Air pre-heater blockage early warning device utilizing machine learning technology

Abstract

The invention relates to the technical field of air pre-heaters, and discloses an air pre-heater blockage early warning device utilizing a machine learning technology, which comprises a sensor module used for detecting air flow and pressure change in an air pre-heater; the data acquisition module is used for acquiring airflow and pressure change data detected by the sensor assembly; the machine learning module is used for analyzing the data acquired by the data acquisition assembly so as to identify the possibility that the air pre-heater is blocked; and the early warning module is used for giving an alarm when the machine learning model recognizes the blockage possibility of the air pre-heater, and the output end of the sensor module is electrically connected to the input end of the data acquisition module. The machine learning module is used for analyzing data collected by the sensor, efficient and accurate recognition of the blockage possibility of the air pre-heater can be achieved, and therefore the early warning module is triggered to give an alarm to help to take measures in time to prevent or solve the blockage problem.

Description

Technical Field

[0001] The present invention relates to the technical field of air preheaters, and in particular to an air preheater blockage warning device utilizing machine learning technology. Background Art

[0002] The air preheater blockage warning device is an intelligent device that uses machine learning technology to monitor and warn of air preheater blockage. The air preheater is a device used to filter particulate matter and pollutants in the air. Once blocked, the air purification effect may decrease or even damage the equipment.

[0003] The air preheater blockage warning device uses machine learning technology to monitor and analyze air flow, pressure, temperature and other data in real time, and identifies the characteristics of blockage by building a model. Once a possible blockage is detected, the device will issue an alarm and provide relevant warning information so that the operator can handle it in time. Through machine learning technology, the warning device can continuously learn and optimize the warning model to improve the accuracy and timeliness of the warning. At the same time, the device can also share data with other systems to achieve intelligent management and maintenance.

[0004] The existing technology can only deal with the blockage of the air preheater, perform maintenance and cleaning, but cannot provide early warning of the blockage of the air preheater, making it impossible to detect and perform maintenance and cleaning in time, resulting in obstructed air circulation, affecting the cooling efficiency of the air-conditioning system and reducing the cooling effect. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an air preheater blockage warning device using machine learning technology, which solves the problem that the prior art is unable to warn of air preheater blockage, and thus is unable to detect and perform maintenance and cleaning in time, resulting in obstructed air circulation, affecting the refrigeration efficiency of the air-conditioning system, and causing a decrease in the refrigeration effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an air preheater blockage warning device using machine learning technology, comprising:

[0007] A sensor module, wherein the sensor module is used to detect airflow and pressure changes in the air preheater;

[0008] A data acquisition module, the data acquisition module is used to collect airflow and pressure change data detected by the sensor assembly;

[0009] A machine learning module, the machine learning module is used to analyze the data collected by the data collection component to identify the possibility of air preheater blockage;

[0010] An early warning module, which is used to issue an alarm when the machine learning model identifies the possibility of air preheater blockage;

[0011] The output end of the sensor module is electrically connected to the input end of the data acquisition module, and the output end of the data acquisition module is electrically connected to the input ends of the machine learning module and the early warning module respectively.

[0012] Preferably, the sensor module includes a pressure sensor, an airflow sensor, a temperature sensor and a humidity sensor. The pressure sensor is used to detect changes in gas pressure inside the air preheater. The airflow sensor is used to monitor the airflow conditions in the air preheater, including airflow velocity and flow rate. The temperature sensor is used to monitor temperature changes inside the air preheater. The humidity sensor is used to monitor humidity changes inside the air preheater.

[0013] Preferably, the data acquisition module includes a sensor interface, a data collector, a data storage device, a data transmission unit and data processing software. The sensor interface is used to connect various sensors in the air preheater blockage warning system. The data collector is responsible for acquiring real-time data from various sensors and integrating, processing and storing the data. The data storage device is used to store the collected data. The data transmission unit is used to transmit the collected data to a machine learning model for analysis and processing. The data processing software is used to preprocess, clean and convert the collected data.

[0014] Preferably, the machine learning module includes a data preprocessing unit, a feature extraction unit, a machine learning algorithm unit, a model training unit, a model evaluation unit and an early warning trigger unit. The data preprocessing unit is responsible for performing preprocessing operations such as cleaning, conversion and normalization on the collected data. The feature extraction unit is used to extract relevant features from the original data. The machine learning algorithm unit includes a supervised learning algorithm and an unsupervised learning algorithm, which are used to learn patterns and rules from the data. The model training unit is used to input the preprocessed data into the machine learning algorithm for model training to establish a prediction model. The model evaluation unit evaluates the trained model to ensure the accuracy and reliability of the model. The early warning trigger unit triggers the early warning device to issue an alarm based on the output result of the machine learning model.

