Fire alarm processing method, system and terminal based on internet of things wisdom fire control

Abstract

This invention belongs to the field of smoke and fire alarm technology, specifically relating to a smoke and fire alarm processing method, system, and terminal based on IoT-based smart fire protection. This invention calculates the overall offset time to correct the real-time collected smoke and fire alarm signals based on an alarm response time sequence formed from historical smoke and fire alarm datasets. Based on a set of alarm data nodes determined in the corrected signal, a comprehensive fitting value is calculated as a reference benchmark for subsequent corrections. Therefore, through macroscopic correction of historical data and iterative optimization of recent data, dynamic and adaptive precision calibration of the alarm response time is achieved, overcoming the insufficient accuracy problem caused by using fixed offset values ​​for correction, thereby improving the accuracy of the timestamp of the starting point of the fire alarm event signal characteristics.

Description

Technical Field

[0001] This invention belongs to the field of smoke and fire alarm technology, specifically relating to a smoke and fire alarm processing method, system, and terminal based on Internet of Things-based smart fire protection. Background Technology

[0002] In the field of fire prevention and control, automated fire suppression systems improve emergency response speed and control efficiency through early automated identification and handling of fires. With the improvement of intelligence, fire suppression systems are gradually replacing traditional manual inspection and passive response modes by utilizing advanced sensing and control technologies.

[0003] Currently, traditional automated smoke alarm and fire suppression linkage systems exhibit significant time lags in practical applications. From sensor detection to fire signal transmission and processing, and finally to the activation of the fire suppression device, the entire process suffers from a noticeable time delay. During this time, the initial fire point may have rapidly shifted location or spread, causing the fire suppression device to still be aimed at the initial alarm location, resulting in a spatial mismatch between the target and the actual fire point, and thus failing to effectively suppress the fire. Furthermore, the control of extinguishing agent spraying largely relies on simple threshold-based methods, continuously or at a fixed flow rate once a fire is detected. This lack of precision and quantitative adjustment based on dynamic factors such as the real-time scale, development trend, and type of combustible material of the fire is insufficient.

[0004] To address the aforementioned issues, this invention provides a smoke alarm processing method, system, and terminal based on Internet of Things (IoT) smart fire protection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a smoke and fire alarm processing method, system, and terminal based on Internet of Things-based smart fire protection. This method can reduce the false alarm rate of smoke and fire alarms, enable timely fire extinguishing operations, and assist in the operation of fire control and alarm systems.

[0006] This invention is achieved through the following technical solution: The smoke alarm handling method based on IoT-based smart fire protection includes the following steps: By processing the smoke alarm signals collected in real time from the smoke monitoring equipment, alarm data nodes are generated; specifically, this includes: performing filtering processing on the smoke alarm signals to output filtered signals; performing correction on the filtered signals to identify the starting point of the signal characteristics representing the fire alarm event in the corrected signals, and determining the timestamp of the signal characteristic starting point as the timestamp of the alarm data node; The alarm delay period is determined based on the timestamp of the latest generated alarm data node and the current system time; When the alarm delay period is determined to be longer than the preset alarm tolerance period, an alarm delay prompt will be output.

[0007] Preferably, performing correction on the filtered signal includes: Based on historical smoke alarm datasets, the overall offset time is calculated; and the response time information contained in the filtered signal is corrected according to the overall offset time.

[0008] Preferably, based on historical smoke alarm datasets, the overall offset time is calculated as follows: The historical fireworks alarm dataset is processed to extract alarm response times and form an alarm response time sequence; The overall offset time is calculated based on the alarm response time sequence.

[0009] Preferably, the historical smoke alarm dataset is processed to extract the alarm response time and form an alarm response time sequence, including: The data in the historical fireworks alarm dataset are sorted according to their respective alarm response times. The time difference between the alarm response times of adjacent data after sorting is calculated. Data with time differences greater than a preset filtering threshold are selected and their alarm response times are extracted to form an alarm response time sequence.

[0010] This invention also discloses a smoke alarm and fire handling system based on Internet of Things (IoT) smart fire protection, comprising the following modules: The signal acquisition module is used to acquire the smoke and fire alarm signals output by the smoke and fire monitoring equipment in the area to be tested in real time. The node generation module is used to receive smoke alarm signals, calculate the overall offset time based on historical smoke alarm datasets, and generate alarm data nodes by performing filtering and correction on the smoke alarm signals. The delay monitoring module is used to respond to the alarm data nodes generated by the node generation module, determine the alarm delay period between the timestamp of the alarm data node and the current system time, and output an alarm delay prompt when the alarm delay period is greater than the preset alarm allowable period.

