Dangerous chemical leakage intelligent early warning system based on multi-source sensor fusion

Abstract

The invention discloses a dangerous chemical leakage intelligent early warning system based on multi-source sensor fusion. The system comprises a data acquisition layer, a data transmission layer, a data processing layer and an early warning layer user interface layer. The data acquisition layer is provided with a plurality of sensors to acquire relevant data of dangerous chemicals in real time; the data transmission layer adopts a mode of combining a wireless communication technology and a wired communication technology to ensure that data is safely transmitted to the central monitoring platform; the data processing layer cleans, fuses and analyzes the data, improves the accuracy and reliability of the data by using a multi-sensor fusion algorithm and a machine learning algorithm, and evaluates the leakage risk; the early warning layer generates an early warning signal according to a risk assessment result, and notifies related personnel in multiple modes; and the user interface layer provides a visual operation interface and a data display platform, so that a user can make decisions and manage conveniently. According to the system, the early warning accuracy is improved, real-time monitoring is realized, the management efficiency is improved, the safety is enhanced, safety accidents are effectively prevented, and the personnel and property safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of safety management of hazardous chemicals, and specifically to a system for realizing intelligent early warning of hazardous chemical leakage by using multi-source sensor fusion technology. Background Art

[0002] In industries such as chemical engineering, energy, and pharmaceuticals, the use and management of hazardous chemicals are extremely important. However, due to the flammable, explosive, toxic and other characteristics of hazardous chemicals, once a leakage accident occurs, it may trigger serious safety accidents, environmental pollution and economic losses. Traditional methods for monitoring hazardous chemical leakage mainly rely on manual inspections and single-sensor monitoring. Manual inspections require a large amount of manpower, material resources and time, and have problems such as low efficiency, poor real-time performance, and being easily affected by human factors, making it difficult to detect leakage risks in a timely manner. Although single-sensor monitoring can improve the monitoring efficiency to a certain extent, due to its perceptual limitations and uncertainties, it often cannot comprehensively and accurately obtain the leakage information of hazardous chemicals, resulting in insufficient accuracy and reliability of early warnings.

[0003] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, multi-source sensor fusion technology has gradually been applied to various fields. This technology improves the comprehensiveness and accuracy of information acquisition by integrating data from different sensors, providing a new solution for early warning of hazardous chemical leakage. Multi-source sensor fusion technology comprehensively utilizes the information obtained by different sensors, avoids the perceptual limitations and uncertainties of a single sensor, forms a more comprehensive perception and recognition of the environment or target, and improves the external perception ability of the system. Currently, multi-source sensor fusion technology has been widely applied in fields such as fault detection, remote sensing, human health monitoring, robot systems, human-computer interaction, target recognition and tracking.

[0004] In the aspect of early warning of hazardous chemical leakage, the application of multi-source sensor fusion technology is still in the process of continuous development and improvement. In some research and practices, attempts have been made to deploy various types of sensors (such as temperature sensors, pressure sensors, gas sensors, liquid level sensors, vibration sensors, etc.) in various links of the production, storage, and transportation of hazardous chemicals, collect a variety of key parameters in real time, and improve the accuracy and timeliness of leakage early warnings through data fusion and analysis. For example, gas sensors, vibration sensors and pressure sensors are deployed along the hazardous chemical transportation pipeline, and through multi-sensor data fusion, the pipeline leakage risk can be detected in a timely manner.

[0005] Although multi-source sensor fusion technology has certain advantages in the early warning of hazardous chemical leaks, there are still some disadvantages and challenges at present. First, in the process of multi-source sensor data fusion, problems such as inconsistent data formats and different data rates may be encountered, and data preprocessing and conversion are required to ensure the consistency and comparability of data. Second, the data of different sensors may be affected by interference factors such as noise and errors, and effective data cleaning and filtering algorithms need to be adopted to improve the quality and reliability of data. In addition, the complexity of multi-source sensor fusion algorithms is relatively high, which requires a large amount of computing resources and time, and may affect the real-time performance of the system. In practical applications, it is also necessary to consider how to improve the response speed of the system on the premise of ensuring the accuracy of early warning to meet the real-time requirements of hazardous chemical leak early warning.

