Remote monitoring system and method for chemical combustible gas detection based on Internet of Things

Abstract

The invention relates to the technical field of instrument remote monitoring systems, in particular to a remote monitoring system and method for chemical combustible gas detection based on the Internet of Things, and the overall architecture of the remote monitoring system comprises a detection terminal, a server and a client. The detection terminal consists of a plurality of gas detection instruments and is used for detecting the concentration of combustible gases such as methane and hydrogen; the server is mainly composed of a Mosquitto message proxy server, an Nginx reverse proxy server, a back-end application program, a MySQL database and a machine learning data analysis module, and is used for receiving and analyzing data; and the client comprises a mobile client and a Web client and is used for displaying data to a user and providing real-time alarm and notification. The NB-IoT and LoRaWAN hybrid communication technology, the adaptive communication strategy, the Kalman filtering algorithm and the machine learning data analysis module are adopted, so that the system has high reliability, high accuracy and high intelligent level, can meet application requirements in industrial environments such as chemical plants and the like, and provides powerful guarantee for safe production of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of instrument remote monitoring systems, and in particular to a remote monitoring system and method for chemical combustible gas detection based on the Internet of Things. Background Art

[0002] With the deepening of global industrialization, the scale and complexity of the chemical industry are increasing. In the chemical production process, the use and generation of various combustible gases have become the norm, such as methane, hydrogen, ethylene, etc. These combustible gases are highly flammable and explosive. Once leaked during production or storage, they may cause major safety accidents such as fire and explosion, endangering the lives of personnel and causing huge property losses. Therefore, real-time and effective monitoring and control of these combustible gases has become the key to ensuring the safe production of chemical companies. However, traditional gas detection methods mainly rely on fixed or handheld detection instruments, which require regular manual inspections. There are problems such as poor real-time performance, limited monitoring range, and large influence of human factors. It is difficult to obtain the dynamic changes of on-site gas concentrations in a timely and accurate manner.

[0003] In addition, the production environment of chemical plants is usually extremely complex, with large plant areas, dense equipment, and a large number of metal structures, pipelines, and obstacles. These factors will interfere with the propagation of wireless signals, leading to signal attenuation, insufficient coverage, and other problems. Existing communication technologies such as Wi-Fi and ZigBee are difficult to achieve stable long-distance data transmission in this environment, and cannot meet the requirements of real-time, reliability, and coverage for combustible gas monitoring. At the same time, the security and integrity of data transmission are also important factors that must be considered, especially in areas involving safe production. Data leakage or tampering may lead to wrong decisions and further increase safety risks. Therefore, there is an urgent need for a communication technology that is suitable for complex industrial environments and can reliably transmit data, as well as a remote monitoring system that can monitor and warn the concentration of combustible gases in real time, so as to improve the safety management level of chemical companies and prevent the occurrence of safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a remote monitoring system and method for combustible gas detection in chemical plants based on the Internet of Things. Aiming at the complex environment of chemical plants, hybrid communication technology and advanced data processing algorithms are adopted to realize real-time monitoring, reliable transmission and early warning analysis of combustible gas concentration, thereby improving the accuracy and safety of gas detection.

[0005] The present invention provides the following technical solutions:

[0006] A remote monitoring system for chemical combustible gas detection based on the Internet of Things, comprising a detection terminal, a server and a client; the detection terminal is composed of a plurality of gas detection instruments, which are installed at different locations in a chemical plant, each gas detection instrument is dedicated to detecting combustible gases such as methane and hydrogen, and is equipped with an explosion-proof housing suitable for hazardous environments; the detection terminal adopts a hybrid communication module integrating NB-IoT and LoRaWAN technologies to ensure reliable data transmission in complex industrial environments; the gas detection instrument has a built-in embedded microprocessor, implements specific data filtering and anomaly detection algorithms, improves the accuracy and reliability of gas concentration measurement, and uploads the processed data to the server.

[0007] Furthermore, the communication module of the gas detection instrument adopts an adaptive communication strategy to select the best communication mode between NB-IoT and LoRaWAN according to network conditions, signal strength and data priority, thereby optimizing the efficiency and reliability of data transmission.