[0011] Preferably, the node generation module is configured to process the historical fireworks alarm dataset to form an alarm response time sequence, and calculate the overall offset time based on the alarm response time sequence.

[0012] Preferably, the node generation module is further configured to: after performing filtering processing on the smoke alarm signal, correct the response time information contained in the filtered signal according to the overall offset time.

[0013] Preferably, the historical smoke alarm dataset is processed to extract the alarm response time and form an alarm response time sequence, including: The data in the historical fireworks alarm dataset are sorted according to their respective alarm response times. The time difference between the alarm response times of adjacent data after sorting is calculated. Data with time differences greater than a preset filtering threshold are selected and their alarm response times are extracted to form an alarm response time sequence.

[0014] Preferably, the signal acquisition module is configured as follows: The smoke alarm signal is filtered to output a filtered signal; the filtered signal is then corrected to identify the starting point of the signal feature representing the fire alarm event in the corrected signal, and the timestamp of the starting point of the signal feature is determined as the timestamp of the alarm data node.

[0015] This invention also discloses a smoke alarm processing terminal based on IoT-based smart fire protection, comprising: Processor; memory that communicates with the processor; The memory stores a computer program, which includes instructions. When executed by the processor, the instructions cause the terminal to perform the aforementioned IoT-based smart fire protection smoke alarm processing method. Beneficial effects

[0016] 1. This invention calculates the overall offset time based on the alarm response time sequence formed by the historical smoke and fire alarm dataset to correct the real-time collected smoke and fire alarm signals. Based on a set of alarm data nodes determined in the corrected signal, a comprehensive fitting value is calculated as a reference benchmark for subsequent correction. Therefore, through macro-correction of historical data and iterative optimization of recent data, dynamic and adaptive precision calibration of alarm response time is achieved, overcoming the problem of insufficient accuracy caused by using fixed offset values ​​for correction, thereby improving the accuracy of the timestamp of the starting point of fire alarm event signal characteristics.

[0017] 2. After completing the calibration and determining the latest alarm data node, this invention further defines the time difference between the timestamp of the node and the current system time as the alarm delay period, and determines whether it is greater than the preset alarm allowable period. If it is greater, an alarm delay prompt and alarm efficiency are output, thereby completing the calibration of the alarm response time and establishing a quantitative monitoring and early warning mechanism for the performance of the smoke and fire alarm handling system itself. This enables maintenance personnel to promptly detect and handle potential system performance degradation problems and achieve preventive maintenance of the smoke and fire alarm handling system. Attached Figure Description

[0018] Figure 1 This is one of the method flowcharts of the present invention; Figure 2 This is the second flowchart of the method of the present invention; Figure 3 This is a system module diagram of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely for explaining the invention and are not intended to limit the scope of protection of the invention. Example 1

[0020] like Figures 1-2 As shown, this embodiment provides a smoke alarm handling method based on IoT-based smart fire protection, specifically including the following steps: S1. Identify and register the smoke and fire monitoring equipment in the area to be tested; Smoke and fire monitoring equipment includes smoke sensors, heat detectors, or infrared thermal imagers. The unique identification information of the smoke and fire monitoring equipment is uploaded to the smart fire protection system for registration and filing. Historical smoke and fire alarm data associated with the area to be monitored are collected from the historical database of the smart fire protection system to generate a historical smoke and fire alarm dataset.

[0021] Furthermore, historical smoke alarm data includes alarm response time and fault type information when the alarm event occurs. This allows for pre-screening of the data based on the fault type information, clearly distinguishing between real fire alarm events and invalid historical data caused by non-fire alarm events, thus ensuring the quality and reliability of the historical smoke alarm dataset from the source.

[0022] Non-fire alarm incidents include those caused by equipment malfunction, communication network interruption, or human error.

[0023] S2. Process the acquired historical smoke alarm dataset to form an alarm response time sequence; Based on the alarm response time, the valid alarm records in the historical fire alarm dataset are sorted by time dimension; after sorting, the time difference between the alarm response times of two adjacent alarm records is calculated sequentially; the calculated time difference is compared with the preset filtering threshold, and the dense and repeated alarm records triggered by a single fire alarm event and generated in a short period of time are grouped into a single valid event. Specifically, if the time difference is less than the filtering threshold, the next alarm record is considered redundant and is removed; if the time difference is greater than or equal to the filtering threshold, the alarm record is retained. Through the above filtering process, representative alarm response times are extracted, and these times together constitute the alarm response time sequence, providing high-quality data input for subsequent calculations of the inherent overall delay characteristics of the system.

