Combustible gas concentration detection alarm method and device

Abstract

The invention discloses a combustible gas concentration detection alarm method and device, and relates to the technical field of gas detection alarm, and the method comprises the steps: collecting combustible gas concentration data in an environment through multiple types of sensors, generating a real-time concentration signal, carrying out noise suppression processing, and obtaining preprocessed concentration data; leakage prediction is carried out, a gas leakage prediction result is generated, similarity calculation is carried out in combination with historical sensing scene data, and a gas leakage risk level is determined; and triggering the multi-stage alarm strategy, and executing the multi-stage alarm strategy to link the intelligent equipment to perform safety control. The technical problems that in the prior art, multiple combustible gases cannot be comprehensively detected, detection data are prone to being interfered by the environment, prospective prediction of gas leakage risk levels is lacked, data privacy protection is insufficient, consequently, the combustible gas concentration detection accuracy is insufficient, and alarming is not timely and accurate enough are solved, intelligent home linkage is achieved, and the detection efficiency is improved. The technical effects of improving the combustible gas detection accuracy and reducing the false alarm rate are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to combustible gas detection, and in particular to a combustible gas concentration detection alarm method and device. Background Art

[0002] If combustible gases such as natural gas and liquefied petroleum gas leak and mix with air to a certain concentration, they may cause explosions, fires and other serious accidents when encountering excitation sources such as electric sparks generated by turning on electrical appliances. Traditional household combustible gas detection equipment has many shortcomings. Most of them use a single sensor and can only detect specific types of combustible gases. They cannot cope with the coexistence of multiple combustible gases in complex home environments such as kitchens. Moreover, they are easily affected by environmental interference such as the noise of kitchen appliances and Wi-Fi signals during operation, resulting in inaccurate gas concentration data and difficulty in accurately determining whether a gas leak has occurred, which seriously misleads the accurate judgment of combustible gas leaks. More importantly, traditional detection methods lack early warning capabilities. They usually alarm only when the gas leak reaches a high concentration and danger is about to occur, and cannot provide early warning at the early stage of the leak. In addition, there is no comprehensive judgment based on home scenarios (such as whether someone is at home, whether there is an open flame source, etc.), which makes the risk level classification inaccurate and difficult to link smart home devices for effective safety processing, such as being unable to close gas valves and start ventilation equipment in time.

[0003] Therefore, the current relevant technologies have the problems of being unable to comprehensively detect multiple combustible gases, detection data being easily affected by environmental interference, lacking forward-looking predictions of gas leakage risk levels, and insufficient data privacy protection, which in turn lead to technical problems such as insufficient accuracy in combustible gas concentration detection and insufficiently timely and accurate alarms. Summary of the invention

[0004] The present application provides a combustible gas concentration detection alarm method and device, which solves the technical problems existing in the prior art, such as the inability to comprehensively detect multiple combustible gases, the susceptibility of detection data to environmental interference, the lack of forward-looking prediction of gas leakage risk levels, and insufficient data privacy protection, which in turn lead to insufficient combustible gas concentration detection accuracy and untimely and inaccurate alarms. The application realizes smart home linkage and achieves the technical effect of improving the accuracy of combustible gas detection and reducing the false alarm rate.

[0005] The present application provides a combustible gas concentration detection and alarm method, which includes: collecting combustible gas concentration data in an environment through multiple types of sensors to generate a real-time concentration signal, performing noise suppression processing on the real-time concentration signal to obtain pre-processed concentration data; performing leakage prediction based on the pre-processed concentration data to generate a gas leakage prediction result, performing similarity calculation based on the gas leakage prediction result combined with historical sensor scene data to determine the gas leakage risk level; triggering a multi-level alarm strategy according to the gas leakage risk level, and executing the multi-level alarm strategy to link smart devices for safety control.

[0006] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: obtaining a sensor data set, dynamically calibrating the sensor data set according to ambient temperature parameters and ambient humidity parameters, and determining sensor calibration data; performing gas concentration analysis based on the sensor calibration data, and generating the real-time concentration signal according to the concentration value; traversing the real-time concentration signal for pulse processing, and extracting abnormal pulse signals; using a wavelet transform algorithm to filter the abnormal pulse signal to generate a smooth concentration curve; and correcting the concentration value according to the smooth concentration curve to obtain the preprocessed concentration data.

[0007] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: constructing a time series concentration distribution diagram based on the smooth concentration curve, performing feature analysis according to the time series concentration distribution diagram, and determining dynamic leakage characteristics; retrieving historical sensing scene data to perform scene feature analysis and determine scene feature vectors; performing multi-dimensional similarity matching on the dynamic leakage characteristics and the scene feature vectors to generate a scene matching degree set; performing weighted clustering analysis on the scene matching degree set to obtain multiple scene feature clusters, traversing the multiple scene feature clusters for association screening, and determining an associated historical scene group, wherein the associated historical scene group includes multiple leakage event probabilities; constructing a leakage prediction model based on the multiple leakage event probabilities and the dynamic leakage characteristics, performing leakage prediction through the leakage prediction model, and generating a gas leakage prediction result; and performing risk decision analysis in combination with the gas leakage prediction result to determine the gas leakage risk level.

[0008] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: obtaining multiple historical gas usage scenario data, the multiple historical gas usage scenario data including gas equipment type data, usage period data and environmental parameter data; correlating the environmental parameter data with the gas equipment type data and analyzing them to determine leakage event data; calculating a similarity matrix between real-time sensing scenario data and the historical sensing scenario data according to the usage period data; performing linear regression analysis on the leakage event data based on the similarity matrix to construct the leakage prediction model.

[0009] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: establishing a sliding time window according to the smooth concentration curve, performing concentration calculation based on the sliding time window, and obtaining the concentration change rate; performing concentration duration detection on the sliding time window based on the concentration change rate, and extracting the peak duration; counting the number of times the concentration value in the sliding time window exceeds a preset warning line to determine the fluctuation frequency; combining the concentration change rate, the peak duration and the fluctuation frequency as a three-dimensional feature vector to obtain the dynamic leakage feature.

[0010] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: multi-dimensional sensor acquisition based on historical scenes to obtain multi-dimensional sensor data; time alignment processing is performed on the multi-dimensional sensor data to generate a scene data stream; feature dimension reduction is performed on the scene data stream using an autoencoder to generate a scene feature vector; and the leakage event label is associated with the scene feature vector and stored to generate the historical sensor scene data.

[0011] In a possible implementation, the combustible gas concentration detection alarm method also performs the following processing: matching the gas leakage risk level with a multi-level alarm strategy library to generate an alarm control instruction set; parsing based on the alarm control instruction set to generate an alarm priority sequence, triggering the local alarm module to execute the alarm according to the alarm priority sequence, and generating a linkage instruction; distributing the linkage instruction to the target smart device to control the target smart device to perform safety operations.

