A smart fire smoke induction alarm method and system based on the Internet of Things

Abstract

The present application relates to the technical field of smoke alarm, in particular to a kind of intelligent fire-fighting smoke sensing alarm method and system based on Internet of Things, comprising the following steps: installing spectrum analysis sensor in the key area of building, capture spectrum signal, according to preset time interval collection smoke spectrum data, and be transmitted to cloud platform by Internet of Things network node, unified data format, generate spectrum dataset.In the present application, by capturing and analyzing the spectral characteristic curve of smoke, different types of smoke can be distinguished, efficient differentiation between fire smoke and non-fire smoke is achieved, false alarm phenomenon is reduced, combined with real-time environmental data, dynamically adjust the sensitivity of the sensor and the alarm threshold, still can maintain high reliability in complex and variable environment, at the same time, it can trigger multiple device response synchronously, quickly start fire sprinkler, smoke exhaust equipment and other mechanisms, and real-time fire alarm signal is transmitted to relevant departments, shorten the response time, optimize the linkage effect of fire emergency handling.

Description

Technical Field

[0001] This invention relates to the field of smoke alarm technology, and in particular to a smart fire smoke sensing alarm method and system based on the Internet of Things. Background Technology

[0002] The field of smoke alarm technology involves the development and implementation of devices and systems for detecting smoke, toxic gases, and other fire-related indicators. The core objective is to improve safety by detecting smoke and fire signs early, thereby triggering timely alarms and response measures. Smoke alarm systems can be based on various sensing technologies, such as photoelectric (using light scattering to detect smoke particles) and ionization (detecting smoke through changes in air ions). With technological advancements, modern smoke alarm systems also integrate Internet of Things (IoT) technology, enabling real-time data transmission, remote monitoring, and intelligent response, enhancing system efficiency and user experience.

[0003] Among these, the intelligent fire-fighting smoke detection and alarm method primarily enhances the functionality of traditional smoke detection and alarm systems through smart technology. Utilizing IoT technology, it can collect environmental data in real time, analyze the data by connecting to a cloud platform, and promptly warn users of potential fire risks. Furthermore, it can be integrated with other smart home devices, such as automatically activating air purifiers and shutting down ventilation systems, to reduce the harm caused by fire. Its main applications include fire prevention and early response in residential, commercial, and industrial areas, improving the speed and efficiency of fire response and significantly enhancing the safety of people and property.

[0004] Existing technologies largely rely on single sensor types to detect changes in smoke particles or ions, lacking the ability to accurately identify smoke types in complex environments. They suffer from a high false alarm rate when distinguishing between non-fire smoke (such as tobacco smoke and cooking smoke) and fire smoke, making accurate alarms difficult to guarantee. Regarding environmental variable handling, traditional equipment fails to dynamically adapt to changes in wind speed, humidity, and other environmental factors, leading to decreased detection performance in scenarios with strong airflow or high humidity. Data transmission largely depends on local storage and processing, lacking a unified remote data integration and analysis mechanism, resulting in poor real-time information and limiting the coverage and response speed of fire monitoring. Alarm systems are typically triggered by a single device, lacking the ability to coordinate responses across multiple devices, making it difficult to quickly activate a comprehensive fire-fighting mechanism. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a smart fire smoke sensing alarm method and system based on the Internet of Things.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a smart fire smoke sensing alarm method based on the Internet of Things, comprising the following steps:

[0007] S1: Install spectral analysis sensors in key areas of the building to capture spectral signals, collect flue gas spectral data at preset time intervals, and transmit the data to the cloud platform through IoT network nodes to unify the data format and generate a spectral dataset.

[0008] S2: Based on the spectral dataset, analyze the spectral characteristic curves of the flue gas, pinpoint the positions of key absorption peaks, match and classify the spectral peaks with the standard spectral range according to the flue gas type, determine the flue gas type based on the matching results, and generate flue gas classification results.

[0009] S3: Based on the flue gas classification results, combined with the real-time wind speed and humidity data collected by the environmental monitoring sensors, analyze the degree of influence of wind speed and humidity on flue gas propagation, combine the environmental variables with the sensitivity parameters of the flue gas sensor, calculate a new sensitivity threshold, and generate the adjusted sensor parameters.

[0010] S4: Based on the adjusted sensor parameters, after detecting the type of fire smoke, activate the preset response device, transmit the fire alarm signal to the fire sprinkler head and trigger the sprinkler head water supply mechanism, and simultaneously send the fire alarm signal to the smoke prevention and ventilation equipment to generate a fire alarm response signal.

[0011] S5: Based on the fire alarm response signal, send a real-time fire alarm notification to the receiving terminal of the fire department through the IoT gateway, upload the fire alarm time, type and triggering device data, record all smoke monitoring and response processes, and generate emergency communication and data records.

[0012] As a further aspect of the present invention, the spectral dataset includes smoke spectral feature values, timestamp data, and sensor deployment location records; the smoke classification results include fire smoke category identifiers, non-fire smoke category identifiers, and spectral absorption peak location information; the adjusted sensor parameters include updated sensitivity thresholds and dynamic alarm trigger values; the fire alarm response signals include fire sprinkler activation signals, smoke prevention and ventilation control signals, and fire alarm status identifiers; and the emergency communication and data recording include fire alarm event notification data, equipment trigger records, and fire alarm event time-series logs.

[0013] As a further aspect of the present invention, the specific steps for installing spectral analysis sensors in key areas of a building to capture spectral signals, collecting flue gas spectral data at preset time intervals, and transmitting the data to a cloud platform via an Internet of Things (IoT) network node to generate a spectral dataset with a unified data format are as follows:

[0014] S101: Install spectral analysis sensors in key areas of the building to capture spectral signals. By adjusting the sampling frequency and spectral band range, the flue gas spectral signal is sampled in segments. Combined with the sensor interface protocol, the collected data is transmitted to the Internet of Things network node in the form of digital signals to generate the raw dataset.