[0024] The filtering threshold is a preset time difference.

[0025] S3. During normal system operation, within a preset time period, continuously and in real time collect the smoke and fire alarm signals output by the smoke and fire monitoring equipment. A smoke alarm signal is a time-series data stream that contains the original response time information at the moment the signal is generated. This information is the direct target of subsequent time correction.

[0026] S4. Calculate the overall offset time and perform signal correction based on that time; Specifically: The real-time acquired smoke alarm signal is filtered to remove abnormal fluctuations in signal amplitude that are outside the preset normal range due to transient electromagnetic interference or sensor noise, thereby outputting a smooth filtered signal. Based on the generated alarm response time sequence, the overall offset time is calculated. The specific process is as follows: calculate the time interval between each pair of adjacent times in the alarm response time sequence to obtain a set of initial overall offsets; perform statistical averaging on the set of initial overall offsets, such as calculating their arithmetic mean, to generate an overall offset time that can characterize the overall delay characteristics of the current fire protection system from sensing to recording. The process determines whether the rate of change of the overall offset time compared to historical values ​​exceeds a preset trend threshold to ensure the stability of the correction process. This trend threshold is used to determine whether the magnitude of change of the newly calculated overall offset time compared to its historical values ​​is within a stable range, thereby deciding whether to adopt the new calculated value to avoid introducing abnormal fluctuations. Specifically: If the rate of change of historical values ​​does not exceed the preset trend threshold, it indicates that the overall delay characteristics are stable. In this case, the overall offset time calculated in this instance is adopted to adjust the response time information contained in the filtered signal in advance to output the corrected signal. If the rate of change of historical values ​​exceeds the preset trend threshold, it indicates that there are abnormal fluctuations in the overall delay characteristics. In this case, the overall offset time calculated in this instance is abandoned to avoid introducing incorrect correction amounts.

[0027] The trend threshold is a preset rate of change limit, which ranges from 5% to 15%, preferably 10%.

[0028] Furthermore, this method also includes maintaining a deviation distribution table, which is configured to record and continuously update all historically calculated and stability-verified overall offset times. After a new overall offset time is calculated, it is compared with the historical data distribution in the deviation distribution table to identify and remove statistical outliers, thereby improving the accuracy and stability of the overall offset time calculation.

[0029] Among them, the historical data distribution is preferably based on the historical mean and standard deviation.

[0030] S5. Based on the obtained corrected signal, determine the alarm data node and calculate the fitting correction parameters for adaptive optimization. Specifically, the starting point of signal features that can characterize the onset of a real fire alarm event is identified in the corrected signal. The preferred starting point of signal features is the time point when the signal amplitude rises continuously and its slope first exceeds a certain specific threshold.

[0031] In practical applications, the starting point of this signal characteristic can correspond to the inflection point where the concentration of flame or smoke begins to increase sharply, and the corresponding timestamp is determined as the alarm data node.

[0032] By repeatedly executing steps S3 to S5 over a period of time, a set of alarm data nodes is generated. To achieve adaptive optimization of the alarm response time, a set of fitting correction parameters is calculated based on this set of alarm data nodes. The specific process is as follows: The timestamps of the alarm data nodes are aggregated into a timestamp set, and the arithmetic mean of the timestamp set is calculated, i.e., the set mean. For each timestamp in the set, its positive deviation relative to the set mean is calculated. Based on the positive deviation, a set of fitting correction parameters is generated. The built-in logic of this generation process is: timestamps with smaller positive deviations are assigned larger weights, while timestamps with larger positive deviations are assigned smaller weights. This set of weights constitutes a set of fitting correction parameters, so that the subsequent correction benchmark calculation results are biased towards the numerical range of faster response in the historical records, thereby actively guiding the system to shorten the alarm response time.

[0033] The positive deviation is the time difference between the timestamp of a single alarm data node and the arithmetic mean of the set, which is used to quantify the timeliness of each alarm response relative to the average response level.

[0034] S6. Based on the calculation results of step S5, calculate and determine the dynamic reference benchmark; The specific calculation process is as follows: Based on a set of fitting correction parameters, the timestamps of a set of alarm data nodes are weighted and averaged to obtain a comprehensive fitting value. This comprehensive fitting value is determined as a reference benchmark for subsequent correction of smoke and fire alarm signals. The fitting correction parameters are the weights; In subsequent system operation, the reference benchmark is used to replace or further optimize the overall offset time calculated in step S4, thereby providing a more stable and adaptive optimization standard and forming a closed-loop feedback mechanism for continuous optimization.

