County fire early warning analysis system based on big data model training technology
By using drones and ground sensors in the county for fire risk monitoring and using big data models for in-depth analysis, the problem of traditional fire early warning systems in the county is solved, and more efficient and accurate fire early warning is achieved.
Patent Information
- Application Number
- CN202510129736.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-05
- Publication Date
- 2025-05-02
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional fire prevention monitoring systems have problems such as limited patrol range and low patrol efficiency in fire warnings within counties, and the complex terrain, artificial dense distribution and construction distribution have increased the difficulty of fire warnings.
The county fire early warning and analysis system based on big data model training technology is adopted, and the high maneuverability and wide coverage capabilities of the drone are used, combined with fixed-point monitoring of ground sensors and cameras, to achieve comprehensive and real-time monitoring of fire risks in the county. Through the big data model, the collected data is analyzed in-depth to improve the accuracy and timeliness of fire warnings.
Comprehensive and real-time monitoring of fire risks in counties has been achieved, the accuracy and timeliness of fire warnings have been improved, and the number and flight frequency of drones can be adjusted dynamically according to the characteristics of the county, which has improved the warning effect.
Smart Images
Figure CN119920055A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of fire warning technology, and in particular to a county fire warning analysis system based on big data model training technology. Background Art
[0002] Forest fire is one of the eight major natural disasters in the world, with the characteristics of wide occurrence, strong suddenness, great destructiveness and high danger. The traditional fire prevention monitoring system mainly relies on manual ground patrol and high-point patrol from watchtowers, which has problems such as limited patrol range and low patrol efficiency. The prevention method of forest fire has not been applied to other types of fires in the county.
[0003] However, fire warning based on the county level is more difficult due to the more complex terrain, densely populated areas, and construction distribution compared to forest fires. Often, there is an overuse of resources to achieve a good warning effect. Summary of the invention
[0004] In response to the deficiencies in the prior art, the present invention provides a county fire warning analysis system based on big data model training technology, which utilizes the high maneuverability and wide coverage capability of drones, combined with fixed-point monitoring by ground sensors and cameras, to achieve comprehensive and real-time monitoring of fire risks in the county, and conducts in-depth analysis of the collected data through big data models to improve the accuracy and timeliness of fire warnings.
[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A county fire warning analysis system based on big data model training technology includes: A drone module is used to perform flight inspection missions within the county and collect video and image data related to fire risks; Ground sensors and camera modules are set up at fixed locations within the county to monitor fire risks at fixed points and work in conjunction with the drone modules; A data processing center for receiving and processing data from drone modules and ground sensors and camera modules; The big data model training and analysis module builds and trains fire warning models based on historical fire data and real-time monitoring data, and conducts real-time analysis and warning of fire risks within the county.
[0006] Preferably, the drone module comprises: UAV fleets dynamically adjust the number of drones based on county area, terrain complexity, population density, and fire risk level; The flight area setting function divides the county into multiple grid areas and focuses on monitoring high-risk areas; The flight frequency setting function, including daily patrol frequency and emergency response mechanism, ensures a rapid response when a fire occurs.
[0007] Preferably, the ground sensor and camera module includes: Various types of sensors to monitor temperature, smoke concentration and fire-related parameters; High-definition cameras to capture images and videos of the fire scene; Data transmission device, used to transmit monitoring data to the data processing center in real time.
[0008] Preferably, the data processing center includes: A data receiving module, used for receiving data from the drone module and the ground sensor and camera module; Data fusion module, used to fuse multi-source data to form more complete and accurate fire warning information; Data storage module, used to store historical fire data and real-time monitoring data.
[0009] Preferably, the big data model training and analysis module includes: Model building submodule, which builds a fire warning model based on historical fire data and real-time monitoring data; Model training submodule, which uses large-scale data sets to train and optimize the model; The real-time analysis submodule inputs the real-time monitoring data into the trained model to perform real-time fire warning analysis.
[0010] Preferably, the model building submodule further includes: Data collection and preprocessing steps, used to collect and preprocess historical fire data and real-time monitoring data; Feature extraction step, extracting features useful for fire warning from the original data; Model selection and design steps: select the appropriate algorithm and design the model architecture based on data characteristics and business needs.
[0011] Preferably, the model training submodule further includes: Data partitioning step: divide the data set into training set, validation set and test set; Hyperparameter tuning step: Tune the model’s hyperparameters to find the optimal model configuration; Iterate the training steps, iterate the training process multiple times and evaluate the model performance.