[0015] S102: Based on the original dataset, duplicate data are directly compared, filtered, and deleted; data with different field types are uniformly encoded; and field mapping rules are set for consistency calibration to obtain a standardized dataset.

[0016] S103: Based on the standardized dataset, the data is transmitted to the cloud database in segments through a batch transmission mechanism. Acquisition time, geographical location and spectral feature labels are added to the data, and the data is stored in a structured and hierarchical manner to generate a spectral dataset.

[0017] As a further aspect of the present invention, based on the aforementioned spectral dataset, the spectral characteristic curves of the flue gas are analyzed, the positions of key absorption peaks are determined, and the spectral peaks are matched and classified with the standard spectral range according to the flue gas type. The flue gas type is then determined based on the matching results, and the specific steps for generating the flue gas classification result are as follows:

[0018] S201: Based on the spectral dataset, by reading the light intensity values ​​in the spectral data point by point and constructing a continuous spectral curve, peak detection is performed on the spectral curve to identify the local maximum value region, the peak center position is calculated and the corresponding light intensity value is recorded, and the contour and position of the key peak are confirmed by curve fitting to generate spectral feature peak data.

[0019] S202: Based on the spectral characteristic peak data, the peak positions are matched by comparing with the standard spectral range, the absorption peak parameters that meet the range conditions are extracted, and a unique identifier is assigned to each calibrated absorption peak to generate key absorption peak calibration data.

[0020] S203: Based on the calibration data of the key absorption peaks, calculate the similarity score between the calibration peak parameters and the standard spectral data of the flue gas type, associate the peak features with the corresponding flue gas type, perform flue gas type matching and classification, and generate flue gas classification results.

[0021] As a further aspect of the present invention, the similarity score is calculated according to the formula:

[0022] ;

[0023] Calculations are performed, in which, Represents the similarity score. This represents the peak parameter value of the flue gas sample at the key absorption peak. This represents the peak parameter value in the standard spectral data corresponding to the flue gas type. This represents the total energy value of the absorption peak obtained in the actual test. This represents the pre-set standard absorption peak energy value.

[0024] As a further aspect of the present invention, based on the flue gas classification results and combined with real-time wind speed and humidity data collected by environmental monitoring sensors, the influence of wind speed and humidity on flue gas propagation is analyzed. The environmental variables are then combined with the sensitivity parameters of the flue gas sensor to calculate a new sensitivity threshold. The specific steps for generating the adjusted sensor parameters are as follows:

[0025] S301: Collects real-time wind speed and humidity data through environmental monitoring sensors, performs time series grouping of wind speed data and extracts the wind speed mean of each group, performs interval statistical analysis of humidity data and analyzes frequency distribution characteristics, and simultaneously integrates the data to generate environmental variable impact data.

[0026] S302: Based on the flue gas classification results and the environmental variable impact data, calculate the correlation between the mean wind speed, humidity frequency distribution characteristics and flue gas diffusion parameters, compare the impact of wind speed and humidity on flue gas diffusion parameters, and perform a joint impact analysis of wind speed and humidity through normalization to generate environmental impact analysis data.

[0027] S303: Based on the environmental impact analysis data, perform joint adjustment of sensor sensitivity parameters, calculate a new threshold by weighting the combined effects of wind speed and humidity with the current sensor sensitivity parameters, group and archive the calculation results according to parameter type, and generate the adjusted sensor parameters.

[0028] As a further aspect of the present invention, the new threshold is defined according to the formula:

[0029] ;

[0030] Calculations are performed, in which, Represents the new threshold. The measured value representing wind speed. The measured value represents the relative humidity.

[0031] As a further aspect of the present invention, based on the adjusted sensor parameters, after detecting the type of fire smoke, the preset response device is activated to transmit the fire alarm signal to the fire sprinkler head and trigger the sprinkler head water supply mechanism, and the fire alarm signal is simultaneously sent to the smoke prevention and ventilation equipment. The specific steps for generating the fire alarm response signal are as follows:

[0032] S401: Based on the adjusted sensor parameters, perform smoke type detection. By receiving the spectral feature signal of the smoke sensor in real time and comparing it point by point with the new threshold, extract the smoke type data that matches the new threshold condition and generate a fire alarm trigger signal.

[0033] S402: Based on the fire alarm trigger signal, activate the preset response device, start the sprinkler water supply mechanism by transmitting the signal to the fire sprinkler control unit, simultaneously send the signal to the smoke prevention and ventilation device and activate the ventilation mode, and generate a fire equipment response status record.

[0034] S403: Based on the fire equipment response status record, transmit the fire equipment response status data to the central fire control platform, record the trigger time and response equipment number, monitor the equipment operation status in real time, and generate a fire alarm response signal.

[0035] As a further aspect of the present invention, based on the fire alarm response signal, a real-time fire alarm notification is sent to the receiving terminal of the fire department through an IoT gateway, the fire alarm time, type, and triggering device data are uploaded, and all smoke monitoring and response processes are recorded to generate emergency communication and data records. The specific steps are as follows:

[0036] S501: Based on the fire alarm response signal, the fire alarm signal is received through the Internet of Things gateway, the trigger time, type and device number are extracted and converted into structured data, combined into standard data packets according to data priority and sent to the fire department receiving terminal to generate fire alarm notification data.

