A combustible gas monitoring and early warning method and system based on the Internet of Things

Abstract

The present invention discloses a combustible gas monitoring and early warning method and system based on the Internet of Things, which relates to the field of combustible gas monitoring technology. The method comprises: obtaining structural design information of the target area, performing equivalent division and Internet of Things equipment deployment; obtaining M combustible gas sensing data streams through regional sensing network monitoring; building a dual channel for combustible gas analysis, including a gas concentration analysis channel and a leakage analysis channel; presetting a combustible gas safety threshold, performing early warning judgment on the combustible gas sensing data stream, and obtaining combustible gas parameters in the area to be warned; and constructing a multi-level linkage early warning mechanism to perform linkage early warning control on the combustible gas parameters in the area to be warned. The method solves the technical problems existing in the prior art, such as unreasonable division of monitoring areas, failure to consider dynamic factors in data analysis, and lack of linkage strategy for hierarchical zoning, which result in poor accuracy and timeliness of monitoring and early warning, and achieves the technical effect of improving the timeliness and accuracy of combustible gas monitoring and early warning.

Description

Technical Field

[0001] The present application relates to the technical field related to combustible gas monitoring, and specifically to a combustible gas monitoring and early warning method and system based on the Internet of Things. Background Art

[0002] With the rapid development of industrialization and urbanization, combustible gases (such as natural gas, liquefied petroleum gas, and methane) are increasingly used in industrial production, energy supply, and daily life. However, once combustible gases leak and reach a certain concentration, they can easily cause serious accidents such as fires and explosions. Traditional combustible gas monitoring has limited coverage, delayed response, and is unable to dynamically adapt to complex environments. This is especially true in scenarios such as large industrial plants, underground pipelines, or high-rise buildings, where gas leaks may occur in hidden areas. Existing monitoring methods lack targeted analysis of the spatial structure and gas diffusion characteristics of the target area, making it difficult to detect hidden dangers in a timely manner. They also ignore dynamic factors such as gas escape velocity and diffusion path, which can easily lead to false alarms or missed reports. They cannot fully reflect the dynamic diffusion trend of gas leaks, resulting in inaccurate and timely risk monitoring and early warning.

[0003] Therefore, in the current relevant technologies, there are technical problems such as unreasonable division of monitoring areas, failure to consider dynamic factors in data analysis, and lack of linkage strategies for hierarchical zoning, which lead to poor accuracy and timeliness of monitoring and early warning. Summary of the Invention

[0004] This application provides a combustible gas monitoring and early warning method and system based on the Internet of Things, which solves the technical problems existing in the existing technology, such as unreasonable division of monitoring areas, failure to consider dynamic factors in data analysis, and lack of linkage strategy for hierarchical zoning, which lead to poor accuracy and timeliness of monitoring and early warning, and achieves the technical effect of improving the timeliness and accuracy of combustible gas monitoring and early warning.

[0005] The present application provides a combustible gas monitoring and early warning method based on the Internet of Things, which includes: obtaining structural design information of a target area, performing equivalent division and Internet of Things device deployment on the target area based on the structural design information, and obtaining M monitoring areas and M regional sensing networks; obtaining M combustible gas sensing data streams of the M monitoring areas through the M regional sensing networks; establishing a dual combustible gas analysis channel, wherein the dual combustible gas analysis channel includes a gas concentration analysis channel and a leakage analysis channel; presetting a combustible gas safety threshold, performing early warning judgment on the M combustible gas sensing data streams based on the gas concentration analysis channel and the leakage analysis channel according to the combustible gas safety threshold, and obtaining combustible gas parameters of the area to be warned; constructing a multi-level linkage early warning mechanism, and performing linkage early warning control on the combustible gas parameters of the area to be warned based on the multi-level linkage early warning mechanism.

[0006] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: attribute identification of the structural design information to obtain a regional structural attribute set, wherein the regional structural attribute set includes regional scale distribution, regional function, gas equipment distribution, personnel flow and ventilation system; based on the regional structural attribute set, division indicators are extracted and indicator priority is arranged, and equivalent area division rules are configured; according to the equivalent area division rules, the target area is divided into equivalent attributes to obtain M monitoring areas; monitoring demand analysis and Internet of Things equipment deployment are performed on the regional information of each of the M monitoring areas, and M regional perception networks are built.

[0007] In a possible implementation, the IoT-based combustible gas monitoring and early warning method further performs the following processing: performing gas risk assessment on the information of each area in the M monitoring areas in turn to obtain M regional gas risk parameters; performing monitoring demand analysis on the M monitoring areas according to the M regional gas risk parameters to determine M regional gas monitoring indicators; performing IoT deployment strategy analysis on the M monitoring areas based on the M regional gas monitoring indicators to obtain M regional IoT device deployment parameters; deploying IoT devices in the M monitoring areas according to the M regional IoT device deployment parameters to build the M regional perception network.

[0008] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: mining and obtaining a combustible gas concentration data set and a combustible gas escape situation data set; using a time series network to perform concentration trend identification and predictive analysis training on the combustible gas concentration data set to generate a gas concentration analysis channel; using a deep neural network to perform escape situation identification and supervised analysis training on the combustible gas escape situation data set to obtain an escape situation analysis channel; and merging the gas concentration analysis channel and the escape situation analysis channel in series to build the combustible gas analysis dual channel.

[0009] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: obtaining gas escape situation analysis elements, the gas escape situation analysis elements including diffusion path, escape range and escape impact area; performing feature identification on the combustible gas escape situation data set according to the gas escape situation analysis elements to obtain a combustible gas escape feature sample set; using a deep neural network to perform supervised analysis training on the combustible gas escape feature sample set to generate a diffusion path analysis network, an escape range analysis network and an escape impact area analysis network; and parallelly fusing the diffusion path analysis network, the escape range analysis network and the escape impact area analysis network to obtain the escape situation analysis channel.

[0010] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: determining the combustible gas concentration early warning threshold and the combustible gas escape situation early warning threshold according to the combustible gas safety threshold; activating the gas concentration analysis channel according to the combustible gas concentration early warning threshold to perform concentration trend analysis and early warning comparison judgment on the M combustible gas perception data streams to obtain M combustible gas concentration early warning judgment results; performing early warning judgment on the M combustible gas perception data streams based on the M combustible gas concentration early warning judgment results, the combustible gas escape situation early warning threshold and the escape situation analysis channel to determine the combustible gas parameters of the area to be warned.