[0012] In a possible implementation, the combustible gas concentration detection and alarm method also performs the following processing: when the gas leakage risk level is level one, the multi-level alarm strategy library is searched to determine the first alarm control instruction, the local sound and light alarm is triggered and an early warning notification is pushed through the first alarm control instruction, and level one leakage record data is generated; when the gas leakage risk level is level two, the multi-level alarm strategy library is searched to determine the second alarm control instruction, the smart gas valve is closed and the ventilation equipment is started through the second alarm control instruction, and level two leakage record data is generated; when the gas leakage risk level is level three, the multi-level alarm strategy library is searched to determine the third alarm control instruction, an emergency broadcast signal is sent to the security system through the third alarm control instruction, and level three leakage record data is generated; the level one leakage record data, the level two leakage record data, and the level three leakage record data are alarm integrated to determine a leakage alarm record log, and the alarm control instruction set is extracted according to the leakage alarm record log.

[0013] In a possible implementation, the combustible gas concentration detection alarm method also performs the following processing: mapping the alarm control instruction set with the device control permission table of the target smart device to determine the alarm priority sequence; traversing the target smart device to perform status verification, and when the verification passes, triggering the local alarm module according to the alarm priority sequence to generate device operation instructions; encapsulating the device operation instructions into a standardized Internet of Things data packet and sending it to the device gateway to execute the alarm, and determining the linkage instruction.

[0014] The present application also provides a combustible gas concentration detection and alarm device, which includes: a pre-processed concentration data acquisition module, which is used to collect combustible gas concentration data in the environment through multiple types of sensors, generate real-time concentration signals, perform noise suppression processing on the real-time concentration signals, and obtain pre-processed concentration data; a gas leakage risk level determination module, which is used to predict leakage based on the pre-processed concentration data, generate gas leakage prediction results, perform similarity calculations based on the gas leakage prediction results combined with historical sensor scene data, and determine the gas leakage risk level; a multi-level alarm strategy execution module, which is used to trigger a multi-level alarm strategy according to the gas leakage risk level, and execute the multi-level alarm strategy to link smart devices for safety control.

[0015] The present application proposes a method and device for detecting and alarming the concentration of combustible gas. Through multiple types of sensors, the combustible gas concentration data in the environment is collected, a real-time concentration signal is generated, and noise suppression processing is performed to obtain pre-processed concentration data; leakage prediction is performed to generate gas leakage prediction results, and similar calculations are performed in combination with historical sensing scene data to determine the gas leakage risk level; a multi-level alarm strategy is triggered, and the multi-level alarm strategy is executed to link smart devices for safety control. The technical problems existing in the prior art, such as the inability to comprehensively detect multiple combustible gases, the detection data being susceptible to environmental interference, the lack of forward-looking prediction of the gas leakage risk level, and insufficient data privacy protection, which in turn lead to insufficient accuracy in combustible gas concentration detection and insufficient timely and accurate alarms, are solved. Smart home linkage is achieved, and the technical effect of improving the accuracy of combustible gas detection and reducing the false alarm rate is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0017] Figure 1 A schematic flow chart of a combustible gas concentration detection and alarm method provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of a flow chart of a combustible gas concentration detection and alarm method for determining a gas leakage risk level provided in an embodiment of the present application.

[0019] Figure 3 A schematic diagram of the structure of a combustible gas concentration detection and alarm device provided in an embodiment of the present application.

[0020] Description of the reference numerals: pre-processing concentration data acquisition module 10 , gas leakage risk level determination module 20 , multi-level alarm strategy execution module 30 . DETAILED DESCRIPTION

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0022] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0023] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0024] The present application embodiment provides a combustible gas concentration detection alarm method, such as Figure 1 As shown, the method includes: Step S100, collecting combustible gas concentration data in the environment through multiple types of sensors, generating a real-time concentration signal, performing noise suppression processing on the real-time concentration signal, and obtaining pre-processed concentration data.

[0025] Preferably, the combustible gas concentration data in the environment is collected through multiple types of sensors (infrared sensors, electrochemical sensors and semiconductor sensors). Specifically, the gas concentration is detected by utilizing the absorption characteristics of combustible gas to infrared light of a specific wavelength. Different combustible gases absorb infrared light to different degrees. By measuring the intensity change after the infrared light is absorbed, the concentration of the combustible gas can be calculated. Infrared sensors have the advantages of high precision, good stability and strong anti-interference ability. They are particularly suitable for detecting some combustible gases with obvious absorption characteristics in the infrared band, such as methane and ethane. The redox reaction of the combustible gas on the electrode generates an electric current, and the magnitude of the current is proportional to the concentration of the combustible gas. The electrochemical sensor has high selectivity and sensitivity to specific combustible gases, and can detect low concentrations of combustible gases. It is often used to detect combustible gases such as carbon monoxide and hydrogen.

[0026] Preferably, when the semiconductor material contacts the combustible gas, its resistance value will change. When the combustible gas is adsorbed on the semiconductor surface, it will cause the change of the carrier concentration inside the semiconductor, thereby causing the resistance change. The concentration of the combustible gas can be determined by measuring the resistance change. The semiconductor sensor has the advantages of fast response speed, low cost, and small size, but the selectivity is relatively poor and it is easily interfered by other gases. These sensors are used in combination to collect the combustible gas concentration information in the environment in real time and convert it into a corresponding electrical signal, that is, a real-time concentration signal, to achieve comprehensive detection of multiple combustible gases and improve the accuracy and reliability of detection. In the process of data acquisition by the sensor, the real-time concentration signal obtained by the sensor may be interfered by various noises, such as electromagnetic interference in the environment, noise of electrical equipment, etc. The noise will cause the collected gas concentration data to fluctuate or deviate, affecting the accurate judgment of the combustible gas concentration; therefore, the real-time concentration signal is subjected to noise suppression processing, such as noise suppression by digital filtering, wavelet analysis, etc., to remove the noise component in the concentration signal, retain useful concentration information, thereby obtaining pre-processed concentration data, which can more accurately reflect the true concentration of the combustible gas in the environment.

[0027] Furthermore, step S100 also includes step S110, acquiring a sensor data set, dynamically calibrating the sensor data set according to ambient temperature parameters and ambient humidity parameters, and determining sensor calibration data; step S120, performing gas concentration analysis based on the sensor calibration data, and generating the real-time concentration signal according to the concentration value; step S130, traversing the real-time concentration signal for pulse processing, and extracting abnormal pulse signals; step S140, filtering the abnormal pulse signal using a wavelet transform algorithm to generate a smooth concentration curve; step S150, correcting the concentration value according to the smooth concentration curve to obtain the preprocessed concentration data.

[0028] Preferably, infrared sensors, electrochemical sensors and semiconductor sensors are used to monitor combustible gases in the environment, and relevant data such as combustible gas concentration, ambient temperature parameters, ambient humidity parameters, etc. are continuously collected to form a sensing data set, wherein the ambient temperature and humidity will affect the performance of the sensor, resulting in deviations in the measurement results. For example, high temperature may change the physical properties of the sensor element and affect its response to the gas concentration; high humidity may form a water vapor film on the sensor surface, interfering with the normal reaction of the gas and the sensor; the sensing data set is dynamically calibrated according to the ambient temperature parameters and ambient humidity parameters. Specifically, through data analysis, the relationship between the gas concentration data output by the sensor and the ambient temperature and humidity is studied. According to the relationship obtained by the analysis, a polynomial model is selected to describe the influence of ambient temperature and humidity on the gas concentration measurement, and a calibration model is constructed. The sensing data set is then input into the calibration model for dynamic adjustment, and finally more accurate sensing calibration data is obtained.