[0037] S502: Based on the fire alarm notification data, the fire alarm trigger time, type and device number are mapped to the platform data table format one by one through the network interface and uploaded. After the upload is completed, the transmission status is recorded in real time and the transmission result is verified to obtain the fire alarm information upload record.

[0038] S503: Based on the fire alarm information upload record, record the smoke monitoring and response process, extract key feature data from the historical monitoring of the sensors and the device action trigger record, integrate them according to the time sequence, and store the response events after labeling them with data tags to generate emergency communication and data records.

[0039] A smart fire smoke detection and alarm system based on the Internet of Things includes:

[0040] The spectral signal capture module installs spectral analysis sensors in key areas of the building to capture spectral signals, directly compares and filters duplicate data to delete duplicates, and transmits the data in segments to the cloud database through a batch transmission mechanism for structured and hierarchical storage to generate a spectral dataset.

[0041] Based on the spectral dataset, the spectral data analysis module reads the light intensity values ​​in the spectral data point by point and constructs a continuous spectral curve. It performs interval matching of peak positions by comparing them with the standard spectral range, calculates the similarity score between the calibrated peak parameters and the standard spectral data of the flue gas type, performs flue gas type matching and classification, and generates flue gas classification results.

[0042] The environmental data analysis module collects real-time wind speed and humidity data through the environmental monitoring sensor, calculates the correlation between the average wind speed, humidity frequency distribution characteristics and smoke diffusion parameters, and calculates a new threshold by weighting the combined effects of wind speed and humidity with the current sensor sensitivity parameters, thereby generating the adjusted sensor parameters.

[0043] Based on the adjusted sensor parameters, the smoke monitoring and response module receives the spectral characteristic signal of the smoke sensor in real time and compares it with the new threshold point by point to activate the preset response device and generate a fire equipment response status record.

[0044] Based on the fire equipment response status record, the data transmission recording module transmits the fire equipment response status data to the central fire control platform, records the trigger time and response equipment number, extracts and converts it into structured data, sends it to the fire department's receiving terminal, and generates fire alarm notification data.

[0045] Based on the fire alarm notification data, the information integration and reporting module maps the fire alarm trigger time, type, and device number to the platform data table format through the network interface and uploads them, records the smoke monitoring and response process, and integrates key feature data from historical sensor monitoring and device action trigger records according to the time sequence to generate emergency communication and data records.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In this invention, by capturing and analyzing the spectral characteristic curves of smoke, different types of smoke can be distinguished, enabling efficient differentiation between fire smoke and non-fire smoke, reducing false alarms. Combined with real-time environmental data, the sensor sensitivity and alarm threshold are dynamically adjusted, maintaining high reliability even in complex and variable environments. At the same time, it can simultaneously trigger the response of multiple devices, quickly activating mechanisms such as fire sprinklers and smoke exhaust equipment, and transmitting fire alarm signals to relevant departments in real time, shortening response time and optimizing the linkage effect of fire emergency response. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the steps of the present invention;

[0049] Figure 2 This is a flowchart of steps S1 of the present invention;

[0050] Figure 3 This is a flowchart of steps S2 of the present invention;

[0051] Figure 4 This is a flowchart of steps S3 of the present invention;

[0052] Figure 5 This is a flowchart of step S4 of the present invention;

[0053] Figure 6 This is a flowchart of steps S5 of the present invention;

[0054] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0057] Please see Figure 1 A smart fire smoke sensing alarm method based on the Internet of Things includes the following steps:

[0058] S1: Install spectral analysis sensors in key areas of the building to capture spectral signals, collect flue gas spectral data at preset time intervals, and transmit the data to the cloud platform through IoT network nodes to unify the data format and generate a spectral dataset.

[0059] S2: Based on the spectral dataset, analyze the spectral characteristic curves of flue gas, pinpoint the positions of key absorption peaks, match and classify the spectral peaks with the standard spectral range according to the flue gas type, determine the flue gas type based on the matching results, and generate flue gas classification results.

[0060] S3: Based on the flue gas classification results, combined with real-time wind speed and humidity data collected by environmental monitoring sensors, analyze the degree of influence of wind speed and humidity on flue gas propagation, combine environmental variables with the sensitivity parameters of flue gas sensors, calculate a new sensitivity threshold, and generate adjusted sensor parameters.

[0061] S4: Based on the adjusted sensor parameters, after detecting the type of fire smoke, activate the preset response device, transmit the fire alarm signal to the fire sprinkler head and trigger the sprinkler head water supply mechanism, and simultaneously send the fire alarm signal to the smoke prevention and ventilation equipment to generate a fire alarm response signal.

[0062] S5: Based on the fire alarm response signal, it sends a real-time fire alarm notification to the receiving terminal of the fire department through the IoT gateway, uploads the fire alarm time, type and triggering device data, records all smoke monitoring and response processes, and generates emergency communication and data records.

[0063] The spectral dataset includes smoke spectral feature values, timestamp data, and sensor deployment location records. Smoke classification results include fire smoke category identifiers, non-fire smoke category identifiers, and spectral absorption peak location information. Adjusted sensor parameters include updated sensitivity thresholds and dynamic alarm trigger values. Fire alarm response signals include fire sprinkler activation signals, smoke control signals, and fire alarm status identifiers. Emergency communication and data recording include fire alarm event notification data, equipment trigger records, and fire alarm event time-series logs.

[0064] Please see Figure 2 The specific steps of S1 are as follows:

[0065] S101: Install spectral analysis sensors in key areas of the building to capture spectral signals. By adjusting the sampling frequency and spectral band range, the flue gas spectral signal is sampled in segments. Combined with the sensor interface protocol, the collected data is transmitted to the Internet of Things network node in the form of digital signals to generate the raw dataset.