[0011] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: if the warning judgment results of the M combustible gas concentrations are no, the combustible gas parameters of the area to be warned are an empty set; if the warning judgment results of the M combustible gas concentrations are yes, the M combustible gas sensing data streams are warned based on the combustible gas escape situation warning threshold and the escape situation analysis channel to determine the combustible gas parameters of the area to be warned.

[0012] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: performing an escape situation analysis on the M combustible gas sensing data streams based on the escape situation analysis channel to obtain M combustible gas escape situation parameters; performing an early warning judgment on the M combustible gas escape situation parameters according to the combustible gas escape situation early warning threshold to determine the combustible gas parameters of the area to be warned.

[0013] In a possible implementation, the combustible gas monitoring and early warning method based on the Internet of Things also performs the following processing: based on the multi-level linkage early warning mechanism, a linkage early warning analysis is performed on the combustible gas parameters in the area to be warned, and the combustible gas zone early warning level and the combustible gas zone early warning execution equipment are determined; and the combustible gas zone early warning level and the combustible gas zone early warning execution equipment are used to perform combustible gas linkage early warning control.

[0014] The present application also provides a combustible gas monitoring and early warning system based on the Internet of Things, which includes: a structural design information acquisition module, which is used to obtain the structural design information of the target area, and perform equivalent division and Internet of Things equipment deployment on the target area based on the structural design information to obtain M monitoring areas and M regional perception networks; a perception data stream acquisition module, which is used to obtain M combustible gas perception data streams of the M monitoring areas through the M regional perception networks; an analysis dual-channel construction module, which is used to build a combustible gas analysis dual channel, and the combustible gas analysis dual channel includes a gas concentration analysis channel and a leakage analysis channel; an early warning judgment module, which is used to preset a combustible gas safety threshold, and perform early warning judgment on the M combustible gas perception data streams based on the gas concentration analysis channel and the leakage analysis channel according to the combustible gas safety threshold to obtain the combustible gas parameters of the area to be warned; a linkage early warning control module, which is used to construct a multi-level linkage early warning mechanism, and perform linkage early warning control on the combustible gas parameters of the area to be warned based on the multi-level linkage early warning mechanism.

[0015] The present application proposes a method and system for monitoring and early warning of combustible gas based on the Internet of Things to obtain structural design information of the target area, perform equivalent division and deploy Internet of Things equipment; obtain M combustible gas sensing data streams through regional sensing network monitoring; build dual channels for combustible gas analysis, including a gas concentration analysis channel and a leakage analysis channel; preset combustible gas safety thresholds, perform early warning judgments on the M combustible gas sensing data streams, and obtain combustible gas parameters in the area to be warned; and construct a multi-level linkage early warning mechanism to perform linkage early warning control on the combustible gas parameters in the area to be warned. This solves the technical problems existing in the prior art, such as unreasonable division of monitoring areas, failure to consider dynamic factors in data analysis, and lack of linkage strategies for hierarchical zoning, which lead to poor accuracy and timeliness of monitoring and early warning, and achieves the technical effect of improving the timeliness and accuracy of combustible gas monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of a combustible gas monitoring and early warning method based on the Internet of Things is provided in an embodiment of the present application.

[0018] Figure 2A schematic structural diagram of a combustible gas monitoring and early warning system based on the Internet of Things is provided in an embodiment of the present application.

[0019] Explanation of the reference numerals: structural design information acquisition module 10 , perception data stream acquisition module 20 , analysis dual-channel construction module 30 , early warning determination module 40 , linkage early warning control module 50 . DETAILED DESCRIPTION

[0020] 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.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be 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 used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, 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 that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application embodiment provides a combustible gas monitoring and early warning method based on the Internet of Things, such as Figure 1 As shown, the method includes:

[0024] Step S100: Acquire structural design information of a target area, perform equivalent division of the target area and deploy IoT devices based on the structural design information, and obtain M monitoring areas and M regional sensing networks.

[0025] Preferably, the structural design information of the target area is obtained, wherein the structural design information includes the spatial layout, building structure, ventilation conditions, etc. of the target area. For example, if the target area is a large chemical plant, its structural design information includes the length, width, and height of the plant, the distribution direction of the pipelines in the plant (such as the laying path of the gas pipeline, the position of the valves, etc.), the division of various functional areas (such as the reaction area, storage area, office area, etc.), and the position and size of windows and vents. Based on the structural design information, the target area is equivalently divided, that is, the target area is divided into several sub-areas that are relatively equivalent in the sense of combustible gas monitoring, wherein equivalence means that the sub-areas have similar behavior patterns such as combustible gas aggregation and diffusion. For example, according to the principle of equal area or equal spatial volume, the warehouse is divided into multiple monitoring areas. If there are complex structures such as shelves and partitions in the warehouse, the areas along the ventilation duct and away from the ventilation duct may have different gas diffusion characteristics. Considering the influence of the structure on the gas flow, M monitoring areas are reasonably divided, wherein M is a positive integer representing the number of monitoring areas.

[0026] Preferably, IoT devices are deployed in each monitoring area, which may include combustible gas sensors, data acquisition units, and communication units, etc., to achieve effective monitoring of combustible gas in the area. For example, according to the spatial size of each monitoring area and the location where gas may accumulate (such as the top, corners, etc.), a certain number of combustible gas sensors are installed to form a sensing network for real-time sensing of the combustible gas concentration in the surrounding environment and for aggregated transmission. For example, through wired (such as RS-485 bus) or wireless (such as LoRa, ZigBee, etc.) communication technology, the data of each sensor is transmitted to the data acquisition node in one area, and then the corresponding M regional sensing networks are obtained, which cooperate with each other to complete the monitoring task of combustible gas in the entire target area.

[0027] Furthermore, step S100 also includes step S110, performing attribute identification on the structural design information to obtain a regional structural attribute set, wherein the regional structural attribute set includes regional scale distribution, regional function, gas equipment distribution, personnel flow and ventilation system; step S120, extracting division indicators and arranging indicator priorities based on the regional structural attribute set, and configuring equivalent area division rules; step S130, performing equivalent attribute division on the target area according to the equivalent area division rules to obtain M monitoring areas; step S140, performing monitoring demand analysis and IoT device deployment on the regional information of each of the M monitoring areas, and building M regional perception networks.

