Indoor formaldehyde monitoring system based on Internet of Things

Abstract

The invention discloses an indoor formaldehyde monitoring system based on Internet of Things, belongs to the technical field of Internet of Things, and aims to solve the technical problems that an existing formaldehyde detection method is poor in timeliness, limited in coverage range and high in cost. According to the technical scheme, the system comprises a cloud server, a mobile application and / or a webpage platform and a plurality of formaldehyde sensor nodes installed at different indoor positions; the formaldehyde sensor node is used for collecting the formaldehyde concentration in the surrounding environment in real time and uploading the collected data to the cloud server through the wireless communication module; the cloud server is used for receiving the data from each sensor node, performing data analysis and data processing on the received sensor node data, and obtaining the indoor real-time formaldehyde concentration, the change trend of the indoor formaldehyde concentration and a corresponding analysis report; the mobile application and / or the webpage platform are / is used for checking the indoor real-time formaldehyde concentration, the change trend of the indoor formaldehyde concentration and a corresponding analysis report.

Description

Technical Field

[0001] The invention relates to the technical field of Internet of Things, and in particular to an indoor formaldehyde monitoring system based on the Internet of Things. Background Art

[0002] With the widespread use of modern building and decoration materials, indoor formaldehyde pollution is becoming increasingly serious. Formaldehyde is a common volatile organic compound (VOC). Long-term exposure to high concentrations of formaldehyde can cause serious effects on human health, such as respiratory diseases, allergic reactions, and even cancer. Therefore, timely monitoring and control of indoor formaldehyde concentrations is crucial to protecting the health of residents.

[0003] Traditional formaldehyde detection methods usually rely on handheld detectors or laboratory analysis, which have the following shortcomings:

[0004] ① Poor timeliness: Real-time monitoring cannot be achieved, manual operation is usually required, and the detection cycle is long.

[0005] ② Limited coverage: Only specific points can be tested and the formaldehyde distribution in the entire room cannot be fully reflected.

[0006] ③High cost: Professional equipment is expensive and not suitable for ordinary households.

[0007] ④ Lack of intelligent management: Automatic warning is impossible, and it is difficult for users to obtain information on exceeding standards in a timely manner and take measures. Summary of the invention

[0008] The technical task of the present invention is to provide an indoor formaldehyde monitoring system based on the Internet of Things to solve the problems of poor timeliness, limited coverage and high cost in existing formaldehyde detection methods.

[0009] The technical task of the present invention is achieved in the following manner: an indoor formaldehyde monitoring system based on the Internet of Things, the system comprising a cloud server, a mobile application and / or a web platform, and a plurality of formaldehyde sensor nodes installed at different locations indoors;

[0010] The formaldehyde sensor node is used to collect the formaldehyde concentration in the surrounding environment in real time, and upload the collected data to the cloud server through a wireless communication module (such as Wi-Fi, Zigbee, LoRa, etc.);

[0011] The cloud server is used to receive data from each sensor node, and perform data analysis and data processing on the received sensor node data to obtain the real-time indoor formaldehyde concentration, the change trend of indoor formaldehyde concentration and the corresponding analysis report;

[0012] The mobile application and / or web platform is used to view the real-time indoor formaldehyde concentration, the changing trend of the indoor formaldehyde concentration and the corresponding analysis report.

[0013] Preferably, the formaldehyde sensor node uses the MQTT protocol to transmit data to the cloud server to ensure the reliability of data transmission.

[0014] Preferably, the cloud server performs data analysis and data processing on the received sensor node data through a pipeline processing framework, as follows:

[0015] Apache Kafka works with Apache Flink to achieve fast processing;

[0016] Kafka organizes sensor node data through topics and partitions, allowing sensor node data to be persistently stored. At the same time, Kafka supports redundant message backup and partition copy mechanisms to ensure data reliability and fault tolerance. Even in the event of partial node failure, it can also ensure data integrity and availability.

[0017] After Flink reads the sensor node data from Kafka, it processes and calculates the sensor node data in real time. Through the checkpoint mechanism, Flink can restore the task status when a failure occurs to ensure data consistency and integrity, effectively meeting the needs of formaldehyde detection;

[0018] Build a backend service to process data from sensor nodes and provide an interface for the front end to call to obtain sensor node data.

[0019] Preferably, the backend service transmits data via the HTTPS protocol and integrates third-party services to provide intelligent warnings in the form of SMS or email.

[0020] Preferably, the backend service uses a backend technology stack that includes Node.js and Java (Spring Boot).

[0021] Preferably, the intelligent early warning is as follows:

[0022] When the formaldehyde concentration exceeds the set threshold, the mobile application and / or web platform automatically sends an alarm notification and makes suggestions for opening windows for ventilation and / or turning on an air purifier and / or turning on a fresh air system.