[0029] Preferably, the sensor calibration data is subjected to gas concentration analysis, that is, features related to gas concentration are extracted from the sensor calibration data. For example, the output of an electrochemical sensor is a current signal proportional to the gas concentration, and the output of an infrared sensor is a change in light absorption intensity at a specific wavelength. The gas concentration is calculated based on the working principle and mathematical model of the sensor. For example, the output signal of some sensors is linearly related to the gas concentration, and the concentration can be calculated by a simple linear equation C=kS+b, wherein C is the gas concentration, S is the sensor output signal, k is the slope, and b is the intercept. The values ​​of k and b can be obtained by measuring and fitting a gas of known concentration; the calculated gas concentration value is then converted into a signal form suitable for transmission and processing, representing the value of the combustible gas concentration in the environment at different times, such as an analog signal (such as a voltage signal, a current signal) and a digital signal. For example, the concentration value is converted into a 4-20mA current signal, or converted into a digital signal through an analog-to-digital converter (ADC).

[0030] Preferably, the real-time concentration signal is traversed for pulse processing to extract abnormal pulse signals. Specifically, the data points in the real-time concentration signal are checked one by one, and the gas concentration value corresponding to each moment is checked in turn. The pulse signal is identified by setting an appropriate threshold and time window. Assuming that the fluctuation range of the gas concentration is within ±5% under normal circumstances, when the change amplitude of a data point exceeds 10%, and this change only lasts for 1-2 sampling cycles, it may be a pulse signal; after the pulse signal is identified, it is divided into different types according to the amplitude, duration, shape and other characteristics of the pulse. For example, some pulses may be caused by instantaneous interference of the sensor, with a small amplitude and a short duration, while some pulses may be caused by sudden gas leakage, with a large amplitude and a long duration; then an abnormal judgment rule is formulated to judge pulse signals with a large amplitude, a long duration or a high frequency of occurrence as abnormal pulses, which may indicate a sudden leakage of combustible gas, a sensor failure, etc.

[0031] Preferably, a wavelet transform algorithm is used to filter the abnormal pulse signal, that is, to remove noise and interference in the abnormal pulse signal, and generate a smooth concentration curve to more accurately reflect the actual gas concentration change trend. The wavelet transform can decompose the signal into wavelet coefficients of different scales and positions. The wavelet is like a set of "basis functions" with different frequencies and shapes. By performing convolution operations with the original signal, the signal can be analyzed at different time-frequency scales. For abnormal pulse signals, which have mutation characteristics, the wavelet transform can effectively decompose them at different scales, thereby distinguishing useful components and noise components in the signal. Specifically, the abnormal pulse signal is input into the wavelet transform algorithm, and the algorithm will decompose the signal into wavelet coefficients of different scales according to the selected wavelet basis function. In terms of scale, the characteristics of the signal will be presented in different ways. For example, on a larger scale, it mainly reflects the overall trend and low-frequency components of the signal; on a smaller scale, it reflects more of the details and high-frequency components of the signal. The noise and interference in the abnormal pulse signal are usually manifested as high-frequency components, while the real concentration change signal is mainly contained in the low-frequency components; then the threshold is set according to the characteristics of the signal and the noise level, and the signal less than the threshold is attenuated, and the signal greater than the threshold is retained or appropriately enhanced to effectively suppress the noise; the wavelet coefficients after threshold processing are reconstructed into the filtered signal through the inverse wavelet transform to accurately reflect the real change of gas concentration; finally, these filtered data points are connected in chronological order to generate a smooth concentration curve, which more clearly shows the trend of gas concentration change over time.

[0032] Preferably, the concentration value is corrected according to the smoothed concentration curve, that is, the original concentration value corresponding to each time point is replaced by the concentration value corrected according to the smoothed concentration curve, so as to obtain corrected concentration data, which is called preprocessed concentration data. It eliminates the influence of interference such as environmental factors, sensor errors and noise to a certain extent, and can more accurately reflect the gas concentration in the actual environment.

[0033] Step S200, performing leakage prediction based on the pre-processed concentration data to generate a gas leakage prediction result, performing similarity calculation based on the gas leakage prediction result in combination with historical sensing scene data to determine the gas leakage risk level.

[0034] Preferably, a gas leakage prediction model is established by using pre-processed concentration data through data analysis and machine learning. Specifically, a leakage prediction model is constructed based on a collaborative filtering algorithm, that is, by analyzing the similarities between concentration data at different time points or different sensor positions in the pre-processed concentration data, potential patterns and associations in the data are discovered, and historical pre-processed concentration data and known gas leakage events are used as training data. The characteristics of the concentration data (such as concentration change rate, continuous high concentration time period, etc.) are used as input to construct a leakage prediction model based on a collaborative filtering algorithm; then the current pre-processed concentration data is input into the trained prediction model to predict whether a gas leakage is likely to occur and the probability of the leakage, and a prediction result is output to obtain a gas leakage prediction result. The prediction result may be a probability value, indicating the possibility of a gas leakage in a certain time period in the future; it may also be a simple "yes" or "no" judgment, indicating whether a gas leakage is predicted.

[0035] Preferably, historical sensing scene data is obtained, and the historical sensing scene data includes information such as gas concentration data, environmental parameters (such as temperature, humidity, etc.) and actual conditions of whether gas leakage occurred at different times and environmental conditions in the past. A similarity measurement method is selected to compare the similarity between the current gas leakage prediction result and the historical sensing scene data, such as Euclidean distance, cosine similarity, etc. For a data set containing multiple features (such as gas concentration, temperature, humidity, etc.), the Euclidean distance between the current prediction result and the historical data point in the feature space can be calculated, and the smaller the distance, the more similar it is; then the gas leakage prediction result is calculated similarly with each data point in the historical sensing scene data to find the historical scene that is most similar to the current situation. Assuming that the current gas leakage prediction result has a probability value of 0.6 and includes information such as the current gas concentration, temperature and humidity, through similarity calculation, it is found that the gas concentration change trend, temperature, humidity and the probability of gas leakage at that time of a certain scene in the historical data are most similar to the current situation.

[0036] Preferably, the current gas leakage risk is graded based on the results of similarity calculations and in combination with actual gas leakage in historical scenarios. For example, the risk level is divided into three levels: low, medium, and high. If no gas leakage occurred in the historical scenario most similar to the current situation and the similarity is high, the current risk level is determined to be low; if gas leakage occurred in a certain proportion of similar historical scenarios, it is determined to be medium risk; if gas leakage occurred in most of the similar historical scenarios, the current risk level is high; thereby, the risk level is preliminarily determined and then adjusted in combination with environmental conditions, such as whether the current environmental conditions are worse than historical scenarios (such as high temperature, high humidity, etc., which may increase the risk of gas leakage), or whether there are some potential fault hazards in the current equipment status, etc., so as to more accurately reflect the actual gas leakage risk situation.