[0028] Preferably, each key element in the acquired target area structural design information is attributed and categorized, extracting attributes such as regional scale distribution (e.g., area and height of different floors), regional functions (e.g., dining area, office area, shopping mall area), gas equipment distribution (gas pipeline direction, gas meter location, gas valve location), personnel flow (main passages, crowded areas), and ventilation system (vent location, ventilation duct direction, ventilation equipment power), and combining them to form a regional structural attribute set. Based on the regional structural attribute set, partitioning indicators are then extracted, i.e., indicators of significance for combustible gas monitoring area partitioning are selected from the regional structural attribute set. For example, for combustible gas monitoring, gas equipment distribution and ventilation system may be key partitioning indicators. Areas near gas equipment are more prone to combustible gas leaks, and the quality of the ventilation system directly affects the diffusion of combustible gas. The indicators are then prioritized, i.e., the partitioning indicators are prioritized according to their importance to combustible gas monitoring, e.g., gas equipment distribution is selected as the primary indicator, ventilation system as the secondary indicator, and personnel flow as the tertiary indicator. Finally, based on the prioritized division indicators, rules are formulated to divide the target area into equivalent monitoring areas. For example, in areas where gas pipelines pass, monitoring areas are divided at a certain distance (determined according to ventilation conditions) and in similar functional areas (such as areas where the same type of catering stores are concentrated).

[0029] Preferably, according to the equivalent area division rule, the entire target area is divided into M relatively equivalent sub-areas, that is, M monitoring areas, and then the monitoring demand analysis is performed on the information of each area in the M monitoring areas. Specifically, for each divided monitoring area, the specific needs for combustible gas monitoring in the area are determined according to its own characteristics and attributes. For example, in areas where gas equipment is concentrated and the risk of leakage is high, high-precision and high-frequency equipment for monitoring the concentration of combustible gas is required, and the response speed of the leakage alarm is very high; in well-ventilated areas far away from gas equipment, the monitoring demand may be relatively low, and the accuracy and monitoring frequency requirements of the monitoring equipment can be appropriately reduced. Then, based on the results of the monitoring needs analysis, corresponding IoT devices are installed in each monitoring area, including combustible gas sensors (for detecting combustible gas concentrations), communication units (for transmitting sensor data to data acquisition nodes), data acquisition nodes (for aggregating sensor data within a certain range), and communication units. In each monitoring area, IoT devices are deployed to build a perception network that can perceive the combustible gas conditions in the area, namely M regional perception networks, which are independent and collaborative to jointly complete the combustible gas monitoring task of the entire target area, ensuring that the combustible gas data in each monitoring area can be collected and utilized in a timely and accurate manner.

[0030] Furthermore, step S140 also includes step S141, performing gas risk assessment on the information of each area in the M monitoring areas in turn to obtain M regional gas risk parameters; step S142, performing monitoring demand analysis on the M monitoring areas according to the M regional gas risk parameters, and determining the M regional gas monitoring indicators; step S143, performing IoT deployment strategy analysis on the M monitoring areas based on the M regional gas monitoring indicators, and obtaining M regional IoT device deployment parameters; step S144, deploying IoT devices in the M monitoring areas according to the M regional IoT device deployment parameters, and building the M regional perception network.

[0031] Preferably, a gas risk assessment is performed on the information of each of the M monitoring areas in turn, which may include evaluating whether there are gas pipelines, whether there are fire sources, ventilation conditions, and personnel activities in each monitoring area, so as to obtain the possibility of gas concentration exceeding the standard, the speed of gas diffusion after leakage, the probability of explosion, etc., which are used as gas risk parameters for each area to measure the gas risk level of the area; then a monitoring demand analysis is performed on the M monitoring areas, and the content that needs to be monitored in the area is determined according to the gas risk parameters of each area, including monitoring frequency (such as high-risk areas need more frequent monitoring), monitoring range (whether the entire area or some key locations need to be monitored), such as high-precision gas concentration monitoring, real-time ventilation condition monitoring, etc., which are finally used as gas monitoring indicators for the M areas.

[0032] Preferably, based on the gas monitoring indicators of each area, the IoT deployment strategy of the M monitoring areas is analyzed to determine the IoT device deployment strategy for each monitoring area. For example, if the monitoring indicators of a certain area require high-precision gas concentration monitoring and real-time ventilation condition monitoring, the IoT deployment strategy includes installing high-precision gas sensors and ventilation sensors in the area and configuring a higher sampling frequency. Then, the specific IoT device deployment parameters are obtained according to the IoT deployment strategy, including determining the type of sensors to be installed (such as electrochemical sensors for detecting specific gas components), the number of sensors (determined according to the area size and monitoring accuracy requirements), the sensor installation location (at key locations such as near gas pipelines and vents), the communication method (such as wired or wireless communication, determined according to the regional environment and sensor distribution), and the data monitoring frequency and transmission frequency, etc., so as to obtain the IoT device deployment parameters for the M areas.

[0033] Preferably, IoT devices are deployed in M ​​monitoring areas according to IoT device deployment parameters, that is, IoT devices are installed in each monitoring area. Specifically, a sufficient number of high-precision gas sensors are installed according to IoT device deployment parameters, and are distributed around gas tanks, possible leakage points, and other locations. They are connected to data acquisition nodes by wired or wireless means, and then a network capable of sensing the gas conditions in the area is constructed, and finally M regional perception networks are obtained. Each device is responsible for collecting combustible gas data, and then sending the data to the data acquisition node through the communication unit. The data acquisition node performs preliminary processing on the data (such as data fusion, data cleaning, etc.), thereby realizing comprehensive real-time perception of the gas status in the target monitoring area.

[0034] Step S200: Acquire M combustible gas sensing data streams of the M monitoring areas through the M area sensing network monitoring.

[0035] Preferably, the combustible gas data in each monitoring area is obtained through real-time monitoring of M regional sensing networks. For example, the combustible gas sensor collects the combustible gas concentration value in the surrounding environment once every second or every few seconds, and then obtains the combustible gas sensing data stream corresponding to each area, which includes the combustible gas data collected by each sensor in the monitoring area and is arranged in a certain time sequence. For example, the sensing data stream of monitoring area 1 may be that at time point t1, sensor A detects a combustible gas concentration of 10ppm, and sensor B detects 5ppm; at time point t2, sensor A detects 12ppm, and sensor B detects 6ppm, etc., which is used to reflect the dynamic changes of combustible gas in M ​​monitoring areas in real time.

