Fire risk dynamic assessment and early warning method fused with deep learning

Abstract

The invention provides a fire risk dynamic assessment and early warning method fused with a deep learning algorithm. According to the method, fire related data is collected in real time from various sources such as a sensor, data cleaning, standardization and feature extraction are carried out, and high-quality input data is generated. And based on the convolution and recurrent neural network, constructing a fire risk prediction model, and performing model training and verification by analyzing historical fire data. The system can monitor building and environment data in real time, assess fire risks and generate corresponding early warning information. And the early warning decision module compares model output with a set threshold value, and once the risk exceeds the threshold value, the system automatically pushes early warning information, so that managers and fire departments can take measures in time. The system also provides a user interface and a feedback mechanism to help a user to monitor a risk assessment result in real time and continuously optimize system performance. The method has the advantages of accurate data analysis and rapid early warning response, and the possibility of fire occurrence is reduced.

Description

Technical Field

[0001] The present invention relates to the field of fire safety technology, and in particular to a method for dynamic assessment and early warning of fire risks that integrates deep learning, big data analysis, and artificial intelligence algorithms. Specifically, the present invention utilizes advanced deep learning models to mine and analyze massive fire data, and combines multi-dimensional real-time data such as building structure information, historical fire records, fire hazard data, equipment status monitoring data, and environmental dynamic information to form a dynamic and intelligent assessment system for fire risk. The system uses an adaptive risk assessment model to quantitatively rank the fire hazards of different buildings and places, and timely adjusts the assessment results according to changes in different environments, providing efficient and accurate fire warning services. By integrating deep learning technology with multi-source data collection, the system can achieve accurate prediction and graded early warning of fire risks, greatly improving the risk management and control capabilities of the fire department under limited human resources, reducing the probability of fire, and promoting the fire protection field to move towards intelligence and automation. Background Art

[0002] With the rapid development of urbanization and the increase in the number of buildings, fire risks are becoming increasingly prominent, especially in high-density cities and industrial areas. Fire hazards are difficult to detect in a timely manner through traditional manual inspections, leading to frequent accidents.

[0003] Traditional fire prevention and control measures mainly rely on regular manual inspections and hidden danger detections. However, due to limited human resources, the efficiency and accuracy of fire risk monitoring cannot be effectively guaranteed, and the rapidly changing fire risks cannot be responded to in a timely manner.

[0004] Currently, the fire protection field is in urgent need of a fire risk assessment system based on big data and artificial intelligence, so as to achieve rapid identification and response to fire risks through comprehensive data analysis and processing.

[0005] Although there are fire risk prediction methods based on statistical analysis, these methods often ignore the comprehensive use of multi-source data and cannot achieve dynamic assessment of fire risks in complex environments, resulting in insufficient accuracy and real-time performance of prediction results.

[0006] Deep learning technology has significant advantages in large-scale data processing and pattern recognition, and can explore complex relationships hidden in the data, but its application in fire risk monitoring and early warning has not been fully researched and developed.

[0007] Therefore, combining deep learning algorithms with multi-dimensional data sources (such as building structures, historical fire records, environmental factors, etc.) to establish an efficient dynamic fire risk assessment and early warning method will help to significantly improve the accuracy and timeliness of fire prevention and control, and provide scientific decision-making support for fire departments. Summary of the invention

[0008] The present invention provides a fire risk dynamic assessment and early warning method integrating deep learning, aiming to improve the accuracy and real-time performance of fire risk assessment in a data-driven manner, thereby effectively reducing the incidence of fire accidents.

[0009] The present application provides a fire monitoring and early warning method, which adopts the following technical solutions: Collect fire-related data in real time from multiple data sources including buildings, firefighting facilities and environmental monitoring; Clean and standardize the collected raw data to ensure data quality and consistency; Extract key features related to fire risk and generate feature vectors for model training; Establish a fire risk prediction model based on deep learning model, using structures such as convolutional neural network and recurrent neural network; Use historical fire data to train and cross-validate the model to optimize model performance; Real-time risk assessment and warning: Apply the trained model to real-time data streams to achieve dynamic fire risk assessment and warning; analyze influencing factors, determine the corresponding fire warning type and formulate response measures.