[0035] S7. Execute alarm delay judgment logic to monitor the performance of the alarm processing flow in real time; Specifically, obtain the latest generated alarm data node and read its timestamp; Calculate the time difference between the timestamp and the current system time, and define this time difference as the alarm delay period; The calculated alarm delay period is compared with the preset alarm tolerance period. If the alarm delay period is determined to be longer than the preset alarm tolerance period, an alarm delay prompt is immediately output, and the quantified alarm efficiency is output simultaneously.

[0036] For example, a calculation formula can be used to provide system administrators with clear early warnings and quantitative indicators regarding the current alarm handling performance status. The formula is: Alarm Efficiency = 1 - Alarm Delay Period / Preset Alarm Tolerance Period.

[0037] Among them, the alarm delay period is used to quantify in real time the time consumed from the occurrence of the fire alarm event characteristic point to the system completing the node judgment.

[0038] The alarm tolerance period is a preset time threshold that represents the maximum acceptable alarm delay for the system. Example 2

[0039] See Figure 3 This embodiment provides a smoke alarm and fire handling system based on Internet of Things (IoT) smart fire protection, including: The signal acquisition module is configured to acquire in real time the smoke and fire alarm signals output by one or more smoke and fire monitoring devices in the area to be tested.

[0040] This module serves as the system's data entry point, continuously receiving signals from the front-end devices and forwarding them to subsequent processing modules.

[0041] Among them, the smoke and fire alarm signal is a data stream or data packet carrying the original detection information, which includes the response time information of the detector sensing abnormalities such as smoke, temperature or flame and triggering the alarm.

[0042] The node generation module is used to receive the smoke and fire alarm signal forwarded by the signal acquisition module, and to perform in-depth processing on the smoke and fire alarm signal to generate alarm data nodes that can accurately characterize the starting point of the signal characteristics of the fire alarm event.

[0043] The operating logic of this module specifically includes the following steps: The received real-time smoke alarm signal is filtered to eliminate noise or interference that may be introduced during the acquisition and transmission process, such as spurious signals caused by transient electromagnetic interference or transient environmental fluctuations, thereby outputting a filtered signal with a higher signal-to-noise ratio and a clearer waveform.

[0044] Based on the historical fireworks alarm dataset, the overall offset time is calculated. This module accesses the historical fireworks alarm dataset, which stores a large number of historical alarm event records, and sorts the valid alarm records in the historical fireworks alarm dataset according to their respective alarm response times in terms of time dimension; and calculates the time difference between the alarm response times of adjacent valid alarm records after sorting.

[0045] Furthermore, in order to identify time jumps caused by abnormal events such as system restarts, maintenance, or network outages, the following steps are performed: Data with a time difference greater than a preset threshold are filtered out, and the alarm response time of these data is extracted to form an alarm response time sequence that represents the distribution of regular alarm events. Based on the alarm response time sequence, the overall offset time is calculated through statistical averaging. This overall offset time objectively reflects the overall delay characteristics caused by factors such as equipment hardware, signal processing algorithms, and network transmission between the actual occurrence of the fire and the recording of the signal by the system.

[0046] The filtered signal is then corrected. Specifically, the response time information contained in the filtered signal is corrected using the calculated global offset time to remove the systematic delay component from the apparent time of the signal.

[0047] In the corrected signal, signal analysis algorithms, such as edge detection and peak recognition, are used to identify the starting point of the signal features that can characterize the fire alarm event. The timestamp corresponding to this starting point is considered to be the time point that is closest to the actual time of the fire after eliminating the systematic delay. This module determines this timestamp as the timestamp of the newly generated alarm data node and outputs the alarm data node to the delay monitoring module.

[0048] The delay monitoring module is used to monitor the real-time performance of the alarm processing link. It is triggered in response to each alarm data node generated by the node generation module. When a new alarm data node is received, this module immediately performs the following operations: Get the timestamp of the latest generated alarm data node, and at the same time get the current system time.

[0049] The time difference between the timestamp of the alarm data node and the current system time is calculated to determine the alarm delay period.

[0050] Furthermore, the alarm delay period mentioned above measures the total time elapsed from the actual occurrence of a fire to the accurate identification of the event by the system.

[0051] The calculated alarm delay period is compared with the preset alarm allowable period, which is an acceptable upper limit of delay set according to fire safety regulations, system design requirements, or operation and maintenance experience. When it is determined that the alarm delay period is greater than the preset alarm allowable period, the delay monitoring module outputs an alarm delay prompt.