[0023] Preferably, the mobile application and / or web platform supports multi-user management, and users can set different permissions for family members or colleagues to facilitate joint management of indoor air quality.

[0024] Preferably, the system conducts in-depth analysis of historical data, uses big data processing technology Spark or convolutional neural network LSTM (CNN-LSTM) algorithm to perform data analysis, and identifies the patterns and trends of formaldehyde concentration changes through learning of historical data by machine learning models. Based on the analysis results, the changing trend of formaldehyde concentration in a specified future time is predicted (a large amount of data is required for training). The advantage of the convolutional neural network LSTM (CNN-LSTM) algorithm is that it combines the spatial feature extraction capability of CNN and the time series processing capability of LSTM, and can process complex data more flexibly.

[0025] The indoor formaldehyde monitoring system based on the Internet of Things of the present invention has the following advantages:

[0026] (i) The present invention installs multiple formaldehyde sensor nodes indoors, uses wireless communication technology to transmit the collected formaldehyde concentration data to a cloud server, and provides users with real-time monitoring and historical data analysis functions through mobile applications or web platforms; the present invention also has an intelligent early warning function. When the formaldehyde concentration exceeds a preset threshold, an alarm is automatically sent to notify the user to take corresponding measures, which not only improves the management efficiency of indoor air quality, but also provides strong protection for the health of users;

[0027] (ii) The present invention solves the problems of poor timeliness, limited coverage, and high cost in existing formaldehyde detection methods. It realizes real-time monitoring, remote management, and intelligent early warning of indoor formaldehyde concentration through the Internet of Things technology, effectively improving the management efficiency of indoor air quality and protecting the health of users. With the continuous development of the Internet of Things technology and smart home technology, the present invention is expected to be widely used in more scenarios, promoting the intelligent upgrade of the field of indoor air quality monitoring.

[0028] (III) The present invention has an intelligent early warning function, which can predict possible exceeding of the standard in advance according to the changing trend of formaldehyde concentration, and promptly notify the user to take preventive measures; in addition, the present invention can also be integrated with smart home devices (such as air purifiers, fresh air systems, etc.) to achieve automatic control; for example, when the formaldehyde concentration exceeds the standard, the system can automatically start the air purifier to reduce the indoor formaldehyde concentration;

[0029] (iv) The present invention can realize real-time and continuous formaldehyde concentration monitoring, so that users can understand the indoor air quality at any time and avoid health hazards caused by excessive formaldehyde;

[0030] (V) The present invention can fully cover the entire space by installing multiple sensor nodes at different locations indoors, ensuring the accuracy and representativeness of the monitoring results;

[0031] (vi) The present invention has an intelligent early warning function, which can promptly notify the user when the formaldehyde concentration exceeds the standard and provide suggested measures to help the user take quick action;

[0032] (VII) The present invention can be integrated with smart home devices to achieve automated control and further improve the management efficiency of indoor air quality;

[0033] (VIII) Through big data processing and machine learning technology, the system can provide users with personalized suggestions to help users better manage and improve indoor air quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described below in conjunction with the accompanying drawings.

[0035] Attached Figure 1 Schematic diagram of an indoor formaldehyde monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0036] The following is a detailed description of an indoor formaldehyde monitoring system based on the Internet of Things of the present invention with reference to the accompanying drawings and specific embodiments.

[0037] Example:

[0038] As attached Figure 1 As shown, this embodiment provides an indoor formaldehyde monitoring system based on the Internet of Things, which includes a cloud server, a mobile application and / or a web platform, and a plurality of formaldehyde sensor nodes installed at different locations indoors;

[0039] The formaldehyde sensor node is used to collect the formaldehyde concentration in the surrounding environment in real time, and upload the collected data to the cloud server through a wireless communication module (such as Wi-Fi, Zigbee, LoRa, etc.);

[0040] The cloud server is used to receive data from each sensor node, and perform data analysis and data processing on the received sensor node data to obtain the real-time indoor formaldehyde concentration, the change trend of indoor formaldehyde concentration and the corresponding analysis report;

[0041] The mobile application and / or web platform is used to view the real-time indoor formaldehyde concentration, the changing trend of the indoor formaldehyde concentration and the corresponding analysis report.

[0042] The formaldehyde sensor node in this embodiment uses the MQTT protocol to transmit data to the cloud server to ensure the reliability of data transmission.