[0006] Furthermore, the application of multi-source sensor fusion technology in the early warning of hazardous chemical leaks also faces some technical problems. For example, how to reasonably select and deploy sensors to achieve all-round and multi-level monitoring of hazardous chemical leaks; how to optimize multi-source sensor fusion algorithms according to different application scenarios and the characteristics of hazardous chemicals to improve the pertinence and effectiveness of early warning; how to integrate multi-source sensor fusion technology with other related technologies (such as geographic information systems, risk assessment models, etc.) to build a more perfect hazardous chemical leak early warning system, etc., are all issues that need further research and solution. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent early warning system for hazardous chemical leaks based on multi-source sensor fusion, which can timely detect leakage risks, achieve accurate early warning, and improve the safety management level of hazardous chemicals by real-time monitoring the storage environment, usage status and surrounding environment of hazardous chemicals.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] An intelligent early warning system for hazardous chemical leaks based on multi-source sensor fusion, the system includes:

[0010] A data acquisition layer configured to deploy various types of sensors for real-time acquisition of key parameters of the storage environment, usage status and surrounding environment of hazardous chemicals, and the sensors include temperature sensors, pressure sensors, gas sensors, liquid level sensors and vibration sensors;

[0011] A data transmission layer configured to transmit the data collected by the data acquisition layer to the central monitoring platform by combining wireless communication technology and wired communication technology, and use encryption technology to ensure the security and integrity of the data during transmission;

[0012] The data processing layer is configured to clean, fuse, and analyze the data transmitted by the data transmission layer. It uses multi-sensor fusion algorithms to improve the accuracy and reliability of the data, and utilizes machine learning algorithms to deeply mine the fused data to identify potential risk patterns;

[0013] The early warning layer is configured to automatically generate early warning signals based on the risk assessment results of the data processing layer. When a leakage risk is detected, it sends the early warning information to relevant personnel through a preset communication method for timely measures to be taken for disposal;

[0014] The user interface layer is configured to provide an intuitive operation interface and a data display platform, enabling users to view real-time monitoring data, risk assessment results, and early warning information for easy decision-making and management.

[0015] Furthermore, the temperature sensor in the data acquisition layer is used to monitor the temperature changes in the storage environment, the pressure sensor is used to monitor the pressure changes in the storage environment, the gas sensor is used to detect leaked toxic or combustible gases, the liquid level sensor is used to monitor the liquid level changes, and the vibration sensor is used to monitor the vibration of the equipment to predict equipment failures and abnormalities.

[0016] Furthermore, the wireless communication technologies adopted by the data transmission layer include at least one of LoRa, Zigbee, and NB-IoT, the wired communication technologies include at least one of industrial Ethernet and fiber optic communication, and the encryption technology includes the TLS / SSL encryption protocol.

[0017] Furthermore, the multi-sensor fusion algorithms adopted by the data processing layer include the Kalman filter method or the D-S evidence theory for multi-source data fusion, and the machine learning algorithms include decision trees, random forests, or support vector machines for data mining and risk assessment.

[0018] Furthermore, the early warning information generated by the early warning layer includes the leakage location, leakage type, leakage degree, and recommended emergency disposal measures. The sending methods of the early warning information include text messages, emails, and APP push notifications to notify relevant personnel in a timely manner.

[0019] Furthermore, the operation interface and data display platform provided by the user interface layer support customized settings to meet the usage requirements and management habits of different users.

[0020] Furthermore, the system further includes a storage layer configured to store the raw data collected by the data acquisition layer, the data processed by the data processing layer, and the early warning information generated by the early warning layer for subsequent data analysis and historical traceability.

[0021] The intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion of the present invention has the following beneficial effects:

[0022] 1. Improve the accuracy of early warning: Through multi-source sensor fusion technology, integrate data from different sensors, improve the comprehensiveness and accuracy of information acquisition, and thus improve the accuracy of early warning;

[0023] 2. Achieve real-time monitoring: The system can monitor the storage environment, usage status and surrounding environment of hazardous chemicals in real time, and detect leakage risks in a timely manner;

[0024] 3. Improve management efficiency: Automated monitoring and early warning reduce the workload of manual inspections and improve management efficiency;

[0025] 4. Enhance safety: Detect and handle leakage risks in a timely manner, effectively prevent the occurrence of safety accidents, and ensure the safety of personnel and property. Brief Description of the Drawings

[0026] Figure 1 It is a schematic diagram of the system architecture of the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion in the present invention;

[0027] Figure 2 It is a schematic diagram of the implementation process of the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion in the present invention. Detailed Embodiments

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0029] In the description of the embodiments of the present invention, "a plurality" represents at least 2.

[0030] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if the terms "set", "installed", "connected", "connected" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] Embodiment: I.