[0015] Preferably, the supervised learning algorithm includes one or more of a support vector machine, a decision tree and a random forest, and the unsupervised learning algorithm includes one or more of clustering analysis and anomaly detection.

[0016] Preferably, the early warning module includes an early warning trigger, an early warning signal generator and an early warning information transmission unit. The early warning trigger triggers the early warning module according to the output result of the machine learning model, the early warning signal generator is responsible for generating the early warning signal, and the early warning information transmission unit transmits the generated early warning signal to relevant personnel or systems.

[0017] Preferably, the warning module also includes a warning information display unit and a warning processing unit. The warning information display unit displays the warning information on a monitoring screen, a control panel or other equipment so that operators can understand the warning situation in a timely manner. The warning processing unit is responsible for processing the warning.

[0018] Working principle: The sensor module detects the airflow and pressure changes in the air preheater through pressure sensors, airflow sensors, temperature sensors and humidity sensors, and cooperates with the data acquisition module to collect the airflow and pressure change data detected by the sensors, and integrates, processes, stores and transmits them through data collectors, data storage devices, data transmission units and data processing software. At the same time, the machine learning module uses the data preprocessing unit, feature extraction unit, machine learning algorithm unit, model training unit, model evaluation unit and early warning trigger unit to analyze the collected data to identify the possibility of air preheater blockage. When the machine learning module identifies the possibility of air preheater blockage, the early warning trigger in the early warning module will trigger the early warning signal generator, generating The warning information display unit displays the warning information on the monitoring screen, control panel or other equipment so that the operator can timely understand the warning situation. The warning processing unit is responsible for processing the warning, which may include automatically taking measures to solve the blockage problem, or notifying the relevant operators to handle it. This air preheater blockage warning device uses machine learning technology to analyze and process the data detected by the sensor. It can timely and accurately identify the possibility of air preheater blockage and trigger a warning signal to help the operator take timely measures to avoid or solve the blockage problem, so that maintenance and cleaning can be carried out in time to avoid air circulation obstruction and improve the cooling effect.

[0019] The present invention provides an air preheater blockage warning device using machine learning technology.

[0020] Beneficial effects:

[0021] 1. The present invention analyzes the data collected by the sensor through the machine learning module, which can realize efficient and accurate identification of the possibility of air preheater blockage, thereby triggering the early warning module to issue an alarm, helping to take timely measures to prevent or solve the blockage problem.

[0022] 2. The present invention can comprehensively monitor the airflow and pressure changes in the air preheater through the sensor module including a pressure sensor, an airflow sensor, a temperature sensor and a humidity sensor, thereby ensuring the comprehensiveness and accuracy of the data.

[0023] 3. The present invention ensures the completeness and efficiency of data through the data acquisition module.

[0024] 4. The present invention adopts a variety of supervised learning algorithms and unsupervised learning algorithms through machine learning modules, which can better adapt to different early warning needs.

[0025] 5. The present invention can transmit warning information to relevant personnel or systems in a timely manner through the warning module, and display the warning information on monitoring screens, control panels and other devices to help operators understand the warning situation and handle it in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a system flow chart of the present invention; DETAILED DESCRIPTION

[0027] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Example:

[0029] Please see attached Figure 1 The embodiment of the present invention provides an air preheater blockage warning device using machine learning technology, comprising:

[0030] The sensor module is used to detect the air flow and pressure changes in the air preheater;

[0031] A data acquisition module, the data acquisition module is used to collect airflow and pressure change data detected by the sensor assembly;

[0032] A machine learning module, the machine learning module is used to analyze the data collected by the data acquisition component to identify the possibility of air preheater blockage;

[0033] The early warning module is used to issue an alarm when the machine learning model identifies the possibility of air preheater blockage;

[0034] The output end of the sensor module is electrically connected to the input end of the data acquisition module, and the output end of the data acquisition module is electrically connected to the input end of the machine learning module and the early warning module respectively.

[0035] The sensor module includes a pressure sensor, an airflow sensor, a temperature sensor and a humidity sensor. The pressure sensor is used to detect changes in gas pressure inside the air preheater. The airflow sensor is used to monitor the airflow conditions in the air preheater, including airflow velocity and flow rate. The temperature sensor is used to monitor temperature changes inside the air preheater. The humidity sensor is used to monitor humidity changes inside the air preheater.

[0036] The data acquisition module includes a sensor interface, a data collector, a data storage device, a data transmission unit and data processing software. The sensor interface is used to connect various sensors in the air preheater blockage warning system. The data collector is responsible for acquiring real-time data from various sensors and integrating, processing and storing the data. The data storage device is used to store the collected data. The data transmission unit is used to transfer the collected data to the machine learning model for analysis and processing. The data processing software is used to preprocess, clean and convert the collected data.