[0010] The data collection part of the present invention determines the fire risk and issues an early warning through real-time monitoring and evaluation of multiple key parameters.

[0011] Among them, fire risk indicators such as ambient temperature, humidity, smoke concentration, and storage of combustible materials are monitored in real time. Thresholds for key parameters are set based on historical data, such as when the temperature exceeds a certain value or when the smoke concentration reaches a predetermined level. Key parameter data is continuously collected through sensors and monitoring equipment to ensure the real-time and accuracy of the data. Real-time data is analyzed using a deep learning model and compared with the set threshold to determine the fire risk level. When the monitored parameters exceed the threshold, the system automatically triggers an early warning to notify relevant managers and firefighters.

[0012] The present application provides a fire monitoring and early warning system, which adopts the following technical solution: a fire monitoring and early warning system, the system comprising: Data acquisition module, which is responsible for acquiring fire risk related data from sensors, monitoring equipment and related systems in real time; The data processing module cleans, preprocesses and extracts features from these data to ensure data quality; The deep learning model module uses advanced algorithms to analyze and judge the fire risk level and outputs the corresponding risk assessment results; the warning decision module compares the model output with the preset threshold and decides whether to trigger the warning based on the judgment result. Once the fire risk exceeds the threshold, the warning push module will automatically generate warning information and push it to relevant managers and fire departments in real time via SMS, email, etc. The user interface module provided by the system allows users to monitor fire risk assessment results and manage warning information in real time to facilitate emergency response; The feedback mechanism module collects user feedback, continuously optimizes system performance, and improves warning accuracy and response efficiency. Overall, the system integrates real-time monitoring, data analysis, risk assessment, warning push and user management functions, providing strong support for the effective monitoring and prevention of fire risks.

[0013] Through this system, all aspects of fire risk warning are seamlessly connected, and the data collection, processing, analysis, warning and feedback mechanism form a complete closed loop. First of all, the system relies on a variety of sensors and monitoring equipment to obtain key fire-related parameters from multiple data sources in real time to ensure the timeliness and comprehensiveness of the data. By cleaning and extracting features from these data, the system can effectively improve data quality and provide a solid foundation for subsequent model analysis.

[0014] During the fire risk analysis phase, the system processes real-time data through deep learning algorithms and can dynamically predict fire risks. Compared with traditional static analysis methods, deep learning models have the ability to self-learn and optimize, making risk assessment more accurate. The early warning decision module accurately identifies high-risk situations by comparing the prediction results with the preset thresholds, and quickly generates early warnings when the risks exceed the thresholds, and pushes them to relevant responsible persons and management departments in a timely manner.

[0015] In addition, the user interface module enables users to view risk assessment results in real time, understand the fire risk status of buildings or areas, and help deploy fire prevention measures in advance. The system's feedback mechanism ensures that users' opinions and suggestions can continuously optimize the model, further improving the system's early warning accuracy and response efficiency.

[0016] This application provides a deep learning algorithm process, using the following technical solutions: Acquire data from multiple sources and perform cleaning, normalization, and feature extraction to prepare high-quality data for model input; Divide the data into training set, validation set and test set to ensure the generalization ability of the model on different data; Select a deep learning architecture based on the task, build the model and perform forward propagation to calculate the predicted value; Calculate the error through the loss function, use the back-propagation algorithm to calculate the gradient and update the model weights; Repeated training to optimize model parameters and use the validation set to evaluate the model to prevent overfitting; Improve model performance by tuning hyperparameters and use the test set for final evaluation; When the model performance reaches the expected level, it is deployed to the actual environment for real-time prediction.

[0017] The process of deep learning begins with data collection and preprocessing. The system obtains raw data from a variety of sources and ensures that the input data is of high quality and suitable for model training through cleaning, normalization, and feature extraction. Then, the data is divided into training sets, validation sets, and test sets to ensure that the model can be generalized and evaluated on different data sets.

