Intelligent fire safety alarm system for building

Abstract

The invention discloses a building intelligent fire safety alarm system, comprising a multi-source monitoring unit including a fire hydrant monitoring unit, a smoke monitoring unit, a circuit monitoring unit, a video monitoring unit and the like which are respectively responsible for monitoring fire hydrant water pressure, smoke concentration, circuit voltage and real-time video information in a building to ensure comprehensiveness and accuracy of fire data; the data acquisition and transmission module is used for transmitting the data acquired by each monitoring unit to the data acquisition unit in real time through a wireless communication technology and carrying out preliminary summarization processing; the beneficial effects of the invention are that through the multi-source monitoring unit, comprehensive coverage and accurate acquisition of fire data are realized; the intelligent data analysis unit carries out deep mining on mass data by using an advanced algorithm, so that the accuracy and timeliness of fire identification are improved; and a false alarm monitoring processing module is introduced, so that the false alarm rate is effectively reduced, and the overall reliability and user satisfaction of the system are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of building fire protection, and in particular relates to an intelligent building fire safety alarm system. Background Art

[0002] With the frequent occurrence of fire accidents in high-rise buildings, people's lives and property are seriously threatened, especially the fire accidents in high-rise buildings are more harmful; the frequent occurrence of fire accidents also brings unstable factors to the society and has a lot of adverse effects on the society; if the control of fire is not strengthened, it will cause huge economic losses and conflicts.

[0003] A fire monitoring and alarm system for a building with publication number CN115560252A discloses a fire protection monitoring and alarm system including a fire hydrant monitoring unit, a smoke monitoring unit, a circuit monitoring unit, a video monitoring unit, a wireless communication unit, a data acquisition unit, a data analysis unit, a central management unit, an alarm unit, an emergency unit and a data storage unit. The fire hydrant monitoring unit monitors the water pressure and water flow leakage data of the fire hydrants in the building. The smoke monitoring unit, the circuit monitoring unit and the video monitoring unit respectively monitor the smoke concentration data, the circuit voltage and the real-time video monitoring information in the building, and transmit the monitored data information to the data acquisition unit through the wireless communication unit. The data acquisition unit summarizes the monitored data information and transmits it to the data analysis unit. The data analysis unit analyzes the monitored data information transmitted by the data acquisition unit to determine whether the monitored data exceeds a preset threshold value. If the threshold value is exceeded, the alarm unit is notified to alarm and start the emergency unit, and the data information is transmitted to the central management unit and the data storage unit at the same time.

[0004] With the acceleration of urbanization, high-rise buildings are increasing, and their fire safety has become the focus of social attention. Once a fire occurs in a high-rise building, it often causes significant casualties and property losses due to the rapid spread of fire and difficulty in evacuating people. Although the existing fire monitoring and alarm system has improved the fire prevention and control capabilities to a certain extent, there are still problems such as high false alarm rate and untimely alarm response, which affects the effective allocation of fire resources and the rapid control of fire. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent building fire safety alarm system to achieve early warning, accurate judgment and rapid response of fire, significantly reduce the false alarm rate, and improve the efficiency and reliability of fire safety management.

[0006] To achieve the above object, the present invention provides the following technical solution: a building intelligent fire safety alarm system, comprising

[0007] Multi-source monitoring unit: including fire hydrant monitoring unit, smoke monitoring unit, circuit monitoring unit, video monitoring unit, etc., which are responsible for monitoring the water pressure of fire hydrants, smoke concentration, circuit voltage and real-time video information in the building to ensure the comprehensiveness and accuracy of fire data;

[0008] Data collection and transmission module: Through wireless communication technology, the data collected by each monitoring unit is transmitted to the data collection unit in real time, and preliminary summary processing is carried out;

[0009] Intelligent data analysis unit: conducts in-depth analysis of the collected data and uses algorithm models to determine whether the data exceeds the preset threshold, thereby accurately identifying fire hazards or fire conditions;

[0010] Alarm and emergency response module: Once the data analysis unit confirms that there is a fire risk, the alarm unit will be immediately triggered to sound and light alarms, and the emergency unit will be automatically activated to quickly control the fire;

[0011] Central management unit: As the core control center of the system, it is responsible for receiving, storing and displaying all monitoring data and analysis results, while providing remote monitoring and management;

[0012] Data storage unit: used to store all monitoring data, alarm records and system logs for a long time, providing data support for subsequent fire investigation and system optimization;

[0013] False alarm monitoring and processing module: By introducing machine learning algorithms, historical alarm data is learned and a false alarm recognition model is established; when an alarm signal is received, the module will automatically determine whether it is a false alarm. If it is a false alarm, it will be automatically filtered.