[0037] Further, such as Figure 2 As shown, step S200 also includes step S210, constructing a time series concentration distribution diagram based on the smooth concentration curve, performing feature analysis according to the time series concentration distribution diagram, and determining dynamic leakage characteristics; step S220, retrieving historical sensing scene data to perform scene feature analysis and determine a scene feature vector; step S230, performing multi-dimensional similarity matching on the dynamic leakage characteristics and the scene feature vector to generate a scene matching degree set; step S240, performing weighted clustering analysis on the scene matching degree set to obtain multiple scene feature clusters, traversing the multiple scene feature clusters for association screening, and determining an associated historical scene group, wherein the associated historical scene group contains multiple leakage event probabilities; step S250, constructing a leakage prediction model according to the multiple leakage event probabilities and the dynamic leakage characteristics, performing leakage prediction through the leakage prediction model, and generating a gas leakage prediction result; step S260, performing risk decision analysis in combination with the gas leakage prediction result to determine the gas leakage risk level.

[0038] Preferably, according to the smoothed concentration curve, the concentration values ​​at different times are arranged in chronological order to construct a time series concentration distribution diagram to intuitively display the change of gas concentration over time, and then the time series concentration distribution diagram is analyzed to extract the characteristics that can reflect the dynamic change of gas leakage, such as the concentration change rate (rapid rise, slow rise, fall, etc.), concentration peak, the length of time that the concentration continues to be higher than a certain threshold, and the frequency of fluctuations, so as to judge the possibility and severity of gas leakage; retrieve historical sensor scene data, that is, obtain sensor data collected in different scenes in the past from the database, including gas concentration, environmental parameters (such as temperature, humidity, pressure, etc.), sensor location information, and whether leakage occurs and other related data, analyze these historical sensor scene data, extract the key features of each scene, such as a specific combination of environmental parameters, correlation patterns of sensor data, etc., and then quantize and encode the extracted scene features to obtain a scene feature vector containing various feature information of the scene.

[0039] Preferably, the current dynamic leakage feature is compared with the scene feature vectors of each historical scene in multiple dimensions, and the similarity between them is calculated. For example, the concentration change feature, environmental parameter feature and other aspects are compared, and the similarity is measured using Euclidean distance, cosine similarity and the like. Then, the similarity value between each historical scene and the current dynamic leakage feature is obtained by calculation, and they are combined into a scene matching set to reflect the similarity between the current situation and each historical scene. Then, the importance of the gas leakage risk is judged according to different features, and different weights are assigned to the similarity of each dimension. Then, a cluster analysis is performed on the scene matching set, and historical scenes with higher similarity are classified into one category to form multiple scene feature clusters. Then, each scene feature cluster is traversed, and the characteristics of the historical scenes therein are analyzed to screen out historical scenes with a higher correlation with the current situation, that is, to screen out associated historical scene groups with a matching degree higher than a preset threshold, wherein the associated historical scene groups all contain probability records of gas leakage, that is, multiple leakage event records, for subsequent leakage prediction and risk assessment.

[0040] Preferably, a leakage prediction model is constructed by a collaborative filtering algorithm using multiple leakage event probabilities in an associated historical scenario group and current dynamic leakage features, and is trained using historical data to establish a relationship between the leakage event probability and the dynamic leakage features. The current dynamic leakage features are then input into the constructed leakage prediction model, and the model predicts whether a gas leakage will occur and the likelihood of the leakage based on the knowledge and relationships it has learned, and outputs a gas leakage prediction result. Finally, a risk decision analysis is performed by comprehensively considering the gas leakage prediction results, the possible consequences of the leakage (such as threats to personnel safety, property losses, etc.), and other relevant factors (such as the sensitivity of the current environment, etc.). According to the results of the risk decision analysis, the gas leakage risk is divided into different levels, such as high, medium, and low risks. Different risk levels correspond to different response measures and management strategies, so as to respond to possible gas leakage incidents in a timely and effective manner.

[0041] Furthermore, step S200 also includes step S270, performing multi-dimensional sensor acquisition based on historical scenes to obtain multi-dimensional sensor data; step S280, performing time alignment processing on the multi-dimensional sensor data to generate a scene data stream; step S290, using an autoencoder to perform feature dimensionality reduction on the scene data stream to generate a scene feature vector; step S2100, associating the leakage event label with the scene feature vector to generate the historical sensor scene data.

[0042] Preferably, based on various historical scenarios related to combustible gas (such as different indoor environments, different equipment usage conditions, etc.), multiple types of sensors are used to collect data, including combustible gas concentration, which is key data that directly reflects whether there is a gas leak and the extent of the leak; ambient temperature and humidity, which affect the diffusion, aggregation and performance of the sensor of combustible gas; equipment operating status, such as the switch status and operating parameters of the gas equipment, and abnormal operation of the equipment may be related to gas leakage; user behavior log, which records the user's operating behavior on the gas equipment (such as the time of switching the equipment, the setting of the adjustment equipment, etc.), and improper operation of the user may also cause the risk of gas leakage; since different types of sensors may have different time intervals and time starting points for collecting data, time alignment processing is performed on the multi-dimensional sensor data, that is, the data of each dimension is arranged in chronological order, and an accurate timestamp is added to each data point, so that the data of different dimensions are consistent in time, and a scene data stream is generated, which is an ordered sequence containing multi-dimensional data and each data is timestamped. For example, at a specific time point, the scene data stream will simultaneously contain relevant information such as the combustible gas concentration, ambient temperature and humidity, equipment operating status and user behavior log at that moment.

[0043] Preferably, an autoencoder is used to perform feature dimensionality reduction on the scene data stream, wherein the autoencoder is an unsupervised learning neural network model, which consists of two parts: an encoder and a decoder. Specifically, feature dimensionality reduction is to compress high-dimensional raw data (i.e., sensor data containing multiple dimensions) into low-dimensional feature representations. By learning the inherent patterns and structures in the data, the autoencoder can extract the key features that best represent the raw data, remove redundant information, obtain the scene feature vector, retain the information related to the combustible gas in the raw data, and reduce the amount of calculation and storage space requirements; then the leakage event label is associated with the scene feature vector and stored. Specifically, the leakage event label is used to mark whether a combustible gas leakage event has occurred in a certain scene (can be represented by "yes" or "no", or by the numbers 0 and 1). The leakage event label corresponding to each scene is associated with the scene feature vector obtained after feature dimensionality reduction, that is, the information of the leakage event is bound to the feature information of the scene, and then stored to form historical sensor scene data for the training of the leakage prediction model. The relationship between different scene features and leakage events can be learned based on the labeled information, thereby improving the accuracy and reliability of the combustible gas leakage analysis and prediction.

[0044] Furthermore, step S210 also includes step S211, establishing a sliding time window according to the smooth concentration curve, performing concentration calculation based on the sliding time window, and obtaining the concentration change rate; step S212, performing concentration duration detection on the sliding time window based on the concentration change rate, and extracting the peak duration; step S213, counting the number of times the concentration value in the sliding time window exceeds a preset warning line, and determining the fluctuation frequency; step S214, combining the concentration change rate, the peak duration and the fluctuation frequency as a three-dimensional feature vector to obtain the dynamic leakage feature.