[0018] In the model building stage, the appropriate deep learning architecture is selected according to the specific task, and the forward propagation algorithm is used to predict through a multi-layer neural network to calculate the output result. The error between the predicted value and the true value is calculated through the loss function, and then the back propagation algorithm adjusts the model parameters (weights and biases) according to the error, thereby continuously optimizing the model performance.

[0019] During the entire model training process, the validation set is used to evaluate the performance of the model to ensure that it is not overfitting or underfitting. Hyperparameter tuning further optimizes the model performance by adjusting parameters such as learning rate and network depth. Finally, the final evaluation of the model is performed on the test set to ensure its reliability in practical applications. Finally, the verified model is deployed in the actual environment and can process new input data in real time and make predictions.

[0020] This paper proposes a fire risk dynamic assessment and early warning method that integrates deep learning. Through multi-source data collection and cleaning, a comprehensive fire data cloud is established, and a deep learning algorithm is used to analyze real-time data to dynamically assess fire risks and generate early warning information. The system uses loss functions and back propagation to optimize model parameters to ensure high-precision predictions, and improves performance through hyperparameter tuning and model verification. Ultimately, the evaluated model can monitor and predict fire risks in real time, significantly improving the efficiency and accuracy of fire prevention work. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments; Figure 1 It is a flow chart of the fire monitoring and early warning method of the present invention; Figure 2 It is a flow chart of the fire monitoring and early warning system of the present invention; Figure 3This is a flow chart of the deep learning algorithm for the fire monitoring and early warning method of the present invention. DETAILED DESCRIPTION

[0022] The fire risk dynamic assessment and early warning method proposed in this invention aims to achieve real-time monitoring and prediction of fire risk through deep learning technology. This method combines multi-source data to establish a comprehensive fire risk assessment model, aiming to improve the accuracy and timeliness of fire early warning, thereby effectively reducing the incidence of fire accidents.

[0023] Reference Figure 1 , fire monitoring and early warning methods include: S101, real-time acquisition of fire-related information from multiple data sources such as buildings, firefighting facilities, and environmental monitoring; The real-time acquisition of multiple data sources from buildings such as sensor monitoring data, fire protection facilities such as smoke detectors and fire extinguisher status, and environmental monitoring such as temperature, humidity, and wind speed, forms comprehensive fire monitoring information; S102, clean and standardize the collected raw data to improve its quality and consistency; The collected raw data is cleaned, such as deduplication and outlier detection, and standardized, such as normalization, to ensure the quality and consistency of the data, laying the foundation for subsequent analysis; S103, identifying and extracting important features related to fire risk to form a feature vector for model training; Among them, algorithms (such as principal component analysis and feature selection techniques) are used to identify and extract important features related to fire risk, such as temperature change rate, smoke concentration, personnel flow, etc., to generate feature vectors for model training; S104, using deep learning algorithms such as convolutional neural networks and recurrent neural networks to establish a fire risk prediction model; Among them, deep learning algorithms such as convolutional neural networks (CNN) are used to process image data such as fire source identification in surveillance videos and recurrent neural networks (RNN) are used to analyze time series data to establish fire risk prediction models; S105, train the model with historical fire data and perform cross-validation to improve the accuracy and robustness of the model; The model is trained using a historical fire dataset, the model performance is evaluated using a cross-validation method, and an optimization algorithm (such as Adam or SGD) is used to adjust the hyperparameters to improve the accuracy and robustness of the model. S106, applying the trained model to perform dynamic fire risk assessment and early warning on the real-time data stream; The trained model is applied to real-time data streams, using a threshold judgment mechanism to dynamically assess fire risks and generate early warning information such as alarm notifications and emergency response recommendations; S107, assess risk factors, determine the appropriate fire warning type and develop corresponding response strategies; The main factors affecting fire risks are assessed, such as building materials and the status of fire-fighting facilities. Based on the assessment results, the corresponding fire warning types are determined, such as high-risk warnings and low-risk monitoring, and specific response strategies are formulated, such as increasing inspection frequencies and drills.