[0008] Furthermore, the embedded microprocessor implements a Kalman filter algorithm to process raw gas concentration data, reduce noise, and improve measurement accuracy.

[0009] Furthermore, the gas detection instrument includes a local data storage module, which temporarily stores data when the network is interrupted, and automatically uploads the stored data after the network connection is restored.

[0010] Furthermore, the server includes a data analysis module based on machine learning, which uses historical gas concentration data to establish a prediction model to provide early warning of potential gas leaks.

[0011] Furthermore, the server uses a secure communication protocol and adopts end-to-end encryption technology to ensure the security of data transmission between the detection terminal and the server.

[0012] Furthermore, when the gas concentration exceeds the preset safety threshold, the system automatically sends an alarm notification to the client and triggers an automatic safety program to start the ventilation system or shut down the equipment.

[0013] Furthermore, the client includes an alarm module that can receive alarm notifications and display alarm information to the user in real time through a mobile application and a web browser. The user can confirm and respond to the alarm through the client.

[0014] Furthermore, the system includes a redundancy mechanism, where multiple detection terminals monitor overlapping areas, thereby improving the fault tolerance and reliability of gas leak detection.

[0015] A remote monitoring method for chemical combustible gas detection based on the Internet of Things, the method comprising the following steps:

[0016] S1: Install several gas detection instruments at different locations in the chemical plant. Each instrument detects flammable gases such as methane and hydrogen and is equipped with an explosion-proof housing. It is equipped with a communication module that uses NB-IoT and LoRaWAN technology.

[0017] S2: The gas detection instrument collects gas concentration data, uses an embedded microprocessor to implement data filtering and anomaly detection algorithms, and adaptively selects the best communication technology to upload the data to the server;

[0018] S3: The server receives data through the Mosquitto message broker, processes and stores it in a MySQL database, and uses machine learning algorithms to perform real-time data analysis to predict potential gas leaks;

[0019] S4: The server transmits gas concentration data and analysis results to the client through the Nginx reverse proxy server; the client displays the data to the user through the mobile application and web browser, and provides real-time alarms and notifications;

[0020] S5: When the gas concentration exceeds the preset safety threshold, the system automatically sends an alarm notification to the client and triggers the automatic safety program; the user can confirm and respond to the alarm through the client.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] Aiming at the complex industrial environment of chemical plants, the present invention adopts a hybrid communication technology integrating NB-IoT and LoRaWAN, and cooperates with an adaptive communication strategy to dynamically select the best communication mode according to network conditions, signal strength and data priority, which significantly improves the real-time detection of combustible gas and the reliability of data transmission. The embedded microprocessor implements the Kalman filter algorithm to process the collected gas concentration data, reduce noise and improve measurement accuracy. The server side introduces a data analysis module based on machine learning, and uses historical gas concentration data to establish a prediction model to achieve early warning of potential gas leaks, providing strong data support for safety management. The system adopts end-to-end encryption technology to ensure the security of data transmission, and has a redundant mechanism to monitor overlapping areas through multiple detection terminals, thereby improving the fault tolerance and reliability of gas leak detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the module composition of a remote monitoring system for chemical combustible gas detection based on the Internet of Things of the present invention;

[0024] Figure 2 The following is a working diagram of a gas detection instrument in a remote monitoring system of the present invention;

[0025] Figure 3A schematic diagram of the module composition of a server in the remote monitoring system of the present invention;

[0026] Figure 4 The flowchart of the server in the remote monitoring system of the present invention;

[0027] Figure 5 The present invention is a flow chart of a remote monitoring method for chemical combustible gas detection based on the Internet of Things. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 creative work should fall within the scope of protection of the present invention.

[0029] Example: Figure 1 As shown, this embodiment provides a remote monitoring system for combustible gas detection in a chemical plant based on the Internet of Things, and its overall architecture includes three parts: a detection terminal, a server, and a client.

[0030] In this embodiment, the detection terminal is composed of several gas detection instruments installed at different locations in the chemical plant to detect the concentration of combustible gases such as methane and hydrogen, and upload the data to the server. Each gas detection instrument is equipped with an explosion-proof housing suitable for hazardous environments to ensure safe operation under harsh conditions.