[0037] The machine learning module includes a data preprocessing unit, a feature extraction unit, a machine learning algorithm unit, a model training unit, a model evaluation unit and an early warning trigger unit. The data preprocessing unit is responsible for cleaning, converting and normalizing the collected data. The feature extraction unit is used to extract relevant features from the original data. The machine learning algorithm unit includes supervised learning algorithms and unsupervised learning algorithms, which are used to learn patterns and rules from the data. The model training unit is used to input the preprocessed data into the machine learning algorithm for model training to establish a prediction model. The model evaluation unit is used to evaluate the trained model to ensure the accuracy and reliability of the model. The early warning trigger unit triggers the early warning device to issue an alarm based on the output results of the machine learning model.

[0038] The supervised learning algorithm includes one or more of support vector machine, decision tree and random forest, and the unsupervised learning algorithm includes one or more of cluster analysis and anomaly detection.

[0039] The early warning module includes an early warning trigger, an early warning signal generator and an early warning information transmission unit. The early warning trigger triggers the early warning module based on the output results of the machine learning model. The early warning signal generator is responsible for generating early warning signals. The early warning information transmission unit transmits the generated early warning signals to relevant personnel or systems.

[0040] The warning module also includes a warning information display unit and a warning processing unit. The warning information display unit displays the warning information on a monitoring screen, a control panel or other equipment so that operators can understand the warning situation in a timely manner. The warning processing unit is responsible for processing the warning.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An air preheater blockage warning device using machine learning technology, characterized in that: include: A sensor module, wherein the sensor module is used to detect airflow and pressure changes in the air preheater; A data acquisition module, the data acquisition module is used to collect airflow and pressure change data detected by the sensor assembly; A machine learning module, the machine learning module is used to analyze the data collected by the data collection component to identify the possibility of air preheater blockage; An early warning module, which is used to issue an alarm when the machine learning model identifies the possibility of air preheater blockage; The output end of the sensor module is electrically connected to the input end of the data acquisition module, and the output end of the data acquisition module is electrically connected to the input ends of the machine learning module and the early warning module respectively.

2. The air preheater blockage warning device using machine learning technology according to claim 1, characterized in that: The sensor module includes a pressure sensor, an airflow sensor, a temperature sensor and a humidity sensor. The pressure sensor is used to detect changes in gas pressure inside the air preheater. The airflow sensor is used to monitor the airflow conditions in the air preheater, including airflow velocity and flow rate. The temperature sensor is used to monitor temperature changes inside the air preheater. The humidity sensor is used to monitor humidity changes inside the air preheater.

3. The air preheater blockage warning device using machine learning technology according to claim 1 is characterized in that: The data acquisition module includes a sensor interface, a data collector, a data storage device, a data transmission unit and data processing software. The sensor interface is used to connect various sensors in the air preheater blockage warning system. The data collector is responsible for acquiring real-time data from various sensors and integrating, processing and storing the data. The data storage device is used to store the collected data. The data transmission unit is used to transmit the collected data to the machine learning model for analysis and processing. The data processing software is used to preprocess, clean and convert the collected data.

4. The air preheater blockage warning device using machine learning technology according to claim 1, characterized in that: The machine learning module includes a data preprocessing unit, a feature extraction unit, a machine learning algorithm unit, a model training unit, a model evaluation unit and an early warning trigger unit. The data preprocessing unit is responsible for cleaning, converting and normalizing the collected data. The feature extraction unit is used to extract relevant features from the original data. The machine learning algorithm unit includes a supervised learning algorithm and an unsupervised learning algorithm, which are used to learn patterns and rules from the data. The model training unit is used to input the preprocessed data into the machine learning algorithm for model training to establish a prediction model. The model evaluation unit evaluates the trained model to ensure the accuracy and reliability of the model. The early warning trigger unit triggers the early warning device to issue an alarm based on the output result of the machine learning model.

5. The air preheater blockage warning device using machine learning technology according to claim 4 is characterized in that: The supervised learning algorithm includes one or more of a support vector machine, a decision tree and a random forest, and the unsupervised learning algorithm includes one or more of a cluster analysis and anomaly detection.

6. The air preheater blockage warning device using machine learning technology according to claim 1, characterized in that: The early warning module includes an early warning trigger, an early warning signal generator and an early warning information transmission unit. The early warning trigger triggers the early warning module according to the output result of the machine learning model. The early warning signal generator is responsible for generating the early warning signal. The early warning information transmission unit transmits the generated early warning signal to relevant personnel or systems.

7. The air preheater blockage warning device using machine learning technology according to claim 6 is characterized in that: The warning module also includes a warning information display unit and a warning processing unit. The warning information display unit displays the warning information on a monitoring screen, a control panel or other equipment so that operators can understand the warning situation in time. The warning processing unit is responsible for processing the warning.

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