[0033] In industries such as chemical engineering, energy, and pharmaceuticals, the use and management of hazardous chemicals are of utmost importance. However, due to the flammable, explosive, and toxic characteristics of hazardous chemicals, once a leakage accident occurs, it may trigger serious safety accidents, environmental pollution, and economic losses. The present invention aims to provide an intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion. By real-time monitoring of the storage environment, usage status, and surrounding environment of hazardous chemicals, leakage risks can be detected in a timely manner, accurate early warnings can be achieved, and the safety management level of hazardous chemicals can be improved.

[0034] II. System Architecture

[0035] (I) Data Acquisition Layer

[0036] Sensor Types and Deployment Locations

[0037] Temperature sensors: In the storage area of hazardous chemicals, temperature sensors are deployed to monitor the temperature changes in the storage environment. In the chemical warehouse, a temperature sensor is deployed at a certain interval (such as every 5 meters) to comprehensively monitor the temperature distribution in the warehouse;

[0038] Pressure sensors: Similarly, pressure sensors are deployed in the storage area to monitor the pressure changes in the storage environment. For the tanks storing compressed gases, a pressure sensor is installed on each tank to obtain the pressure data inside the tank in real time;

[0039] Gas sensors: Gas sensors are deployed around pipelines and containers to detect leaked toxic or combustible gases. Along the pipelines transporting hazardous chemicals, gas sensors are set at a certain interval (such as every 10 meters) to promptly detect the gas diffusion caused by pipeline leakage;

[0040] Liquid level sensors: Liquid level sensors are deployed near liquid storage tanks to monitor the changes in liquid level. For large liquid chemical storage tanks, liquid level sensors such as ultrasonic liquid level gauges or float type liquid level gauges are used to measure the liquid height in real time;

[0041] Vibration sensors: Vibration sensors are deployed near key equipment to monitor the vibration conditions of the equipment and predict equipment failures and abnormalities. For equipment such as running pumps and compressors, vibration sensors are installed on their motors and key components. By monitoring vibration frequency and amplitude parameters, it can be determined whether the equipment is operating normally.

[0042] Sensor Data Acquisition Frequency

[0043] According to the characteristics and leakage risks of different hazardous chemicals, a reasonable data collection frequency is set. For volatile and easily leakable chemicals, relevant sensors (such as gas sensors and liquid level sensors) can be set to a higher collection frequency, such as collecting data once a minute; while for relatively stable parameters (such as the temperature and pressure in the storage area), the sensors can collect data once every half hour or one hour.

[0044] (2) Data Transmission Layer

[0045] Selection and Application of Wireless Communication Technology

[0046] The LoRa wireless communication technology is adopted to transmit the data collected by the sensors to the nearby gateway devices; LoRa has the characteristics of long distance, low power consumption, and large capacity, and is suitable for application in complex environments such as hazardous chemical storage areas. For example, in a large chemical industrial park, a LoRa gateway is set every certain distance (such as 500 meters) to receive the data sent by the surrounding sensors.

[0047] Supplement of Wired Communication Technology

[0048] The gateway device transmits the data to the central monitoring platform through a wired network (such as industrial Ethernet); during the data transmission process, the TLS / SSL encryption technology is adopted to ensure the security and integrity of the data; for some scenarios with extremely high requirements for data transmission real-time performance and stability, such as the core production area of a large hazardous chemical production enterprise, in addition to wireless communication, industrial Ethernet cables can also be laid to achieve wired data transmission and ensure that the data is stably and efficiently transmitted to the central monitoring platform.

[0049] Security Guarantee for Data Transmission

[0050] During the data transmission process, the TLS / SSL encryption technology is adopted to encrypt the transmitted data. Through digital certificate authentication, it is ensured that only authorized devices and users can access and transmit the data, preventing the data from being stolen or tampered with, and guaranteeing the security and integrity of the data.

[0051] (3) Data Processing Layer

[0052] Data Cleaning Method

[0053] In the central monitoring platform, the big data processing platform (such as Hadoop, Spark) is used to clean and preprocess the collected data; data cleaning includes operations such as removing duplicate data, correcting error data, and supplementing missing data. For example, for the abnormally high or low temperature data collected by the sensors, which may be caused by sensor failures or interference, through data cleaning algorithms, these abnormal data points are identified and removed to ensure the accuracy and reliability of the data.

