Intelligent high-altitude and fire operation safety management system based on risk assessment

Abstract

The invention relates to the technical field of safety engineering, in particular to an intelligent high-altitude and fire operation safety management system based on risk assessment, which comprises a safety helmet module, a data acquisition and transmission module, an edge calculation and processing module, a risk assessment and decision module, an emergency response module and a data analysis and optimization module. The intelligent safety helmet worn by each operator is integrated with a flame sensor and a height sensor, and flame information in an operation environment and height changes of the operators can be collected in real time. Through the low-power-consumption wireless communication technology, the collected data is transmitted to the edge computing device in real time, and the edge computing device calculates the comprehensive risk index through multi-sensor data fusion. And when the risk index exceeds a preset threshold value, the system automatically triggers alarm, sends early warning information to operators and a management platform, and takes emergency measures, so that the accident rate in high-altitude operation and fire operation can be effectively reduced, and the safety management level of the operation environment is improved.

Description

Technical Field

[0001] The present invention relates to the field of safety engineering technology, and in particular to an intelligent high-altitude and hot work safety management system based on risk assessment. Background Art

[0002] In modern industry and construction, aerial work and hot work are inevitable and important links. However, such work has extremely high safety risks. According to statistics, high-altitude falls and fire accidents are the main causes of casualties among workers, seriously threatening their lives. Existing safety management measures mainly rely on manual monitoring and post-event review, lacking real-time monitoring and automatic early warning systems, which can easily lead to misjudgments and missed judgments.

[0003] In the existing technology, although there are some safety equipment for high-altitude operations, such as safety belts and goggles, these devices can only provide passive protection and cannot monitor the dynamic state of operators and environmental changes in real time. Traditional flame detectors and height sensors also mostly operate independently and lack data fusion analysis, resulting in delayed response to abnormal situations. Therefore, there is an urgent need for an intelligent safety management system that can integrate multiple sensors and monitor the working environment and operator status in real time to reduce safety risks.

[0004] In recent years, the rapid development of edge computing and Internet of Things technologies has provided a technical foundation for the construction of intelligent safety management systems. Through real-time data collection, processing and analysis, combined with machine learning and multi-sensor fusion algorithms, the intelligence and automation level of safety management can be significantly improved, thereby improving operational safety. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose an intelligent high-altitude and hot work safety management system based on risk assessment to solve the above-mentioned problems.

[0006] Based on the above objectives, the present invention provides an intelligent high-altitude and hot work safety management system based on risk assessment.

[0007] The intelligent high-altitude and hot work safety management system based on risk assessment includes the following modules:

[0008] Helmet module:

[0009] Each worker is equipped with an exclusive smart safety helmet with integrated flame sensor and height sensor for real-time monitoring of flame and worker's height changes.

[0010] The helmet is also equipped with a wireless communication module to achieve real-time data transmission and alarm functions.

[0011] Data acquisition and transmission module:

[0012] The sensors in the helmet are responsible for collecting data in real time and transmitting the data to the edge computing device using low-power wireless communication technology.

[0013] Data is encrypted during transmission to ensure information security.

[0014] Edge computing and processing module:

[0015] The edge computing device receives data from multiple helmets and performs real-time data preprocessing and anomaly detection.

[0016] The flame sensor and height sensor data are fused and analyzed to calculate the comprehensive risk index.

[0017] Risk Assessment and Decision Making Module:

[0018] Use machine learning models (such as random forests or decision trees) to dynamically assess the risks of operators.

[0019] When the risk index exceeds the preset threshold, the system triggers an alarm and sends a warning signal to the operating personnel and the management platform.

[0020] Emergency Response Module:

[0021] After identifying potential fire or fall risks, the system can automatically take emergency measures, such as cutting off power and sending risk avoidance alerts.

[0022] Record the process of the incident and provide data support for subsequent accident analysis.

[0023] Data analysis and optimization module:

[0024] The system continuously collects historical data, analyzes accidents, and optimizes risk assessment models and sensor settings based on feedback.