[0066] The operation of installing spectral analysis sensors in critical areas of buildings includes selecting the appropriate sensor type, accurately determining the installation location, and calibrating the sensor. The sensor type must be able to accurately capture spectral signals in flue gas for more accurate air quality monitoring. The selection of the installation location should be based on maximizing signal capture efficiency. The calibration process ensures that the data output by the device accurately reflects the actual spectral conditions in the environment. By adjusting the sampling frequency and spectral band, the sensitivity and accuracy of the device in detecting different chemical components can be optimized. Furthermore, the data is transmitted in the form of digital signals through protocols to ensure accurate data transmission and efficient subsequent processing.

[0067] S102: Based on the original dataset, duplicate data is directly compared, filtered, and deleted; data with different field types are uniformly encoded; and field mapping rules are set for consistency calibration to obtain a standardized dataset.

[0068] Based on the original dataset, the first step is to perform data cleaning to remove duplicate data. This process involves directly comparing the completeness and uniqueness of each data entry. After data cleaning, the next step is to encode the discrepancies in the data to unify the data format and enhance the consistency and usability of the dataset. Field mapping involves setting specific transformation rules to ensure that each field in the dataset accurately corresponds to the corresponding field in the database. This process not only improves data processing efficiency but also reduces errors that may occur during data parsing. The resulting standardized dataset is structured and consistent, improving data reliability and the accuracy of subsequent analysis.

[0069] S103: Based on a standardized dataset, data is transmitted to a cloud database in segments through a batch transmission mechanism. Data collection time, geographical location, and spectral feature labels are added to the data, and structured hierarchical storage is performed to generate a spectral dataset.

[0070] By utilizing standardized datasets, the batch uploading and cloud storage of data involves segmenting the data and securely transmitting it to a cloud database. Each data segment is assigned a timestamp, a geographic location tag, and a spectral feature tag. These tags provide rich contextual information for the data, which is helpful for subsequent data analysis and applications. The structured storage of the cloud database is optimized according to the data type and purpose, ensuring the speed and accuracy of data retrieval. The process fully leverages the elasticity and scalability of cloud computing, and the generated spectral dataset provides accurate and real-time data support for subsequent analysis and decision-making.

[0071] Please see Figure 3 The specific steps of S2 are as follows:

[0072] S201: Based on the spectral dataset, the light intensity values ​​in the spectral data are read point by point and a continuous spectral curve is constructed. Peak detection is performed on the spectral curve to identify the local maximum value region, the peak center position is calculated and the corresponding light intensity value is recorded. Combined with curve fitting, the contour and position of the key peak are confirmed, and spectral feature peak data is generated.

[0073] By reading the light intensity values ​​from the spectral data point by point and constructing a continuous spectral curve, according to the formula... Calculate the spectral characteristic peak data. Where, Represents spectral characteristic peaks, Representing the The light intensity value of each data point Representing the Wavelength weights for each data point. Assume the spectral data contains... There are 10 data points, and the light intensity value of each point is 1. Wavelength weight at each point It can be set based on the correlation strength between wavelength and spectral characteristics. For example, for And it has the following light intensity values ​​and wavelength weights: , Spectral characteristic peaks The calculation process is as follows:

[0074] ;

[0075] ;

[0076] This result shows that by using a weighted summation method, the effects of light intensity and wavelength can be comprehensively considered, thereby accurately calculating the characteristic peak and providing precise numerical support for subsequent spectral analysis.

[0077] S202: Based on spectral characteristic peak data, the peak positions are matched by comparing with the standard spectral range, the absorption peak parameters that meet the range conditions are extracted, and a unique identifier is assigned to each calibrated absorption peak to generate key absorption peak calibration data.

[0078] Based on the peak data of spectral characteristics, this stage of processing involves comparing the peak position with the standard spectral range to ensure that the extracted peaks conform to the preset standard spectral range. This comparison process requires a high-precision matching algorithm to ensure accurate identification of the peak position. Each successfully matched peak is assigned a unique identifier, which is indexed in the database to facilitate subsequent queries and analysis. The generated key absorption peak calibration data provides a key reference standard for subsequent analysis and application, increasing the traceability and scientific nature of data processing.

[0079] S203: Based on the key absorption peak calibration data, calculate the similarity score between the calibration peak parameters and the standard spectral data of the flue gas type, associate the peak features with the corresponding flue gas type, perform flue gas type matching and classification, and generate flue gas classification results;

[0080] Similarity score, according to the formula:

[0081] ;

[0082] The calculation is performed, where represents the similarity score. This represents the peak parameter value of the flue gas sample at the key absorption peak. This represents the peak parameter value in the standard spectral data corresponding to the flue gas type. This represents the total energy value of the absorption peak obtained in the actual test. This represents the pre-set standard absorption peak energy value.

[0083] In practice, The light intensity at a specific wavelength can be obtained by performing spectral analysis on a flue gas sample using a spectrometer. For example, the light intensity measured at a wavelength of 650 nm is 120 units.

[0084] parameter This represents the peak parameter in the standard spectral data corresponding to the flue gas type. This value is derived from historical data standardization and is generally a constant value at the same wavelength, such as setting the standard peak value at a wavelength of 650 nm to 100 units.

[0085] parameter This is the total energy of the absorption peak obtained in the actual test, which can be calculated from the integrated spectral data. The total energy of the absorption peak is set to 5000 units when testing the flue gas sample.

[0086] parameter This represents the pre-defined standard absorption peak energy, which is also determined based on the standardization process and average value, and is assumed to be 4500 units.