[0054] Application of Multi-Sensor Fusion Algorithm

[0055] The multi-sensor fusion algorithm (such as Kalman filtering method, D-S evidence theory) is adopted to fuse the data from different sensors, improving the accuracy and reliability of the data. Taking the Kalman filtering method as an example, its basic principle is to use the state equation and observation equation of the system to estimate the state of the system, and continuously update the estimated value according to the observation data to obtain the optimal system state estimate. In this system, the data of the same physical quantity (such as temperature, pressure) collected by multiple sensors can be used as observation data, and these data can be fused through the Kalman filtering algorithm to obtain a more accurate estimated value of the physical quantity.

[0056] The D-S evidence theory is a mathematical theory for dealing with uncertain information, which can fuse the evidence (i.e., data) from different sensors to obtain a more reliable conclusion. For example, for the judgment of gas leakage, the gas concentration data detected by the gas sensor and the temperature and pressure data of the surrounding environment can both be used as evidence, and these evidences can be fused through the combination rules of the D-S evidence theory to improve the accuracy of gas leakage judgment.

[0057] Machine Learning Algorithm Training and Application

[0058] Machine learning algorithms (such as decision tree, random forest, support vector machine) are used to deeply mine the fused data to identify potential risk patterns.

[0059] First, a large amount of historical data is collected, including data during normal operation and data during leakage accidents. These data are labeled and preprocessed as the training data set. Then, a suitable machine learning algorithm is selected to train the training data set to build a risk assessment model. For example, using the random forest algorithm can handle high-dimensional data and has strong generalization ability and anti-overfitting ability. During the training process, the random forest builds multiple decision trees and votes or averages the outputs of these decision trees to obtain the final prediction result. Through the risk assessment model obtained by training, the data collected in real time can be analyzed to evaluate the magnitude and possibility of leakage risk.

[0060] (IV) Early Warning Layer

[0061] Early Warning Signal Generation Logic

[0062] According to the risk assessment results, when a leakage risk is detected, the system automatically generates a warning signal; different risk thresholds are set, and when the assessment results exceed the corresponding thresholds, warnings of different levels are triggered. For example, for the gas leakage risk, thresholds for three risk levels of low, medium, and high are set. When the gas concentration exceeds the low-risk threshold, the system issues a first-level warning to alert the staff; when the gas concentration further rises and exceeds the medium-risk threshold, a second-level warning is issued to prompt the adoption of corresponding emergency measures; if the gas concentration reaches the high-risk threshold, the system immediately issues a third-level warning, activates the emergency plan, and notifies relevant personnel to evacuate and handle it promptly.

[0063] Warning Information Release Method

[0064] The warning information is sent to relevant personnel (such as safety managers and operators) through various methods such as text messages, emails, and APP push; the warning information includes detailed information on the leakage location, leakage type, and leakage degree, so that relevant personnel can take measures to handle it in a timely manner. For example, when the system detects a liquid leakage in a certain chemical warehouse and the leakage degree is moderate, the system automatically generates a warning message and sends it to the warehouse manager via text message. The text message content is: "Warning: A liquid leakage has occurred in [warehouse name], and the leakage degree is moderate. Please go to the scene immediately to check and take corresponding measures." At the same time, the warning information is sent to relevant safety managers through APP push. The push content includes the detailed leakage location (such as the specific area and coordinates of the warehouse), leakage type (liquid, gas), leakage degree (mild, moderate, severe), and recommended emergency handling measures (such as evacuating personnel, closing relevant valves, and starting ventilation equipment).

[0065] (V) User Interface Layer

[0066] Operation Interface Design and Function

[0067] An intuitive operation interface and data display platform are provided; users can view real-time monitoring data, risk assessment results, and warning information through this interface, which is convenient for decision-making and management. The operation interface adopts a graphical design to intuitively display the layout of the hazardous chemical storage area and the positions of each sensor. For example, the layout of the chemical industrial park is shown in the form of a floor plan on the interface, and the positions of each chemical warehouse, pipeline, and equipment are clear at a glance. The sensor icons are distributed at the corresponding positions, and clicking on the sensor icon can view the real-time data and historical data curves of the sensor.

[0068] Data Display and Analysis Function

[0069] The interface display not only includes real-time data, but also provides functions for historical data query and analysis. Users can query historical data and warning records according to time range, chemical type, and risk level conditions, and conduct data analysis and statistics. For example, users can query the temperature change trend of a chemical warehouse in the past month, analyze the temperature fluctuations by plotting a temperature curve, and evaluate the stability of the storage environment. At the same time, the interface also provides a visual display of risk assessment results, using color coding (such as green for low risk, yellow for medium risk, and red for high risk) to intuitively show the risk levels of different areas, helping users quickly understand the overall safety situation.