[0012] Preferably, it also includes: The result display and response module is used to display the fire warning analysis results to the monitoring personnel through a visual interface and set the warning threshold. When the prediction result exceeds the threshold, the alarm system is automatically triggered to notify relevant personnel to respond quickly. Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a county fire warning analysis system based on big data model training technology, which utilizes the high maneuverability and wide coverage of drones, combined with fixed-point monitoring by ground sensors and cameras, to achieve comprehensive and real-time monitoring of fire risks in the county. The collected data is deeply analyzed through big data models to improve the accuracy and timeliness of fire warnings.
[0013] The present invention provides a method for configuring the number of drones, which can provide a reasonable basic number of drone formations based on factors such as county area, terrain complexity, and population density, thereby achieving efficient and reasonable inspections. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0015] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION
[0016] The present invention is described in further detail below with reference to the accompanying drawings.
[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be used for other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.
[0018] Example: See also Figure 1 The present invention provides a county fire warning analysis system based on big data model training technology, including a drone module, a ground sensor and camera module, a data processing center, a big data model training and analysis module, and a result display and response module.
[0019] A drone module is used to perform flight inspection missions within the county and collect video and image data related to fire risks; Ground sensors and camera modules are set up at fixed locations within the county to monitor fire risks at fixed points and work in conjunction with the drone modules; A data processing center for receiving and processing data from drone modules and ground sensors and camera modules; The big data model training and analysis module builds and trains fire warning models based on historical fire data and real-time monitoring data, and conducts real-time analysis and warning of fire risks within the county; The result display and response module is used to display the fire warning analysis results to the monitoring personnel through a visual interface and set the warning threshold. When the predicted result exceeds the threshold, the alarm system is automatically triggered to notify relevant personnel to respond quickly.
[0020] In the implementation of the present application, the drone module is composed of a drone formation. The number of drone formations is dynamically adjusted according to the county area, terrain complexity, population density and fire risk level. Generally, 3-5 drones are equipped to ensure sufficient redundancy in emergency situations. The drone formation dynamically adjusts the number of drones according to the actual monitoring effect and mission requirements. For example, the number of drones is increased during the high-incidence season of fires or special events.
[0021] The drone module has the functions of flight area setting and flight frequency setting. The flight area setting function divides the county into multiple grid areas, and each area is assigned one or two drones for regular inspections. The grid size is flexibly adjusted according to factors such as terrain and building density. High-risk fire areas such as industrial areas, forests, and residential areas are monitored, and the flight frequency and stay time of drones in these areas are increased. The flight frequency setting function includes daily inspection frequency and emergency response mechanism. The daily inspection frequency of drones is set, such as once or more times a day, and is adjusted appropriately according to seasons and weather conditions. After receiving the warning signal from the ground sensor or camera, the drone is immediately dispatched to the target area for rapid inspection, confirm the fire situation and continue to track, to ensure rapid response when a fire occurs.
[0022] In the implementation of the present application, the ground sensor and camera module includes various types of sensors, high-definition cameras, and data transmission devices. Various types of sensors are used to monitor temperature, smoke concentration, and fire-related parameters; high-definition cameras are used to capture images and videos of the fire scene; and data transmission devices are used to transmit monitoring data to a data processing center in real time.
[0023] In the implementation manner of the present application, the data processing center includes a data receiving module, a data fusion module, a data storage module, and the like.
[0024] The receiving module is used to receive data from the drone module and the ground sensor and camera module; the sensors and cameras carried by the drone module transmit data to the ground station or data center in real time, and are uploaded to the receiving module synchronously with the data from the ground sensors and cameras. The data fusion module uses the data fusion algorithm to fuse the multi-source data from the drone module, ground sensors and cameras to form more complete and accurate fire warning information. The data storage module is used to store historical fire data and real-time monitoring data.
[0025] In this application, the high-altitude perspective and wide coverage capability of the drone module are utilized to quickly discover blind spots or hidden fire sources that are difficult to cover by ground sensors and cameras, and rely on ground sensors and cameras to provide more stable and detailed monitoring data; the drone module is integrated with the ground sensor and camera module to work together to achieve complementary advantages, especially for better monitoring effects in fixed areas and specific scenarios.
[0026] In the implementation manner of the present application, the big data model training and analysis module includes: a model building sub-module, a model training sub-module, a real-time analysis sub-module, etc.