[0087] First calculate and The product of, i.e.:

[0088] ;

[0089] calculate and The absolute value of the difference, i.e.:

[0090] ;

[0091] Find the square root of the difference above, that is:

[0092] ;

[0093] Finally, the similarity score is calculated using the previous results:

[0094] ;

[0095] The results show that the obtained similarity scores This score reflects the degree of matching between the flue gas sample and a predetermined standard; a higher value indicates a greater similarity. Based on this score, the type of flue gas can be further determined. If the score reaches a certain threshold, it is identified as a specific type of flue gas, supporting environmental monitoring and control decision-making processes.

[0096] Please see Figure 4 The specific steps of S3 are as follows:

[0097] S301: Collects real-time wind speed and humidity data through environmental monitoring sensors, performs time series grouping of wind speed data and extracts the wind speed mean of each group, performs interval statistical analysis of humidity data and analyzes frequency distribution characteristics, and simultaneously integrates the data to generate environmental variable impact data.

[0098] The process of collecting real-time wind speed and humidity data through environmental monitoring sensors involves the selection and configuration of high-precision sensors. The sensors periodically send the acquired data to the central processing unit. This data is first grouped into time series, with each group including wind speed data within a certain time window. By calculating the average wind speed of each group, the statistical characteristics of wind speed within that time period can be effectively reflected. At the same time, humidity data is processed through interval statistical analysis to analyze the frequency of humidity occurrence in each interval. This process is crucial for understanding and predicting changes in environmental conditions. The concurrent data integration work ensures the integrity and consistency of the data, and the generated environmental variable impact data provides real-time and accurate basic data for subsequent analysis.

[0099] S302: Based on the flue gas classification results and environmental variable impact data, calculate the correlation between the mean wind speed, humidity frequency distribution characteristics and flue gas diffusion parameters, compare the impact of wind speed and humidity on flue gas diffusion parameters, and perform joint impact analysis of wind speed and humidity through normalization to generate environmental impact analysis data.

[0100] The above content calculates the correlation between the average wind speed, the frequency distribution characteristics of humidity, and the flue gas diffusion parameters, according to the formula. Calculate the environmental impact analysis data. Where, Represents the correlation coefficient. and These represent sample values ​​for wind speed and humidity, respectively. and It is the corresponding sample mean. and It is the sample standard deviation. That is the sample size. Assume there are... Sample points, wind speed sample values meters per second, humidity sample value The sample means for wind speed and humidity were respectively meters per second %, Sample standard deviation meters per second %. Correlation coefficient The calculation process is as follows:

[0101] ;

[0102] The results show that wind speed and humidity are highly correlated with flue gas diffusion parameters, providing an accurate basis for quantitative analysis and helping to better understand the impact of environmental variables on flue gas diffusion.

[0103] S303: Based on environmental impact analysis data, perform joint adjustment of sensor sensitivity parameters, calculate a new threshold by weighting the combined effects of wind speed and humidity with the current sensor sensitivity parameters, group and archive the calculation results according to parameter type, and generate the adjusted sensor parameters;

[0104] The new threshold is determined according to the formula:

[0105] ;

[0106] Calculations are performed, in which, Represents the new threshold. The measured value representing wind speed. The measured value represents the relative humidity.

[0107] wind speed and relative humidity This data was actually measured using environmental monitoring equipment. Wind speed The measured speed was 10 m / s, and the relative humidity was... The measured value is 80% (or 0.8 to suit calculations). Data is obtained using standard sensors at the weather station and transmitted to the central processing unit via data transmission technology.

[0108] Calculate wind speed and relative humidity The product of:

[0109] ;

[0110] Calculate the sum and square of the difference between wind speed and humidity:

[0111] ;

[0112] ;

[0113] Calculate the square root of the difference and handle the interior of the absolute value:

[0114] ;

[0115] Divide the sum by the square root of the difference:

[0116] ;

[0117] Add the results together to get the new threshold:

[0118] ;

[0119] The results show that, under conditions of wind speed of 10 m / s and humidity of 80%, the new sensor threshold should be adjusted to 9.083. This represents the sensitivity threshold that the sensor needs to be set under given environmental conditions to ensure it accurately responds to environmental changes and makes appropriate adjustments or reactions. In this way, the sensor parameter adjustment can more accurately match the actual changes in environmental conditions, enhancing stability and reliability.

[0120] Please see Figure 5 The specific steps of S4 are as follows:

[0121] S401: Based on the adjusted sensor parameters, the smoke type is detected. The smoke sensor's spectral feature signal is received in real time and compared point by point with the new threshold. The smoke type data that matches the new threshold condition is extracted and a fire alarm trigger signal is generated.

[0122] In the process of smoke type detection, the adjusted sensor parameters play a crucial role. First, the sensor needs to receive spectral characteristic signals from the smoke in real time. These signals contain key chemical composition information, and each signal point represents the light intensity at a specific wavelength, reflecting the specific type of smoke. Each received spectral signal is compared point-by-point with a pre-set new threshold. This comparison process is implemented through a high-precision algorithm to ensure that every data point matches accurately. When the signal intensity detected by the sensor exceeds the threshold, the system identifies the smoke type corresponding to that spectral characteristic and triggers the corresponding fire alarm signal. Furthermore, the system records all smoke type data that match the new threshold conditions; this data is subsequently used to generate detailed fire alarm trigger records. This process not only ensures the response speed of the fire alarm system but also significantly improves the accuracy of fire alarm detection, providing reliable data support for safety monitoring and emergency response, significantly improving the efficiency and reliability of the fire protection system, and ensuring that measures can be taken quickly and accurately in the event of a fire.