[0043] The cloud server in this embodiment performs data analysis and data processing on the received sensor node data through the pipeline processing framework, as follows:

[0044] ①Apache Kafka and Apache Flink for fast processing;

[0045] ②Kafka organizes sensor node data through topics and partitions, allowing sensor node data to be persistently stored. At the same time, Kafka supports redundant backup of messages and partition copy mechanisms to ensure data reliability and fault tolerance, and even in the event of partial node failure, it can ensure data integrity and availability;

[0046] ③After Flink reads the sensor node data from Kafka, it processes and calculates the sensor node data in real time. Through the checkpoint mechanism, Flink can restore the task status when a failure occurs to ensure data consistency and integrity, effectively meeting the needs of formaldehyde detection;

[0047] ④ Build a backend service to process data from sensor nodes and provide an interface for the front end to call to obtain sensor node data.

[0048] The backend service in this embodiment transmits data via the HTTPS protocol and integrates third-party services to provide intelligent warnings in the form of text messages or emails.

[0049] The backend technology stack used by the backend service in this embodiment includes Node.js and Java (Spring Boot).

[0050] The intelligent early warning in this embodiment is as follows:

[0051] When the formaldehyde concentration exceeds the set threshold, the mobile application and / or web platform automatically sends an alarm notification and makes suggestions for opening windows for ventilation and / or turning on an air purifier and / or turning on a fresh air system.

[0052] The mobile application and / or web platform in this embodiment supports multi-user management, and users can set different permissions for family members or colleagues to facilitate joint management of indoor air quality.

[0053] In this embodiment, the system performs in-depth analysis of historical data, uses big data processing technology Spark or convolutional neural network LSTM (CNN-LSTM) algorithm to perform data analysis, and identifies the patterns and trends of formaldehyde concentration changes through learning of historical data by machine learning models. Based on the analysis results, the changing trend of formaldehyde concentration within a specified time in the future is predicted (a large amount of data is required for training). The advantage of the convolutional neural network LSTM (CNN-LSTM) algorithm is that it combines the spatial feature extraction capability of CNN and the time series processing capability of LSTM, and can process complex data more flexibly.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An indoor formaldehyde monitoring system based on the Internet of Things, characterized in that: The system includes a cloud server, a mobile application and / or a web platform, and a plurality of formaldehyde sensor nodes installed at different locations indoors; The formaldehyde sensor node is used to collect the formaldehyde concentration in the surrounding environment in real time, and upload the collected data to the cloud server through the wireless communication module; The cloud server is used to receive data from each sensor node, and perform data analysis and data processing on the received sensor node data to obtain the real-time indoor formaldehyde concentration, the change trend of indoor formaldehyde concentration and the corresponding analysis report; The mobile application and / or web platform is used to view the real-time indoor formaldehyde concentration, the changing trend of the indoor formaldehyde concentration and the corresponding analysis report.

2. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 1, characterized in that: The formaldehyde sensor node uses the MQTT protocol to transmit data to the cloud server.

3. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 1 or 2, characterized in that: The cloud server performs data analysis and data processing on the received sensor node data through the pipeline processing framework, as follows: Apache Kafka works with Apache Flink to achieve fast processing; Kafka organizes sensor node data through topics and partitions, allowing sensor node data to be persistently stored. At the same time, Kafka supports redundant message backup and partition copy mechanisms to ensure data reliability and fault tolerance. Even in the event of partial node failure, data integrity and availability can be guaranteed. After Flink reads the sensor node data from Kafka, it processes and calculates the sensor node data in real time. Through the checkpoint mechanism, Flink can restore the task status when a failure occurs to ensure data consistency and integrity, effectively meeting the needs of formaldehyde detection; Build a backend service to process data from sensor nodes and provide an interface for the front end to call to obtain sensor node data.

4. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 3, characterized in that: The backend service transmits data via the HTTPS protocol and integrates third-party services to provide intelligent warnings in the form of SMS or email.

5. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 3, characterized in that: The backend technology stack used by the backend service includes Node.js and Java.

6. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 4, characterized in that: The smart warnings are as follows: When the formaldehyde concentration exceeds the set threshold, the mobile application and / or web platform automatically sends an alarm notification and makes suggestions for opening windows for ventilation and / or turning on an air purifier and / or turning on a fresh air system.

7. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 1, characterized in that: The mobile application and / or web platform supports multi-user management, and users can set different permissions for family members or colleagues to facilitate joint management of indoor air quality.

8. The indoor formaldehyde monitoring system based on the Internet of Things according to claim 1, characterized in that: The system conducts in-depth analysis of historical data, uses big data processing technology Spark or convolutional neural network LSTM algorithm to analyze data, and identifies the patterns and trends of formaldehyde concentration changes through machine learning models to learn historical data. Based on the analysis results, it predicts the changing trend of formaldehyde concentration within a specified time in the future.

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