[0024] In the implementation of the fire risk dynamic assessment and early warning system, first of all, key information is obtained from multiple data sources through real-time data collection. These data sources include various sensors in the building, status monitoring of fire protection facilities and environmental monitoring data. This comprehensive data integration can provide more accurate and comprehensive information for fire monitoring, helping relevant departments to understand fire risks in a timely manner.

[0025] Secondly, in order to ensure the quality and consistency of the data, the collected raw data must be cleaned and standardized. This process includes deduplication and outlier detection to eliminate possible noise data. At the same time, data normalization can ensure that data from different sources are analyzed under the same standard, which lays a solid foundation for subsequent feature extraction and model training.

[0026] When building a fire risk prediction model, the system uses advanced deep learning algorithms, such as convolutional neural networks (CNN) and recurrent neural networks (RNN). These algorithms can efficiently process different types of data, including image data and time series data. Through training with historical fire data, the model can be gradually optimized to improve the accuracy and reliability of predictions, thereby providing a scientific basis for fire warnings.

[0027] Convolutional neural networks (CNNs) are mainly used to process image data in fire risk dynamic assessment and early warning systems, such as surveillance videos, building floor plans, and fire scene images. CNNs can automatically extract image features through their unique convolutional layer structure and identify potential fire sources, smoke and other abnormal conditions. The high efficiency of this feature extraction enables the system to quickly locate fire risks in complex visual information, thereby achieving real-time monitoring and early warning, significantly improving the speed and accuracy of fire response.

[0028] On the other hand, recurrent neural networks (RNNs) are mainly used to process time series data, such as changes in temperature, humidity, and smoke concentration from environmental sensors. These data are usually time-dependent, and RNNs can capture the temporal relationships and dynamic features in the data through their cyclic structure. This capability enables the system to analyze the trend of fire risk changes over time, thereby achieving more accurate dynamic risk assessment and helping to predict the possibility of fire.

[0029] Combining the advantages of CNN and RNN, the fire risk dynamic assessment and early warning system can effectively integrate image data and time series data to form a comprehensive fire monitoring solution. This fusion not only improves the system's prediction ability, but also enables it to conduct targeted analysis based on the characteristics of different data types, providing a more scientific and reliable decision-making basis for fire risk management.

[0030] By introducing these two deep learning neural networks, the system can better cope with complex fire risk environments and improve the ability to predict and respond to sudden fires, thus providing strong support for protecting the safety of people and property.

[0031] Once the model training is completed, the system can analyze new data streams in real time to conduct dynamic fire risk assessment and early warning. Using the threshold judgment mechanism, the system can promptly identify potential fire risks and generate early warning information. This mechanism not only improves the efficiency of monitoring, but also significantly reduces the risk of fire.

[0032] Finally, by analyzing the factors affecting fire risk, the system can formulate corresponding response strategies for different risk types. This not only improves the accuracy of fire prevention and control work, but also enables rapid response at critical moments to ensure the safety of personnel and property. Such a systematic approach provides new ideas for fire risk management and helps build a safer environment.

[0033] Reference Figure 2 , the fire monitoring and early warning system includes: S201, the data acquisition module is responsible for obtaining information related to fire risks from sensors and monitoring equipment in real time; The data acquisition module includes the following functions: Sensor data collection: Real-time acquisition of environmental parameters such as temperature, humidity, and smoke concentration, and monitoring of fire risks through various sensors (such as temperature sensors and smoke detectors); Video surveillance integration: Acquire image and video data from surveillance cameras and identify abnormal conditions such as fire or smoke through image recognition technology; System data integration: Integrate data from fire protection systems, building management systems and other related systems to ensure comprehensive monitoring of all potential fire risk sources.