[0027] The model building submodule builds a fire warning model based on historical fire data and real-time monitoring data, and uses machine learning, deep learning and other algorithms to extract and identify fire characteristics.
[0028] The model training submodule uses large-scale data sets to train and optimize the model to improve the model's prediction accuracy and robustness.
[0029] The real-time analysis submodule inputs the real-time monitoring data into the trained model to perform real-time fire warning analysis. The analysis results are displayed to the monitoring personnel through a visual interface to facilitate rapid response.
[0030] Furthermore, the model building submodule includes: The data collection and preprocessing steps are used to collect and preprocess historical fire data and real-time monitoring data; the main source of historical fire data is fire event data from the past few years, including the time, location, type of fire source, weather conditions, environmental factors (such as wind speed, humidity), human factors, etc.
[0031] Feature extraction step: extracting features useful for fire warning from the original data, such as the changing trend and spatial distribution characteristics in time series data; Model selection and design steps: select appropriate algorithms and design model architecture based on data characteristics and business needs. For example, you can use algorithms such as random forest and gradient boosted tree (GBDT) for feature selection and classification prediction; or use convolutional neural networks (CNN) and recurrent neural networks (RNN) to process image and video data.
[0032] The data partitioning step divides the data set into training set, validation set and test set; the training set is used for model training, the validation set is used to adjust model parameters, and the test set is used to evaluate model performance.
[0033] In the hyperparameter tuning step, the model's hyperparameters are tuned to find the optimal model configuration using methods such as grid search, random search, or Bayesian optimization.
[0034] Iterate the training steps, iterate the training process multiple times and evaluate the model performance.
[0035] Furthermore, the real-time analysis submodule includes: The data access step is to establish a real-time data stream access mechanism to ensure that the real-time monitoring data from equipment such as drones and ground sensors can be transmitted to the system in real time.
[0036] Real-time processing step: pre-processing the real-time data, including data cleaning, feature extraction and other steps, to meet the model input requirements.
[0037] In the model reasoning step, the processed real-time data is input into the trained model for fire warning analysis. The model will output the probability of fire occurrence or prediction results.
[0038] It should be noted that the configuration method of the drone formation is 1. Determine the basic weights, which can be determined based on historical data, expert opinions, or preliminary analysis. Specifically, determine the county area ( ), the basic factors affecting the size of the UAV formation, weight ( ) is set to 0.2; determine the terrain complexity ( ): Complex terrain requires more drones to cover blind spots, weight ( ) is set to 0.3; determine the population density ( ): Densely populated areas require higher monitoring density, weight ( ) is set to 0.25; determine the fire risk level ( ): High-risk areas require more attention and resources, weight ( ) is set to 0.25.
[0039] 2. Data collection and scoring. The full score is set to 100. For each county or region, collect specific data on the above factors and score them: County area: Score is based on area size, with larger areas receiving higher scores.
[0040] Terrain complexity: Scored through GIS analysis or field surveys, with complex terrain (such as mountainous areas and areas with intersecting water bodies) scoring higher.
[0041] Population density: Scored based on population density distribution map, with high-density areas scoring higher.
[0042] Fire risk level: Based on historical fire data and expert assessment, high-risk areas score high.
[0043] 3. Calculate the comprehensive weight score. For each region, calculate the comprehensive weight score based on the basic weight and data score:
[0044] 4. The number of drones is configured. A basic range of drone numbers is set based on the county’s comprehensive weight score. For example, 3 drones are configured for scores between 0 and 5, 4 drones for scores between 5 and 7, and 5 or more for scores between 7 and 10.
[0045] In addition, based on the basic configuration, dynamic adjustments will be made according to factors such as seasons and special events. During the high-incidence fire season or special events, the number of drones will be increased by 20%-50% based on the basic configuration.
[0046] 5. Flight area and frequency settings Grid division: The county is divided into grids of different sizes based on factors such as terrain and building density. The grid size can be flexibly adjusted based on the comprehensive weight score, and the grid division in high-risk areas is finer.
[0047] Key area monitoring: Focus on monitoring areas with high comprehensive weight scores (such as industrial areas, forests, residential areas, etc.), and increase the frequency and duration of drone flights in these areas. The flight frequency can be set to multiple times a day or even real-time monitoring according to actual conditions.