[0031] Specifically, each gas detection instrument consists of an embedded microprocessor, a data acquisition module, a hybrid communication module, a local data storage module, a power management module, and an explosion-proof housing. Embedded microprocessor: A high-performance, low-power microcontroller (such as the STM32 series) is used to control the operating status of the instrument, process the collected gas concentration data, and communicate with other modules. The microprocessor implements the Kalman filter algorithm to process the collected gas concentration data, reduce noise, and improve measurement accuracy. Data acquisition module: A highly sensitive combustible gas sensor is configured to detect the concentration of the target gas in real time. The sensor converts the gas concentration into an electrical signal and transmits it to the microprocessor for processing. Hybrid communication module: Integrates NB-IoT and LoRaWAN technologies and adopts an adaptive communication strategy. According to network conditions, signal strength, and data priority, the best communication method is dynamically selected to ensure reliable data transmission and energy efficiency. When the signal is good, low-power LoRaWAN communication is preferred; when the signal is poor or critical data needs to be transmitted, switch to NB-IoT communication. Local data storage module: Configure non-volatile memory (such as EEPROM or Flash) to temporarily store data when the network is disconnected or the communication fails. After the network connection is restored, the stored data is automatically uploaded to avoid data loss.

[0032] Power management module: With low power design, it can be powered by battery or external power supply. The power management module optimizes the energy consumption of each component and prolongs the service life of the equipment. Explosion-proof housing: It complies with relevant safety standards (such as ATEX, IECEx), is suitable for hazardous environments, and protects the internal components of the instrument from external environmental influences.

[0033] like Figure 2 As shown, the working process of the gas detection instrument includes the following steps:

[0034] Data acquisition: The data acquisition module detects the concentration of combustible gas in the environment at a set time interval (such as once per second) and converts the analog signal output by the sensor into a digital signal.

[0035] Data processing: The embedded microprocessor implements the Kalman filter algorithm on the collected gas concentration data, performs calibration and filtering, and obtains accurate gas concentration values.

[0036] Anomaly detection: The microprocessor performs anomaly detection on the processed data to determine whether there is a sudden change in gas concentration or a situation where the gas concentration exceeds the safety threshold.

[0037] Data storage: Store the current gas concentration data, timestamp and other information in the local data storage module to form a historical record.

[0038] Data upload: Through the hybrid communication module, the best communication method is adaptively selected to upload the data packet containing the instrument ID, installation location, working status, detected gas type and gas concentration to the server. Data upload follows the MQTT protocol to ensure the efficiency and reliability of data transmission.

[0039] Abnormal handling: When the gas concentration is detected to exceed the preset safety threshold, the alarm mechanism is triggered immediately and the alarm information is uploaded to the server first.

[0040] In this embodiment, the server is mainly composed of a Mosquitto message proxy server, an Nginx reverse proxy server, a backend application, a MySQL database, and a machine learning data analysis module. Figure 3 shown.

[0041] Specifically, Mosquitto message proxy server: as the core component of the MQTT protocol, it is responsible for receiving messages from each detection terminal and forwarding the messages to the back-end application that has subscribed to the corresponding topic. Nginx reverse proxy server: used to process the client's HTTP / HTTPS requests, distribute them to the back-end application, provide load balancing and security protection functions, support SSL encryption, and ensure the security of data transmission. Back-end application: developed based on common Web development frameworks (such as Spring Boot, Django, etc.), to implement business logic processing, data parsing, storage, and interface provision functions. The application is also responsible for implementing machine learning algorithms to analyze and predict historical data. MySQL database: used to store gas detection data, user information, alarm records, prediction models, etc. Machine learning data analysis module: using historical gas concentration data, machine learning algorithms (such as time series analysis, anomaly detection algorithms, etc.) are used to establish a prediction model, analyze the trend of gas concentration changes in real time, and warn of potential gas leakage risks in advance.

[0042] like Figure 4 As shown, the server workflow includes the following steps:

[0043] Data reception and analysis: The Mosquitto server receives the MQTT messages uploaded by the detection terminal. The back-end application obtains the message content by subscribing to the corresponding topic, parses the message, and extracts information such as instrument ID, installation location, working status, detection gas type, gas concentration, and timestamp.