[0045] Preferably, a sliding time window of fixed length is set on the smooth concentration curve, and the sliding time window is slid on the curve as time goes by to focus on the concentration data within a period of time. Specifically, for the concentration data in each sliding time window, the first-order derivative of the concentration is calculated to indicate the rate of change of the concentration over time. By calculating the first-order derivative of the concentration in the window as the concentration change rate, the speed of the increase or decrease of the gas concentration in the time period can be understood. For example, if the concentration change rate is positive and large, it means that the gas concentration is rising rapidly, and there may be a gas leak and the leakage rate is fast; if the change rate is negative, it means that the concentration is decreasing, and the leakage may be controlled or the environment is stable. The environment is diluting the gas, etc.; within the sliding time window, the length of the continuous period when the detection concentration exceeds the safety threshold, where the safety threshold is a pre-set standard value used to judge the safety risk. By detecting the length of the period when the concentration continuously exceeds the safety threshold, the duration of the gas concentration in a dangerous state can be understood; in the process of the concentration exceeding the safety threshold, a concentration peak may occur, that is, the highest value reached by the concentration. The peak duration refers to the length of time that the concentration remains at a high level near the peak, which can reflect the severity and duration of the gas leakage. If the peak duration is long, it means that the gas leakage is more serious and lasts for a long time, which poses a greater threat to safety.

[0046] Preferably, the number of times the concentration value exceeds a preset warning line within the sliding time window is counted. The preset warning line is used to judge the fluctuation of the concentration. When the concentration frequently exceeds the warning line, it indicates that the gas concentration is in an unstable state. There may be intermittent leakage from the leakage source or environmental factors have a greater impact on the gas distribution, resulting in frequent concentration fluctuations. The higher the fluctuation frequency, the more complex the dynamic changes of the gas leakage. Finally, the calculated concentration change rate, the extracted peak duration, and the statistically obtained fluctuation frequency are combined into a three-dimensional feature vector, which can comprehensively describe the dynamic characteristics of the gas leakage. The concentration change rate reflects the speed of concentration change, the peak duration reflects the severity and duration of the leakage, and the fluctuation frequency illustrates the stability of the concentration and the complexity of the change, thereby more accurately analyzing and judging the gas leakage.

[0047] Furthermore, step S250 also includes step S251, obtaining multiple historical gas usage scenario data, wherein the multiple historical gas usage scenario data include gas equipment type data, usage period data and environmental parameter data; step S252, correlating the environmental parameter data with the gas equipment type data and analyzing them to determine leakage event data; step S253, calculating a similarity matrix between real-time sensing scene data and the historical sensing scene data according to the usage period data; and step S254, performing linear regression analysis on the leakage event data based on the similarity matrix to construct the leakage prediction model.

[0048] Preferably, multiple historical gas usage scenario data are obtained from various data sources (such as the database of the gas company, the records of relevant monitoring equipment, etc.), and the multiple historical gas usage scenario data include gas equipment type data, usage period data and environmental parameter data, wherein the gas equipment type data records the type of gas equipment used, such as gas stoves, gas water heaters, etc.; the usage period data records the usage time of the gas equipment in a day, accurate to a specific time period; the environmental parameter data covers the environmental factors at that time, such as temperature, humidity, air pressure, etc. A comprehensive analysis is performed on the environmental parameter data and the gas equipment type data. Specifically, different types of gas equipment may have different possibilities and characteristics of leakage under different environmental conditions. For example, some gas equipment is more likely to have seal aging in a high temperature and high humidity environment, thereby causing gas leakage; through correlation analysis, the potential relationship between environmental parameters and gas equipment types is found, and then it is determined under what circumstances a gas leakage event occurs, forming leakage event data, which specifically includes information such as the type of gas equipment and environmental parameters corresponding to the leakage.

[0049] Preferably, the real-time acquired sensing scene data (i.e., gas-related data in the current environment, which may include real-time gas concentration, environmental parameters, etc.) is compared with the historical sensing scene data, and the temporal correspondence between the real-time data and the historical data is analyzed based on the usage period data. For example, the real-time data and historical data in the same period (such as 7pm to 9pm) are compared, and then the similarity between the real-time sensing scene data and each historical sensing scene data is calculated using cosine similarity, Euclidean distance, etc., and the calculated similarity values ​​are established as a similarity matrix to show the similarity between the real-time data and each historical data; then the obtained similarity matrix is ​​used to perform linear regression analysis in combination with the leakage event data, and a predictive relationship between certain eigenvalues ​​in the similarity matrix and related environmental parameters, gas equipment type and other factors and whether a leakage occurs or the probability of a leakage is established. By performing linear regression analysis on the leakage event data, the influence weight of each independent variable on the dependent variable is determined, thereby constructing a leakage prediction model, which can predict the possibility of gas leakage in the current situation based on the similarity between the real-time sensing scene data and the historical data and other related factors, and ensure the accuracy of the gas concentration prediction.

[0050] Step S300: triggering a multi-level alarm strategy according to the gas leakage risk level, and executing the multi-level alarm strategy to link smart devices for safety control.

[0051] Preferably, different levels of alarm mechanisms are formulated according to the determined gas leakage risk level, and each level corresponds to different alarm methods and contents. Specifically, a low-risk level alarm only issues a prompt message, such as displaying a yellow warning icon on the relevant monitoring interface, informing that there is a certain gas leakage risk at present, but the situation is relatively minor; a medium-risk level alarm may issue a sound alarm of a specific frequency, and the warning icon on the monitoring interface turns orange and flashes, and at the same time sends detailed alarm information to more relevant personnel, including the approximate location of the leakage, the possible scope of impact, etc.; a high-risk level alarm triggers a strong alarm signal, such as a piercing alarm. The system will make a sound, flash red lights, etc., and clearly indicate that the leakage is critical. At the same time, it will send an emergency notice to all relevant personnel, activate the emergency plan, and require immediate emergency measures. When different levels of alarms are triggered, the smart home devices will be linked to perform safety control to reduce the risks brought by gas leakage. For example, the smart ventilation equipment will be started to speed up air circulation and reduce the concentration of combustible gas; non-critical gas equipment will be automatically shut down to prevent explosions caused by sparks generated by equipment operation; the main gas supply valve will be immediately cut off to prevent combustible gas leakage, and the fire sprinkler system will be started to cool and dust the areas that may be affected to reduce the risk of explosion. Through the multi-level alarm strategy and the safety control of the linkage of smart devices, appropriate measures will be taken according to the severity of the gas leakage risk to ensure safety to the greatest extent.

[0052] Furthermore, step S300 also includes step S310, matching the gas leakage risk level with the multi-level alarm strategy library to generate an alarm control instruction set; step S320, parsing based on the alarm control instruction set to generate an alarm priority sequence, triggering the local alarm module to execute the alarm according to the alarm priority sequence, and generating a linkage instruction; step S330, distributing the linkage instruction to the target smart device to control the target smart device to perform safety operations.