[0070] User Permission Management and Customization Settings

[0071] Considering the usage requirements and management habits of different users, the user interface layer supports customization settings. At the same time, user permission management is implemented, and different permissions are assigned according to the user's role (such as administrator, operator, ordinary user). For example, administrators can perform system configuration, user management, and permission setting operations; operators can view real-time data, receive warning information, and make simple operation responses; ordinary users may only be able to view some public data and warning information. Through customization settings, users can adjust the interface layout, data display method, and warning reminder method according to their own needs, improving the usability and applicability of the system.

[0072] III. Specific Implementation Cases

[0073] (I) Application of Leak Warning in the Hazardous Chemical Warehouse of a Chemical Enterprise

[0074] System Deployment Situation

[0075] In the hazardous chemical warehouse of a chemical enterprise, this intelligent warning system is deployed. A total of 50 temperature sensors, 30 pressure sensors, 20 gas sensors, 15 liquid level sensors, and 10 vibration sensors are deployed in the warehouse, covering all areas and key equipment of the warehouse.

[0076] Data Collection and Transmission

[0077] The temperature sensors collect data every 10 minutes, the pressure sensors collect data every 15 minutes, the gas sensors collect data every 5 minutes, the liquid level sensors collect data every 30 minutes, and the vibration sensors collect data every minute. The collected data is sent to 5 gateway devices around the warehouse through LoRa wireless communication technology, and the gateway devices then transmit the data to the central monitoring platform through industrial Ethernet. During the data transmission process, TLS / SSL encryption technology is used to ensure the security of the data.

[0078] Data Processing and Risk Assessment

[0079] The central monitoring platform uses the Hadoop big data processing platform to clean and preprocess the collected data; the Kalman filtering method is used to fuse the temperature and pressure data to improve the accuracy of the data; at the same time, the random forest machine learning algorithm is used to analyze the fused data, build a risk assessment model, and through the training of historical data, the model can accurately identify various risk modes such as gas leakage, liquid leakage, and equipment failure, and evaluate the magnitude and possibility of the leakage risk.

[0080] Early Warning and Response

[0081] When the system detects a leakage risk, it automatically generates a warning signal according to the risk level. For example, when a gas sensor detects that the concentration of toxic gas in a certain area of the warehouse is gradually increasing and exceeds the set low-risk threshold, the system issues a first-level warning and pushes notifications to the warehouse management personnel via text message and APP; after receiving the warning, the management personnel immediately go to the site to check and find that the gas leakage is caused by the loosening of a certain pipeline connection. The management personnel promptly close the relevant valves and arrange for maintenance personnel to repair it, avoiding the further expansion of the accident.

[0082] (2) Application of Leakage Warning for a Certain Hazardous Chemical Transportation Pipeline

[0083] System Deployment

[0084] Along a certain long-distance hazardous chemical transportation pipeline, gas sensors, vibration sensors, and pressure sensors are deployed at regular intervals (such as every 200 meters). A total of 100 gas sensors, 50 vibration sensors, and 30 pressure sensors are deployed to achieve full-line monitoring of the pipeline.

[0085] Data Collection and Transmission

[0086] The gas sensor collects data every 5 minutes, the vibration sensor collects data every minute, and the pressure sensor collects data every 10 minutes; the collected data is sent to multiple gateway devices along the line through LoRa wireless communication technology, and the gateway devices then transmit the data to the central monitoring platform through optical fiber communication. The TLS / SSL encryption technology is used during the data transmission process to ensure data security.

[0087] Data Processing and Risk Assessment

[0088] At the central monitoring platform, the collected data is cleaned and preprocessed; the D-S evidence theory is used to fuse the data from gas sensors, vibration sensors, and pressure sensors to improve the accuracy of leakage judgment. At the same time, the support vector machine machine learning algorithm is used to analyze the fused data to identify the risk patterns of pipeline leakage; through the training of a large amount of historical data, the model can accurately judge whether the pipeline leaks and the approximate location of the leakage according to the changes in sensor data.