[0123] S402: Based on the fire alarm trigger signal, activate the preset response device, start the sprinkler water supply mechanism by transmitting the signal to the fire sprinkler control unit, simultaneously send the signal to the smoke prevention and ventilation equipment and activate the ventilation mode, and generate a fire equipment response status record;

[0124] Based on the fire alarm trigger signal, the core task at this stage is to effectively transmit the signal to the fire sprinkler control unit and activate the sprinkler water supply mechanism to respond to the fire. Specifically, after the fire alarm trigger signal is generated by the sensor, it is immediately transmitted to the central control unit of the fire protection system via wired or wireless network. The central control unit quickly analyzes the signal, confirms its validity, and then sends instructions to each associated fire sprinkler control unit. Upon receiving the activation instruction, the control unit triggers the sprinkler activation mechanism, thereby starting the water supply. Simultaneously, the signal is also transmitted to the smoke control and ventilation system. Upon receiving the activation signal, the smoke control and ventilation equipment automatically opens the exhaust fans according to the design preset, increasing the exhaust volume and quickly expelling smoke and harmful gases. This series of actions ensures that preliminary fire extinguishing and smoke control measures can be implemented in the early stages of a fire, reducing potential losses and injuries caused by the fire. The generated fire equipment response status records provide detailed operational data for future analysis and evaluation.

[0125] S403: Based on the fire equipment response status record, transmit the fire equipment response status data to the central fire control platform, record the trigger time and response equipment number, monitor the equipment operation status in real time, and generate fire alarm response signals;

[0126] After the fire equipment response status record is generated, data processing and transmission become crucial follow-up steps. At this point, every response and its status from the fire equipment is recorded in real time, including the specific trigger time, the responding equipment number, and its operational status. This information is then sent to the central fire control platform, which serves as the information aggregation and monitoring center for the entire fire protection system. Through a high-speed data communication network, response data is rapidly uploaded to the platform, ensuring the timeliness and accuracy of the information. The monitoring software on the platform processes and displays the received data, updating the equipment's operational status and response in real time. Furthermore, the platform has data analysis capabilities, enabling it to aggregate and analyze historical data, identify potential system vulnerabilities or equipment malfunctions, and further guide the maintenance and upgrades of fire equipment. This complete link from equipment response to data monitoring not only improves the fire protection system's responsiveness and safety management efficiency but also supports rapid decision-making in emergencies, enhancing overall safety assurance.

[0127] Please see Figure 6 The specific steps of S5 are as follows:

[0128] S501: Based on the fire alarm response signal, the fire alarm signal is received through the Internet of Things gateway, the trigger time, type and device number are extracted and converted into structured data, and the data is combined into standard data packets according to the data priority and sent to the fire department receiving terminal to generate fire alarm notification data.

[0129] In the fire alarm response signal reception process, the IoT gateway plays a crucial data relay role. First, the gateway receives fire alarm signals from sensors, including key information such as trigger time, fire alarm type, and device number. The data is converted into a structured format in real time to ensure the accuracy and efficiency of subsequent processing. Then, the system prioritizes the data according to its urgency level and assembles it into a standard data packet. This data packet collectively contains all the necessary fire alarm information, ensuring that the fire department's receiving terminal can quickly acquire and process this information. The data packet is sent through a secure network channel, with each step accompanied by strict security protocols to ensure the security and integrity of the information during transmission. The generated fire alarm notification data not only provides the fire department with real-time fire alarms but also, through precise information recording, provides foundational data for subsequent investigation and analysis, greatly improving the efficiency and effectiveness of emergency response.

[0130] S502: Based on fire alarm notification data, the fire alarm trigger time, type and device number are mapped to the platform data table format and uploaded through the network interface. After the upload is completed, the transmission status is recorded in real time and the transmission result is verified to obtain the fire alarm information upload record.

[0131] Based on fire alarm notification data, the entire data upload and recording process involves several key steps. First, the fire alarm trigger time, type, and device number are extracted item by item from the received data and mapped to the central platform's data table to ensure compatibility of all uploaded data with the platform's storage format. After data mapping, the system uploads the information to the central monitoring platform via a network interface. This upload process is equipped with advanced data encryption and security verification measures to ensure the security and reliability of data transmission. After the upload is complete, the system not only records the status of each transmission but also verifies the transmission results to ensure the accuracy and integrity of the data. The generated fire alarm information upload records not only provide fire departments with crucial fire alarm handling data but also ensure the transparency and traceability of the data processing workflow, enhancing response capabilities and management efficiency in emergency situations.

[0132] S503: Based on fire alarm information upload records, record the smoke monitoring and response process. By extracting key feature data from historical sensor monitoring and equipment action trigger records, integrate them according to time sequence, and store the response events after labeling the data, generate emergency communication and data records.

[0133] In the process of generating emergency communications and data logs, integrating and tagging historical monitoring data becomes a core task. Key features, including smoke composition, concentration, and trends, are extracted from the historical monitoring data of the sensors. The data is integrated according to time series to ensure the timeliness and continuity of information. Simultaneously, the trigger records of all relevant equipment actions are captured and recorded, such as the activation time and duration of response measures like water spraying and smoke extraction. The data is tagged by the system for rapid identification and access in subsequent analysis and investigation. Ultimately, all information is stored in a secure database, providing crucial data support for future safety analysis, equipment maintenance, and upgrade decisions, ensuring continuous system optimization and improved accuracy of emergency response.

[0134] Please see Figure 7 A smart fire smoke detection and alarm system based on the Internet of Things includes:

[0135] The spectral signal capture module installs spectral analysis sensors in key areas of the building to capture spectral signals, directly compares and filters duplicate data to delete duplicates, and transmits the data in segments to the cloud database through a batch transmission mechanism for structured and hierarchical storage to generate a spectral dataset.