[0034] S202, the data processing module cleans, preprocesses and extracts features from the collected data to ensure high data quality; The data processing module includes the following functions: Data cleaning: remove duplicate, erroneous and invalid data to ensure data accuracy and reliability; Data preprocessing includes: Standardization: Standardize data from different sources so that they can be compared on the same scale; Normalization: ensure that data of different dimensions can be processed uniformly to improve model training results; Feature extraction includes: Key feature identification: Extract features related to fire risk, such as temperature change rate, humidity fluctuation, etc. Feature vector generation: Convert the extracted features into a vector form suitable for model input to prepare for subsequent deep learning model training.

[0035] S203, the deep learning model module uses advanced algorithms to evaluate the fire risk level and outputs the risk assessment results; wherein the deep learning model module includes the following functions: Model selection and construction include: Convolutional Neural Network (CNN): used to process image data and automatically extract visual features related to fire risk; Recurrent Neural Network (RNN): Process time series data and analyze the changing trends of environmental parameters over time; Model training includes: Historical data training: Use historical fire data to train the model and identify potential patterns of fire occurrence; Cross-validation: Use cross-validation technology to ensure the generalization ability and stability of the model; Risk assessment: The model outputs the fire risk level and provides real-time assessment results to facilitate subsequent decision-making.

[0036] S204, the warning decision module compares these evaluation results with the preset threshold to decide whether to trigger a warning. Once the risk exceeds the threshold, the warning push module will automatically generate a notification and quickly send it to relevant managers and fire departments via SMS or email; The early warning decision module includes the following functions: Threshold setting: Set the warning threshold of fire risk based on historical data and expert experience; Risk judgment: Compare the risk assessment results output by the model with the preset threshold to determine whether to trigger an early warning; Early warning information generation: After determining that the risk exceeds the threshold, early warning information is automatically generated to ensure the timeliness and accuracy of the information.

[0037] S205, the user interface module of the system enables users to monitor risk assessment results and manage warning information in real time to facilitate emergency response; The warning push module includes the following functions: Real-time monitoring interface: Provides a user-friendly interface that allows users to view fire risk assessment results and historical data in real time; Early warning management function: Users can manage and view all early warning information to ensure continuous attention to fire risks; Interactive function: Allows users to adjust threshold settings and parameters as needed, providing flexible system management options.

[0038] S206, the feedback mechanism module collects user opinions, continuously optimizes the system's performance, and improves the accuracy of warnings and response efficiency. Overall, the system design integrates multiple functions such as real-time monitoring, data analysis, risk assessment, warning push and user management, providing comprehensive support for effective monitoring and prevention of fire risks; The feedback mechanism module includes the following functions: User feedback collection: Regularly collect user feedback on system functions and performance to understand problems and needs in actual use; System optimization: Based on user feedback, the system is continuously optimized and upgraded to improve the accuracy of early warning and response efficiency; Performance evaluation: Evaluate the system’s predictive effectiveness and make adjustments and improvements based on historical events and user feedback.

[0039] Reference Figure 3 , the fire monitoring and early warning method includes the following deep learning algorithm processes: S301, data collection and preprocessing: Obtain fire-related data from various data sources, clean and normalize them, and extract key features to ensure high-quality data for model input; S302, data set division: the preprocessed data is divided into a training set, a validation set, and a test set to ensure that the model has good generalization ability and can adapt to changes in different data sets; S303, model building and forward propagation: select the appropriate deep learning architecture according to the specific task requirements, build the model and perform forward propagation to calculate the prediction results; S304, Error evaluation and back propagation: Use the loss function to evaluate the error between the prediction result and the true label, calculate the gradient through the back propagation algorithm and update the model weight; S305, model training and validation: repeatedly perform training, optimize model parameters, and use validation sets to monitor model performance to prevent overfitting; S306, Hyperparameter tuning and performance evaluation: Further improve model performance by adjusting hyperparameters, and use the test set for final evaluation to verify the accuracy and robustness of the model; S307, model deployment and real-time application: When the model performance reaches the expected standard, it will be deployed in the actual environment to achieve real-time fire risk prediction and assessment.