[0044] Data storage: The parsed data is written into the corresponding table of the MySQL database through the backend application to ensure data integrity and consistency.

[0045] Real-time data analysis: The machine learning data analysis module analyzes newly received data in real time, compares it with historical data, updates the prediction model, and predicts potential gas leakage risks.

[0046] Alarm processing: The back-end application determines whether the gas concentration exceeds the standard or has an abnormal trend based on the preset safety threshold and analysis results. If it exceeds the standard or an abnormality is detected, an alarm message is generated, recorded in the database, and the alarm notification mechanism is triggered.

[0047] Data provision: The backend application provides a RESTful API interface for clients to query real-time data, historical data, alarm information, and analysis results.

[0048] Secure communication: The server uses end-to-end encryption technology to ensure the security of data transmission between the detection terminal and the server, preventing data from being stolen or tampered with.

[0049] In this embodiment, the client includes a mobile client and a Web client. The user can remotely view the working status and gas concentration data of the gas detection instrument and receive alarm notifications through a mobile phone application or a web browser.

[0050] Specifically, the mobile client is a mobile application developed based on the Android or iOS platform. It mainly includes the following functional modules: Data display module: displays real-time and historical gas concentration data in the form of lists, charts, etc., and supports multiple filtering and sorting methods. Alarm module: receives alarm notifications pushed by the server, supports pop-up reminders, sounds, vibrations, etc. to inform users, and users can view alarm details and handle them. User management module: supports user login, registration, permission settings, etc. to ensure the security of the system and the confidentiality of data. Map positioning module: combined with the installation location of the instrument, displays the distribution of each detection point on the map, so that users can intuitively understand the on-site situation. Safety control module: when receiving an alarm notification, the user can remotely trigger the automatic safety program through the client to start the ventilation system or shut down related equipment.

[0051] In addition, the Web client is a web application accessed based on a browser, suitable for use on a computer. It mainly includes the following functional modules: Real-time monitoring: real-time refresh display of the data and status of each detection instrument, providing rich visual charts. Historical data query: provides data filtering, export and other functions, supports query by time, instrument ID, gas type and other conditions, and facilitates users to analyze data. Alarm management: view historical alarm records, handle alarm events, generate alarm reports, and support report exports in multiple formats. Data analysis: provides advanced functions such as statistical reports, trend analysis, anomaly detection, etc. to assist management decisions. System configuration: users can adjust system parameters such as security thresholds, alarm methods, and permission settings as needed.

[0052] like Figure 5 As shown, the remote monitoring method provided in this embodiment includes the following steps:

[0053] S1: Install several gas detection instruments at different locations in the chemical plant. Each instrument detects flammable gases such as methane and hydrogen and is equipped with an explosion-proof housing. It is equipped with a communication module that uses NB-IoT and LoRaWAN technology.

[0054] S2: The gas detection instrument collects gas concentration data, uses an embedded microprocessor to implement data filtering and anomaly detection algorithms, and adaptively selects the best communication technology to upload the data to the server;

[0055] S3: The server receives data through the Mosquitto message broker, processes and stores it in a MySQL database, and uses machine learning algorithms to perform real-time data analysis to predict potential gas leaks;

[0056] S4: The server transmits gas concentration data and analysis results to the client through the Nginx reverse proxy server; the client displays the data to the user through the mobile application and web browser, and provides real-time alarms and notifications;

[0057] S5: When the gas concentration exceeds the preset safety threshold, the system automatically sends an alarm notification to the client and triggers the automatic safety program; the user can confirm and respond to the alarm through the client.

[0058] In addition, the following is a supplementary explanation of the remote monitoring method. According to the layout and production requirements of the chemical plant, gas detection instruments are installed in the areas that need to be monitored. During installation, record the ID, installation location and type of detected gas of each instrument, and configure the communication parameters, adaptive communication strategy and preset security thresholds. Then, deploy the server-side application on the data center or cloud server, including Mosquitto, Nginx, back-end application, MySQL database and machine learning data analysis module. Configure the domain name, SSL certificate and firewall policy to ensure the security and stability of the system. Users need to install the mobile client APP on their mobile phones, or access the Web client through a browser on their computers to complete user registration and permission configuration.