[0089] Early Warning and Response

[0090] When the system detects the risk of pipeline leakage, it immediately generates a warning signal. For example, when a section of the pipeline leaks, the gas sensor detects an abnormal increase in the surrounding gas concentration, the vibration sensor detects abnormal pipeline vibration, and the pressure sensor detects a decrease in pipeline pressure; the system fuses these data, determines that the pipeline is leaking, issues a level-three warning, and notifies relevant safety management personnel and emergency response personnel through multiple methods such as text messages, emails, and APP push; the warning information includes the approximate location of the leakage (such as a section of the pipeline), the type of leakage (gas leakage), the degree of leakage (severe), and the recommended emergency response measures (such as immediately closing relevant valves, evacuating surrounding personnel, and organizing emergency repairs). After receiving the warning, relevant personnel quickly take measures, effectively controlling the impact of the leakage accident and avoiding greater safety accidents and environmental pollution.

[0091] IV. Conclusion

[0092] This intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion deploys various types of sensors at the data acquisition layer to monitor the storage environment, usage status, and surrounding environment of hazardous chemicals in real time; at the data transmission layer, a combination of wireless and wired communication technologies is used to ensure the secure transmission of data; at the data processing layer, multi-sensor fusion algorithms and machine learning algorithms are used to deeply mine and risk-assess the data; at the early warning layer, warning signals are generated according to the risk assessment results and notified to relevant personnel through multiple methods; at the user interface layer, an intuitive operation interface and data display platform are provided to facilitate users' decision-making and management. The system has been applied in multiple actual scenarios, effectively improving the safety management level of hazardous chemicals, timely discovering and handling leakage risks, preventing the occurrence of safety accidents, and ensuring the safety of personnel and property.

[0093] The above describes the present invention and its implementation manners. This description is not restrictive, and what is shown in the drawings is only one of the implementation manners of the present invention. The actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural manners and embodiments without creative efforts without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. An intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion, characterized in that: The system includes: The data collection layer is configured to deploy various types of sensors for real-time collection of key parameters of the storage environment, usage status and surrounding environment of hazardous chemicals, including temperature sensors, pressure sensors, gas sensors, liquid level sensors and vibration sensors; The data transmission layer is configured to use a combination of wireless communication technology and wired communication technology to transmit the data collected by the data collection layer to the central monitoring platform, and encryption technology is used to ensure the security and integrity of the data during transmission; The data processing layer is configured to clean, fuse and analyze the data transmitted by the data transmission layer, use multi-sensor fusion algorithms to improve the accuracy and reliability of the data, and use machine learning algorithms to conduct in-depth mining of the fused data to identify potential risk patterns; The early warning layer is configured to automatically generate early warning signals based on the risk assessment results of the data processing layer. When a leakage risk is detected, the early warning information is sent to relevant personnel through a preset communication method so that timely measures can be taken to deal with it; The user interface layer is configured to provide an intuitive operating interface and data display platform, enabling users to view real-time monitoring data, risk assessment results and early warning information to facilitate decision-making and management.

2. According to claim 1, the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion is characterized by: The temperature sensor of the data acquisition layer is used to monitor the temperature changes of the storage environment, the pressure sensor is used to monitor the pressure changes of the storage environment, the gas sensor is used to detect the leakage of toxic gas or flammable gas, the liquid level sensor is used to monitor the change of liquid level, and the vibration sensor is used to monitor the vibration of the equipment to predict equipment failure and abnormality.

3. According to claim 1, the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion is characterized by: The wireless communication technology used in the data transmission layer includes at least one of LoRa, Zigbee, and NB-IoT, the wired communication technology includes at least one of industrial Ethernet and fiber optic communication, and the encryption technology includes TLS / SSL encryption protocol.

4. According to claim 1, the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion is characterized by: The multi-sensor fusion algorithm adopted by the data processing layer includes Kalman filtering or DS evidence theory for multi-source data fusion, and the machine learning algorithm includes decision tree, random forest or support vector machine for data mining and risk assessment.

5. According to claim 1, the intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion is characterized by: The warning information generated by the warning layer includes the leakage location, leakage type, leakage degree and recommended emergency disposal measures. The warning information is sent in the form of SMS, email, and APP push to notify relevant personnel in a timely manner.

6. The intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion according to claim 1 is characterized by: The operation interface and data display platform provided by the user interface layer support customized settings to meet the usage requirements and management habits of different users.

7. An intelligent early warning system for hazardous chemical leakage based on multi-source sensor fusion according to any one of claims 1 to 6, characterized in that: The system further includes a storage layer configured to store the original data collected by the data collection layer, the data processed by the data processing layer, and the warning information generated by the warning layer for subsequent data analysis and historical tracing.

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