[0036] Step S300: Building a dual combustible gas analysis channel, wherein the dual combustible gas analysis channel includes a gas concentration analysis channel and a leakage analysis channel.

[0037] Preferably, the gas concentration analysis channel refers to a model used to analyze combustible gas concentrations, focusing primarily on changes in combustible gas concentration within the monitoring area. For example, combustible gas sensors (such as electrochemical sensors, catalytic combustion sensors, or infrared sensors) installed within the monitoring area acquire gas concentration data in real time, convert the detected concentration values ​​into electrical or digital signals, and transmit them to a data processing unit. Parameters such as average concentration, peak concentration, and concentration change rate are calculated to obtain concentration analysis results, which are used to determine whether the combustible gas exceeds a safety threshold and thus issue an early warning. The escape analysis channel is a model used to analyze the escape of combustible gas (including the source location, diffusion direction, diffusion range, and diffusion rate of the gas leak). For example, multiple sensors strategically arranged within the monitoring area are used to infer the approximate location of the leak source by utilizing differences in the spatial distribution of gas concentrations. Simultaneously, the gas diffusion direction and range are analyzed in combination with information from the ventilation system and air flow direction. The diffusion rate is then analyzed in combination with the gas's physical properties (such as density and molecular weight) and environmental conditions (such as temperature and airflow velocity), thereby determining the extent of the hazardous area.

[0038] Furthermore, step S300 also includes step S310, mining and obtaining a combustible gas concentration data set and a combustible gas escape situation data set; step S320, using a time series network to perform concentration trend identification and predictive analysis training on the combustible gas concentration data set to generate a gas concentration analysis channel; step S330, using a deep neural network to perform escape situation identification and supervised analysis training on the combustible gas escape situation data set to obtain an escape situation analysis channel; step S340, merging the gas concentration analysis channel and the escape situation analysis channel in series to build the combustible gas analysis dual channel.

[0039] Preferably, a combustible gas concentration dataset is obtained by collecting data over a long period of time using combustible gas sensors deployed in actual environments (such as factory workshops, underground garages, etc.), that is, a data set on the changes in combustible gas concentration over time in different scenarios; a combustible gas escape situation dataset is obtained through simulation experiments or actual accident case collection, including the escape situation when the combustible gas leaks, such as the location of the leak source, the time of the leak, the range of gas diffusion, the diffusion speed and other information. For example, in a closed laboratory, a combustible gas leakage point is artificially set, and the diffusion process of the gas in space after the leak is recorded by high-speed cameras and multiple gas sensors, including data such as the direction, range and speed of the diffusion, and the data is compiled into a dataset.

[0040] Preferably, a time series network is used to perform concentration trend identification and predictive analysis training on the combustible gas concentration dataset, wherein the time series network is a neural network used to process time series data, such as a long short-term memory network (LSTM) or a gated recurrent unit (GRU). The long short-term memory network can process data with a time sequence, capture the time dependency in the data, and learn the trend of gas concentration changes over time. Specifically, by inputting the combustible gas concentration dataset into the time series network, the network learns the trend pattern of gas concentration rising, falling, or remaining stable. For example, when a combustible gas leaks, the concentration tends to rise rapidly in a short period of time, and the concentration tends to gradually decrease after the leak source is blocked. After training with a large amount of data, the time series network can perform trend prediction on new gas concentration data, and finally generate a gas concentration analysis channel, which can analyze the combustible gas concentration data in real time, predict the future trend of concentration changes, and detect potential dangerous situations in time.

[0041] Preferably, a deep neural network is used to perform leakage identification and supervised analysis training on the combustible gas leakage data set, wherein the deep neural network (DNN) is a neural network structure with multiple hidden layers, which can learn complex data patterns and process various forms of data, such as images, time series, etc., for analyzing the combustible gas leakage data. Specifically, the combustible gas leakage data set is input into the deep neural network, and different leakage conditions are marked, such as the leakage source location (coordinates in space), diffusion range (parameters such as the diffusion radius centered on the leakage source), and diffusion speed (diffusion distance per unit time). The mapping relationship between the leakage situation and the data features is obtained through supervised learning. For example, the network learns that under specific environmental conditions (such as wind speed, ventilation conditions, etc.), gas will diffuse in a certain direction after leakage, and the diffusion speed is related to the leakage amount and environmental factors. After training, the deep neural network can analyze the new leakage data to obtain a leakage analysis channel for real-time judgment of the leakage state of the combustible gas, including the location of the leakage source, the direction and range of gas diffusion, etc.

[0042] Preferably, the gas concentration analysis channel and the escape situation analysis channel are combined to form a dual channel for combustible gas analysis, and the data processing and analysis results of the two channels are integrated. For example, the gas concentration analysis channel provides trend and prediction information of concentration changes, and the escape situation analysis channel provides information on the escape status (such as the location of the leakage source, the diffusion range, etc.). After integration, a more comprehensive understanding of the dangerous situation of combustible gas can be obtained. When the gas concentration analysis channel predicts that the concentration is about to reach the dangerous threshold, and the escape situation analysis channel determines the location of the leakage source and the diffusion range, an alarm can be issued in time, and specific dangerous area information can be provided so that effective emergency measures can be taken, thereby improving the accuracy and reliability of combustible gas monitoring and early warning.

[0043] Furthermore, step S330 also includes step S331, obtaining gas escape situation analysis elements, wherein the gas escape situation analysis elements include diffusion path, escape range and escape impact area; step S332, feature identification of the combustible gas escape situation data set according to the gas escape situation analysis elements to obtain a combustible gas escape feature sample set; step S333, using a deep neural network to perform supervised analysis training on the combustible gas escape feature sample set to generate a diffusion path analysis network, a escape range analysis network and a escape impact area analysis network; step S334, parallel integration of the diffusion path analysis network, the escape range analysis network and the escape impact area analysis network to obtain the escape situation analysis channel.