[0025] Beneficial effects of the present invention:

[0026] 1. The present invention integrates flame sensors and height sensors into the safety helmets of operators, and has the following beneficial effects: the system can collect the height changes and flame states of operators in real time, ensure timely response when danger occurs, and greatly improve the early warning capability of emergencies; through comprehensive analysis of the data of flame sensors and height sensors, the system can provide more accurate risk assessment, reduce the probability of misjudgment and missed judgment, and improve the safety of the work site; once abnormal flame or height changes are detected, the system will immediately trigger the alarm mechanism to notify operators and managers in time, effectively reduce the incidence of accidents, and protect the lives of operators; data processing through edge computing devices can reduce data transmission delays, achieve faster decision-making and response, and ensure that measures are taken quickly in dangerous situations; the system has the ability of self-learning and optimization, and continuously improves the accuracy of risk assessment models through historical data analysis, making safety management more intelligent and personalized; through the application of intelligent safety management systems, it can improve the safety awareness of operators, form a good safety culture, and promote continuous progress in safety management of enterprises. In summary, the present invention provides an efficient and intelligent safety management solution through technological innovation, which will provide strong protection for the safety of aerial operations and hot work, help reduce accidents, and improve the safety and management efficiency of the working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of the system operation flow structure of an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a hardware module of an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of a data processing module according to an embodiment of the present invention;

[0031] Figure 4 A schematic diagram of a risk assessment module according to an embodiment of the present invention;

[0032] Figure 5 A schematic diagram of an emergency response module according to an embodiment of the present invention;

[0033] Figure 6 Schematic diagram of a data analysis and optimization module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0035] The intelligent high-altitude and hot work safety management system based on risk assessment includes six modules: hardware design, data acquisition and transmission, real-time monitoring and processing, multi-sensor fusion analysis, risk assessment and decision-making, and emergency response and processing. The core goal of this system is to reduce the risks of high-altitude and hot work and ensure the safety of operators through intelligent technology and real-time data analysis.

[0036] 1. System Hardware Design and Integration

[0037] Safety helmet integrated design

[0038] Wearing a helmet is a necessary protective measure when performing operations at height or involving fire. Therefore, each worker will be equipped with a dedicated smart helmet with integrated flame sensors and height sensors to monitor flames and changes in personnel height in the working environment in real time.

[0039] Flame sensor: The detection range covers the front field of view, and is mainly used to detect the spectral characteristics of the flame and determine the presence, intensity and type of the flame.

[0040] Height sensor: used to monitor the height changes of workers, capable of sensing tiny height fluctuations to ensure accurate capture of falling risks during high-altitude operations.

[0041] In addition, the helmet will also integrate the following components:

[0042] Wireless communication module: used for real-time data transmission, supports low-power LoRa, Wi-Fi or 5G modules, and ensures that data can be uploaded to edge computing devices or cloud platforms efficiently and stably.

[0043] Power management module: Provides a battery system with long battery life and supports wireless charging or solar-assisted charging when necessary.

[0044] Alarm and prompt device: When the flame sensor or height sensor detects an abnormality, the safety helmet can emit an audible and visual alarm or vibrate to remind the operator.

[0045] Edge computing devices

[0046] Edge computing devices will be deployed at key locations in the work area to process data streams from each helmet in real time. Each edge computing node has the following functions:

[0047] Data preprocessing: denoising and smoothing sensor data to filter out potential anomalies.

[0048] Local decision-making: For some simple risks (such as flame intensity exceeding a preset threshold or a sharp drop in height), edge nodes can quickly make preliminary decisions, trigger alarms, and reduce response delays.

[0049] Data transmission and synchronization: Edge devices will transmit processed data to the cloud system for in-depth analysis and storage.

[0050] Edge computing selection

[0051] Edge computing devices use small, low-power industrial edge servers or gateways, such as the NVIDIA Jetson series, Intel NUC, or Raspberry Pi 4. The specific choice depends on the number of sensors, data processing complexity, and network environment.

[0052] The edge device needs to have multi-interface support (such as Wi-Fi, LoRa, BLE) to receive sensor data from the helmet, and have sufficient computing power to run local algorithms.