[0059] Through the detailed description of the above embodiments, the present invention provides a remote monitoring system and method for combustible gas detection in chemical plants based on the Internet of Things, which successfully realizes real-time monitoring, reliable transmission and safety warning of combustible gas concentration in the complex environment of chemical plants. The system adopts hybrid communication technology integrating NB-IoT and LoRaWAN, adaptive communication strategy, Kalman filter algorithm and machine learning data analysis module, with high reliability, high accuracy and high intelligence level, which can meet the application requirements in industrial environments such as chemical plants, and provide strong guarantee for the safe production of enterprises.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A remote monitoring system for chemical combustible gas detection based on the Internet of Things, including a detection terminal, a server and a client, characterized in that: The detection terminal consists of several gas detection instruments installed in different locations of the chemical plant. Each gas detection instrument is dedicated to detecting combustible gases such as methane and hydrogen, and is equipped with an explosion-proof casing suitable for hazardous environments. The detection terminal adopts a hybrid communication module that integrates NB-IoT and LoRaWAN technologies to ensure reliable data transmission in complex industrial environments. The gas detection instrument has a built-in embedded microprocessor that implements specific data filtering and anomaly detection algorithms to improve the accuracy and reliability of gas concentration measurements, and uploads the processed data to the server.

2. The system according to claim 1, characterized in that The communication module of the gas detection instrument adopts an adaptive communication strategy to select the best communication mode between NB-IoT and LoRaWAN according to network conditions, signal strength and data priority, thereby optimizing the efficiency and reliability of data transmission.

3. The system according to claim 2, characterized in that The embedded microprocessor implements a Kalman filter algorithm to process raw gas concentration data, reduce noise, and improve measurement accuracy.

4. The system according to claim 3, characterized in that The gas detection instrument includes a local data storage module, which temporarily stores data when the network is interrupted and automatically uploads the stored data after the network connection is restored.

5. The system according to claim 4, characterized in that The server includes a data analysis module based on machine learning, which uses historical gas concentration data to establish a prediction model to provide early warning of potential gas leaks.

6. The system according to claim 5, characterized in that The server uses a secure communication protocol and adopts end-to-end encryption technology to ensure the security of data transmission between the detection terminal and the server.

7. The system according to claim 6, characterized in that When the gas concentration exceeds the preset safety threshold, the system automatically sends an alarm notification to the client and triggers the automatic safety program to start the ventilation system or shut down the equipment.

8. The system according to claim 7, characterized in that The client includes an alarm module, which can receive alarm notifications and display alarm messages to users in real time through mobile applications and web browsers. Users can confirm and respond to alarms through the client.

9. The system according to claim 8, characterized in that The system includes a redundancy mechanism, with multiple detection terminals monitoring overlapping areas to improve the fault tolerance and reliability of gas leak detection.

10. A remote monitoring method for chemical combustible gas detection based on the Internet of Things, characterized in that: Using the system according to any one of claims 1 to 9, the method comprises the following steps: S1: Install several gas detection instruments at different locations in the chemical plant. Each instrument detects flammable gases such as methane and hydrogen and is equipped with an explosion-proof housing. It is equipped with a communication module that uses NB-IoT and LoRaWAN technology. S2: The gas detection instrument collects gas concentration data, uses an embedded microprocessor to implement data filtering and anomaly detection algorithms, and adaptively selects the best communication technology to upload the data to the server; S3: The server receives data through the Mosquitto message broker, processes and stores it in a MySQL database, and uses machine learning algorithms to perform real-time data analysis to predict potential gas leaks; S4: The server transmits gas concentration data and analysis results to the client through the Nginx reverse proxy server; the client displays the data to the user through the mobile application and web browser, and provides real-time alarms and notifications; S5: When the gas concentration exceeds the preset safety threshold, the system automatically sends an alarm notification to the client and triggers the automatic safety program; the user can confirm and respond to the alarm through the client.

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