[0053] Preferably, a multi-level alarm strategy library pre-stores a variety of detailed alarm strategies for different risk levels, each strategy specifies the corresponding alarm method, alarm object and alarm content, etc. The determined gas leakage risk level is compared and matched with the content in the multi-level alarm strategy library to find the corresponding alarm strategy, and then specific alarm control instructions are generated according to the matched alarm strategy to form an alarm control instruction set. For example, if the risk level is high risk, the matched alarm strategy may include issuing a high-decibel alarm, sending an emergency notice to all relevant personnel, etc. The corresponding instructions will include instructions for controlling the alarm device to issue an alarm sound, as well as information such as the specific content of the notification and the recipient; the generated alarm control instruction set is then parsed to analyze the importance of each instruction According to the analysis results, the alarm control instructions are sorted by priority to form an alarm priority sequence. For example, in a high-risk situation, the instruction to immediately cut off the gas supply may have the highest priority, while the instruction to send a notification has a relatively low priority. Then, according to the alarm priority sequence, the local alarm modules are triggered in turn to perform corresponding alarm operations, wherein the local alarm module may include an audible and visual alarm, etc., which can issue different forms of alarms according to the alarm control instructions. In the process of executing the alarm operation, a linkage instruction is generated according to the alarm control instruction and the actual execution situation, which is used to control the smart home devices to work together to deal with combustible gas leakage. For example, when the local alarm module issues a high-risk alarm, the linkage instructions include closing the gas valve, starting the ventilation equipment, etc.

[0054] Preferably, the generated linkage instruction is sent to the corresponding target smart home devices, such as smart gas valves, smart ventilation systems, smart environmental monitoring equipment, etc. These devices perform safety operations according to the linkage instruction requirements. For example, after the smart gas valve receives the closing instruction, it will automatically close the valve to cut off the gas supply; after the smart ventilation system receives the start-up instruction, it will start working to accelerate air circulation to reduce the concentration of combustible gas; and in the process of smart home devices performing safety operations, the execution status of the equipment (such as whether the valve is successfully closed, the operating power of the ventilation equipment, etc.) and the changes in environmental concentration (real-time acquisition of combustible gas concentration data through environmental monitoring equipment) are monitored in real time, and the execution intensity of the alarm strategy is dynamically adjusted according to the monitoring information. For example, if the combustible gas concentration decreases slowly after the ventilation equipment is running, the operating power of the ventilation equipment may be increased, thereby realizing intelligent combustible gas monitoring and control.

[0055] Further, step S310 also includes step S311, when the gas leakage risk level is level one, searching the multi-level alarm strategy library to determine the first alarm control instruction, triggering the local sound and light alarm and pushing the early warning notification through the first alarm control instruction, and generating level one leakage record data; step S312, when the gas leakage risk level is level two, searching the multi-level alarm strategy library to determine the second alarm control instruction, closing the smart gas valve and starting the ventilation equipment through the second alarm control instruction, and generating level two leakage record data; step S313, when the gas leakage risk level is level three, searching the multi-level alarm strategy library to determine the third alarm control instruction, sending an emergency broadcast signal to the security system through the third alarm control instruction, and generating level three leakage record data; step S314, integrating the level one leakage record data, the level two leakage record data, and the level three leakage record data, determining the leakage alarm record log, and extracting the alarm control instruction set according to the leakage alarm record log.

[0056] Preferably, when it is determined that the gas leakage risk level is level one (usually indicating that the risk is relatively low), a search is performed in the multi-level alarm strategy library to determine the first alarm control instruction applicable to the level one risk. The main function of the instruction is to trigger the local sound and light alarm to emit sound and light alarms to alert nearby personnel to potential gas leakage. At the same time, an early warning notification is pushed, such as sending messages to mobile phones, computers and other terminal devices of relevant staff to inform them that there is currently a level one gas leakage risk. At the same time, level one leakage record data is generated, recording relevant information of the level one gas leakage event, such as the time and location of the leakage, the triggered alarm action, etc.; when the gas leakage risk level rises to level two (indicating that the risk level has increased), the multi-level alarm strategy library is also searched to determine the second alarm control instruction applicable to the level two risk, which is mainly used to operate smart home devices, that is, through this instruction, the smart gas valve is closed to cut off the gas supply from the source to prevent the leakage from further expanding; at the same time, the ventilation equipment is started to accelerate air circulation and reduce the concentration of combustible gas in the environment, thereby generating level two leakage record data to record the details of the level two gas leakage event, including valve closing time, ventilation equipment start time and other information.

[0057] Preferably, when the gas leakage risk level reaches level three (indicating a higher risk and a more urgent situation), a search is still performed in the multi-level alarm strategy library to determine the third alarm control instruction applicable to the level three risk, which is used to send an emergency broadcast signal to the security system, that is, to broadcast an emergency notice to the relevant area (such as the entire building or a specific dangerous area) to inform personnel that the situation is critical and that they need to take rapid response measures, such as evacuation, etc., and then generate level three leakage record data to record relevant information of the level three gas leakage incident, such as the time and content of the broadcast; then the level one leakage record data, the level two leakage record data and the level three leakage record data are alarm integrated, that is, the leakage record data of different risk levels are aggregated to form a leakage alarm record log, and according to the leakage alarm record log, all alarm control instructions are extracted to form an alarm control instruction set, which is used to evaluate the execution effect of the alarm strategy, or when a similar gas leakage incident occurs, it can be used as a reference to quickly determine the measures to be taken; thereby achieving an orderly response and recording of gas leakage incidents to ensure the reduction of safety risks.

[0058] Furthermore, step S320 also includes step S321, mapping the alarm control instruction set with the device control permission table of the target smart device to determine the alarm priority sequence; step S322, traversing the target smart device to perform status verification, and when the verification passes, triggering the local alarm module according to the alarm priority sequence to generate device operation instructions; step S323, encapsulating the device operation instructions into a standardized Internet of Things data packet and sending it to the device gateway to execute the alarm, thereby determining the linkage instruction.

[0059] Preferably, the device control permission table of the target smart device records the control permission of each target smart device (such as smart gas valves, ventilation equipment, security broadcasting systems, etc.), such as which instructions can operate the device, and the importance and priority of different operations. The instructions in the alarm control instruction set are matched with the device control permission table. According to the priority regulations for different operations in the device control permission table, the priority of each instruction in the current situation is determined, and then an alarm priority sequence sorted by priority is formed, which determines the execution order of security operations and ensures that important operations can be executed first. Then all target smart devices are checked one by one to verify their The system checks the online status (whether the device is connected to the network and can communicate normally) and controllability (whether the device can operate according to the instructions). If a device is found to be offline, it will automatically switch to the backup control node (backup communication path or control method) to ensure that the device can be controlled. When all target smart devices have passed the status verification, the local alarm module will be triggered in sequence according to the alarm priority sequence to perform the corresponding alarm operations (such as sound and light alarms). At the same time, according to the alarm control instruction set and the current status of the device, specific operation instructions are generated for each target smart device, clarifying the actions that the device needs to perform, such as the closing instruction of the smart gas valve and the starting instruction of the ventilation equipment.

[0060] Preferably, these instructions are encapsulated into standardized IoT data packets to ensure that device operation instructions can be accurately and securely transmitted in the IoT environment. In addition to the device operation instructions, the data packets also include a timestamp (recording the time when the instructions are sent, for time synchronization and operation tracing) and a digital signature (used to verify the integrity of the data packet and the identity of the sender, and prevent the data packet from being tampered with or forged); the encapsulated data packet is sent to the device gateway (the intermediate device connecting the smart device and the network), and then transferred to the corresponding target smart device to perform alarm and control operations. In this process, linkage instructions are determined, which clarifies how the various smart devices work together to respond to gas leakage incidents, thereby realizing linkage control between devices, and realizing effective management and control of target smart devices in the gas leakage alarm system, thereby improving reliability and safety.