[0044] Preferably, gas leakage analysis elements are obtained, including diffusion path, leakage range and leakage impact area, wherein the diffusion path refers to the trajectory of combustible gas diffusion in space after leakage, for example, gas may start from the leakage source (such as a broken gas pipeline) and diffuse along the ventilation duct, door and window gaps or air flow direction. Diffusion path analysis can determine the main direction of gas diffusion and the areas it may pass through; the leakage range indicates the area covered by the diffusion of combustible gas after leakage, which may be a two-dimensional area (such as the diffusion area on the ground) or a three-dimensional spatial range (such as the diffusion height and area in the room). The leakage range analysis helps to determine the size of the hazardous area; the leakage impact area refers to the area affected by the leakage of combustible gas, which may include adjacent rooms, buildings or other sensitive areas (such as crowded areas, near fire sources, etc.). The leakage impact area analysis is used to evaluate the potential impact of leakage events on the surrounding environment and personnel.

[0045] Preferably, for the combustible gas escape situation data set, feature annotation is performed based on the gas escape situation analysis elements (diffusion path, escape range, and escape impact area). That is, for each gas leakage event record, the specific path of gas diffusion (such as diffusion from the broken pipe along the corridor to the stairwell), the escape range (such as diffusion to the area within 10 meters from the leakage source), and the area affected by the escape (such as affecting nearby offices and conference rooms) are clearly identified, and the original data set is converted into a sample set with feature identification, that is, the combustible gas escape feature sample set. Then, a deep neural network is used to conduct supervised analysis training on the sample set of combustible gas leakage characteristics. Specifically, the deep neural network is trained using a sample set with diffusion path feature identifiers to learn how to predict the gas diffusion path based on the input leakage data (such as the location of the leakage source, gas type, ventilation conditions, etc.), and generate a diffusion path analysis network; another deep neural network is trained using a sample set with leakage range feature identifiers to learn the factors affecting the leakage range (such as leakage volume, gas density, obstacles, etc.), and to predict the area covered by the leakage, and generate a leakage range analysis network; a third deep neural network is trained using a sample set with leakage impact area feature identifiers, taking into account factors including the range and direction of gas diffusion, the layout of the surrounding environment, etc., to learn how to predict which specific areas (such as nearby rooms, buildings, etc.) will be affected by the leakage event, and generate a leakage impact area analysis network. The model parameters of the leakage analysis channel training process are shown in Table 1:

[0046] Table 1. Training parameters of the channel model for emission analysis

[0047]

[0048] Preferably, the diffusion path analysis network, the escape range analysis network and the escape impact area analysis network are finally connected in parallel to form a comprehensive escape situation analysis channel, which can simultaneously process the input combustible gas escape data and analyze them through the three networks respectively, and output the comprehensive analysis results of the diffusion path, escape range and escape impact area, and quickly provide the possible diffusion direction, coverage range and specific affected areas of the gas.

[0049] Step S400: preset a combustible gas safety threshold, perform early warning judgment on the M combustible gas sensing data streams based on the gas concentration analysis channel and the escape situation analysis channel according to the combustible gas safety threshold, and obtain combustible gas parameters of the area to be warned.

[0050] Preferably, a combustible gas safety threshold is preset based on the type of combustible gas, the use environment, etc. For example, the lower explosion limit of methane is 5% (volume fraction), and the safety threshold is set as a certain proportion of the lower explosion limit, such as 25%, i.e., 1.25%. When the monitored methane concentration reaches or exceeds the preset safety threshold, it is considered that there is a potential safety risk; then, the real-time monitored combustible gas concentration data is analyzed according to the gas concentration analysis channel to obtain a gas concentration analysis result; and the combustible gas sensing data stream is analyzed according to the emission analysis channel to predict the gas diffusion path, emission range, and emission impact area, and determine the emission analysis result; Then, combined with the gas concentration analysis results and the escape analysis results, when the gas concentration reaches the safety threshold, but the escape range is small and will not affect important areas, it may be judged as a low-level warning; when the gas concentration is high and the escape situation is serious (such as spreading to multiple important areas), it is judged as a high-level warning; after the warning is triggered, the combustible gas parameters in the monitoring collection area are monitored, including concentration value, concentration change trend, escape path, escape range, escape impact area, etc. For example, the highest gas concentration near the leakage point, the speed of gas concentration increase, the direction and range of gas diffusion, and the affected areas (such as nearby operating rooms, storage rooms, etc.) are recorded.

[0051] Furthermore, step S400 also includes step S410, determining the combustible gas concentration warning threshold and the combustible gas escape situation warning threshold according to the combustible gas safety threshold; step S420, activating the gas concentration analysis channel according to the combustible gas concentration warning threshold to perform concentration trend analysis and warning comparison judgment on the M combustible gas perception data streams to obtain M combustible gas concentration warning judgment results; step S430, performing warning judgment on the M combustible gas perception data streams based on the M combustible gas concentration warning judgment results, the combustible gas escape situation warning threshold and the escape situation analysis channel to determine the combustible gas parameters of the area to be warned.

[0052] Preferably, based on the combustible gas safety threshold, with reference to the lower explosion limit, occupational exposure limit, etc. of the combustible gas, a concentration limit (combustible gas concentration warning threshold) is set to trigger a concentration warning. At the same time, the combustible gas escape warning threshold is set in combination with the safety considerations of the escape situation, including the escape range threshold, the escape velocity threshold, and the escape impact area threshold. For example, the escape range threshold is set to 10 meters from the leak source, the escape velocity threshold is set to 0.5 meters per second, and the escape impact area threshold is set to include areas with dense personnel. When the escape situation reaches the threshold, the escape situation warning is triggered. Then, according to the combustible gas concentration warning threshold, the gas concentration analysis channel is activated to perform concentration trend analysis and warning comparison judgment on M combustible gas sensing data streams. Specifically, for each combustible gas sensing data stream, the gas concentration analysis channel analyzes its concentration change trend, for example, predicting the future concentration change trend through a time series network and comparing it with the preset concentration warning threshold. If the predicted concentration will exceed the threshold in the future, or the current concentration has exceeded the threshold and has a continuous upward trend, it is judged as a warning, and finally M combustible gas concentration warning judgment results are obtained.