[0053] Edge device deployment solution

[0054] Centralized deployment: Deploy edge devices at multiple strategic points in the work area to cover the entire work site. Each edge computing node can connect to multiple hard hat devices, process their data streams and transmit them to the cloud.

[0055] Distributed deployment: Deploy multiple edge computing nodes in each partition of the operating area to share the processing tasks of sensor data in different partitions, reduce latency and improve the response speed of the system.

[0056] Network Architecture

[0057] Mesh networks or self-organizing network technologies such as LoRaWAN are used to ensure stable communication between edge nodes and between sensor devices and edge nodes. The gateway device synchronizes part of the processed data to the cloud via 4G / 5G or wired networks.

[0058] Edge computing tasks

[0059] Local preprocessing: Preprocess the sensor data, including denoising, data smoothing, and anomaly detection.

[0060] Preliminary decision-making: For some simple risk situations, edge devices can make preliminary decisions directly (such as triggering an alarm) without waiting for the cloud to process the results.

[0061] Gradual warning: When the risk exceeds a certain threshold, the edge device will generate warning information and immediately transmit it to the cloud or relevant management personnel

[0062] 2. Data Collection and Transmission

[0063] Real-time data collection

[0064] Flame sensor data: The flame sensor captures the light intensity and wavelength information of the flame and detects the flame spectrum characteristics. Multiple sets of data are collected every second to ensure real-time performance.

[0065] Height sensor data: The height sensor monitors the real-time height of the operator and calculates the height change amplitude in combination with the accelerometer inside the sensor. Through frequent data sampling (multiple times per second), every tiny height fluctuation of the operator can be captured.

[0066] Data transmission and communication architecture

[0067] Wireless communication network: Use LoRa, Wi-Fi Mesh or 5G network to transmit the data of the helmet to the edge computing device in real time. LoRa network is suitable for low-power communication in a large range, while Wi-Fi Mesh is suitable for high-bandwidth transmission in local areas.

[0068] Data encryption and compression: Data is encrypted during transmission to ensure data security, and compression algorithms are used to reduce bandwidth usage.

[0069] 3. Real-time monitoring and processing

[0070] Data preprocessing and anomaly detection

[0071] The edge computing device performs preliminary processing on the received sensor data:

[0072] Data cleaning: Use sliding window method or moving average method to remove noise and smooth the data of flame sensor and height sensor. Through data denoising, ensure that the system will not generate false alarms due to short-term environmental changes.

[0073] Anomaly detection: Real-time monitoring of sensor data to see if it exceeds a preset threshold. For example, if the flame intensity exceeds a certain threshold (such as 0.7), or the height changes by more than 2 meters, the system will immediately mark it as an abnormal situation.

[0074] Real-time monitoring algorithm:

[0075] Data cleaning and preprocessing algorithms

[0076] Moving average or exponential smoothing algorithms smooth sensor data in real time, filtering out instantaneous noise and outliers.

[0077] Anomaly detection: Use Z-score or sliding window method to monitor the changes in sensor data. If the flame or height data changes sharply within a certain period of time, an abnormal mark is triggered.

[0078] Formula (moving average method):

[0079]

[0080] Among them, S t is the smoothed data at time t, X t-i is the flame detection data of the past n moments. 2.2 Flame spectrum anomaly detection algorithm

[0081] The flame sensor detects spectral characteristics to monitor the presence and intensity of flames in real time. The system compares the flame sensor data with the flame spectrum library to determine whether the spectrum meets specific flame characteristics (such as common acetylene flames, methane flames, etc.).

[0082] Threshold-based flame detection algorithm:

[0083] After the spectral data is compared, if the flame spectrum characteristic value exceeds the preset threshold (such as flame intensity > 0.7), the system will consider that there may be a flame.

[0084] Spectral detection formula:

[0085]

[0086] The flame spectrum model is as follows:

[0087] Flame Spectrum Library

[0088] The flame spectrum model stores the spectral characteristics of different fuel flames (such as acetylene, propane, natural gas, etc.). Each flame has a specific wavelength and light intensity distribution, and the system matches the corresponding flame type based on the spectral characteristics detected by the sensor.