[0061] In the above, refer to Figure 1 A combustible gas concentration detection alarm method according to an embodiment of the present invention is described in detail. Figure 3 A combustible gas concentration detection and alarm device according to an embodiment of the present invention is described.

[0062] A combustible gas concentration detection alarm device according to an embodiment of the present invention is used to solve the technical problems existing in the prior art, such as the inability to comprehensively detect multiple combustible gases, the detection data being susceptible to environmental interference, the lack of forward-looking prediction of gas leakage risk levels, and insufficient data privacy protection, which in turn lead to insufficient accuracy in combustible gas concentration detection and insufficient timely and accurate alarms. It realizes smart home linkage and achieves the technical effect of improving the accuracy of combustible gas detection and reducing the false alarm rate. Figure 3 As shown, a combustible gas concentration detection and alarm device includes: a pre-processing concentration data acquisition module 10, a gas leakage risk level determination module 20, and a multi-level alarm strategy execution module 30.

[0063] The pre-processed concentration data acquisition module 10 is used to collect the combustible gas concentration data in the environment through multiple types of sensors, generate a real-time concentration signal, perform noise suppression processing on the real-time concentration signal, and obtain pre-processed concentration data; the gas leakage risk level determination module 20 is used to predict leakage based on the pre-processed concentration data, generate a gas leakage prediction result, perform similarity calculation based on the gas leakage prediction result combined with historical sensor scene data, and determine the gas leakage risk level; the multi-level alarm strategy execution module 30 is used to trigger the multi-level alarm strategy according to the gas leakage risk level, and execute the multi-level alarm strategy to link smart devices for safety control.

[0064] The specific configuration of the pre-processing concentration data acquisition module 10 will be described in detail below. The pre-processing concentration data acquisition module 10 further includes: acquiring a sensor data set, dynamically calibrating the sensor data set according to the ambient temperature parameter and the ambient humidity parameter, and determining the sensor calibration data; performing gas concentration analysis based on the sensor calibration data, and generating the real-time concentration signal according to the concentration value; traversing the real-time concentration signal for pulse processing, and extracting abnormal pulse signals; filtering the abnormal pulse signal using a wavelet transform algorithm to generate a smoothed concentration curve; and correcting the concentration value according to the smoothed concentration curve to obtain the pre-processing concentration data.

[0065] The specific configuration of the gas leakage risk level determination module 20 will be described in detail below. The gas leakage risk level determination module 20 further includes: constructing a time series concentration distribution map based on the smooth concentration curve, performing feature analysis according to the time series concentration distribution map, and determining dynamic leakage features; retrieving historical sensing scene data for scene feature analysis to determine scene feature vectors; performing multi-dimensional similarity matching on the dynamic leakage features and the scene feature vectors to generate a scene matching degree set; performing weighted clustering analysis on the scene matching degree set to obtain multiple scene feature clusters, traversing the multiple scene feature clusters for association screening, and determining an associated historical scene group, wherein the associated historical scene group contains multiple leakage event probabilities; constructing a leakage prediction model based on the multiple leakage event probabilities and the dynamic leakage features, performing leakage prediction through the leakage prediction model, and generating a gas leakage prediction result; performing risk decision analysis in combination with the gas leakage prediction result to determine the gas leakage risk level.

[0066] The specific configuration of the gas leakage risk level determination module 20 will be described in detail below. The gas leakage risk level determination module 20 further includes: obtaining multiple historical gas usage scenario data, the multiple historical gas usage scenario data including gas equipment type data, usage period data and environmental parameter data; performing correlation analysis on the environmental parameter data and the gas equipment type data to determine leakage event data; calculating a similarity matrix between the real-time sensing scenario data and the historical sensing scenario data according to the usage period data; performing linear regression analysis on the leakage event data based on the similarity matrix to construct the leakage prediction model.

[0067] The specific configuration of the gas leakage risk level determination module 20 will be described in detail below. The gas leakage risk level determination module 20 further includes: establishing a sliding time window according to the smoothed concentration curve, performing concentration calculation according to the sliding time window, and obtaining a concentration change rate; performing concentration duration detection on the sliding time window based on the concentration change rate, and extracting the peak duration; counting the number of times the concentration value in the sliding time window exceeds the preset warning line, and determining the fluctuation frequency; combining the concentration change rate, the peak duration and the fluctuation frequency as a three-dimensional feature vector to obtain the dynamic leakage feature.

[0068] The specific configuration of the gas leakage risk level determination module 20 will be described in detail below. The gas leakage risk level determination module 20 further includes: performing multi-dimensional sensing acquisition based on historical scenes to obtain multi-dimensional sensing data; performing time alignment processing on the multi-dimensional sensing data to generate a scene data stream; using an autoencoder to perform feature dimension reduction on the scene data stream to generate a scene feature vector; associating and storing the leakage event label with the scene feature vector to generate the historical sensing scene data.

[0069] The specific configuration of the multi-level alarm strategy execution module 30 will be described in detail below. The multi-level alarm strategy execution module 30 further includes: matching the gas leakage risk level with the multi-level alarm strategy library to generate an alarm control instruction set; parsing based on the alarm control instruction set to generate an alarm priority sequence, triggering the local alarm module to execute the alarm according to the alarm priority sequence, and generating a linkage instruction; distributing the linkage instruction to the target smart device to control the target smart device to perform a safety operation.

[0070] The specific configuration of the multi-level alarm strategy execution module 30 will be described in detail below. The multi-level alarm strategy execution module 30 further includes: when the gas leakage risk level is level one, the multi-level alarm strategy library is searched to determine the first alarm control instruction, the local sound and light alarm is triggered and the warning notification is pushed through the first alarm control instruction, and the first-level leakage record data is generated; when the gas leakage risk level is level two, the multi-level alarm strategy library is searched to determine the second alarm control instruction, the intelligent gas valve is closed and the ventilation equipment is started through the second alarm control instruction, and the second-level leakage record data is generated; when the gas leakage risk level is level three, the multi-level alarm strategy library is searched to determine the third alarm control instruction, and the security system is sent an emergency broadcast signal through the third alarm control instruction to generate the third-level leakage record data; the first-level leakage record data, the second-level leakage record data, and the third-level leakage record data are alarm-integrated to determine the leakage alarm record log, and the alarm control instruction set is extracted according to the leakage alarm record log.

[0071] The specific configuration of the multi-level alarm strategy execution module 30 will be described in detail below. The multi-level alarm strategy execution module 30 further includes: mapping the alarm control instruction set with the device control permission table of the target smart device to determine the alarm priority sequence; traversing the target smart device to perform status verification, and when the verification passes, triggering the local alarm module according to the alarm priority sequence to generate device operation instructions; encapsulating the device operation instructions into a standardized Internet of Things data packet and sending it to the device gateway to execute the alarm, and determining the linkage instruction.