[0053] Preferably, for each monitoring area, its concentration warning judgment result and escape situation are comprehensively considered. If the concentration warning judgment result is a warning, and the escape situation analysis channel predicts that the escape situation reaches the escape situation warning threshold (such as the escape range exceeds the set threshold, the escape impact area includes the dangerous area, etc.), then the area is determined to be an area to be warned; then the combustible gas parameters of the area to be warned are monitored and determined, including concentration value, concentration change trend, escape path, escape range, escape impact area, etc. For example, the combustible gas concentration in the area to be warned is 1.5% (exceeding the threshold), the escape range is 12 meters away from the leakage source (exceeding the escape range threshold), and the escape impact area includes nearby operating rooms (crowded areas), which are used for early warning response measures, such as timely notification of personnel evacuation, initiation of emergency treatment procedures, etc.

[0054] Furthermore, step S430 also includes step S431, if the warning judgment results of the M combustible gas concentrations are no, the combustible gas parameters of the area to be warned are an empty set; step S432, if the warning judgment results of the M combustible gas concentrations are yes, the M combustible gas sensing data streams are warned based on the combustible gas escape warning threshold and the escape analysis channel to determine the combustible gas parameters of the area to be warned.

[0055] Preferably, when making flammable gas concentration early warning judgments for each monitoring area, if all judgment results are negative, that is, the flammable gas concentration in no monitoring area reaches or exceeds the preset flammable gas concentration early warning threshold, it is considered that there is no obvious flammable gas concentration risk in the current area, and the flammable gas parameters of the area to be warned are an empty set; if the flammable gas concentration early warning judgment result in one or more monitoring areas is positive, that is, the flammable gas concentration reaches or exceeds the preset concentration early warning threshold, the escape situation analysis channel is further used to make a more comprehensive early warning judgment. Specifically, the escape situation analysis channel will be based on the flammable gas escape situation early warning threshold (such as the escape range). The system analyzes the corresponding combustible gas sensing data stream based on the surrounding threshold, escape velocity threshold, escape impact area threshold, etc., to obtain the gas diffusion path, diffusion range and possible affected areas. Then, based on the results of the escape situation analysis, it determines the combustible gas parameters of the area to be warned, including but not limited to concentration parameters (such as the current concentration value of the combustible gas and the concentration change rate), escape path parameters (the main path and direction of gas diffusion), escape range parameters (the area covered by the escape, such as the distance from the leak source and the area involved) and escape impact area parameters (the specific areas affected by the escaped gas, such as nearby rooms, buildings, crowded areas, etc.).

[0056] Furthermore, step S432 also includes step a, performing a leakage analysis on the M combustible gas sensing data streams based on the leakage analysis channel to obtain M combustible gas leakage parameters; and step b, performing a warning judgment on the M combustible gas leakage parameters according to the combustible gas leakage warning threshold to determine the combustible gas parameters of the area to be warned.

[0057] Preferably, a fugitive emission analysis channel is used to perform fugitive emission analysis on M combustible gas sensing data streams, including diffusion path analysis, fugitive emission range analysis, and fugitive emission impact area analysis, thereby obtaining M combustible gas fugitive emission parameters, including diffusion path (diffusion trajectory of gas in space), fugitive emission range (area covered by gas diffusion), and fugitive emission impact area (specific areas affected by fugitive gas, such as nearby rooms, buildings, etc.); then the fugitive emission parameters of each monitoring area are compared with the corresponding early warning threshold value, and for the monitoring area that meets the early warning conditions, its combustible gas parameters are collected as the combustible gas parameters of the area to be warned, including fugitive emission parameters (diffusion path, fugitive emission range, fugitive emission impact area) and related concentration parameters (such as current concentration, concentration change trend, etc.), which are used for early warning response measures, such as issuing an alarm, activating an emergency plan, organizing personnel evacuation, etc.

[0058] Step S500: construct a multi-level linkage warning mechanism, and perform linkage warning control on the combustible gas parameters in the area to be warned based on the multi-level linkage warning mechanism.

[0059] Step S500 further includes step S510, performing a linkage warning analysis on the combustible gas parameters in the area to be warned based on the multi-level linkage warning mechanism, and determining the combustible gas zone warning level and the combustible gas zone warning execution equipment; step S520, using the combustible gas zone warning level and the combustible gas zone warning execution equipment to perform combustible gas linkage warning control.

[0060] Preferably, the multi-level linkage warning mechanism refers to dividing the warning into multiple levels according to the degree of danger and escape situation of the combustible gas, and enabling warnings of different levels to trigger corresponding linkage measures to achieve a comprehensive assessment and timely response to the combustible gas risk. Specifically, based on the multi-level linkage warning mechanism, the combustible gas parameters of the warning area are subjected to linkage warning analysis, and the combustible gas parameters of the warning area are collected, including concentration value, concentration change trend, escape path, escape range, escape impact area, etc., and the warning level is determined in combination with the combustible gas parameters according to the grading standard of the multi-level linkage warning mechanism. For example, the warning level is divided into low-level warning, medium-level warning and high-level warning; and then, according to the warning level, the warning execution equipment that needs to be activated is determined, which may include sound and light alarms, ventilation systems, emergency evacuation broadcast systems, gas emergency shut-off devices, etc. For example, in the case of a high-level warning, the sound and light alarms may be activated to remind on-site personnel, the ventilation system may be turned on to reduce the gas concentration, the emergency evacuation broadcast system may be activated to guide personnel to evacuate, and the gas emergency shut-off device may be turned off to prevent the source of gas leakage. The combustible gas zoning warning level data is shown in Table 2:

[0061] Table 2 Data table of combustible gas zone warning levels (example)

[0062]

[0063] Preferably, the corresponding warning execution equipment is activated according to the determined zoning warning level. For example, when the system determines that it is a medium-level warning, the sound and light alarm and ventilation system are automatically activated. When it is determined to be a high-level warning, in addition to activating the sound and light alarm and ventilation system, the emergency evacuation broadcast system and the gas emergency shut-off device are also automatically activated. Through the coordinated work of the warning execution equipment, effective control of the combustible gas risk is achieved. For example, the sound and light alarm promptly reminds on-site personnel to pay attention to the danger, the ventilation system can reduce the concentration of combustible gas and reduce the risk of explosion; the emergency evacuation broadcast system guides personnel to evacuate the dangerous area quickly and orderly; the gas emergency shut-off device can cut off the gas supply and control the source of the leak. The multi-level linkage warning mechanism can take appropriate measures according to the severity of the risk.

[0064] In the above, refer to Figure 1A combustible gas monitoring and early warning method based on the Internet of Things according to an embodiment of the present invention is described in detail. Figure 2 A combustible gas monitoring and early warning system based on the Internet of Things according to an embodiment of the present invention is described.