[0089] Flame spectrum characteristic values:

[0090] The peak wavelength range, peak intensity and wavelength energy distribution of different types of flames. The system determines the flame type by comparing with these characteristic values.

[0091] Flame detection model

[0092] The KNN classification algorithm or decision tree model is used to determine whether it is a dangerous flame based on the spectral data collected by the sensor. For example, the typical wavelength of acetylene flame is 590nm, and the typical wavelength of methane flame is 810nm. The system uses the KNN algorithm to compare and classify the detected spectral data with the features in the flame spectrum library.

[0093] KNN formula:

[0094]

[0095] Among them, x is the real-time spectral data of the sensor, and y is a flame feature vector in the flame spectrum library.

[0096] When the spectral matching degree exceeds a certain threshold, the system considers that there is a fire risk in the area and issues a warning signal.

[0097] Height change detection algorithm: Using sliding window and threshold detection methods, the system monitors the height changes of operators in real time. When a sharp drop in height is detected (for example, a drop of more than 2 meters), the system considers that the operator may have fallen and immediately triggers an alarm.

[0098] Height sensor anomaly detection algorithm

[0099] Height sensors are used to monitor the real-time height changes of operators, and the risk of falling is determined based on speed and acceleration.

[0100] Sliding window method: If the height change exceeds a certain safety threshold (such as a drop of more than 2 meters) within a certain time window (such as within 1 second), the system considers that a fall may have occurred.

[0101] Fall detection formula:

[0102]

[0103] in, is time t 0 The height at is time t 1 The height at that time.

[0104] Multi-dimensional abnormal monitoring

[0105] The system will also comprehensively analyze multiple sensor data such as flame, temperature, and air pressure, and further accurately analyze abnormal situations in combination with environmental sensors (such as gas sensors, temperature sensors, etc.). For example, when a flame is detected and the operator is working at a high altitude, the system will mark the event as a serious risk.

[0106] 4. Multi-sensor fusion analysis

[0107] Sensor Data Fusion

[0108] Adopt multi-sensor fusion algorithm, combine the data of flame sensor and height sensor, improve the accuracy of system judgment and reduce false alarms:

[0109] Weighted average fusion: Flame intensity, height change, and environmental data are weighted to generate a comprehensive risk score.

[0110] Bayesian reasoning fusion: Through Bayesian reasoning, the abnormal detection results of different sensors are jointly reasoned to calculate the probability of fire or fall at the work site.

[0111] Formula example:

[0112] Bayesian inference formula:

[0113]

[0114] Where P(Fire|Data) is the probability of fire given the sensor data, and P(Data|Fire) is the likelihood of sensor data under fire conditions.

[0115] The data fusion strategy is as follows:

[0116] Multi-sensor fusion methods such as weighted averaging or Bayesian reasoning are used to combine flame detection data with height sensor data to improve judgment accuracy and reduce false alarms. For example, when the flame sensor detects a flame, but the height sensor data shows that the operator's position has changed little, it may be a false alarm; if the height also changes abnormally, the system considers the risk to be high.

[0117] Bayesian reasoning fusion algorithm

[0118] The results of flame detection and height detection are jointly calculated through Bayesian reasoning:

[0119]

[0120] Where P(Fire|Data) is the probability of a fire given the data, and P(Data|Fire) is the known number of fire hours.

[0121] According to the probability, P(Fire) is the prior probability of fire.

[0122] V. Risk Assessment and Decision-making

[0123] The risk assessment model system uses a random forest or decision tree model to dynamically assess the risk of each operator based on the data from the flame sensor and height sensor. The model is trained with historical data to enable it to adapt to changes in various working conditions.

[0124] Risk index formula: Risk_Index=w 1 ×Fire_Intensity+w 2 ×Height_Change+w 3 ×Environmental_Factors, where Fire_Intensity is the flame intensity, Height_Change is the height change, and Environmental_Factors are environmental factors (such as gas concentration and temperature).

[0125] The risk assessment model is as follows:

[0126] Risk Assessment Logic

[0127] The system uses a random forest or decision tree model to comprehensively analyze multiple sensor data and calculate the current risk index of the operator. The risk index is weighted based on the intensity and height change of the flame sensor and other sensor data in the operating environment (such as temperature, gas concentration, etc.).

