper sampling frequencies are set for different parameters so as to obtain accurate data in real-time and historical data analysis;
the edge equipment is deployed as edge equipment with calculation and communication capabilities, the edge equipment comprises an Internet of things gateway and an edge server, data compression and lightweight preliminary analysis are carried out while the edge equipment processes sensor data, the pressure of data transmission and cloud computing is reduced, the edge equipment is combined with an edge algorithm model, and the edge equipment is used for real-time anomaly detection and automatically triggering an alarm mechanism;
the cloud platform and the data storage are cloud computing and storage platforms with elasticity and high availability, the cloud computing and storage platform comprises a cloud server and a cloud database, a data storage architecture is designed, the data storage architecture comprises a time sequence database and a big data storage, a large amount of sensor data is processed and stored, and advanced data security policies comprising data encryption, identity verification and access control are implemented to protect the privacy and integrity of the data;
the data preprocessing and enhancement is to further preprocess the sensor data, the preprocessing is to remove noise and interpolate the data, and the machine learning and deep learning technology is utilized to extract, reduce and enhance the characteristics of the data so as to capture hidden modes and relevance;
the data analysis and algorithm modeling are enclosed as utilizing cloud high-performance computing resources, complex data analysis and modeling are carried out on the preprocessed data, machine learning, deep learning, statistics and optimization algorithms are applied to determine the state of a gas system, find abnormal behaviors, conduct trend prediction and performance optimization, the accuracy and robustness of a model are continuously optimized, and adaptation to actual system changes and new data modes is achieved through online learning and migration learning technologies;
the advanced control strategy is generated based on the data analysis and modeling result, and a higher control strategy is generated, which comprises fuzzy control, model predictive control and reinforcement learning technology application, so as to realize optimized gas system control and performance;
the control command is issued and executed to convert the generated advanced control strategy into an executable control command, and the executable control command is issued through a cloud application program and edge equipment, and the edge equipment controls actual gas equipment according to the command by utilizing an automation and adjustment technology, wherein the gas equipment is a valve and an electric actuator;
the real-time monitoring and feedback is to monitor the state and the running condition of the gas system in real time, realize the timely pushing and feedback of data through the edge equipment and the cloud platform, combine the real-time monitoring and control strategy evaluation result, carry out closed-loop control, adjust the control parameters and strategies in real time, respond to the system change and optimize the control performance;
the autonomous fault diagnosis and maintenance is to use cloud algorithm and model to implement autonomous fault diagnosis and maintenance function, monitor and diagnose sensor fault, equipment fault and abnormal working condition, and trigger early warning, alarming and maintenance operation according to analysis result.
According to the invention, through deployment and data preprocessing of the edge equipment, rapid processing and analysis of sensor data and real-time monitoring and control of the state of the gas system can be realized, the real-time responsiveness of the system is improved, machine learning and deep learning algorithms are utilized to analyze and model the data, the recognition capability of the state and behavior of the gas system is improved through data preprocessing and feature extraction, a more accurate control strategy is provided, advanced control strategy generation and application such as fuzzy control, model predictive control and reinforcement learning are adopted, control parameters and strategies can be automatically adjusted according to different application scenes and changes of the gas system, more intelligent and self-adaptive control is realized, autonomous fault diagnosis and prediction of the gas system are realized by means of cloud algorithms and models, faults and abnormal conditions are recognized and predicted through real-time monitoring and analysis, maintenance measures are adopted in advance, loss caused by system faults is avoided, centralized storage, backup and safety of the sensor data are ensured through application of a cloud platform and data storage, data loss and information leakage are avoided, the integrity and privacy of the data are ensured, the deployment and the edge equipment is ensured, the calculation resource is not to be dependent on the computing resource, the practical demand is reduced, and the calculation resource is not met, and the practical demand is reduced, and the calculation resource is flexible and the system is flexible and flexible is applied.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. A cloud edge cooperation-based intelligent gas sensing control method is characterized by comprising the following steps of: the method comprises the following steps:
step 1, collecting sensor data;
step 2, deploying edge equipment;
step 3, the cloud platform and the data are stored;
step 4, data preprocessing and enhancement;
step 5, data analysis and algorithm modeling;
step 6, generating an advanced control strategy;
step 7, issuing and executing a control instruction;
step 8, monitoring and feeding back in real time;
and 9, autonomous fault diagnosis and maintenance.
2. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the sensor data acquisition is to install a plurality of gas sensors to monitor a plurality of parameters of a gas system, the gas sensors are one or more of gas flow sensors, pressure sensors, temperature sensors and humidity sensors, an integrated circuit and advanced sensing technology are used to ensure that the sensors have high precision, low power consumption and long service life, and proper sampling frequencies are set for different parameters so as to obtain accurate data in real-time and historical data analysis.
3. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the edge equipment is deployed as edge equipment with calculation and communication capabilities, the edge equipment comprises an Internet of things gateway and an edge server, data compression and lightweight preliminary analysis are carried out while sensor data are processed by the edge equipment, the pressure of data transmission and cloud computing is reduced, and the edge equipment is combined with an edge algorithm model and used for real-time anomaly detection and automatic triggering of an alarm mechanism.
4. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the cloud platform and the data storage are cloud computing and storage platforms with elasticity and high availability, the cloud computing and storage platform comprises a cloud server and a cloud database, a data storage architecture is designed, the data storage architecture comprises a time sequence database and a big data storage, a large amount of sensor data is processed and stored, an advanced data security policy is implemented, and the data security policy comprises data encryption, identity verification and access control, and data privacy and integrity are protected.
5. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the data preprocessing and enhancement is to further preprocess the sensor data, the preprocessing is to remove one or more of noise, filling missing values and data interpolation, and the machine learning and deep learning technologies are utilized to extract, reduce and enhance the characteristics of the data so as to capture hidden modes and relevance.
6. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the data analysis and algorithm modeling is enclosed as utilizing cloud high-performance computing resources to carry out complex data analysis and modeling on the preprocessed data, and machine learning, deep learning, statistics and optimization algorithms are applied to determine the state of a gas system, discover abnormal behaviors, conduct trend prediction and performance optimization, continuously optimize the accuracy and robustness of a model and realize adaptation to actual system changes and new data modes through online learning and transfer learning technologies.
7. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the advanced control strategy is generated based on the data analysis and modeling result, and a higher-level control strategy is generated, which comprises the application of fuzzy control, model predictive control and reinforcement learning technology, so as to realize the optimized gas system control and performance.
8. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the control command issuing and executing is to convert the generated advanced control strategy into an executable control command, and issue the control command through a cloud application program and edge equipment, wherein the edge equipment controls actual gas equipment according to the command by utilizing an automation and adjustment technology, and the gas equipment is a valve and an electric actuator.
9. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the real-time monitoring and feedback is to monitor the state and the running condition of the gas system in real time, realize the timely pushing and feedback of data through the edge equipment and the cloud platform, combine the evaluation result of the real-time monitoring and control strategy, perform closed-loop control, adjust the control parameters and the strategy in real time, respond to the system change and optimize the control performance.
10. The intelligent control method for gas sensing based on cloud edge cooperation as claimed in claim 1, wherein the intelligent control method is characterized by comprising the following steps: the autonomous fault diagnosis and maintenance is to implement autonomous fault diagnosis and maintenance functions by utilizing a cloud algorithm and a model, monitor and diagnose sensor faults, equipment faults and abnormal working conditions, and trigger early warning, alarming and maintenance operations according to analysis results.
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