[0072] A combustible gas concentration detection and alarm device provided in an embodiment of the present invention can execute a combustible gas concentration detection and alarm method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0073] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0074] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A combustible gas concentration detection and alarm method, characterized in that: The method comprises: Collecting the combustible gas concentration data in the environment through multiple types of sensors, generating a real-time concentration signal, performing noise suppression processing on the real-time concentration signal, and obtaining pre-processed concentration data; Perform leakage prediction based on the pre-processed concentration data to generate a gas leakage prediction result, perform similarity calculation based on the gas leakage prediction result combined with historical sensing scene data to determine the gas leakage risk level; A multi-level alarm strategy is triggered according to the gas leakage risk level, and the multi-level alarm strategy is executed to link smart devices for safety control.

2. A combustible gas concentration detection and alarm method as claimed in claim 1, characterized in that: The multi-type sensors include infrared sensors, electrochemical sensors, and semiconductor sensors, and the noise suppression process includes: Acquire a sensor data set, dynamically calibrate the sensor data set according to an ambient temperature parameter and an ambient humidity parameter, and determine sensor calibration data; Performing gas concentration analysis based on the sensor calibration data, and generating the real-time concentration signal according to the concentration value; Traversing the real-time concentration signal to perform pulse processing and extract abnormal pulse signals; The abnormal pulse signal is filtered using a wavelet transform algorithm to generate a smooth concentration curve; The concentration value is corrected according to the smoothed concentration curve to obtain the pre-processed concentration data.

3. A combustible gas concentration detection and alarm method as claimed in claim 2, characterized in that: The method of performing leakage prediction based on the pre-processed concentration data to generate a gas leakage prediction result, performing similarity calculation based on the gas leakage prediction result combined with historical sensing scene data to determine the gas leakage risk level includes: Constructing a time series concentration distribution diagram based on the smoothed concentration curve, performing feature analysis according to the time series concentration distribution diagram, and determining dynamic leakage features; Retrieve historical sensing scene data to perform scene feature analysis and determine scene feature vectors; Perform multi-dimensional similarity matching on the dynamic leakage feature and the scene feature vector to generate a scene matching degree set; Performing weighted cluster analysis on the scene matching degree set to obtain multiple scene feature clusters, traversing the multiple scene feature clusters for correlation screening, and determining a correlation historical scene group, wherein the correlation historical scene group includes multiple leakage event probabilities; Building a leakage prediction model according to the multiple leakage event probabilities and the dynamic leakage characteristics, performing leakage prediction through the leakage prediction model, and generating a gas leakage prediction result; A risk decision analysis is performed in combination with the gas leakage prediction result to determine the gas leakage risk level.

4. A combustible gas concentration detection and alarm method as claimed in claim 3, characterized in that: A leakage prediction model is constructed according to the multiple leakage event probabilities and the dynamic leakage characteristics, the method comprising: Acquire multiple historical gas usage scenario data, wherein the multiple historical gas usage scenario data include gas equipment type data, usage period data and environmental parameter data; Correlation analysis is performed on the environmental parameter data and the gas equipment type data to determine leakage event data; Calculating a similarity matrix between the real-time sensing scene data and the historical sensing scene data according to the usage period data; A linear regression analysis is performed on the leakage event data based on the similarity matrix to construct the leakage prediction model.

5. A combustible gas concentration detection and alarm method as claimed in claim 3, characterized in that: Constructing a time series concentration distribution graph based on the smoothed concentration curve, performing feature analysis according to the time series concentration distribution graph, and determining dynamic leakage features, the method comprising: Establishing a sliding time window according to the smoothed concentration curve, performing concentration calculation according to the sliding time window, and obtaining a concentration change rate; Performing concentration duration detection on the sliding time window based on the concentration change rate, and extracting the peak duration; Counting the number of times the concentration value exceeds a preset warning line within the sliding time window to determine the fluctuation frequency; The concentration change rate, the peak duration and the fluctuation frequency are combined as a three-dimensional feature vector to obtain the dynamic leakage feature.

6. A combustible gas concentration detection and alarm method as claimed in claim 1, characterized in that: The method for constructing the historical sensing scene data includes: Conduct multi-dimensional sensing collection based on historical scenarios to obtain multi-dimensional sensing data; Performing time alignment processing on the multi-dimensional sensing data to generate a scene data stream; Using an autoencoder to perform feature dimension reduction on the scene data stream to generate a scene feature vector; The leakage event label is associated with the scene feature vector and stored to generate the historical sensing scene data.

7. A combustible gas concentration detection and alarm method as claimed in claim 1, characterized in that: Triggering a multi-level alarm strategy according to the gas leakage risk level, executing the multi-level alarm strategy and linking smart devices to perform safety control, the method includes: According to the gas leakage risk level, the alarm control instruction set is matched with the multi-level alarm strategy library to generate an alarm control instruction set; Parsing based on the alarm control instruction set, generating an alarm priority sequence, triggering the local alarm module to execute the alarm according to the alarm priority sequence, and generating a linkage instruction; The linkage instruction is distributed to the target smart device to control the target smart device to perform a security operation.

8. A combustible gas concentration detection and alarm method as claimed in claim 7, characterized in that: According to the gas leakage risk level, matching is performed with a multi-level alarm strategy library to generate an alarm control instruction set, the method comprising: When the gas leakage risk level is level one, the multi-level alarm strategy library is searched to determine a first alarm control instruction, a local sound and light alarm is triggered by the first alarm control instruction and a warning notification is pushed, thereby generating level one leakage record data; When the gas leakage risk level is level 2, the multi-level alarm strategy library is searched to determine the second alarm control instruction, and the smart gas valve is closed and the ventilation equipment is started through the second alarm control instruction to generate level 2 leakage record data; When the gas leakage risk level is level three, the multi-level alarm strategy library is searched to determine the third alarm control instruction, an emergency broadcast signal is sent to the security system through the third alarm control instruction, and level three leakage record data is generated; The first-level leakage record data, the second-level leakage record data, and the third-level leakage record data are integrated for alarm, a leakage alarm record log is determined, and the alarm control instruction set is extracted according to the leakage alarm record log.

9. A combustible gas concentration detection and alarm method as claimed in claim 7, characterized in that: Based on the alarm control instruction set, the alarm priority sequence is generated, the local alarm module is triggered to execute the alarm according to the alarm priority sequence, and the linkage instruction is generated, the method includes: Determine the alarm priority sequence according to mapping the alarm control instruction set with the device control permission table of the target intelligent device; Traversing the target intelligent device to perform status verification, and when the verification passes, triggering the local alarm module according to the alarm priority sequence to generate device operation instructions; The device operation instruction is encapsulated into a standardized Internet of Things data packet and sent to the device gateway to execute the alarm and determine the linkage instruction.

10. A combustible gas concentration detection and alarm device, characterized in that: The device is used to implement a combustible gas concentration detection and alarm method according to any one of claims 1 to 9, and the device comprises: A pre-processing concentration data acquisition module is used to collect combustible gas concentration data in the environment through multiple types of sensors, generate real-time concentration signals, perform noise suppression processing on the real-time concentration signals, and obtain pre-processing concentration data; A gas leakage risk level determination module, used to perform leakage prediction based on the pre-processed concentration data, generate a gas leakage prediction result, perform similarity calculation based on the gas leakage prediction result combined with historical sensing scene data, and determine the gas leakage risk level; The multi-level alarm strategy execution module is used to trigger the multi-level alarm strategy according to the gas leakage risk level, and execute the multi-level alarm strategy to link smart devices for safety control.

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