[0136] The spectral data analysis module is based on the spectral dataset. It reads the light intensity values ​​in the spectral data point by point and constructs a continuous spectral curve. It performs interval matching of peak positions by comparing with the standard spectral range, calculates the similarity score between the calibrated peak parameters and the standard spectral data of the flue gas type, performs flue gas type matching and classification, and generates flue gas classification results.

[0137] The environmental data analysis module collects real-time wind speed and humidity data through environmental monitoring sensors, calculates the correlation between the average wind speed, humidity frequency distribution characteristics and smoke diffusion parameters, and calculates a new threshold by weighting the combined effects of wind speed and humidity with the current sensor sensitivity parameters, thereby generating adjusted sensor parameters.

[0138] Based on the adjusted sensor parameters, the smoke monitoring and response module receives the spectral characteristic signal of the smoke sensor in real time and compares it with the new threshold point by point to activate the preset response device and generate a fire equipment response status record.

[0139] The data transmission recording module transmits the response status data of the fire equipment to the central fire control platform based on the fire equipment response status record, records the trigger time and response equipment number, extracts and converts it into structured data, sends it to the fire department's receiving terminal, and generates fire alarm notification data.

[0140] The information integration and reporting module, based on fire alarm notification data, maps and uploads the fire alarm trigger time, type, and equipment number to the platform data table format through a network interface, records the smoke monitoring and response process, and integrates key feature data from historical sensor monitoring and equipment action trigger records according to time sequence to generate emergency communication and data records.

[0141] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for a smart fire smoke sensing alarm based on an Internet of Things, characterized in that, The method comprises the following steps: installing a spectrum analysis sensor in a key area of a building to capture a spectrum signal, collecting smoke spectrum data according to a preset time interval, and transmitting the data to a cloud platform through an Internet of Things network node, unifying a data format, and generating a spectrum data set; based on the spectrum data set, analyzing the spectrum characteristic curve of the smoke, calibrating the position of the key absorption peak, matching and classifying the spectrum peak value and the standard range according to the standard spectrum range of the smoke type, and judging the smoke type according to the matching result to generate a smoke classification result; based on the smoke classification result, combining the real-time wind speed and humidity data collected by the environmental monitoring sensor, analyzing the influence degree of wind speed and humidity on smoke propagation, combining the environmental variables with the sensitivity parameters of the smoke sensor, calculating a new sensitivity threshold, and generating adjusted sensor parameters; based on the adjusted sensor parameters, after detecting the type of fire smoke, activating a preset response device, transmitting a fire alarm signal to a fire sprinkler and triggering a sprinkler water supply mechanism, synchronously sending a fire alarm signal to a smoke exhaust device to generate a fire alarm response signal; based on the fire alarm response signal, sending a real-time fire alarm notification to a receiving terminal of a fire department through an Internet of Things gateway, uploading fire alarm time, type and trigger device data, and recording all smoke monitoring and response processes to generate emergency communication and data records; The specific steps of analyzing the spectrum characteristic curve of the smoke based on the spectrum data set, calibrating the position of the key absorption peak, matching and classifying the spectrum peak value and the standard range according to the standard spectrum range of the smoke type, and judging the smoke type according to the matching result to generate a smoke classification result are as follows: based on the spectrum data set, reading the light intensity value in the spectrum data point by point and constructing a continuous spectrum curve, detecting and identifying the local maximum value region of the spectrum curve, calculating the peak center position, and recording the corresponding light intensity value, combining curve fitting to confirm the contour and position of the key peak, and generating spectrum characteristic peak data; based on the spectrum characteristic peak data, matching the peak position by comparing the standard spectrum range, extracting the absorption peak parameters that meet the range condition, and assigning a unique identification code to each calibrated absorption peak to generate key absorption peak calibration data; based on the key absorption peak calibration data, calculating the similarity score of the calibrated peak value parameters and the standard spectrum data of the smoke type, associating the peak characteristics with the corresponding smoke type, and performing smoke type matching and classification to generate a smoke classification result; The similarity score is calculated according to the formula: ； performing a calculation, wherein representing a similarity score, representing a peak parameter value of the flue gas sample on a key absorption peak, representing a peak parameter value in standard spectrum data corresponding to the flue gas type, representing a total energy value of the absorption peak obtained in the actual test, representing a pre-set standard absorption peak energy value. 2.The IoT-based intelligent fire smoke sensing alarm method according to claim 1, characterized in that, The spectrum data set includes smoke spectrum characteristic values, timestamp data and sensor deployment location records, the smoke classification result includes fire smoke category identification, non-fire smoke category identification and spectrum absorption peak position information, the adjusted sensor parameters include updated sensitivity threshold and dynamic alarm trigger value, the fire alarm response signal includes fire sprinkler start signal, smoke exhaust control signal and fire alarm state identification, and the emergency communication and data records include fire alarm event notification data, device trigger record and fire alarm event time sequence log. 3.The IoT-based intelligent fire smoke sensing alarm method according to claim 1, wherein, The specific steps for installing spectral analysis sensors in key areas of a building to capture spectral signals, collecting flue gas spectral data at preset time intervals, and transmitting the data to a cloud platform via IoT network nodes to generate a spectral dataset with a unified data format are as follows: Spectral analysis sensors are installed in key areas of the building to capture spectral signals. By adjusting the sampling frequency and spectral band range, the flue gas spectral signal is sampled in segments. Combined with the sensor interface protocol, the collected data is transmitted to the Internet of Things network node in the form of digital signals to generate the raw dataset. Based on the original dataset, duplicate data are directly compared, filtered, and deleted; data with different field types are uniformly encoded; and field mapping rules are set for consistency calibration to obtain a standardized dataset. Based on the standardized dataset, the data is transmitted to the cloud database in segments through a batch transmission mechanism. Acquisition time, geographical location, and spectral feature labels are added to the data, and the data is stored in a structured and hierarchical manner to generate a spectral dataset. 4.The IoT-based intelligent fire smoke sensing alarm method according to claim 1, wherein, Based on the flue gas classification results, and combined with real-time wind speed and humidity data collected by environmental monitoring sensors, the influence of wind speed and humidity on flue gas propagation is analyzed. The environmental variables are then combined with the sensitivity parameters of the flue gas sensors to calculate a new sensitivity threshold. The specific steps for generating the adjusted sensor parameters are as follows: Real-time wind speed and humidity data are collected by environmental monitoring sensors. The wind speed data is grouped into time series and the mean wind speed of each group is extracted. The humidity data is statistically analyzed by interval classification and frequency distribution characteristics. The data is integrated and unified to generate environmental variable impact data. Based on the flue gas classification results and the environmental variable impact data, the correlation between the mean wind speed, humidity frequency distribution characteristics and flue gas diffusion parameters is calculated respectively. The effects of wind speed and humidity on flue gas diffusion parameters are compared, and the joint impact analysis of wind speed and humidity is performed by normalization to generate environmental impact analysis data. Based on the environmental impact analysis data, the sensor sensitivity parameters are jointly adjusted. The combined effects of wind speed and humidity are weighted and calculated with the current sensor sensitivity parameters to calculate a new threshold. The calculation results are grouped, archived and recorded according to parameter type to generate the adjusted sensor parameters.