[0065] According to an embodiment of the present invention, a combustible gas monitoring and early warning system based on the Internet of Things is used to solve the technical problems existing in the prior art, such as unreasonable division of monitoring areas, failure to consider dynamic factors in data analysis, and lack of linkage strategies for hierarchical zoning, which lead to poor accuracy and timeliness of monitoring and early warning. This system achieves the technical effect of improving the timeliness and accuracy of combustible gas monitoring and early warning. Figure 2 As shown, a combustible gas monitoring and early warning system based on the Internet of Things includes: a structural design information acquisition module 10, a perception data stream acquisition module 20, an analysis dual-channel construction module 30, an early warning determination module 40, and a linkage early warning control module 50.

[0066] A structural design information acquisition module 10 is used to obtain the structural design information of the target area, perform equivalent division and IoT device deployment on the target area based on the structural design information, and obtain M monitoring areas and M regional perception networks; a perception data stream acquisition module 20 is used to obtain M combustible gas perception data streams of the M monitoring areas through the M regional perception networks; an analysis dual-channel construction module 30 is used to build a combustible gas analysis dual channel, and the combustible gas analysis dual channel includes a gas concentration analysis channel and a leakage analysis channel; an early warning judgment module 40 is used to preset a combustible gas safety threshold, perform early warning judgment on the M combustible gas perception data streams based on the gas concentration analysis channel and the leakage analysis channel according to the combustible gas safety threshold, and obtain the combustible gas parameters of the area to be warned; a linkage early warning control module 50 is used to construct a multi-level linkage early warning mechanism, and perform linkage early warning control on the combustible gas parameters of the area to be warned based on the multi-level linkage early warning mechanism.

[0067] The specific configuration of the structural design information acquisition module 10 will be described in detail below. The structural design information acquisition module 10 further includes: attribute identification of the structural design information to obtain a regional structural attribute set, wherein the regional structural attribute set includes regional scale distribution, regional function, gas equipment distribution, personnel flow, and ventilation system; extracting division indicators and ranking indicators based on the regional structural attribute set, and configuring equivalent area division rules; dividing the target area into M monitoring areas according to the equivalent area division rules; performing monitoring demand analysis and IoT device deployment on the regional information of each of the M monitoring areas, and building M regional perception networks.

[0068] The specific configuration of the structural design information acquisition module 10 will be described in detail below. The structural design information acquisition module 10 further includes: performing a gas risk assessment on the information of each of the M monitoring areas in sequence to obtain M regional gas risk parameters; performing a monitoring demand analysis on the M monitoring areas based on the M regional gas risk parameters to determine M regional gas monitoring indicators; performing an IoT deployment strategy analysis on the M monitoring areas based on the M regional gas monitoring indicators to obtain M regional IoT device deployment parameters; and deploying IoT devices in the M monitoring areas according to the M regional IoT device deployment parameters to establish the M regional sensing network.

[0069] The specific configuration of the dual-channel analysis module 30 will be described in detail below. The dual-channel analysis module 30 further includes: mining and acquiring a combustible gas concentration dataset and a combustible gas emission data set; using a time series network to perform concentration trend identification and predictive analysis training on the combustible gas concentration dataset to generate a gas concentration analysis channel; using a deep neural network to perform emission identification and supervised analysis training on the combustible gas emission data set to obtain an emission analysis channel; and merging the gas concentration analysis channel and the emission analysis channel in series to establish the dual-channel combustible gas analysis.

[0070] The specific configuration of the dual-channel analysis module 30 will be described in detail below. The dual-channel analysis module 30 further includes: obtaining gas escape analysis elements, which include diffusion path, escape range, and escape impact area; performing feature identification on the combustible gas escape data set according to the gas escape analysis elements to obtain a combustible gas escape feature sample set; using a deep neural network to perform supervised analysis training on the combustible gas escape feature sample set to generate a diffusion path analysis network, a escape range analysis network, and a escape impact area analysis network; and parallelizing and fusing the diffusion path analysis network, the escape range analysis network, and the escape impact area analysis network to obtain the escape analysis channel.

[0071] The specific configuration of the early warning determination module 40 will be described in detail below. The early warning determination module 40 further includes: determining a combustible gas concentration early warning threshold and a combustible gas escape situation early warning threshold based on the combustible gas safety threshold; activating the gas concentration analysis channel according to the combustible gas concentration early warning threshold to perform concentration trend analysis and early warning comparison and determination on the M combustible gas sensing data streams to obtain M combustible gas concentration early warning determination results; performing early warning determination on the M combustible gas sensing data streams based on the M combustible gas concentration early warning determination results, the combustible gas escape situation early warning threshold, and the escape situation analysis channel to determine the combustible gas parameters of the area to be warned.

[0072] The specific configuration of the early warning determination module 40 will be described in detail below. The early warning determination module 40 further includes: if the M combustible gas concentration early warning determination results are negative, then the combustible gas parameters of the area to be warned are an empty set; if the M combustible gas concentration early warning determination results are positive, performing an early warning determination on the M combustible gas sensing data streams based on the combustible gas escape situation early warning threshold and the escape situation analysis channel to determine the combustible gas parameters of the area to be warned.

[0073] The specific configuration of the early warning determination module 40 will be described in detail below. The early warning determination module 40 further includes: performing an escape analysis on the M combustible gas sensing data streams based on the escape analysis channel to obtain M combustible gas escape parameters; performing an early warning determination on the M combustible gas escape parameters according to the combustible gas escape warning threshold to determine the combustible gas parameters of the area to be warned.

[0074] The specific configuration of the linkage warning control module 50 will be described in detail below. The linkage warning control module 50 further includes: performing linkage warning analysis on the combustible gas parameters in the warning area based on the multi-level linkage warning mechanism, determining the combustible gas zone warning level and the combustible gas zone warning execution device; and performing combustible gas linkage warning control using the combustible gas zone warning level and the combustible gas zone warning execution device.

[0075] The combustible gas monitoring and early warning system based on the Internet of Things provided by an embodiment of the present invention can execute the combustible gas monitoring and early warning method based on the Internet of Things provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0076] Although the present application makes various references to certain modules in the system 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.