[0014] As a preferred technical solution of the present invention, the in-depth analysis includes data preprocessing; data exploration and descriptive analysis; in-depth mining and predictive analysis; business insight and decision support.

[0015] As a preferred technical solution of the present invention, the data preprocessing includes data cleaning and data integration.

[0016] As a preferred technical solution of the present invention, the algorithm model construction method is as follows:

[0017] Collect real-time data from each monitoring unit;

[0018] Data preprocessing: including data cleaning, data integration and data standardization;

[0019] Select features that have a significant impact on the judgment results based on business needs and data characteristics;

[0020] Choose the appropriate algorithm based on the nature of the problem and the characteristics of the data;

[0021] Depending on the selected algorithm, historical data or simulated data is used to train the model. During the training process, the model parameters need to be continuously adjusted to optimize the model's performance.

[0022] As a preferred technical solution of the present invention, the emergency unit includes an automatic water sprinkler fire extinguishing system; a gas fire extinguishing system; a smoke exhaust system; an emergency lighting and evacuation indication system; and a fire broadcasting system.

[0023] As a preferred technical solution of the present invention, the false alarm identification model construction method is as follows:

[0024] Collect historical alarm data from various alarm systems;

[0025] For data that is not marked as false alarms, it needs to be manually labeled by experts;

[0026] Preprocess the data;

[0027] Select features that have a significant impact on false alarm identification based on business requirements and algorithm characteristics;

[0028] Perform necessary transformations on selected features;

[0029] Based on feature selection, further feature engineering is performed;

[0030] Choose the appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data;

[0031] Divide the processed data into training set, validation set and test set;

[0032] Train the machine learning model using the training set data and adjust the model parameters to optimize performance.

[0033] As a preferred technical solution of the present invention, the historical alarm data includes alarm time, alarm type, alarm location, alarm reason and marking information of whether it is a false alarm.

[0034] As a preferred technical solution of the present invention, the conversion includes encoding and normalization.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The multi-source monitoring unit achieves comprehensive coverage and accurate collection of fire data; the intelligent data analysis unit uses advanced algorithms to conduct in-depth mining of massive data to improve the accuracy and timeliness of fire identification;

[0037] Introducing the false alarm monitoring and processing module effectively reduces the false alarm rate and improves the overall system reliability and user satisfaction;

[0038] Once a fire is confirmed, the system can immediately activate the alarm and emergency response module to quickly control and extinguish the fire and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figure 1 , which is the first embodiment of the present invention, and provides a building intelligent fire safety alarm system, comprising

[0043] Multi-source monitoring unit: including fire hydrant monitoring unit, smoke monitoring unit, circuit monitoring unit, video monitoring unit, etc., which are responsible for monitoring the water pressure of fire hydrants, smoke concentration, circuit voltage and real-time video information in the building to ensure the comprehensiveness and accuracy of fire data;

[0044] Data collection and transmission module: Through wireless communication technology, the data collected by each monitoring unit is transmitted to the data collection unit in real time, and preliminary summary processing is carried out;

[0045] Intelligent data analysis unit: conducts in-depth analysis of the collected data and uses algorithm models to determine whether the data exceeds the preset threshold, thereby accurately identifying fire hazards or fire conditions;

[0046] Alarm and emergency response module: Once the data analysis unit confirms that there is a fire risk, the alarm unit will be immediately triggered to sound and light alarms, and the emergency unit will be automatically activated to quickly control the fire;

[0047] Central Management Unit: As the core control center of the system, it is responsible for receiving, storing and displaying all monitoring data and analysis results, while providing remote monitoring and management;

[0048] Data storage unit: used to store all monitoring data, alarm records and system logs for a long time, providing data support for subsequent fire investigation and system optimization;

[0049] False alarm monitoring and processing module: By introducing machine learning algorithms, historical alarm data is learned and a false alarm recognition model is established; when an alarm signal is received, the module will automatically determine whether it is a false alarm. If it is a false alarm, it will be automatically filtered.