[0128] Risk assessment formula:

[0129] Risk_Index=w 1 ×Fire_Intensity+w 2 ×Height_Change+w 3 ×Environment_Factor where w 1 ,w 2 ,w 3 is the weight of each feature, which is determined by training data.

[0130] Risk assessment model training

[0131] The model is trained with a large amount of historical operation data to determine the weights of different features and risk thresholds. Supervised learning methods (such as decision trees and random forests) are used to classify various types of sensor data and predict the possibility of high, medium, and low risks. Random forest formula

[0132]

[0133] Model testing and validation

[0134] The model is tested using historical data sets and real-time data in simulated operating environments to verify its accuracy and robustness in different scenarios, and is continuously optimized by adjusting model parameters (such as thresholds and feature weights).

[0135] Decision making and feedback

[0136] Gradual warning: When the risk index exceeds the set threshold, the system sends a warning signal to the operator through the alarm and vibration device, prompting him to avoid danger.

[0137] Automatic response: For highly dangerous situations, such as falling from height or a sharp increase in flame intensity, the system can automatically cut off power or hot work equipment and send an emergency notification to the security management platform.

[0138] VI. Emergency Response and Handling

[0139] On-site warning

[0140] When the system determines that there is a risk of fire or falling, the operator's helmet will emit an audible and visual alarm or vibrate to remind the operator to respond quickly and avoid potential danger.

[0141] Background monitoring and remote assistance

[0142] The system will monitor the status of operators in real time, send alarm information to managers and emergency response teams through the cloud platform, and provide key data such as the real-time location, height, flame intensity, etc. Combined with the video surveillance system, managers can remotely confirm the on-site situation and command evacuation.

[0143] Accident handling and analysis

[0144] After each incident, the system will record the complete sensor data and processing process. After an accident occurs, the system will automatically generate a report to analyze the cause of the accident, optimize the risk assessment model and early warning strategy.

[0145] 7. Model Optimization and System Iteration

[0146] Data Feedback and Model Optimization

[0147] The system will continuously collect data during the operation process, and through historical data and feedback mechanisms, continuously optimize the risk assessment model and sensor threshold settings to improve the system's prediction accuracy.

[0148] System expansion and upgrade

[0149] In the future, more sensors (such as temperature and gas detection sensors) can be introduced to further enhance the system's comprehensive monitoring capabilities. At the same time, AI and machine learning algorithms can be used to analyze more environmental factors and provide the system with more powerful prediction capabilities.

[0150] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Intelligent high-altitude and hot work safety management system based on risk assessment, characterized by: include: A plurality of intelligent helmets, each of which is integrated with a flame sensor and a height sensor, a wireless communication module, an alarm device and a power management module, and is used to monitor the environment and working status of the operator in real time; An edge computing device, connected to the helmet, performs data preprocessing, preliminary decision making and data upload to a cloud platform; Cloud computing platform for storing, analyzing and visualizing sensor data to provide risk assessment and decision support.

2. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The flame sensor adopts spectrum analysis technology, and can detect the characteristic spectrum of the flame and judge the flame type and intensity.

3. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The height sensor uses laser or ultrasonic distance measurement technology to monitor the height changes of the operator in real time.

4. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The edge computing device includes a data acquisition module, a data processing module and an alarm module, which are responsible for receiving and processing data and triggering alarms.

5. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The cloud computing platform uses machine learning algorithms to analyze historical data to optimize risk assessment models and provide safety recommendations to operators.

6. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 5 is characterized in that: The risk assessment model generates a risk index through multi-sensor data fusion calculation and divides the risk levels into high, medium and low.

7. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The smart helmet can automatically trigger preset emergency response measures when detecting flames or height abnormalities, including sound and light alarms and sending alarm signals to management personnel.

8. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The cloud computing platform can generate event reports, recording all sensor data, abnormal events and emergency response processes.

9. The intelligent high-altitude and hot work safety management system based on risk assessment according to claim 1 is characterized in that: The wireless communication module supports multiple communication protocols, including LoRa, Wi-Fi and 5G, to meet the communication needs in different environments.

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