[0077] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A combustible gas monitoring and early warning method based on the Internet of Things, characterized in that: The method comprises: Acquire structural design information of a target area, perform equivalent partitioning and IoT device deployment on the target area based on the structural design information, and obtain M monitoring areas and M regional sensing networks; Acquiring M combustible gas sensing data streams of the M monitoring areas through the M regional sensing network monitoring; Build a dual channel for combustible gas analysis, which includes a gas concentration analysis channel and a leakage analysis channel; Preset a combustible gas safety threshold, and perform early warning judgment on the M combustible gas sensing data streams based on the gas concentration analysis channel and the escape situation analysis channel according to the combustible gas safety threshold to obtain combustible gas parameters of the area to be warned; Constructing a multi-level linkage early warning mechanism, and performing linkage early warning control on the combustible gas parameters in the area to be warned based on the multi-level linkage early warning mechanism; The construction of a dual-channel combustible gas analysis system includes: Mining and obtaining combustible gas concentration data sets and combustible gas escape situation data sets; Using a time series network to perform concentration trend identification and prediction analysis training on the combustible gas concentration data set to generate a gas concentration analysis channel; Using a deep neural network to perform emission identification and supervised analysis training on the combustible gas emission data set to obtain an emission analysis channel; The gas concentration analysis channel and the escape situation analysis channel are connected in series and combined to build the combustible gas analysis dual channel; The obtaining of the emission analysis channel includes: Obtaining gas escape analysis elements, wherein the gas escape analysis elements include diffusion path, escape range, and escape impact area; Performing feature identification on the combustible gas escape situation data set according to the gas escape situation analysis elements to obtain a combustible gas escape feature sample set; A deep neural network is used to perform supervised analysis training on the combustible gas escape feature sample set to generate a diffusion path analysis network, a escape range analysis network, and a escape impact area analysis network; The diffusion path analysis network, the dispersion range analysis network and the dispersion impact area analysis network are connected in parallel and integrated to obtain the dispersion situation analysis channel.

2. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 1, characterized in that: The obtaining of M monitoring areas and M regional sensing networks includes: Attribute identification is performed on the structural design information to obtain a regional structural attribute set, wherein the regional structural attribute set includes regional scale distribution, regional function, gas equipment distribution, personnel flow, and ventilation system; Extracting partitioning indicators and arranging index priorities based on the regional structural attribute set, and configuring equivalent regional partitioning rules; Divide the target area into M monitoring areas by performing equivalent attribute division according to the equivalent area division rule; Perform monitoring demand analysis and IoT device deployment on the information of each of the M monitoring areas, and build M regional perception networks.

3. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 2, characterized in that: The construction of M regional awareness networks includes: Performing gas risk assessment on the information of each of the M monitoring areas in sequence to obtain M regional gas risk parameters; Performing a monitoring demand analysis on the M monitoring areas according to the M regional gas risk parameters to determine the M regional gas monitoring indicators; Performing IoT deployment strategy analysis on the M monitoring areas based on the gas monitoring indicators of the M areas to obtain IoT device deployment parameters for the M areas; IoT devices are deployed in the M monitoring areas according to the IoT device deployment parameters of the M areas, and the M area perception networks are built.

4. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 1, characterized in that: The method of obtaining the combustible gas parameters of the area to be warned includes: Determining a combustible gas concentration warning threshold and a combustible gas escape warning threshold based on the combustible gas safety threshold; activating the gas concentration analysis channel according to the combustible gas concentration warning threshold to perform concentration trend analysis and warning comparison judgment on the M combustible gas sensing data streams, thereby obtaining M combustible gas concentration warning judgment results; Based on the M combustible gas concentration warning judgment results, the combustible gas escape warning threshold and the escape analysis channel, a warning judgment is performed on the M combustible gas sensing data streams to determine the combustible gas parameters of the area to be warned.

5. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 4, characterized in that: The step of determining the combustible gas parameters in the area to be warned includes: If the M combustible gas concentration warning judgment results are negative, the combustible gas parameters of the area to be warned are an empty set; If the warning judgment results of the M combustible gas concentrations are yes, a warning judgment is performed on the M combustible gas sensing data streams based on the combustible gas escape warning threshold and the escape analysis channel to determine the combustible gas parameters of the area to be warned.

6. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 5, characterized in that: The step of performing early warning judgment on the M combustible gas sensing data streams based on the combustible gas escape situation early warning threshold and the escape situation analysis channel to determine combustible gas parameters in the area to be warned includes: performing a leakage analysis on the M combustible gas sensing data streams based on the leakage analysis channel to obtain M combustible gas leakage parameters; Perform a warning judgment on the M combustible gas escape condition parameters according to the combustible gas escape condition warning threshold, and determine the combustible gas parameters of the area to be warned.

7. The combustible gas monitoring and early warning method based on the Internet of Things according to claim 1, characterized in that: The method of performing linkage early warning control on the combustible gas parameters in the area to be warned based on the multi-level linkage early warning mechanism includes: Based on the multi-level linkage warning mechanism, a linkage warning analysis is performed on the combustible gas parameters in the area to be warned, and the combustible gas zone warning level and the combustible gas zone warning execution device are determined; The combustible gas zoning warning level and the combustible gas zoning warning execution equipment are used to perform combustible gas linkage warning control.

8. A combustible gas monitoring and early warning system based on the Internet of Things, characterized in that: The system is used to implement the combustible gas monitoring and early warning method based on the Internet of Things according to any one of claims 1 to 7, and the system includes: A structural design information acquisition module is used to obtain structural design information of a target area, perform equivalent partitioning of the target area and deploy IoT devices based on the structural design information, and obtain M monitoring areas and M regional sensing networks; A sensing data stream acquisition module, configured to acquire M combustible gas sensing data streams of the M monitoring areas through the M regional sensing network monitoring; An analysis dual-channel building module is used to build a combustible gas analysis dual-channel, which includes a gas concentration analysis channel and a leakage analysis channel; An early warning judgment module is used to preset a combustible gas safety threshold, perform early warning judgment on the M combustible gas sensing data streams based on the gas concentration analysis channel and the escape situation analysis channel according to the combustible gas safety threshold, and obtain combustible gas parameters of the area to be warned; The linkage warning control module is used to build a multi-level linkage warning mechanism, and perform linkage warning control on the combustible gas parameters in the area to be warned based on the multi-level linkage warning mechanism.

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