[0050] In this embodiment, preferably, the in-depth analysis includes

[0051] Data preprocessing

[0052] Data cleaning: remove duplicate data, process missing values, detect and process outliers, etc. to ensure the accuracy and completeness of data;

[0053] Data integration: Integrate data from different sources and formats into a unified data structure to facilitate subsequent analysis;

[0054] Data Exploration and Descriptive Analysis

[0055] Statistical description: Describe the basic distribution characteristics of the data by calculating the mean, median, mode, standard deviation and other statistics of the data;

[0056] Visual presentation: Use charts (such as bar charts, line charts, scatter charts, pie charts, etc.) to intuitively display the distribution, trends and correlations of data;

[0057] Deep Mining and Predictive Analysis

[0058] Data mining: Use data mining techniques (such as association rules, cluster analysis, classification and prediction) to extract hidden patterns, associations and trends from data;

[0059] Prediction model: Based on historical data and market trends, build prediction models (such as time series analysis, machine learning models, etc.) to predict future data changes or business trends;

[0060] Business insights and decision support

[0061] Business insights: Through in-depth analysis, the business logic and rules behind the data are revealed, providing managers with in-depth business insights;

[0062] Decision support: Based on the analysis results, data support is provided for strategic planning, resource allocation and other decisions, helping to achieve accurate decision-making and efficient operations.

[0063] In this embodiment, preferably, the algorithm model construction method is as follows:

[0064] Collect real-time data from various monitoring units (such as smoke monitoring units, circuit monitoring units, etc.);

[0065] Data preprocessing: including data cleaning (removing noise, processing missing values, etc.), data integration (integrating data from different sources into a unified format) and data standardization (converting data into a unified dimension or range for comparison and analysis);

[0066] Feature selection and extraction

[0067] Select features that have a significant impact on the judgment results based on business needs and data characteristics; for example, in smoke monitoring, smoke concentration may be a key feature; then, the values ​​of these features need to be extracted or calculated from the raw data;

[0068] Algorithm selection and model building

[0069] Algorithm selection: Choose an appropriate algorithm based on the nature of the problem and the characteristics of the data. In this scenario, since the main task is threshold judgment, complex machine learning algorithms are not required. Instead, simple conditional judgment statements or threshold comparison algorithms are used. However, if the combined effects of multiple factors need to be considered, or if the accuracy and robustness of judgment need to be improved, more complex algorithms such as decision trees, support vector machines (SVMs), or neural networks are used.

[0070] Model building: Based on the selected algorithm, use historical data or simulated data to train the model; during the training process, the model parameters need to be continuously adjusted to optimize the model's performance.

[0071] In this embodiment, preferably, the emergency unit includes an automatic sprinkler fire extinguishing system; a gas fire extinguishing system; a smoke exhaust system; an emergency lighting and evacuation indication system; and a fire broadcasting system.

[0072] Example 2

[0073] See also Figure 1 , which is the second embodiment of the present invention, is based on the previous embodiment, except that:

[0074] The false alarm identification model is constructed as follows:

[0075] Collect historical alarm data from various alarm systems. This data should include information such as alarm time, alarm type, alarm location, alarm reason, and whether it is a false alarm.

[0076] For data that is not marked as false alarms, experts need to manually label it to ensure the accuracy of the data;

[0077] Data cleaning: remove duplicate data, missing values, outliers, etc. to ensure data quality;

[0078] Feature selection: Select features that have an important impact on false alarm identification based on business requirements and algorithm characteristics; these features include alarm time, alarm type, alarm location, environmental parameters (such as temperature, humidity, smoke concentration, etc.), etc.