[0040] The fire risk dynamic assessment and early warning method of this application can monitor and analyze fire risk data in real time by integrating deep learning technology, thereby providing more accurate fire prediction and early warning information. This system not only improves the efficiency of fire risk assessment, but also responds to potential fire threats in a timely manner, effectively reduces the incidence of fire, enhances the scientificity and foresight of fire safety management, and provides strong technical support for fire prevention and emergency response.

Claims

1. A fire monitoring and early warning method integrating deep learning includes: S101, collects fire-related information from multiple data sources such as buildings, firefighting equipment, and environmental monitoring systems in real time; S102, cleaning and standardizing the collected raw data to ensure the quality and consistency of the data; S103, analyzing and extracting key features that are highly correlated with fire risk, and generating feature vectors for model training; S104, using deep learning algorithms such as convolutional neural network (CNN) and recurrent neural network (RNN) to build a fire risk prediction model; S105, training and cross-validating the model based on historical fire data to further optimize its accuracy and stability; S106, applying the trained model to real-time data streams to conduct dynamic assessment and early warning of fire risks; S107, analyze and evaluate risk factors, determine the type of fire warning, and develop corresponding emergency response plans.

2. According to claim 1, by analyzing the influencing factors of fire risk, the system can formulate corresponding response strategies for different risk types, which not only improves the accuracy of fire prevention and control work, but also can respond quickly at critical moments to ensure the safety of personnel and property. Such a systematic approach provides new ideas for fire risk management and helps to build a safer environment.

3. A fire monitoring and early warning system integrating deep learning includes: S201, the data acquisition module is responsible for acquiring multi-source data related to fire risks from sensors and monitoring equipment in real time; S202, the data processing module cleans, preprocesses and extracts features from the collected data to ensure data quality and consistency; S203, the deep learning model module analyzes the fire risk through advanced algorithms and outputs corresponding risk assessment results; S204, the warning decision module compares the risk assessment result with the set threshold value to decide whether to trigger the warning. Once the threshold value is exceeded, the warning push module will immediately generate a notification and send it to relevant personnel and the fire department via SMS or email; S205, the user interface module allows users to view fire risk assessment and warning information in real time to support emergency management and response; S206, the feedback mechanism module is responsible for collecting user feedback, continuously optimizing system functions, and further improving warning accuracy and response efficiency.

4. The design of the system according to claim 2 integrates multiple functions such as real-time monitoring, data analysis, risk assessment, early warning push and user management, providing comprehensive support for effective monitoring and prevention of fire risks.

5. A fire monitoring and early warning method integrating deep learning The deep learning algorithm process includes: S301, firstly, obtain fire-related data from multiple data sources, clean and normalize the data, and extract key features to ensure the high quality of model input data; S302, then divide the processed data into a training set, a validation set, and a test set to ensure that the model can achieve good generalization ability on different data sets; S303, selecting a suitable deep learning architecture according to task requirements, building a model and performing forward propagation, and calculating a predicted value; S304, using the loss function to evaluate the error between the model prediction result and the actual value, calculating the gradient through the back propagation algorithm and updating the model weight; S305, continue training to optimize model parameters, and monitor the performance of the model through the validation set to prevent the model from overfitting; S306, further improving the model performance by adjusting the hyperparameters, and evaluating the final model using the test set to verify its accuracy and robustness; S307, after the model training is completed, it is deployed to the actual application environment to achieve real-time fire risk prediction and dynamic assessment.

6. According to claim 3, the algorithm process ensures the high quality of model input data through multi-data source collection, cleaning and feature extraction, combines deep learning architecture for training, optimizes model performance, and prevents overfitting through validation sets, ensuring that the model has good generalization ability and prediction accuracy, and is finally deployed in practical applications. It can assess fire risks in real time and accurately predict potential fire hazards. This process effectively improves the accuracy and real-time performance of fire risk monitoring, can provide timely warnings before a fire occurs, and help relevant personnel take early response measures, thereby reducing the probability and losses of fire.

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