5. The fire smoke sensing and alarming method based on the Internet of Things according to claim 1, characterized in that, Based on the adjusted sensor parameters, after detecting the type of fire smoke, the preset response device is activated, the fire alarm signal is transmitted to the fire sprinkler head and the sprinkler head water supply mechanism is triggered, and the fire alarm signal is simultaneously sent to the smoke prevention and ventilation equipment. The specific steps for generating the fire alarm response signal are as follows: Based on the adjusted sensor parameters, smoke type detection is performed. By receiving the spectral feature signal of the smoke sensor in real time and comparing it point by point with the new threshold, smoke type data that matches the new threshold conditions is extracted and a fire alarm trigger signal is generated. Based on the fire alarm trigger signal, the preset response device is activated. The signal is transmitted to the fire sprinkler control unit to start the sprinkler water supply mechanism. At the same time, the signal is sent to the smoke prevention and ventilation equipment and the ventilation mode is activated, and a fire equipment response status record is generated. Based on the fire equipment response state record, the fire equipment response response state data is transmitted to the central fire control platform, the trigger time and the response equipment number are recorded, the equipment operation condition is monitored in real time, and the fire alarm response signal is generated. 6.The IoT-based intelligent fire smoke sensing alarm method according to claim 1, wherein, Based on the fire alarm response signal, real-time fire alarm notification is sent to the receiving terminal of the fire department through the Internet of Things gateway, fire alarm time, type and trigger device data are uploaded, and all smoke monitoring and response processes are recorded, and the specific steps of generating emergency communication and data record are: Based on the fire alarm response signal, the fire alarm signal is received through the Internet of Things gateway, the trigger time, type and equipment number are extracted and converted into structured data, combined into a standard data packet according to the data priority, and sent to the receiving terminal of the fire department, and the fire alarm notification data is generated; Based on the fire alarm notification data, the fire alarm trigger time, type and equipment number are mapped and uploaded one by one with the platform data table format through the network interface, and after uploading, the transmission state is recorded in real time and the transmission result is verified, and the fire information upload record is obtained; Based on the fire information upload record, the smoke monitoring and response process is recorded, the key feature data of the historical monitoring of the sensor and the device action trigger record are extracted, integrated according to the time sequence, and stored after labeling the response event data label, and the emergency communication and data record is generated.

7. A smart fire smoke sensing alarm system based on Internet of Things, characterized in that, The Internet of Things-based intelligent fire smoke sensing alarm method according to any one of claims 1-6, the system comprises: The spectrum signal capture module installs spectrum analysis sensors in key areas of the building, captures spectrum signals, directly compares and filters repeated data, and transmits the data to the cloud database in batches through a batch transmission mechanism for structured hierarchical storage to generate a spectrum data set; The spectrum data analysis module, based on the spectrum data set, reads the light intensity values in the spectrum data point by point and constructs a continuous spectrum curve, matches the peak value position in the interval by comparing the standard spectrum range, calculates the similarity score of the calibrated peak value parameter and the smoke type standard spectrum data, and performs smoke type matching and classification to generate a smoke classification result; The environmental data analysis module collects real-time wind speed and humidity data through the environmental monitoring sensor, calculates the correlation of wind speed average, humidity frequency distribution characteristics and smoke diffusion parameters, and performs weighted operation on the joint influence of wind speed and humidity and the current sensor sensitivity parameter to calculate a new threshold value, and generates an adjusted sensor parameter; The smoke monitoring response module, based on the adjusted sensor parameter, receives the spectrum feature signal of the smoke sensor in real time and compares it with the new threshold value point by point, activates the preset response device, and generates a fire equipment response state record; The data transmission record module, based on the fire equipment response state record, transmits the fire equipment response response state data to the central fire control platform, records the trigger time and the response equipment number, extracts and converts it into structured data, and sends it to the receiving terminal of the fire department to generate fire alarm notification data; The information integration reporting module maps and uploads the fire alarm trigger time, type and device number item by item with the platform data table format based on the fire alarm notification data through the network interface, records the smoke monitoring and response process, integrates according to the time sequence by extracting the key feature data and device action trigger record of the historical monitoring of the sensor, and generates the emergency communication and data record.

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