[0052] Furthermore, alarm delay notifications can take various forms, preferably including: sending a highlighted alarm to the monitoring interface of operations and maintenance personnel, recording an emergency-level system log, or notifying relevant operations and maintenance personnel via SMS, email, etc., to prompt them to pay attention to and investigate the possible causes of slow system response. These causes include, but are not limited to, network congestion, excessive server load, or front-end device failure. Example 3

[0053] This embodiment provides a smoke alarm processing terminal based on IoT-based smart fire protection, including: Processor; memory that communicates with the processor; The memory stores a computer program, which includes instructions. When executed by the processor, the instructions cause the terminal to perform a smoke and fire alarm handling method based on IoT-based smart fire protection.

Claims

1. A smoke alarm handling method based on IoT-based smart fire protection, characterized in that, Includes the following steps: By processing the smoke alarm signals collected in real time from the smoke monitoring equipment, alarm data nodes are generated; specifically, this includes: performing filtering processing on the smoke alarm signals to output filtered signals; performing correction on the filtered signals to identify the starting point of the signal characteristics representing the fire alarm event in the corrected signals, and determining the timestamp of the signal characteristic starting point as the timestamp of the alarm data node; The alarm delay period is determined based on the timestamp of the latest generated alarm data node and the current system time; When the alarm delay period is determined to be longer than the preset alarm tolerance period, an alarm delay prompt will be output.

2. The smoke and fire alarm handling method based on IoT-based smart fire protection according to claim 1, characterized in that, Correcting the filtered signal includes: Based on historical smoke alarm datasets, the overall offset time is calculated; and the response time information contained in the filtered signal is corrected according to the overall offset time.

3. The smoke and fire alarm handling method based on IoT-based smart fire protection according to claim 2, characterized in that, Based on historical smoke alarm datasets, the overall offset time was calculated as follows: The historical fireworks alarm dataset is processed to extract alarm response times and form an alarm response time sequence; The overall offset time is calculated based on the alarm response time sequence.

4. The smoke and fire alarm handling method based on IoT-based smart fire protection according to claim 3, characterized in that, The historical smoke alarm dataset is processed to extract alarm response times, forming an alarm response time sequence including: The data in the historical fireworks alarm dataset are sorted according to their respective alarm response times. The time difference between the alarm response times of adjacent data after sorting is calculated. Data with time differences greater than a preset filtering threshold are selected and their alarm response times are extracted to form an alarm response time sequence.

5. A smoke and fire alarm handling system based on Internet of Things-based smart fire protection, characterized in that, Includes the following modules: The signal acquisition module is used to acquire the smoke and fire alarm signals output by the smoke and fire monitoring equipment in the area to be tested in real time. The node generation module is used to receive smoke alarm signals, calculate the overall offset time based on historical smoke alarm datasets, and generate alarm data nodes by performing filtering and correction on the smoke alarm signals. The delay monitoring module is used to respond to the alarm data nodes generated by the node generation module, determine the alarm delay period between the timestamp of the alarm data node and the current system time, and output an alarm delay prompt when the alarm delay period is greater than the preset alarm allowable period.

6. The smoke and fire alarm processing system based on IoT-enabled smart fire protection according to claim 5, characterized in that, The node generation module is configured to process the historical fireworks alarm dataset to form an alarm response time sequence, and calculate the overall offset time based on the alarm response time sequence.

7. The smoke and fire alarm handling system based on IoT-enabled smart fire protection according to claim 6, characterized in that, The node generation module is also configured to: after performing filtering on the smoke alarm signal, correct the response time information contained in the filtered signal based on the overall offset time.

8. The smoke and fire alarm processing system based on IoT-enabled smart fire protection according to claim 6, characterized in that, The historical smoke alarm dataset is processed to extract alarm response times, forming an alarm response time sequence including: The data in the historical fireworks alarm dataset are sorted according to their respective alarm response times. The time difference between the alarm response times of adjacent data after sorting is calculated. Data with time differences greater than a preset filtering threshold are selected and their alarm response times are extracted to form an alarm response time sequence.

9. The smoke and fire alarm processing system based on IoT-enabled smart fire protection according to claim 5, characterized in that, The signal acquisition module is configured as follows: The smoke alarm signal is filtered to output a filtered signal; The filtered signal is corrected to identify the starting point of the signal feature representing the fire alarm event in the corrected signal, and the timestamp of the starting point of the signal feature is determined as the timestamp of the alarm data node.

10. A smoke alarm processing terminal based on IoT-based smart fire protection, characterized in that: include: processor; Memory that communicates with the processor; The memory stores a computer program, which includes instructions. When executed by the processor, the instructions cause the terminal to perform the smoke and fire alarm handling method based on IoT-based smart fire protection as described in any one of claims 1 to 4.

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