[0079] Data conversion: Perform necessary conversions on selected features, such as encoding and normalization, so that machine learning algorithms can better process them;

[0080] Based on feature selection, further feature engineering is performed to improve the performance of the model; feature engineering includes:

[0081] Feature derivation: creating new features by combining or transforming existing features;

[0082] Feature selection optimization: Use statistical methods or machine learning algorithms to automatically select the optimal feature combination;

[0083] Choose the appropriate machine learning algorithm: Choose the appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data. For the problem of false alarm identification, commonly used algorithms include decision trees, random forests, support vector machines, neural networks, etc.

[0084] Divide the data set: divide the processed data into training set, validation set and test set; the training set is used to train the model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the model performance;

[0085] Model training: Use the training set data to train the machine learning model and adjust the model parameters to optimize performance.

[0086] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A building intelligent fire safety alarm system, characterized in that: include Multi-source monitoring unit: including fire hydrant monitoring unit, smoke monitoring unit, circuit monitoring unit, video monitoring unit, etc., which are responsible for monitoring the water pressure of fire hydrants, smoke concentration, circuit voltage and real-time video information in the building to ensure the comprehensiveness and accuracy of fire data; Data collection and transmission module: Through wireless communication technology, the data collected by each monitoring unit is transmitted to the data collection unit in real time, and preliminary summary processing is carried out; Intelligent data analysis unit: conducts in-depth analysis of the collected data and uses algorithm models to determine whether the data exceeds the preset threshold, thereby accurately identifying fire hazards or fire conditions; Alarm and emergency response module: Once the data analysis unit confirms that there is a fire risk, the alarm unit will be immediately triggered to sound and light alarms, and the emergency unit will be automatically activated to quickly control the fire; Central management unit: As the core control center of the system, it is responsible for receiving, storing and displaying all monitoring data and analysis results, while providing remote monitoring and management; Data storage unit: used to store all monitoring data, alarm records and system logs for a long time, providing data support for subsequent fire investigation and system optimization; False alarm monitoring and processing module: By introducing machine learning algorithms, historical alarm data is learned and a false alarm recognition model is established; when an alarm signal is received, the module will automatically determine whether it is a false alarm. If it is a false alarm, it will be automatically filtered.

2. The intelligent building fire safety alarm system according to claim 1, characterized in that: The in-depth analysis includes data preprocessing; data exploration and descriptive analysis; deep mining and predictive analysis; business insights and decision support.

3. The intelligent building fire safety alarm system according to claim 2, characterized in that: The data preprocessing includes data cleaning and data integration.

4. The intelligent building fire safety alarm system according to claim 1, characterized in that: The algorithm model construction method is as follows: Collect real-time data from each monitoring unit; Data preprocessing: including data cleaning, data integration and data standardization; Select features that have a significant impact on the judgment results based on business needs and data characteristics; Choose the appropriate algorithm based on the nature of the problem and the characteristics of the data; Depending on the selected algorithm, historical data or simulated data is used to train the model. During the training process, the model parameters need to be continuously adjusted to optimize the model's performance.

5. The intelligent building fire safety alarm system according to claim 1, characterized in that: The emergency unit includes an automatic water sprinkler fire extinguishing system; a gas fire extinguishing system; a smoke exhaust system; an emergency lighting and evacuation indication system; and a fire broadcasting system.

6. The intelligent building fire safety alarm system according to claim 1, characterized in that: The false alarm identification model construction method is as follows: Collect historical alarm data from various alarm systems; For data that is not marked as false alarms, it needs to be manually labeled by experts; Preprocess the data; Select features that have a significant impact on false alarm identification based on business requirements and algorithm characteristics; Perform necessary transformations on selected features; Based on feature selection, further feature engineering is performed; Choose the appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data; Divide the processed data into training set, validation set and test set; Train the machine learning model using the training set data and adjust the model parameters to optimize performance.

7. The intelligent building fire safety alarm system according to claim 6, characterized in that: The historical alarm data includes alarm time, alarm type, alarm location, alarm reason, and marking information of whether it is a false alarm.

8. The intelligent building fire safety alarm system according to claim 6, characterized in that: The conversion includes encoding and normalization.

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