[0048] Daily inspection and emergency response: Set the frequency of daily inspections by drones and respond quickly according to early warning signals. In emergency response, drones should be dispatched to high-risk areas to confirm and track fires.
[0049] In specific implementation, if the comprehensive weight score is 7.5 points, the basic configuration is 5 drones. If it is currently in the high-incidence season of fire, it will be dynamically adjusted to 6-7 drones, and the monitoring frequency and stay time of key areas will be strengthened.
[0050] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.
Claims
1. A county fire early warning analysis system based on big data model training technology, characterized by: include, A drone module is used to perform flight inspection missions within the county and collect video and image data related to fire risks; Ground sensors and camera modules are set up at fixed locations within the county to monitor fire risks at fixed points and work in conjunction with the drone modules; A data processing center for receiving and processing data from drone modules and ground sensors and camera modules; The big data model training and analysis module builds and trains fire warning models based on historical fire data and real-time monitoring data, and conducts real-time analysis and warning of fire risks within the county.
2. According to claim 1, a county fire early warning analysis system based on big data model training technology is characterized by: The drone module includes: UAV fleets dynamically adjust the number of drones based on county area, terrain complexity, population density, and fire risk levels; The flight area setting function divides the county into multiple grid areas and focuses on monitoring high-risk areas; The flight frequency setting function, including daily patrol frequency and emergency response mechanism, ensures a rapid response when a fire occurs.
3. According to claim 2, a county fire early warning analysis system based on big data model training technology is characterized by: Configuration method of the drone formation: Determine the basic weights, which can be determined based on historical data; Data collection and scoring, with a full score of 100 points, for each county or region, collect specific data on county area, terrain complexity, population density, and fire risk factors, and score them; Calculate the comprehensive weight score. For each region, calculate the comprehensive weight score based on the basic weight and data score; The number of drones is configured, and a basic range of drone numbers is set according to the county’s comprehensive weight score; Flight area and frequency settings.
4. According to claim 3, a county fire early warning analysis system based on big data model training technology is characterized by: The formula for calculating the comprehensive weight score is: Determine the county area ( ), the weight of the basic factors affecting the size of the UAV formation ( ); Determine the terrain complexity ( ): Complex terrain requires more drones to cover the weight of blind spots ( ); Determine the population density ( :Densely populated areas require a higher monitoring density weight ( ); Determine the fire risk level ( ): Weighting of high-risk areas that require more attention and resources ( ).
5. According to claim 1, a county fire early warning analysis system based on big data model training technology is characterized by: The ground sensor and camera module includes: Various types of sensors to monitor temperature, smoke concentration and fire-related parameters; High-definition cameras to capture images and videos of the fire scene; Data transmission device, used to transmit monitoring data to the data processing center in real time.
6. The county fire early warning analysis system based on big data model training technology according to claim 1 is characterized by: The data processing center includes: A data receiving module, used for receiving data from the drone module and the ground sensor and camera module; Data fusion module, used to fuse multi-source data to form more complete and accurate fire warning information; Data storage module, used to store historical fire data and real-time monitoring data.
7. The county fire early warning analysis system based on big data model training technology according to claim 1 is characterized by: The big data model training and analysis module includes: Model building submodule, which builds a fire warning model based on historical fire data and real-time monitoring data; Model training submodule, which uses large-scale data sets to train and optimize the model; The real-time analysis submodule inputs the real-time monitoring data into the trained model to perform real-time fire warning analysis.
8. The county fire early warning analysis system based on big data model training technology according to claim 7 is characterized by: The model building submodule also includes: Data collection and preprocessing steps, used to collect and preprocess historical fire data and real-time monitoring data; Feature extraction step, extracting features useful for fire warning from the original data; Model selection and design steps: select the appropriate algorithm and design the model architecture based on data characteristics and business needs.
9. The county fire early warning analysis system based on big data model training technology according to claim 8 is characterized by: The model training submodule also includes: Data partitioning step: divide the data set into training set, validation set and test set; Hyperparameter tuning step: Tune the model’s hyperparameters to find the optimal model configuration; Iterate the training steps, iterate the training process multiple times and evaluate the model performance.
10. The county fire early warning analysis system based on big data model training technology according to claim 1 is characterized by: Also includes: The result display and response module is used to display the fire warning analysis results to the monitoring personnel through a visual interface and set the warning threshold. When the predicted result exceeds the threshold, the alarm system is automatically triggered to notify relevant personnel to respond quickly.